( ) the Evolution of the Internet in the Business Sector

The Evolution of the
Internet in the Business
Sector:
Web 1.0 to Web 3.0
Pedro Isaías
Universidade Aberta (Portuguese Open University), Portugal
Piet Kommers
University of Twente, The Netherlands
Tomayess Issa
Curtin University, Australia

A volume in the Advances in E-Business Research
(AEBR) Book Series

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CIP Data
The evolution of the internet in the business sector : Web 1.0 to Web 3.0 /
Pedro Isaias, Piet Kommers, and Tomayess Issa, editors.
pages cm
Includes bibliographical references and index.
ISBN 978-1-4666-7262-8 (hardcover : alk. paper) -- ISBN 978-1-4666-7263-5 (ebook) -- ISBN 978-1-4666-7265-9 (print
& perpetual access) 1. Business enterprises--Computer network resources. 2. Information technology--Economic aspects.
I. Isaias, Pedro, editor. II. Kommers, Piet A. M., editor.
HD30.37.E96 2015
384.3’1--dc23
2014036396
This book is published in the IGI Global book series Advances in E-Business Research (AEBR) (ISSN: 1935-2700; eISSN:
1935-2719)
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ISSN: 1935-2700
EISSN: 1935-2719
Mission

Technology has played a vital role in the emergence of e-business and its applications incorporate
strategies. These processes have aided in the use of electronic transactions via telecommunications networks for collaborating with business partners, buying and selling of goods and services, and customer
service.  Research in this field continues to develop into a wide range of topics, including marketing,
psychology, information systems, accounting, economics, and computer science. 
The Advances in E-Business Research (AEBR) Book Series provides multidisciplinary references
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find this collection, or individual books, extremely useful. This collection contains the highest quality academic books that advance understanding of e-business and addresses the challenges faced by
researchers and practitioners. 

Coverage

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•
•
•

E-marketing
E-business Systems Integration
Web Advertising
Trust, Security, and Privacy
E-procurement Methods
E-Business Strategies
Telework and Telecommuting
Web Service-Based Business Systems
B2B E-marketplaces
Online Consumer Behavior

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Titles in this Series

For a list of additional titles in this series, please visit: www.igi-global.com

The Evolution of the Internet in the Business Sector Web 1.0 to Web 3.0
Pedro Isaías (Universidade Aberta (Portuguese Open University), Portugal) Piet Kommers (University of Twente,
The Netherlands) and Tomayess Issa (Curtin University, Australia)
Business Science Reference • copyright 2015 • 390pp • H/C (ISBN: 9781466672628) • US $235.00 (our price)
RFID Technology Integration for Business Performance Improvement
In Lee (Western Illinois University, USA)
Business Science Reference • copyright 2015 • 337pp • H/C (ISBN: 9781466663084) • US $225.00 (our price)
Integrating Social Media into Business Practice, Applications, Management, and Models
In Lee (Western Illinois University, USA)
Business Science Reference • copyright 2014 • 325pp • H/C (ISBN: 9781466661820) • US $225.00 (our price)
Electronic Payment Systems for Competitive Advantage in E-Commerce
Francisco Liébana-Cabanillas (University of Granada, Spain) Francisco Muñoz-Leiva (University of Granada, Spain)
Juan Sánchez-Fernández (University of Granada, Spain) and Myriam Martínez-Fiestas (ESAN University, Perú)
Business Science Reference • copyright 2014 • 393pp • H/C (ISBN: 9781466651906) • US $215.00 (our price)
Trends in E-Business, E-Services, and E-Commerce Impact of Technology on Goods, Services, and Business
Transactions
In Lee (Western Illinois University, USA)
Business Science Reference • copyright 2014 • 347pp • H/C (ISBN: 9781466645103) • US $185.00 (our price)
Interdisciplinary Perspectives on Business Convergence, Computing, and Legality
Reema Khurana (Institute of Management Technology-Ghaziabad, India) and Rashmi Aggarwal (Institute of
Management Technology-Ghaziabad, India)
Business Science Reference • copyright 2013 • 354pp • H/C (ISBN: 9781466642096) • US $165.00 (our price)
Research and Development in E-Business through Service-Oriented Solutions
Katalin Tarnay (University of Pannonia, Hungary & Budapest University of Technology and Economics, Hungary)
Sandor Imre (Budapest University of Technology and Economics, Hungary) and Lai Xu (Bournemouth University,
UK)
Business Science Reference • copyright 2013 • 328pp • H/C (ISBN: 9781466641815) • US $185.00 (our price)

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Editorial Advisory Board
Ibrahim Al-Oqily, Hashemite University, Jordan
Nada Dabbagh, George Mason University, USA
Noraida Dominguez, University of Puerto Rico at Carolina, Puerto Rico
Ruslan R. Fayzrakhmanov, Vienna University of Technology, Austria
Isabela Gasparini, Universidade do Estado de Santa Catarina (UDESC), Brazil
Arnon Hershkovitz, Tel Aviv University, Israel
Achmad Nizar Hidayanto, Universitas Indonesia, Indonesia
Theodora Issa, Curtin University, Australia
Roberto Pereira, University of Campinas (UNICAMP), Brazil
Sonia San-Martín, Universidad de Burgos, Spain

List of Reviewers
Leonelo D. A. Almeida, Federal University of Technology Paraná (UTFPR), Brazil
Amit Chauhan, Florida State University, USA
Filipe Roseiro Côgo, Federal University of Technology at Paraná (UTFPR), Brazil
Cinzia Colapinto, Ca’ Foscari University of Venice, Italy
Alexandra I. Cristea, University of Warwick, UK & Coventry University, UK
Rodolfo Miranda de Barros, State University of Londrina, Brazil
Alona Forkosh-Baruch, Levinsky College of Education, Israel & Tel Aviv University, Israel
Marcos H. Kimura, Santa Catarina State University (UDESC), Brazil
Márcio J. Mantau, Santa Catarina State University (UDESC), Brazil
André Luís Menolli, Universidade Estadual do Norte do Paraná (UENP), Brazil
Hilário Oliveira, Federal University of Pernambuco, Brazil
Peldon, Curtin University, Australia
Tanti Kartika Sari, Curtin University, Australia
Lei Shi, University of Warwick, UK & Coventry University, UK
Agnis Stibe, University of Oulu, Finland
Ebenezer Uy, De La Salle – College of Saint Benilde, Philippines
Thomas Zefferer, Graz University of Technology, Austria

Table of Contents

Foreword .............................................................................................................................................. 15
10.4018/978-1-4666-7262-8.chfwd

Preface .................................................................................................................................................. 17
10.4018/978-1-4666-7262-8.chpre

Section 1
Web and Business Models
10.4018/978-1-4666-7262-8.chs01

10.4018/978-1-4666-7262-8.chs01

Chapter 1
Are Signals a Solution to Perceived Risk and Opportunism in Mobile Shopping? Gender
Differences and Similarities.....................................................................................................................1
Sonia San-Martín, Universidad de Burgos, Spain
10.4018/978-1-4666-7262-8.ch001

10.4018/978-1-4666-7262-8.ch001

10.4018/978-1-4666-7262-8.ch001::1

Chapter 2
Models and Approaches for Web Information Extraction and Web Page Understanding.....................25
Ruslan R. Fayzrakhmanov, Vienna University of Technology, Austria
10.4018/978-1-4666-7262-8.ch002

10.4018/978-1-4666-7262-8.ch002

10.4018/978-1-4666-7262-8.ch002::1

Section 2
Web Applications
10.4018/978-1-4666-7262-8.chs02

10.4018/978-1-4666-7262-8.chs02

Chapter 3
A Roadmap on Awareness of Others in Accessible Collaborative Rich Internet Applications.............52
Leonelo D. A. Almeida, Federal University of Technology-Paraná (UTFPR), Brazil
M. Cecília C. Baranauskas, University of Campinas (UNICAMP), Brazil
10.4018/978-1-4666-7262-8.ch003

10.4018/978-1-4666-7262-8.ch003

10.4018/978-1-4666-7262-8.ch003::1

10.4018/978-1-4666-7262-8.ch003::2

Chapter 4
A Usability Evaluation of Facebook’s Privacy Features Based on the Perspectives of Experts and
Users......................................................................................................................................................73
Márcio J. Mantau, Santa Catarina State University (UDESC), Brazil
Marcos H. Kimura, Santa Catarina State University (UDESC), Brazil
Isabela Gasparini, Santa Catarina State University (UDESC), Brazil
Carla D. M. Berkenbrock, Santa Catarina State University (UDESC), Brazil
Avanilde Kemczinski, Santa Catarina State University (UDESC), Brazil
10.4018/978-1-4666-7262-8.ch004

10.4018/978-1-4666-7262-8.ch004

10.4018/978-1-4666-7262-8.ch004::1

10.4018/978-1-4666-7262-8.ch004::2

10.4018/978-1-4666-7262-8.ch004::3

10.4018/978-1-4666-7262-8.ch004::4

10.4018/978-1-4666-7262-8.ch004::5

Chapter 5
HTML Segmentation for Different Types of Web Pages.......................................................................98
Evelin Carvalho Freire de Amorim, Departamento de Ciência da Computação (UFMG),
Brazil
10.4018/978-1-4666-7262-8.ch005

10.4018/978-1-4666-7262-8.ch005

10.4018/978-1-4666-7262-8.ch005::1

Section 3
Social Networking Sites
10.4018/978-1-4666-7262-8.chs03

10.4018/978-1-4666-7262-8.chs03

Chapter 6
Understanding Employee Attitudes to SNS Implementation in the Australian Banking Sector.........121
Tanti Kartika Sari, Curtin University, Australia
10.4018/978-1-4666-7262-8.ch006

10.4018/978-1-4666-7262-8.ch006

10.4018/978-1-4666-7262-8.ch006::1

Chapter 7
Teacher-Student Relationship in the Facebook Era.............................................................................145
Alona Forkosh-Baruch, Levinsky College of Education, Israel & Tel Aviv University, Israel
Arnon Hershkovitz, Tel Aviv University, Israel
10.4018/978-1-4666-7262-8.ch007

10.4018/978-1-4666-7262-8.ch007

10.4018/978-1-4666-7262-8.ch007::1

10.4018/978-1-4666-7262-8.ch007::2

Chapter 8
Examining the Opportunities of Social Networking Adoption in the Health Care Systems...............173
Peldon, Curtin University, Australia
10.4018/978-1-4666-7262-8.ch008

10.4018/978-1-4666-7262-8.ch008

10.4018/978-1-4666-7262-8.ch008::1

Section 4
Web, Technology, and Social Influence
10.4018/978-1-4666-7262-8.chs04

10.4018/978-1-4666-7262-8.chs04

Chapter 9
We Have Good Information for You: Cognitive Authority and Information Retrieval on the Web....191
Filipe Roseiro Côgo, Federal University of Technology at Paraná (UTFPR), Brazil
Roberto Pereira, University of Campinas (UNICAMP), Brazil
10.4018/978-1-4666-7262-8.ch009

10.4018/978-1-4666-7262-8.ch009

10.4018/978-1-4666-7262-8.ch009::1

10.4018/978-1-4666-7262-8.ch009::2

Chapter 10
A Web-Based Method for Ontology Population..................................................................................211
Hilário Oliveira, Federal University of Pernambuco, Brazil
Rinaldo Lima, Federal University of Pernambuco, Brazil
João Gomes, Federal University of Pernambuco, Brazil
Fred Freitas, Federal University of Pernambuco, Brazil
Rafael Dueire Lins, Federal University of Pernambuco, Brazil
Steven J. Simske, Hewlett-Packard Labs, USA
Marcelo Riss, Hewlett-Parckard do Brasil, Brazil
10.4018/978-1-4666-7262-8.ch010

10.4018/978-1-4666-7262-8.ch010

10.4018/978-1-4666-7262-8.ch010::1

10.4018/978-1-4666-7262-8.ch010::2

10.4018/978-1-4666-7262-8.ch010::3

10.4018/978-1-4666-7262-8.ch010::4

10.4018/978-1-4666-7262-8.ch010::5

10.4018/978-1-4666-7262-8.ch010::6

10.4018/978-1-4666-7262-8.ch010::7

Chapter 11
User Engagement in Feedback Sharing through Social Influence.......................................................234
Agnis Stibe, University of Oulu, Finland
Harri Oinas-Kukkonen, University of Oulu, Finland
10.4018/978-1-4666-7262-8.ch011

10.4018/978-1-4666-7262-8.ch011

10.4018/978-1-4666-7262-8.ch011::1

10.4018/978-1-4666-7262-8.ch011::2

Section 5
Web and Education
10.4018/978-1-4666-7262-8.chs05

10.4018/978-1-4666-7262-8.chs05

Chapter 12
Creating a Community of Practice in Learning...................................................................................259
Ebenezer Uy, De La Salle – College of Saint Benilde, Philippines
Eusebio Yu, De La Salle – College of Saint Benilde, Philippines
10.4018/978-1-4666-7262-8.ch012

10.4018/978-1-4666-7262-8.ch012

10.4018/978-1-4666-7262-8.ch012::1

10.4018/978-1-4666-7262-8.ch012::2

Chapter 13
Influence of Perceived Quality of Official University Websites to Perceived Quality of University
Education and Enrollment Intention....................................................................................................278
Achmad Nizar Hidayanto, Universitas Indonesia, Indonesia
Fanny Rofalina, Universitas Indonesia, Indonesia
Putu Wuri Handayani, Universitas Indonesia, Indonesia
10.4018/978-1-4666-7262-8.ch013

10.4018/978-1-4666-7262-8.ch013

10.4018/978-1-4666-7262-8.ch013::1

10.4018/978-1-4666-7262-8.ch013::2

10.4018/978-1-4666-7262-8.ch013::3

Chapter 14
Students as Customers: Participatory Design for Adaptive Web 3.0...................................................306
Lei Shi, University of Warwick, UK
Alexandra I. Cristea, University of Warwick, UK
Craig Stewart, Coventry University, UK
10.4018/978-1-4666-7262-8.ch014

10.4018/978-1-4666-7262-8.ch014

10.4018/978-1-4666-7262-8.ch014::1

10.4018/978-1-4666-7262-8.ch014::2

10.4018/978-1-4666-7262-8.ch014::3

Chapter 15
The Open Innovation Paradigm: Can Digital Storytelling Generate Value for the Educational
Field?....................................................................................................................................................332
Luca Ganzerla, Ca’ Foscari University of Venice, Italy
Cinzia Colapinto, Ca’ Foscari University of Venice, Italy
Elena Rocco, Ca’ Foscari University of Venice, Italy
10.4018/978-1-4666-7262-8.ch015

10.4018/978-1-4666-7262-8.ch015

10.4018/978-1-4666-7262-8.ch015::1

10.4018/978-1-4666-7262-8.ch015::2

10.4018/978-1-4666-7262-8.ch015::3

Compilation of References . .............................................................................................................. 354
10.4018/978-1-4666-7262-8.chcrf

About the Contributors .................................................................................................................... 396
10.4018/978-1-4666-7262-8.chatc

Index ................................................................................................................................................... 405
10.4018/978-1-4666-7262-8.chidx

Detailed Table of Contents

Foreword .............................................................................................................................................. 15
10.4018/978-1-4666-7262-8.chfwd

Preface .................................................................................................................................................. 17
10.4018/978-1-4666-7262-8.chpre

Section 1
Web and Business Models
10.4018/978-1-4666-7262-8.chs01

10.4018/978-1-4666-7262-8.chs01

Chapter 1
Are Signals a Solution to Perceived Risk and Opportunism in Mobile Shopping? Gender
Differences and Similarities.....................................................................................................................1
Sonia San-Martín, Universidad de Burgos, Spain
10.4018/978-1-4666-7262-8.ch001

10.4018/978-1-4666-7262-8.ch001

10.4018/978-1-4666-7262-8.ch001::1

There is usually a situation of information asymmetry in mobile shopping. According to signaling theory
and applying it to a mobile commerce context, the authors propose a model with some cognitive and
experiential quality signals from the vendor and the site that can reduce shopper perceived risk and fear
of opportunism: vendor reputation, site design, personalized service, and personalized information.
However, as some literature states that male and female behaviour is somewhat different, the authors
analyze differences and similarities between men and women in those perceptions of signals, risk, and
opportunism. The authors collected information from 447 mobile shoppers, and results show some
gender differences, which involve implications for managers when segmenting their potential market of
mobile shoppers by gender. Women are more sensitive to signals, whereas for men the reduction of risk
is key to less perceived opportunism.
10.4018/978-1-4666-7262-8.ch001

Chapter 2
Models and Approaches for Web Information Extraction and Web Page Understanding.....................25
Ruslan R. Fayzrakhmanov, Vienna University of Technology, Austria
10.4018/978-1-4666-7262-8.ch002

10.4018/978-1-4666-7262-8.ch002

10.4018/978-1-4666-7262-8.ch002::1

This chapter discusses the main challenges addressed within the fields of Web information extraction
and Web page understanding and considers different utilized Web page representations. A configurable
Java-based framework for implementing effective methods for Web Page Processing (WPP) called WPPS
is presented as the result of this analysis. WPPS leverages a Unified Ontological Model (UOM) of Web
pages that describes their different aspects, such as layout, visual features, interface, DOM tree, and the
logical structure in the form of one consistent model. The UOM is a formalization of certain layers of a
Web page conceptualization defined in the chapter. A WPPS API provided for the development of WPP
methods makes it possible to combine the declarative approach, represented by the set of inference rules
and SPARQL queries, with the object-oriented approach. The framework is illustrated with one example
scenario related to the identification of a Web page navigation menu.
10.4018/978-1-4666-7262-8.ch002

Section 2
Web Applications
10.4018/978-1-4666-7262-8.chs02

10.4018/978-1-4666-7262-8.chs02

Chapter 3
A Roadmap on Awareness of Others in Accessible Collaborative Rich Internet Applications.............52
Leonelo D. A. Almeida, Federal University of Technology-Paraná (UTFPR), Brazil
M. Cecília C. Baranauskas, University of Campinas (UNICAMP), Brazil
10.4018/978-1-4666-7262-8.ch003

10.4018/978-1-4666-7262-8.ch003

10.4018/978-1-4666-7262-8.ch003::1

10.4018/978-1-4666-7262-8.ch003::2

Web 2.0 represents a shift from static to highly dynamic, participative, and collaborative Web. However,
most of Rich Internet Applications (RIAs) are still not accessible; as a consequence, universal participation
is still far from being a reality. Providing accessible means for “awareness of others” (i.e. a perception
of the activities of others in the system) is essential in RIAs to enable collaboration among all users.
This chapter explores, through a systematic literature review, studies approaching the topic in accessible
collaborative RIAs. The authors also identify the technologies proposed, extended, or used by those
studies. As results they characterize the studies in the area and clarify the state-of-the-art of technologies
for supporting awareness of others. Finally, the authors propose a set of guidelines aiming at supporting
the design of mechanisms for awareness of others in collaborative RIAs.
10.4018/978-1-4666-7262-8.ch003

Chapter 4
A Usability Evaluation of Facebook’s Privacy Features Based on the Perspectives of Experts and
Users......................................................................................................................................................73
Márcio J. Mantau, Santa Catarina State University (UDESC), Brazil
Marcos H. Kimura, Santa Catarina State University (UDESC), Brazil
Isabela Gasparini, Santa Catarina State University (UDESC), Brazil
Carla D. M. Berkenbrock, Santa Catarina State University (UDESC), Brazil
Avanilde Kemczinski, Santa Catarina State University (UDESC), Brazil
10.4018/978-1-4666-7262-8.ch004

10.4018/978-1-4666-7262-8.ch004

10.4018/978-1-4666-7262-8.ch004::1

10.4018/978-1-4666-7262-8.ch004::2

10.4018/978-1-4666-7262-8.ch004::3

10.4018/978-1-4666-7262-8.ch004::4

10.4018/978-1-4666-7262-8.ch004::5

The issue of privacy in social networks is a hot topic today, because of the growing amount of information
shared among users, who are connected to social media every moment and by different devices and
displays. This chapter presents a usability evaluation of the privacy features of Facebook’s social network.
The authors carry out an evaluation composed by three approaches, executed in three stages: first by the
analysis and inspection of system’s features related to privacy, available for both systems (Web-based
systems and mobile-based systems, e.g. app). The second step is a heuristic evaluation led by three experts,
and finally, the third step is a questionnaire with 605 users to compare the results between specialists
and real users. This chapter aims to present the problems associated with these privacy settings, and it
also wants to contribute for improving the user interaction with this social network.
10.4018/978-1-4666-7262-8.ch004

Chapter 5
HTML Segmentation for Different Types of Web Pages.......................................................................98
Evelin Carvalho Freire de Amorim, Departamento de Ciência da Computação (UFMG),
Brazil
10.4018/978-1-4666-7262-8.ch005

10.4018/978-1-4666-7262-8.ch005

10.4018/978-1-4666-7262-8.ch005::1

Search engines manage several types of challenges daily. One of those challenges is locating relevant
content in a Web page. However, the concept of relevance in information retrieval depends on the problem
to be solved. For instance, the menu of a website does not impact the results of an algorithm to detect
duplicate Web pages. An HTML segmentation algorithm partitions a Web page visually in such a way
that parts from a same partition are semantically related. This chapter presents two strategies to segment
different types of Web pages.
10.4018/978-1-4666-7262-8.ch005

Section 3
Social Networking Sites
10.4018/978-1-4666-7262-8.chs03

10.4018/978-1-4666-7262-8.chs03

Chapter 6
Understanding Employee Attitudes to SNS Implementation in the Australian Banking Sector.........121
Tanti Kartika Sari, Curtin University, Australia
10.4018/978-1-4666-7262-8.ch006

10.4018/978-1-4666-7262-8.ch006

10.4018/978-1-4666-7262-8.ch006::1

The Australian banking sector has utilised Social Networking Sites (SNS) to support companies’
sustainability through customer engagement. Numerous studies have been conducted on issues associated
with SNS, including teamwork, communication, trust, and security. These studies have usually been
concerned with the perspectives and attitudes of customers and organizations, and sometimes, employers.
This chapter is based on a Master’s degree dissertation research aimed at filling the gap by investigating
the opinions of the Australian banking sector employees, in particular those who use the SNS application
as a work tool. The Honeycomb framework is used as the theoretical basis with six underlying factors
being ascertained as a result of Exploratory Factor Analysis (EFA). The findings are discussed, and
recommendations are proposed which are intended to increase the benefits to be gained from SNS
adoption. The analysis results make a significant theoretical, methodological, and practical contribution.
10.4018/978-1-4666-7262-8.ch006

Chapter 7
Teacher-Student Relationship in the Facebook Era.............................................................................145
Alona Forkosh-Baruch, Levinsky College of Education, Israel & Tel Aviv University, Israel
Arnon Hershkovitz, Tel Aviv University, Israel
10.4018/978-1-4666-7262-8.ch007

10.4018/978-1-4666-7262-8.ch007

10.4018/978-1-4666-7262-8.ch007::1

10.4018/978-1-4666-7262-8.ch007::2

The popularity of social networking sites has facilitated new modes of teacher-student communication,
conveying the potential of changing teacher-students interaction. The goal of this chapter is to examine
students’ and teachers’ perceptions of student-teacher SNS-based relationships in the Facebook era and
to supply evidence that supports decision making. The authors present two studies involving secondary
school Israeli students and teachers, examining the relations between Facebook-based student-teacher
communication and student-teacher relationships. Findings suggest that Facebook communication may be
beneficial but highlight conflicting issues. The authors discuss the implications of these studies, offering
recommendations that include comprehensive support of teachers in developing new ICT literacies. They
recommend further research as a means of providing educational policymakers and stakeholders with
evidence to assist with informed decision making, as well as a means to empower teachers by allowing
them to make decisions based on their educational beliefs.
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Chapter 8
Examining the Opportunities of Social Networking Adoption in the Health Care Systems...............173
Peldon, Curtin University, Australia
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Social Network Sites (SNSs) are known for providing the opportunity to quickly spread information faster
than any other mode because of its ease of accessibility and ability to reach wider populations. The purpose
of this chapter is to examine the opportunities of adopting Social Networking (SN) in the healthcare
systems. Based on the current literature review, using a social network will enhance communication,
collaboration, connection, coordination, and knowledge sharing. The healthcare profession of Bhutan
undertook the survey for this study. Three new factors were generated from this study, namely 4Cs; it
was found that the use of social networking enhances communication, coordination, collaboration, and
connection with patients and among healthcare professionals. The second factor, Green and Sustainability,
social networking enables the reduction of the carbon footprint, and the third factor is Exchange Knowledge
via use of social networking.
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Section 4
Web, Technology, and Social Influence
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Chapter 9
We Have Good Information for You: Cognitive Authority and Information Retrieval on the Web....191
Filipe Roseiro Côgo, Federal University of Technology at Paraná (UTFPR), Brazil
Roberto Pereira, University of Campinas (UNICAMP), Brazil
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Through the concept of Cognitive Authority, information relevance and quality have been related to the
expertise/skill of those who publish and share information on the Web. This chapter discusses how the
concept of cognitive authority can be used in order to improve the information retrieval on folksonomybased systems. The hypothesis is that a ranking scheme that takes into account the cognitive authority
of the information sources provides results of higher relevance and quality to users. To verify this
hypothesis, the Folkauthority approach is adopted; a ranking scheme called AuthorityRank is proposed;
and an information retrieval system, named AuthoritySearch, is built. A real social network is used to
simulate the authority relationship among users, and the AuthorityRank scheme is compared with the
tf-idf scheme using the NDCG metric. The results indicate a statistically significant improvement in the
quality and relevance of the information obtained through the use of the AuthorityRank scheme.
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Chapter 10
A Web-Based Method for Ontology Population..................................................................................211
Hilário Oliveira, Federal University of Pernambuco, Brazil
Rinaldo Lima, Federal University of Pernambuco, Brazil
João Gomes, Federal University of Pernambuco, Brazil
Fred Freitas, Federal University of Pernambuco, Brazil
Rafael Dueire Lins, Federal University of Pernambuco, Brazil
Steven J. Simske, Hewlett-Packard Labs, USA
Marcelo Riss, Hewlett-Parckard do Brasil, Brazil
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The Semantic Web, proposed by Berners-Lee, aims to make explicit the meaning of the data available
on the Internet, making it possible for Web data to be processed both by people and intelligent agents.
The Semantic Web requires Web data to be semantically classified and annotated with some structured
representation of knowledge, such as ontologies. This chapter proposes an unsupervised, domainindependent method for extracting instances of ontological classes from unstructured data sources available
on the World Wide Web. Starting with an initial set of linguistic patterns, a confidence-weighted score
measure is presented integrating distinct measures and heuristics to rank candidate instances extracted
from the Web. The results of several experiments are discussed achieving very encouraging results, which
demonstrate the feasibility of the proposed method for automatic ontology population.
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Chapter 11
User Engagement in Feedback Sharing through Social Influence.......................................................234
Agnis Stibe, University of Oulu, Finland
Harri Oinas-Kukkonen, University of Oulu, Finland
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Organizations continuously strive to engage customers in the services development process. The Social
Web facilitates this process by enabling novel channels for voluntary feedback sharing through social
media and technologically advanced environments. This chapter explores how social influence design

principles can enhance the effectiveness of socio-technical systems designed to alter human behavior
with respect to sharing feedback. Drawing upon social science theories, this chapter develops a research
framework that identifies social influence design principles pertinent to persuasive systems that facilitate
user engagement in feedback sharing. The design principles are then implemented in an information
system and their effects on feedback sharing are explored in an experimental setting. The main findings
of this chapter contribute to research related to social influences on user behavior and to the practice of
designing persuasive information systems.
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Section 5
Web and Education
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Chapter 12
Creating a Community of Practice in Learning...................................................................................259
Ebenezer Uy, De La Salle – College of Saint Benilde, Philippines
Eusebio Yu, De La Salle – College of Saint Benilde, Philippines
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Social media plays a huge part in Filipinos’ lives. In the area of learning, the proponents observed the
emergence of an online community of practice using Facebook groups that has over 350 members. The
aim of the chapter is to answer the question: How do online communities of practice engage students
to learn and build new knowledge? The objective is to propose a framework that will guide readers to
build their own online community of practice based on its learning context. To achieve the objective,
the proponents use the inductive approach of grounded theory using action research. Results show that
community members used different Facebook features to support their ongoing community of practice.
Further studies may also assess the applicability of the framework in other areas of development.
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Chapter 13
Influence of Perceived Quality of Official University Websites to Perceived Quality of University
Education and Enrollment Intention....................................................................................................278
Achmad Nizar Hidayanto, Universitas Indonesia, Indonesia
Fanny Rofalina, Universitas Indonesia, Indonesia
Putu Wuri Handayani, Universitas Indonesia, Indonesia
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This chapter aims to analyze the impact of a university’s website quality to the intentions of prospective
students to enroll at the university. The proposed model was tested by distributing questionnaires to thirdyear senior high school students around Jakarta. Respondents were asked to follow a series of instructions
to access the websites of two universities, the University of Muhammadiyah Malang and the Indonesian
Islamic University. After completing the task, respondents were asked to complete a questionnaire to
evaluate website quality. Based on the analysis of 117 valid questionnaires, it is concluded that website
quality influences the perception by prospective university students of quality university education,
which subsequently affects the intention of prospective students to enroll at the university. The finding
confirms that the quality of official websites can be used as an extrinsic attribute to signal the quality of
education at the university; thus, its optimal maintenance must be endeavoured.
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Chapter 14
Students as Customers: Participatory Design for Adaptive Web 3.0...................................................306
Lei Shi, University of Warwick, UK
Alexandra I. Cristea, University of Warwick, UK
Craig Stewart, Coventry University, UK
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The World Wide Web is changing, from the early Web 1.0 to the Social Web 2.0 and beyond to Web 3.0
interfaces, but more importantly, the users of the Web are also changing, and their numbers are increasing
rapidly in line with this evolution. In e-Learning, it is essential to be able to keep up with these trends and
provide personalized social interaction. Here, our main customers are our students, but these customers
do not come unprepared: they already have a great deal of Web experience, especially in the areas of
Social Networking Sites (SNS) and online interaction. Thus, it is essential to improve approaches used
in the past, where learners were only involved in the receiving part of the delivery process. This chapter
therefore proposes and explores applying participatory design methodologies in the early stages of the
social adaptive educational hypermedia system design process, showing also its benefits for further
design, implementation, and usage.
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Chapter 15
The Open Innovation Paradigm: Can Digital Storytelling Generate Value for the Educational
Field?....................................................................................................................................................332
Luca Ganzerla, Ca’ Foscari University of Venice, Italy
Cinzia Colapinto, Ca’ Foscari University of Venice, Italy
Elena Rocco, Ca’ Foscari University of Venice, Italy
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The aim of this chapter is to shed light on an emerging educational and business paradigm, stemming
from the digital revolution and the opportunities disclosed by Open Innovation. The central idea behind
Open Innovation is that, in a world of widely distributed knowledge, companies cannot afford to rely
entirely on their own research but should instead buy or license processes or inventions from other actors.
After pointing out the potential benefits of digital storytelling and of Web 2.0 and 3.0 for promoting
pedagogical and organizational innovation, the authors present an application of the Open Innovation
Paradigm in education: the Value Generating Framework. The chapter offers empirical evidence of the
benefits through an in-depth analysis of the alliance between the Italian Zoo “Parco Natura Viva” and
the Italian foundation “Radio Magica.” This knowledge-intensive, collaborative, value network paradigm
is more successful than the previous firm-centric paradigm.
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Compilation of References . .............................................................................................................. 354
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About the Contributors .................................................................................................................... 396
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Index ................................................................................................................................................... 405
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xv

Foreword

If one of the more difficult tasks you face is keeping abreast of communications technology for either
your workplace or your personal life, then this is a crucial book for you. So many feel that they are overwhelmed by the current technology and using only small aspects of what their hardware and software
are capable of performing. Yet more and more Web-based capability is certainly coming and there are
real and perceived expectations that force us to continue to learn and use these new IT possibilities.
This book is unique, as it not only looks from present to future usage of the Web as it evolves from
Web 1.0 towards Web 3.0 but also spans the crucial gaps between theory and practical applications for
several important user groups. The book spans the private and public sectors and the world of schools
and academia.
While addressing these sectors individually, it also subtly urges a blending of collaborative efforts
between them to cope with emerging local and global issues and opportunities. The possibilities of
the emerging Web allow innovative approaches with wide ranges in scale and diversity that go beyond
simple public awareness or marketing and commercial solutions delivered in one-way dissemination.
The book shows that the ever-evolving Web is a marvelous tool, which when properly used can enable
humanity to harness the plethora of far-flung autonomous creativity to address much larger and crucial
issues. Engaging and channeling the collective creativity of interdisciplinarity and intercultural perspectives, until recently the domain of only a select handful of enormous transnational corporations, is now
a reality for all with Web access and language capabilities.
Compiled from a select group of global IT visionaries, the book explains the current reality and possibilities of cloud-based computing. Also dealing with the present, the book addresses the building and
use of crowd-based collaborative applications that can serve not only small groups of specialized users
but can be tailored to assist related needs anywhere in the world. This book draws your attention to the
growing need for interdisciplinary teams and the endemic need for variety and diversity.
The book contends that Web 3.0 is the future website for every sector, including business. It is assumed that even small corporations, universities, and schools can utilize the connectivity of the Web to
enhance product design, adaptation, and acceptance, whether of an emerging commercial product or a
new classroom curricula development.
Often the awareness of a new feature when shared spawns myriad localized adaptations that launch
yet more cycles of creative innovations. This phenomena of Web-enabled co-creation nourishing creative industries and non-profit services can more effectively serve society than the current and former
untargeted broadcasting of information, education, and marketing campaigns.
I found this wide-ranging book to be useful and engaging, moving from the provision of a deeper
understanding of the evolutionary aspect of the Web to date to looking into the future possibilities of

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Web 3.0. I was comforted by the practical forays beyond theory and historical context into specific
case studies and research that enhanced my own thinking of how I could more effectively welcome this
emerging IT tsunami. As an education advisor, I was engrossed in the recommendation of the authors
that children and other e-learners be involved in design processes at an early age. Other research into
issues such as teacher education and the contribution of social networking had me thinking of possibilities of enhancing professional development practice in remote schools or developing countries. I was
enticed to think of IT conundrums such as the numerous recession recoveries, including the last one of
2008 that resulted in largely jobless economic growth due in part to the new Web-based tools at hand.
Yet it is during the recessions that IT training of workers is either reduced or inappropriately simplified.
Reading reminded me that along with these wonderful possibilities there is of necessity a need for the
presence of a moral and value-based foundation for the Web. Freedom and openness to information and
knowledge is a principle of the Web, but how do we address not only Web etiquette but also the underlying
values of servitude, access, respect of intrinsic cultures perspectives, etc. as Web 3.0 emerges? How can
Web 3.0 play a positive role in bringing comfort and peace to humankind? The book is a provocateur.
Charles Hopkins
York University, Canada

Charles Hopkins is currently the United Nations Education, Scientific, and Cultural Organization Chair at York University in
Toronto, Canada, where as well as lecturing in the graduate program, he coordinates an international network of teacher education institutions from over 70 countries collaboratively working upon the reorientation of elementary and secondary teacher
education to address sustainable development. Hopkins is a senior advisor regarding Education for Sustainable Development
(ESD) to UNESCO, the UN University, The National Board of Education of Finland, and The Beijing Academy of Educational
Sciences. His current research is looking for the possible relationship between systemic adoption of ESD policy and practice
and traditional perceptions of quality education in high-scoring PISA countries. He is also an advisor to both UNESCO and the
Government of Japan regarding the UNESCO World Conference on ESD to be held in Aichi Nagoya in 2014. Within Canada,
Hopkins is the Co-Director of the Sustainability and Education Academy (SEdA), which is a collaborative professional development program designed to assist senior education leaders, ministry of education officials, and teacher education institutions in
reorienting entire school systems to address sustainability. He is also Chair of the Board of the John Dearness Environmental
Society and serves on the boards of a number of education-related non-governmental organizations. Previously, Charles was
a teacher, a principal, a superintendent of curriculum, and a regional superintendent with the Toronto Board of Education. A
long-time leader in the fields of education and ESD, Hopkins has lectured and presented papers in over 70 countries. He is the
author of a textbook on ecology, of numerous book chapters and journal articles, and has appeared in television documentaries
and PBS in the USA dealing with education, the environment, and sustainable development.

xvii

Preface

Information and Communication Technology (ICT) is essential to the national and international business
sector to improve job performance and productivity. ICT is not limited to a specific software package or
home. This tool aims to enrich and expand communication, collaboration, cooperation, and connection
between employees and employers, employees and stakeholders, and enhance job productivity and user
satisfaction.
Web use in the business sector improves data management, supports the availability of Internet mobility, stimulates creativity and innovation, encourages globalization, and enhances customer satisfaction
via communication, collaboration, cooperation, and connection.
The Web is divided into three types: Web 1.0, Web 2.0, and Web 3.0. Web 1.0 refers to connecting
information and shared read-write hypertext space, while Web 2.0 is known as the participative Web, as
it allows users to connect via social networking with more interaction with less control. Web 3.0 refers to
connecting intelligence and is known as Semantic Web; in other words, it identifies Web-based data so
that searches can be more effective, and the information is part of the network. Web 3.0 is considering
the future of every sector, including business.
This book covers many topics in relation to the journey of Web 1.0 to Web 3.0, particularly Web and
business models, Web applications, social networking sites, the Web, technology, and social influence,
and the Web and education.
This book presents a set of guidelines and principles of Web 1.0, Web 2.0, and Web 3.0 adoption in
the business sector, since this tool aims to improve job performance, productivity, increase profile, and
reduce cost. Furthermore, this book aims to support researchers and academics’ work by sharing the latest
technologies among their students nationally and internationally, especially in the higher-education sector.

SECTION 1: WEB AND BUSINESS MODELS
Chapter 1, “Are Signals a Solution to Perceived Risk and Opportunism in Mobile Shopping? Gender
Differences and Similarities,” written by San-Martín, explores the issue of information asymmetry in
mobile shopping. In this chapter, a model with some cognitive and experiential quality signals from the
vendor and the site that can decrease shopper perceived risk and fear of opportunism—vendor reputation, site design, personalized service, and personalized information—is presented. In this study, the
author explores the concept of m-shopping, which has been less researched than electronic shopping.
In this context, it analyzes signals as means to reduce perceived risk and opportunism in a m-shopping
environment. In addition, a theoretical approach (signaling theory) is utilized to validate the author’s

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hypotheses. Empirically, this research considers vendor and site signals as possible solutions that can
help to resolve user perception of risk and opportunism when making purchases with the mobile phone,
which is a variable that has not been addressed in m-shopping.
Chapter 2, “Models and Approaches for Web Information Extraction and Web Page Understanding,”
by Fayzrakhmanov, discusses the major challenges addressed within the background of Web Information
Extraction (WIE) and Web Page Understanding (WPU) and reflects on different Web page representations
leveraged in Web Page Processing (WPP). The author introduces the expression Web Page Processing
(WPP) and its connection with the context of WIE and WPU and accomplishes a comparative analysis
of different approaches in terms of leveraged Web page models. In addition to the discussion of some
aspects regarding the field of WIE and WPU, it presents a configurable Java-based framework (derived
from the conducted analysis of different approaches and using Web page representations) for implementing effective and robust methods for WPP called WPPS.

SECTION 2: WEB APPLICATIONS
Chapter 3, “A Roadmap on Awareness of Others in Accessible Collaborative Rich Internet Applications,”
by Almeida and Baranauskas, focuses on the concept of Rich Internet Applications (RIAs). Therefore,
the authors explore, by using a systematic literature review, studies approaching this concept in accessible collaborative RIAs. The proposed SLR (Systematic Literature Review) is based on four review
questions: (a) disabilities being considered, and the geographical context of the authors, (b) awareness
of others, (c) recommendations, guidelines, and design patterns (named RecGuidPat for simplification),
and (d) involved technologies. Besides the presentation of the literature review, the authors categorize
the technologies presented in the literature review and elucidate in terms of the state-of-the-art of the
technologies. In addition, the chapter proposes some guidelines with the purpose to support the design
of mechanisms for awareness of others in collaborative RIAs.
Chapter 4, “A Usability Evaluation of Facebook’s Privacy Features Based on the Perspectives of
Experts and Users,” by Mantau, Kimura, Gasparini, Berkenbrock, and Kemczinski, presents a usability
evaluation of the privacy features (privacy features available in May and June of 2012) of a well-know
social network, Facebook. The authors performed an evaluation in three stages by using three approaches:
the analysis and assessment of the system’s features concerning privacy issues (from Web-based system
and mobile-based system), a heuristic evaluation led by three experts, and a questionnaire with 605 users
to measure the results between specialists and real users. This study presents the issues associated with
the privacy settings and also wants to help improve the user interaction with this social network. After
Facebook’s privacy features had been updated, the authors re-evaluated the Web- and mobile-based
environment to verify which of the initial problems encountered (mentioned in this study) in the first
heuristic evaluation were solved and which of these continue.
In Chapter 5, “HTML Segmentation for Different Types of Web Pages,” by Amorim, the author
explores the idea that search engines deal with several types of challenges on a daily basis, such as locating relevant content in a Web page. The central goal of this chapter is to segment different kinds of
Web pages. In this chapter, the author presents two strategies to segment different types of Web pages.
Consequently, this study has the purpose to describe general methods for HTML segmentation and
compare two general HTML segmentation methods, namely ETL HTML segmentation and the so-called
TPS segmentation. Furthermore, it also examines some topical methods, the main results of HTML

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segmentation algorithms, and issues to be explained in HTML segmentation. Finally, a new model to
categorize segments in HTML documents is proposed by the author.

SECTION 3: SOCIAL NETWORKING SITES
Chapter 6, “Understanding Employee Attitudes to SNS Implementation in the Australian Banking Sector,” by Sari, focuses on the usage of SNS (Social Networking Sites) by the Australian Banking Sector.
The purpose is to analyze the opinion of the Australian Banking sector employees, in particular those
who use SNS applications as their working tool. Therefore, the employees from Australian banks listed
in the APRA (Australian Prudential Regulation Authority) list in March 2013 were encouraged by
email to participate in an online survey to analyze the central question, What are the factors required
that trigger SNS implementation in the Australian banking sector from the employee perspective? This
survey gathered 130 responses with 87% completion rate and yielded 113 used cases. In addition, in this
research, the Honeycomb framework jointly with 4C guidelines proved appropriate as the base theory
when analysing SNS adoption in the Australian banking industry.
In Chapter 7, “Teacher-Student Relationship in the Facebook Era,” by Forkosh-Baruch and Hershkovitz, students’ and teachers’ perceptions of student-teacher SNS-based relationships in the Facebook
era are observed. The purpose of the authors is to aid in the implementation of SNS in the education
segment with empirical evidence that sustain decision making. Here, the main objective is to present
the emergence of Web 2.0 into educators’ lives and its effect on teacher-student relationships and communication. The authors express this phenomenon through an exploratory study, examining students’
and teachers’ perceptions of student-teacher communication via SNS. This chapter explores two studies
concerning lower and higher secondary school Israeli students and teachers. These studies analyze the
relations between Facebook-based student-teacher communication and student-teacher relationships.
In general, the results suggest that Facebook communication may be helpful, but they emphasize some
conflicting issues.
Chapter 8, “Examining the Opportunities of Social Networking Adoption in the Health Care Systems,” by Peldon, explores, in general, the opportunities of adopting Social Network Sites (SNSs) and,
in particular, examines the opportunities of Social Networking (SN) in the healthcare systems. This
chapter investigates the behaviors towards social networking implementation as one of the means to
communicate with patients and among healthcare professionals themselves in Bhutan. The study was
carried out by conducting a questionnaire (using the “Qualtrics” online survey software). The target
was the healthcare professionals of Bhutan with a sample size of 154 participants including Bhutanese
doctors, physicians, specialists, and nurses. From this research, three factors were generated regarding
the aspect of opportunities, namely the 4Cs (Communication, Coordination, Collaboration, and Connection), green and sustainability, and exchange knowledge. The main purpose of this chapter was to
establish whether the healthcare professionals in Bhutan are eager to adopt social networking as a model
for communication with patients and among healthcare professionals themselves in the future. From
the sample used in this research, the author concludes that 63% of healthcare professionals are keen to
adopt social networking as a way of communication among themselves and with patients in the present
and in the future.

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SECTION 4: WEB, TECHNOLOGY, AND SOCIAL INFLUENCE
In Chapter 9, “We Have Good Information for You: Cognitive Authority and Information Retrieval
on the Web,” by Côgo and Pereira, it is argued how the concept of cognitive authority can be used to
develop and improve the information retrieval on folksonomy-based systems. The authors propose a
ranking scheme that considers the cognitive authority of the information sources, and doing so provides
results of higher significance and quality to users. To validate this proposal, the Folkauthority approach
was implemented, a ranking scheme named AuthorityRank was proposed, and an information retrieval
system, called AuthoritySearch, was developed. In addition, a social network was used to reproduce the
authority relationship among users, and the AuthorityRank scheme was compared with the tf-idf scheme
using the NDCG metric. The authors state that by adopting Folkauthority approach, it is possible to
improve the relevance and quality of the results of a query by giving more importance to certain sources
of information when calculating the ranking of the retrieved information.
Chapter 10, “A Web-Based Method for Ontology Population,” by Oliveira, Lima, Gomes, Freitas, Lins,
Simske, and Riss, focuses on an unsupervised, domain-independent method for extracting instances of
ontological classes from unstructured data sources available on the WWW/Internet. Here, the proposed
method is capable of extracting occurrences of ontological classes from unstructured sources of information
written in natural language available on the Web. The method is based on a Confidence-weighted Score
function (ConfScore) that incorporates different measures and heuristics to rank candidate instances.
According to the authors, the proposed method focuses on the task of Ontology Population (OP), which
does not alter the structure of the ontology (i.e., no changes in the hierarchy of classes and/or relationships are performed). The updating task is restricted to the set of instances of concepts, relationships,
and properties of an input ontology.
Chapter 11, “User Engagement in Feedback Sharing through Social Influence,” by Stibe and
Oinas-Kukkonen, emphasizes how social influence design principles can improve the effectiveness of
socio-technical systems designed to modify human behavior with respect to sharing feedback. Within
the context of social science theories, this chapter presents a research framework that identifies social
impact design principles pertinent to influential systems that assist user engagement in feedback sharing.
The design principles are then put into practice in an information system and their effects on feedback
sharing are investigated in an experimental context. In order to identify the social impact design principles, the relevant background is defined and a review of the associated literature is given. The key aim
of this review is to provide knowledge about the social influence principles that are significant in this
context and to develop a theory-driven research framework. The major results of this study add valor
to research connected to social influences on user behavior and to the practice of designing persuasive
information systems.

SECTION 5: WEB AND EDUCATION
Chapter 12, “Creating a Community of Practice in Learning,” by Uy and Yu, presents a study that has
the purpose to answer the general question, How do online communities of practice engage students to
learn and build new knowledge? In addition, the authors examine the emergence of online communities
of practice in Facebook Groups that were created in the field of teaching and learning. Therefore, with
this research, the authors propose a framework that will allow users to build their own online community

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of practice in their own specific learning context. This study will facilitate the teaching and learning
environment by creating a platform in which learners may successfully collaborate and learn from each
other. In practice, the purpose of this research is to investigate the impact of online Communities of
Practice in learning contexts. As a result, codes and common themes were identified and a model was
created to describe the communities of practice and a framework was developed to guide educators in
creating their own communities of practice using social media.
Chapter 13, “Influence of Perceived Quality of Official University Websites to Perceived Quality
of University Education and Enrollment Intention,” by Hidayanto, Rofalina, and Handayani, suggests a
model to examine how the quality of a university official website operates as a signal of quality of university education as commercial service. This research examines whether the excellence of a university’s
website, which is evaluated based on reliability, responsiveness, functionality, accessibility, information quality, and interface, can be developed as an extrinsic quality to indicate the quality of university
education. The authors focused on educational service, particularly in higher education. Through this
study, the authors try to contribute to the use of signaling theory to foresee how the quality of a website
influences the perceived quality of services in university education contexts.
Chapter 14, “Students as Customers: Participatory Design for Adaptive Web 3.0,” by Shi, Cristea,
and Stewart, focuses on the notion that the educational environment must follow the development of the
Web and must adapt to the new context. The authors propose and explore applying participatory design
methodologies in the early stages of the social adaptive educational hypermedia system design process,
showing also its benefits for further design, implementation, and usage. In this chapter, it is demonstrated
how students and other e-Learning users can be involved in the design process by applying a participatory
design methodology in the early stage of the development of a social-AEHS. Consequently, the authors
report their case study that imitated a large co-designer experiment in a small format and extracted an
ordered list of initial application requirements. Therefore, the authors conclude that it is vital to get the
students and the learners involved in the whole system design process.
Chapter 15, “The Open Innovation Paradigm: Can Digital Storytelling Generate Value for the Educational Field?” by Ganzerla, Colapinto, and Rocco, presents a new emerging educational and business
paradigm: the open innovation paradigm. The authors explore an application of the Open Innovation
Paradigm in the context of education: the Value-Generating Framework. In addition, empirical substantiation of the benefits of this paradigm is given through an in-depth analysis of the alliance between the
Italian Zoo Parco Natura Viva and the Italian foundation Radio Magica. Based on the identification
of four educational challenges, the authors describe the framework of intervention and analysis of the
Value-Generating Framework, and they discuss the advantages of the application of the Open Innovation
paradigm both in profit and non-profit contexts. The main purpose of this chapter is to create a connection between Media and Education.
Pedro Isaías
Universidade Aberta (Portuguese Open University), Portugal
Piet Kommers
University of Twente, The Netherlands
Tomayess Issa
Curtin University, Australia

Section 1

Web and Business Models

1

Chapter 1

Are Signals a Solution
to Perceived Risk
and Opportunism in
Mobile Shopping?

Gender Differences and Similarities
Sonia San-Martín
Universidad de Burgos, Spain

ABSTRACT
There is usually a situation of information asymmetry in mobile shopping. According to signaling theory
and applying it to a mobile commerce context, the authors propose a model with some cognitive and
experiential quality signals from the vendor and the site that can reduce shopper perceived risk and
fear of opportunism: vendor reputation, site design, personalized service, and personalized information.
However, as some literature states that male and female behaviour is somewhat different, the authors
analyze differences and similarities between men and women in those perceptions of signals, risk, and
opportunism. The authors collected information from 447 mobile shoppers, and results show some
gender differences, which involve implications for managers when segmenting their potential market of
mobile shoppers by gender. Women are more sensitive to signals, whereas for men the reduction of risk
is key to less perceived opportunism.

INTRODUCTION
Nowadays vendors face new challenges with the
widespread use of new technologies, which is a
new means of electronic shopping. Among those
challenges, the mobile phone can be considered an
extension of Internet to operate. The penetration

of mobile phones is clearly increasing and allows
many daily operations such as shopping. There are
about 6 billions of mobile telephone subscriptions
in the world and more than 90% of the population have access to mobile networks ITU (2013).
The mobile phone is the most used device in the
world. According to data of Telecommunications

DOI: 10.4018/978-1-4666-7262-8.ch001

Copyright © 2015, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.


Are Signals a Solution to Perceived Risk and Opportunism in Mobile Shopping

Market Comission (TMC) in Spain, the number of
electronic transactions in Spain reached a maximum at the end of 2012 (40 millions of operations
in the fourth trimester), but data mainly refer
to Internet. In the context of mobile commerce
(m-commerce), a study of Google in Spain (Our
Mobile Planet, 2012) emphasizes the importance
of smartphones in the way consumers buy. 82%
of Spanish consumers have searched for a product
or service with a smartphone and it is decisive in
the shopping decision (24% of smartphone users
have bought with it). Forecasts about m-commerce
are great and in 2013 it is expected a growth of
44% in comparison with 2012 (reaching 235.400
millions of dollars) (Gartner, 2013). In addition, a
IAB Spain research study (2012) finds that 59%
of the Internet users own a smartphone and that 8
out of 10 mobile users surf the Internet using their
devices. For the purposes of this chapter, mobile
shopping (m-shopping) refers to the activities of
consumers who use wireless Internet service when
shopping and purchasing using mobile phones
(Ko et al., 2009).
Both e-commerce and m-commerce include
advantages for shopping such as convenience,
quick shopping, wide assortment of products,
timetable flexibility or less stress while shopping
(San-Martín & Camarero, 2008), but there are
some differential advantages of mobile phones
for consumers: ubiquity, personalization, mobility,
localization and interactivity (Lee, 2005), which
are key to the adoption of mobile phones to buy
(Gillian & Drennan, 2005). Some of the impediments are also common to online and mobile
shopping –e.g. lack of physical contact, greater
transaction costs and technical problems- (SanMartín & Camarero, 2008), but there are others
that are especially related to mobile channels, such
as low size of the screens, lack of specific antivirus software for mobile phones and inadequate
standardization of payment methods (San-Martín
et al., 2013). There are also several firm benefits
deriving from the particularities of m-shopping, as
it is a convenient, rapid, and fully interactive means

2

of communication, which has great potential for
segmentation (San-Martín, 2013). Nevertheless,
it represents an important challenge for firms,
given that the development of this promising sales
channel is still at an early stage.
When the consumer has to choose the most
appropriate mobile vendor (m-vendor) to buy from
(to establish the relationship with), a problem of
information asymmetry may appear (Mishra et
al., 1998; Kirmani & Rao, 2000; Singh & Sirdeshmukh, 2000). Due to a lack of information, the
consumer has difficulties to distinguish the true
quality of different products/services and the true
behavior of vendor firms (Mishra et al., 1998; Pavlou et al., 2007) and has, therefore, fear of vendor
opportunism. Opportunism is self-interest seeking
and can manifest itself, in an active (e.g. lying) or
a pasive form (e.g. shirking) (Wathne & Heide,
2000). This information asymmetry is greater
when consumers do not have information about
the vendor and mobile shoppers (m-shoppers) feel
there are lack of interaction and advice, lack of
experience with this kind of shopping, impossibility to touch or try products and services, distrust
when paying and transferring personal data with
the mobile phone, lack of knowledge, too small
mobile phone screens.
In order to reduce perception of risk and
opportunism, there are different factors –signals- that vendors can use to facilitate shoppers
the evaluation of products and services (Wathne
& Heide, 2000). Signals can be defined as firm
characteristic or cue that reveals information about
product quality or firm behaviour and that offers
firm costs or revenues as a hostage, bond, promise
or commitment (Kirmani & Rao, 2000; Mishra
et al., 1998). Signals sent to consumers are that
such investments would not be worthwhile for a
low-quality vendor that would not en-joy repeat
purchases (Mishra et al., 1998). Signals can be a
good solution to the perceived risk and opportunism problem in B2C relationships (Mishra et al.,
1998; Singh & Sirdeshmukh, 2000). Although
there are studies that analyze signals in the online


Are Signals a Solution to Perceived Risk and Opportunism in Mobile Shopping

and off-line context, there are few that analyze
them in a m-shopping context (Yeh & Li, 2009).
In this chapter, we are considering design and
personalization, which are especially related with
m-shopping, different from an online shopping,
as the size of the mobile phone screen is limited
and because mobile phones are a personal tool
and computers are not. According to Rowley
(2004), the effect of signals in distant commerce
is greater than in other media, due to the fact that
in this medium the possibility of personalizing
the messages (signals) increases in accordance
with the profile of consumers and their location.
The effectiveness of signals is related to the
existence of prepurchase information scarcity,
postpurchase information clarity and bond vulnerability (if promised quality is false, vendor image
or income will be damaged or lost, especially if
signals are default-dependent). Signalling will be
a viable strategy when a non-signalling strategy
provides a bigger payoff than does signalling for
the low-quality firm and occurs the opposite for
the high-quality firm (what is called a separating
equilibrium) (Kirmani & Rao, 2000).
Literature has determined that demographic
and contextual characteristics such as gender
(Zhang et al., 2007; Kolsaker & Payne, 2002)
influence the purchase experience. Gattiker et al.
(2000) affirm that personal characteristics have
an influence and that they interact with mental
processes such as perception, reasoning, passing
judgments and making decisions. In this chapter,
we wonder if there are differences between men
and women in perceptions of quality signals and
in their ability to reduce perceived risk and fear of
vendor opportunism. Therefore, the objective of
this study is to study gender differences and similarities regarding the influence of four cognitive
and experiential signals- reputation, personalized
information and service and design- on perception
of risk and opportunism. The research question can
be summed up as: do men and women perceive in
a same or in a different way quality signals, risk
and opportunism when shopping with the mobile

phone?. We will test the proposed hypotheses with
a wide sample of Spanish m-shoppers, a country
where there is no similar study. In literature, we
have not found strategies to address opportunism
problems in a m-shopping context taking into account shopper gender.
This chapter makes some interesting contributions to literature. First, we focus on m-shopping,
which has been less researched than electronic
shopping. In that context, we analyze signals as
ways to reduce perceived risk and opportunism in
a m-shopping context. To that aim, a sound theoretical approach –the signalling theory- is used to
justify our hypotheses. Our research empirically
take into account vendor and site signals as possible
solutions that can help to solve user perception of
risk and opportunism when making a shopping
with the mobile phone, which is a variable that
has not been addressed in m-shopping. Second,
and in spite of being one of the most common
segmentation criteria used by firms to address their
target, gender research is not conclusive regarding
the adoption of mobiles phones to buy. Third, as
an empirical contribution we test the model for
a sample of m-users that have already bought
with their mobile phone. Most existing research
focuses on potential shoppers –not in users that
have bought- and do not deal with variables such
as perceived risk, opportunism and signals.

BACKGROUND
Perception of Risk and
Opportunism in Mobile Shopping
Consumers usually perceive more risk in distance
shopping in comparison with offline shopping
(Akaah & Korgaonkar, 1988; Tan, 1999; Kim et
al., 2009; Koenig-Lewis et al. 2010). In general,
the more the risk, the less the probability of the
transaction taking place. Risk diminishes in distant
shopping in the evaluation of alternatives phase of
the shopping process and increases in the shopping

3


Are Signals a Solution to Perceived Risk and Opportunism in Mobile Shopping

phase (Cunningham et al., 2005; Laroche et al.,
2005), which is the one analyzed in this chapter.
As previously stated, there can be a shopping
problem in mobile contexts when consumers
perceive risk when they have to choose the mvendor/site to establish the exchange with. As in
electronic contexts, in mobile environments there
are not tangible indicators of the quality of the
product, the purchase is affected by problems of
safeguards and privacy and higher risk is expected
(Li & Yeh, 2010), thus risk is higher than in an
offline context. Risk involves the likelihood of
privacy invasion in terms of impossibility to see,
touch or try in distance shopping and the risk of
high privacy and personal loss potential together
with a lack of consumer experience with new
electronic services (Pavlou et al., 2007) such as
mobile ones (Schierz et al., 2010).
In m-commerce there are some causes of
risk such as spam, phishing, changing seller ID,
location, unauthorized use and contract terms
before, during or after purchase (Mallat, 2007),
which can make buyers avoid these markets. Some
typical risks in m-shopping refer to the need to
provide personal information to the vendor and
the suspicion that after payment is processed, the
goods purchased could not arrive (Giannakis &
Batten, 2011; Singh & Sirdeshmukh, 2000). If a
technology fails to provide the expected result,
it will result in a loss for the consumer (Im et al.
2008; Laukkanen & Kiviniemi, 2010). Several
works studying perceived risk in the context of
mobile marketing treat it essentially as a fear of
providing personal or banking data to a firm and
of loss of privacy (Bauer et al. 2005; Gao et al.
2010; Riquelme and Ríos, 2010).
All those risks can lead to a need for controlling
the vendor or the fear that the vendor will seek
its own interest by eluding its responsibility, not
fulfilling its promises or hiding relevant information for the shopper. Hence, the insecurity when
shopping in a m-site can lead to a perception of
opportunism.

4

H1: Perceived risk increases perceived opportunism.

Signalling in Mobile Shopping
There are different kinds of signals that the
shopper can use to infer quality or vendor behavior, perceive less risk and be less afraid of
opportunism. Following Kirmani & Rao (2000),
signals can be classified into default-independent
(sale-independent and sale-dependent signals)
and default-dependent (revenue-risking and costrisking) signals (Table 1). They do not study those
signals for online or mobile contexts and that is
why in this chapter other specific cues that can
serve as signals of quality or behavior in these new
contexts of shopping are included. In this chapter,
we consider signals such as personalization, which
can indicate firm investment in providing specific
information and products adapted to customers.
This shows commitment, bonds and promises of
quality or behavior and customers can penalize
the firm if promised personalization is not true.
Following that signals typology, we have considered both default-independent signals (design and
reputation, which are sale-independent signals)
and default-dependent signals (personalization,
which is a revenue risking signal). From the mvendor side, they can send signals to the market so
that the consumer can make inferences about the
quality provided and about the vendor behaviour
and intentions (signalling). From the m-shopper
side, they can make an effort in searching for detailed information of different vendors (screening
the potential m-vendors) and infer information
about service quality and m-vendor behaviour
with the help of the informative signals sent by
the m-vendor or site to the market.
Another possible classification of signals
is between cognitive and experiential ones.
Wolfinbarger and Gilly (2003) suggest that online customers can show two types of behavior:
experience-based behavior (purchases for fun
and enjoyment, which are more related to emo-


Are Signals a Solution to Perceived Risk and Opportunism in Mobile Shopping

Table 1. Types and characteristics of signals
Default-Independent Signals
Sale-Independent

Default-Contingent Signals

Sale-Contingent

Revenue-Risking

Cost-Risking

Example

Retailer investment in
reputation

Low introductory Price

High Price

Warranties

Characteristics

• Publicly visible
expenditures
• Repeat purchase is
important
• Fixed monetary loss

• Private expenditures
during sales transaction
• Repeat purchase is
important
• Monetary loss variable
or semi-variable

• Future revenues at risk
• Repeat purchase is
important
• Monetary loss in the
future

• Future cost at risk
• Repeat purchase is
irrelevant
• Monetary loss in the
future

Appropriate When

Buyer cannot be
identified easily

Buyer can be identified
easily

Frequently purchased
nondurables

Durables

Utility for the Buyer

It is not received directly

It is received directly

It is not received directly

It is received directly

Source: Adapted from Kirmani & Rao (2000).

tions) and goal-oriented or utilitarian shopping
(task-oriented, efficient, rational, and deliberate
shopping, which is more related to cognition).
San-Martín & Camarero (2008) have also applied
those types of signals in e-commerce. The signals
that we are going to propose in this study are especially relevant in a m-shopping context and refer
both to the m-vendor (reputation) and the m-site,
which are cognitive (personalization) and experiential (design). Moreover, we confirmed they are
important signals for m-shoppers according to
the content analysis performed with information
obtained in a pre-test with 15 consumers, as we
will comment in the empirical research section.
M-Vendor Reputation. It is the extent to which
buyers believe a selling organization is honest
and concern about its customers (Jarvenpaa et
al., 1999). Reputation indicates quality of the
products and services, given the absence of vendors who advise the m-shopper. These promises
will be especially valued by the individuals who
feel greater risk in distance purchasing (Xiong &
Liu, 2005). Reputation is an important signal to
reduce risk as in many cases it is transferred from
offline contexts to electronic contexts and it is
supposed that a reputable vendor will find it easy
to sell electronically than a not-known vendor. If
promised quality or implicitly promised vendor

behavior is false, vendor reputation will be lost
(Chen et al., 2010; Shao & Li, 2009). Xiong &
Liu (2005) and Jarvenpaa et al. (1999) consider
that customers will favour sites that represent a
merchant with good reputation in the market. Siau
& Shen (2003) suggest that m-vendor reputation
contributes to the reduction of risk and initial
trust formation.
Design. The site is the only means of communicating with customers; thus, its appearance,
aesthetics and structure are of great importance
(Li & Yeh, 2010). Site visual design describes the
appeal that user interface and aesthetics presents
to customers (Kim & Lee, 2002; Wu et al., 2014),
refers to the balance, emotional appeal, or aesthetics of a site and it may be expressed through
colors, shapes, font type, music or animation (Cyr
et al., 2006). Design is relevant in a m-shopping
context as it is impossible to touch and try products
electronically. Given the fact that users actually
interact through their mobile device interface,
the design and development of effective mobile
interfaces can be a major determinant for the penetration and growth of m-shopping (Manganari et
al., 2007). Cyr et al. (2006) and Li & Yeh (2010)
suggest that aesthetics might be an important part
of designing an overall enjoyable user experience
with mobile devices. A m-shopping environment

5


Are Signals a Solution to Perceived Risk and Opportunism in Mobile Shopping

may also be effective in encouraging transactions
(Yeh & Li, 2009) and in reducing risk and fear
of inappropriate or opportunistic behavior on the
part of the m-vendor.
Personalization. It is the ability of a vendor to
tailor products, services, information and the shopping environment to satisfy individual customers
(Srinivasan et al., 2002). This is an important
signal in m-shopping contexts, as it is related to
the especial characteristic of mobile phones in
comparison with other means of buying and selling
and scarcely investigated in m-shopping literature
(Lee, 2005; Zarmpou et al., 2012). Personalization
is not so possible in offline and online selling than
in mobile selling as the mobile phone is a personal
tool and goes with the consumer anywhere and
anytime. We propose that both personalized information and personalized service are relevant
signals of quality and vendor behaviour and are the
basis of one-to-one marketing (Hoffman & Novak,
1996). In fact, mobile phones allow personalized
information, products and services, more than
other instruments for commerce, and this can be an
advantage for the m-vendor. In a similar vein and
for online shopping, Thirumalai & Sinha (2011)
distinguish decision (information) and transaction
(products/services bought) personalization. The
mobile environment allows marketers to build
profiles of its customers and develop an interactive relationship so that marketing actions can be
personalized to meet the needs of a small or even
individual segment of homogeneous audience
(Lee, 2005). Zarmpou et al. (2012) consider that
personalization is a relevant relationship driver in
m-shopping contexts. Lee (2005); Li & Yeh (2010)
and Shao & Li (2009) suggest that personalization
help to reduce risk and be confident when buying
in m-shopping contexts. Therefore, it is expected
that m-shoppers will perceive less risk and will
have less fear of opportunism if they perceive that
they are treated as unique customers and receives
personalized products, services and information.
The influence of vendor signals on shopper
opportunism may be direct or indirect (through

6

risk). Reputation, design and personalization are
m-site signals which will have a direct effect on
risk in the m-site, as signals contribute to reduce
information asymmetry and risk, following the signalling theory. Moreover, such characteristics may
also affect perceived opportunism indirectly: once
signals have reduced perceived risk, reputation,
design and personalization can also reduce fear
of m-vendor opportunism as the m-shopper will
not need to control the vendor and will not think
that he/she is going to be deceived by the firm.
H2: Vendor reputation reduces perceived risk
(H2a) and perceived opportunism (H2b).
H3: Mobile site visual design reduces perceived
risk (H3a) and perceived opportunism (H3b).
H4: The offer of personalized information reduces
perceived risk (H4a) and perception of opportunism (H4b).
H5: The offer of a personalized service reduces
perceived risk (H5a) and perception of opportunism (H5b).

Differences between Men and
Women When Perceiving Signals,
Risk, and Opportunism
Shopper gender has been one of the most frequently
analysed personal characteristics when studying
B2C relationships. Attitudinal and behavioural
differences between men and women have been
investigated in various empirical studies related to
approval of technology in online purchases (Chang
& Samuel, 2004; Rodgers & Harris, 2003; Luo
et al., 2006; San-Martín & Jiménez, 2011), but
there are not many studies for mobile purchases.
In our opinion, studies must continue to research
the true value of gender as a moderating variable
of various stimuli or factors linked to m-shopping
decisions and that is one of the reasons to test
gender effects in this study.
Research about gender and risk is not conclusive (Coley & Burgess, 2003). On the one hand,
some studies have failed to identify gender dif-


Are Signals a Solution to Perceived Risk and Opportunism in Mobile Shopping

ferences in certain phases of purchase (Okazaki,
2004). Bonn et al. (1998) do not identify gender
differences when studying the tendency to search
for travel-related information on Internet. Eckel
& Grossman (2008) do not observe gender differences in behaviour when subjects are exposed to
risk. On the other hand, many studies in different
purchasing contexts do show gender differences.
It seems that, in general, women are more risk
averse than men and have a lower preference for
competitive environments, especially when they
face a risky situation (Croson & Gneezy, 2009;
Brody, 1993), as it happens in a m-shopping
context. Other research shows that men are more
overconfident than women (Soll & Klayman,
2004). Jayawardhena et al. (2010) state that men
and women respond in different ways in a mobile
context and that men are more decisive and especially value perceived control as m-shopping
is a functional activity destined more for men.
Moreover, women tend to evaluate all the available
information, whereas men are more prone to trust
in a unique attempt of searching for information
(Meyers-Levy, 1989; Putrevu, 2001).
The study carried out by Luo et al. (2006) suggests that women seek elements that help them to
reduce risk in on-line purchasing. Women attach
greater relevance to the elements that mitigate
risk and allow privacy and security in the use of
technologies (Garbarino & Strahilevitz, 2004;
Bartel-Sheehan, 1999). Women accorded greater
importance to elements (i.e. signals) that might
mitigate any possible risk and loss of privacy and
security (Bartel Sheehan, 1999; Citrin et al., 2003;
Garbarino & Strahilevitz, 2004). On the contrary,
men seek to develop their own identity, they are
more independent in their purchase process and
have more utilitarian motives when buying (Citrin
et al. 2003), thus it is expected that they are less
influenced by vendor signals.
It is possible that the relations proposed in
H1-H5 differ in accordance with the gender of
the m-shopper. Signals can be more important
in the case of women as they are more receptive

to signals and are more concerned to reductions
of risk when making decisions, especially to
personalization because it allows the shopper to
feel there is a close relationship. It appears that
females are more involved in buying activities,
value salespeople more than males do and are
more concerned about privacy, attach greater
value to relationships with salespeople and value
relational aspects of the purchase more than men
(Slama & Tashlian, 1985; Gilbert & Warren, 1995;
Iacobucci & Ostrom, 1993). Due to the fact that
background is controversial and no study has
analyzed the reduction of risk and opportunism
through signals, we propose a general hypothesis
and try to obtain and explain innovative results
in this field.
H6: Gender moderates relations between quality
signals, perceived risk and opportunism.
Figure 1 reflects all the previous hypotheses
we propose to test in this chapter.

EMPIRICAL STUDY
A sample of 447 m-shoppers was collected using
C.A.T.I. (Computer Assisted Telephone Interviews) system in June 2012. They were randomly
selected from a national panel of m-users, but they
had to have bought at least once with the mobile
phone to be included in the sample. We think that
collecting information from m-shoppers is interesting and innovative in Spain because m-shopping
is in its infant stage and it is essential to have firsthand information from early adopters to know their
perceptions and opinions to improve m-vendor
strategies and because they are the ones that can
emit positive word-of-mouth and be decisive in the
difussion process of m-shopping (Rogers, 2003).
Most existing studies are based on convenience
samples with m-users and not shoppers. The type
of Spanish m-shopper is mainly young (less than
25 years old), with secondary studies, and with a

7


Are Signals a Solution to Perceived Risk and Opportunism in Mobile Shopping

Figure 1. Proposed model

Table 2. Sample characteristics
Gender

Age

Education

Male

60.2%

<18

0.4%

No qualification

6.5%

<900€

54.6%

Female

29.8%

18-24

44%

Primary School

18.1%

901-1200€

13.9%

25-34

41.2%

Secondary School

19.2%

1201-1500€

12.1%

35-44

13.4%

Vocational Training

25.3%

1501-2000€

11.6%

45-54

0.7%

Degree

29.8%

2001-3000€

5.7%

>54

0%

Master

1.1%

3001-4000€

1.4%

>4000€

0.7%

low social and economic level. Products or services
acquired with the mobile phone are principally
music and participation in contests as ONTSI
(2012) also shows. More specifically, our sample
has participated in contests with the mobile phone
(31%), has bought music (28%), videogames and
applications (15%), spectacle tickets (14%) and
small payments such as parking (12%). Therefore,
they are all of low-involvement. There is not divergence among these product categories regarding
the variables of the proposed model, according
to Anova analyses. Sample descriptions in terms
of demographics are shown in Table 2.
In the questionnaire, the members of the survey
were asked to indicate the product/service and the

8

Monthly Income

company from which they had lastly bought by
mobile phone and, regarding these answers, they
were asked about the signals, perception of risk
and opportunism and profile variables. In order to
measure the proposed variables, 5-position Likert
scales were employed. These scales were adapted
to the context of the study basing on the indicators proposed in the literature and with the help
of a pre-test performed with 15 consumers who
had already bought with the mobile phones. They
were selected by convenience sampling, asking a
first question regarding if they had bought with
the mobile phone or not, then personal in-depth
semi-structured interviews with those consumers were maintained. All the interviews were


Are Signals a Solution to Perceived Risk and Opportunism in Mobile Shopping

recorded, transcribed and revised by me and three
colleagues. The average duration of interviews
was one hour. Consumers participating in the pretest also helped us to select the most interesting
signals in m-shopping. They were asked to cite
ten essential characteristics or cues that can help
them when evaluating products and vendors, when
buying with the mobile phone and that are differential of mobile contexts putting them in order
of priority. Then, following guidelines of content
analysis, mentioned cues were classified in categories and compared with the terms commonly
used in literature. Content analysis is a method of
codifying the text of writing into various groups
or categories based on selected criteria (Krippendorff, 2012). The categories obtained were:
aspects referring to personalization of contents,
service and information (cited by 12 consumers),
aspects regarding the vendor (mainly reputation)
(cited by 8 consumers) and some factors related
to site design (cited by 7 consumers). Besides,
most consumers in this sample indicated risk
when introducing bank and personal data as a
relevant aspect in shopping processes made with
mobile phones (cited by 10 consumers). Once the
questionnaire was developed, it was distributed
to those 15 consumers in order to see if it was
clearly understandable and to adapt some scales
with items that were originally formulated in
relation to tradicional or online shopping into mshopping. The translations of scales from English
into Spanish was done with the help of a native
proof-reader, colleagues and people participating
in the pre-test.
The measurement indicators for the variables
proposed in our model are based on the works related as follows. The scale of reputation was taken
from Ganesan (1994) and Jarvenpaa et al. (1999).
According to these studies, reputation includes
worrying about the other party, having bad reputation in the market (reverse coded), being known as
honest and fair. Ganesan applies it to B2B relations
and Jarvenpaa et al. (1999) to online shopping.
Design aesthetics scale was developed according

to Cyr et al. (2006), who integrates visual design
into TAM model. The scale of personalization
was developed according to scale of customization of Srinivasan et al. (2002), who applied it to
an e-commerce context. Following results in the
pre-test and considering the especial advantages
of the mobile phone to buy, two items related
to provided information in a quick and optiman
manner adapted to consumer needs were added
to the items related to personalization. Then we
separated all the indicators into the ones referred
to personalization of information and the ones
referred to personalization of customer service,
with the help of the pre-test and the exploratory
factor analysis. Perceived risk when paying and
providing information through a m-site was measured using the scale of security of Schierz et al.
(2010) as a reference. These authors especially
focus on the importance of preserving security
when paying and giving private data through a
shopping site. They apply the scale with TAM
variables to m-shopping and therefore it was not
necessary to adapt it to our context. The scale of
perception of opportunism was developed using
John (1984), Ping (1993) and Wathne & Heide
(2000). These authors indicate that perceived
opportunism includes avoiding responsibility, not
fulfilling contracts or promises, hiding relevant
information, always seeking for their own benefit,
even damaging customers’ interests. Those types
of opportunism involve not only active opportunism (e.g. not fulfilling promises), but also passive
opportunism (e.g. hiding relevant information).
These studies have been applied to traditional
shopping contexts, but now we want to apply it
to m-shopping as there are not an opportunism
scale already adapted to online or mobile contexts,
to the extent of our knowledge. All scales were
formulated in relation to a m-shopping context and
pre-tested with the help of 15 consumers. Table 3
shows the descriptive statistics of the variables.
As can be seen in the means test, no differences
of means were found in the two groups for most
of the indicators.

9


Are Signals a Solution to Perceived Risk and Opportunism in Mobile Shopping

Table 3. Items, descriptive statistics, and mean tests
Variables
Reputation
(α=0.751; CR=0.902;
AVE=0.700)

Perceived Risk
(α=0.888; CR=0.795;
AVE=0.792)

Personalized Information
(α=0.871; CR=0.910;
AVE=0.715)

10

Description

Men Mean

Women Mean

Mean Test (Sig)

-This m-vendor is wellknown.

4.029

4.101

-0.467 (0.640)

-This m-vendor is
acknowledged as a leader
in its sector.

4.104

4.124

-0.094 (0.925)

-This m-vendor is known
for its bad reputation
(reverse coded).

3.171

3.017

0.536 (0.592)

-This m-vendor is known
because it worries about
its customers.

4.000

4.309

-1.136 (0.257)

-Most consumers think
that this m-vendor has a
good reputation of being
fair in the market.

4.256

4.629

-1.461 (0.145)

-This m-vendor has a
reputation of offering
good products and
services.

3.654

3.910

-1.257 (0.209)

-This m-vendor makes
sure that the risk that
an unauthorized person
enters in my m-payment
process is high.

3.439

3.292

0.741 (0.459)

-This m-vendor assures
that the risk of abusing of
my personal information
is high when I pay with
the mobile.

3.271

3.135

0.707 (0.480)

-This m-vendor makes
sure that the risk of
abusing of my banking
information is high.

3.316

3.213

0.501 (0.617)

-I think that the services
of m-payment of this
m-vendor are secure.

3.223

3.129

0.531 (0.595)

-This m-vendor offers me
packs of information in a
timely manner.

2.803

2.725

0.318 (0.751)

-This m-vendor offers
me optimal information
according to where I
am and in what I am
interested in each case.

2.922

3.056

-0.522 (0.602)

-The advertising I receive
from this m-vendor
adapts to my situation.

3.253

3.477

-0.781 (0.435)


Are Signals a Solution to Perceived Risk and Opportunism in Mobile Shopping

Table 3. Continued
Variables
Personalized Service
(α=0.773; CR=0.847;
AVE=0.603)

Design of the M-Site
(α=0.723; CR=0.862;
AVE=0.760)

Perceived Opportunism
(α=0.715; CR=0.810;
AVE=0.621)

Description

Men Mean

Women Mean

Mean Test (Sig)

-This m-vendor has the
ability to answer my
specific questions in an
adequate way.

4.033

4.179

-0.657 (0.511)

-This m-vendor has the
ability to answer my
specific questions in an
quick way.

3.925

4.140

-0.995 (0.320)

-This m-vendor allows me
to ask for personalized
products/services.

3.750

3.949

-0.730 (0.466)

-This m-vendor makes me
feel as a unique customer.

2.260

2.213

0.232 (0.817)

-This m-site contains
images that make a better
experience of shopping.

3.658

3.708

-0.227 (0.820)

-This m-site has an
attractive, modern and
professional design.

4.026

4.011

0.078 (0.938)

-When I buy on this
m-vendor, I suspect that
the vendor can be lying.

3.777

3.904

-0.800 (0.424)

-I am afraid that this
m-vendor will only
search its benefit.

2.584

2.887

-1.857 (0.064)

-I am afraid that this
m-vendor may hide
relevant information for
customers.

2.673

2.831

-0.993 (0.321)

-I think that this
m-vendor would avoid
their responsibility if
people did not notice it.

3.349

3.567

-1.171 (0.242)

-I think that this
m-vendor would not
fulfill their obligations as
it would derive a benefit
avoiding it.

3.431

3.539

-0.631 (0.540)

A preliminary analysis of the data with SPSS
18.0 was completed, to detect data that could
distort the results. In order to test unidimensionality of the variables of the model, we performed
an exploratory factor analysis and we obtained
six variables corresponding to the six variables
proposed in the model, in this order of impor-

tance: risk, reputation, personalized information,
perceived opportunism, personalized service and
design. Those six factors accounted for 63.99% of
the variance of the variables of the model. Table
3 also shows Cronbach alpha (α), composite
reliability (CR) and average variance extracted
(AVE) of each scale, which are acceptable values

11


Are Signals a Solution to Perceived Risk and Opportunism in Mobile Shopping

Table 4. Results of the multisample estimation of the model
Female Shoppers

Male Shoppers

RISK

OPORT

RISK

OPORT

-0.021

-0.264**

-0.066

-0.234**

(-0.263)

(-3.303)

(-0.968)

(-3.518)

DESI

-0.155*

-0.059

-0.038

-0.0209

(-1.797)

(-0.712)

(-0.559)

(-0.317)

PERS.INF

-0.169**

-0.231**

-0.125*

-0.116*

(-2.080)

(-2.918)

(-1.895)

(-1.812)

-0.0895

-0.221**

-0.050

-0.065

(-1.063)

(-2.714)

(-0.740)

(-0.991)

--

0.068

--

0.243**

REPUT

PERS.SER
RISK

(0.939)
Goodness of Fit

X =0.0077 (P=0.89); CFI = 0.989; RMSEA = 0.04; NFI = 0.990; IFI = 0.990 GFI = 0.987

in all cases (higher than 0,6 or 0,7 the first two
and higher than 0,5 the third one) (Anderson &
Gerbing, 1988; Bagozzi & Yi, 1988).
Given that our objective is to compare the same
model for two different samples (178 women and
269 men), we performed a multisample confirmatory factor analysis to test configural and metric
invariance of the measurement models of both
groups and they were acceptable. As a result of
this validation process, 2 items of visual design
were eliminated because one of them presented
a low R2 and the measurement errors of another
item were correlated with other variables. With
the six constructs and for the two samples, we
estimated a multisample analysis with LISREL
8.7, which allows us to know if hypotheses are
supported in both cases, to know the loadings and
significance of each coefficient for both groups
and have common goodness of fit indexes of the
global estimation with both groups simultaneously (Chin, 1995; Jöreskog & Sörbom, 1996).
Multi-group analysis was performed using a
hierarchical approach to compare the Chi-Square
of the two sub-samples and to calculate an overall
Chi-square difference. In this case, a model that
imposed equality constraints parameters across

12

(4.098)

2

the subgroups (totally restricted model) was
compared with the general non-restricted model
(Chi-square difference value of 16.696 (p< 0.05),
which shows that there is a general moderating
effect of gender in the model. In order to test in
more detail the differences across groups (the
moderating effect of gender in each relation),
each path was tested one at a time, comparing
the constraint model (the structural parameter
fixed) with the non-constraint model. Significant
differences across groups for each coefficient are
indicated with asterisks (Table 4).
Regarding H1, perceived risk increases perceived opportunism only in the case of men. As for
H2, results do not vary between men and women,
as reputation reduces perceived opportunism
(H2b is supported in both cases), but does not
reduces perceived risk (H2a is not supported in
any case). Design is not relevant at all to reduce
risk nor opportunism for men, but it can reduces
risk in the case of women (H3a is supported for
women and H3b is not supported in any case).
As H4 postulated, personalized information can
reduce risk and opportunism in both cases. Finally,
personalized service can decrease the possibility
of perceiving opportunism, but not risk in the case


Are Signals a Solution to Perceived Risk and Opportunism in Mobile Shopping

Table 5. Supported hypotheses considering gender differences

H1: Risk-Opportunism (+)

Female M-Shoppers

Male Shoppers

Is there a Gender
Difference?

No

Yes

Yes (In Sign)
No

H2a: Reputation-Risk (-)

No

No

H2b: Reputation-Opportunism (-)

Yes

Yes

H3a: Design-Risk (-)

Yes

No

H3b: Design-Opportunism (-)

No

No

H4a: Personalized Inf-Risk (-)

Yes

Yes

H4b: Personalized InfOpportunism

Yes

Yes

H5a: Personalized Service-Risk (-)

No

No

H5b: Personalized ServiceOpportunism (-)

Yes

No

Yes (In sign and size)
Yes (in size)

Yes

H6 is supported as there are significant differences in proposed model according to gender

of women and it does not have any significant
impact in men (H5a is not supported in any case
and H5b is supported for women). Therefore and
according to our results, H6 is supported as there
are some interesting and significant differences
between women and men (specifically, there are
important differences in H1, H3a and H5b and
slight differences in coefficient size in H2b and
H4), thus it is convenient to consider the effect
of gender in the proposed model. Table 5 summarizes supported hypotheses in the case of male
and female shoppers. Figure 2 offers a compari-

son of significant effects of the estimation of the
model for women and men in order to see them
more clearly.
As the model suggest the possibility of a partial
mediation effect of risk in the model, we compared
the fit of the model that considers full mediation
of risk (Chi=31.626 (p=0.0004); RMSEA=0.09;
CFI=0.917; IFI=0.923; NFI=0.891; GFI=0.969)
with the fit of the model that considers partial
mediation (the one in this chapter) and with the
fit of the model without risk, only with direct
effects (Chi=0.113 (p=0.637); RMSEA=0.06;

Figure 2. Graphical representation of significant effects in the structural model for women and men

13


Are Signals a Solution to Perceived Risk and Opportunism in Mobile Shopping

CFI=0.970; IFI=0.955; NFI=0.954; GFI=0.975)
and the fit is better in the partial mediation model.
Besides, the indirect effects of signals on perceived
opportunism through risk are not significant.
Therefore, direct effects are more important than
indirect effects in the model and perceived risk
does not work properly as a mediating variable
in the model.

DO FEMALE AND MALE
M-SHOPPERS PERCEIVE SIGNALS,
RISK AND OPPORTUNISM IN
SIMILAR OR DIFFERENT WAYS?
In this chapter, we have identified and considered the possibility of a problem deriving from
taking advantage of information asymmetry in a
m-shopping context: m-shopper fear when selecting the m-vendor for m-shopping (perception of
opportunism). In order to address the commented
information asymmetry problem and drawing upon
the signalling theory, several proposed hypotheses
here have examined the influence of signals on mshopper opportunism and most of them have been
supported for women and not for men. Therefore,
female shoppers use vendor signals to infer quality
or behavior and vendors can send these signals
to the market to be chosen by female shoppers to
establish and maintain shopping relations.
Consistent with the findings by Chen et al.
(2010) and Kirmani & Rao (2000) and irrespectively of m-shopper gender, vendor reputation is
found to reduce perceived risk and to diminish
early adopters scepticism regarding opportunistic
transactions. Reputation should be highlighted in
order to contribute to the reduction of risk and
opportunism as other authors state (Xiong & Liu,
2005). Besides, advertising and promotion of mshopping should highlight the good image and
prestige of the m-site and vendor in its message.
In this sense, it is important that vendors develop
security protocols and try to receive accreditation
by reputable institutions (Pavlou et al., 2007;

14

Giannakis & Batten, 2011, Swamynathan et al.,
2010; Xiong & Liu, 2005).
Although reputation is the main signal to
reduce early m-shopper risk and perceived opportunism both for women and men, our findings
allow us to state that women are also receptive to
more signals probably because they do not have
as much experience shopping with the mobile
phone as men. They especially value the offer of
personalized services and products and information. The importance of personalization and risk
in m-shopping contexts was also highlighted by
Zarmpou et al. (2012) for m-shopping contexts,
but without a distinction of demographic characteristics. This detailed personalization is not
possible when shopping with the computer, thus
it can be a especially valued signal of quality and
good intentions of the m-vendor in the case of
m-shopping.
In our study, it is shown that visual design is
a relevant signal for women but less than others,
such as reputation to reduce perceived risk and
opportunism as it is only appearance and aesthetics, probably because of the type of products
mainly bought by the sample (applications, music
downloads and participations in contexts), which
are of low involvement. Therefore, the investment
in design may not be regarded as a useful cue to
reduce m-shopper risk and opportunism (it is a
small effect in the case of women and none effect in the case of men), which is different from
previous studies that state the relevance of design
in m-commerce (Li & Yeh, 2010). It does not
work as a signal of quality and this result can be
due to the fact that the sample has already bought
with their mobile phones and it influences only
the first time they buy with the mobile phone or
maybe because m-shoppers are used to distance
shopping and are not so impressed by images and
visual design of the m-site. Hence, site developers (especially the ones operating in the field of
applications for the mobile phone) should focus
less on design, especially if their target are men.


Are Signals a Solution to Perceived Risk and Opportunism in Mobile Shopping

In the case of men, if there is a perception of
risk in payment and transferring personal data, they
will also feel the need of controlling the m-vendor
and they can perceive that it is possible that the mvendor hides relevant information, does not fulfil
the promises made, avoids responsibility or only
looks for its benefit. In contrast, the perception
of risk in the case of women does not seem to be
related to the perception of m-vendor opportunism, maybe because opportunism involves more
risks than introducing personal and financial data
in the m-site, it is a more general concept than
financial and operational risk. Therefore, even in
the case of reducing perceived risk, women can
perceive m-vendor opportunism and that is why
they search for more cues or signals of quality
and behaviour.
In short, it is necessary that there is a good
reputation of the m-vendor in the market, the offer of personalized information and service and
an attractive visual design to reduce both risk and
opportunism in the case of women. The mere fact
of reducing women perception of financial risk
cannot by itself reduce perceived opportunism,
but it does help in the case of men.

SOLUTIONS AND
RECOMMENDATIONS
Firms around the world can take advantage of the
high penetration of mobile telephony and daily
habits of smartphone users, as it is a great opportunity for implementing m-shopping strategies.
According to ONTSI (2012), the perception of
risk when giving bank and personal data through
a site is an important impediment to Internet commerce in Spain, thus it is key for the m-vendor to
develop means of reducing perceived risk of their
target market of m-shoppers. However, not all
the vendors know how to manage relations with
their m-shoppers and do not know differences
according to their gender, which are useful for the

design and planning of their strategies addressing
their targets.
Given that m-shopping might entail a competitive advantage for the firm, especially at times of
economic uncertainty such as the current crisis,
vendors need to show early adopters that they are
reliable and non-opportunistic vendors. The use
of cognitive and experiential signals of product
quality and good vendor behaviour to achieve that
aim is especially important if the target of their
business are female consumers.
Companies selling with the mobile phone
should communicate clear and accessible information on privacy and security to reduce perceived
risk by consumers (Wang et al., 2004). This reduction of perceived risk is important for women as
signals influence them and it is important for men
as risk impacts perceived opportunism. Hence,
companies that want to enter or improve m-selling
should carry out studies on the transmission of
credible signals through their web sites and prepare
suitable gender-based signalling strategies in accordance with the target population. Our results
show that reputation is an important signal for all
shoppers, both for women and men. Due to the
increased number of women using Internet and
mobile phones in purchase decisions, companies
should give priority to the incorporation of systems
on their web sites that guarantee security in the
purchase process.
M-vendors should maintain a good reputation
in the market as this reveals as a relevant signal
for all the shoppers. On the contrary, visual design
seems not to be so important, which is inconsistent with other studies (Li & Yeh, 2010). Content
providers should develop and offer the specific
information that mobile users need and the personalization of contents and service to different
users, but especially in the case of women. Men
also value, to a certain extent, the offering of personalized information and advertising directed to
them, but definitively women are the m-shoppers
that mostly appreciate personalized information,
advertising, products, services, contents, adapta-

15


Are Signals a Solution to Perceived Risk and Opportunism in Mobile Shopping

tion of contents in time and place. In the case of
male targets, m-vendors should facilitate the clear
and secure introduction of personal and financial
data to reduce opportunism.

FUTURE RESEARCH DIRECTIONS
In the m-shopping research, there is still much
to be done. In order to compare the different
perspectives of the asymmetric relationship in mshopping, a future study could take into account
m-vendor point of view and its true intentions and
objectives when they send signals to the market.
Future research should address if the signals that
are perceived as more useful by m-shoppers are
the ones in which the m-vendor mostly invest or
are the ones mostly used to indicate quality and
behavior. Moreover, it would be interesting to analyze the moderating effect of the shopping channel,
e.g. computer vs smartphones as characteristics
are different and related signals or quality can
be affected by the shopping tool. Nevertheless,
there are some studies that suggest that in the
future there will be a unique virtual environment
of shopping without distinction of the shopping
channel (IAB Spain research, 2012).
In the study presented in this book chapter,
we have only analyzed one country (Spain) and
therefore results are limited to a certain kind of
products (low involvement products). Spain is
a country that has its particular characteristics
and it is a country where mobile phones are not
widely used to buy. In this sense, the results of this
study are valid for early adopters and cannot be
generalized to other types of shoppers. It would be
interesting to replicate this study in other cultural
contexts (considering different lifestyles) or with
other degree of development and considering
differences produced by the product or service
offered and by the m-shopping adopter. To this
aim, it would be advisable to validate the scales
in other contexts and use a wider scale for design
that includes usability apart from visual appeal-

16

ing. More signals of quality could be included in
the model (e.g. price premium, interactivity and
warranties).
Another proposal to improve this research
is to look into the used systems of payment by
women and men because it could influence
perceived risk and determinants of it in the case
of each target. Moreover, as there is still a lack
of empirical research in this area, future studies
should incorporate these and other variables,
such as relational variables -trust, satisfaction and
buying intentions-, to enrich the understanding of
m-shopping. In this sense, the focus of this book
chapter has been the impact of signals, but signals
do not seem to be the best way to reduce perceived
risk and opportunism in the case of men. Therefore
in the future it could be analyzed if other kind of
variables, such as satisfaction, is a better way to
reduce perceived risk by men.
Future research should analyze the moderating effect of m-shopping experience and other
demographic and socioeconomic characteristics
on the proposed model. Insofar as the perceived
risk is lower and individuals confront a distance
purchase as something habitual or a part of their
routine buying, it is probable that they will pay
less attention to signals (Rodgers et al., 2005;
San Martín et al., 2011) and that their perceived
risk and opportunism will be determined to a
lesser extent by these types of signals which the
m-vendor/site can use. Finally, we think that age
can also be a determinant variable in m-shopping
adoption both in the case of women and in the
case of men.

CONCLUSION
This book chapter contributes to a gap in the
academic literature in which there are not similar
studies regarding m-shopping. This study could
therefore be seen as a starting point for others on
the usefulness of signals that firms should take into
account to reduce perceived risk and opportunism


Are Signals a Solution to Perceived Risk and Opportunism in Mobile Shopping

when introducing commercial mobile platforms
to sell their products or services. Besides, our
study represents an interesting contribution to
the study of the moderating role of gender in a
mobile context. The main objective of this study
was to empirically analyze if men and women are
different regarding perceptions of signals, risk and
opportunism in the asymmetric relationships that
can exist in m-shopping. In our opinon, this line
of research is worth pursuing because it is one
of the most commonly used variables in market
segmentation and selection of the target population due to its accessibility and simplicity (Coley
& Burgess, 2003). To the best of our knowledge,
this study is the only one in the current literature
on m-shopping that addresses gender role in the
problem of information asymmetry that occurs
in m-shopping. In summary, the contributions
of this study are as follows. The first contribution of this study derives from the application
of variables from signalling theory to the study
of m-shopping. In this sense, this work signifies
an advance in the study of m-shopping and the
ways and the role of signals in reducing risk and
perceived opportunism and we do not know any
similar study for the m-shopping context. By applying the signalling theory to the m-shopping
context, we reach a better understanding of some
essential cognitive and experiential constructs and
shopping decisions that have been overlooked in
most m-shopping literature. The second contribution of this research lies in the analysis of gender
differences, which has relevant academic, but
also managerial implications. Results confirm
that gender is an important criterion to take into
account, and in each case, the effect of the signals
on perceived risk and opportunism is different.
The third contribution of this research is related
to the fact that we have used a sample of m-users
who have already bought with the mobile phone
(all them) and this is the first of this kind in Spain
as others are only based on information gathered
from users, not shoppers and none of them about
signals, perceived risk and opportunism.

Reputation is a key aspect in the reduction
of perceived opportunism for both women and
men, which indicates that once a buyer perceives
a reputable m-vendor or site, it should be key to
reduce risk related to the introduction of personal
and financial data in the m-site, irrespective of
the buyer’s gender. As women are less confident,
even if they manage to reduce their perceived
risk in payment and when introducing personal
data on the web through the inference of quality
and vendor behaviour with the help of m-vendor
signals, they still have a fear of opportunism. On
the contrary, for men it seems that the reduction
or risk in payment is enough to reduce fear of
vendor opportunism.
Maybe females are in a previous step of adopting m-shopping in comparison with men and
therefore are more sensitive to signals, which
help consumers in the first stages of adoption of
a technology to buy. Maybe men are the shoppers
that are less affected by superficial aspects of
shopping and are more focused on security and
risk in payment. In fact, some authors affirm that
as penetration and acceptance of technologies
such as the mobile phone increase, the moderating effect of gender will diminish (Zhou et al.
2007; Coley & Burgess, 2003), although from our
point of view, there will always exist differences
in perceptions, attitudes and behavior. It would
be advisable to repeat this study in the future as
segmentation criteria must be frequently revised
and gender role can change.

ACKNOWLEDGMENT
This work was supported by the Fundación Ramón
Areces (Spain) through funding (Grant reference
2010/00134/001) for a research project on the
future of m-commerce.

17


Are Signals a Solution to Perceived Risk and Opportunism in Mobile Shopping

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24

KEY TERMS AND DEFINITIONS
Default-Dependent Signals: Signals in which
the monetary loss occurs only when the firm
defaults on its claim.
Default-Independent Signals: Signals in
which the monetary loss occurs independently of
whether the firm defaults on its claim.
Mobile Shopping: The buying of products
and services to consumers through mobile phones.
Personalization: The ability of a vendor to
tailor products, services, information and the shopping environment to satisfy individual customers.
Reputation: The extent to which buyers believe a selling organization is honest and concern
about its customers.
Signal: Firm characteristic or cue that reveals
information about product quality or firm behaviour and that offers firm costs or revenues as a
hostage, bond, promise or commitment.
Visual Design: The appeal that user interface
and aesthetics presents to customers.

25

Chapter 2

Models and Approaches for
Web Information Extraction
and Web Page Understanding
Ruslan R. Fayzrakhmanov
Vienna University of Technology, Austria

ABSTRACT
This chapter discusses the main challenges addressed within the fields of Web information extraction
and Web page understanding and considers different utilized Web page representations. A configurable
Java-based framework for implementing effective methods for Web Page Processing (WPP) called WPPS
is presented as the result of this analysis. WPPS leverages a Unified Ontological Model (UOM) of Web
pages that describes their different aspects, such as layout, visual features, interface, DOM tree, and
the logical structure in the form of one consistent model. The UOM is a formalization of certain layers
of a Web page conceptualization defined in the chapter. A WPPS API provided for the development of
WPP methods makes it possible to combine the declarative approach, represented by the set of inference
rules and SPARQL queries, with the object-oriented approach. The framework is illustrated with one
example scenario related to the identification of a Web page navigation menu.

INTRODUCTION
Information is an inalienable part of today’s life.
This fact is clearly evident in the ongoing development and expansion of the World Wide Web
(the Web)—a huge information platform that has
provided vast opportunities for people by making it
possible to effectively solve various tasks in business, education, science, and our everyday lives.
With the help of the Web, a person can pay bills,
buy products and services, complete university

degrees online, search for and read articles, keep
contact with their friends and so much more.
The Web contains a vast amount of information
represented mainly on web pages in unstructured
and semi-structured forms. Web resources (i.e.
web pages) are primarily intended for human
consumption and thus their information content
is not accessible for automatic processing. The
necessity of developing methods for web page
understanding (WPU) and wrappers for web information extraction (WIE) is based on the need for

DOI: 10.4018/978-1-4666-7262-8.ch002

Copyright © 2015, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.


Models and Approaches for Web Information Extraction and Web Page Understanding

this information in computer-aided systems (e.g.
web form understanding for the meta-search or
extraction of prices and sentiments for the competitive intelligence) and the implementation of
different aspects of the Semantic Web and relevant
use cases (e.g. connecting recognized entities on
web pages with open data sources through Linked
Data technology, improving the performance of
information retrieval and query answering systems). Many methods and approaches for WPU
and WIE (hereinafter referred to as methods)
have been developed that target different forms
of web page representation: the source code (X/
HTML, XML), DOM tree (or tag tree), and visual representation rendered by the web browser
engine (e.g. Firefox’s Gecko, Chrome’s Blink or
Internet Explorer’s Trident). Each of these aspects
of a web page has its purpose, advantages and
disadvantages; however, consideration of web
page visual models is known to ensure the development of more robust and effective methods
which can be applied over a wider range of web
pages (Fayzrakhmanov, 2013, sec. 2.4). This is
due to the fact that merely the visual representation analyzed by the user exclusively reflects the
semantics and logical structure of a web page.
Furthermore, the analysis of visual cues also gives
a unique possibility to leverage certain principles
and laws of Gestalt theory which in turn reflects
processes of human object recognition (KrüplSypien, Fayzrakhmanov, Holzinger, Panzenböck,
& Baumgartner, 2011; Xiang, Yang, & Shi, 2007)
for developing more robust methods.
A conceptual gap between the source code (i.e.
the XML, X/HTML code and thus the DOM tree)
and layout structure has been growing even larger
(Oro, Ruffolo, & Staab, 2010) in recent years,
forcing developers and researchers increasingly
often to focus on visual features rather than the
source code. This tendency is related to the use
of various front-end technologies from the open
web stack, such as X/HTML, CSS and JavaScript, in the web development process. These
technologies thus impart a property of application

26

with rich functionality to the contemporary web
pages. Therefore, their automatic analysis should
be performed on their rendered state, taking into
account their visual and functional aspects.
In the absence of a standard to describe a web
page’s visual appearance suitable for WPU and
WIE, the development of new methods generally
encounters the challenge of defining necessary
features and relationships. To overcome this
problem and provide a convenient means for
developing new methods and approaches, a Web
Page Processing System (WPPS) was developed
along with the underlying Unified Ontological
Model (UOM) which formalizes the most required aspects of the web page conceptualization
introduced in this chapter. The proposed UOM
describes different aspects of a web page—its
interface (web forms, links, images, etc.), layout,
perceptible visual features, DOM tree, and logical
structure—in one consistent and easily extensible
model. During the development of the UOM,
different styles of representing the layout of web
pages (Kong, Zhang, & Zeng, 2006; Kovacevic,
Diligenti, Gori, & Milutinovic, 2004; Oro et
al., 2010), PDF (Hassan, 2010), and scanned
documents (Aiello et al., 2002) were considered.
WPPS is a means for developing new, effective
and robust methods analyzing different forms
of web page representations and profiting from
both declarative and object-oriented approaches
by employing the introduced bridged adapter
software design pattern.
The analysis presented in this chapter is mainly
based on the work of Fayzrakhmanov (2013),
where the interested reader can not only find a
detailed description of various concepts and aspects presented, but an underlying theory as well.
This chapter first introduces the term web page
processing (WPP) and its relation to the fields of
WIE and WPU, then conducts a comparative analysis of different approaches in terms of leveraged
web page models. It presents a conceptualization
of the web page and the UOM as a formalization
of selective aspects of the conceptualization. The


Models and Approaches for Web Information Extraction and Web Page Understanding

Figure 1. Web page processing: Generic classification diagram

chapter discusses an object-oriented abstraction of
the UOM and introduces a Web Page Processing
System for developing WPP methods, its API and
GUI. The chapter is concluded with a discussion
of future research directions and a short summary.

WEB PAGE PROCESSING (WPP)
The term web page processing (WPP) was loosely
inspired by document understanding (DU), a field
of research where the term “document processing” is related to the analysis and understanding
of mainly raster (or scanned) documents (Aiello, Monz, Todoran, & Worring, 2002). Other
formats, such as formatted textual and PDF, are
also considered in DU (Hassan, 2010). WPP is
applied to web pages and consists of the three
main processes: 1) transformation of a web page
into the required model (alternative or standard
representations, such as the source code or DOM
tree, can be utilized), 2) application of the method
of WIE or WPU developed and 3) conversion of the
obtained results into other formats or integration
into the external systems. It plays an important
role in research fields such as information search

(Nie, Wen, & Ma, 2008), web data mining (Hiremath & Algur, 2010), web adaptation (Gu, Chen,
Ma, & Chen, 2002), web accessibility (Mahmud,
Borodin, & Ramakrishnan, 2007), business intelligence (Baumgartner, Frölich, & Gottlob, 2007),
information integration (Bernstein & Haas, 2008),
and other fields (Ferrara, De Meo, Fiumara, &
Baumgartner, 2012).
There are two main areas of research within
the field of WPP depending on the type of task to
be solved: WIE and WPU. The classification of
different fields and their relations are illustrated
in Figure 1.

Web Information Extraction (WIE)
Web information extraction (WIE) is related to
the identification of relevant facts on a web page
and their representation in the structured form.
The problem of information extraction can be
considered as a problem of querying unstructured information resources and acquiring the
structured results. The main means of assessing
the effectiveness of a WIE system are based on
precision and recall (Miao, Tatemura, Hsiung,
Sawires, & Moser, 2009), which are both adopted

27


Models and Approaches for Web Information Extraction and Web Page Understanding

from the field of information retrieval. Thus, the
challenge of developing WIE, which within the
required time interval extracts information from
certain sources, with the required precision and
recall is of high concern. In terms of implementing
WIE, researchers and developers apply different
methods and approaches, including data mining
and machine learning (Liu, Grossman, & Zhai,
2003), logic programming (Baumgartner et al.,
2001), automaton-based methods (Hsu & Dung,
1998), various heuristics (Luo et al., 2009), as
well as approaches based on natural language
processing (NLP) (Cowie & Lehnert, 1996) and
ontologies (Holzinger, Krüpl, & Herzog, 2006;
Nédellec & Nazarenko, 2005).
WIE refers to the well-known fields of research
such as information extraction (IE) and web data
extraction (WDE). Examples of IE tools are
NoDoSE (Adelberg, 1998), RAPIER (Califf &
Mooney, 1999) and Crystal (Soderland, Fisher,
Aseltine, & Lehnert, 1995). Information extraction (IE) methods extract relevant facts, such as
events, appointments and quotations, from textual
content presented in natural language (Mitkov,
2005). IE also used in sentiment analysis that often
refers to the opinion mining for specific products
or services. Mainly dedicated to the analysis of
plain text and based on NLP techniques, classical methods of IE cannot be directly applied to a
web page. This is due to the fact that web pages
usually have a complex structure with elements
possessing various semantic roles (e.g., a navigation menu, main content, calendar, table, and
logotype) and providing different functionalities
(e.g., a link, button, and element with drag-anddrop function). Furthermore, contemporary web
pages are web applications with rich interface
and thus cannot be treated as formatted textual
documents. Therefore, IE methods applied to web
pages require a supplementary analysis of the web
page structure (e.g. segmentation). Due to the
presence of multi-media content, IE from web
pages can be accompanied with the application
of OCR (optical character recognition) methods.

28

Web data extraction (WDE) in turn targets data
to be identified on a web page, for example, the
name and price of a product, timetable of a flight,
or opening hours. WDE methods mainly analyze
the structure of a web page established by the
source code or DOM tree and have very limited
analysis of textual content which usually boils
down to the application of regular expressions.
The majority of contemporary web pages and
specifically those from the Deep Web are generated
by the web server (“on-the-fly”) upon the user
request based on the data stored in the back-end
databases. These systems are usually called web
content management systems. Thus, the problem
of extracting web data is very often considered as
an issue of data records extraction from a certain
database with unknown schema and mapping it
into the database with a known schema. Examples
of tools and approaches implementing this concept
are IEPAD (Chang & Lui, 2001), ExAlg (Arasu
& Garcia-Molina, 2003), DEPTA (Zhai & Liu,
2005) as well as the approach introduced by
Lanotte et al. (2014).
WIE consists of two main phases: wrapper
induction and direct information extraction (or
wrapper application). A wrapper is a template,
description, or program for extracting relevant
data or information. A wrapper is created during
the first phase. It can be performed manually, in
semi-automatic, or automatic manner (Chang,
Kayed, Girgis, & Shaalan, 2006). Wrappers reflect intrinsic (e.g., HTML tag, part of speech,
or color) and relative (e.g., sequence of elements
within the source code, position within the DOM
tree, or spatial alignment) features of objects to be
identified. However, the most common techniques
are based on absolute or relative position of the
required information object within some structural
representation of a web page, such as the source
code (Crescenzi, Mecca, & Merialdo, 2001), DOM
tree (Gottlob, Koch, Baumgartner, Herzog, &
Flesca, 2004), or graph-based structure (Zhu, Nie,
Wen, Zhang, & Ma, 2005). In the second phase,
the wrapper is applied for the certain set of web


Models and Approaches for Web Information Extraction and Web Page Understanding

pages which possesses the required properties.
Extracted structured information as the result of
this phase is then integrated into other applications
or transformed into other formats.
An overview of various WIE and primarily
WDE methods, approaches, and tools can be
found in the works of Chang et al. (2006), Ferrara et al. (2012), Kuhlins, & Tredwell (2003),
Laender, Ribeiro-Neto, & da Silva (2002) and
Sarawagi (2008).

WEB PAGE REPRESENTATIONS

Web Page Understanding (WPU)

There are four main groups of web page representations used in various methods of WPP: textual,
source code, tree, and visual. The latter is divided
into quantitative and qualitative representations.
A textual representation reflects the textual
content of a web page. It is usually generated by
the analysis of the source code (or the DOM tree
with JavaScript applied). All markup is removed,
and all textual content is laid out according to
the presentation semantics of X/HTML tags
itself (without consideration of the corresponding CSS style sheets). Textual serialization can
also be acquired via a web page segmentation
procedure and subsequent depth-first traversal
over the segmentation tree (Aiello et al., 2002;
Yesilada, 2011).
Information extraction from a one-dimensional
text usually includes grammatical and syntactic
analysis. However, text files containing data
structures, such as lists and tables, need additional
analysis of a two-dimensional layout of a text.
Wrappers of IE systems can be represented in
the form of logical rules, automata, or templates.
The most famous techniques for IE are based on
regular languages such as regular expressions
(Chang et al., 2006) used for extracting date, time,
and price. Noteworthy NLP tools include GATE,
LAPIS, The Dragon Toolkit, Stanford Named
Entity Recognizer, RAPIER, NoDoSE, Crystal,
and OpenCalais web service.
A source code of a web page written in X/
HTML or XML is a marked text which reflects the
structural characteristics of the content. Most of

Web page understanding (WPU) is related to understanding the logical structure of a web page and
its elements. The most common challenges include
segmentation of the web page on logically consistent blocks (Yesilada, 2011), table recognition
(Gatterbauer, Krüpl, Holzinger, & Herzog, 2005),
search form understanding (Furche et al., 2012),
and web page labeling (Nie et al., 2008). All these
methods have many applications related to web
data mining, improving effectiveness of information search (He, Cai, Wen, Ma, & Zhang, 2007;
Nie et al., 2008) and enhancing web accessibility
(Guo, Mahmud, Borodin, Stent, & Ramakrishnan,
2007). Very often a WPU is incorporated into the
IE methods. For example, Li, Liu, Obregon, &
Weatherston (2007) and also Liu, & Meng (2006)
adapt VIPS, a web page segmentation algorithm,
for data records extraction.
WPU is related to document understanding
in that it has a similar goal although its object of
research is different: raster document. Some applicable techniques for scanned documents can also
be applied for web pages after certain necessary
modifications are made which take into account
their fragmented nature (the CSS Object Model).
For instance, the XY-cut algorithm (Ha, Haralick,
& Phillips, 1995) invented for raster document
segmentation, was adapted by Gatterbauer et al.
(2005) for table recognition on web pages. Moreover, metrics such as precision and recall used in
WIE are also applied in WPU.

Various WPP methods leverage different representations of web pages which define their
peculiarities such as: effectiveness, efficiency,
robustness, and the set of web pages the WPP
method can be applied to.

Web Page Representations
in Web Page Processing

29


Models and Approaches for Web Information Extraction and Web Page Understanding

the relevant WIE techniques are based on regular
expressions and string alignment with string edit
distance metrics utilized. It is worth mentioning
the following WIE tools operating on the source
code level: SoftMealy (Hsu & Dung, 1998), IEPAD
(Chang & Lui, 2001), RoadRunner (Crescenzi
et al., 2001), DEByE (Laender et al., 2002), and
ExAlg (Arasu & Garcia-Molina, 2003).
A tree structure is generally presented by the
DOM tree or tag tree (usually modeled as an ordered unranked labeled tree) based on the source
code. A tree structure is isomorphic to the source
code if the latter is valid and well-defined, and
scripts which can change the tree are not applied.
Most of the well-known techniques which operate
on this representation are distinguished by using
XML technologies (e.g., XPath), tree alignment
method, and tree edit distance metrics. Examples
of tree-based tools and approaches are MDR of
B. Liu et al. (2003) (a heuristics-based approach),
Lixto Visual Wrapper (a visual and interactive
wrapper generation and data extraction tool)
developed by Baumgartner, Frölich, & Gottlob
(2007), the method for record extraction presented
by Miao et al. (2009), FiVaTech (a tool, dedicated
to automatically extracting objects with repetitive
structure) introduced by Kayed & Chang (2010),
OXPath (a rich XPath 1.0-based WDE language
with possibilities of interaction with web pages)
developed by Furche, Gottlob, Grasso, Schallhart,
& Sellers (2011).
A quantitative visual representation is usually
leveraged together with a tree representation.
Therefore, it is often defined as a tree enriched
with data acquired from the CSS Object Model
computed by the web browser engine. The data
quantitatively reflects attributes and relationships between objects. The major quantitative
information considered in relevant WPP methods
are coordinates of the CSS boxes, their width,
height, distance between boxes in pixels, and
color encoded in RGB. It is important to mention tools and approaches based on quantitative

30

visual information such as ViNTs (a system for
automatic wrapper generation for search engines)
realized by Zhao, Meng, Wu, Raghavan, & Yu
(2005), ViPER (an automatic web data extraction
tool specializing on data records) introduced by
Simon & Lausen (2005), the heuristics for article
extraction (i.e., news stories, encyclopedia entries, or single blog posts) presented by Luo et al.
(2009), VSAP (an automatic approach, in which
quantitative information is analyzed for mining
data records) of Hiremath & Algur (2010), and
MDRMTA (a method for structured object mining based on maximum text content comparison)
developed by Rahaman et al. (2010).
A qualitative visual representation is usually
modeled as a graph which reflects a set of web
page elements of different types with various
relations defined on this set. The relations specified for the rendered web page are mainly spatial
relationships between web page elements (mostly
CSS boxes), such as topology, alignment, distance,
and direction expressed qualitatively (Handbook,
2007). Thus, the analysis of a rendered web page
ultimately boils down to the analysis of its spatial
characteristics (its layout). Qualitative characteristics possess a number of advantages over
quantitative ones. In particular, they are used to
provide information in a comprehensive form for
both humans and computers and reflect various
spatial configurations of objects to be identified.
This is the basis for conducting automatic spatial
reasoning which is in some sense an analogy of
human reasoning (Handbook, 2007).
There are four main groups of web page representations used in WPP:
•

Models Based on Inclusion Relationship:
Which can be obtained by the application
of web page segmentation algorithms
(Yesilada, 2011), such as XY-cut (Ha et
al., 1995) and VIPS (Cai, Yu, Wen, & Ma,
2003). The most prominent approaches leveraging this model include the approach


Models and Approaches for Web Information Extraction and Web Page Understanding

•

•

•

for identifying and extracting tabular data
(Gatterbauer et al., 2005) and techniques
for extracting data records on the result
pages of web databases and search engines
(Li et al., 2007; W. Liu & Meng, 2006; Oro
& Ruffolo, 2011).
Models Based on Direction and
Alignment Relationships: In which relations are set between adjacent elements.
The classic example is a layout modeled
by means of an adjacency graph for data
extraction from PDF files (Hassan, 2010).
Models of this type are utilized, for instance, in the method based on the twodimensional CRF model for automatically
extracting web page objects (Zhu et al.,
2005) and in the web page classification
(Kovacevic et al., 2004).
Models Based on Interval Relations:
Utilized, for example, in the automatic
comparative analysis of web page segments (Cosulschi, Constantinescu, &
Gabroveanu, 2004) and the automatic extraction of data record sections from search
engine result pages (Zhao, Meng, & Yu,
2006).
Models Rich with a Variety of Spatial
Relationships: Are integrated in one consistent model. Examples of approaches leveraging these models are those based on
the graph grammar for web page adaptation for mobile devices (Kong et al., 2006)
and SXpath, a WDE language extending
XPath 1.0 and operating over DOM tree
enriched with different spatial relations
(Oro et al., 2010). These models are usually limited to certain tasks to be solved in
spite of the fact that they integrate different
spatial relations.

It is worth mentioning that methods utilizing
web page models based on qualitative features
usually do not operate over the DOM tree.

Analysis of Web Page
Representations and
Relevant WPPS Methods
Figure 2 schematically presents a general overview
of various methods and techniques applied for
different web page representations.
As it is symbolically depicted in Figure 2, complexity of the web page representation increases
from the textual representation to the qualitative
visual. However, robustness of the methods also
significantly increases for both the textual and
qualitative visual representations. This tendency
is confirmed by the fact that textual and visual
representations are natural forms of information
representation for human beings. Therefore, methods operating on these representations often reflect
and simulate processes which the human being
utilizes. For example, part of speech recognition,
sentence splitting, entity recognition for the text,
and analysis of relative spatial allocation of elements including their size, color, and typographical
characteristics for the rendered web page. Also
important is the Gestalt theory (Ware, 2004) which
investigates the peculiarities of human perception. It is mentioned in the work of Krüpl-Sypien
et al. (2011) as a principal means for analyzing
web page visual representation. Some aspects of
Gestalt theory are also used in the analysis of the
quantitative spatial characteristics by Xiang et al.
(2007). From the source code to the visual representation, the graph of robustness is also related
to the change frequency and number of relevant
design patterns. The source code and DOM tree
are prone to more frequent changes compared to
the visual representation. Furthermore, the number
of various visual design patterns and corresponding fashions of spatially arranging information
objects is considerably less than the set of various
ways of coding them. It is also important to note
that from the source code to the qualitative visual
representation, the efficiency of methods generally deteriorates due to the limitations imposed by

31


Models and Approaches for Web Information Extraction and Web Page Understanding

Figure 2. The survey of contemporary tools and techniques applied for different web page representations

the required generation of relevant models with
different levels of complexity.
The superiority of methods based on the DOM
tree over methods based on the source code is
largely confirmed in the work of Kayed & Chang
(2010), where the authors claim that FiVaTech
is more effective than ExAlg (Arasu & GarciaMolina, 2003), and in the paper of B. Liu et al.
(2003), in which the authors demonstrate that
MDR outperforms IEPAD (Chang & Lui, 2001).
Furthermore, Baumgartner et al. (2007) and Gottlob et al. (2004) demonstrate the effectiveness of
leveraging the DOM tree in contrast to the source
code by example of Lixto Visual Wrapper. The effectiveness of methods which additionally analyze
quantitative spatial characteristics in contrast to
methods merely based on the treelike structure

32

is proved in works by Hiremath & Algur (2010),
Rahaman et al. (2010), Simon & Lausen (2005),
and Zhao et al. (2005), where the authors compare
their approaches (VSAP, MDRMTA, ViPER, and
ViNTs respectively) to MDR (Liu et al., 2003).
The superiority of methods considering qualitative
spatial characteristics against methods considering
quantitative spatial characteristics is demonstrated
by Oro & Ruffolo (2011). The authors compare
their SILA approach to ViNTs (Zhao et al., 2005).
SILA also outperforms the tree-based MDR.
In general, all of the aforementioned web page
representations are still actively used for WPP. A
representation can be chosen based on the task
and the type of information to be extracted or
analyzed. For example, textual representation is
the most suitable if a web page mainly contains


Models and Approaches for Web Information Extraction and Web Page Understanding

textual information (e.g., a monologic text) and
the task of IE is posed. The source code and
tree representations are best suited for the WDE
from web pages which are modified relatively
seldom, have moderately simple structures (e.g.,
the source code explicitly reflects the structure of
web objects), or have regularities in their structures which can be mined and reflect features of
the object to be extracted (e.g., web pages of the
Deep Web generated with the specific template
from the back-end database). The source code and
DOM tree are well-studied, have corresponding
standards, and plenty of various approaches and
tools. Rendered (visualized) web pages are considered if the analysis of spatial configuration or
visual characteristics is required, for example, for
the table recognition, product list extraction, and
web page segmentation.
Thus, there are two main challenges which are
to be resolved in regards to the conducted analysis:

A WEB PAGE CONCEPTUALIZATION
AND THE UNIFIED
ONTOLOGICAL MODEL

•

Web Page Conceptual Model

•

In contrast to the source code and DOM
tree, there is no unified model or standard for visual representation of the web
page suitable for WPP. In developing new
methods, the researcher always encounters
the problem of designing a new model or
structure which can hold all the necessary
features and relationships. This aspect is
reflected further in the web page conceptualization and the Unified Ontological
Model.
There is a relatively small amount of methods leveraging visual cues. Various visual
features and relationships are used in different methods, and therefore the issue of
discovering the appropriate visual cues
which will favor the development of efficient and robust approaches of WPP is of
paramount concern. This aspect is reflected in the WPPS system introduced further
in the chapter.

In terms of modeling web pages, it is necessary to
define their general conceptual model reflecting
their various aspects as well as to formalize relevant objects and characteristics. At the moment,
there is no unified web page conceptualization or
model which can contain information necessary
for developing WPP methods, discovering various
potentially important attributes and relations. This
section thus introduces a web page conceptualization, which represents a web page as a sequence
of layers providing a different level of abstraction
necessary for different types of tasks, as a proposal
for further research related to the problem of WPP
and development of the Semantic Web technology.
A Unified Ontological Model also described in
this section formalizes specific layers.

A generic conceptual model of the web page
(Fayzrakhmanov, 2013; Krüpl-Sypien et al., 2011)
consists of nine abstraction layers that serve different tasks (see Figure 3): 1. Source code layer
is represented by the X/HTML, or XML source
code in conjunction with the JavaScript code,
CSS style sheets, and additional objects such as
multimedia files, Java applets, Flash, Silverlight
applications, etc. 2. Web page rendering layer
consists of the DOM tree (generated by a web
browser engine from the source code by applying CSS rules and JavaScript code). The existing
methods that work on this level make use of the
DOM tree and computed CSS attributes to localize the visualized DOM elements. 3. Geometric
layer is the result of the analysis of the web page
layout and, in particular, spatial characteristics of
visualized DOM elements (e.g. CSS boxes). This
layer describes the geometry of a web page layout
expressing information primarily in qualitative
form. It plays an important role in the analysis of

33


Models and Approaches for Web Information Extraction and Web Page Understanding

the spatial configurations of a web page and detecting objects corresponding to particular spatial
patterns. 4. Interface layer provides information
regarding the functional role of elements on web
pages and defining the functionalities of graphical user interfaces. 5. Gestalt layer is intended for
reflecting the process of human visual perception according to Gestalt theory, its main laws
and principles. This layer allows the possibility
to investigate such a process and its use in the
problem of web page understanding. A Gestalt
layer is based on the geometric and interface layers
while also taking into account different visually
perceptible features. 6. Data structure layer uses
data structures to model various logical objects
on a web page. For instance, a navigation menu
can be represented as a list or tree and an article
with sections and subsections can be represented
as a tree whereas data that is visually arranged in
a table can be mapped into its logical counterpart
such as a coherent table. 7. Layer of web specific
objects contains common genre-independent web
objects, such as navigation menus, headers, footFigure 3. Layered conceptual model of the web page

34

ers, and main content. 8. Layer of domain specific
objects refers to web objects of particular web page
genres. For the web forum genre, for example,
these objects include forum thread, topic, post,
and reply. 9. Layer of textual content semantics
is dedicated to representing the meaning of the
textual content, its linguistic characteristics and
logical (semantic) structure.
Although each layer addresses a different level
of abstraction, all of them serve the same purpose
in the spirit of the Semantic Web: to enable a more
precise and machine-understandable semantic
description of a web page.

The Unified Ontological Model
of Web Pages (UOM)
The Unified Ontological Model (UOM) is a formalization of the web page conceptualization that is
suitable for automatic processing. The goal of the
UOM is to formalize all the layers of the web page
conceptualization, in particular layers 2–9. The
current version of the UOM predominately formal-


Models and Approaches for Web Information Extraction and Web Page Understanding

izes the conceptualizations of layers 2–4 and 6.
Thus, the UOM consists of two main sub-models:
the Physical Model (PM) and the Logical Model
(LM). The PM is a set of domain ontologies that
correspond to the physical layers of the conceptual
representation of the web page; it is a conjunction
of the Extended DOM (DOM*), corresponding
to the web page rendering layer, the Block-based
Geometric Model (BGM), corresponding to the
geometric layer, and the Interface Model (IM) of
the interface layer. The Extended DOM incorporates DOM trees of web pages together with
computed CSS attributes in the one consistent
model. The BGM (Fayzrakhmanov, 2012a) is
the main sub-model of the PM and describes the
layout and geometric structure of a web page by
means of quantitative and qualitative information (e.g. interval relations, topology, alignment
and direction). A theoretical background of this
model is presented in (Fayzrakhmanov, 2013, ch.
3). The Interface Model represents the functional
elements of a web page (e.g. links, buttons, images
and HTML 5 specific elements such as article,
section, canvas and data) and basic structures
(e.g. lists and tables), which are acquired due to
the analysis of the Extended DOM. The LM is
a set of domain ontologies which model certain
aspects of logical characteristics of the web page:
data structure, web specific and domain specific
objects. In general, the LM corresponds to layers 6–9 and can be naturally extended by other
ontologies and datasets by means of Linked Data.
The LM is used in conjunction with the PM as
the annotation of certain objects of the PM and
serves as their interpretation. OWL 2 DL (with
RDF-based semantics) is utilized for modeling
the UOM as it provides us with the necessary
expressive power.
Interested readers are encouraged to refer to
works of Fayzrakhmanov (2013, ch. 3, 4).

WEB PAGE PROCESSING
BASED ON THE UNIFIED
ONTOLOGICAL MODEL
WPP processes can be represented consisting
of three main phases when applied to the UOM
(see Figure 4): 1) Physical Model instantiation
(analysis of a web page’s technical layers), 2) web
page understanding and direct web information
extraction, and 3) information transformation and
integration.
Physical Model instantiation is based on the
data models provided by the web browser engine
(i.e., DOM trees and CSSOMs). This process can
be controlled by the specified configuration which
defines necessary constraints (i.e., what should be
modeled and materialized in the certain instance
of the PM). In turn, WPU and direct WIE are
performed leveraging the PM. The aim of these
two processes from the viewpoint of the UOM is
to provide an interpretation in the form of the LM
for the concepts of the PM by means of domain
ontologies and Linked Data technology. Thus, in
terms of the UOM, WIE is a process of the PM
analysis performed according to the query specified. A query can be represented by the wrapper
which defines the necessary information to be
extracted as well as its main characteristics. It
can be realized as an algorithm, template or a
query over ontology such as a SPARQL query. In
terms of the UOM, methods of WPU operate over
the PM by mainly targeting the whole web page.
The principal goal of WPU methods is to derive
semantics of a web page hidden in the materialized PM according to the viewpoint indicated.
The viewpoint defines the necessary granularity
required for describing the web page semantics and
realizing it in the materialized LM. For example,
WPU can be limited to the web page segmentation
and building a segmentation tree in the LM, or it
can consider more web specific objects such as a
navigation menu, main content, header, or footer.
Information transformation is a process aimed at
providing information in the form appropriate for

35


Models and Approaches for Web Information Extraction and Web Page Understanding

Figure 4. Data flow diagram of web page processing

external applications. It can be represented as an
XML document, tuples in the relational database,
or assertions in the knowledge base.

AN OBJECT-ORIENTED
ABSTRACTION FOR THE UNIFIED
ONTOLOGICAL MODEL
An application of the UOM in the WPP requires
a presence of mechanisms which enable leveraging both declarative and imperative (procedural)
approaches. This requirement is connected to the
active development of various methods of WPP
which are mainly implemented using imperative
languages.
Ontology provides ample opportunities of
applying various declarative approaches which
enable automatic reasoning and logical deduction
as well as querying. Examples of declarative languages used for WDE from the ontology include
Datalog± (Calì, Gottlob, Lukasiewicz, & Pieris,
2011), SWPML (Krüpl-Sypien et al., 2011), and

36

HιLεX (Manna, Oro, Ruffolo, Alviano, & Leone,
2012).
In the set of imperative languages, those based
on the object-oriented paradigm are the most
suitable for representing certain aspects of the
ontology. For instance, they introduce concepts of
class and object (as instance of certain class), their
attributes (data fields) and procedures (methods).
Thus, a class can be associated with the class in the
OWL ontology while an object can be associated
with the object in OWL, and data fields can be
represented by the properties in OWL.

A Required Abstraction
A certain level of abstraction is required to provide access to the instances of the UOM for both
declarative and object-oriented approaches (see
Figure 5).
However, this abstraction is not necessary
for declarative languages such as SPARQL and
SWPML. This is due to the fact that the UOM
and the PM, in particular, can be easily spelled


Models and Approaches for Web Information Extraction and Web Page Understanding

Figure 5. A required abstraction from the ontology for applying methods based on the object-oriented
paradigm

into the RDF syntax which is a target ontology
representation for leveraging these languages. In
contrast, Datalog± and HιLεX require certain
transformations into the relevant representation
which enables their straightforward applications.
For example, Datalog± requires representation of
the UOM in the Datalog± style with intensional
and extensional databases for TBox and ABox
respectively. For HιLεX, the ontology should be
represented in terms of OntoDLP. This transformation is out of the scope of this chapter.
A difference in the nature of ontological and
object-oriented languages thus demands the creation of an object-oriented abstraction over the
UOM which can be specified by the following
requirements to ensure their seamless integration:
1) independence from the applied reasoner from
the predefined set of reasoners; 2) robustness
against partial instantiation of the PM (if only

relevant objects and properties are materialized in
the ontology); 3) possibility to choose a preferred
mode for obtaining properties (e.g., obtaining the
distance between objects by querying the ontology
or computing it “on-the-fly” based on coordinates); 4) constant and reciprocal synchronization
between the ontology and object-oriented layer.

A Bridged Adapter
In this chapter, the problem of implementing the
object-oriented abstraction is considered from a
practical point of view. Thus, a design pattern
which incorporates the introduced requirements
was developed.
A bridged adapter software design pattern
(Fayzrakhmanov, 2012b) is based on patterns
such as adapter, bridge, and factory method. This
pattern is recommended if it is needed to provide

37


Models and Approaches for Web Information Extraction and Web Page Understanding

access to a certain adapted object (adaptee) which
has an interface or structure different from what
is required, and where the adaptee either does
not have a strictly defined interface (behavior)
or its structure is not permanent. For instance, an
individual of the OWL ontology representing a
block in the BGM can have some subset of relevant
datatype and object properties materialized while
some subset of other properties is taken to be computed upon request with parameters of fuzziness
specified, and which can also be changed. In this
case, an interaction with such an object and its use
in algorithms can be problematic. Furthermore,
it can lead to the so-called problem of the boilerplate code. As a solution to this issue, the pattern
proposed allows the developer to have the same
adapter for the ontological object (individual)
while implementations, which map different interfaces, are selected according to the specification
of the object (ontology configuration).
In Figure 6, a class diagram depicts the
proposed design pattern. Adaptee represents an
adapted object (which in our case is an individual
of the ontology) and has a predefined configuration represented by the class Configuration.
An interface AbstractAdapter is instantiated by
the AdapterFactory and is a representation of
the Adaptee required by the Client. An Adapter
class implements the interface AbstractAdapter
mapping it into the Adaptee by means of the
AbstractImplementor provided by the ImplementorFactory during the instantiation of the Adapter.
The ImplementorFactory dynamically creates an
implementation of the AbstractImplementator interface, which corresponds to the object wrapped
(adaptee) and its configuration. Library contains
all necessary implementations for various valid
configuration parameters of the adapted object.
In terms of the UOM, a library should have an
implementation of all necessary basic queries.
Thus, a bridged adapter pattern can be applied to certain classes in the ontology providing the developer with required adapters whose
implementations correspond to the ontology

38

configuration. The Adaptee can represent certain
classes provided by the ontology frameworks
with low-level API, such as Jena and OWL API.
Adapters, symbolically represented by the AbstractAdapter interface and the Adapter class,
can form a taxonomy of interfaces and classes
reflecting a subsumption of classes within the
ontology. For instance, for ontological assertions subclassOf(Button, WebFormElement),
subclassOf(TextField, WebFormElement), and
subclassOf(WebFormElement, WebForm), the
corresponding Java classes Button and TextField
can be used for adapting different adaptees forming two disjoint sets, whereas a WebFormElement
represents both types of adaptees. When designing
the taxonomy of adapters, it is recommended to
follow the guidelines presented by Kalyanpur,
Pastor, Battle, & Padget (2004). In addition,
JOPA (Kremen & Kouba, 2012) can be used for
generating the taxonomy and basic functionalities
of adapters. As such, the problem of type casting
can be resolved either on the ontology level or the
abstraction level.
The introduced bridged adapter pattern allows
the possibility to implement a solution satisfying
the requirements specified earlier. According
to the ontology configuration in particular, the
developer can define which reasoners should be
applied and which objects and properties can
be instantiated, as well as how they should be
computed in compliance with Req. 1–3. This approach does not make a strict separation between
object-oriented model and ontology. All object
fields are not stored within the object-oriented
model but are acquired or computed based on the
information stored in the ontology. Modification
of object fields is also immediately reflected in the
ontology. This ensures the possibility of conjunctive utilization of declarative methods provided
by the ontology framework and object-oriented
program and satisfies Req. 4. The bridged adapter
software design pattern plays an important role
in the WPPS framework.


Models and Approaches for Web Information Extraction and Web Page Understanding

Figure 6. Software design pattern “bridged adapter”

A WEB PAGE PROCESSING
SYSTEM (WPPS)
A Web Page Processing System (WPPS) is intended
for: 1) the rapid development of new methods for
web page understanding and information extraction tasks; 2) leveraging benefits of declarative and
object-oriented approaches in accordance with the
bridged adapter design pattern; 3) investigating
the abundant forms of web page representations,
relations and features formalized with the UOM for
detecting those most appropriate for solving specified problems (Fayzrakhmanov, 2012b, 2012c).
The framework provides various parameters
for configuring ontological models and modes
of their generation. Thus, the developer can
specify a set of models, attributes, and relations

the WPPS framework should instantiate in the
UOM as well as methods for their computation
(e.g., whether to store attributes and relations in
the ontology or compute them “on-the-fly” based
on the quantitative or basic qualitative relations).
WPPS makes it possible to control a level of fuzziness for computing attributes and relations and
provides a unified access interface via a WPPS
API independent from a particular configuration
of the UOM. Moreover, an integrated R-tree index
is built taking into account inaccuracy (fuzziness)
specified for corresponding qualitative spatial
relations such as containment and intersection.
The R-tree provides a possibility of performing
efficient queries against the geometric space of
a web page (i.e., the BGM) with the complexity
of search between O(logmN) and O(N). All these

39


Models and Approaches for Web Information Extraction and Web Page Understanding

factors contribute to the novelty of the WPPS
framework and its effectiveness in developing
new web page processing methods.

The general architecture of the WPPS framework is illustrated in Figure 7. It consists of several
components:

Architecture

•

The WPPS framework is an Eclipse (Indigo) RCP
based cross-platform application implemented in
the Java language (JDK 1.7.0). It was successfully
tested on different operating systems, such as
Ubuntu, Mac OS X, Windows XP, and Windows 7.
WPPS has XULRunner (version 1.9.2 corresponding to Firefox of the version 3.6) integrated for
rendering web pages. WPPS also utilizes the ATF
project plug-ins of the version 0.3.0, which enables seamless integration of XULRunner within
the Eclipse RCP platform and conveys additional
graphical components and widgets for interacting
with the web browser (i.e., XULRunner).
Figure 7. Architecture of the WPPS framework

40

•

•

UOM Manager: Realizes the UOM by
means of the Jena ontology framework and
applies required reasoners. It also provides
access to the ontologies by means of an
API and SPARQL engine primarily implemented by Jena.
Configuration Manager: Is responsible
for configuring the WPPS framework
and controls settings of an instance of the
UOM and modes of computing features
and relations.
Core: Provides the basic functionality to
interact with an instance of the UOM via
the UOM manager. As such, it possesses
a collection of different implementations


Models and Approaches for Web Information Extraction and Web Page Understanding

•

•

•

•

•

relevant to various valid configurations
of the WPPS and allows the application
of SPARQL queries and logical inference
rules, which are handled by Jena. The core
is also responsible for processing different
inaccuracies (fuzziness) while computing
qualitative attributes and relations.
Adapter Layer: Implements the bridged
adapter software design pattern and enables the application of heuristics over the
ontologies. Thus, the use of the adapter
layer enables leveraging both declarative
and object-oriented paradigms.
Physical Model Generator: Is responsible
for generating the PM of the UOM relevant
to a certain web page and a configuration
provided. DOM trees and CSSOMs are
acquired from the integrated XULRunner.
This component is easily extensible for
other web browsers and sources (for example, PDF document).
WPPS API: Is based on the adapter layer
and provides the main functionality necessary for developing web page processing
methods.
WPPS GUI: Is a convenient interface for
invoking developed methods, applying
different configurations by the user and
investigating various aspects of web page
representation.
Web Page Processing Methods: Is a
set of methods, primarily represented as
Eclipse’s plug-in fragments, with predefined configurations designed for solving specific problems.

WPPS Configuration
Configuration of the WPPS framework is represented by an XML file which allows the developer
of a new method to control the process of model
generation including the computation of attributes
and features, speeding up the process of building an
instance of the PM and ensuring efficient interac-

tion via the WPPS API provided (Fayzrakhmanov,
2013, sec. 5.3.2). All the configuration parameters
can be grouped into several categories: 1) model
configuration, 2) object configuration, 3) property
configuration, 4) fuzziness, 5) relevant web page
area, and 6) simplification.
A model configuration specifies the topology
of instantiated sub-models of the UOM as well
as inference logical rules and reasoners applied.
Object configuration mainly defines the number
of object classes to be instantiated within the
ontology. Property configuration specifies representation of OWL properties in the ontology as
well as the mode of computing them “on-the-fly”.
For example, some properties can be computed
by WPPS based on basic ones on the API level
(e.g., width based on the coordinates of an object’s
endpoints or alignment between objects based on
the interval relations). WPPS also allows derivation of properties based on the logical rules and
subsumption hierarchies on the ontology level. The
developer can also specify fuzziness in computing
different qualitative spatial relations taking into
account the inaccuracy in rendering web page and
spatial configurations of web objects. He can also
set the area of a web page canvas which should
be considered in the process of PM instantiation
(Figure 4) and invoke the simplification process
which omits all invisible objects (which are about
37.7% of all objects on a web page on average).

WPPS API
The API of the WPPS framework provides all
the necessary functionalities for the developer
in realizing WPP methods. The functions of the
basis API can be split into three main groups: 1)
selectors, 2) processing functions, and 3) statistical functions.
1. Selectors are those functions that allow the
selection of a specified subset of objects from
the instance of the UOM. Extracted ontological
individuals are adapted by the corresponding Java
objects by the use of the bridged adapted design

41


Models and Approaches for Web Information Extraction and Web Page Understanding

pattern and wrapped by a Java object of type
IResults representing them as a sequence. Selection can be performed based on the type of object
(e.g., “image,” “box,” or “html link,” see. Listing
1, i), a predicate specified (viii), or SPARQL
query. Furthermore, the object contained (iv) or
intersecting specified area can be selected; in this
case R-tree is involved for efficiency.
2. Processing functions were designed to process wrapped objects acquired from the instance
of the UOM by means of selector functions. The
following functions are available when treating a
result collection of objects as a set: intersection,
union, selection. Moreover, functions of this group
make it possible to order elements as well as group
result objects into subsets, split the result into
sequences (vii), and form trees and grids using
the predicates specified by the developer.
3. Statistical functions provide means for
computing aggregated values, such as mean,
median, variance, minimal and maximal values,
over a set of objects and set of pairs of adjacent
objects in the result sequence. The latter is useful for computing some characteristics regarding
relationships between adjacent objects in the
result collection, such as average spatial distance
between neighboring elements.
Example 1. To demonstrate, Listing 1 presents
a wrapper implemented using the WPPS API
for extracting a horizontally oriented navigation menu. A navigation menu is defined as a
sequence of menu items with spatial relations
“east-orthogonal-visible-block-of” (ix) and
“bottom-aligned-with” (x) defined between them.
Each menu item is a link (v) containing nonempty
textual elements (vi). The expected location is on
the top of a web page in a rectangular area with
the height of 250 px (iii) (see Listing 1).
The screenshots in Figure 8 demonstrate the
result of applying the wrapper specified in Example 1 (with minor necessary modifications).
As we can see, the wrapper reflects the natural
definition of the navigation menu perceived by

42

the user and can be applied on a wide range of
web pages with different source codes.
WPPS was applied in different problems including web accessibility (Fayzrakhmanov, 2013,
sec. 6.5) and basic web object identification (Kordomatis et al., 2013). In these fields, WPPS was
leveraged as a platform for rapidly building other
applications. Furthermore, an exhaustive evaluation and detail demonstration of the application
of the WPPS API is presented in Fayzrakhmanov
(2013), sections 5.3.4, 5.4.2 and 5.5.

WPPS GUI
A GUI of WPPS (see Figure 9) enables users and
developers to apply different web page processing methods and visualize results. A WPPS GUI
contains versatile visual components provided by
the ATF project for visualizing and modifying the
DOM tree and CSSOM. It also contains a component (Ontologies Graph view) for visualizing
the UOM by means of graph diagrams.

FUTURE RESEARCH DIRECTIONS
Due to the constantly growing interest in leveraging visual representations of a web page, it is
important to further improve the UOM as well as
formalize other layers of a web page conceptual
model. Furthermore, the formalization of the
Gestalt layer can be a huge step towards the development of more robust algorithms reflecting
certain mental processes performed by humans
during pattern recognition. It is also crucial that
the logical layers be investigated further and various web specific and domain specific objects be
defined in publicly available ontologies. The use
of such datasets with Linked Data technology can
dramatically improve accessibility of web pages
for both humans and computers.
The problem of providing object-oriented representation of the application domain for a given
ontology is one of the most important challenges


Models and Approaches for Web Information Extraction and Web Page Understanding

Listing 1. The source code based on the WPPS API for horizontally oriented navigation menu identification
// 1. get all links, which contain nonempty textual elements,
// from the top part of a web page
IWebDocumentBlock doc = api.getObjectByType (// (i)
IWebDocumentBlock.class);
Rectangle2D area = doc.getTopWebPage()
.as(IQntBlock.class).getArea(); // (ii)
area.yMax = 250; //px (iii)
IResults res = api.getObjectsContainedInArea(// (iv)
area, new IIEFilter() {
public EFilterResult apply(IQntBlock v) {
if (v.canAs(IHtmlLink.class) // (v)
&& v.as(IHtmlLink.class).getString().length() > 0) // (vi)
return EFilterResult.ACCEPT;
else return EFilterResult.REJECT; } });
// 2. join objects by relations EAST_ORTHOGONAL_VISIBLE_BLOCK_OF
// and BOTTOM_ALIGNED_WITH
// (vii)
res = api.groupInSeq(res, new IIEPredicate2() { // (viii)
public Boolean apply(IInstanceAdp v1
, IInstanceAdp v2) {
IQltBlock b1 = v1.as(IQltBlock.class);
IQltBlock b2 = v2.as(IQltBlock.class);
return b2.hasRelation(b1
, EBlockQltRelation
.EAST_ORTHOGONAL_VISIBLE_BLOCK_OF) // (ix)
&& b2.hasRelation(b1
, EBlockQltRelation
.BOTTOM_ALIGNED_WITH); // (x)
} });

Figure 8. Screenshots of results of identifying a horizontally oriented navigation menu with the use of
WPPS

43


Models and Approaches for Web Information Extraction and Web Page Understanding

Figure 9. Screenshot of the WPPS GUI. The figure demonstrates the result of applying a wrapper
“Cathegory” for a web page edition.cnn.com

considered in model-driven engineering. In order
to make substantial contributions to model-driven
engineering, further research should target the
challenge of automatic generation of the objectoriented abstraction and relevant API according
to the requirements defined. This research should
also consider issues of instantiating a model which
is unknown in advance but is compliant with certain requirements or possess certain properties.
Another interesting challenge consists of the
integration of knowledge bases represented by

44

means of formalisms differing from RDF and
OWL, for example, Datalog± or HιLεX. This will
allow the application of alternative approaches
for WPP.
Furthermore, limitations of the application of
various membership functions (reflecting inaccuracies on a web page layout) for computing
qualitative spatial relations and R-tree should be
investigated as well.


Models and Approaches for Web Information Extraction and Web Page Understanding

CONCLUSION
This chapter gives a broad overview of different
approaches and main principles of web information extraction (WIE) and web page understanding (WPU). It discusses the main forms of web
page representations and proposes a web page
conceptualization which reflects different aspects
of web pages. This conceptualization is proposed
as a classification of different abstractions of
a web page and intended for further research
and formalization. In this chapter, the Unified
Ontological Model (UOM) is presented which
formalizes the main aspects of a web page, such
as technical, visual and logical data structure
layers. This model is also considered as a part
of web page processing (WPP) that defines the
challenge of applying imperative approaches and
building certain abstraction for the UOM. This
was implemented in terms of the bridged adapter
software design pattern. The proposed models
were realized in WPPS, a Java-based framework
for developing effective and robust methods and
approaches that address problems in the fields of
WIE and WPU. WPPS has API, which provides
basic functionality for querying and processing
data obtained from the ontology. Interested readers
are encouraged to refer to Fayzrakhmanov (2013).

ACKNOWLEDGMENT
This research is supported by the Austrian Science Fund (FWF) under grant P25207-N23 and
by ZIT–Die Technologieagentur der Stadt Wien
under grant 943997 (RankEx project).

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KEY TERMS AND DEFINITIONS
Information Extraction: Is a method for analyzing texts expressing facts in natural language

49


Models and Approaches for Web Information Extraction and Web Page Understanding

and extracting relevant pieces of information from
these texts (Nédellec & Nazarenko, 2005).
Information Extraction: Is the process of
automatically identifying facts of interest from
pieces of text, and so transforming free text into
a structured database (Corney, Byrne, Buxton,
& Jones, 2008).
Web Data Extraction System: Is a software
system that automatically and repeatedly extracts
data from web pages with changing content and
delivers the extracted data to a database or some
other application (Baumgartner, Gatterbauer, &
Gottlob, 2009).
Web Information Extraction: Is a process of
identifying the relevant facts on a web page, tak-

50

ing into account the complexity of the web page
structure and the content in natural language, and
their representation in the structured form.
Web Page Processing: Refers to the analysis
of a web page for extracting specific facts or
understanding its logical structure and semantics.
Web Page Understanding: Is related to understanding the logical structure of a web page
and its elements.
Wrapper: 1. Is a template, description, or
program for extracting relevant data or information. 2. A program that identifies the desired data
on target pages, extracts the data and transforms it
into a structured format (Baumgartner et al., 2009).

Section 2

Web Applications

52

Chapter 3

A Roadmap on Awareness
of Others in Accessible
Collaborative Rich
Internet Applications
Leonelo D. A. Almeida
Federal University of Technology-Paraná (UTFPR), Brazil
M. Cecília C. Baranauskas
University of Campinas (UNICAMP), Brazil

ABSTRACT
Web 2.0 represents a shift from static to highly dynamic, participative, and collaborative Web. However,
most of Rich Internet Applications (RIAs) are still not accessible; as a consequence, universal participation is still far from being a reality. Providing accessible means for “awareness of others” (i.e. a
perception of the activities of others in the system) is essential in RIAs to enable collaboration among
all users. This chapter explores, through a systematic literature review, studies approaching the topic in
accessible collaborative RIAs. The authors also identify the technologies proposed, extended, or used
by those studies. As results they characterize the studies in the area and clarify the state-of-the-art of
technologies for supporting awareness of others. Finally, the authors propose a set of guidelines aiming
at supporting the design of mechanisms for awareness of others in collaborative RIAs.

INTRODUCTION
The second wave of websites, also known as
Web 2.0 brought significant innovations not only
regarding technology, but also on social aspects.
In the Web 2.0, aspects such as user participation,
collaboration, real-time interaction, awareness of
others (i.e. an understanding of the activities of

others, which provides a context for our own activity (Dourish & Bellotti, 1992) while in a shared
interaction space), and social networking are in
the spotlight (Gibson, 2008). Also, websites are
being gradually extended to Web applications,
renamed Rich Internet Applications (RIAs). Such
applications are increasingly becoming more
robust and some of them can already rival with

DOI: 10.4018/978-1-4666-7262-8.ch003

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A Roadmap on Awareness of Others in Accessible Collaborative Rich Internet Applications

desktop applications. Mesbah, van Deursen, & Roest (2012) point out some reasons for the adoption
of RIAs: no installation effort on the client-side;
everybody using the most recent version; access
from anywhere with Internet access (both to applications and user data); new collaboration and
community building opportunities.
Interaction spaces in RIAs may be highly dynamic, and content updates may involve responses
for user requests and environment updates (i.e.,
updates automatically triggered by the application). Both types of updates occur without requiring reload of the whole page. The environment
updates are especially important in collaborative
RIAs (i.e., RIAs in which users collaborate with
each other in shared interaction spaces). A significant amount of such updates is concerned with
enabling awareness of others’ identity, presence,
actions, objects, etc.
The novel possibilities enabled by RIAs are
essential to support relevant aspects of the Web
2.0 as participation and collaboration. On the other
hand, there is an increasing concern on the access
to RIAs. RIAs should not only be accessible by
every person but also provide equivalent possibilities of participation and collaboration (Mori,
Buzzi, Buzzi, Leporini, & Penichet, 2011). Initially
focused on people with disabilities and the AT (Assistive Technologies e.g., voice browsers, screen
readers) used by them to access computer applications, accessibility may the extended to every
person since all of us are different in diverse aspects
of life and we are constantly affected by context
and temporary constraining situations (e.g., in a
noisy environment, a broken arm). Accessibility
features are not limited to the User Interface (UI),
they can also influence the application architecture
and overall features (Jeschke, Vieritz, & Pfeiffer,
2008); however, diverse problems can be solved by
providing semantic for elements and relationships
among them. In (collaborative) RIAs semantically
meaningful elements must also provide information about state changes and dynamic updates in a

(shared) interaction space, especially those related
to awareness of others.
Since RIAs involve the proposition and extension of web guidelines and standards (e.g.
HTML5, CSS3, WAI-ARIA), authoring tools,
development frameworks, user agents (any software that retrieves, renders and facilitates end
user interaction with web content (World Wide
Web Consortium [W3C], 2013b)), and other supportive technologies, there are several ongoing
researches and products. However, the knowledge
about this research topic is still fragmented and
usually results in a lack of standardized behavior
for the existing RIAs. As a consequence, people,
especially those with disabilities, face several
problems while interacting with RIAs (Buzzi,
Buzzi, Leporini, Mori, & Penichet, 2010).
This chapter presents a Systematic Literature
Review (SLR) of studies approaching awareness
of others in collaborative accessible RIAs. The
proposed SLR is based on four review questions
encompassing: (a) disabilities being considered,
and the geographical context of the authors, (b)
awareness of others, (c) recommendations, guidelines and design patterns (named RecGuidPat for
simplification), and (d) involved technologies.
The review questions are formally defined in the
review planning. The SLR results are divided into
three parts: (1) an overview of the reviewed studies
and focus on aspects related to the first two review
questions, (2) RecGuidPat for accessibility in RIAs
(3) technologies present in the reviewed studies.
This chapter presents and analyzes the main
findings regarding the overview of the reviewed
studies and the technologies approached by them.
This chapter is organized as follows: Next
subsection presents background information in
which this research is situated. Following, we
present the SLR process. Then, we present the
SLR results, followed by a discussion on them,
and the final remarks.

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A Roadmap on Awareness of Others in Accessible Collaborative Rich Internet Applications

BACKGROUND
The term Web 2.0 was proposed by Tim O’Reilly
(2005) in order to identify a movement towards a
more participative and collaborative Web. Web
2.0 does not imply a new version of the Web; it
coexists with those traditional static page-oriented
websites. Rather, Web 2.0 applications involve
existing and novel technologies in order to produce highly dynamic and interactive applications.
The Web applications perceived as providing
functionalities similar to desktop applications are
called Rich Internet Applications (RIAs). Initially
focused on Flash-based applications, currently
RIAs are employed to refer to Web applications,
regardless of the technology used.
Common issues present in (collaborative)
RIAs are navigation history not properly stored
in user agents (Odell, 2009), lack of awareness
of asynchronous content updates, and overload
of notifications. Moreover, RIAs represent a
change of interaction paradigm since people that
use AT such as screen readers are tied to a linear
navigation approach, while content updates in
RIAs move focus from an area of the application
to another without following a rigid sequence.
There are diverse efforts regarding Web accessibility guidelines (e.g., W3C, 2012), authoring
tools that support the development of accessible
web content and applications (e.g., Dojo®, Google
Web Toolkit - GWT®), accessibility evaluation
tools (e.g., semi-automatic evaluation), techniques
(e.g., tally queues (Thiessen and Hockema, 2010)),
and others. W3C offers a set of guidelines for accessibility in RIAs, the WAI-ARIA (W3C, 2011),
that provides additional roles, states, and properties to the markup in order to expose dynamic
updates and the semantic of widgets to the AT.
Moreover, the HTML5 increases semantics for
the page elements as header, footer, and navigation menu (currently implemented using “div”
elements, which lack semantic value) by adding,
respectively, new tags “head”, “footer”, “nav”; for

54

new more advanced input types (e.g., date picker);
and a better integration with scripts.
Despite these recent efforts from the Web community, some concepts still deserve special focus
to guarantee that all people are able to perceive
each other in the environment; this is the main
motive in this chapter for tracing a road map into
this subject.

THE REVIEW METHOD
The SLR process adopted in this chapter is based
on Kitchenham (2004), which proposed a SLR
process for the Software Engineering domain.
Next, we present the review protocol used in this
research.

Review Questions
As the objective of this research was to gather
the knowledge spread across different distribution vehicles (e.g. journals, conferences, books,
industry guidelines) regarding awareness of others
in accessible (collaborative) RIAs, the SLR was
organized around 4 review questions (see Table 1).

Methods
Since the review process was a SLR it is necessary to provide enough information in order to
enable future researches to extend or derivate it.
The methods employed in the review process (see
Figure 1) consisted of: studies selection, data extraction, and data synthesis. The studies selection
is divided into two steps: automatic retrieving from
digital libraries, and manual inclusion or exclusion
of studies based on a set of predefined criteria.
The review process was conducted by one reviewer and by one supervisor. The SLR followed
the procedures for checking the uniformity and
validity of data extraction activities, as recommended in (Kitchenham, 2004). The protocols for
studies selection and data extraction were itera-


A Roadmap on Awareness of Others in Accessible Collaborative Rich Internet Applications

Table 1. Primary and secondary review questions
    Review Questions
RQ1. What are the disabilities approached in the studies involving accessibility in (collaborative) RIAs?
RQ1.1. Where (geographically speaking) are such studies conducted?
RQ1.2. What is the focus of the contributions?
RQ2. Are there studies on awareness of others in collaborative RIAs?
RQ3. What are the sets of recommendations, guidelines, and design patterns that contribute for awareness of others and accessibility in
(collaborative) RIAs?
RQ4. What are the tools, platforms, APIs, libraries, and AT used, proposed, and/or considered in the development of accessible
(collaborative) RIAs?

Figure 1. Overview of the review process

tively fine-tuned (see the lozenges in Figure 1).
Next, we provide details of the methods adopted
in this review.
Studies selection. It consists in retrieving
studies using automated queries (see Code 1,
henceforth referred as Q1) from digital libraries
and selecting or discarding them based on a set
of requirements from the review protocol. Q2
appends the expression “AND (“awareness” or

“aware”)” to Q1. The digital libraries considered
were: ACM Digital Library, IEEE Xplore Digital
Library, Science Direct, Scopus, and Springer
Link. No additional filter as year of publication,
type of study, etc was considered.
After the automatic querying, a manual evaluation based on a set of criteria (see Code 2) was
necessary to determine which of the selected
studies would be included or excluded from the

55


A Roadmap on Awareness of Others in Accessible Collaborative Rich Internet Applications

review. That step considered the title, abstract
and keywords of the studies. For studies without
abstracts it was also considered the introduction
section. Also, synonyms were accepted (e.g., web
application and web tool for RIAs). In that step
the reviewer did not consider other metadata as
authors, or vehicles of publication. The criterion
of availability on the Web considered even the
studies that are hosted in websites that require
user authentication, considering the reviewers’
access constraints.
Code 1. Query for retrieving studies from the
digital libraries (Q1)

others. The extraction form is composed of three
parts: a general overview of the study, the contributions regarding RecGuidPat, and the contributions
regarding software and hardware technologies. In
this chapter we present the results from the first
and third parts. The second part is not subject of
this chapter since it involves extensive documentation for analysing and reorganizing the RecGuiPat
identified by the review. Next we present the form
elements in detail.
Extraction Form - Part 1. The overview consisted of three components: general information,
population and author’s expectations for the topic.

(“accessible for all” OR “accessibility” OR
“universal access” OR “universal design”) AND
(“recommendations” OR “guidelines” OR
“patterns”) AND
(“rich internet applications”)

•

•

Code 2. The expression for inclusion/exclusion
of studies in the review process
(“approaches accessibility in RIAs”
OR “approaches RecGuidPat for accessible RIAs” OR “approaches accessibility in collaborative RIAs” OR
“approaches RecGuidPat for accessible
collaborative RIAs” OR “approaches
awareness of others in collaborative RIAs” OR “approaches RecGuidPat
for awareness of others in accessible
collaborative RIAs”) AND (“is written
in English” AND “is available on the
Web” AND NOT “is an index” AND NOT
“is duplicated”)

Data extraction. It involved the analysis of all
studies selected in the previous step. This SLR
classifies the reviewed studies in dimensions such
as type of contribution regarding RecGuidPat,
software, products evaluation approaches, and

56

•

General Information: This component
involved the identification of the reviewed
study and the conducted analysis, main
contributions, concepts for (collaborative)
RIAs, contributions related to awareness of
others, and possible relevant text excerpts;
Population: Involved (a) the target audience of the reviewed study, (b) the main
product of the reviewed study (when applicable), and (c) people that participated directly in the reviewed study (e.g.,
in a requirement analysis or in a product
evaluation),
Authors’ insights for the review topic.

Extraction Form - Part 3. Technologies
involved two components: technologies identification and occurrences of technologies in the
reviewed studies.
1. Technologies Identification: Provides
identification for technologies that appeared
in the reviewed studies, based on the fields:
(A1) Unique identifier (for internal purposes); (A2) Technology name; (A3) URL
or DOI of the technology provider; (A4)
Distribution license; (A5) Category of the
technology - initially it was considered the
essential components of Web accessibility
as suggestions (W3C, 2005); (A6) Is the


A Roadmap on Awareness of Others in Accessible Collaborative Rich Internet Applications

technology accessible? Suggested values:
yes (explicitly evaluated), yes (affirmed
by the reviewed study’s authors), no, not
informed, not applicable; (A7) Only for
(collaborative) RIAs: (A7.1) Domain of the
RIA; (A7.2) Type of interaction. Suggested
values: collaboration (i.e., people in a group
have a common objective and act on the same
shared artifact), cooperation (i.e., people in
a group have a common objective however,
in order to reach it, act on artifacts in subgroups or individually), mixed (i.e., people
shift constantly between individual and
collaborative activities), individual; (A7.3)
Mechanisms for contributing to awareness
of others (only those explicitly mentioned
in the reviewed study);
2. Occurrences of Technologies in the
Reviewed Studies: Provides additional
information regarding every occurrence
of technologies in the reviewed studies.
Fields: (B1) Study in which the technology
appeared; (B2) Unique identifier of the
technology (Field A1); (B3) Version of the
technology (when applicable); (B4) The
approach employed on the technology classified into four groups, following a precedence
order:
a. Citation (Lower Precedence): The
technology is only mentioned by the
reviewed study;
b. Use (Precedes Citation): The technology is used in the reviewed study,
without any modification or evaluation;
c. Evaluation (Precedes Use and,
Consequently, Citation): The technology is object of evaluation in the
reviewed study;
d. Extension and Proposition Are
Mutually Exclusive (Both Precedes
the Previously Mentioned): Extension
involves the modification of the
technology in the reviewed study.
Proposition consists of a new technol-

ogy being proposed in the reviewed
study.
Data synthesis. The SLR adopted a mixed
approach of qualitative (population addressed
by the studies and the methods for that, types of
evaluation, strategies of architectures, concepts for
(collaborative) RIAs, and so on) and quantitative
(most frequent vehicles of publication, geographical distribution of the reviewed studies, addressed
disabilities, frequency of use of technologies, and
so on) results.
As this SLR addresses RIAs, we used only
free RIAs (Google Docs®, IBM Many-Eyes®,
Wordle®, and Cacoo®1) as tools for planning,
conducting, and reporting the research.

RESULTS
This section presents the results from the studies
selection (automatic querying and manual inclusion/exclusion). Following, it presents the results
from the Part1 – Overview and, finally, from the
Part 3 – Technologies.
The automatic querying was conducted on
October 2011, between 4th and 18th. The queries
were submitted to the reported digital libraries.
This activity resulted in 290 studies selected from
the digital libraries. Springer Link retrieved more
studies (97) followed by ACM Digital Library (73),
and IEEE Xplore Digital Library (55); Scopus
retrieved 43 studies (34 of them already retrieved
from the other digital libraries), and ScienceDirect retrieved 22 studies. After the automatic
retrieving, the selected studies were submitted
to the manual inclusion/exclusion activity. In
contrast to the previous activity, the ACM Digital
Library was the one with more studies selected
for review (35), while Springer Link was the one
with more discarded studies (67) and 29 studies
selected for review. The activity also selected 13
studies from IEEE Xplore Digital Library, 1 from
ScienceDirect, and 1 from Scopus. Finally, after

57


A Roadmap on Awareness of Others in Accessible Collaborative Rich Internet Applications

Figure 2. Tag cloud of the reviewed studies keywords

the two-step studies selection, 79 studies were
selected for the data extraction. The complete list
of included and excluded studies can be found at
http://bit.ly/MssBqg.
The results from the selection activity indicate
the absence of studies addressing recommendations, guidelines, and patterns for awareness of
others in collaborative RIAs and a limited number
of studies focusing on awareness of others in collaborative RIAs. Regarding the discarded studies,
5 were written in languages different from English,
11 studies were not available in the Web (considering the reviewers’ access to digital libraries),
22 studies were just indexes to other studies; 42
studies were duplicated; and 131 studies did not
meet the criteria of this SLR.

Studies Approaching Awareness
of Others in Accessible
Collaborative RIAs
Based on the reviewed studies we verified that
since 2007 this topic has consistently been target
of publications (average of 15 studies per year,
standard deviation of 1.9 studies). Next, we present the results organized by: concepts for (collaborative) RIAs, geographical distribution (Review
Question 1.1) and type of contribution (Review
Question 1.2), and contributions for awareness of
others (Review Question 2).

58

Keywords. As the tool employed to generate
the tag clouds relies on single words, blank spaces
among words of an expression were suppressed
in order to keep semantics. Figure 2 presents the
resulting tag cloud based on expressions from
the keywords. The word “accessibility” receives
great emphasis while the term “Web” was contextualized in expressions such as “Semantic Web”,
“Web accessibility” and “Web 2.0”. Regarding
collaborative RIAs, the only evidence of them is
the term “groupware”. Also, it is evident the focus
on visual disabilities by considering the terms
“blind users”, “blind”, “visually impaired”, and
“screen reader”.
Concepts for (collaborative) RIAs. The most
frequent synonym for RIAs is “Web 2.0 applications”. For collaborative RIAs the terms “groupware” and “collaborative” were more frequent.
The next expressions present the variations of the
terms. The character “|” means an exclusive OR
operator and the characters “[” and “]” represent
a group of optional words:
•
•

RIAs: [dynamic | interactive | rich [sophisticated]] Web [2.0 | 2.0 Internet | -based |
2.0-based] applications
Collaborative RIAs: [web-based] groupware [application | system] and collaborative [tool | software | environment]


A Roadmap on Awareness of Others in Accessible Collaborative Rich Internet Applications

The concept of RIA and its characteristics
can be summarized as: RIA creates a new breed
of dynamic Web applications, also known as
Web 2.0, which enables users to act as content
providers. RIAs combine the benefits of the Web
distribution architecture with the UI interactivity
and multimedia support of desktop environments.
RIAs “emulate the functionality of” / “give the
feeling of” / “can rival” desktop applications, by
supporting the single-page paradigm; by combining semantics, style and behavior; and through
asynchronous client-server communication. The
interaction in RIAs is far more complex and offers new levels of user interactivity through a
Web browser. Content in RIAs is highly dynamic,
more attractive and interactive, and can be updated
in small chunks without requiring reloading the
entire Web page. User interfaces are more varied
and responsive to user actions.
Geographical and publishing distribution. Figure 3 presents the geographical distribution of the
contributions grouped by the type of contribution.
U.S.A. holds the largest number of contributions,
followed by Italy, and Spain. The most common
type of contribution is the “proposition/extension
of tool(s)” (37 contributions), followed by “proposition/extension of RecGuidPat” (21), “evaluation
of tool(s)” (18), “surveys” (17), and “evaluation
of RecGuidPat” (5). By grouping the results by
geographical region, we verify that Europe holds
more contributions from the studies, followed by
North America, Asia, and South America. In Europe, there is a predominance of studies focused
on proposition or extension of tools (27), while in
the North America there is a slight predominance
of surveys (10). Only North America and Europe
presented contributions regarding evaluation of
RedGuidPat (5 contributions). Similarly to Europe,
in Asia there is a predominance of contributions
proposing or extending tools.
The vehicles with the largest number of
reviewed studies are the International Crossdisciplinary Conference on Web Accessibility
(19 studies), the International Conference on

Universal Access in Human-Computer Interaction
(13), and the International ACM SIGACCESS
Conference on Computers and Accessibility (5).
The other vehicles (slightly more than 50%) do
not hold more than 3 studies each and most of
them hold only 1 study. The International Crossdisciplinary Conference on Web Accessibility
holds contributions from 10 different countries,
while U.S.A. holds most of the contributions (11).
The International Conference on Universal Access
in Human-Computer Interaction holds contributions from 7 different countries.
Awareness of others. The importance of awareness of others for collaborative RIAs is highlighted
by diverse studies (e.g., in synchronous communication (Thiessen and Hockema, 2010), in collaborative browsing (Maly, Zubair, & Li, 2000),
in collaborative editing (Mori, Buzzi, Buzzi,
Leporini, & Penichet, 2011), in socially-based
reporting of web accessibility problems (Takagi,
Kawanaka, Kobayashi, Itoh, & Asakawa, 2008)).
However, no study approached recommendations,
guidelines, or design patterns for awareness of
others, even for evaluation considerations. In order
to summarize the contributions, we rearranged
them as requirements using an adaptation of the
categories of questions for awareness of others
proposed by Gutwin and Greenberg (2002) i.e.,
who, what, where, when, and how.
•
•

Who: Information on other connected people should be accessibly provided (Mori,
Buzzi, Buzzi, Leporini, & Penichet, 2011);
What: Information on what other people
are doing should be available and easy to
obtain at any time (Chen & Raman, 2008;
Mori, Buzzi, Buzzi, Leporini, & Penichet,
2011). As content updates, the user must
be aware of the update even if those parts
are not focused. For people using screen
reader, this involves identifying and automatically speaking portions of the web
page. In the context of synchronous communication, Thiessen and Hockema (2010)

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A Roadmap on Awareness of Others in Accessible Collaborative Rich Internet Applications

Figure 3. Contributions according to authors’ country of affiliation and type of contribution

•
•

•

60

highlighted the need of providing information about chat and subscription invitations, and new messages;
Where: No direct reference was identified;
When: Changes caused by world events
(i.e., not by user request) should be announced according to the appropriate politeness setting for that region (Thiessen
& Hockema, 2010). The politeness setting
defines the moment information should be
presented (usually by voice synthesizing);
How: As content updates, users must be
able to access the new content and not be
unduly interrupted in their current task and
not be overwhelmed with too much information. Thiessen and Hockema (2010) and

Chen and Raman (2008) suggest that notifications should be prioritized, filtered and
queued (when appropriate) in ways that accommodate people needs.

Technologies for Awareness in
Accessible Collaborative RIAs
The Data Extraction identified 535 occurrences
of technologies in the reviewed studies. Those
occurrences involved 241 different technologies.
The complete list of technologies can be found
at http://bit.ly/1aez4Qu. Also, in addition to the
suggested categories of technologies (Field A5)
we identified extensions and new elements that
integrate the Web accessibility architecture, such


A Roadmap on Awareness of Others in Accessible Collaborative Rich Internet Applications

Figure 4. Number of occurrences of technologies grouped by category of technologies (horizontal axis)
and approach (sections in bars): (a) all the categories; (b) only the categories evaluation, extension,
and proposition

as: APIs, authoring tools (for user agents, applications, content, AT), evaluation (client-side,
server-side, static, temporal, based on states, and
others), component libraries, disability simulators,
transcoding (automatic, based on annotations, and
others). This section covers the Review Question 4.
Approaches and categories of technologies.
The categorization of the technologies that appeared in the reviewed studies (Field B4) aimed
at situating the technologies according to the
components of Web accessibility. Moreover, the
classification of approaches on the technologies
that appeared in the reviewed studies aimed at
clearly identifying the contributions. Figure 4
presents two graphics regarding the categories
and approaches adopted in each occurrence. The
first graphic (Figure 4.a) presents an overview
of all occurrences, while the second (Figure 4.b)
focuses on the approaches that involved the technologies more directly (i.e., evaluating, extending
or proposing the technologies).
In the Figure 4.a it is possible to verify that AT,
User Agents, and Evaluation Tools are the three
categories with more occurrences (see Figure
4.a). On the other hand, when considering the
approaches that assess or modify (i.e., evaluation,
extension, proposition) such technologies (see
Figure 4.b) the results change significantly. The

category RIA/CRIA appears as the most frequent
followed by Evaluation Tools and AT. User agents
are just the fifth more frequent. API, libraries and
authoring tools for user agents are cited or used in
the reviewed studies, however none study extended
or proposed any of them.
Regarding the approaches on technologies,
there is a predominance of citations (337 occurrences) and usages (109) of technologies,
as expected. Moreover, in Figure 4.b there is a
predominance of evaluations (49 occurrences)
and propositions (26) of technologies, and just
few studies extending (14) technologies already
available.
The 20 technologies that appeared more frequently in the reviewed studies (henceforth called
of “top 20”) were cited at least 5 times each.
Figure 5 presents the tree-map of occurrences of
technologies grouped by category. The 4 technologies more mentioned involve only AT (JAWS, 41
occurrences, and Window-Eyes, 18 occurrences)
and user agents (Mozilla Firefox, 38 occurrences,
and Microsoft Internet Explorer, 27 occurrences).
Moreover, the top 20 represents about 42% (226)
of all occurrences.
When considering the approaches on the top
20 technologies we verified that most of the occurrences are citations (136) or usages (68). The

61


A Roadmap on Awareness of Others in Accessible Collaborative Rich Internet Applications

Figure 5. Tree-map of the occurrences of the top 20 technologies grouped by category. Color and size
both indicate the number of occurrences.

only approach “proposition” among the top 20 is
the HearSay3, a non-visual user agent (Borodin,
Bigham, Stent, & Ramakrishnan, 2008). Other
15 occurrences consisted of evaluations and 6 to
extensions.
Distribution License. Regarding the distribution licenses, 73 technologies are open source
(e.g., GPL, EPL, Apache License), 60 were not
clearly informed in the reviewed studies, 47 are
free access websites, other 20 are free access
desktop technologies, and 39 are commercial
software. The distribution license of the top 20
technologies involved 8 open source, 6 free-access
(4 of them are on the Web), 5 private, and 1 not
informed by the reviewed studies. Among the 4
most frequent technologies, 3 of them are private.
More than half of the RIAs (24 of 43) approached
in the reviewed studies offer free access to their
functionalities (e.g., e-Buddy, Facebook, Flickr)
and some of them (7) are distributed as open
source (e.g., AChat-PHP)2.

62

Accessibility of the approached technologies.
Other aspect analyzed is the accessibility of the
technologies. This aspect is usually not informed
in the studies (125 technologies), while 64 technologies were verified as accessible, and other 9
were mentioned as accessible, however the studies
do not point to a formal evaluation of them, 28
technologies were considered not accessible, 15
technologies were not classified (e.g., back-end
technologies as APIs). Also, 13 of the top 20 were
considered accessible by the reviewed studies,
and all the 4 most frequent technologies are accessible. Regarding the accessibility of the RIAs
approached in the reviewed studies we verified
that only 7 of them are considered accessible
(e.g., IBM Social Accessibility Project (Takagi,
Kawanaka, Kobayashi, Itoh, & Asakawa, 2008),
Google Accessible Search, ReefChat3) and other 3
were mentioned as accessible, however without a
formal evaluation. Still regarding the accessibility
of the approached RIAs, 23 RIAs were considered


A Roadmap on Awareness of Others in Accessible Collaborative Rich Internet Applications

not accessible by the reviewed studies and for
other 9 RIAs this information is not provided by
the reviewed studies.
Vehicles of publication. The vehicles with
the largest number of reviewed studies are the
International Cross-disciplinary Conference on
Web Accessibility (19 studies), the International
Conference on Universal Access in HumanComputer Interaction (13), and the International
ACM SIGACCESS Conference on Computers and
Accessibility (5). When considering the technologies approached by the studies the same ranking
applies regarding the proposition of technologies.
For extensions, the Journal of Symbolic Computation appears in second, while the other vehicles
had no more than 1 extended technology each.
For evaluations, the International Conference
on Dependability of Computer Systems appears
in first (especially due to an study that involved
evaluation of diverse technologies i.e. Dworak,
2008), followed by the International Conference
on Universal Access in Human-Computer Interaction, and the International Cross-disciplinary
Conference on Web Accessibility.
In average, citations involved 65% of the approaches to technologies. When considering the
other types of approach grouped by digital library
we observed that citations are the most common
in all of them (standard deviation of 11%). The
second most common type of approach in the ACM
Digital Library is the “use” (16%); “evaluation” in
IEEE Xplore Digital Library (23%); “extension”
in Science Direct (22%); and “use” in Springer
Link (28%). Proposition was the third most common in ACM Digital Library, and fourth or last
in the others.
Classification for Components of Web Accessibility. W3C classifies the main components of
Web accessibility (W3C, 2005) i.e., authoring
tools, evaluations tools, user agents & media
players, and assistive technologies. This research
identified the approached tools and linked them
to W3C’s categories of components and added
some sub-categories when necessary. We identi-

fied authoring tools for Web content (e.g., Dojo
and Sencha Ext JS) and also for authoring other
components of the Web architecture (e.g., WebKit for user agents)4. Moreover, authoring tools
are supposed to be accessible and offer a level of
abstraction in order to allow people with diverse
proficiency on IT to work with them.
Evaluation tools are still a challenge when
designing (collaborative) RIAs due the dynamic
nature and the richness of the components available in those applications. In the reviewed studies, evaluation methods varied (a) from manual
to automatic evaluations, (b) from the original
DOM provided by Web server to the code rendered by the user agent, (c) regarding the points
of verification we verified studies based on states
and DOM invariants (Mesbah, van Deursen, &
Roest, 2012), temporal analysis (Dworak, 2008),
(d) still, there was social-based approaches using
social networking services for identifying and
reporting accessibility problems on websites
(Takagi, Kawanaka, Kobayashi, Itoh, & Asakawa,
2008). Also, some technologies were focused on
implementing the guidelines sets already available
as WCAG, Section508, Stanca Act (e.g., ATRC
Accessibility Checker) and others extended such
guidelines by including factors regarding context
of use as assistive technologies and access devices
(e.g., Vigo, Kobsa, Arrue, & Abascal, 2007) or
focusing on specific aspects (e.g. color contrast).
The limits among categories of technologies
are becoming fuzzy. Currently, some technologies tend to provide all the resources as a whole
pack. Thus, in several cases, a specific technology aggregates categories as user agents and
assistive technologies, authoring and evaluation
tools. The same occurs with the roles users play
while interacting with Web-related technologies,
since abstraction layers provide simpler means
for performing tasks before restricted to Web
professionals as, for example, creating a new page,
uploading a video to a web server. Consequently,
users and roles while interacting with technolo-

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A Roadmap on Awareness of Others in Accessible Collaborative Rich Internet Applications

gies are increasingly more related to the task and
less to the person.
Browsers and media players approached by the
reviewed studies involved, beyond the mainstream
software (e.g., Mozilla Firefox, Microsoft Internet
Explorer), text-based navigation, co-browsing
(i.e., users remotely sharing the navigation through
the user agent, e.g., Niederhausen, Pietschmann,
Ruch, & Meißner, 2010), and navigation through
multi-part objects (Hardesty, 2011).
Other two components receive special attention
in the development of RIAs, they are the Accessibility APIs (e.g., Linux Accessibility Toolkit,
MacOS Accessibility Protocol, and Microsoft
Active Accessibility), usually provided by the
operating systems or graphical environments, and
the transcoding applications. The last involves a set
of applications that aims at performing transformations on the Web content to make it accessible.
For that, different approaches are being proposed
as pattern matching (Sato, Miyashita, Takagi, &
Asakawa, 2007) and annotation on style sheets
(Lunn, Harper, & Bechhofer, 2009).
People involvement. Another concern when investigating accessibility in RIAs is the way people
are involved in the reviewed studies. In only 15
of the reviewed studies the participants directly
involved were people with visual disabilities (Almeida & Baranauskas, 2012). People with other
disabilities were not directly involved. Considering the approaches on technologies, we verified
that, for the proposition of new technology, the
participation of users occurred during the evaluation phase (for 13 occurrences on technologies),
and, in 11studies, no involvement was reported.
Among the 13 participations, 10 of them involved
only IT professionals and, in only 3, people with
no expertise on IT. For extensions, 7 occurrences
of involvement occurred during the evaluation
phase, 3 during the requirement analysis, and 7
did not involve participants directly. Also, 8 of
the participations involved only IT professionals,
while only 2 involved people with no expertise
on IT. There were 58 evaluations and 50 of them

64

involved only IT professionals, whereas 8 involved
people with no expertise.
RIAs approached by the studies. In addition
to the general fields for identifying technologies
in the extraction form, other data were collected
for (collaborative) RIAs (see Field A7). For the
field “Domain” (A7.1) it was possible to verify
a considerable diversity of areas. Based on the
collected data, those domains were grouped into:
management, navigation, sharing, communication, social networking, education, authoring,
e-commerce, and games. Figure 6 presents the
tree-map of those groups and the respective subgroups. The size of the rectangles represents the
number of different technologies in the sub-group
and the color the number of occurrences. The
groups of domains “communication”, “sharing”,
and “social networking” concentrate most of
the occurrences of technologies. Regarding the
number of different technologies, the group “navigation” joins the other three groups as the most
frequent. The sub-group with more occurrences is
the “mainstream” social networks (e.g., Facebook,
Twitter), followed by the sub-group “information
sharing”. Four sub-groups equalized in number
of different technologies: “mainstream” social
networks, “synchronous” communication, “task”
management, and “co-browsing” navigation.
RIAs that provide functionalities for cooperation and collaboration among users are one of the
most prominent trends. Among those RIAs, we
highlight the social networks, collaborative text
and image editors, and sharing of multimedia.
However, the development of such applications is
considerably more complex, so that accessibility
features frequently are not fully available. In the
context of this SLR, only 4 of the 24 collaborative
or cooperative RIAs that appeared in the reviewed
studies were considered (or formally evaluated)
as accessible.
Awareness of others is one of the main features in a collaborative RIA; however, projecting
accessible mechanisms for supporting awareness
in collaborative RIAs still demands investigation.


A Roadmap on Awareness of Others in Accessible Collaborative Rich Internet Applications

Figure 6. Tree-map of RIAs grouped by domain

Among the technologies that provide such mechanisms only in 9, aspects related to awareness of
others were discussed: 4 synchronous communication tools, 2 social networks, 1 collaborative text
editor, 1 a co-browsing tool, and 1 geographical
maps tool. Among the synchronous communication tools, diverse features consisted in adding
flexibility to the presentation of information for
supporting awareness e.g., the refresh rate of messages, messages sorting, and filters of messages by
user name. Others focused on defining strategies
for informing users of updates e.g., audio cues for
new messages, number of new messages in each
conversation session, tally queues for categorizing
and provide on-demand information of the updates
(Thiessen & Hockema, 2010). Both collaborative
text editors and co-browsing tools present the
feature of knowing the focus of the other users
in the shared interaction space, essential for collaboration involving a same object.
Limitations of the research. Despite being
formally defined and conducted according to the
recommendations for SLR, the activity of manual inclusion/exclusion presented some fragility.
Some studies selected by analyzing only the title
and abstract did not present actual relation to the
research topic and were excluded in the activity of
data extraction. Since there were studies unduly

included, there is also the possibility of having
excluded studies that should be included. One
fact that drew our attention is the almost complete
absence of studies from the CSCW community.
We hypothesize that possible causes are the differences in terminologies between the CSCW
and Web communities, and the limited focus on
accessibility in groupware.

DISCUSSION
The next paragraphs discuss the systematic literature review results.
Approached disabilities. The tag cloud of keywords evidenced the high frequency of the terms:
“visual disabilities”, “blind users”, “blind”,
“visual impairments”, and “screen reader”. Also,
among the 31 studies that focused on specific disabilities, 29 of them addressed visual disabilities
(e.g., Buzzi, Buzzi, Leporini, Mori, & Penichet,
2010) while only 2 approached other disabilities
(related to ageing). Some factors that could be
attributed to this tendency are: visual disabilities
seem to be the most frequent disabilities group
in some populations (e.g. Brazil (2010)); there
is a belief that visual impairments are the more
challenging group of disabilities for the current

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A Roadmap on Awareness of Others in Accessible Collaborative Rich Internet Applications

user interfaces design; and even the superficial
or absent knowledge about other disabilities and
their impact on users of such systems. The results
of this chapter indicate that more attention should
be put on other disabilities (e.g. dyslexia, deafness) that also can bring issues for people when
interacting with (collaborative) RIAs.
Many of the analyzed studies are attempts
to cope with new dimensions brought by such
highly dynamic and interactive applications.
Currently, there are not much widely accepted
answers for essential topics such as evaluation
methods, integration of accessibility concerns to
development processes, interaction models for
AT, just to name a few examples.
Geographical overview. The analysis of the
geographical distribution of the reviewed studies
and the types of contributions indicate that this
research topic is mostly concentrated in Europe,
followed by North America and Asia (probably
these results are consistent to the general distribution of studies for other topics in computer
science). Also, by analyzing the type of contribution we observed some tendency according
to countries. U.S.A holds most of the surveys;
studies from Italy focused mostly on evaluation
of RIAs, and Germany and U.S.A proposed and/
or extended recommendations, guidelines, and
design patterns. The country with more studies
addressing the proposition of tools is Spain.
Studies approaching evaluation of recommendations, guidelines, and design patterns came from
Canada, Italy, Spain, and U.S.A.
Awareness of others. Collaborative RIAs are
one of the most challenging types of RIAs since
they considerably change the Web pages paradigm. Collaborative RIAs that offer synchronous
features among users are even more complex.
Those applications are instigating the development of new technologies and protocols for the
Web e.g., streaming without requiring additional
plug-ins and resources for distributed computing,
and requiring additional skills for Web developers
e.g., distributed computing. However, less than

66

20% of the investigated collaborative RIAs were
considered accessible by the reviewed studies.
Moreover, just a few studies approached awareness of others in those collaborative RIAs and
the focus was limited to providing flexibility
to dynamic aspects and the strategy of updates
notification. Despite of relevant, other aspects
regarding awareness of others also deserve attention e.g., awareness of privacy & visibility
grants during the interaction, groups constitution
and joining, providing equivalent information for
people with different abilities and from different contexts, in order to establish and maintain
collaboration.
By articulating the findings from the SLR
with some additional information from emerging
standards, we propose a small and not exhaustive
set of guidelines for awareness of others in collaborative accessible RIAs (see Table 2).
Evaluation methods. Beyond answering the
review questions, the SLR provided us with additional information that can give some indications
about open issues. A question is how accessibility should be treated in development life cycles.
Currently a significant part of studies (20% of
the reviewed studies in this work) focuses on
evaluation methods. A number of different approaches are already available, varying: (a) from
manual to automatic processes or even involving
people with disabilities; (b) from considering
strictly disabilities to extending it to functional
constraints (e.g. a broken arm, a noisy interaction environment); (c) from static to temporalor state-based analysis of the interaction in the
shared space, and a number of different factors.
W3C is working on a conformance methodology for website accessibility (W3C, 2014). On
the other hand, many other approaches consider
that accessibility conformance would be better
conducted if considered not as an evaluation
step but across the whole life cycle (e.g., Shelly
& Barta, 2010).
Assistive technologies. AT should provide
support for the emerging standards. Hence, even


A Roadmap on Awareness of Others in Accessible Collaborative Rich Internet Applications

Table 2. Guidelines for awareness of others in collaborative accessible RIAs
Guidelines
1. A collaborative RIA should provide mechanisms to support awareness of others to be able to answer the questions (based on Gutwin
and Greenberg (2002)): (a) With whom are people interacting? (b) What are people doing? (c) Where are people interacting? (d) When
do events happen? (e) How do those events occur?
2. Dynamic content should be semantically marked with properties, roles, and states (e.g., WAI-ARIA): this will enable AT to
dynamically present updates. Markup should be kept valid in every state transition.
3. Dynamic content must be visually discernible from the static ones. Whether not clearly highlighted such content can be ignored by
people, especially those with low vision acuity, elderly, people in noisy environments, and other situations in which the attention is
compromised by other elements.
4. Information about content updates should be available anytime or at least while they can be required by people in a shared interaction
space.
5. The load of information about content updates should be adequate to the user’s pace. It should be carefully dimensioned and,
preferably, customizable by the user. Moreover, content updates should be organized and/or queued and/or grouped in order to reduce the
load of information.
6. The politeness for notification of content updates should be determined according to the relevance of the information and should avoid
disrupting people interaction. Whenever possible it should be customizable by the user.

some assumptions widely accepted (e.g., linear
navigation and single voice paradigms adopted
by screen readers) may have to be reconsidered.
For instance, consider collaborative RIAs that
involve synchronous communications among
three or more people. It can become hard to keep
awareness of the conversation when considering linear presentation. Maybe an approach of
multi-voice, simultaneous speaking and gender
aware for screen readers should better fit that
scenario. Multimedia resources as avatars for
sign language are still lacking in most of the
Web. Despite the existence of diverse solutions
for that, it seems to be disinterest in investing in
this type of assistive technology. Maybe solutions
for coupling this type of resource to operating
systems and/or web browsers should be a more
effective approach.
Access to and accessibility of the technologies. The distribution of the technologies is also
a concern among people with disabilities. The
results of the SLR demonstrate that three of the
four most frequent technologies employed in the
researches are private. Also, it is knowledgeable
that AT are expensive products and, unfortunately, most of the main software enterprises

develop accessible solutions considering the
compatibility with such expensive AT, while
other open source ATs are usually less privileged.
Also, despite the emphasis on providing features
that allow people with different skills to build
the Web, the SLR demonstrated that only about
one third of the investigated technologies were
assessed as accessible.
New versus extended software solutions.
The analysis of the approaches on technologies
revealed that most works that directly involve
development of computer-based technologies
focus on proposition of new technologies or evaluation of existing ones. The extension of tools still
received little attention in the reviewed studies.
That fact can be considered a natural consequence
of the very preliminary state-of-the-art in the
area. However, efforts on joint development of
technologies should be considered since the early
stages aiming at speeding up the development
of the area, consolidating solutions and good
practices and, in a near future, consolidating
standards. While WAI-ARIA is concerned on
providing semantic for HTML tags and expose
dynamic updates so that they can be notified to
AT, there are a number of other aspects to be

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A Roadmap on Awareness of Others in Accessible Collaborative Rich Internet Applications

considered when developing (collaborative) RIAs
e.g., update rate, navigation strategy, awareness
of others.
Among the categories of technologies that
emerged from the SLR we observed that Authoring Tools for (collaborative) RIAs was the fourth
category in terms of number of evaluations,
propositions, and extensions. However, most of
those technologies focus on providing components with semantic increments from WAI-ARIA,
while collaboration aspects receive less attention.
Building robust authoring tools is essential in
order to change the status of the development
of Web applications from a craft practice to a
consistent and accessible-by-conception process.
People engagement. Designing for all is a
challenging goal and, in the Web, this challenge
is even greater (Hendler, Shadbolt, Hall, BernersLee, & Weitzner, 2008). Most of the modern
process models for software engineering involve
participation of target people in some activities in
the software lifecycle. However, considering the
results of this SLR, we observe that about half of
propositions and extensions of technologies does
not directly involve target people. Also, most of the
involvements occur during the evaluation activity
and they usually involve only IT professionals.
Thus, we believe that practices for people engagement should be improved in research in the area.
Methods and techniques from Human-Computer
Interaction and Software Engineering constitute
a valuable framework of methods and techniques
for this purpose.
Components of Web accessibility. Additionally to the components already reported by the
W3C the results from the SLR, allowed us to
highlight two other components (transcoding
tools and accessibility APIs). Accessibility APIs
are usually bound to operating systems or graphical environments. On the other hand, those APIs
are essential components in the Web architecture
since they are responsible for receiving the data
from user agents and exposing them to the AT.
Transcoding tools are still an emerging type of

68

tool used to provide some level of accessibility for non-accessible content. In the future,
those tools will probably be integrated to other
components such as user agents and AT. In this
work we explicitly included those tools, once
transcoding has the potential of helping with the
legacy content in the Web that is not supposed
to be updated in a near future.

FINAL REMARKS
The awareness of others in a shared space has
been acknowledged as an important issue in
collaborative Rich Internet Applications. This
chapter presented a study regarding awareness
of others in accessible collaborative RIAs. The
results involved the characterization of the
contributions for this topic, the geographical
distribution of the analyzed studies, and an
analysis of the technologies approached by the
reviewed studies.
The study was conducted through a systematic literature review, whose protocol may be
used anytime to include the last publications
in the field, extending the results so far. Some
relevant findings are: the prevalence of studies
focusing on visual disabilities, the variety of
approaches for evaluating (collaborative) accessible RIAs, the limited number of solutions
extending existing solutions and the prevalence
of studies proposing new tools, and the need
for more engagement of the target people of the
studies during the software development lifecycle. Finally, the analysis of the contributions
regarding awareness of others were compiled
in a set of guidelines for design and/or evaluation of mechanisms for awareness of others in
collaborative accessible RIAs.
Future directions of this research involve
periodical updates to this SLR, and stimulate
others to extend or mix it, in order to build up
a long-term overview of the research on awareness of others in collaborative accessible RIAs.


A Roadmap on Awareness of Others in Accessible Collaborative Rich Internet Applications

Hopefully, we expect to provide up-to-date information about the state-of-the-art and emerging
research opportunities on this research topic.

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A Roadmap on Awareness of Others in Accessible Collaborative Rich Internet Applications

KEY TERMS AND DEFINITIONS
Authoring Tools: Any web-based or non-webbased application(s) that can be used by authors
(alone or collaboratively) to create or modify web
content for use by other people (other authors or
end users) (W3C, 2013a).
Awareness of Others: Awareness of others is
an understanding of the activities of others, which
provides a context for our own activity (Dourish
and Bellotti, 1992).
CSCW: Computer Supported Cooperative
Work is a multidisciplinary research area focused
on collaborative environments and the technologies that support collaborative work.
Groupware: Any software that supports collaborative activities.
Guidelines: Propose how something should
be done or how to measure something.
Rich Internet Application: It is a new breed
of dynamic Web applications, also known as
Web 2.0, which enables users to act as content
providers. RIAs combine the benefits of the Web
distribution architecture with the UI interactivity
and multimedia support of desktop environments.
Standards: One or a set of guidelines that
are recognized as correct by an authority or by
general consent.
Systematic Literature Review: A systematic
literature review is a means of identifying, evaluat-

72

ing and interpreting all available research relevant
to a particular research question, or topic area,
or phenomenon of interest (Kitchenham, 2004).
User Agent: Any software thar retrieves,
renders and facilitates end user interaction with
web content (W3C, 2013b).

ENDNOTES


1



2



3



4

The tools are available at http://docs.google.
com/, for Google Docs; http://www-958.
ibm.com/software/data/cognos/manyeyes,
for IBM ManyEyes; http://www.wordle.
net, for Wordle; and https://cacoo.com, for
Cacoo.
The tools are available at http://www.ebuddy.
com, for e-Buddy; http://www.facebook.
com, for Facebook; http://www.flickr.com,
for Flickr; and http://atutor.ca/achat, for
AChat-PHP.
The tools are available at http://www.google.
com/accessibility/labs/search/, for Google
Accessible Search; and http://www.reefchat.
org, for ReefChat.
The tools are available at http://dojotoolkit.
org/, for Dojo Toolkit; http://www.sencha.
com/products/extjs, for Sencha Ext JS, and
http://www.webkit.org/, for WebKit.

73

Chapter 4

A Usability Evaluation of
Facebook’s Privacy Features
Based on the Perspectives
of Experts and Users
Márcio J. Mantau
Santa Catarina State University (UDESC), Brazil
Marcos H. Kimura
Santa Catarina State University (UDESC), Brazil
Isabela Gasparini
Santa Catarina State University (UDESC), Brazil
Carla D. M. Berkenbrock
Santa Catarina State University (UDESC), Brazil
Avanilde Kemczinski
Santa Catarina State University (UDESC), Brazil

ABSTRACT
The issue of privacy in social networks is a hot topic today, because of the growing amount of information shared among users, who are connected to social media every moment and by different devices
and displays. This chapter presents a usability evaluation of the privacy features of Facebook’s social
network. The authors carry out an evaluation composed by three approaches, executed in three stages:
first by the analysis and inspection of system’s features related to privacy, available for both systems
(Web-based systems and mobile-based systems, e.g. app). The second step is a heuristic evaluation led by
three experts, and finally, the third step is a questionnaire with 605 users to compare the results between
specialists and real users. This chapter aims to present the problems associated with these privacy settings, and it also wants to contribute for improving the user interaction with this social network.

DOI: 10.4018/978-1-4666-7262-8.ch004

Copyright © 2015, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.


A Usability Evaluation of Facebook’s Privacy Features Based on the Perspectives of Experts and Users

INTRODUCTION
From its beginning, the Internet had provided a
series of applications that directly influence the
daily lives, including e-mail, e-commerce applications, e-learning, and various other web-based
services. Currently, web experiences have a new
category of applications, related to users’ social
relations. Many of these applications have been
distinguished by their wide use, such as social
networks. A social network can be characterized
as a set of autonomous participants, focusing ideas
and resources around shared values and interests
(Marteleto, 2001). A social network can also be
understood as a collection of individuals linked
together by a set of relations (Downes, 2005).
According to Nielsen (Nielsen Group, 2009),
social networks have attracted millions of users,
and social media, a term used to identify the
content created and disseminated through social
interactions, has become the fourth most popular
online category – ahead of personal email. This
popularity is associated with a common feature
among all social networks: creating and sharing
content. This content can be in many ways, from
writing informing activities during the day, even
multimedia content such as photos and videos.
There are several types of social networks,
among which we named: professional networks
(e.g. LinkedIn) and networks to share specific
information, such as short messages (e.g. Twitter),
photos (e.g. Instagram) and videos (e.g. Youtube).
Another type of social networks is the one that connects users to friends, such as Facebook - a social
network created in 2004. Nowadays Facebook is
one of the most accessed sites of Internet, and
also the social network with the largest number
of users (Alexa, 2012).
Facebook allows users to create profiles and
manage a list of users with whom they share connections. Users can view and scroll through their
lists, and lists created by other users of the system.
Profiles can be accessible to anyone with a Facebook account or can be private, with information

74

available in accordance with the privacy policies
defined by the user.
Several features and settings for privacy control
and customization of personal information are
available on Facebook. These two aspects - user
control and user personalization/customization
- are very important to HCI (Human Computer
Interaction) area, since they can make the systems
easy to learn and use, and they are tools that bring
effectiveness, efficiency, safety and satisfaction
to the system during the navigation. The usable
interfaces increase user satisfaction by providing
greater comfort in their operation.
As shown, it is observed that there is great importance in analyzing the usability of the system.
In this work we provide an extended, more detailed
overview of the results introduced by Kimura et
al. (2012), where we investigated the usability of
Facebook’s privacy features and settings in the
two available interfaces: (i) web-based interface,
accessed by the web browser, and (ii) the mobile
interface, accessed by the official application
(app) provided by Facebook. For this purpose
the procedure adopted for the evaluation was to
investigate all features and settings related to the
privacy aspects, making the inspection of the
entire environment (web-based and mobile), and
registering all the privacy settings found. Later,
we performed the heuristic evaluation technique
proposed by Nielsen (Nielsen & Mack, 1994),
and the whole process of evaluation and analysis
of encountered problems, highlighting the main
problems in each environment. Then, we applied a
questionnaire with 605 Facebook’s users, focused
on the problems identified by the heuristic evaluation. Finally we analyzed the problems encountered by experts and the questionnaire answers to
understand what were the major complaints and
problems and then, we verified if the problems
still appear in the Facebook’s features.


A Usability Evaluation of Facebook’s Privacy Features Based on the Perspectives of Experts and Users

BACKGROUND
Usability has been an important theme extensively
studied in the human–computer interaction (HCI)
field, and researchers in this field have emphasized
the successful interaction between a human and a
computer as a key factor in designing and implementing a variety of computing systems (Lee &
Kozar 2012). Evaluation is integral to the design
process. Evaluators collect information about
users’ or potential users’ experiences in order to
improve its design, when users’ interacting with
a prototype, a computer system, a component of
a computer system, an application (Rogers, Sharp
& Preece, 2011). Usability is considered to be one
of the most important quality factors for Web applications, and the challenge of developing more
usable Web applications has led to the emergence
of a variety of methods, techniques, and tools with
which to address Web usability issues (Fernandez,
Insfran & Abrahão, 2011).
Web applications should be usable in order
to be accepted by users and to improve their success probability (Fernandez, Abrahão & Insfran,
2013). What constitutes a good web site has
been traditionally explained by relating it to user
and usability. In other words, a successful and
preferable web site generally refers to one with
high usability, which is user-friendly and usercentered in interface and functional aspects (Lee
& Koubek, 2010).
This section presents an overview of the fundamental aspects of this research, especially on
usability evaluation of interactive systems.
According to Mackenzie (2013) there are three
common approaches for conducting research in
Human Computer Interaction (HCI): (1) the observational method, (2) the experimental method,
and (3) the correlational method. All three are
empirical as they are based on observation or
experience, however there are differences and
these follow from the goals of the research and
from the expertise and style of the researcher
(Mackenzie, 2013).

The observational method includes a collection
of common techniques used in HCI research, including interviews, field investigations, contextual
inquiries, field studies, focus groups, think aloud
protocols, storytelling, walkthroughs, cultural
probes, etc. In the experimental method (also
called the scientific method), knowledge is acquired through controlled experiments conducted
in laboratory settings. Acquiring knowledge imply
(a) gathering new knowledge and (b) studying
existing knowledge for the purpose of verifying,
refuting, correcting, integrating, or extending.
The correlational method involves looking for
relationships between variables. For example, a
researcher might be interested in knowing if users’
privacy settings in a social networking application
are related to their personality, level of education,
age, gender, etc. Data are collected on each item
and then relationships are examined (e.g., it might
be apparent in the data that users with certain
personality traits tend to use more strict privacy
settings than users with other personality traits
(Mackenzie, 2013).
Another classification in proposed by Rogers, Sharp and Preece (2011), where the authors
classify evaluations into three broad categories,
depending on the setting, user involvement, and
level of control. The first one is the (1) controlled
settings involving users (e.g. laboratories), where
user’s activities are controlled in order to test hypotheses and measure or observe certain behaviors
(the main methods are usability testing and experiments). The second is (2) natural settings involving
users (e.g. online communities): there is little or
no control of users’ activities in order to determine
how the product would be used in the real world
(the main method is the use of field studies) and
the third is (3) any settings not involving users:
consultants and researchers critique, predict, and
model aspects of the interface in order to identify
the most obvious usability problems (the range
of methods includes inspections, heuristics, walkthroughs, models, and analytics) (Rogers, Sharp
& Preece, 2011).

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A Usability Evaluation of Facebook’s Privacy Features Based on the Perspectives of Experts and Users

In this chapter we focus on (a) the setting not
involving users, especially the inspection and
heuristic methods and (b) an user’s evaluation by
a questionnaire, described below.
The inspection methods allow the evaluator to
examine a solution of HCI to predict the possible
consequences of an interface problem. These
methods do not involve the direct participation of
users and to inspect an interface, the evaluators
are put in the place of a potential user to try to
identify problems that users can face when they
interact with the system (Barbosa & Silva, 2010).
There are several evaluation methods for inspection, such as heuristic evaluation, cognitive
walkthrough, using recommendations and guidelines, and so on (Nielsen & Mack, 1994). For the
purpose of this work, it was chosen the heuristic
evaluation proposed by Nielsen because: it is the
assessment method most frequently used (Rogers,
Sharp & Preece, 2011); it is widely accepted in
the HCI area, being used in different contexts and
interfaces; and it is well known by the evaluators.
An heuristic evaluation is a usability inspection method in which the evaluation is performed
based on a set of guidelines called heuristics, and
desirable characteristics that describe the interaction, guiding the evaluators to systematically
inspect the interface for problems that affect the
usability (Nielsen & Mack, 1994). The heuristic
evaluation method requires a group of evaluators to
examine Web artifacts (commonly user interfaces)
in compliance with commonly accepted usability
principles called heuristics (Fernandez, Abrahão
& Insfran 2013).
The evaluation is based on trial and it is supported by confidence in the experience of those
who evaluates the interface (Rocha & Baranauskas, 2003). It is recommended that three to five
evaluators are involved. This method is fast and
has a considerably lower cost compared to other
methods (Nielsen & Mack, 1994).
Conduct a heuristic evaluation consists in
analyzing the interface to report problems, according to the heuristics and the expertise of

76

the evaluators, seeking to inconsistencies in the
interface about the principles of usability. In this
method the experts examine the system and make
a diagnosis of problems and barriers that users are
likely to encounter during the interaction (Barbosa
& Silva, 2010).
The set of heuristics proposed by Nielsen allows
an evaluator to analyze and evaluate an interface.
The Table 1 presents a summary of heuristics
(Nielsen & Mack, 1994; Barbosa & Silva, 2010):
For each problem found according to the
heuristics, it should be associated with a severity
that is based on a combination of three factors
(Rocha & Baranauskas, 2003): (i) The frequency
with which it occurs: it is common or rare; (ii) the
impact of the problem when it occurs: it is easy
or difficult to overcome it; (iii) the persistence of
the problem: a problem that occurs only once and
the user can overcome long as you know it exists,
or if users repeatedly be bothered by it.
These factors influence the severity levels
used in the evaluation, which can be classified as
(Nielsen & Mack, 1994): (0) It is not necessarily
seen as a usability problem; (1) Cosmetic problem that does not need to be fixed, unless there is
time and resource available; (2) Minor usability
problem, with low priority of fixing it; (3) Major
usability problem, with high priority of fixing it;
(4) Catastrophic usability problem, where it is
imperative to fix it. In the following section we
present the heuristic evaluation process conducted
in the Facebook’s privacy settings.
Questionnaires may be used for getting initial
responses that can then be analyzed to choose
people to interview or to get a wider perspective
on particular issues that have arisen elsewhere
(i.e. data gathering in the establish requirements
process), or the questionnaire might be used to get
opinions and views about specific suggestions for
the kind of help that would be most appreciated
– measuring the user satisfaction (i.e. in the evaluation process) (Rogers, Sharp & Preece, 2011).
Collecting data about user´s satisfaction can
be achieved by a user satisfaction questionnaire.


A Usability Evaluation of Facebook’s Privacy Features Based on the Perspectives of Experts and Users

Table 1. Summary of Nielsen’ heuristics
H1. Visibility of System Status: The system should provide appropriate feedback to users within a reasonable time;
H2. System Compatibility with the Real World: The system uses terms familiar to the user rather than software-oriented terms. Realworld conventions should be followed for information to appear in sequential order and logic;
H3. User Control and Freedom: To provide emergency exits to misguided actions of users (such as undo and redo);
H4. Consistency and Standards: A user should not wonder whether different words, situations or actions mean the same thing. Follow
platform conventions;
H5. Error Prevention: Making a design that prevents the error before it happens. Eliminate conditions that lead to errors and provide
users with a confirmation option before finalizing the action;
H6. Recognition Rather than Recall: Make visible all objects, actions and options. The user should not have to remember information
from one to another dialogue. Instructions should be visible or be easy retrieval when needed;
H7. Flexibility and Efficiency of Use: Provide ways to expertise users speed up interaction and support novice users;
H8. Aesthetic and Minimalist Design: Dialogues should not contain irrelevant or rarely needed information. Display only the
information that is important and needed;
H9. Help Users Recognize, Diagnose and Recover from Errors: Error messages should be expressed in clear language indicating the
problem and constructively suggest a solution;
H10. Help and Documentation: Provide help and documentation. This information should be easy to find, focused on the task and the
user; and it should not be extensive.

It is used to find out how users actually feel about
using the product, through asking them to rate it
along a number of scales, after interacting with it
(Rogers, Sharp & Preece, 2011). Questionnaires
are a series of questions designed to be answered
asynchronously, i.e. without the presence of the
investigator, and they may be on paper or online.
Online questionnaires are interesting because they
reach a large number of people quickly, they reach
participants that are geographically separated,
they receive faster response rate and automatic
transfer the responses into a database (Rogers,
Sharp & Preece, 2011).

METHODOLOGY
This section presents our methodology to evaluate
the usability of Facebook’s privacy features and
settings. Our methodology of usability evaluation
put together three different approaches, accomplished in three stages: (a) an inspection of the
Facebook’s functionalities, done by three experts
(HCI researchers); (b) an heuristic evaluation,
performed by the same three evaluators and, (c)

an questionnaire to measure user’s satisfaction,
answered by 605 Facebook’s users. In the next
sections we discuss each approach of our methodology.

Inspection of the Privacy Features
The first stage of this work was the inspection
through functionalities and interface units of the
Facebook (web-based and mobile system), seeking
to collect the features that contain privacy settings.
This inspection was conducted and executed by
three experts (HCI researchers). It was out of our
scope Facebook’s synchronous features (e.g. chat),
ads and third-party applications. Since our target
public was Brazilians’ teenagers and young adults,
our inspections were in the Portuguese version of
both systems. After the individual analysis of each
environment, a comparison about the existence of
the privacy settings for each interface was made.
The result was arranged as shown in Figure 1, in
order to help the experts in the second stage of
our evaluation process. It can be observed that
there are many features in only one of the interfaces, and other features are available in different

77


A Usability Evaluation of Facebook’s Privacy Features Based on the Perspectives of Experts and Users

Figure 1. Inspection of the privacy settings of Facebook (June 2012): mobile versus traditional environment

locations for the web-based interface and for the
mobile-based interface.

Heuristic Evaluation Procedure
The heuristic evaluation consisted of three evaluators (the same experts from the first step), all with
prior expertise of the evaluation method, and also
knowledge of the environment to be evaluated.
About the Scope and Materials, the heuristic
evaluation had these resources: (i) the web-based
system was accessed from the Mozilla Firefox
web browser - version 13.0 under the Windows

78

7 Operating System, and Google Chrome web
browser - version 19.0.1084.56 under the Windows
7 Operating System, (ii) the mobile system was
accessed via the official Facebook application
- version 4.1.1 - under the IOS 5.1.1 Operating
System, running on a Ipad2 tablet device, (iii)
the mobile system was accessed via the official
Facebook application - version 1.9.5 - under the
Android 2.2 Operating System, running on a Samsung Galaxy P1000 tablet device. The accesses
were made during the months of May and June
2012. It should be noted here that the official applications are similar for both the IOS operating


A Usability Evaluation of Facebook’s Privacy Features Based on the Perspectives of Experts and Users

system and for the Android operating system,
with few differences that do not interfered in the
heuristic evaluation, and it was not necessary to
evaluate the two systems separately.
We created three new users of Facebook, selecting as the default language Portuguese-Brazilian,
each evaluator was responsible for a user. All
evaluators navigated to the privacy features of
Facebook, which were previously inspected and
documented as shown in Figure 1, using both the
web-based and the mobile systems. The evaluator also interacted among them and with others
outside the group, to verify and test the features.
The evaluation was performed in the traditional
web-based interface of Facebook, and we did not
evaluate the timeline interface.
The evaluation procedure was performed in
three steps, detailed below:

the problem; 2 - Violated heuristics; 3 - Severity
level; 4 - Location of the problem in the interface.
With the individual heuristic evaluations
reports, it was generated a general consolidated
report. The Figure 2 shows a Venn diagram with an
overview of the problems found by each evaluator.
We notice that there are problems that were
identified by more than one evaluator, and also
by all the three evaluators. The Figure 3 shows
the problems found in traditional/web-based
(Figure 3a) and mobile (Figure 3b) environments,
respectively. Some problems were found in traditional (web-based) and mobile environment,
showing that recurring problems can occur in
both environments.

1. Initial exploration of the system, based on
the result of the found features (shown in
Figure 1);
2. Evaluation period, where each evaluator
used the Facebook interface around 3 hours,
separately, by inspecting systems at least
twice, the first being to understand the flow
of system information and the other focusing
specifically on the goal of the work (in total
about 1 hour and a half for each system).
At this stage, the evaluators inspected the
privacy and different components of each
reported problem encountered by associating
them with the violated usability heuristics.
Each evaluator individually generated a
report, presenting a formal record of the
evaluation;
3. Final section, with the consolidation of the
evaluation, where evaluators identified all
the problems encountered, discussing their
severity and suggesting solutions.

The Facebook’ privacy features was also evaluated
based on an online questionnaire. Questionnaires
are a well-established technique for collecting
data and users’ opinions (Preece et al., 2011).
We developed a questionnaire based on heuristic
evaluation results, in order to clarify or deepen
understanding.
The questionnaire was composed by 14 questions, divided into four main groups: (i) three
questions of user demographic; (ii) four questions
of user profile, to identify the user experience on
the system; (iii) three questions related to their
knowledge about Facebook’s privacy features and
setting; and (iv) four questions related to major
problems of Facebook’s privacy settings identified
by the heuristic evaluation.
The questions were available to the participant
only after they have read the initial explanation
about the work and they understood that their
participation was anonymous. Only after they
agreed to participate in the study, the questions
were presented. Even so, the participants could
leave the study at any time. The participants’
privacy was guaranteed and they did not identify
themselves at any time.

For each problem identified were described
the following information: 1 - Description of

User’s Satisfaction Evaluation
by a Questionnaire

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A Usability Evaluation of Facebook’s Privacy Features Based on the Perspectives of Experts and Users

Figure 2. Overview of encountered problems

Figure 3. Distribution of the problems encountered on the different interfaces

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A Usability Evaluation of Facebook’s Privacy Features Based on the Perspectives of Experts and Users

Figure 4. Lack of more refined privacy options in the user’s gender settings

We speeded it on social networks such as
Twitter and Facebook itself, so that the questions
were answered voluntarily. We also carried out
a “marketing” campaign along with famous entertainment blogs in Brazil for the dissemination
of our research, making research quickly spread
through social networks. The data from users’
responses was gathered during two weeks, and the
questionnaire was available on-line at (http://goo.
gl/OW0zL). As a result, we obtain a total of 605
responses of Facebook´s users, and data analysis
is presented in the results section.

RESULTS
The results of the heuristic evaluation by three
experts in split in two section, according the
environment of the problem was found (web
environment versus mobile environment). After,
the results of the user´s satisfaction acquired by
the questionnaire is presented.

Problems Identified by Heuristic
Evaluation in the Web Environment
The problems identified in the web environment
are described as follows, sorted by decreasing
severity degree.
Problem #1: Inconsistency on the privacy
level settings of the user gender registration. The
settings options for the privacy level in “Basic
information/Gender” do not follow the same pattern set in other places (as showed on Figure 4).
This problem violates the H4 (Consistency and

standards) and it is classified as severity level 4
(Catastrophic usability problem). The user cans
only sets up whether the information is displayed
or not. In other settings, it is possible to perform a
more refined customization (e.g. choose between
Public, Friends, or Just me visibility option).
Problem #2: There is no option to control the
information displayed only to the user (“Just me”
option). The user can not choose between “No”
or “Just me” options if he/she does not want his/
her personal information to be available to other
users (as shown in the Figure 5). This problem
violates H4 (Consistency and standards) and it is
classified as severity level 4 (Catastrophic usability
problem), because this feature is not available. The
system only allows to erase or not the information.
Problem #3: There is no delete button to
some registered items. In some features, there
is no “Delete” button. Thus, the user does not
know, for example, how to delete a song from
his/her favorite songs list. This problem occurs
over several options on the editing profile features
(e.g. musics, books, films, games). The user must
use the keyboard (the “DEL” key) to delete any
information that he wants to remove from his/her
list. This problem violates H1 (Visibility of system
status) and H3 (User control and freedom) and it
is classified as severity level 4 (Catastrophic usability problem). The user cannot identify a way
to delete some information that was added to his/
her preferences. There is not a way to perform
this action on the interface, only by keyboard and
there is no explanation about it.
Problem #4: No option to cancel the action
being performed. In some presentation units,

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A Usability Evaluation of Facebook’s Privacy Features Based on the Perspectives of Experts and Users

Figure 5. Absence of some privacy options

there is no option to cancel the action performed
by the user. The system automatically runs the
task, without asking whether it really should be
executed. This problem violates H3 (User control
and freedom) and H4 (Consistency and standards)
and it is classified as severity level 3 (Problem
with high priority to fix).
Problem #5: Set the privacy level in the
fields with multiple items. Fields that allow the
addition of several items just have the ability to
set the privacy level for the whole group. If new
information is added, it is not clear to the user if it
will be applied only to the specific field or to the
whole group. This problem occurs for the other
fields that let user add multiple information, and
the user does not know about the privacy level
applied (this problem is shown in Figure 6). This
problem violates H4 (Consistency and standards)
and H5 (Error prevention) and it is classified as
severity level 3 (Problem with high priority to
fix). The user can usually modify his/her profile,
but he cannot change the privacy level of each
specific information. Likewise, when the users are
entering new information, it is not evident that the

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privacy level is applied to only informed record
or to any other previously registered information.
Problem #6: Difficulties in identifying the
profile displayed to others Facebook’s users.
The information privacy levels can be: “Public”,
“Friends”, “Just me”, and “Personalized”. These
privacy levels are identified by icons. However, it
is not clear to the user what privacy level is being
displayed on his/her profile (as showed in Figure
7). This problem occurs in several portions of the
system. This problem violates H5 (Error prevention) and H6 (Recognition rather recall), and it
is classified as severity level 2 (Minor usability
problem, with low priority of fixing it). The user
cannot identify at what privacy level the content
is being displayed. The displayed icons are not
always intuitive to the users. A novice Facebook’s
user will have difficulty in identifying the icons
functions.
Problem #7: Messages/Information presented
in different languages in the same interface. In
some parts of the web-based interface, especially
in the error messages, the configuration options
and Privacy Policy, the information appears written in a different language selected by user. Figure


A Usability Evaluation of Facebook’s Privacy Features Based on the Perspectives of Experts and Users

Figure 6. Inconsistency in the fields of multiple items in the web-based interface set to Brazilians’ language

8 presents an example which some information
is only displayed in English on the Brazilian’
web-based interface. This problem violates H4
(Consistency and standards) and it is classified
as severity level 2 (Minor usability problem, with
low priority of fixing it). The user can perform
the task only if he understands the information
presented in the other language.
Problem #8: Lack of the button to save the
changes in some forms. Some forms on the user
profile do not have the button to save the user’s
changes. The forms “Education and work”,
“Activities of interest” and “Sports” have this
problem. This problem violates H4 (Consistency
and standards) and H5 (Error prevention), and it
is classified as severity level 2 (Minor usability
problem, with low priority of fixing it). The user
can perform the task, but it is unclear to him
whether the changes were saved or not.
Problem #9: A different interface form for
editing profile. When the user edits his/her profile,
the interface does not follow the same pattern used
in other pages. The only button in the interface

is the “Done” button. There is no specific record
button, such as “Save” or “Apply”. This problem is
shown on Figure 9. Another problem in the same
interface is the lack of a “Cancel” button. This
problem violates H3 (User control and freedom)
and H4 (Consistency and standards), and it is classified as severity level 2 (Minor usability problem,
with low priority of fixing it), because the user
can perform the tasks although the interfaces are
different, even if with a certain difficulty.
Problem #10: Output screen button is in a
different language. In some interfaces, the exit
button is displayed in another language; different
from the user has set. Figure 9 shown an example
of user configuration interface that displays some
information in other language, different from that
the user has set up (in this case, the exit button
“Done” is in English). This problem violates H4
(Consistency and standards) and it is classified
as severity level 2 (Minor usability problem, with
low priority of fixing it). Although the language
is presented in English, the user probably can
understand the output button.

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A Usability Evaluation of Facebook’s Privacy Features Based on the Perspectives of Experts and Users

Figure 7. Lack of labels on the privacy icons

Problem #11: A poor explanation about the
customization options. This problem occurs as
showed on Figure 10. This problem violates H5
(Error prevention), and it is classified as severity
level 2 (Minor usability problem, with low priority of fixing it). Despite the lack of clarity in the

explanation of the functionality, the user can still
perform the task.
Problem #12: Different information about
the same Privacy Settings for web-based and
mobile-based interfaces. This problem violates
H4 (Consistency and standards), and it is classi-

Figure 8. Part of the information displayed in English on the Brazilian’ web-based interface

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A Usability Evaluation of Facebook’s Privacy Features Based on the Perspectives of Experts and Users

Figure 9. Exit button displayed only in English on the Brazilian’ web-based interface

fied as severity level 2 (Minor usability problem,
with low priority of fixing it).
Problem #13: Standard Privacy Control has
no clear explanation. In this resource, there is a
default privacy option. However, the information about this feature is not described clearly
to the user. The user can be confused about the
meaning of “Timeline and Tagging Settings”,
and if this option can be used by users who have
a Blackberry device. This problem violates H4
(Consistency and standards) and it is classified as
severity level 1 (Cosmetic problem that does not
need to be fixed, unless there is time and resource
available). Despite the lack of clarity in the system
explanation, the user can still perform the task.

Problems Identified by
Heuristic Evaluation in the
Mobile Environment
The problems identified in the mobile environment
are described as follows, sorted by decreasing
severity degree.
Problem #1: The option to change the privacy
level is disabled. The icons presented on the “News
feed” in the mobile interface are displayed but are
not available to click. This problem violates H3
(User control and freedom) and it is classified as

severity level 4 (Catastrophic usability problem).
There is no changing option in the mobile interface.
Problem #2: The option to delete a published
status is not clear. In the mobile-based interface,
after the user posts a new status, it is not clear
how he can delete it. This problem violates H3
(User control and freedom) and it is classified as
severity level 4 (Catastrophic usability problem).
To delete a published status, the user must slide
horizontally the publication, using the mobile
touch function, when the “Delete” option appears,
but this function is not explained or specified in
any location.
Problem #3: It is not possible to change the
privacy level in the user’ photos section. On the
mobile-based interface, there is no option to
change the privacy level of user’ published photos. This problem violates the H3 (User control
and freedom) and it is classified as severity level
4 (Catastrophic usability problem). In the webbased interface, the user can change the privacy
level by clicking on “Edit photo”. However, on
mobile-based interface, users cannot control this
option when using the same feature.
Problem #4: Lack of standardizing the user
status. In the mobile-based interface, the system
does not offers custom privacy options to the functionality of changing the user status. In this way,

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A Usability Evaluation of Facebook’s Privacy Features Based on the Perspectives of Experts and Users

Figure 10. Label with poor information on the web-based interface

the system prevents the user to make more refined
chooses such as: “Friends of friends” or “Specific
people”. This problem violates H3 (User control
and freedom) and H4 (Consistency and standards),
and it is classified as severity level 4 (Catastrophic
usability problem). The system should offer this
option to provide a more standardized interface
and greater control in both environments.
Problem #5: There are no options of displaying
private information (“Just me”). There is a lack
of options such as “Nobody” or “Just me”, if the
user do not want to share his/her personal information with others users. This problem violates H4
(Consistency and standards) and it is classified as
severity level 4 (Catastrophic usability problem).
Problem #6: Lack of a few privacy settings options. Several user’ basic information that exist in

86

the profile settings interface cannot be changed in
the privacy level. These privacy settings exist and
can be changed on the web-based interface. This
problem violates H4 (Consistency and standards)
and it is classified as severity level 4 (Catastrophic
usability problem). On the mobile-based interface
it is not possible to configure this option.
Problem #7: The Privacy Policy is showed
in English language. In this case, users who do
not master this language will not be able to understand the information (this problem occurs as
presented in Figure 11). This problem violates the
H4 (Consistency and standards) and H10 (Help
and documentation), and it is classified as severity
level 3 (Problem with high priority fix). The user
might not understand the terms of the Facebook’
Privacy Policy.


A Usability Evaluation of Facebook’s Privacy Features Based on the Perspectives of Experts and Users

Figure 11. Privacy Policy is presented in English

Problem #8: Lack of some system messages in
the mobile-based interface. When the user makes
a change of his/her registration (e.g. changing
the privacy of his/her birth date) the system does
not highlight the change, and there is no clear
message showing which item was modified. This
problem violates H1 (Visibility of system status),
H2 (System compatibility with the real world),
and H5 (Error prevention), and it is classified
as severity level 3 (Problem with high priority
fix). This problem hinders user to understand the
system status.
Problem #9: A conflict occurs when searching
for Privacy Policy on the mobile-based interface.
In the web-based interface, when the Facebook’
engine searches the term “privacy”, the system
suggests as a first option the Privacy Settings Interface. However, on the mobile-based interface
this option is not suggested at all. In this case, the
system only exhibit communities and/or people
that have the word “privacy” in their name. This
problem violates H4 (Consistency and standards)
and it is classified as severity level 2 (Minor usability problem, with low priority of fixing it). The
two versions (web and mobile-based interface)
exhibit different results for the same search.
Problem #10: Privacy Policy with information
overload and visual pollution. Some information

regarding to the privacy policy in mobile-based
version is not available and others are presented
in a very extensive way for the user. For example,
the policy related to the privacy terms is extremely
extensive and it is presented in one page to the
user, making user to scroll several times to view
the content. There is no search engine to help in
finding any particular information. This problem
violates H8 (Aesthetic and minimalist design) and
H10 (Help and documentation), and it is classified
as severity level 2 (Minor usability problem, with
low priority of fixing it). The user may not have
access to certain information relating to the policy
of the data in the mobile version and likewise, there
is a difficulty in user to get all documentation.
Problem #11: Conflicting information on
web and mobile-based interfaces. On Privacy
Settings, the explanation of “Control privacy
by publishing” is presented in different way on
the two environments. This problem violates H4
(Consistency and standards) and it is classified
as severity level 2 (Minor usability problem, with
low priority of fixing it).
It is noticed that despite being found more
problems in the web-based environment the problems encountered in the mobile-based environment have higher severity degree. Furthermore,
some problems were found in both environments,

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A Usability Evaluation of Facebook’s Privacy Features Based on the Perspectives of Experts and Users

Figure 12. Facebook’s users’ distribution by age (Socialbakers, 2014b)

such as lack of privacy options and the different
information content for the same functionality.
Based on the problems encountered, the group
analyzed the possible problems that would actually be found by Facebook users. An important
issue is that the vast majority of Brazilian users
of Facebook are young, which often interact and
share their information. As shown in Figure 12,
the largest age group is currently 18-24 years
(31,9%), followed by the users in the age of 2534 years (27,3%). There are 48% male users and
52% female users in Brazil, compared to 46% and
54% in United States and 75% and 25% in India
(the two countries with more Facebook’ users in
February 2014) (Socialbakers, 2014a).
By these data we realized the importance of
this age group for Facebook, and so, we designed
a questionnaire focused at this audience, because
they may be impacted by the privacy issues of
Facebook, and they interact and share more their
information on-line in social networks than any
other age group. We presented our questionnaire
in some on-line communities and blogs in Brazil.

88

Questionnaire Results
Regarding the first question, about the 605
participants’ age, we had 2.81% up to 12 years,
19.33% from 13 to 18 years, 41.82% from 19 to
25 years and 36.04% of 26 years or more. Note
that although Facebook only allows registration
of users 13 years or more, were collected 17 users
below this age group who use the social network.
Besides, we realized our dissemination in the entertainment blogs focused to young people had a
good result, as more of 61% of participation were
in the range of 13 to 24 years old. The second
question was related to the gender of participants,
with the result: 48.26% are female and 51.74%
male. Question 3 identifies the Brazilians region
(State) of participant residence. Figure 13 shows
the distribution of users by location. Among the 20
Brazilians’ state that participated in the study, the
highest participation in the research was “Paraná”
and “São Paulo”, and the study had also Brazilians
users living abroad.
Question 4 was about to how long time the
user has an account in Facebook. The results
were 20.49% of participants use it less than one
year, 41.16% use it between 1 and 2 years, and
38.35% use it 2 years or more. About how often


A Usability Evaluation of Facebook’s Privacy Features Based on the Perspectives of Experts and Users

Figure 13. Distribution of users by location

participant uses Facebook (addressed in question
5), the results were: 0.66% of the users use it once
a week, 2.31% use it rarely; 10.25% use it a few
times a week; 28.10% use it at least once a day;
and 58.68% use it many times a day. It may be
noted also that the selection of participants was
very interesting, since the majority of them uses
Facebook quite frequently, and thus may have
different problems related to privacy settings.
Question 6 asked users about what kind of
devices (computers or mobile devices) they have
been using to access Facebook, and this question
could have more than one answer. As a result we
obtained: 41.99% of the users access Facebook
only by their computer or notebook; 1.99% access Facebook only by their mobile devices (e.g.
smartphone, cellphone, tablet), and the majority,
56.02% access Facebook by both types of devices

(computers or mobile devices), showing that our
comparison between web-based interface versus
mobile-based interface is important, and the
differences between them may result in a major
usability problem for users. The seventh question
asked respondents who marked both options in the
previous question (e.g. who uses different devices),
which device they prefer to use Facebook, and most
people preferred computer device - with 83.77%
of users, 15.34% preferred mobile devices and
0.89% did not answered the question. This may be
related with the findings of heuristic evaluation,
where more severity problems were detected in
the mobile system.
When asked about the knowledge of the
Facebook’s privacy settings (question 8), 18.85%
of participants claim no knowledge of them,
against 81.15% who were aware of them. When

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A Usability Evaluation of Facebook’s Privacy Features Based on the Perspectives of Experts and Users

we analyze this issue in relation to the users’ age,
we can recognize that younger people have less
knowledge about privacy settings, since within
the range under 13 years, 52.95% of the participants had no knowledge of the privacy settings;
in the range from 13 to 18 years the percentage
was 34.49%; in the range from 19 to 25 years the
percentage was 14.98%; and up to 25 years, it was
10.58% of users who did not know the privacy
settings. In the next question 9, they were asked
whether they had changed any privacy setting,
and the answers were: 19.01% of the participants
have not made changes, and 80.99% had made
changes. For the 80.99% who have already made
changes in the privacy settings (495 people), we
asked about the settings they had changed. The
answers are grouped in Figure 14, and we realize
that the majority of the responders are concerned
only with the default privacy settings, with who
can see their photos, with who can publish in their
wall, and the privacy setting regarding to phone
and address.
The following questions dealt with problems
identified in heuristic evaluation. Question 11 was
related to the lack of options to cancel executed
actions of users. The result was that 70.10% of
the participants did not miss the lack of option to
cancel, versus 29.49% that missed such option.
The lack of an option to cancel operations in the
system can make the user confuse, not knowing
what to do to change a setting and return to the
previous status. Users not have to wonder where
they have to go to cancel previous actions, nor
leaving the unwanted state without cancel the
changes they did not want. This problem was
reported by 146 people, confirming the result
obtained by the heuristic evaluation.
Question 12 treated the lack of a delete button
in the area of their personal tastes. The results
showed that 55.44% of people did not miss the
delete button, but 44.56% of people said yes, they
miss this feature in the system. This problem had
been detected by the heuristic evaluation with the
highest severity. Question 13 asked people about

90

the difficulty in visualizing information in English
language, even when the interface’s language
was set up to Portuguese. We asked this issue
because heuristic evaluation detected it in some
parts of both web-based help system and mobile
help system, and also detected buttons in the
system that had such inconsistency. The answers
were that 69.75% of participants did not see it as
a problem, but 30.25% considered it a difficulty
if they had been seeing information in English,
which represents a very significant amount of
people, if we compare that with the amount of
hits Facebook has.
Question 14 asked people if they had some
problems to make changes in the privacy settings
on Facebook, and 82.31% of participants had
no trouble, 16.36% of users had problems, and
1.33% did not answer it. Of those participants
who had problems, we asked them to write the
problems found (the only open question in this
questionnaire). All responses were analyzed and
categorized into several terms (or labels), similar
to building an affinity diagram, that is a data collection approach. The most commonly terms found
in the responses of users were arranged in a tag
cloud that we translated here to English words,
as shown in Figure 15.
The main problems identified were also detected by heuristic evaluation. It is important to
see that the majority of young people were not
concerned about their privacy online. As result,
they did not pay much attention about the privacy
settings of Facebook. As the participants’ age
increased, they had more concerns about their
privacy settings. We were able to confirm the
results of heuristic evaluation, especially related
to the highest problems (e.g. those which severity are 4). Others problems (that were identified
by the experts and that were not explicit asked to
the respondents in a closed question) were in fact
verified in the open question, such as the lack of
information, information that is hard to use and
difficult to understand, failures, hidden options,
and hard configuration of privacy settings.


A Usability Evaluation of Facebook’s Privacy Features Based on the Perspectives of Experts and Users

Figure 14. Privacy configurations more used on Facebook

DISCUSSION AND NEW
SIGNIFICANCE: FACEBOOK
CHANGES SINCE THE
USABILITY EVALUATION
Since the Facebook’s privacy settings have been
updated from June 2012 (both on web-based and
on mobile-based environment), we reevaluated
the two environments to verify which problems
identified in our heuristic evaluation were solved
and which of these persists.
Table 2 presents the problems that were identified on web-based environment. Of the 13 usability problems originally raised in our heuristic
evaluation, 8 were solved, 1 was partly solved and
4 yet persist.
Table 3 presents the problems that were identified on mobile-based environment. Of the 11 us-

ability problems originally raised in our heuristic
evaluation, 7 were solved, and 4 yet persist.

LIMITATIONS
We examine in this paper the usability evaluation
of Facebook’s privacy settings available in May
and June of 2012, composed by an evaluation
process formed by three approaches and executed
in three stages. We first analyzed the systems
features and privacy setting of each environment,
the web-based one accessed via web browser, and
the mobile-based system, an official Facebook
application available for devices such as smartphones and tablets.
The second stage of the evaluation was heuristic evaluation conduction, performed by three
experts, which identified potential usability prob-

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A Usability Evaluation of Facebook’s Privacy Features Based on the Perspectives of Experts and Users

Figure 15. Tag Cloud of problems reported by Facebook’s users translated to English

lems that users might face. The final stage of the
evaluation was completion a questionnaire with
Facebook’s users, analyzing whether the problems
encountered earlier were really a problem for the
end-user. We relied on a voluntary participation
by 605 Facebook users, where the vast majority
uses Facebook frequently. The issue of privacy
and ethical issues was secured by first accepting
an informed consent participating in the research,
and also because we did not request any individual
information.
After the Facebook’s privacy settings have
been updated, we re-evaluated the web and
mobile-based environment to check which of the
initial problems encountered in our first heuristic
evaluation were solved and which of these persist.
The results show that 8 of 13 initial problems in
web-based environment were solved, 1 was partly
solved and 4 yet persist. Regarding to the mobilebased environment, 7 of 11 initial problems were
solved and 4 yet persist.
In this second evaluation, we found 9 persistent problems in both environments (one of
these partially solved). Regarding the problems
severity, 4 of it were classified as severity level 4

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(Catastrophic usability problem), 2 were classified
as severity level 3 (Problem with high priority
to fix) and 2 were classified as severity level 2
(Minor usability problem, with low priority of
fixing it). The heuristics violated were H4 (Consistency and standards) with 4 problems related,
H5 (Error prevention) with 3 problems related,
H6 (Recognition rather recall) with 2 problems
related and H1 (Visibility of system status), H2
(System compatibility with the real world), and
H3 (User control and freedom) with 1 problem
related each.
This second analysis shows that Facebook’s
privacy setting still have some usability problems.
They seem small, given the immensity of Facebook’s features; however, the majority of these
problems have high severity, which can cause an
obstacle to the user, when using the system.

CONCLUSION
The goal of this work was not examining which
method was the best approach, but discussing
how the different results types can be found and


A Usability Evaluation of Facebook’s Privacy Features Based on the Perspectives of Experts and Users

Table 2. Facebook changes on web-based environment
Problems

Description

Problems #1, #2,
#5, and #6

The problems persist. There were no significant changes in these usability problems.

Problem #3

The problem was solved. The information add method was changed. Now, the system uses the “Like” function to
add and the “Unlike” to delete.

Problem #4

The problem was solved. In all Facebook features there is a “Cancel” option (presented as a link or a button) when
an action is being performed.

Problem #7

The problem was partly solved. As presented in Figure 16, in some features, information are presented in different
languages (e.g. on Privacy Policy, the information appears written in a different language used by user), see Figure
16.

Problem #12

The problem was solved. However, in some features, the web-based environment yet has more configuration
options compared to the mobile-based environment.

Problem #8, #9,
#10, #11, and #13

The problems were solved. These problems were not identified in the new web-based environment.

Figure 16. Privacy Policy date is only in English

Table 3. Facebook changes on mobile-based environment
Problems

Description

Problem #3

The problem persists. In some features, it is not possible to change the privacy level.

Problems #1, #2, #4
and #5

The problems were solved. These configuration options are available. The user can edit the privacy level on
the “News feed”, delete appropriately one publication, and select a custom privacy option (he can choose the
“Personalized” or the “Just me” options).

Problem #6

The problem persists. The web-based environment has more configuration options compared to the mobile-based
environment.

Problem #7

The problem was solved. The Privacy Policy terms are showed in the user language.

Problem #8

The problem persists. After changing the privacy of a feature, the user must click “Save” for the changes to take
effect. The system does not highlight the change, and there is no clear message showing which item was modified.

Problem #9

The problem persists. On web-based environment, the Facebook’ engine searches the term “privacy” and suggests
the Privacy Settings Interface. However, on the mobile-based interface the same search returns a different result.

Problems #10 and
#11

The problems were solved. The information has been standardized for both environments.

93


A Usability Evaluation of Facebook’s Privacy Features Based on the Perspectives of Experts and Users

identifying problems related to privacy settings.
This work showed the importance of performing
a usability evaluation, and identifies potential
problems that users can face when using a social networking. Obviously, this study does not
answer all the usability problems highlighted in
the literature, but it identifies a direction facing
problems found by an inspection method and by
users’ participation.
This work addressed as a broader result to
our community, presenting the problems associated with these privacy settings and we wanted
contributing to the improvement of users’ interaction with the Facebook social network. We are
planning to execute others usability evaluations
in the privacy settings of others social networks
and try to understand the different dimension of
privacy establish by the different groups of users.
Further research can be conducted to assess the
privacy issues in relation to other social networks,
and how the Facebook’ privacy features and settings differs from other social networks. Based
on these results, we could define a framework for
privacy evaluation to overcome the privacy issues
and to present ways to reduce these problems.

Fernandez, A., Abrahão, S., & Insfran, E. (2013).
Empirical validation of a usability inspection
method for model-driven Web development.
Journal of Systems and Software, 86(1), 161–186.
doi:10.1016/j.jss.2012.07.043

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Albert, W., & Tullis, T. (2013). Measuring the user
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Albert, W., Tullis, T., & Tedesco, D. (2010). Beyond the Usability Lab: Conducting Large-scale
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Barbosa, S. D. J., & Silva, B. S. (2010). Interação
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(in Portuguese)
Bevan, N. (2009) Extending Quality in Use to
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M. Kurosu (Ed.): Human Centered Design, HCII
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Cairns, P., & Cox, A. L. (2008). Research methods for human-computer interaction. Cambridge
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Cooper, A., Reimann, R., & Cronin, R. (2007).
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Dix, A., Finlay, J., Abowd, G., & Beale, R. (2004).
Human-Computer Interaction (3rd ed.). Prentice
Hall.
Dumas, J. S., & Loring, B. A. (2008). Moderating Usability Tests: Principles and Practices for
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KEY TERMS AND DEFINITIONS
Effectiveness: The accuracy and completeness
with which specified users can achieve specified
goals in particular environments.
Efficiency: The resources expended in relation to the accuracy and completeness of goals
achieved.
Expert: A person who has a comprehensive
and authoritative knowledge of or skill in a particular area.
Heuristic Evaluation: A heuristic evaluation
is a usability inspection method for computer
software that helps to identify usability problems


A Usability Evaluation of Facebook’s Privacy Features Based on the Perspectives of Experts and Users

in the user interface (UI) design. It specifically
involves evaluators examining the interface and
judging its compliance with recognized usability
principles (the “heuristics”).
Human-Computer Interaction (HCI): Human–computer interaction involves the study, planning, design and uses of the interaction between
people (users) and computers.
Inspection Method: The inspection method
allows the evaluator to examine a solution of
HCI to predict the possible consequences of an
interface problem.
Observational Method: The observational
method includes a collection of common techniques used in HCI research, including interviews,
field investigations, contextual inquiries, field
studies, focus groups, think aloud protocols,
storytelling, walkthroughs, cultural probes, etc.
Privacy: The state or condition of being free
from being observed or disturbed by other people.
It is the ability of an individual or group to seclude
themselves or information about themselves and
thereby express themselves selectively. The boundaries and content of what is considered private
differ among cultures and individuals, but share
common themes. When something is private to
a person, it usually means there is something to
them inherently special or sensitive. The domain
of privacy partially overlaps security, including
for instance the concepts of appropriate use, as
well as protection of information. Privacy may
also take the form of bodily integrity.

Satisfaction: The comfort and acceptability
of the work system to its users and other people
affected by its use.
Social Network: It is a network of social
interactions and personal relationships. It is a
dedicated website or other application that enables
users to communicate with each other by posting
information, comments, messages, images, etc.
Usability Problem: A usability problem is an
aspect of the system and/ or a demand on the user
which makes it unpleasant, inefficient, onerous
or impossible for the user to achieve their goals
in typical usage situations.
Usability: Usability can be defined by the
effectiveness, efficiency and satisfaction with
which specified users achieve specified goals in
particular environments. It is the state or condition
of being usable; the degree to which an object,
device, software application, etc. is easy to use
with no specific training. Usability refers to the
ease with which a User Interface can be used by
its intended audience to achieve defined goals.
Usability incorporates many factors: design,
functionality, structure, information architecture,
and more.
User Interface (UI): The hardware, or software, or both that enables a user to interact with
a computer. The term user interface typically
refers to the visual presentation and its underlying
software with which a user interacts.

97

98

Chapter 5

HTML Segmentation for
Different Types of Web Pages
Evelin Carvalho Freire de Amorim
Departamento de Ciência da Computação (UFMG), Brazil

ABSTRACT
Search engines manage several types of challenges daily. One of those challenges is locating relevant
content in a Web page. However, the concept of relevance in information retrieval depends on the
problem to be solved. For instance, the menu of a website does not impact the results of an algorithm to
detect duplicate Web pages. An HTML segmentation algorithm partitions a Web page visually in such
a way that parts from a same partition are semantically related. This chapter presents two strategies to
segment different types of Web pages.

INTRODUCTION
Search engines manage redundant and nonstructured content daily. However, redundant and
non-structured data generate problems that affect
the performance of search engines. For example,
redundant data are not useful for a query; nevertheless redundant data can be exhibit in results if they
are not removed from the dataset. Partitioning a
web page into cohesive visual pieces and selecting
the most relevant piece can improve algorithms
for detection of redundant data. The task of partitioning a web page into cohesive visual pieces
is called HTML segmentation.
Web browsing in mobile devices is also enhanced by HTML segmentation (Yin & Lee,
2004). The web browser of a mobile device can

partition a web page and exhibit the most relevant
part of the web page in the center of the screen.
This feature improves the user’s experience in the
mobile device.
Another task to be solved by HTML segmentation is the ranking quality of standard web pages
searching schemes (Fernandes, Moura, da Silva,
Ribeiro-Neto, & Braga, 2011). Ranking of web
pages is an important task in Information Retrieval
and search engines are concerned about the best
ranking of web pages.
There are two main types of HTML segmentation techniques: general or topical. The latter
technique segments only specific types of web
pages, for instance blogs or news. Although
topical techniques achieve robust results they are
inflexible for particular Information Retrieval

DOI: 10.4018/978-1-4666-7262-8.ch005

Copyright © 2015, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.


HTML Segmentation for Different Types of Web Pages

tasks. General techniques face the challenge of
finding a model that conciliates features from
different web pages like personal web pages and
e-commerce web pages.
Considering that general techniques for HTML
segmentation are uncommon and still constitute
a challenge for the data mining area, because
web pages displays relevant content in different
ways. For instance, describing news web pages
and an e-commerce web page in one model is not
an intuitive task.
This chapter has the following goals:
1. Describing general techniques for HTML
segmentation;
2. Comparing two general HTML segmentation techniques. The first strategy is called
ETL HTML segmentation and the second
strategy is called TPS segmentation.
The remaining of this chapter also reviews
some topical techniques, the main results of HTML
segmentation algorithms and issues to solve in
HTML segmentation.

BACKGROUND
HTML segmentation covers concepts from information retrieval and data structures. The following subsection defines data structures concepts
employed in HTML segmentation algorithms.
The next subsection describes how HTML segmentation improves some tasks of the information
retrieval area.

Data Structures Concepts
A rendering web page is purely an HTML document in its visual form. However, a more suitable
representation of HTML is required in order to
automatically extract information from web pages.
Therefore algorithms that process web pages use
a data structure called DOM (Document Object

Model) Tree, which defines a logical structure of
documents and the way a document is accessed and
manipulated (Le Hégaret, P. Wood, L., & Robie,
J., 2000). The process of building a DOM Tree
transforms each HTML tag into a DOM node,
which also involves assigning attributes of a tag to
the corresponding DOM node. Figure 1 shows an
HTML code on the left side and its corresponding
DOM Tree on the right side.
Web browsing requires a visual representation
of HTML though. Besides that, a rendering web
page allows the user to locate continuous visual
parts of his or hers concern, for instance, menu,
product pictures, and many others parts. The concept of HTML segment is based on these visual
parts of a rendering web page. For instance, an
e-commerce web page usually shows the following visual parts: describing product, pictures of
product, reviews of products, and so on.
Chakrabarti, Kumar, and Punera (2008)
formally defined HTML segment as a visual
continuous and cohesive piece of a web page.
HTML segmentation tasks aims to find a set of
web page segments in the same way a human
divides a web pages in different semantic parts.
By using this kind of segmentation, it is possible
to improve the ranking quality of standard web
pages searching schemes (Fernandes et al., 2011)
(Song et al., 2004). Also, the duplicate detection
of web pages is enhanced by HTML segmentation
algorithms (Chakrabarti et al.,2008).
Due to the importance of HTML segmentation
in Information retrieval, many studies proposed
different solutions to segment web pages. One
of the first techniques developed to segment
web pages was the VIPS algorithm (Microsoft
Research, 2003). VIPS algorithm assigns to
each segment a value called Degree of Coherence, which measures how coherent a segment
is. DOM structure and visual cues are used to
compute the degree of coherence, which ranges
from 1 to 10. Degree of coherence has the following properties:

99


HTML Segmentation for Different Types of Web Pages

Figure 1. HTML code and its DOM Tree

•
•

The greater the degree of coherence is,
the more consistent the content within the
block;
In the hierarchy tree, the degree of coherence of the child is not smaller than the one
of its parents.

VIPS algorithm comprises three steps: segment extraction, separator detection and content
structure construction. In segment extraction step,
the DOM tree is traversed starting in the root node,
and each DOM node is evaluated whether it is a
block or not a block. In the case that a DOM node
is not a segment, its children are processed in the
same way. Next, for each extracted segment, the
algorithm assigns a degree of coherence. Separator detection step discovers separators in the web
page and assigns a weight for each separator. The
content structure construction step traverses the
DOM tree starting in the root node in order to
check the granularity of extracted segment. The
process of segmentation is applied one more time
whether the segment does not meet the granularity
requirements. Five human volunteers evaluated
results in 600 web pages and the measurement
had four grades: Perfect, Satisfactory, Fair, and
Bad. Volunteers judged 93% of theweb pages as
being perfect or satisfactory.

100

Velloso and Dorneles (2013) describe a HTML
segmentation technique in order to detect the
main content of a web page. Authors argue that
data records, like lists of products or services,
compose the main content of a web page. Velloso
and Dorneles’ technique is based on the Tag Path
concept, which can be defined as a string that
describes an absolute path from the root of DOM
Tree until a given node. This technique executes
four main steps: converting a DOM Tree to a tag
path set, searching for a tag path that represents
the main content, filtering noises in tag path set
and pruning the DOM Tree; leaving only the
main content. Commercial and institutional web
pages from 23 web sites were used to evaluate
the method, and the performance metrics were
percentage of pruning in DOM Tree and main
content preservation. The algorithm reduced
46.22% of DOM nodes in web pages and 20 web
sites preserved their main content. However, the
authors mention the following cases where their
technique produces poor results: pages with little
difference between regions, pages where the main
content is subdivided into more than one region,
and pages where the main content is smaller than
the rest.
Some studies apply Machine Learning to
HTML segmentation. Machine Learning techniques, which are well known in the Information


HTML Segmentation for Different Types of Web Pages

Retrieval area, also provide efficient ways to find
Pattern in Data. Chakrabarti et al. (2007) applied
isotonic smoothing to find irrelevant segments
in web pages, although the Adjusted Rand Index
achieved slightly above 0.5. Song et al. (2004) segmented web pages using a heuristic, however the
authors used Machine Learning to rank segments
according to its index importance. Chakrabarti et
al. (2008) employed Machine Learning to segment
web pages. In this approach, HTML segmentation
is formulated like two optimization problems:
correlation clustering formulation (CCUTS) and
energy-minimization cuts formulation (GCUTS).
Both approaches represent the DOM Tree as a
weighted graph. The weights of the edges denote
the cost of placing the end points of an edge in
different segments. The appropriate segmentation
is found when the weights of the edges that minimize the formulation function are minimized. In
order to evaluate the performance of CCUTS and
GCUTS, the authors used the Adjust Rand Index
(ARI) and the Normalized Mutual Information
(NMI), which will be explained later. One-hundred
and five web pages randomly retrieved from the
web composed the dataset. GCUTS achieved 0.6
in ARI and 0.76 in NMI, while CCUTS achieved
0.46 in ARI and 0.64 in NMI.
Other studies propose heuristics to segment
web pages. Yi, Liu, and Li (2003) proposed a
new data structure, called SOMTree (Site Object
Model Tree), based in the DOM Tree design style.
Fernandes et. al (2011) developed a new version
of SOMTree to detect segments in web sites. Both
SOMTree approaches are at the site level, which
means that they demand a set of web pages from
a specific site to build a model for such web site.
This kind of approach requires a model for each
web site, which can generate a huge amount of
models and effort to produce such models.
Although visual features do not distinguish
techniques, their employment produces competitive results. (Chakrabarti et al., 2008) (Kohlschütter, & Nejdl, 2008) (Song et al., 2004). Visual

features provide clues that are related to the segment definition. However, extraction of visual
features is an expensive process.
Kohlschütter, Fankhauser, and Nejdl (2010)
and Weninger, Hsu and Han (2010) consider
that some segments are irrelevant; therefore both
researches attempt to detect useful content in web
pages. The first approach tested generic web pages
in their method and the second test only tested
blogs web pages. Although both methods have
competitive results, HTML segmentation considers the whole web page and therefore is more
flexible with regard to the ranking task.

HTML Segmentation and
Information Retrieval
Most HTML segmentation techniques aims to
improve a significant information retrieval task:
ranking of web pages. This task assigns scores to
web pages according to a given query. In order
to compute scores of web pages, search engines
process web pages and extracts text content from
web pages. Then, the text content is separated into
words. Each word is stored in an index file with a
pointer to the web page that includes the processed
word. When a user submits a query, search engines
look for query words in the index file and compute
similarity between query and web pages. Vector
space model is an efficient method to compute
similarity between query and web pages (Yolle,
Baeza-Yates, Ribeiro-Neto, 1999). The following equation describes the Vector Space Model
formula, where dj is a web page, q is a query, wij
is the weight of the i-th word in the j-th web page,
wiq is the weight of i-th word in query, and N is
the number of terms in query.

∑ (w
N

sim (d j ,q ) =





i =1

∑

N

2
i =1 i , j

w

i, j

wi,q )

∑

N


w 




(1)

2
i =1 i ,q

101


HTML Segmentation for Different Types of Web Pages

The web page retrieval method described
above, considers all words in the same web page
to be equally important. However, when the user
is looking for a web page, typically information
in the main content of web page is more relevant
than information in both the menu and header of
the web page. Zaragoza, Craswell, Taylor, Saria,
and Robertson (2004) proposed a method that applies positive weights to words of title and anchor
text. Results were superior compared to a model
where words on the title and anchor text have the
same weight of another words in web pages. The
strategy employed by Zaragoza et. al may also be
used to compute web page score. Such strategy
uses Equation 1 to compute sim for each segment
then the final web page score is a weighted mean
of segment similarities.
Although search engines results are satisfactory, improvements in ranking tasks are still
necessary because of the fast growing of the web.
As segmenting web pages of all kinds can be
improved, ranking task can take advantage from
these future improvements.

i.e., the strategy will build a segment model for
each web site. The main problem with this kind
of strategy is the growing of models, since new
web sites emerge every day.
Besides the types of strategy, visual features
are a crucial issue in HTML segmentation. A time
performance test with 1000 web pages was done
using a popular software1 to extract visual features
from the DOM Tree. The average time to process
the DOM Tree visually took 32.48 seconds, while
without the use of the software the average time
of the proposed algorithm in this chapter was
0.03 seconds. Even if search engines own a robust
infrastructure, a huge amount of data would be
hard to process visually. Despite the fact that visual
features yield competitive results, performance
should be considered for large volumes of data.
Few studies are dedicated to create one model
for different kinds of web pages. These strategies
raise a problematic issue: how to build a model that
conciliates web pages with distinct styles? In order
to describe some differences between web pages,
consider Table 1. The number of nodes per segment
in each dataset varies in significant amounts. For
instance, while Blogs segments comprise almost 30
nodes per segment, Ig segments comprise around
13 nodes per segment. These numbers depict one
aspect of the differences between web pages.
Naturally when a user is browsing the web,
he or she notices others divergences between the
layout of web pages. For instance, a personal web
page displays a simple layout, possibly with two
main segments: a menu and a text segment. On the
other hand, a product web page in an e-commerce
web site presents several segments: menu, product
pictures, product descriptions, and so on.

STRATEGIES TO SEGMENT
GENERIC WEB PAGES
The previous section described several proposals to solve the HTML segmentation problem.
Nevertheless each proposal has specific problems.
Topical oriented strategies are limited in their
scope, since Search Engines process different
types of web pages and this kind of strategy would
cause the overhead of identifying the type of web
page. Another common strategy is site oriented,
Table 1. Average number of nodes per segment

102

Dataset

ns = #Nodes in Segment/#Nodes in Tree

Standard Deviation (ns)

CNN

22.25

18.26

Blogs

27.36

26.60

Ig

13.16

11.65


HTML Segmentation for Different Types of Web Pages

In order to deal with differences between web
pages, a machine learning strategy must be trained
in several kinds of web pages. Unlike heuristics,
Machine Learning builds a model to handle distinct features in the best possible way. The main
disadvantage of Machine Learning strategies is
the manual labeling of data. However, the labeling
process can be performed by some heuristic and
manual correction is applied later.
Machine Learning techniques are ancient
in computer science, thus there is a variety of
techniques to serve diverse purposes. As Machine Learning demands a significant amount
of labeling data, only recently data mining is
applying Machine Learning to classify data.
Technology companies have been mining data
before Machine Learning became widespread.
A technical report from Microsoft (Microsoft
Research, 2003) describes a heuristic method to
improve the performance of ranking quality of
web pages. Also a partnership between IBM and
Berkeley University proposed a method to discover
and eliminate irrelevant segments of web pages
(Bar-Yossef, & Rajagopalan, 2002).
Mining web pages has been researched since
the Web 1.0. The main characteristics of Web
1.0 are static web sites, the bow-tie structure
of web and content generated by a small group
(Cormode, & Krishnamurthy, 2008). Nowadays,
the business sector still builds its web sites with
web 1.0 structure.
However costumer contributions are taking an
important role in web; reviews and complains in
social media are a concern for the business sector.
Social media, blogs and wikis represent much of
the Web 2.0. Content generated in Web 2.0 is also
non-structured and massive.
HTML segmentation in Web 2.0 is easier
because many web sites are generated automatically, however a problem arises: noise content.
Sometimes, customer contribution in web is
unimportant for company or is full of grammatical and semantic mistakes, which can affect the
understandability of the text. In order to avoid

incomprehensive comments, some web portals
provide a review system with grades; whether
the review is relevant, other customers validate
it. The business sector can be benefited by this
kind of strategy, the since data mining algorithms
of Web 1.0 can still be used.
Besides Web 1.0 and Web 2.0, another promising technology is arising: Web 3.0. Berners-Lee,
Hendler, and Lassila (2001) defined Web 3.0 as
the Semantic Web, i.e., a Web with “meanings”.
Semantic Web has been evolving for more than
10 years with the goal of “bringing structure to
the meaningful content of Web pages, creating an
environment where software agents roaming from
page to page can readily carry out sophisticated
tasks for users” (Berners-Lee et al, 2001).
Although the Semantic Web had not evolved
as predicted by Berners-Let e al. (2001), some
companies recently started using web semantic
tools in information retrieval. Google Company
(Singhal, 2012) launched the Knowledge Graph,
which is based on the Freebase2. Knowledge graph
expanded the traditional information retrieval of
Google Search Engine through concepts meanings.
Besides web pages, query results also return concepts from Knowledge Graph related to the query.
For instance, when we search Google about the
Brazilian soccer player Neymar, facts about Neymar’s life and related personalities are also listed
apart. However, only some queries take advantage
of the Knowledge graph, since several concepts
are not registered in the Knowledge Graph yet.
Some data mining techniques, like HTML
segmentation, can benefit the Semantic Web
though. NELL is a project from Carnegie Mellon University that aims at mapping strings from
HTML documents to concepts (Lohr, 2013). In
order to perform the mapping, NELL retrieves web
pages and then employs the following techniques,
namely: Machine Learning, Natural Language
Processing, and Logic. Whether we consider that
relevant segments in web page own concepts,
thus a project like NELL can apply data mining
techniques.

103


HTML Segmentation for Different Types of Web Pages

SOLUTIONS AND
RECOMMENDATIONS
HTML documents prevail in the present time and
will do for a long time. Therefore in this chapter
we describe two different solutions to segment of
web pages. First solution is an enhanced version
developed by Amorim (2012), which employed
Machine Learning in HTML segmentation. Second Solution is a slightly modified version of
Tag Path Sequence (TPS) HTML segmentation,
which is a heuristic developed by Velloso and
Dorneles (2013).
Amorim’s (2012) strategy used Entropy
Guided Transformation Learning (ETL) (Milidiú,
Santos, & Duarte, 2008) and a Natural Language
Modeling to segment web pages. Although, ETL
segmentation applies a Natural Language Processing (NLP) Model to the problem, the approach
works in any language, since only inspiration
comes from NLP and none NLP techniques are
used. Also several statistical and structural features

are extracted from web pages. For performance
reasons, visual features are absent in this model.
TPS strategy is a simple heuristic based on the
concept of tag path. Velloso and Dorneles applied
TPS segmentation to extract the main regions of
web pages, thus we made a few modifications to
extract all segments in web pages.

ETL HTML Segmentation Strategy
ETL is a strategy that combines the advantages
of Decision Trees (DT) Learning and Transformation-Based Learning (TBL). The key idea of
ETL is to use decision tree induction to obtain
templates. Next, TBL strategy is used to generate transformation rules. Figure 2 shows the ETL
method.
DT learning is one of the most widely used
machine learning algorithms, and it performs a
partitioning of the training set using principles
of Information Theory. The learning algorithm
conducts a general to specific search in the fea-

Figure 2. ETL: Entropy guided transformation learning

104


HTML Segmentation for Different Types of Web Pages

ture space, which is a set of values that describes
data. For instance, Table 1 describes data for
the HTML segmentation problem. The most
informative feature is added to a tree structure at
each step of the search. Information Gain Ratio,
which is based on the data entropy, is normally
used as the informativeness metric. The objective is to construct a tree using a minimal set of
features, which efficiently divides the training
set into classes of observations. After the tree is
grown, a pruning step is carried out in order to
avoid overfitting, which means that the machine
learning algorithm learnt random error or noise.
One of the most used algorithms for induction of
a DT is the C4.5 (Quinlan, 1993). Quinlan’s C4.5
system is used throughout this research.
Transformation Based error-driven Learning
(TBL) is a successful machine learning algorithm
introduced by Eric Brill (1995). TBL has since
been used for several Natural Language Processing
tasks, such as part-of-speech (POS) tagging (Brill,
1995) and Phrase Chunking (Milidiu et al.,2008).
TBL uses an error correcting strategy. Its main
scheme is to generate an ordered list of rules that
correct classification mistakes, which have been
produced by an initial classifier, in the training
set. Figure 3 describes how TBL works. In the
first step the baseline system performs an initial
classification for the untagged text. In the second
step the method compares the results classification
with the truth. Whenever a classification error is
found, all the rules that can correct it are generated by instantiating the templates. This template
instantiation is done by capturing some contextual
data of the sample being corrected. The third step
computes the rules scores, i.e., errors repaired and
errors created. If there is not a rule with a score
above a threshold, the learning process is stopped.
The fourth step selects the best scoring rule, stores
it in the set of learned rules and applies it to the
training set. The process returns to step two until
there is no rule left to select.
ETL DT template extraction includes a depthfirst traversal of the DT. For each visited node,

Figure 3. Transformation based learning

ETL creates a new template that combines its
parent node template with the feature used to split
the data at that node.
The ETL HTML segmentation strategy has
three main steps. The first step processes a DOM
Tree and extracts the features of the DOM nodes.
The second step generates three classifiers. Finally,
the third step can classify web pages.
The first step extracts the following twelve
features: nwords, nchars, nlinks, nimages,
nanchortextlength, tag, imgheight, imgwidth,
level, avgLenWords, hasCssClass, nchildren and
nchildrennnodes. Nwords is the number of words
in the subtree rooted in the current DOM node.
Nchars is the number of characters in the subtree
rooted in the current DOM node. Nlinks is the
number of links in the subtree rooted in the current DOM node. Nimages is the number of images
in the subtree rooted in the current DOM node.
Nanchortextlength is the number of characters
of an anchor text if the current HTML tag is a
link. Tag is the HTML label of the current DOM
node. Imgheight is the height of an image if the
current HTML tag is an image. Imgwidth is the

105


HTML Segmentation for Different Types of Web Pages

width of an image if the current HTML tag is an
image. Level is the level of the current node in
the DOM Tree. AvgLenWords is the average of
words length in the subtree rooted. HasCssClass
is a binary feature. When some css class is applied
to the current node in a DOM Tree, the feature
value is True, otherwise it is False. Nchildren is
the absolute number of the children in the subtree
rooted. Nchildrennodes is the relation between
the number of the children by the number of the
tree nodes. Amorim (2012) performed experiments with all listed features, except for: nchars,
nchildren and nchildrennodes.
The extracted features capture some intuitive
design attributes. As an illustration, let us consider
an e-commerce web page describing a product.
The menu of an e-commerce web page is a segment which has few words and several links, all of
them with a short length anchor text. The product
image is another segment. The product description
is another segment, which has a lot of text and
few links and images. These kinds of segment
characteristics are also found in other types of web
pages for two main reasons. First, because people
build web pages using some design principles in
order to help user navigation throughout the page.
The second reason is because many web pages are
automatically generated based on some templates.
The second step generates three classifiers,
which use DOM nodes as token units. A token
representation is often used in the Natural Language Processing (NLP) area. A token is a concept
represented by a string of characters, usually
categorized according to some given rules. The
process of splitting a string of characters into its
constituent tokens is called tokenization. One
NLP task that uses text tokenization is the Noun
Phrase Chunking. This task consists in recognizing
Noun Phrase (NP) text segments. In the following
example brackets indicate the four noun phrase
chunks in the illustrative sentence:
[He] reckons [the current account deficit] will
narrow to [only \# 1.8 billion] in [September].

106

The usual approach to build an NP Chunker
is by modeling it as a token classification task.
Each token is tagged in the IOB1 style, where:
O means that the token is not a NP; I means that
the token is part of an NP and B is used for the
first token of an NP chunk that follows another
NP chunk. The IOB1 tagging style is shown in
the following example:
He/I reckons/O the/I current/I account/I
deficit/I will/O narrow/O to/O only/I \#/I 1.8/I
billion/I in/O September/I.
Table 2, three DOM nodes from Figure 4 are
represented as tokens. Feature labels identify
table columns.
The web page segments are given by the DOM
node chunks. The oldest parent inside a segment is
labeled with B, which means that the node begins
the corresponding segment. If a node is part of a
segment the label of the node is I. If a node is not
part of a segment the label of the node is O. In
Table 2, node 36 begins a segment, so the chunk
feature value for node 36 is B. Nodes 37 and 38 are
inside a segment, so their chunk feature value is I.
In order to generate a classifier to HTML
segments, the second step performs three local
subtasks: entity identification, candidate relation
generation and relation recognition. This approach
is based on the RelHunter method (Fernandes et
al., 2010), which models the target structures as

Figure 4. A DOM tree piece


HTML Segmentation for Different Types of Web Pages

Id

nwords

Nchars

nlinks

nimages

nanchortextlength

tag

imgheight

imgwidth

level

nchildren

nchildrennnodes

chunk

Table 2. DOM node features

36

61

305

18

6

0

div

0

0

5

11

0.027

B

37

8

32

3

1

0

div

0

0

6

5

0.012

I

38

0

0

1

1

0

div

0

0

7

2

0.004

I

a relation over entities. Then those subtasks are
detailed and exemplified.
Entity Identification. This is a local subtask
which detects simple entities. An entity comprises
a type and a set of ordered nodes, considering the
order of the Depth-First Search Algorithm. Additionally, one of the nodes’ entity is defined as the
head entity (gray nodes in Figure 5). The Entity
Identification subtask is decomposed into several
node classification tasks, one for each entity type.
Thus, the original corpus is used to train several
classifiers, also one per entity type. The outcome
of this subtask is the entity set.

In HTML segmentation, there are two entity
types: nodes that start a segment and nodes that
end a segment. These entities comprise only one
node, which is the head entity. In order to identify
these entities, two binary classifiers are trained:
one for the start node identification subtask and
another for the end node identification subtask.
The start classifier labels nodes as start node (S)
or not start node (NS) and the end classifier labels
nodes as end node (E) or not end node (NE).
Candidate Relation Generation. At this phase,
the entity set is determined and hence a domainspecific procedure is used to generate the candi-

Figure 5. Segment detection scheme

107


HTML Segmentation for Different Types of Web Pages

date relation. Each instance within this relation
represents a candidate structure, which can be
true or false candidates. For the HTML segment
identification task, the candidate relation is generated with all entity pairs composed by a start
segment node and an end segment node, such that
the start node does not occur after the end node.
Relation Recognition. This is a classification
subtask in which a binary classifier discriminates
between true and false candidates within the
candidate relation. In order to train this classifier, an entry for each candidate is created in the
relation dataset built. This dataset comprises two
feature sets: local and global. The local features
are related to the candidate entities. For each
entity, the features of the head token are copied
from the original corpus to the relation dataset.
Figure 5 shows two local features: the first is the
snwords feature that represents the value of the
nwords feature in the entity node labeled as 2;
the second is enwords that represents the value
of nwords feature in the entity node labeled as 7.
The global features carry information about
the candidate and its entities related to their whole
segment, whose definition is task dependent. For
instance, in HTML segmentation task a segment
is the HTML segment. In order to capture global
features, the segment is split into three fragments.
In fact, given a candidate, the three fragments are
the subtree rooted at the left brother of the start
entity, the subtree between start entity and end
entity and the subtree rooted in the right brother
of the end entity. The Figure 5 highlights these
three fragments in the DOM tree fragment. The
global features inform about the occurrence of
relevant elements within each considered fragment. The definition of a relevant element is
application specific. In the HTML segmentation
task, the following elements are relevant: tags,
which consider the following HTML tags p, div,
l, a, lu, li, table, tr, td, h1, h2,i ; links; images;
nchars; hasCssClass; nwords; nodeid ; nchildren;
nchildrennnodes; start nodes; end nodes. For each
relevant element and fragment, three global fea-

108

tures are generated in the relation dataset: a flag
indicating the occurrence of the element within
the fragment, a counter showing the frequency
of the element and sometimes a feature specific
frequency value.
Five global features were added to Amorim’s
(2012) model. The first feature added was nchars,
which rather than the number of characters, fragments obtains the number of nodes with less than
five characters. The second feature added was
nwords, which Amorim (2012) already employed
with a flag indicating the occurrence of words in
fragments. However, this model also computes
text density, which is which is the ratio between
the number in tokens in fragment and the number
of lines in fragment (Kohlschütter, &Nejdl, 2008).
In this model, one line has at most 80 characters.
The third feature added was nodeid, which the
algorithm computes as the absolute difference
between start node and end node. The fourth
feature added was nchildren, with the algorithm
counting nodes having more than 10 children.
The fifth features added was nchildrennnodes,
with the algorithm counting nodes having values
below 0.05 nchildren/nnodes value.

TPS HTML Segmentation Strategy
Several studies employ visual features in HTML
segmentation. However, visual features affect the
performance of algorithm, thus we decided to
compare ETL to a generally oriented algorithm
that does not use visual features. TPS HTML
segmentation (Velloso, & Dornelles, 2013) is
the only strategy that follows these requirements.
TPS is based on the concept of Tag Path (TP),
which is a string describing the absolute path
from the root of the DOM tree to a given node.
For instance, whether the DOM tree of Figure 4
is rooted in the node 36 then the node 42 has the
following tag path: div_div_h2. The underline
(“_”) character joins HTML tags. Although this
example is devoid of style, the authors also join
styles in TP.


HTML Segmentation for Different Types of Web Pages

Tag Path Sequence is the set of the TP of all
nodes of a given DOM tree with n nodes, and is
represented symbolically by the following ordered
sequence TPS[1..n] = (TP1,TP2,TP3,…,TPn-1,TPn).
Each different TP in a TPS is represented by a
symbol, for which a code is assigned. For example,
the TPS for subtree rooted in node 36 of Figure
4 is TPS = (1, 2, 3, 4, 5, 6, 7, 8, 8, 8, 8). The tree
is traversed in a depth-first search, therefore 1
is the code for the TP of node 36, 2 is the code
for node 38, 3 and 4 are the codes for node 38
children’s, and so on.
The set of all distinct symbols in a TPSa of
size n is called alphabet, which formally is Σa =
{α|∃TPSa[i] = α ∧ 1 ≤ i ≤ n }. For the subtree of
Figure 4 whose root is node 26, the alphabet is Σ
= {1,2,3,4,5,6,7,8}. Two other concepts used in
TPS segmentation algorithm: Tag Path frequency
set and Frequency thresholds set. The first concept
is a set of pairs in format the (s,f), where s is symbol and f is the frequency of s appearances in an
given TPS. The latter concept is a set composed
only by frequencies of the Tag Path frequency set.
Velloso and Dornelles build these definitions
to specify what is a region, which is the union
of Tag Path Sequences without Tag Paths in
common. Region is the most important concept,
since the TPS segmentation algorithm looks for
these entities.
The searching for regions in TPS is based on
the idea that different regions on an web page
are composed by different Tag Paths; therefore
Tag Path Sequences of two different regions are
disjoint. In order to remove irrelevant nodes, Tag
Paths that occur less than three times are removed
from the Tag Path Sequence. The main steps in
TPS algorithm are the following:
1. Converting a DOM Tree to a Tag Path
Sequence: The algorithm performs a DepthFirst Search in a DOM tree and concatenates
HTML tags and CSS styles of each DOM
node in order to create a TP to TPS of a given

DOM Tree. For each TP created an integer
code is assigned to it;
2. Searching for the Main Region: TPS
originally looks for the main region of a web
page. In this step, Tag Path frequency set
and Frequency thresholds are built. Then, a
loop is performed until a region that is 20%
bigger than other is found. However in order
to find several regions this step is executed
until 90% (value computed in the training
dataset) of the DOM nodes are in the Tag
Path Sequence;
3. Filtering Alphabet: All the symbols of
a given Tag Path Sequence compose an
alphabet, nevertheless Tag Paths with low
frequencies are irrelevant. This step is performed inside the previous main loop of steps
in order before to help finding main region.
Besides these steps, the TPS algorithm performs a DOM tree pruning. After the pruning, the
authors measure the accuracy of noise removal.
The average of noise removal was 77.03%.

EXPERIMENTS AND RESULTS
This section presents an empirical evaluation of
the proposed approach. First, the dataset used
in the training and test steps is described. Then,
the metrics that are used to evaluate the HTML
segmentation techniques are explained. Finally,
the empirical findings are described.
The dataset used in this paper experiments is
the same dataset used by Fernandes et al. (2011).
The dataset is comprised by 34460 web pages of
Ig web site (a popular Brazilian portal), 9540 web
pages of Cnn web site, and 109456 web pages
of the following tech blogs: Boing Boing, Cnet,
Engadget, Gizmodo, Google, LifeHacker, Mashable, Slash Filme, and Techcrunch.
From the dataset used by Fernandes two different sets are separated. The first one is comprised
by 254 web pages from blogs of technology, 173

109


HTML Segmentation for Different Types of Web Pages

web pages from Ig portal and 82 web pages from
Cnn portal. The second one is the original dataset detailed before. The former dataset is called
MIXPORTALS and the latter dataset is called
PORTALS2. Fernandes et al. (2011) labeled the
original corpora using the VIPS algorithm (Microsoft Research, 2003) and correcting manually.
Kohlschutter and Nejdl (2008), Fernandes et
al. (2011) and Chakrabarti et. al used ARI and
NMI in order to evaluate their strategies. Although
information retrieval tasks commonly evaluate
performance by precision and recall metrics, these
are inadequate to the HTML segmentation problem. The reason is that segmenting web pages is
a clustering problem, where related DOM nodes
are clustered inside a segment.
Strehl and Ghosh (2003) introduced NMI for
the evaluation of clustering algorithms. NMI
normalizes Mutual Information metric. The idea
of Mutual Information is computing how much
information segmentations U and V share. Equation 2 computes Mutual Information between
two segmentations U = {U1,U2,…,UR} and V=
{V1,V2,…,VC}.
I (U ;V ) = ∑ ∑P (U i ,Vj ) log
U i ∈UVj ∈V

P (U i ,Vj )
P (U i ) P (Vj )



(2)
Probability P(Ui,Vj) is calculated as in Equation 3, where N is the number of DOM nodes of
the segmented web page . This equation computes
probability of identified segment and real segment
contain same node. Hence, the higher value of
Equation 3, the more similar identified segment
and real segment is.
P (U i ,Vj ) =

U i ∩Vj
N



(3)

Probability P(Ui) describes the chance of a node
from U belonging to that segment. This computa-

110

tion is also used in entropy definition. In addition
to Mutual Information, NMI also uses the entropy
concept, which is computed by Equation 4.
H (U ) =−
 ∑P (U i ) log p (U i )

(4)

U i ∈U

Finally Equation 5 gives the formulation of
NMI. Explanation for this Equation is the same
explanation of Equation 2, except that NMI values
are between 0 and 1.
NMI (U ;V ) =

I (U ;V )
H (U ) H (V )



(5)

NMI ranges from 0 to 1 and higher values indicate higher quality. The intuition of NMI is the
more information two solutions share, the more
similar they are. However according to Strehl and
Ghosh (2003) the number of clusters increases
the NMI value.
Strehl and Ghosh (2003) also describe ARI
measure, which is the agreement between detected
segments.
The RAND index between two partitions of
a set of objects measures the fraction of pairs of
objects that are either grouped together or placed
in separate groups in both partitions. Hence, higher
RAND index values assigned to segmentations
output by our algorithm indicate better quality.
The disadvantage of RAND index is that it does
not takes a constant value when there are random
segmentations. Then ARI adjusts the values of
the RAND index so that it is upper bounded by
1 and scores 0 for a random segmentation. Table
3 lists agreements nij between each pair of segment (Ui,Vj).
Hence, Equation 6 uses the Contingency Table
to compute ARI value. This Equation intends to
measures agreements between partitions, which
are computed by the nij term. Value of ARI can
be negative, since the expected value


HTML Segmentation for Different Types of Web Pages

Table 3. The contingency table nij = |Ui ∩ Vj|
U/V

V1

V2

…

VC

Sums

U1

n11

n12

…

n1C

a1

U2

n21

n22

…

n2C

a2

⋮

⋮

⋮

⋱

⋮

⋮

UR

nR1

nR2

…

nRC

aR

Sums

b1

b2

…

bC

Σij nij =N N

 a 
b  N 
 j   
  i 

 ∑  2  ∑  2  /  2 


 i   j    

the test set is used to estimate the error rate of the
trained classifier.
The training stage was performed in the MIXPORTAL corpus. Table 4 details the performance
of the proposed approach using ETL HTML
Chunking technique in corpora PORTALS. The
NMI numbers in Table 4 are the average of the
NMI of all web pages and the ARI numbers in
Table 4 are the average of the ARI of all web pages.
The result of each collection in PORTALS is in
separate for better understanding. Table 4 also
shows the results of baseline experiment. The
baseline results are the ones based on the final
model described in (Amorim, 2012).
Table 5 exhibits different values from Amorim
(2012), which have been caused by changes in
dataset. The new dataset comprises more Blogs
and less CNN documents. Regardless the changes
in dataset, one feature that impacted positively
the measurements: text density feature. The file
of rules generated by training of the entities pairs
provides clues about the importance of another
features: hasCssClass of right fragment and level
of end node. Rules with high score include these
two features. On the other hand, other features
seem to be more important to the end classification

the index is greater than the agreements.
b  N 
a 
n  
j  
 i 
 ij  
−


∑ ij  2  ∑ i  2  ∑ j  2  /  2 


ARI =







b  N 


b
a
a
1
 i  
 i 
 j   
 i 
+
−




  

  /  

2  ∑ i  2  ∑ j  2   ∑ i  2  ∑ j  2   2 

 


(6)

According to Fernandes et. al (2011), both
measures represents the same idea: how much
each classified segmentation is similar to the
real segmentation. Nonetheless, in order to keep
consistency with related researches both measures
are computed.

Empirical Results
In order to test ETL HTML chunking strategy,
a holdout method was applied. In the holdout
method the original dataset is divided into two
different corpora: a training set and a testing set.
The training set is used to train the classifier and

Table 4. Results in PORTALS corpora using ETL to HTML chunking
Corpus

NMI (Baseline)

ARI(Baseline)

NMI

ARI

Ig

0.74

0.96

0.76

0.97

CNN

0.71

0.96

0.74

0.97

Tech Blogs

0.77

0.92

0.75

0.98

111


HTML Segmentation for Different Types of Web Pages

Table 5. Comparing NMI: TPS and ETL to HTML chunking
Corpus

TPS

ETL HTML Chunking

Ig

0.61

0.76

CNN

0.57

0.74

Tech Blogs

0.62

0.75

task and to the start classification task. The file
of rules of end classification task highlights the
following features: nchildren, which was absent
in Amorim’s (2012) model; anchortextlength;
nimages; nlinks; ul HTML tag; and imgheight.
Other features also appear in file of rules of end
classification, however these features are in the
highest score rules. The file of rules of start classification also provides features that are relevant
for this classification task. Imgheight equal 0 is
a good clue that the node doesn’t start a segment.
In corpora title tag is frequently labeled as a segment, therefore the rule with the second highest
score explains this pattern; tag title, level 2 and
nlinks 0. Besides that, other features also provide
good clues to start nodes, for example: nlinks,
avgLenWords, tag, nimages.
Table 5 compares TPS strategy and ETL HTML
Chunking strategy by NMI metrics. Both strategies use the PORTALS corpora to evaluate their
techniques. ARI is not use in TPS comparison
because ARI measures how similar segments
are. However, TPS strategy is a heuristic that
consider segments in different way that we had
the labeled dataset.
The training time was 214 minutes. The time
for test step is less then one second per page. The
machine used is a High-Memory Extra Large Instance with 17.1 GB memory, 6.5 ECU (2 virtual
cores with 3.25 EC2 Compute Units each) and
64-bit platform, where one EC2 Compute Unit
(ECU) provides the equivalent CPU capacity of a
1.0-1.2 GHz 2007 Opteron or 2007 Xeon processor. The entire infrastructure used in these tests is
from Amazon Web Services.

112

NMI was the metric chosen to compare TPS
and ETL HTML segmentation because the definition of segment is not relevant. ARI requires that
the node in a partition of algorithm results be in
the same partition in labeled results. However,
the algorithm can understand a big segment as
several smaller segments. Due to the nature of
TPS – big regions are more relevant than others
– it is reasonable compare ETL and TPS using
NMI metric.
Even if NMI is the best metric to compare ETL
and TPS, ETL HTML Chunking has far better
results in regards to the NMI measurement. This
happens because the segments that ETL HTML
Chunking strategy identifies have nodes outside
the segments, causing the Adjusted Rand Index
value to grow. On the other hand, the Normalized
Mutual Information decreases, because the shared
information between a classified segment and a
labeled segment is lower due to the extra nodes
inside the segment. The features hasCssClass and
avgLenWords positively influence ARI, although
these features have a negative impact in NMI
numbers.
TPS algorithm is simpler than ETL segmentation algorithm. Also, TPS is a heuristic, which
means that training data are unnecessary. Nevertheless, ETL HTML Chunking strategy only needs
a small set (around 500 web pages) of heterogeneous web pages to construct a model that can be
applied in different kind of pages. The training
time in our dataset took 214 minutes. Despite the
fact that training time has increased, the training
is performed only once and produces only one
model for different types of page. It is important
to highlight that ETL HTML Chunking technique


HTML Segmentation for Different Types of Web Pages

uses just a small set of features, so ETL HTML
Chunking can improve NMI in a future work.

FUTURE RESEARCH DIRECTIONS
Data mining is a recent research field. However
two main trends can be observed in data mining:
statistical analysis and semantic concepts. Both
trends can take advantage of techniques whose
goal is detecting the main content of web pages.
HTML documents seems to prevail in web for
a long time, nevertheless HTML will be evolve
over time. Some web pages already incorporate
concepts tag in HTML code.
Despite HTML evolution, unstructured data
and web design patterns will still be widespread
along the web, thus HTML segmentation will
be relevant for the information retrieval area.
Certainly the semantic web will be more crucial
in the future to rank results. On the other hand
search engines will still process non-structured
documents with embedded content structure. The
business sector has almost the same scenario.
Like nowadays, queries will be executed on nonstructured data in companies. Nevertheless, only
large companies are investing in data mining of
Web 1.0. The business sector is exploiting Web
2.0 though; despite the fact that Web 1.0 is a huge
part of web.
Semantic web should also surpass some
obstacles in order to be adopted by the business
sector. Inference Engines applied by the semantic
web have expensive performances, since structured
information possesses several properties and
relationships (Guo, Pan, & Heflin, 2005). Also
efforts towards to ontologies that cover different
languages, are merging with old technologies, and
are harmonizing with each order are in progress.
Besides the web paradigms, data mining also
has to evolve. HTML segmentation for different
kinds of web pages should explore more structural features. As the web grows, performance is
a concern more than ever. Web 2.0 is responsible

for the fast grow of web content, however these
content remains unstructured. Therefore tasks
like HTML segmentation also should also explore
Natural Language Process (NLP) in order to find
helpful algorithm. For instance, words in relevant
segments can be noun phrases or irrelevant segments can contain only nouns. NLP is a growing
research field that HTML segmentation should
test. Kohlschütter and Nejdl (2008) employed text
density feature in their heuristic, which achieved
competitive results. Although NLP features can
be language related, NLP performance is more
robust than visual features.
Data mining also begins to be concerned about
the huge volume of data (Lohr, 2012). Therefore the
performance of the feature extraction should be an
issue to worried with. Although companies have been
gathering infrastructure from the cloud, companies
should be looking at fast algorithms as well.
The ETL HTML segmentation model can
take advantage of these ideas. The only linguistics feature added to the model was text density.
Likewise, the only style feature was hasCssClass,
which proved to be a relevant feature (Amorim,
2012). Many studies explored style features in site
models, with positive impact in results (Fernandes
et al, 2011) (Song et al, 2004).
Although, ETL was applied in some NLP
problems successfully, it is worth to try some of
the Machine Learning Algorithms in the proposed
model (Milidiu et al, 2008) (Fernandes, Pires,
Santos, & Milidiu, 2009). Performance in training time on ETL is expensive when compared
to SVM algorithm for example. Thus whether
an ML algorithm with less expensive training
produce competitive results is also a future study
to be developed.

DISCUSSION
Segmenting different kinds of web pages is the
main subject of this chapter. Also, in the beginning of this chapter the challenge of finding a

113


HTML Segmentation for Different Types of Web Pages

model that conciliates features from different
web pages has been stated. Nonetheless, applying
a machine learning technique in different kind
of web pages presented strong results compared
to a heuristic technique. Therefore applying an
efficient method of segmenting different kinds
of web pages can improve that process different
kinds of web pages, for instance ranking of web
pages in search engine schemes.
A new model to classify segments in HTML
document was proposed as well. Although ETL
performed well in the described model, ETL consumes a significant amount of time in training.
This disadvantage is even more critical whether
we consider that a search engine collects new web
pages all the time, and then add some new web
pages to segmentation model. On the other hand,
it is worth to experiment another machine learning techniques, since there are machine learning
techniques which are more robust than ETL. The
proposed ETL method to segment web pages also
can be tested in web pages of same kind. Probably,
machine learning model would produce better
results to same type of web pages, since web
pages of same type usually owns similar layouts.
Since Amorim (2012) results, another proposals has been made, which proves that HTML
segmentation still a topic of research. Ma, Wen,
Song, and Liu (2013) describes a method that
computes the importance of segments in web
pages. The method proposed by Bing, Lam, and
Wong (2013), detects segments that are data
records. This research is useful to build knowledge bases, which are valuable to semantic web.
Zeleny and Thwin (2013) segment web pages
using visual features, since they provide helpful clues to identify segments. Although visual
features are expensive to process, Zeleny and
Thwin describe a robust method to handle visual
features. Win and Thwin (2014) employ visual
features to segment web pages as well, and they
obtain strong results. Another recent research that
employs visual features is proposed by Murthy,
Raghunandan e Suresha (2014). Besides visual

114

clues, the authors deal with dynamic web pages.
Recent researches in HTML segmentation diminish drawbacks of employing visual clues, since
they improve HTML segmentation performance.
Therefore, new strategies to decrease performance
drawbacks of visual clues should be investigate in
order to enhance segmentation accuracy.

CONCLUSION
The web page segmentation task represents an
important step to improve the solution to classical
Information Retrieval problems. In this task, an
HTML page is divided into semantically coherent
smaller items. Several proposals were proposed
in order to solve HTML segmentation. However,
some issues remains unsolved, like: visual features,
topic oriented techniques and site oriented techniques. Visual features affect HTML segmentation
performance, which is an issue to consider since
search engines deals with huge contents. Topic
oriented techniques usually produce competitive
results, nevertheless for some general information
retrieval tasks they are not useful. Site oriented
techniques are only useful to some information
retrieval tasks as well whereas a model for each
web site will be created.
Techniques that segment several types of web
pages are not common, and some of them employ
visual features. We aim at proposing an HTML
segmentation technique in which visual features
are not adopted, since we consider performance a
crucial issue. Here, a strategy is proposed where
each node from the web page DOM tree corresponds to a token unit and a web page segment to
a token chunk. The Machine Learning approach
proposed is based on the Entropy Guided Transformation Learning algorithm. This is an efficient
strategy to learn structural features from a DOM
Tree. The empirical findings indicate that ETL
HTML Chunking strategy is competitive with
related work. The usual metrics to evaluate the
HTML segmentation task are the Normalized


HTML Segmentation for Different Types of Web Pages

Mutual Information, which reaches 70%, and the
Adjusted Rand Index, on which reaches 90%.
TPS technique is an alternative technique
to HTML segmentation. Tests were performed
in order to evaluate TPS, and results provided
competitive results. However, Normalized Mutual
Information values achieved by TPS are from
values achieved by ETL technique. We also have
to consider that TPS original purpose is detect
only the main regions, which could affect the
obtained results. However, other techniques for
different kinds of web pages are unable to reproduce or use visual features (Kohlschütter, &
Nejdl, 2008) (Chakrabarti et al, 2008). The main
advantage of TPS technique compared to ETL is
time performance in the training process. While
ETL requires a huge processing time in order to
compute entropy, TPS builds model for detecting
segments in few seconds. The main disadvantage
of TPS compared to ETL is the accuracy of results. NMI and ARI achieved superior results in
the ETL algorithm.
Time performance is an important issue to
search engine, since it deals with huge amounts
of data. Therefore, although the TPS strategy
achieved inferior results, for practical purposes
it is worth to experimenting TPS in ranking
strategy. Also, as the main goal of TPS is detect
main areas in web pages, probably other tasks in
information retrieval can take advantage of this
robust technique. For instance, detecting areas
in web pages to extract tables, and then extract
semantic knowledge.
The business sector should be concerned
with about data mining information in HTML
documents, since unstructured documents will
prevail for a long time. Also companies should
consider time performance as a crucial feature of
data mining systems, because the user contribution to web content no longer requires technical
expertise. Web 2.0 provides tools that enables the
production of content by the to non-expert user
produce content, for instance blogs and social
media. Structured content is another issue that

business should be worried. Giving meaning to
strings enable a more efficient to data mining,
however semantic content is insufficient to data
mining process. Classical techniques of data mining have been enhanced with semantic concepts.
Nevertheless semantic concepts does not develop
functionalities that scientists desire, therefore
companies should embody semantic meaning to
their data and take advantage of well-established
data mining techniques.

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HTML Segmentation for Different Types of Web Pages

KEY TERMS AND DEFINITIONS
Adjusted Rand Index: Rand index is a formula
that measures agreements between partitions. In
order to measure the agreements a contingency
table is build. A contingency table computes the
agreements between all parts of two given partitions. Because rand index is not a normalized
metric, Adjusted Rand Index was proposed. Then
Adjusted Rand index ranges between 0 and 1 and
measure agreements between to partitions.
Entropy: Entropy is a concept from statistical
area, which is modeled by the equation
1
H (S ) =∑pi log2
. pi is the probability of
pi
i
i-th item occur in space S.
HTML Segmentation: HTML segmentation
is the process of dividing web pages into semantically coherent smaller items.
Information Retrieval: Information Retrieval
is the process of finding relevant information for
the user. These process can be manual or automatically. Nowadays, the most prominent area
in information retrieval is information retrieval
in the web.

Machine Learning: Machine Learning is
subfield of Artificial Intelligence. The methods
that Machine Learning comprises employ data
with known information in order to learn patterns.
These patterns are applied in new data, and then
information is discovered in data without known
information.
Normalized Mutual Information: Given two
partition U and V, Normalized Mutual Information
measures the joint probability of some part U i ∈ U
and some part Vj ∈ V . The meaning of the joint
probability between two parts is to evaluate the
similarity between the classified partition and the
real partition. The values of normalized mutual
information ranges between 0 and 1.
Vector Space Model: Vector Space Model
is a formula employed by search engines to rank
web pages given a query. The formula of vector
space model is the dot product between a vector
representing a document and a vector representing a query.

ENDNOTES
1
2




https://github.com/ariya/phantomjs
http://www.freebase.com/

119

Section 3

Social Networking Sites

121

Chapter 6

Understanding Employee
Attitudes to SNS
Implementation in the
Australian Banking Sector
Tanti Kartika Sari
Curtin University, Australia

ABSTRACT
The Australian banking sector has utilised Social Networking Sites (SNS) to support companies’ sustainability through customer engagement. Numerous studies have been conducted on issues associated with
SNS, including teamwork, communication, trust, and security. These studies have usually been concerned
with the perspectives and attitudes of customers and organizations, and sometimes, employers. This
chapter is based on a Master’s degree dissertation research aimed at filling the gap by investigating the
opinions of the Australian banking sector employees, in particular those who use the SNS application
as a work tool. The Honeycomb framework is used as the theoretical basis with six underlying factors
being ascertained as a result of Exploratory Factor Analysis (EFA). The findings are discussed, and
recommendations are proposed which are intended to increase the benefits to be gained from SNS adoption. The analysis results make a significant theoretical, methodological, and practical contribution.

INTRODUCTION
In the last five years, we have seen the proliferation of applications based on the Web 2.0 platform
being utilised by various industries, including
the financial industry, for internal and external
communication. One of the most widely-adopted
Web 2.0 based application types is SNS. This type
of Web 2.0 application was intended to facilitate

human interaction by incorporating multi-faceted
functionalities; however, along with the advantages, there are also risks associated with user
interaction. Technology and infrastructure development, such as telecommunication, hardware and
software systems have helped to increase public
access to the internet.
As of April 2012, Internet World Stats (2012)
recorded that Australia has 14,189, 557 users

DOI: 10.4018/978-1-4666-7262-8.ch006

Copyright © 2015, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.


Understanding Employee Attitudes to SNS Implementation in the Australian Banking Sector

(68.2% internet population) and ranked it second
in the world after the USA with 205,493,713 users
(68.7% population). This means that Australian
banks have a huge opportunity for tapping the
hitherto unrealized market opportunities. The “Big
4”: Australia and New Zealand Bank (ANZ), Commonwealth Bank of Australia (CBA), National
Australia Bank (NAB) and Westpac Bank (Westpac) initiated the adoption of SNS. Nevertheless,
from 2009 when the Web 2.0 adoption started until
2011, the majority of Australian banks launched
an SNS application (i.e. blog) as a temporary trend
evidenced by the exclusion of this channel from
their corporate websites (Senadheera, Warren, &
Leitch, 2011).
The web technology evolution forced both
business and industry to begin shifting their
perspective. They started by giving customers or
community a higher priority (Hanna, Rohm, &
Crittenden, 2011), emphasising the organisations’
internal and external engagement (Communique,
2010) and anticipating that they would increase
their economic value through social capital (Annabi & McGann, 2012). In the corporate context,
participation and sharing are part of collaboration
which includes the effort to preserve tacit knowledge (Hemsley & Mason, 2012).
The very popular Social Media is also associated with Web 2.0 due its similar characteristics
such as crowdsourcing, blogging and user-rich
experience (O’Reilly, 2005). However, Social
Media refers to the tools whilst SNS is more a
type of channel. Hartshon (2010) highlighted five
points of difference between these two: definition, communication style, return of investment,
timely response and asking or telling. Senadheera
et al. (2011) defined Social Media as “web based
services that provided space for in-depth social
interaction to share, discuss and collaborate, facilitated by one or more media rich functionalities”
and Social Media Services as “individual websites
that form the new media landscape” (p. 2). Boyd
and Ellison (2007) suggested that SNS emphasise the communication channel used to connect

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with other people who are already in a particular
extended social network. They defined SNS as:
a web-based services that allow individuals to (1)
construct a public or semi-public profile within a
bounded system, (2) articulate a list of users with
whom they share a connection, and (3) view and
traverse their list of connections and those made
by others within the systems (p. 211).
This definition of SNS is consistent with the
aim of the study as mentioned in the Abstract. The
understanding is constructed from four aspects: 1)
the investigation results, whether a specific factor
impacted on their effective use of this application;
2) the identified implications of SNS adoption
for staff motivation and job satisfaction, 3) the
implication for staff’s communication skills, who
use SNS as their working tools; and 4) the degree
of collaborative interaction among the staffs and
customers in the Australian banking sector. This
chapter is organised into several sections. First,
it will examine the current literatures with respect to SNS adoption by the Australian banking
sector, followed by a discussion of the research
methodology adopted in the dissertation stages.
Next, data analysis, in particular the EFA results,
and the conclusions derived from the findings will
be presented. In the final section, the limitations
of this research are acknowledged, the contributions are stated, and a set of recommendations for
further study are proposed.

BACKGROUND
SNS Characteristics
After the Web 2.0 technologies were introduced
by Dale Dougherty in 2004, O’Reilly (2005)
discussed Web 2.0’s core capabilities which
comprise: an application with web as platform,
harnessing collective intelligence, remix-able data
source and data transformation, focus on service


Understanding Employee Attitudes to SNS Implementation in the Australian Banking Sector

software and ability to provide rich-user experiences. Later, the Web 2.0 technologies emerged as
Blogs, Wiki, Virtual Worlds, Social Networking,
Podcasts and Mashups, with SNS being the most
popular (Anderson, 2007).
The IT industry leaders utilised its popularity to
build team collaboration, increase communication
and exchange industry knowledge both horizontally and vertically (Barker, 2008). Some notable
characteristics are: it democratises technology
(Strategic Direction, 2009), promotes openness,
increases communication and reduces the distance
between levels in a vertical hierarchy (Rai, 2012).
The adoption also countered other employers’
concerned about losing productivity due to time
used by their employees to access to the internet
and internet-based applications (Young, 2010).
Therefore, the SNS implementation as indicated
had a positive impact on both employees and
employers in general industries (Burrus, 2010)
as it could encourage creativity and innovative
improvement in the organisation (Rai, 2012).

AUSTRALIAN BANKS AND
THE EMERGENCE OF BANK
2.0 IN AUSTRALIA
Financial institutions in Australia abide by the
regulations set by the Australian Prudential
Regulation Authority (APRA), Australian Securities and Investment Commission (ASIC) and the
Reserve Bank of Australia (RBA). There is also
an association called ABA – Australian Bankers
Association with 24 members (Australian Bankers’ Association Inc., 2013). According to APRA
(2013) and ABA (2012), Australian banking is a
pivotal contributor in finance and insurance industry leading with 33% of the market capitalization of
the Australian Stock Exchange (ASX) followed by
Metals and Mining (25%) and Energy and Utilities
(11%). As of April 2013, APRA (2013) lists the
composition of banks operating in Australia as:
19 Australian-owned banks, 8 foreign subsidiary

banks, 40 foreign bank branches and 99 building
societies and credit unions.
The finance sector is the largest contributor to Australian output by 11% (A$135 billion
gross value). The Big Four survived the GFC
in 2007 and, together with another nine banks
globally, had a AA++ Moody’s rating in 2010.
It is believed that competition and technology
adoption are among the factors driving bank
productivity. In May 2004, the ABA Fraud Task
Force and the Australian High Tech Crime Centre
(AHTCC) were given The National Response Plan,
a protocol that formalised the banks’ responses
to cybercrime (Australian Bankers’ Association
Inc., 2012). Australia financial industry also
must comply with Australia’s Privacy Act 1998
(AUSTRADE, 2011).
Australia Statistic recorded that as of February
2012, the financial industry employed 211,900
people which accounted for 1.86% of jobs in
Australia’s employed workforce. The banking
sector employed more females compared with the
national average (54.3% and 45.45% respectively).
Administrative and clerical positions comprised
43.9% of banking workforces, followed by 35.5%
of the professionals and 16.1% managerial positions. In terms of age, in this industry there are
31.3% people aged between 25-34 years, 11.4%
of staff aged under 25 and a total of 8.1% aged
55 or over (AUSTRADE, 2011).
Generally, there are several driving factors
that put pressure on or encouraged banks to follow other industries in the adoption of SNS. The
first factor was the desire to capture the untapped
opportunities and counter the effects of the GFC
– which led customers and the general public to
demand more transparency (Bonson & Flores,
2011). Another factor was that 44% of the customers using SNS as a communication medium
demanded that banks pay attention to their voice
(Ernst & Young, 2012). Also, the composition of
the workforce changed as retiring baby boomers
made way for Gen Y (DiRomualdo, 2006).

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Understanding Employee Attitudes to SNS Implementation in the Australian Banking Sector

Banking institutions are commonly perceived
as conservative, unattractive and commandcontrol-based organisations (Thefinancialbrand.
com, 2011a, 2011b) because it is one of the most
heavily regulated industries (Bonson & Flores,
2011; Jackson, 2012). If deciding to adopt SNS,
this sector also should consider several risk factors
commonly shared by other industries such as loss
of control of data and information (Burrus, 2010),
security (Abu-Nimeh, Chen, & Alzubi, 2011)
and privacy and trust (Fogel & Nehmad, 2009).
However, the specific obstacles to adoption were
seen to be the industry’s traditional regulations
and compliance measures about disclosure and
bank practice rather than technology or capacity
(Littleton, 2012; Tung, 2012).
A wide range of issues were discussed in the
early days of SNS adoption by Australian banks,
such as products or services (eMarketingConnected.com.au, 2011), brand identity, (Senadheera
et al., 2011), policy and guideline staffs and
customers (Angus, 2012) and resource management for social media team (Elsworth, 2012). A
few saw this new technology as a potential threat
to organisations (Zappone, 2011). Disputes with
industrial relations were experienced by some of
the Big 4 banks (Shapcott, 2010; Stafford, 2012;
Thomler, 2011). Those clashes demonstrated
that it is crucial to devise and implement careful
strategic planning to accommodate the internal
and external relationship changes. Together, SNS
and mobile banking forced the Australian banks
to change their operational processes by repositioning their organisations in the light of their
customers (Bender, 2012; Nielsen, 2012). After
meeting all these challenges, CBA and NAB were
rewarded for their successful initiation of SNS
applications, taking full advantage of what they
had to offer; as a result, they were ranked 10th and
14th respectively among the top 20 banks in the
world (Thefinancialbrand.com, 2013).
These achievements were the result of the concerted efforts of all parties: the industry regulators,
senior managements and the staffs. Nevertheless,

124

any discussions about SNS implementation, particularly in the Australian banking sector, that
considered the employee’s point of view were
not specifically evaluated or investigated in terms
of the relationship between the various facets
of SNS functionalities (Howard, 2009; KPMG
International, 2012).

Advantages and Challenges of SNS
Implementation in Financial Sector
Banks implement SNS more carefully compared
with other industries, namely technology-focused
and media-focused industries (Mukherjee, 2011).
In weighing the advantages and disadvantages,
several factors are considered prior to implementation such as: the membership verification process,
governance framework and data management of
social application content, to name a few (J. Hair,
2012). Future SNS apps can be selected according to factors such as specific features, a target
group of users, a unique application feature and
the ability to facilitate effective communication
(Hanna et al., 2011). When setting their adoption goals, the banks were advised to consider
the following factors: brand enhancement, cost
reduction, customer satisfaction, innovation and
revenue (The Economist, 2008).
Compliance, Human Resources and Technology are the three barriers generally associated
with SNS adoption (Accenture, 2011). As much
as 56% of banking institutions expressed their
concern about failure to comply with policy and
regulation (MHP Communications, 2011) and they
also needed to prepare a new operational model to
maintain discretion and customer confidentiality
(Chui et al., 2012).
The human resources barrier covers a wide
range of factors including workplace training
(Burrus, 2010), rules and policy for internal and
outsource staffs (Kuikka & Äkkinen, 2011), different level of interest on application adopted due
to age or generation of the employees (Stelzner,
2012) and lack of proactive understanding from


Understanding Employee Attitudes to SNS Implementation in the Australian Banking Sector

the organisation themselves in promoting the
potential value of SNS to improve the employee
productivity (Ferro, Divine, & Zachry, 2012), to
name a few.
In terms of technology, compared with
other banking applications, SNS is considered
as immature and its viability relies heavily on its
popularity (Accenture, 2011). Therefore, banks
need to make the right technology investment to
align the SNS apps with their existing systems
(Koehler-Kruener & Tay, 2013) i.e. Customer
Relation Management System, record keeping
(Tay & Basilliere, 2012) and data analytics (Accenture, 2011). Record management is one of
the compliances required by industry regulators
such as the Australian Competition and Consumer
Commission (ACCC), APRA, ASIC and Privacy
Act to name a few (AUSTRADE, 2011).

Implication of SNS Adoption
in Financial Institutions
Whilst SNS adoption could improve productivity
as a result of worker interaction up to 25% due to
improved collaboration, coordination and communication (Chui et al., 2012), internal and external
impacts and implications should also be expected.

Internal Implications
Drakos (2009) reported that SNS adoption could
improve talent assimilation, project team productivity and overcome cross-cultural challenges in a
company with ethnic diversity. Exemplifying the
Italian banking sector, they found a positive impact on communication and collaboration among
employees (Corso, Martini, & Piva, 2009). As
SNS promote a sharing and openness-oriented
culture, this technology can act as an informal
medium of knowledge exchange among employees, vertically and horizontally, within and across
the organisation (Chui et al., 2012). An organisation can expect better and more comprehensive
collaboration by selecting the right combination

of applications and getting experts involved in
the communication, infusing innovative ideas,
and nurturing and strengthening any weak ties
(Jarrahi & Sawyer, 2012). C-suite involvement is
recommended to initiate knowledge exchange as it
creates an environment that encourages cognitive
and collaborative Intellectual Capital in generating
knowledge creation (Shih, Chang, & Lin, 2010).
SNS adoption also applies to the creation of
several new positions. Sixty-five percent of the
companies required their Marketing, Public Relations, Customer Services, IT and Legal staff to
prioritise SNS tasks in the job descriptions (Ragan, 2012). Along with process, a specialist team
dedicated to SNS technology could be established
to address the talent gap (Accenture, 2011).
Conversely, the following challenges may
arise: SNS applications could exacerbate any
existing tension between the organisation and the
employee (Ford & Mason, 2012); it could be costly
to upgrade the infrastructure as the recommended
bandwidth should have broadband capacity (International Telecommunication Union, 2013); and IT
Security must be increased due to the improved
quality and increased quantity of malware (KPMG
International, 2011).

External Impact
According to Watson-Manheim (2011), in the
organisational context, SNS can serve as a medium or space to enhance both professional and
personal identities internally and with external
organisation. Yet, Andrejevic (2011) argued that
if seen in a larger context, there is a possibility
that the use of SNS will tend to privatize a community rather than publicize it. With the finance
industry regulated for online transparency, other
industries may look to it for inspiration (Bonson
& Flores, 2011). Any external impact that is likely
to occur will depend on the corporate-consumer
dialogue and the policy that is established. When
banks utilise SNS as a campaign tool, they should
consider its promptness and ensure that the

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Understanding Employee Attitudes to SNS Implementation in the Australian Banking Sector

campaign is aligned and congruent with the selfimage presented, brand preference and customer
satisfaction (Jamal, 2004).

UNDERPINNING THEORY
The Honeycomb framework of Kietzmann,
Hermkens, McCarthy, and Silvestre (2011) comprises seven blocks representing the basic SNS
functionalities and a four-point guideline named
4Cs. The blocks are not mutually exclusive, nor
does each one have to be present when examining
the specific factor of the user experience and its
implication for organisations. The IDENTITY
block represents the extent to which it will reveal personal information; CONVERSATIONS
measure the extent to which a user will use SNS
as the main communication channel; SHARING ascertains the extent to which the user will
exchange, distribute and receive data through
social media; PRESENCE measures to what
extent a user identifies another user’s existence;
RELATIONSHIPS measure how a user of SNS
sees their relationship with other users. REPUTATION is used to understand how they identify
themselves and others and GROUPS is used to
understand whether users can form a community or
establish a hierarchy. The 4Cs -Curate, Congruity,
Cognize and Chase- provide guidelines on how a
company can monitor, understand and respond to
SNS activities in strategies development. Cognize
means the user is able to understand the SNS
functionality and the implications of engaging
with their customers in order to understand their
needs. Congruity assists in evaluating whether
the adopted SNS suits the firm’s goals or strategy.
Curate refers to how employees as the agent of
the company are able to understand how often,
when and who will participate in the conversation.
Chase emerges following the user’s evaluation of
their engagement activity.
The seven blocks have been used in various
fields of studies. It was used as an evaluation

126

tool on the Brazilian social network “Vila na
Rede” (Pereira, Baranauskas, & da Silva, 2010);
by Vitkauskaitė (2011) who combined this with
Hofstede’s individualism-collectivism, and social
media strategy by Australian banks by Senadheera
et al. (2011). This framework is also applicable for
practitioners, i.e. when preparing a business case
(Li, Webber, & Cifuentes, 2012) or when a firm
is selecting the right social network applications
(Taprial & Kanwar, 2012).
This research used seven blocks and the 4Cs
guidelines as its theory base. These were adopted
after the relationships between seven functionalities and the level of awareness from the users’
experience working with SNS tools were first
identified from the literature review. The 4Cs
were included to weight users’ observations
for each functionality. Figure 1 illustrates the
framework and the 4Cs implication for each of
the functionalities.

RESEARCH QUESTIONS,
RESEARCH METHODS,
AND PARTICIPANTS
Research Questions
Along with the identified relationships and level
of awareness, a gap was noticed when studying the
trend of SNS adoption by the Australian banking
sector (Balnaves, 2012; Senadheera et al., 2011),
the mindset and culture of banking sector (King,
2010) and the workforce composition (Rai, 2012).
Apparently, the perceptions of the employees
-particularly in the Australian banking sector- as
the active agents of adopted SNS are yet to be assessed. This was used as the basis for the research
questions which consist of one primary question
and two secondary questions.
Primary question: “What are the factors that
trigger SNS implementation in the Australian
banking sector from the employee perspective?”


Understanding Employee Attitudes to SNS Implementation in the Australian Banking Sector

Figure 1. The Honeycomb framework with 4Cs guidelines adapted from Kietzmann et al. (2011)

This question is intended to reveal whether
the Australian banks utilise SNS to accommodate
the specific requirements of their customers or as
part of a generic strategy regardless of the unique
needs of their customers.
The two secondary questions are:
1. “Would the implementation of popular SNS
(such as FB and Twitter) in the workplace increase staff motivation and job satisfaction?”
This question seeks to ascertain whether the
adoption of popular SNS encourages employees
to achieve better performance as their work now
is supported with more user-friendly applications.
2. “Does the adoption of SNS application
improve communication skills and collaborative performance among staffs and
customers of the business?”
This enquiry examines employees’ attitudes
to their company’s decision to adopt SNS as a
new working tool for customer engagement and
whether employees will perceive the company’s

decision as good, moderate or unacceptable.
“Good” means the strategy to use popular SNS
as a working tool is accepted. “Moderate” means
they accept the usage of the tool and see this as just
a temporary trend that is following and profiting
from Social Customer Relationship Management
(SCRM). “Unacceptable” means that a corporation should not consider the adoption of popular
social technology due to risks associated with
security and data management and they found
that SNS does not improve employee skills or
work performance.

Research Methods
The survey was prepared, designed, managed and
processed using Qualtrics online. There are several
significant benefits of using an online survey; first,
it offers flexibility of time and location. Second,
the features support the analysis process as it is
capable of generating raw data in various formats,
allowing it to be integrated with other applications. The research approach is positivism-realist;
this mixed methodology suits IS research where
IT is the central concern and the adoption of the

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Understanding Employee Attitudes to SNS Implementation in the Australian Banking Sector

technology involves various fields (Mora, Gelman,
Forgionne, Petkov, & Cano, 2009).
The survey has three types of questions: the
close-ended ones that categorised and list options
in the demographic section, the rating questions,
and open-ended questions used in the seven
groups which represent seven functionalities of
the framework. The purpose of each group is:
1. Identity: Seeks perception of awareness,
understanding, value and result expectation
from working with the SNS;
2. Conversations: Measure employees’ understanding of the communication generated in
various types of SNS;
3. Sharing: Seeks whether information management has been applied and how they
weight the incoming information reliability
and its usefulness to assist their task,
4. Presence: To determine, the employee
awareness of digital footprint and its impact
on their organisation’s virtual presence;
5. Relationships: Ascertain whether the employees recognized the value of using SNS
as a working tool and whether they already
included the value in their working strategy;
6. Reputation: To ascertain the attentiveness
on the internal and external impact of using
or working with SNS in their organisation;
7. Groups: To determine whether the participants have recognized the value associated
with their internal community, i.e. improvement of collaboration and communication
included the need to have specific SNSrelated policies and guidelines.
Each group has four questions where each
question signifies the 4Cs value; (i.e. first question
represents Cognize value, the second for Congruity
and so on). The open-ended questions allow the
respondents to share their perspectives or observations, giving the researcher the opportunity to
obtain the participants’ insight.

128

For measurement purposes, the open questions
apply coded variables and category questions used
dichotomous scale and 5-point Likert scale. The
rating questions followed a 5-point Likert scale
comprise the choices: “Strongly Disagree”, “Disagree”, “Agree”, “Strongly Agree” and “Unable to
Judge”. The “Unable to Judge” was positioned last
to reduce the incidence of neutral answers; also,
this expression is less threatening to the participant than “Do Not Know” (Saunders, Lewis, &
Thornhill, 2009). In data processing, any kind of
data collected involves interpretation in order to
recognize what seen, known data is produced by
particular examples, how it relates to or compares
with other findings and how to describe the relationships between the variables (Blaikie, 2003).

Sample of Participants
Employees from Australian banks listed in the
APRA list in March 2013 were invited by email
to participate in the online survey. The sample
population was established based on their positions and job descriptions in their LinkedIn profile.
The selection allowed sample diversity based on
their professional background and ensured that
the invitees had some degree of contact with
SNS applications either directly or indirectly.
The researcher then joined about 50 professional
groups on the LinkedIn site to approach appropriate population sample from various departments
such as HR, Marketing, IT and Customer Service.
The groups were combined to comprise various
interest discussion groups: Social Media Australia
and New Zealand, Customer Services and Social
Media, Digital Marketing and Australia IT. The
survey was conducted from the first week of
March 2013 until 20th May 2013, extended from
the original closing date of 10th April 2013 in order
to achieve the minimum number of respondents
required for Factor Analysis. Figure 2 illustrates
the eight main steps of survey dissemination
adapted from Issa (2007).


Understanding Employee Attitudes to SNS Implementation in the Australian Banking Sector

Figure 2. Online survey dissemination process adapted from Issa (2007)

DATA ANALYSIS AND RESULTS
Data Analysis
SPSS ver 21 was used for statistical analysis; the
Descriptive, Independent t-Test measured the
agreed level for each functionality, Correlation
Test with cross-tabulation and EFA. Two of five
types of variables (Creswell, 2003) were used for
EFA: Independent variable (IV) and Dependent
variable (DV). The IV is the explanatory variable

and manipulates other variables; DV is the outcome variable that looks for any change (Robson,
2011). In exercising EFA, the Gender category
is set as IV; the research questions which have a
continuous Likert scale from “1” (Strongly Disagree) to “5” (Unable to Judge) are set as DV. An
independent t-Test was conducted to compare the
means of the two groups: the male and the female
and together with descriptive analysis, the agreed
level for both genders was measured.

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Understanding Employee Attitudes to SNS Implementation in the Australian Banking Sector

The Respondents
The survey collected 130 responses with 87%
completion rate and yielded 113 use cases. The
demographic statistic indicated the respondents
comprised 61.1% Male and 38.9% Female; and the
workforce was dominated by Gen X. Respondents
who are working in mobile banking, digital banking and ATM services were grouped as Service
Channels. The social media team accounted for
only 4% of the total sample. Table 1 provides
further details of the respondents.
From the t-Test, which set a 95% confidence
level, the outcome suggested that the female group
Table 1. Respondents’ profiles

130

has higher level, although they are half in number
compared to the male group (Table 2 presents the
complete t-Test result).

Reliability and EFA
The Reliability test was conducted pre and post
EFA to measure the consistency of all the item
samples. The first run for all 28 items resulted in
a Cronbach’s Alpha α value of .933, indicating a
good internal consistency between each functionality according to George and Mallery’s rule of
thumb (Gliem & Gliem, 2003). The α from the
final EFA was .862 for N = 14, indicating that


Understanding Employee Attitudes to SNS Implementation in the Australian Banking Sector

Table 2. Descriptive and t-Test result

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Understanding Employee Attitudes to SNS Implementation in the Australian Banking Sector

Table 3. Pattern matrix – initial run: Pattern Matrixa
Factor
RQNo.

Questions

1

2

3

4

5

RQ2.2

CONVERSATIONS-All comments or contacts received
provided various levels of awareness on how to respond to
customer inquiries.

.913

RQ2.1

CONVERSATIONS-Visitor or customer comments
provided better insight regarding products and / or service.

.858

RQ2.3

CONVERSATIONS-I learn new ways to “listen” and “to
chime-in” to the conversation.

.690

RQ4.4

PRESENCE-It should follow specific guidelines and
policy.

.437

RQ2.4

CONVERSATIONS- Improve my traditional conversation
ability.

.368

RQ3.3

SHARING-Improves my work quality and performance.

.952

RQ3.1

SHARING-Allows me to follow up and track down issues
easier.

.817

RQ3.4

SHARING -Gives me more accountable information.

.772

RQ3.2

SHARING-Increase the quality of communication between
client/customer and with stakeholders.

.515

RQ4.1

PRESENCE-Is an effective tool to be recognized in the
digital community?

.826

RQ4.2

PRESENCE-Expands my skill to build, grow and maintain
community membership.

.753

RQ5.3

RELATIONSHIPS-Enable me to see clearly on who is
linking to whom and also what content was sending across
that connection.

RQ6.1

REPUTATION-Exposes my work or workplace to wider
(global) community, as well as I learn about others
organization.

.528

RQ7.3

GROUPS-Increase collaboration and communication with
work peers, customers and the management.

.429

RQ4.3

PRESENCE-Is a good way to shorten the distance between
me and my customer?

.314

RQ7.1

GROUPS-Provides different approach and specific policy
to protect customer and the organization.

.983

RQ7.4

GROUPS-Policy and guideline could align with the
customer engagement.

.761

RQ5.4

RELATIONSHIPS-Act as a communication interface with
the public, what to share and how to engage them.

.522

RQ5.2

RELATIONSHIPS-Express a different perspective of
invisible sight of my works.

.322

RQ1.4

IDENTITY-Makes my work approachable for our current
and prospective customers.

.908

RQ1.3

IDENTITY-Provides additional values which is important
for my department.

.805

RQ1.2

IDENTITY-Provide levels of trust, confidentiality and
communication effectiveness.

.449

6

7

-.301

-.305

.609

.411

continued on following page

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Understanding Employee Attitudes to SNS Implementation in the Australian Banking Sector

Table 3. Continued
Factor
RQNo.

Questions

1

2

3

4

5

6

RQ0.1

Gender

RQ6.2

REPUTATION-All responses could be used to measure
work effectiveness.

.788

RQ1.1

IDENTITY-Provide prompt response and deliver better
services to internal and external customers.

.533

RQ6.3

REPUTATION-We expect broad ranges of attention posted
by the public and customers.

.391

RQ6.4

REPUTATION-It could create a virtual “resume” for me
and my team.

.384

RQ5.1

RELATIONSHIPS-Assist my working relationship with
internal and external organizations.

7

.378

.383

“Extraction Method: Principal Axis Factoring.
Rotation Method: Promax with Kaiser Normalization.a”
a. Rotation converged in 8 iterations.

the variable’s reliability was still retained after
the EFA was conducted.
EFA was chosen as an appropriate means of
factor analysis to examine the primary research
question taking into consideration that: (1) the
primary objective of this research is to examine the
unobserved elements represented in the original
framework as expressed in the primary research
questions; and (2) the researcher has little knowledge about the amount of specific and inaccurate
variance in the Australian banking sector (J. F.
Hair, Black, Babin, & Anderson, 2010). Principal
Axis Factoring (PAF) was used as this serves the
primary purpose of identifying and examining the
factors that are shared by the functionalities of
the framework; also, it is reliable in finding the
co-variation between items in the functionalities.
PROMAX was chosen as this rotation type is able
to produce a Factor Matrix and Pattern Matrix
where functionalities are “not assumed to be
uncorrelated with each other” (p. 114).
There were 104 valid cases (N=104) from a
total of 114 responses processed for EFA as missing values were excluded automatically by SPSS.

A normality distribution is not required for EFA
(Lyytinen & Gaskin, 2012). Reliability correlation
established with the value of .86 above the recommend .6 for the KMO for Measure Sampling Adequacy (MSA) and the Bartlett’s Sphericity Test’s
was significant at χ2 (406) = 1555.26, p <.05. The
commonalities of the factors indicated >60% or
16 of 28 items, providing enough factors to meet
the variance required. All, except for RQ0.1 and
RQ4.4, have a value above .3, further confirming
that each item shared a common variance with
other items. Seven factors with Eigenvalue > 1.00
gave 66.261% cumulative percentage Total Variance Explained. The Factor Correlation Matrix
screened for discriminant validity indicated that
no variable was corrected as all values shown are
<.7 for the seven retained factors.
The Pattern Matrix (see Table 3) shows four
variables RQ2.2, RQ2.3, RQ3.3 and RQ7 which
have high loadings (.913, .991, .952 and .983
respectively), thereby representing the significant
unique contribution to the factor (Hair et al. 2010).
The two lowest loadings are from RQ4.2 (.314)
and RQ5.2 (.322).

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Understanding Employee Attitudes to SNS Implementation in the Australian Banking Sector

Results
The high loading values indicate that the participants have learned new ways to “listen” and
to “chime-in” to the conversation (RQ2.3) and
recognise the need to apply a different approach
and specific policies to protect both the customer
and the organisation (RQ7.1). Respondents are
also aware that communication through SNS improves the quality of their work and performance
(RQ3.3) and all comments or contact received
made them aware of the necessity to prepare prior
to responding to customer inquiries (RQ2.2), as
responses from interactive communication can
influence a user’s perception and attitude (Walther
& Bunz, 2005). The lowest loadings indicate that
the employees do not believe that the use of SNS
usage can help to improve their social skills by
establishing and managing community membership; nor can they see how SNS as a working tool
could give them an opportunity to present the
other facets of their works (RQ5.2).
In terms of these two variables, the positive
implications may be perceived only by the participants who extensively use the SNS for their work
task, (i.e. the social media team). The common
factors were sorted by the size of the factor coefficient (see Table 4); six factors were identified
and labelled as:
1. Awareness of feedback from the bank
customers;
2. Improvement of work quality and
performance;
3. Engagement in dialogue with internal and
external stakeholders;
4. Bank compliance with policies and
guidelines;
5. Credibility is established at both individual
and organisational levels; and
6. Acknowledgement given to the employee of
their work deliverables.

134

These six underlying factors answered the
primary question. Those factors enhanced the
initial framework that can be referred to as a BE
(Banking Employee) Honeycomb as illustrated
in Figure 3 (the new six factors are in the elipse
and connected with the intial functionality/ functionalities with the dash line; and the box informs
the specific facet and the implication from the
respective functionality).
The second question is answered after further
analysis because the questions asked were not
directly specified to measure the workers’ motivation and job satisfaction. Staff motivation was
measured using several variables: Issues tracking
using SNS (RQ3.1), Accountability of information
(RQ3.4) which leads to increased trust between
internal stakeholders and the opportunity to be
acknowledged by the wider community outside the
organisation (RQ6.1). The Likert scale’s agreed
level and t-Test calculation were returned with
signicant result for those variables. The t-Test
results indicated that respondents agreed that SNS
assists employees to understand customer requests
and requirements (RQ1.1) and the information
from SNS is valuable and should be taken into
account (RQ3.4). By linking these two results,
the researcher concluded that if the staffs value
the shared information, this can be extended to
promote and increase the trust factor within the
organisation. In addition, responses to RQ6.1 (acknowledgement of the wider community outside
the organisation) reflect that the employees are
aware that the community values their professional
expertise. This positive acceptance can encourage
employees to perform better, leading to increased
satisfaction as a result of sharing knowledge or
making a greater effort (Li et al., 2012).
The third question was answered after a thorough assessment of the prevalent variant that was
established from the emerging factors; communication skill and the collaborative performance
skill are selected as two notable characteristics.
The first characteristic led to Factors 1 and 3
being established. The responses suggested that


Understanding Employee Attitudes to SNS Implementation in the Australian Banking Sector

Table 4. Pattern matrix final run- factors labeled: pattern matrix
Factor
RQNo.

Labels

Functionalities

1

Awareness of
feedbacks from the
bank customer

CONVERSATIONS-All comments or contacts received provided
various levels of awareness on how to respond to customer inquiries.

.913

CONVERSATIONS-Visitor or customer comments provided better
insight regarding products and / or service.

.858

RQ2.3

CONVERSATIONS-I learn new ways to “listen” and “to chime-in”
to the conversation.

.690

RQ4.4

PRESENCE-It should follow specific guidelines and policy.

.437

RQ2.4

CONVERSATIONS- Improve my traditional conversation ability.

.368

RQ2.2
RQ2.1

RQ3.3

.952

SHARING-Allows me to follow up and track down issues easier.

.817

RQ3.4

SHARING-Gives me more accountable information.

.772

RQ3.2

SHARING-Increase the quality of communication between client/
customer and with stakeholders.

.515

RQ4.1
RQ4.2

Engagement
in dialogue
with internal
and external
stakeholders

3

PRESENCE-Is an effective tool to be recognized in the digital
community?

.826

PRESENCE-Expands my skills to build, grow and maintain
community membership.

.753

RQ5.3

RELATIONSHIPS-Enable me to see clearly on who is linking to
whom and also what content was sending across that connection.

RQ6.1

REPUTATION-Exposes my work or workplace to wider (global)
community, as well as I learn about others organization.

.528

RQ7.3

GROUPS-Increase collaboration and communication with work
peers, customers and the management.

.429

RQ4.3

PRESENCE-Is a good way to shorten the distance between me and
my customer?

.314

RQ7.1

-.305

.983

GROUPS-Policy and guideline could align with the customer
engagement.

.761

RQ5.4

RELATIONSHIPS-Act as a communication interface with the
public, what to share and how to engage them.

.522

RQ5.2

RELATIONSHIPS-Express a different perspective of invisible sight
of my works.

.322

RQ1.4
RQ1.3

Credibility is
established at both
individual and
organisation level

RQ1.2
RQ0.1
RQ6.2

6

7

.411

IDENTITY-Makes my work approachable for our current and
prospect customer.

.908

IDENTITY-Provides additional values which important for my
department.

.805

IDENTITY-Provide levels of trust, confidentiality and
communication effectiveness.

.449

Gender

.378

REPUTATION-All responses could be used to measure work
effectiveness.

.788

IDENTITY-Provide prompt response and deliver better services to
internal and external customers.

.533

RQ6.3

REPUTATION-We expect broad ranges of attention posted by the
public and customers.

.391

RQ6.4

REPUTATION-It could create a virtual “resume” for me and my
team.

.384

RQ5.1

RELATIONSHIPS-Assist my working relationship with internal and
external organizations.

RQ1.1

Acknowledgement
given to the
employee upon their
work deliverables

5

.609

GROUPS-Provides different approach and specific policy to protect
customer and the organization.

RQ7.4

Policy & guideline
compliance by the
bank

4

-.301

SHARING-Improves my work quality and performance.

RQ3.1

Improvement on
work quality and
performance

2

.383

135


Understanding Employee Attitudes to SNS Implementation in the Australian Banking Sector

Figure 3. BE Honeycomb augmented the Honeycomb Framework from Kietzmann et al. (2011)

participants are starting to take notice of the new
way of listening and joining the conversation
(RQ2.3) which is part of Factor 1, in order to be
more responsive to the customer enquiries and are
heeding customer feedback in order to improve the
quality of the products and or services (RQ2.1).
Likewise, the stakeholders’ engagement capability is regarded as a conversation capability. This
capability is reflected in RQ4.1, RQ4.2 and RQ5.3
and grouped as Factor 3.
Reiterating an earlier observation, collaboration is one of the values that are expected to stem
from the adoption of SNS. RQ1.4 was initially
intended to measure the collaborative performance
skill (make my work approachable), together with
RQ5.1 (assist working relationship with internal

136

and external organisation) and RQ7.3 (increase
collaboration and communication with work peers,
customers and management). However, RQ1.4
was grouped with other variables under Factor
5, and, RQ5.1 and RQ7.3 returned a low loading
value (.383 and .429 respectively).
These results indicate two challenges: ownership and authorization (Kuikka & Äkkinen, 2011)
possibly facing the organisation when they attempt
to improve collaboration by utilising the adopted
SNS. The t-Test results and agreed level showed
this intention has been acknowledged; yet there
is still room for improvement in the use of SNS
as working tools, especially for collaborative
performance.


Understanding Employee Attitudes to SNS Implementation in the Australian Banking Sector

All these answers show the positive relationship
between the utilization of SNS apps as working
tools and the improvement in staff motivation
and job satisfaction in Australia’s banking sector.
Further, this indicated the employee perceived as
“GOOD” their company’s decision to use SNS
as a new working tool for customer engagement,
thereby supporting its adoption.
Further assessment leads to two interesting
outcomes. First, regarding the employees’ attitude
to policies and guidelines, they agreed with their
employers that having policies and guidelines
pertaining specifically to SNS could serve the
organisation’s interests and ensure compliance
with financial regulators concerning privacy and
confidentiality. This may reduce the earlier concern that the SNS would jeopardize productivity
and lead to abuse of an organisation’s resources
(Young, 2010).
Second is Collaboration; referring to Table 4,
the combination of Factors 5 and 6 indicated that
SNS was not fully utilised as a means of promoting internal collaboration. RQ7.3 under GROUP
returned low scores and was grouped under Factor
3 (Engagement) rather than Factor 6. This result
indicated that silo mentality still dominate in most
organisations. To bridge the gap, the involvement
of senior management through internal SNS could
encourage staff participations and initiate interaction between senior and junior staffs as one means
of preserving tacit knowledge (Hamza, 2011). In
order to encourage and improve collaboration
in the workplace, banks need to have a strategic
plan and utilise simple and familiar tools (Corso
et al., 2009).

SIGNIFICANCE FOR THEORY,
METHODOLOGY AND PRACTICE
The Honeycomb framework together with 4C
guidelines were appropriate to use as the base
theory when analysing SNS adoption in the Australian banking industry. The seven functionalities

provide the structure required to analyse data
results. Like other studies, additional theory that
specifically addresses the essence of research
context and topic should be adopted to ensure a
more comprehensive investigation process and
acceptable results. The methodology signified
through statistical analysis where the result presented the correlation between functionalities and
underlying factors were identified. Regarding its
practical contribution, the EFA results informed
a significant low level of collaboration as the
staff still perceived that the adoption of the SNS
as a work tool was mainly for commercial and
branding purposes.

LIMITATION AND
RECOMMENDATIONS
Three significant limitations emerged during the
course of this research. The first is the issue of
sample size; a greater number of valid responses
would have been desirable for the purpose of
EFA. Moreover, the scope was limited as it was
restricted to the banking sector in Australia; hence,
the findings cannot be confidently generalised
to other countries; also the analysis focused on
gender and excluded the age variable for the EFA.
The research findings have led to several recommendations. First, banks can optimize their SNS
investment by taking advantage of the potential
benefits that SNS can bring to an internal organisation such as encouraging knowledge exchange and
improving employee motivation which positively
impacts on work quality and performance. Second,
regarding the REPUTATION functionality and the
new observed value labelled Factor 6, it appears
that SNS can be considered as one of the parameters used to evaluate employee performance.
Third, SNS can be used to encourage and engage
the employee in collaborative activities to bridge
the silo construct. With the transition workforce
generation, collaboration is necessary in order to
preserve tacit knowledge (Hamza, 2011).

137


Understanding Employee Attitudes to SNS Implementation in the Australian Banking Sector

FUTURE RESEARCH

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107–116). IGI Global. doi:10.4018/978-1-46661559-5.ch007

KEY TERMS AND DEFINITIONS
BE Honeycomb: Banking sector Employee
Honeycomb where six factors constructed from
EFA augmented the Honeycomb framework.
Collaboration: In organisation’s context of
participation and sharing knowledge; collaboration refers to a set of act includes planning, making
decision, problem solving, setting goals, assuming
responsibility, working together, communicating
and coordinating openly.
Likert’s Scale Agreed Level: Number of
respondents whom selected the option Agreed to
Strongly Agree on each research question; where
each question measured with 5 points Likert scale.
Ownership and Authorization: Two issues
occurred in the organisation that adopted SNS;
these issues challenge the structures of traditional
organisation’s hierarchy (clear segmented departments functions) and employee’s initiative when
their company assigned them to engage with their
stakeholders or customers through SNS apps.
Silo Mentality: An attitude found in the organisation or institution that inhibit the flow of
knowledge sharing between departments.
SNS Apps: SNS applications facilitate people
meeting like minds, comprises both professional
and social networking.
Tacit Knowledge: One of two types of knowledge; the knowledge that is understood resides
within the knower’s mind.

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Chapter 7

Teacher-Student Relationship
in the Facebook Era
Alona Forkosh-Baruch
Levinsky College of Education, Israel & Tel Aviv University, Israel
Arnon Hershkovitz
Tel Aviv University, Israel

ABSTRACT
The popularity of social networking sites has facilitated new modes of teacher-student communication,
conveying the potential of changing teacher-students interaction. The goal of this chapter is to examine
students’ and teachers’ perceptions of student-teacher SNS-based relationships in the Facebook era and
to supply evidence that supports decision making. The authors present two studies involving secondary
school Israeli students and teachers, examining the relations between Facebook-based student-teacher
communication and student-teacher relationships. Findings suggest that Facebook communication may
be beneficial but highlight conflicting issues. The authors discuss the implications of these studies, offering recommendations that include comprehensive support of teachers in developing new ICT literacies.
They recommend further research as a means of providing educational policymakers and stakeholders
with evidence to assist with informed decision making, as well as a means to empower teachers by allowing them to make decisions based on their educational beliefs.

EDUCATIONAL PARADIGM
CHANGES IN THE KNOWLEDGE
ERA: INTRODUCTION TO
SNS IN EDUCATION
Throughout human history, knowledge was the
driving wheel behind societies. In the modern,
post-industrial society, many consider knowledge
to be the single most valuable commodity (cf. Bell,
1976; Masuda, 1980). Indeed, in the 1990s, the

phrase for describing the changing society was
altered from “information society” to “knowledge
society”, focusing on ideas rather than on explosion of information, hence shifting attention to
21st century skills for gaining knowledge. To this
day, the two concepts – information society and
knowledge society – tend not to be differentiated
(Anderson, 2008). Information and communication technologies are key issues in the transformation of society. Specifically, the growing usage

DOI: 10.4018/978-1-4666-7262-8.ch007

Copyright © 2015, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.


Teacher-Student Relationship in the Facebook Era

and popularity of Web 2.0 applications created
new modes of collaboration and communication
(Cheung & Lee, 2010), thereby facilitating social
change (Olson, 1994). Still, with regards to the
knowledge society, fundamental change have occurred in the past few decades; hence, change is
not only in terms of amount or scope, but rather
fundamental, causing transformation and vast
implications on all aspects of our lives (Mioduser,
Nachmias & Forkosh-Baruch, 2008). Within the
realm of social media, several fundamental issues
arise, related to, e.g., self-exposure, intimacy, and
self-expression (Amichai-Hamburger & Vinitzky,
2010; Livingstone, 2008; Lowenthal, 2009; Marwick & Boyd, 2010).
The knowledge era challenges society with
a paradigm shift, setting demands for new tools
and skills. According to Simon (1996), the world
is becoming more complex. New paradigms
emerged as a result of lifelong learning, emphasizing self-directed learning, constructivism and
constructionism, emphasizing collaboration and
social learning. Furthermore, new literacies are
offered for the information age (cf. Lankshear &
Knobel, 2006; Leu, Kinzer, Coiro, & Cammack,
2004; Mioduser, Nachmias & Forkosh-Baruch,
2008). Previous paradigms may not be relevant
any more when new concepts emerge. With regards to education, we encounter new pedagogical
paradigms, e.g., new assumptions, concepts and
practices that shape our views of reality. New
paradigms emerge when a current paradigm cannot
meet demands of society (Brummelhuis & Kuiper,
2008). Such a fundamental change is considered
a paradigm shift (Kuhn, 1970).
The emergence of Web 2.0 applications created
a growing population of collaborators worldwide,
interacting and communicating beyond boundaries
of time and space. This facilitated the creation of
new types of interfaces, i.e., online social networks,
where the users are at the center of the knowledge creation, rather than information (Chueng
& Lee, 2010). Hence, a new paradigm emerged:

146

the creation of networks of people worldwide, for
educational as well as social purposes.
Teachers, as well as educational systems at
large, can benefit from these changes by facilitating contemporary educational paradigms (Abbott,
2005), allowing teachers to “[engage] in an authentic relationship with students where teachers
know and respond with intelligence and compassion to students and their learning” (Rodgers &
Raider-Roth, 2006). This pattern of teacher-student
communication challenges traditional paradigms
in which communication is limited and based on
traditional teacher-student relationship and roles.
Hence, the evolving knowledge society and
the emergence of information and communication
technologies into our lives present complex challenges to educators and policymakers worldwide.
Education requires adjustment to these changes
in learning and teaching (e.g., the emergence of
self-driven lifelong learning), in the shattering of
boundaries (e.g., massive open online courses –
MOOCs – as a new mode of distance education),
as well as new meaning to emerging educational
paradigms (e.g., constructivism, project based
learning) (Brown & Adler, 2008). Schools, as a
result, are prone to changes in the information era,
as are all realms of our life, e.g., business or governance. Time and space boundaries are blurring
and the information flow is constantly growing;
this challenges teachers’ role as educators in 21st
century Web-based education, creating potential
for advanced teacher-student collaboration, cooperation, and connection in ways unfamiliar to
traditional education (UNESCO, 2011).
However, some changes are difficult to adopt
by educational policymakers as they dramatically
challenge long-established norms and traditions.
The emergence of social networking sites, one of
the prominent building stones of the Web 2.0 era,
is perhaps the most salient example of the need to
address unfamiliar educational scenarios.
Social networking sites (SNS) are websites
that enable their users to construct a public or
semi-public profile and to build a personal inner


Teacher-Student Relationship in the Facebook Era

network of connections (Boyd & Ellison, 2007);
SNS have become the most popular websites
on the Internet and have been adopted by many
teenagers worldwide (Boyd, 2008; Ellison &
Boyd, 2013; Lenhart, 2007). SNS pedagogical
usages have been extensively discussed from
several points of view, e.g., the instruction point
of view, learning possibilities and challenges for
formal and informal teaching and learning (e.g.,
Mazman & Usluel, 2010; Veletsianos, Kimmons
& French, 2013). However, there is still lack of
research regarding yet another angle related to
the use of SNS in education, that is, the social
aspects of SNS-mediated communication between students and teachers. This is the unique
angle we discuss in this chapter, based on studies
examining the relationships between students and
teachers who are closely familiar with the SNS
world. Mainly, we focus our attention on middleand high-school students, as this population has
special characteristics when it comes to SNS use
and perceptions (cf. Boyd, 2014). Furthermore,
while many studies have explored Facebook educational affordances in higher-education, studies
in the context of secondary schools are limited
(cf. Hew, 2011).
SNS challenge the traditional dichotomy
between students and teachers, as well as help
re-inventing student-teacher out-of-class communication in so many ways. Hence, SNS carry
educational promises, such as allowing available,
enriched, and contextual student-teacher communication, or facilitating multi-channel learning
experiences. Notwithstanding, SNS may also
generate some undesired consequences, from
being a waste of time to extreme cases of cyberbullying, making this issue highly controversial
in public opinion. The moral and educational
concerns involving SNS-based student-teacher
communication reinforce the need for balancing
opportunities and risks within policy (Livingstone
& Brake, 2009).
Some intriguing questions have been raised
regarding student-teacher connections on SNS

and their effect on student-teacher relationships
in “real-life”, and vice versa. Even the very term
used in many SNS to describe connected users,
“friends”, may challenge traditional studentteacher hierarchy, as traditionally teachers are
allowed some power over their students even when
close relationship between the two are developed
(Ang, 2005; Jaimeson & Thomas, 1974; Steinfield, Ellison, & Lampe, 2008; Vie, 2008). Albeit,
closeness and friendship may be different in SNS
compared to the real world (e.g., Subrahmanyam
et al., 2008; Van House, 2007).
This should be viewed in light of the everchanging role of teachers in the information era,
as a result of blurring, even breakdown, of time
and space boundaries (MacFarlane, 2001; Scardamalia & Bereiter, 2006). This changes teaching
paradigms, as well as the learning experience in
its broader sense. However, although technology
is a major factor in schools nowadays, teachers
and students are still crucial players in this process
(Ertmer, 2005; Elmore, 1996; Lambert, 2007).
Teachers may change their role from “a sage on
the stage” to “a guide on the side”, that is, from
a formalized, well-established format of traditional teaching and learning to a contemporary
educational paradigm focusing on connecting to
students, mentoring and assisting them (Abbott,
2005; Wong et al., 2006). SNS-based communication plays a major role in this change, extending the scope and setting in which teachers and
students communicate. This may affect, in turn,
mutual perceptions and beliefs (Mazer, Murphy, &
Simonds, 2009), thereby changing student-teacher
relationships, followed by an even greater change
in traditional hierarchical structures in schools.
For this reason, school authorities and policymakers have been pondering about their position
regarding student-teacher SNS-based communication. Educational policymakers worldwide have
adopted different educational approaches, often
prohibiting teacher-student communication via
SNS altogether. Public discussion on teacherstudent communication via SNS reflects the com-

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Teacher-Student Relationship in the Facebook Era

plex nature of this issue, and overall demonstrates
the difficulty in adapting novelties in large-scale
systems and organizations. However, most policies
are not based on empirical evidence.
The purpose of this chapter is to present the
emergence of Web 2.0 into educators’ lives and
its effect on teacher-student relationships. We
propose demonstration of this phenomenon via
an exploratory study conducted by the authors,
examining students’ and teachers’ perceptions of
student-teacher communication via SNS. The mission and concerns of the proposed chapter focus
on the merits of SNS in contemporary education,
as well as its shortcomings, from both teachers’
and students’ points of view. We were especially
concerned with the lack of evidence-based research in secondary education (Hew, 2011), where
teacher-student connections are vital.
For meeting this goal, we have conducted
two qualitative studies, interviewing students
and teachers regarding their beliefs and usage
of Facebook as a means for student-teacher
communication (for elaboration on the students’
perspective see Hershkovitz & Forkosh-Baruch,
2013). Participants in the qualitative exploratory
studies to be reported in this chapter include Israeli
secondary-school students and teachers within
the Israeli public education system. In-depth
semi-structured interviews with all teachers and
students allowed us to explore their experience
on Facebook and their perceptions of teacherstudent communication and relationships in the
Facebook era.
We conducted qualitative content analysis
utilizing a methodological framework suggested
by Ang, titled “Teacher-Student Relationships
Inventory” (TSRI) for assessing student-teacher
relationships (Ang, 2005), and as a result, gained
some interesting insights. The framework was
developed as a survey-based tool for assessing
satisfaction, instrumental help, and conflict in
student-teacher relationships from the teachers’
point of view (full details are given in the Related
Work section). We analysed the participants’ state-

148

ments related to teacher-student communication
in SNS and to teacher-student relationship in today’s SNS era. Statements about teacher-student
relationships were assigned to one of three categories, based on TSRI axes: a) Satisfaction; b)
Instrumental Help; and c) Conflict. Classifications
were validated under strict procedures. Following a detailed methodological description, the
chapter will present findings from the teachers’
and students’ studies; findings from both studies
may provide a more comprehensive, multi-faceted
view on the topic.
Overall, our study suggests that teacher-student
relationships via SNS are mostly a reflection of
teachers’ educational views regarding their role, in
terms of instruction and interpersonal interactions
with their students. Hence, teachers leading close
relationship with their students benefit greatly
from ubiquitous connections beyond school hours,
while others, who prefer more distant and formal
relationship with their students in class, are prone
to limit their interaction also beyond school hours.
Hence, while banning teacher-student SNS-based
communication is irrelevant to the latter, it might
be harmful to the former.
In fact, as both students and teacher indicated,
student-teacher communication via Facebook
barely exists, mostly because students lack interest in their teachers’ private life. However, when
it does exist, it might contribute greatly to both
students and teachers, even if limited only to
administration-oriented messages, mainly because
it is accessible and convenient (truly natural for
students). Only in rare cases students exhibit rich
communication on Facebook with an exceptional
teacher, thereby emphasizing the importance of
the human factor rather than technology itself, and
yet benefiting from Web 2.0-based technology as a
facilitator of these relationships. We also highlight
some conflictual situations, some of which are
unique to the SNS-based communication.
Our study raises some fundamental themes
regarding technology utilization in the education sector, especially Web 2.0 SNS interfaces.


Teacher-Student Relationship in the Facebook Era

Teacher-student befriending on Facebook is
related to a feeling of satisfaction in class. Also,
teacher-student communication on Facebook
facilitates a feeling of closeness between them.
Facebook, in terms of instrumental help, is a convenient means of communication for students and
teachers compared to other Web-based interfaces
and platforms; however, students mostly communicate with teachers via Facebook when beneficial, and sometimes will online-communicate
with their teachers about topics they would not
do so otherwise. Educators are also challenged by
conflictual experience in class as a routine; this is
translated to Facebook communication, and vice
versa. Moreover, Facebook-based communication
may create new types of conflicts and dilemmas.
When occurring, conflictual situations tend to be
resolved in private communication channels; furthermore, teachers will normally ignore students’
online-reported improper behavior, and intervene
only in extreme conditions. All of these may
both empower and challenge teachers’ role as a
source of authority in the classroom (Maranto &
Barton, 2010).
The chapter concludes with a discussion on
the implications of these studies, thereby raising
awareness to the rich potential of SNS among
students and teachers (rather than flatten it to rare
cases of extreme conflicts). Implications include
the need for comprehensive support of teachers
in developing new literacies, needed to utilize
Web 2.0 SNS interfaces, for the benefit of their
students. We emphasize teacher-student relationships in the SNS-era as potentially contributing
added value to the school milieu. These research
findings might also have implications on the uses
of SNS in other hierarchy-based settings, e.g.,
between students and instructors, or between
employers and their superiors. Hence, we also
recommend further research into these aspects as
a means of providing educational policymakers
and stakeholders with findings that may enable
informed decision-making.

The chapter is arranged as follows. In the next
section we present the two most common approaches of policymakers world-wide to teacherstudent SNS-based communication. Then, we refer
to related work, with regards to both empirical
studies and theoretical frameworks. Nest, we thoroughly describe the case study of Israel. Finally,
we draw conclusions and recommendations.

POLICIES FOR USING (OR NOT
USING) SNS IN EDUCATION
In light of ambivalent consequences as a result of
the impact of SNS on teacher-student relationships,
educational policymakers have been contemplating their standpoint on this issue. In Israel, where
the study reported in this chapter was conducted,
the Ministry of Education first banned teacherstudent connections and communication altogether
via SNS (Israeli Ministry of Education, 2011); our
study was carried out during the period before and
immediately after this regulation was published,
in the midst of a rigorous public debate on this
issue. However, about a year and a half after the
regulation was published, another regulation was
published by the Ministry, this time referring to
the educational affordances of SNS as a learning
environment, specifically mentioning Facebook
(Israeli Ministry of Education, 2013), as it is the
most popular SNS used in Israel (Alexa, 2014).
According to the updated regulation, teacher and
students are allowed, even encouraged, to communicate on Facebook following these rules:
Communication is permitted only via a closed
group, opened by the teacher and administrated by
her or him or by a designated student; the group
members will be only the teacher and her or his
students, and communication is allowed only by
using a separate Facebook profile (different from
the personal one, if such exists), lacking any personal information, for both teachers and students.
The latter collides with Facebook Statement of
Rights and Responsibilities1, which clearly states:

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Teacher-Student Relationship in the Facebook Era

“You will not create more than one personal account”. Overall, this change of regulations and
the administration of a Facebook-specific rule
demonstrate educational policymakers’ confusion
regarding SNS at large.
When examining how this issue is approached
worldwide, we realized that some states, cities,
or school districts in the US have already banned
teacher-student connections and communication
via SNS, the largest of them being the New York
City Department of Education (NYC Department
of Education, 2012). The State of Missouri was
also in the progress of banning such relationships,
however from very different motives. While the
NYC Department of Education was worried by
“the fact that in this digital era, the lines between
professional and personal endeavors are sometimes
blurred” (NYC Department of Education, 2012, p.
1), the Missouri suggested regulations were part
of the State attempts of protecting students from
sexual harassment. The Missouri’s proposed Act,
entitled Amy Hestir Student Protection Act, was
named after a former student of Missouri public
school system who at the time was sexually assaulted by a teacher with whom she communicated online, and originally stated that “teachers
also cannot have a nonwork-related website that
allows exclusive access with a current or former
student” (Amy Hestir Student Protection Act,
2011). Eventually, however, this Act was omitted
by the State’s Governor. The State of Louisiana
enacted a law that requires school employees to
use only school-provided means when contacting
students by phone, e-mail, or any other electronic
means, and to discuss only “educational services”
(Louisiana Revised Statuses, 2009; (Q).(2)(b)).
In addition to NYC, local school districts in at
least 13 States have also barred teacher-student
contact via social media (cf. Schroeder, 2013).
Other countries also take similar approach. In
Australia, for example, Queensland Department
of Education, Training and Employment (2013)
states in its Standards of Practice: “Employees
must not use personal social networking sites

150

including Facebook and Twitter, to contact or
access students enrolled in any state educational
facility.” (p. 9).
Other regulators have chosen to warn rather
than to ban. For example, The Teaching Council
of Ireland (the organization that regulates the
teaching profession in that country), in their Code
of Professional Conduct for Teachers states that
“Teachers should […] ensure that any communication with pupils/students, colleagues, parents,
school management and others is appropriate,
including communication via electronic media,
such as e-mail, texting and social networking
sites” (The Teaching Council, 2012, p. 7). These
examples, although different by their purpose,
share an important common ground: They all
refer to SNS as yet another means of communication (rather than, for example, a potentially rich
platform for learning), hence generalize their own
agenda of student-teacher relationships towards
social media. In that sense, public discussion on
teacher-student communication via SNS, mostly
on policymakers’ and regulators’ level, is vital to
the debate within the education system, reflecting the difficulty to adapt novelties. These issues
have potential impact on educational trends, and
thereby on society at large, however only little
they are based on empirical evidence

RELATED WORK ON TEACHERSTUDENT RELATIONSHIP
IN THE SNS-ERA
The issue of SNS in education in general and
with relation to teacher-student relationships in
particular, should be viewed in a broader context
of the role of SNS in the community as a facilitator
of student engagement and of authentic, meaningful learning. This may be achieved first and
foremost by trust and closeness between teachers
and their students; however, this issue is highly
controversial. Mazer, Murphy and Simonds (2007;
2009) have demonstrated the complexity of mixed


Teacher-Student Relationship in the Facebook Era

attitudes towards teachers’ use of SNS. In their
studies, college students were shown fake profiles
of an instructor, depicting different levels of selfdisclosure (e.g., photos of the teacher in social settings, including with friends in public locations or
at-home family photos, or a face-shot only). They
were then asked about their overall thoughts of
the instructor and of her course. Findings suggest
that students who viewed the Facebook profile of
a teacher high in self-disclosure showed higher
levels of motivation and affective learning and a
more positive classroom climate, as well as higher
levels of the teacher’s trustworthiness and caring,
than students who viewed the Facebook profile of
a teacher low in self-disclosure. However, when
asked about the appropriateness of a teacher use of
Facebook, almost 40% of the students responded
with somewhat- or very inappropriate responses.
When measuring the appropriateness of different types of student-teacher SNS-interactions,
findings suggest that students perceive passive
behaviours of teachers (e.g., reading through the
education info, viewing photos) more appropriate
than active ones (e.g., commenting on a photo,
starting a chat) when interacting with them (Teclehaimanot & Hickman, 2011). This is in line with
adolescents’ perceptions of Facebook as a “closed
area” for youngsters to connect with each other,
therefore not suitable for adults, including teachers,
or for teaching and learning (e.g., Starkman, 2007).
A topic often discussed with regards to students’ and/or teachers’ use of SNS is the connection between in-class and out-of-class behavior.
Callaghan and Bower (2012) have shown that
online school-related activity via SNS was related
to student-teacher in-class relationship reciprocally: More positive student-teacher relationship
within the class had led to more on-task learning
behaviours, and teacher engagement in online
activity strengthened these relationships in return.
As teacher-student relationship is a key to
many aspects of learning and teaching, this
topic has attracted numerous studies. Although
the reported study in this chapter is qualitative

in its nature, it was conducted as a basis for an
extended, quantitative analysis. Therefore, while
looking for theoretical frameworks for evaluating teacher-student relationship, we intentionally
explored such that include quantitative measuring
tools, eventually choosing Ang’s (2005) TeachersStudent Relationship Inventory.
The Teachers-Student Relationship Inventory
(TSRI) is a self-report tool for measuring teacherstudent relationships using teacher perceptions
regarding their relationship with current students.
Contrary to the vast majority of its predecessors,
TSRI is a multidimensional scale dedicated to
measuring teacher-student relationship that had
been validated within large populations.
Other often-used teacher-report tools for
measuring teacher-student relationship include
Pianta’s (1992) Student-Teacher Relationship
Scale (STRS). STRS is a 28-item, 5-point Likert
type, questionnaire, originally consisting of 28
items on 3 sub-scales: Conflict, Closeness, and
Dependency. A shorter, 15-item version, of this
tool omits the Dependency axis. Teacher Relationship Inventory (TRI) (Hughes, Cavell, & Willson,
2001) is another teacher-reported, 22-item, 5-point
Likert type questionnaire, measuring Support and
Conflict in teacher-student relationships. Both
STRS and TRI aim on studying teacher-student
relationship in the early ages, mainly kindergarten and primary grades. TRI has been often used
with other tools to measure students’ perception
of teacher-student relationships, in specific interviewing children about their relationship with
the teacher, as well as about the extent of support
other children (their peers) get from the teacher
(e.g., Hughes & Kwok, 2007; Meehan, Hughes,
& Cavell, 2003).
Children’s perception was also measured by
Relationship With Teacher questionnaire, a 9-item,
3-point Likert style inventory, which focuses on
perceived support from their teachers and is suitable for elementary school-age (Blankemeyer,
Flannery, & Vazsonyi, 2002). Some studies also
measured teacher-student relationship at the global

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Teacher-Student Relationship in the Facebook Era

level rather than at an individual level; for example,
Midgley, Feldlaufer, and Eccles (1989) had used
a student-report questionnaire on teacher support,
in which students were asked to rank statements
about the relationship of the teacher and the class
(e.g., “The teacher is friendly to us”).
The only other similar scale that existed before
the development of TSRI that had been validated
is STRS (Pianta, 1992). However, as mentioned,
it was studied in the context of much younger
students (usually, preschool to early elementary
school grades; more recently, it was validated with
relation to a population of teachers of children aged
3-12 years old by Koomen et al., 2012). It is argued
that middle school students rely on teachers for
emotional support very differently compared to
younger children (e.g., Wentzel, 1996); this might
affect teachers’ perceptions of teacher-student
relationship at large. Hence, TSRI was validated
with teachers regarding elementary-, middle- and
high-school students (Ang, 2005; Ang et al., 2008).
An example of the differences between the two
inventories can be shown in STRS’ Dependency
axis, which is probably irrelevant when dealing
with older students, as adolescents are mainly
characterized by their sense of autonomy and
control (cf. Ang, 2005). Actually, even STRS
was later modified omitting the Dependency
axis, as this dimension had shown relatively low
Cronbach’s alpha values (cf. Koomen et al., 2012).
Hence, STRS was left with only the Conflict and
Closeness dimensions. TSRI had been validated
to measure values of three dimensions altogether:
Conflict, Instrumental Help, and Satisfaction.
STRS’ Closeness is parallel to TSRI’s Closeness;
however, Ang’s inventory also examines practical
aspects of teacher-student relationship.
TSRI has 14 self-report items, each of which
is ranked on a 5-point Likert scale. Its dimensions
are: 1) Satisfaction, which refers to positive experiences between students and teachers (e.g., “I enjoy
having this student in my class”); 2) Instrumental
help, which refers to teachers’ relationship with
students from the practical point of view: being

152

a source of assistance, either academic or nonacademic (e.g., “If the student has a problem at
home, he/she is likely to ask for my help”); and 3)
Conflict, which refers to negative teacher-students
experiences (e.g., “This student frustrates me more
than most other students”). For the full inventory
see the Appendix.
Consequently, we preferred Ang’s (2005)
TSRI scale over the other tools, mainly because
this inventory focuses on the relevant population
of school-age SNS-users (that is, middle- and
high-school students). The study presented in this
chapter was conducted under Ang’s framework,
with its three components - Satisfaction, Instrumental Help, and Conflict - driving our protocol
for interviewing and leading our analysis.

THE CASE OF ISRAEL: STUDENTS’
AND TEACHERS’ PERSPECTIVE
In light of the growing debate is Israel and worldwide, exhibited in policy regulations initiated by
Ministries of Education in many countries, and
considering the fact that often such decisions are
not evidence-based, the study reported herewith
was initiated. Issues highlighted in this study may
have potential impact on educational trends, and
thereby on the Web 2.0 society at large.
The purpose of this exploratory study is to
explore perceptions of teacher-student communication and relationships via SNS, specifically
Facebook, from both the teachers’ and the students’
perspective. We utilized a qualitative exploratory
approach aimed to generate insights leading to a
large-scale study. Specifically, the objectives of the
reported study are: a) To explore current trends in
student-teacher communication via Facebook; b)
To understand teachers’ and students’ perception
of Facebook-users vs. non-users; c) To identify
issues regarding student-teacher relationships on
Facebook and their effect on face to face relationships, and vice versa.


Teacher-Student Relationship in the Facebook Era

Table 1. Demographics and SNS characteristics of student participants
Student

Gender

Age

Grade in
2011/12

Freq. of Using Facebook

Joined
Facebook
[Years]

No. of FacebookFriends

AR

M

17

12

Stopped using recently; before –as long as
3 hours/day

5

400-500 (before
stop using)

BA

M

15.5

10

Approx. 2 hours/day

3

466

CY

F

18

Recently
graduated

Using about 5 times/day, overall about an
hour/day

4

~800

ES

F

13.5

8

“Many times a day”

2.5

270

HHY

F

14.5

9

1-2 hours/day, formerly as long as 4 hours/
day

2

544

NA

F

15

10

“Every other day”

2

600

NE

F

16

11

“A lot of time, it’s always open”

2.5

734

OH

M

14

9

Tried for 2 months, then stopped using

1.5 (when
tried)

N/A

RGA

F

17.5

12

Once a day for about 30 minutes

2.5

500-600

N/A

N/A

1.5

211

YA

M

17

11

Not using Facebook at all

YG

M

14.5

9

Once in a while, only for 5 minutes to
catch updates

Participants reported here included Israeli
secondary-school students (N=11, ages 13.518y/o, 5 males and 6 females) and teachers (N=5,
27-54y/o, first-year to 32 years of teaching experience, 3 males, 2 female) from various parts of
the country, all studying or teaching in the Israeli
public education system. A description of the
research population is given in Table 1 (students)
and Table 2 (teachers).
Semi-structured interviews were conducted
with all participants, exploring their experience on
Facebook and their perceptions of student-teacher
communication and relationships as a result. Interviews were carried out by both authors simultaneously via audio/audiovisual communication
using Skype software during August-December
2011. Interview length was between 20-60 minutes. Interviews were recorded, fully transcribed
by the authors before analysis.

Qualitative content analysis was conducted
with the unit of analysis being teachers’ and
students’ statements related to teacher-student
communication in SNS and to teacher-student
relationship in today’s SNS era. We used the Direct Content Analysis method (Hsieh & Shannon,
2005) with variables derived from Ang’s (2005)
TSRI; specifically, each statement about teacherstudent relationships was categorized as related
to Satisfaction, Instrumental Help, or Conflict.
Each of the authors had coded one interviewtranscript and later reviewed the other author’s
coding. Then, the authors discussed the coding
and came to an agreement regarding conflictual
coding schemes. The remaining interviews were
coded by one author each. Within each axis of
the framework, a few main themes arose, each of
which was examined separately for teachers and
for students. Then a comparative examination was
carried out, identifying mutual themes.

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Teacher-Student Relationship in the Facebook Era

Table 2. Demographics and SNS characteristics of teacher participants
Teacher

Gender

Age

Status

Teaching Experience

EH

F

54

married+3

32 yrs; 10th-12th grades;
Theater and Field Studies

Logs in daily

5

~500 (mostly
current/former
students)

EF

M

28

single

3 yrs; 10th-12th grades;
History and Civics

30-40 minutes a day

5

~1,200; not
students

ER

M

39

married+3

4 yrs; 11th-12th grades;
Biology & class teacher

At least 30 minutes
a day

3

~600, mostly
former/current
students

MY

F

35

single

11 yrs; 10th-12th grades;
English (EFL) & class
teacher

~7-8 hours a week;
opened a second,
teaching-only, profile

4

personal profile:
~100 (50 of
whom former
students);
professional
profile: ~50

NN

M

27

single

first year teaching; 9th10th grades; History and
Civics

Never used Facebook; recently
joined Twitter for personal
purposes

ANALYSIS OF SNS-BASED
STUDENT-TEACHER
COMMUNICATION
Description of Communication
The students’ interviews indicate that studentteacher communication via Facebook barely exists,
in spite of Facebook being the most popular SNS
in Israel and one of the top Web 2.0 interfaces.
In fact, most students do not have teachers as
their Facebook friends, and estimate that this is
true for their peers too. This is mainly because
students mostly perceive Facebook as a “closed
territory” for youngsters; hence they do not see
it as a place suitable for adults at large. As one of
the interviewed students asserted:
I didn’t want to add [my teacher as a Facebookfriend], I don’t think it’s a place for a teacher,
where all of my friends are, my private life, things
that I share, it would have been much more convenient for me if she wouldn’t use Facebook at
all. (CY, F:18)

154

Freq. of Using
Facebook

Joined
FB
[Years]

No. of FBFriends

Does not connect
with students

The teachers had utilized different types of
SNS-based communication with their students,
based on their personal educational beliefs. We
identified at least three types of such communication. The first type of communication is liberal, in
which teachers allowed their present students to
access their profile, that is, accepted their students’
Facebook-friendship request. Almost always in
these cases, Facebook-friendship requests were
initiated by students (sometimes with the teacher
announcing in class that students who want to connect with her or him on Facebook were welcome
to do so). Students of liberal teachers were able
to see every Facebook-activity that was shared
by them (unless the teacher prevented them from
viewing certain activities by changing privacy settings). The second type is conservative, in which
teacher-students communication was limited to
closed class-related Facebook groups. These
teachers prevented their current students’ access
to their Facebook Wall or any other non-group
activity. Usually, this was simply implemented by
opening a second Facebook account for professional use only. The third type of communication


Teacher-Student Relationship in the Facebook Era

is the non-existing, in which the teachers prevent
any kind of SNS-mediated communication with
their current students, basically not approving
any friendship requests if initiated by students.
De facto, in both the liberal and the conservative
modes, student-teachers actual communication is
mostly limited to class-related correspondence,
with both parties respecting each other’s privacy.
Both students and teachers who have experienced Facebook-based communication agree that
Facebook is a convenient, accessible and versatile
means of communication. For students who have
experience with various Web 2.0 platforms, it is a
major interactive tool. Teachers, on the other hand,
often find it as the “place” where their students
are, its richness making it an extension to real-life
events. As one of the teachers asserted:
It gives me an option to communicate with my
students continuously… Most of the communication is one-on-one, by chats and messages… I
use it as a communication platform… If I want
to talk with the students – this is where they are”.
(ER, 39:M)
Facebook allows synchronous text-based correspondence that would not take place otherwise
had communication been constricted to school
boundaries, or if alternative means of electronic
communication were being used: Web 1.0 platforms are less user-friendly and less ubiquitously
accessible through mobile devices; indeed, e-mail
use has declined among teens in recent years,
along with a dramatic increase in texting, which
demonstrates the preference for availability and
immediacy2. These availability and immediacy
make Web 2.0 platforms, like Facebook, a best
practice of communication.
However, students question their teachers’
competency when using such a complex platform
as Facebook. Students expect their teachers to be
proficient in Facebook utilization, and to use it
skilfully and wisely:
Older teachers were on Facebook, I don’t think
they understood the general idea, so it’s like, it

seemed to them that they should add everybody
they knew… (CY, F:18)
If they ever let teachers be students’ friends,
then teachers would have to go through like a
Facebook lesson. Because students, well, nobody
my age on Facebook would write things like ‘I
hate this or that kid’ on their wall, but rather in
a chat. (YG, M:14.5)

Students’ and Teachers’ Perceptions of
Users vs. Non-users among Teachers
We asked both students and teachers to describe
the unique characteristics of teachers who use
Facebook, as opposed to those who don’t use it,
assuming that there are clear criteria differentiating the two (i.e., differentiating Web 2.0-oriented
teachers from old-school teachers). Most interviewees were able to differentiate users from
non-users. However, criteria varied considerably.
For example, one of the students used criteria related to the teacher’s characteristics, relationships
with students, and the subject matter taught by
the teacher, and linked them together in a rather
complex manner:
I can tell almost by first look if someone has
a Facebook account... The more the teacher has
good connections with his students and the less
he’s perceived old-fashioned, he probably has
a Facebook account. But a teacher that is more
attached to his domain than to his students, even
if they like him, and he’s like more old-fashioned
- then he probably does not have a Facebook account... (OH, M:14)
The list of parameters according to which
the students categorize user/non-user teachers
consists of some interesting criteria. Overall,
teachers who have Facebook accounts are young
(this was inconclusive), technology-updated, have
good connection with students, are open-minded
and have free time. Hence, teenage students think
that Facebook users among their teachers are those
who are more similar to them in their conduct.

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Teacher-Student Relationship in the Facebook Era

The teachers’ list of parameters was strikingly
similar, however relations between the parameters
were more complex. Teachers mentioned young
age, informal and more easy-going connections
with students, and proficiency in ICT utilization
as predictors of being active on Facebook. However, they stated that while Facebook users may
be young and have ICT proficiency, older and
more experienced teachers may exhibit intensive
communication with their students as well; hence
age and experience in teaching may compensate
for lesser ICT skills. Teachers were very candid
about their notions on this issue:
[Teachers who use Facebook] don’t exactly
have technological talent as far as I know, but
I don’t think you need in [using] Facebook too
much. It’s a matter of reading the instructions
and doing what it says… But they try for their
students, yes, definitely. (MY, 35: F)
[Not connecting with students via Facebook] is
divided between teachers that are old and totally
don’t connect to this world […] and the young who
don’t want the kids to be part of their personal
life. (EH, 54: F)

Students’ and Teachers’ Perceptions
of Student-Teacher Relationships
As detailed above, we analysed students’ and
teachers’ interview scripts based on Ang’s (2005)
framework, categorizing statements into one of
three categories: Satisfaction, Instrumental Help,
or Conflict. We present herewith the main themes
arising from the data.
Satisfaction: The satisfaction axis refers to
positive experiences between students and teachers. Two main themes arise from both students’
and teachers’ interviews regarding this axis:
•

156

Teacher-student befriending on Facebook
is related to a feeling of satisfaction in
class;

•

Teacher-student
communication
on
Facebook facilitates a feeling of closeness
between the two.

Teacher-student befriending on Facebook is
related to a feeling of satisfaction in class. The
act of becoming Facebook-friends requires both
parties’ agreement, one of the parties being the
initiator of this request and the other approving
it. In all the cases discussed in our interviews,
never had a teacher initiated a friendship request
from one of her or his current students. An
interesting question arises as to the timing of
students’ friendship request from their teachers.
The students participating in our study indicated
that friendship-request from teachers and teachers’ acceptance of these requests is related to the
physical class atmosphere:
It’s like mostly the cooler teachers have Facebook
accounts, and the class is cooler with that teacher,
not really teacher-teacher-teacher, but sort of more
like a friend. (NE, F:16)
Mostly it’s the students who send friend requests
to teachers. Which teachers accept? The ones
that go along with their students, who go with
the flow. (AR, M:17)
One of the students had gone as far as stating
clearly that:
Only teachers that are… like… nice to students
and all, maybe they have [a Facebook account],
they add the kids. (BA, M:15.5)
Things are not different from the teachers’ point
of view. They too indicate that close and satisfying
in-class relationships are “translated” to outsidethe-classroom (including online) relationship:
I have a special relationship with my students,
that’s not related to Facebook… this openness of
mine is also connected to the fact that I don’t have


Teacher-Student Relationship in the Facebook Era

a problem with Facebook… they communicate
with me personally, they talk to me directly lots
of times. (EH, 54: F)
I think [the use of Facebook] reflects the way in
which I conduct myself in school. The accessibility, the commitment… the informality sometimes.
(ER, 39: M)
The enmeshment between social media and
the physical world is inevitable. This leads to the
second theme.
Teacher-student communication on Facebook
facilitates a feeling of closeness between the two.
Based on our analysis of the interviews we conducted, we found that the very communication
via Facebook causes closeness between students
and teachers in new ways. Two teacher quotes
demonstrate this theme:
I think it’s very… it enables closeness also between
teachers and students, not personal necessarily,
but to give a better feeling also in school. In fact,
to create some kind of friendship, let’s say. (MY,
35: F)
I feel that the fact that I share with them, like I
let them into… I want them to share with me. I
mean, I want to be a significant part of their lives,
not to be the teacher who ’pukes knowledge‘ on
them. (ER, 39: M)
From the students’ point of view, similar
feelings arise, highlighting the effects of communication via Facebook on what is happening
in the traditional class zone. Hence, there is great
potential for connectedness, which makes the
students feel closer to their teachers. Here are
two examples of students’ statements related to
this theme:
Nothing can compare to the advantages that
Facebook gives us. It’s like getting to know the
teacher from closer… For some students it helps

to connect to the teacher and to understand the
material better. (RGA, F:17.5)
There’s a teacher, that, like, confirmed me [as a
Facebook-friend], and she has students that she
now teaches on Facebook. I see she’s really active
and comments to kids’ posts and students comment on hers, and she comments on ex-students’
posts, and it really connects between her and her
students. (NE, F:16)
This reflects reciprocal teacher-student relationship based on equality, and may be either
an outcome of students feeling close to teachers,
facilitated by their befriending on Facebook, or a
result of teachers feeling they can stretch relationship with students to the extreme.
Instrumental Help: Instrumental help refers
to teacher-student relationship from the practical
point of view. Two main themes arose from both
students’ and teachers’ interviews:
•
•

Facebook is a convenient means of Web
2.0 communication for students and teachers compared to other interfaces;
Students mostly communicate with teachers via Facebook when beneficial and
sometimes will online-communicate with
their teachers about topics they would not
do so otherwise.

Facebook is a convenient means of Web
2.0 communication for students and teachers
compared to other interfaces. Due to its unique
characteristics – e.g., enabling both synchronous
and asynchronous traits, and allowing a rich
interface and various features – Facebook is
first and foremost a convenient means of communication. Both students and teachers find this
convenience a great opportunity for broadening
communication between each other and making
it more efficient. Students’ responses related to
this theme emphasize that mere communication
improvements might be a good enough reason to

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Teacher-Student Relationship in the Facebook Era

enrich the correspondences with their teachers
using this new media:
I think it’s another pretty good means of communication with the teacher, that you don’t have
to call at certain hours3… And it’s not like on the
phone, that it’s, she’s in the middle of something,
and it’s bugging her. If she’s connected, then she’s
probably at home and not elsewhere. (NA, F:15)
There are cases in which teachers give homework...
and not all students succeed... and if it was possible to communicate with the teacher not only
by phone... because the hours are not always
convenient for them, then you could ask a question
on Facebook, in a place where everybody sees it,
and then you could solve the problem and also
help everyone solve it. (YG, M:14.5)
Facebook allows fluent interaction with teachers for practical usage. This has impact on the way
students perceive teachers, showing that teachers
care about their students. The immediacy of the
messaging on Facebook is an incentive for using
it, rather than other online school system or even
face-to-face communication. Also, when all else
fails, Facebook is perceived as the most likely
alternative for practical interaction, as mentioned
by one of the students:
I don’t talk to him on Facebook. I do it only if I
want to send him a paper or something that we
don’t succeed in sending by e-mail. Mostly the usage of Facebook is for transferring messages and
papers… or if you can’t send e-mail. (ES, M:13.5)

look for each one individually for writing her
or him, and now, say I want to hold a meeting,
or if someone from the class want to let know of
something that happened to her or him, they can
do it by one click, it’s nicer and more accessible
(EH, 54:F)
Students mostly communicate with teachers
via Facebook when this is beneficial for them
and sometimes will online-communicate with
their teachers about topics they would not do so
otherwise. Students seem to choose Facebook to
interact with their teachers mainly for practical
purposes: It is a popular Web 2.0 interface, used
within and mostly outside educational context.
Usually they confine their interaction with their
teachers to practical information, e.g., messages,
clarifications, and are very particular in their scope
of communication:
This teacher uploads the homework [to Facebook],
so it’s easier for me, so I added her [as a Facebook
friend]. (BA, M:15.5)
I think [Facebook communication] is good only
in cases in which students want to talk with teachers about social problems, or about the material
taught, or such, but generally, I don’t see any
special reason for it. (YA, M:17)

From the teachers’ point of view, Facebook
allows different types of communication channels
and unique features for broadcasting information;
hence, teachers find Facebook efficient for keeping their students updated:

Communication via Facebook has the advantage of being timely, mostly when utilizing
synchronous communication. But more than being
convenient, teachers reported using the chat (instant messaging) option for private conversations
with students, which would not have occurred
otherwise, had they not been online via Facebook.
For example, students’ chats about seating arrangements, or issues regarding personal problems in
school and beyond, as portrayed by the following
example of one of the teachers:

One thing that I really like is the group that I
created for my class… Before that, I needed to

I was contacted by a student with Anorexia… I saw
her in the hallway after the school vacation, and

158


Teacher-Student Relationship in the Facebook Era

I told her how good looking she was and that’s
it. Later that day, I’ve got a friendship request
from her on Facebook, and she contacted me via
the chat and told me why she was looking good
and all that went on with her life and that she got
hospitalized, and it became a very personal correspondence… I think that it was more easy for
her that way [rather than face-to-face](EH, 54:F)
Conflict: The conflict axis refers to negative
teacher-students experiences. Three main themes
are evident from both student and teacher interviews regarding this axis:
•
•
•

Web 2.0 environments, specifically SNS,
can generate conflictual situations.
Conflictual situations are usually discussed
via private communication channels;
Conflictual experience in real-life is translated to Facebook communication, and
vice versa.

Web 2.0 environments, specifically SNS, can
generate conflictual situations. Even the mere
concept of “friendship” between students and
their teachers via Facebook is conflictual. As
stated earlier, Facebook friendship can only be
established upon agreement of both sides. Students
do not connect on Facebook with teachers with
whom they have conflicts in real-life (demonstrating again that the physical world and the virtual
world are intertwined):
There are a few teachers that I don’t like, [they
are] annoying, […] I don’t hate them, but I also
don’t want to be in any connection with them.
(ES, M:13.5)
When invited to be their teachers’ friends,
some students prefer to decline due to privacy
issues; notwithstanding, these situations are rare
– almost always students were the initiators of
Facebook friendship in our study. Teachers who
share their private life with their students in class

handle Facebook communication similarly, while
teachers who keep students out of their private life
do so also when communicating via Facebook.
Students’ privacy is being kept by teachers, with
limited interventions based on teachers’ judgment.
Interestingly, most of our interviewees (students
as well as teachers) specifically mentioned that
the teachers’ private life is to be kept private. For
example:
Students would not want to add their teachers...
because then they would know stuff about them...
I have no need to know what teachers are doing
in their private life. (YG, M:14.5)
When befriended, student-teacher Facebookmediated communication might create conflicts
that would not occur otherwise. As one teacher
(who is intentionally not connected to his students
via Facebook) expressed it:
In some way, it will… bring me into situations
and dilemmas that I don’t want to be part of.
Say, hypothetically, ok? A student is posting a
status: ‘I feel bad, I’m thinking to commit suicide’,
something like that… Now, I see it, as a teacher
and as a home-class teacher. Should I respond?
Shouldn’t I? Should I take it seriously? Not to
take it seriously? Shall I contact the student’s
parents? And if he was just kidding, then from
now on – every time one of my students is posting about what she or he is going through, do I
need to contact them personally? Should I react
at all? (EF, 28:M)
As opposed to the Satisfaction themes, in which
strong links between real-life and SNS behaviors
are evident, when teachers encounter conflictual
situations via SNS, they may act very differently
than had these situations been encountered in
school. For example:
From time to time I do see things that they [the
students] write… but I ignore… I do so for all kinds

159


Teacher-Student Relationship in the Facebook Era

of things, I smooth over it… They write that they
were cheating on the exam. You know, there are
dilemmas: What to do with it? Not to do with it…
Someone wrote ‘I sneaked out of last class today’.
If it happened in the real-world, I couldn’t ignore
it. If I heard two students talking in the hallway
about how they cheated in an exam, I should have
reported it to their teacher, I didn’t have a choice.
[Q: So, what’s the difference?] Even if I heard it
in the hallway, I would prefer not to hear it, and
here [on Facebook] I have the option not to hear
it (ER, 39:M)

In addition to these public vs. private channels, a third popular mode of communication
used variously by students and teachers is group
interfaces, most commonly exhibited either by a
Facebook Group or a group Chat. Both teachers
and students are aware of the public vs. private
continuum with regards to communication with
each other, and know how to cope with conflictual
situations, either by restraining their communication or by shifting to a more private space. The
following quotes demonstrate these strategies
among teachers and students:

Sometimes I choose not to respond, because,
you know, they are kids, they have all kinds of
nonsense between them, so I sometimes choose
not to respond… (EH, 54:F)

First, there was a group to our class, and then,
because they wanted to join our teacher, they say:
‘Well, we shall open another group’… to say what
we [really] want while in the first group we will
inform about cancellations and such so she [the
teacher] could see it. It was like a lie, to open the
new group. (NA, F:16.5)

Conflictual situations are usually discussed via
private communication channels. We found that
students selectively use communication channels
offered by Facebook for conveying conflictual topics in their communication with their teachers. In
particular, the Wall and one-on-one Chat components are seen as mere opposites: while the former
seems to be perceived as “public” by students, that
is, generally accessible to every Facebook friend,
the latter enables private communication hidden
from Facebook friends that were not specifically
chosen for the communication. Consequently,
teachers and students perceive conflictual communication in student-teacher relationships as
suitable for private communication channels but
not for public ones. This was mentioned by one of
the students who described an incident involving
viewing a teacher’s Wall after searching for her
profile on Facebook:
One teacher wrote to another teacher on her Wall:
‘I’m back from school, I have a headache’, and
the other teacher replied: ‘It must be [a certain]
class’, and they continued… You see, they didn’t
use the Chat for that discussion. (YG, M:14.5)

160

I signed-in to my teacher profile, then began a
group chat, and they [the students] always write
there: ‘[The teacher] is listening’ or ‘[The teacher]
is looking’ and ‘Talk nicely’… [The students]
know that once they open a group chat, I can see
it too. They can open a private chat without me.
(MY, 35: F)
Conflictual experience in real-life is translated
to Facebook communication, and vice versa. We
encountered strong and explicit mutual influences
between the virtual Facebook space and realworld experiences. Students indicate that access
to information about their teachers’ lives may be
abused in real-life setting. Also, they were afraid
of teachers using such conflictual information
against them. For example:
It might hurt, I’m sure it might… If a student is
exposed to what the teacher is doing [during
times out of school], he might use it against the
teacher. (YA, M:17).


Teacher-Student Relationship in the Facebook Era

It depends on the kid, whatever he shares on
Facebook and the content that he uploads. I’m not
sure that he would like his teachers to know what
he’s doing during his free time, as they’ll use it
against him in school. (RGA, F:17.5)
Overall, our findings suggest that Facebook,
being a popular SNS, is a unique means of communication, very different from face-to-face as
well as other online communication channels.
However, there are strong and rather complex
links between teacher-student relationships in the
real-world and on Facebook.

CONCLUSION AND
RECOMMENDATIONS
This chapter focuses on teacher-student relationship in the secondary school. Hence, it is
important to emphasize the lack of research of
secondary school students. In the beginning of
2010, Hew (2011) reviewed 539 articles reporting
on empirical studies about students’ and teachers’ use of Facebook. With no exception, all of
the reviewed articles which presented empirical
studies, included participants from higher education institutes (either colleges or universities),
and none focused on high-school students. As
Koles and Nagy (2012) concluded, there might
be a big difference between teenagers and young
adults with regards to the effects of SNS use on
school-related measures. Reviewing more recent
studies (published since 2010), as well as extending our scope to empirical studies published
pre- and post-2010 that discuss SNS other than
Facebook, we could still barely find works discussing SNS-mediated student-teacher relationships
(as opposed to pedagogical uses mentioned in
some publications) in secondary schools. This
emphasizes the need for more empirical research
within these populations, which are the ones that
are mostly influenced by national, federal or state
policy makers’ and regulators’ decisions.

Indeed, teacher-student communication (mainly when referring to secondary-school students)
via SNS is still highly controversial. With only
little research on this topic, policymakers tend
to emphasize disadvantages rather than benefits,
often leading to banning teacher-student relationships via SNS. Overall, our study suggests that
teacher-student relationships via SNS are mostly
a reflection, sometimes a magnifier, of teachers’
attitudes, beliefs and educational views regarding
their role, in terms of instruction and interpersonal
interactions with their students (cf. Callaghan
& Bower, 2012). Hence, teachers leading close
relationship with their students benefit greatly
from continuing connections beyond school hours;
so do their students. Communicating via online
environments might be seen as communicating
out of class. In this sense, our findings well fit
into the literature on out-of-class communication
between teachers and students. Findings indicate
that out-of-class communication is positively
correlated with faculty perceptions of instructor
empathy and relational topics, concentrating on
how different types of relationships bring about
different states of affect and involvement; this
mostly concerns class-related topics (e.g., Nadler
& Nadler, 2000; Nadler & Nadler, 2001). These
findings well resonate with our findings regarding
the Satisfaction and Instrumental Help axes. Since
evidence shows that out-of-class communication
between students and teachers promotes student
satisfaction and motivation to learn (cf. Jones,
2008), SNS as a means of out-of-class communication might also contribute accordingly. All in
all, out-of-class communication is not mandatory
for teachers who prefer a more distant and formal
relationship with their students; for such teachers,
banning teacher-student SNS-based communication is irrelevant.
Moreover, teacher-student communication may
prove to be beneficial for teachers and students
that share close and supportive relationship online,
while in class they may not share such closeness.
This is true mainly for adolescent students that are

161


Teacher-Student Relationship in the Facebook Era

more likely to use Web 2.0 interfaces, specifically
SNS, as substitutes for interpersonal relationships
in school, in alliance with the social compensation theory (Zwica & Danowski, 2008). Hence,
banning teacher-student SNS interactions may
hinder teacher-student communication for some
of the more introvert students altogether.
Our findings support previous studies indicating on the critical role of teachers’ educational
beliefs and internal factors in their technology
integration practices, specifically Web 2.0-based
platform (Ertmer, 2005; Ertmer, OttenbreitLeftwich, Sadik, Sendurur, & Sendurur, 2012;
Sang, Valcke, van Braak, & Tondeur, 2010;
Windschitl & Sahl, 2002). These findings might
have implications on the study of student-teacher
relationships in general.
With regards to the student-teacher Facebook
relationship, our study suggests that students lack
interest in their teachers’ personal lives, and do
not wish to expose their own personal experiences to their teachers. In addition, Facebook
is perceived by both students and teachers as a
convenient Web 2.0 communication tool, hence its
popularity. However, as popular as it is, students
see Facebook as a social platform designed for
youngsters; hence teachers are not welcome. This
is in line with previous studies suggesting that
teenagers find SNS suitable for keeping a desired
self-representation that is often to be shared only
with “real” friends and not with adults (family
members included) (e.g., Brandtzæg, Heim &
Kaare, 2010; Durrant, Frohlich, Sellen & Uzzell,
2011; Livingstone, 2008). Therefore, communication with teachers is limited to cases in which it
is most beneficial for students. Only in rare cases
students demonstrate a rich, Facebook-mediated
connection with an exceptional teacher, thereby
emphasizing the importance of the human factor
rather than technology itself. Hence, SNS-based
teacher-student communication can contribute
to students’ interpersonal relationships and to
higher levels of trust and intimacy, possibly
leading to better student-teacher relationships

162

altogether (e.g., Sheer & Fung, 2007). However,
when using this communication channel, students
expect their teachers to exhibit Web 2.0 skills (as
expected in 21st century education) in the ways
they approach their students, e.g., synchronous vs.
asynchronous communication, public vs. private
channels, etc. – just like students master Web
2.0 skills and practice them when using SNS in
general and Facebook in particular. Therefore,
considerable change might be needed in the technological toolbox of knowledge and skills which
teachers are expected to master in the knowledge
society (Lankshear & Knobel, 2006; Leu, Kinzer,
Coiro, & Cammack, 2004; Mioduser, Nachmias
& Forkosh-Baruch, 2008).
Hence, one potential obstacle for teachers
attempting to use SNS is the lack of sufficient
technological literacy. Indeed, SNS are complex
Web 2.0-based platforms requiring teachers and
students to utilize both ICT skills and social
skills. There is a clear need to supply teachers
with elaborate technological skills for using Web
2.0 SNS, as expected by their students. Assisting
teachers with the needed IT skills for using SNS
may therefore prove to be as important to their
teaching agenda as supporting their instructional
strategies.
As Davis (2003) emphasized, knowledge of
student-teacher relationships is often embedded within knowledge of a particular underlying approach (for example, motivation studies,
being mostly interested in teachers as effective
instructors, define “good” student-teacher relationships as those that support motivation and
learning in classroom). Our study suggests that
the technological Web 2.0 media through which
these relationships are facilitated (e.g., SNS) may
also be a critical component of teacher-student
relationships. Hence, the need for proficiency in
this media is crucial for teachers.
Dilemmas are an integral part of an educator’s
daily practice, within the classroom as well as in the
digital world. However, educators engaging in SNS
might be exposed more often to conflictual situa-


Teacher-Student Relationship in the Facebook Era

tions that may challenge the standard dichotomy
of teachers vs. students that many teachers rely
on as their source of authority in the classroom
(Maranto & Barton, 2010). That said, SNS, or
the Internet in general, should not be blamed for
the erosion of teachers’ authority; moreover, administration support regarding conflict-handling
by teachers is necessary for optimal educational
solution (e.g., Cothran & Ennis, 1997; Jaimeson
& Thomas, 1974).
The solution of banning SNS-based communication, already taken by some policymakers, is
potentially destructive to the education system, as
it might negatively affect the way teachers believe
that the system offers support for them and for
their students; moreover, it may send a message
to students about the degree of trust and support
of the system to its teachers. The alternative solution is promoting and supporting an effective
use of SNS, as pursued with 21st century skills in
general, which Ministries of Education worldwide
implement and promote via national programs.
Students, educators, parents, and policymakers
should be well aware of the risks and challenges
with which they are faced by using SNS. We argue
that teacher-student relationships in the SNS-era
may have bearing on additional aspects of the
school milieu, e.g., teaching and learning, achievements, and parents’ involvement in schools. We
strongly recommend further research into these
aspects, providing policymakers with insight on
these issues, which will support evidence-based
decisions.

Recommendations
Recommendations include multi-level action,
regarding research, policy, and application (i.e.,
students, teachers, and pre-service teachers).
Research: (Table 3) We live in an era in which
the landscape of our digital culture is constantly
changing. Web 2.0 tools change our conceptualization with regards to social settings, educational
setting being a private case of a social milieu

(Boyd, 2012). This complex phenomenon requires
extensive research. Moreover, the necessity of a
research community focusing on SNS in K-12
education is evident, as these platforms are growingly becoming popular by students worldwide.
Specifically, research should be focused more
on educational uses of SNS that are not merely
pedagogical, but rather social and emotional. We
claim that the well-being of the students may
play a vital role in their academic performance;
hence, emotional and social aspects of the educational milieu, whether in school or online, via
SNS, should be extensively studied, attempting to
broaden the body of knowledge regarding teacherstudent relationship.
Policy: (Table 4) Policy needs to lean on extensive evidence-based research, as the use of SNS
for professional educational purposes and lifelong
learning is growing. The term “communities of
practice” (CoPs) is being replaces by “networks
of practice” (NoPs) (Ranieri, Manca & Fini,
2012). Many teachers are well into SNS usage,
just as their students are. Closing this channel of
communication, which is a convenient means of
interaction in cases which both parties communicate willingly, is the easiest path for coping with
this new media. However, it may lead to using
ineffective alternative platform, as well as superficial communication, rather than coping with
the most salient digital interfaces currently being
used worldwide. More importantly, it might harm
teachers and/or students who find it empowering.
Practice: (Table 5) Implementation of SNS in
education is complex, as it reflects a shift from
the limited face-to-face (or online) hierarchical
relationship to a medium that offers opportunities for innovative, engaging and effective learning, conveying a message of novel pedagogy
(Conole & Culver, 2010). Hence, teachers and
students need to be prepared for this new media.
First, they need to be proficient SNS users, i.e.,
knowledgeable in utilizing the network of their
choice for communicating and learning, as well
as familiar with general practices of privacy

163


Teacher-Student Relationship in the Facebook Era

Table 3. Do and don’t recommendations
Do Recommendations

Don’t Recommendations

Research on emotional as well as social aspects of SNS
implementation in education is highly needed – do it.

Do not restrict to pedagogical aspects of SNS implementation in
education.

Studies should include SNS usage within school context and
beyond, as student-teacher relationship is not restricted to physical
boundaries.

Do not use only one type of methodology – pursue the issue
of teacher-student relationship in the SNS era, which is in high
need of additional research evidence, with qualitative as well as
quantitative data.

Table 4. Do and don’t recommendations
Do Recommendations

Don’t Recommendations

Base policy decisions on evidence-based research.

Do not ban SNS-based communication: this is potentially
destructive to the education system.

Develop a liberal policy enabling teachers to act upon their
educational beliefs.

Do not attempt a policy of “one size fits all”: policy should reflect
a pluralistic view of SNS implementation in education.

Trust teachers with decision-making regarding their relationship
with their students – just as they are trusted with teaching them.

Table 5. Do and don’t recommendations
Do Recommendations

Don’t Recommendations

Encourage teachers to develop their ICT literacy, including skills
and competencies, in order to use SNS wisely.

Do not lose boundaries to a point of chaos: teachers are teachers,
not their students’ “buddies”.

Make informed decisions: achieve proficiency as SNS users,
knowing to utilize SNS for communicating and learning, as well as
in privacy issues and data usage

Avoid discussing conflictual situations in public communication
channels – private vs. public channels of communications have
different purposes.

Begin practice in pre-service teacher education.

Do not lose your students’ respect: they trust you!

and data use (Pybus, 2013; Watson, Besmer, &
Lipford, 2012; Wisniewski, Lipford, & Wilson,
2012). Teachers should be supported by a wide
safety-network (so to speak), enabling them the
freedom to interact wisely and efficiently with their
students. In-service training within this context
should exceed instrumental modes, focusing also
on ethical, social and cultural issues, as well as
on actual real-life dilemmas.
The teacher may be considered an agent in the
social-personal interaction with the student, exercising the ability to exert control over the students’
lives in proactive engagement (Fox, 2010). SNS
enable the learning about others through reciprocal exchange of ideas and thoughts as well as

164

reflecting on current issues, to begin with within
the pre-service milieu. This in turn may lead to
modification of the teaching practice altogether.
When referring to paradigm shift, this is a desired
type of change. Hence, pre-service teachers should
also experience formal as well as informal SNS
uses under supervision and training conditions.
This new line of training will give back pre-service
teachers, when becoming teachers, the power to
practice diverse modes of interaction with their
students, including extending it beyond school
hours or physical space, upon wishing to do so.
Their actions will be based on informed decisions, conveying a message of trust and respect


Teacher-Student Relationship in the Facebook Era

as beginning teachers when entering a modern
and renewed education system.
Awareness to the challenges posed by social
media to the education milieu is of great importance. Notwithstanding, teachers should be given
some space in deciding about utilizing SNS for
interacting with their students. This is similar
to deciding about closeness and intimacy with
students in “real life”, within school and beyond.
This chapter emphasizes the need for further research in this area, aimed to assist policymakers in
decision-making regarding SNS implementation
in educational settings. SNS may support teachers’
professional development, as well as students’
academic, social and emotional advancement, as
well as facilitate a more pluralistic educational
approach worldwide.

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KEY TERMS AND DEFINITIONS
Educational Policies: Rules and regulations
usually bestowed by the government (federal or
state), as a means of achieving norms and standards
within the education system. These regulations are
usually phrased by policymakers and published by
officials in Ministries of Education in each country.
Facebook-Era: The era beginning in the mid90s of the former century, in which the emergence
of Facebook created new modes of interaction
as well as new social opportunities between its
members; this, in turn, created new challenges in
the field of education.
ICT: Information and Communication Technologies, including local as well as online interfaces applications and platforms.

Paradigm Shift: A fundamental change,
in which new paradigms emerge as a result of
a situation in which a current paradigm cannot
meet the demands of society. Consequently, new
assumptions, concepts and practices that shape
our views of reality emerge.
Secondary School: The second and third
common schooling periods that follows elementary (primary) school and followed by
higher-education. Grades and ages of children in
secondary schools vary in different countries, but
usually include 7th to 12th grades (approx. 13 to
18 years old).
Social Networking Site: A site in which the
main purpose is to build and foster relationships
between its users. This is usually achieved by a
user setting-up an online profile and connecting
to other users’ profiles, hence creating her or his
online social network. Content and communication features vary heavily between these sites,
and may include text, documents, images, videos,
games, and so on.
Teacher-Student Relationships: Any interaction, verbal or non-verbal, between teachers
and their students, whether in school or beyond
school hours.

171

Teacher-Student Relationship in the Facebook Era

APPENDIX
Table 6 shows the inventory used to assess teacher-student relationships.
Table 6. Teacher-student relationship inventory (TSRI) (Ang, 2005)
ttem

Dimension

1

Satisfaction

2

Instrumental Help

3

Satisfaction

Original Item
I enjoy having this student in my class
If the student has a problem at home, he/she is likely to ask for my help
I would describe my relationship with the student as positive

4

Conflict

5

Satisfaction

This student frustrates me more than most other students in my class

6

Instrumental Help

7

Conflict

I cannot wait for this year to be over so that I will not need to teach this student next year

8

Conflict

If this student is absent, I feel relieved

9

Instrumental Help

If this student needs help, he/she is likely to ask me for help

10

Instrumental Help

The student turns to me for a listening ear or for sympathy

11

Conflict

12

Instrumental Help

13

Satisfaction

I am happy with my relationship with this student

14

Satisfaction

I like this student

If this student is absent, I will miss him/her
The student shares with me things about his/her personal life

If this student is not in my class, I will be able to enjoy my class more
The student depends on me for advice or help

Taken from https://www.facebook.com/legal/terms [accessed November 21, 2013].
According to Pew Internet and American Life Project, Tech usage over time. Available at: http://www.pewinternet.org/Trend-Data(Teens)/Usage-Over-Time.aspx [accessed November 21, 2013].
3
It is a common practice in Israel for teachers to let their students call them on the home phone or mobile phone during pre-defined times
in the afternoon/evening.
1
2

172

173

Chapter 8

Examining the Opportunities
of Social Networking Adoption
in the Health Care Systems
Peldon
Curtin University, Australia

ABSTRACT
Social Network Sites (SNSs) are known for providing the opportunity to quickly spread information
faster than any other mode because of its ease of accessibility and ability to reach wider populations.
The purpose of this chapter is to examine the opportunities of adopting Social Networking (SN) in the
healthcare systems. Based on the current literature review, using a social network will enhance communication, collaboration, connection, coordination, and knowledge sharing. The healthcare profession
of Bhutan undertook the survey for this study. Three new factors were generated from this study, namely
4Cs; it was found that the use of social networking enhances communication, coordination, collaboration, and connection with patients and among healthcare professionals. The second factor, Green and
Sustainability, social networking enables the reduction of the carbon footprint, and the third factor is
Exchange Knowledge via use of social networking.

1. INTRODUCTION
Social networking is commonly known as a platform on which social networks or social relations
are built among people sharing similar interests. It
is also defined as a public sphere wherein millions
of people around the world interact and socialize.
It enables people to become a part of a virtual
community by sharing opinions, ideas and informations with people having similar interests. The
rapid rise in social networking sites (Livingstone

& Brake, 2010) has shifted industries’ focus to
online communities through dedicated websites;
moreover, it has been integrated into their daily
practices. Similarly, it has also introduced freedom
and flexibility in the workplace by providing a diverse range of new ideas, viewpoints and opinions.
SNSs are designed to provide information
and education pertaining to health care issues,
diseases, treatments and medications. Some of
the common advantages of SNSs include: ability
to freely communicate; and improved coordina-

DOI: 10.4018/978-1-4666-7262-8.ch008

Copyright © 2015, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.


Examining the Opportunities of Social Networking Adoption in the Health Care Systems

tion, collaboration and cooperation. SNSs can
also enable users to better understand information
and enrich their knowledge. As a green initiative, the use of SNSs will help to reduce carbon
emissions and contribute to sustainability and
green awareness. Although there are several risk
associated with use of social networking such as
data integrity, information security and privacy
but this chapter focuses only on opportunities of
adopting SN. However this study also explores,
the attitudes towards social networking adoption as
one of the modes for communicating with patients
and among health care professionals themselves
in Bhutan. This study was carried out using the
qualitative method and the survey questionnaire
was distributed and collected using an online
survey tool.
The chapter is organized as follows: section 1
presents brief introduction on social networking;
section 2 covers the social network aspects; section
3 discusses the methodology, which is followed
by analysis and finally it presents the conclusion.

2. SOCIAL NETWORK ASPECTS
Social networking is a platform for building social networks or social relations among people
sharing similar interests. Social networks can be
profit-based sites that encourage people of similar
backgrounds and similar interests to initiate relationships with one another (McLennan & Howell,
2010, p. 12). Social networking is a public sphere
in which millions of people around the world interact and socialize in an open environment (O’
Bien & Torres, 2012). Boyd and Ellison (2008)
stated that a social network is a web-based service
which allows individuals to establish a public or
semi-public profile depending on users’ interests.
It allows users to share, interact and communicate
with anyone with whom they share a connection.
SNSs are used mostly for the purpose of remaining
connected to people who are already part of the
user’s social network. It is also said that SN is on

174

the rise and continuing to expand. According to
Boyd and Ellison (2008)
Social networking is defined as web-based services
that allow individual to “(1) construct a public
of semi-public profile within a bounded system,
(2) articulate a list of other users with whom they
share a connection, and (3) view and traverse
their list of connections and those made by others
within the system” (p. 211)
Grabner-Kräuter (2009) mentions that the
Web 3.0 is a term used to describe advanced
Internet technology such as social networking,
blocks and wikis and it is said to have more collaboration among Internet users than traditional
Web. SNS enables people to become the part of
a virtual community by sharing opinions, connect
and socialize with people of similar interests (O’
Bien & Torres, 2012). O’Brien (2009) states that
SNSs and powerhouse in social networking media
sell ads targeting individual users based on data
gathered from their personal information. SNSs
enable the public to be active and selective by
making information available online and making
it easy for them to compare and decide based on
their requirements.
SNS provides an opportunity to quickly spread
real-time information (DeAngelis, 2011; Erica,
Lorincz, & Dahlweid, 2012) and at the same time it
delivers the platform that helps businesses to seize
opportunities (Bonchi, Castillo, & Gionis, 2011).
According to Bonchi et al. (2011), networking
is the key to employee excellence as it increases
the opportunity of having multiple advisors and
mentors. McLennan and Howell (2010) state that
SNSs have also changed the way people communicate, interact and respond mainly in terms
of professional and personal relationships. It also
helps to increase mobility by enabling people to
interact, share information and freely discuss any
topics with one another. Information spread faster
through SNSs than through a real-life network


Examining the Opportunities of Social Networking Adoption in the Health Care Systems

since digital information can be easily copied
and searched.
Nowadays, almost everyone has integrated the
SNSs into their daily practices (Boyd & Ellison,
2008; McLennan & Howell, 2010). SNSs have several technical features such as a friend list, profile
display picture, chat feature and messages. It is
very easy to open an SNS account; all it requires
is the completion of forms containing a series of
questions. The user has the right to control the
visibility of his/her profile in many SNSs, although
there are a few exceptions. It also has a feature
for leaving private messages, posting comments,
photo, video sharing and instant messaging (Boyd
& Ellison, 2008). SNSs are accessible by anyone
with an Internet connection and one can easily
join a site simply by providing basic information
such as name, age, and location; moreover, the
user can control the visibility of his/her profile by
protecting the access to it. The following section
mainly discuses on the opportunity of using SN
in health care system.

2.1 Opportunities of SNS
As mentioned by Rooney (2009), the main advantage of a technology-driven health care system
is the ready availability of online blogs, videos
and podcasts which convey and share health care
messages similar to the traditional way of educating a patient which was possible only during a
personal consultation with a doctor. Technology
has made it possible for doctor and patient to
maintain a professional relationship beyond the
office or clinic (Rooney, 2009). Weblogs, instant
messaging and video chats are reengineering the
way that health care professionals and patients
interact (Hawn, 2009). SNSs are a powerful and
cost-effective tool that most organizations utilize.
The benefits to be gained by using SNSs also
depend on competent Internet and media literacy.
Although there are several opportunities of
using SN; this study generated new factors which
has been named as 4C: which stands for commu-

nication, collaboration, coordination and connection. The advantages of SNSs include: ability to
freely communicate; and improved coordination,
collaboration and cooperation with patients’ and
among health care professionals. Green and
Sustainability as a green initiative, the use of
SNSs will help to reduce carbon emissions and
contribute to sustainability and green awareness.
Exchange Knowledge SNSs can also enable users
to better understand information and enrich their
knowledge by sharing it through social media. The
major benefit of using SNSs has been explained
more in details in following part.

2.1.1 Communication
Communication takes place when individuals or
groups share the same communication codes or
motives, values or goals. Communication can be
done through different modalities such as email,
instant message, blogging and photo and video
sharing (Krauter, 2010). These modalities can
enrich communication and improve quality of services as a result of better communication (Hawn,
2009). SNSs can support real-time communication
and quickly spread information using multimedia
formats both in the form of audio and visual media; moreover, it can disseminate information and
facilitate communication globally. Social media is
a relatively new tool with which to communicate
with peers, family, friends, colleagues, patients and
potential customers (Pike, 2011). The study done
by Huang and Dunbar (2013) showed that using
social media as a two-way communication channel
is a much more effective means for hospitals to
connect to their clients/patients. It is a promising and universal platform enabling health care
professionals and patients to easily share medical
information; it also helps patients to receive faster
service and doctors can timely check their patients’
feedback. SNSs provide the opportunity of delivering significant and emotional support to patients
even though they might be geographically distant.
SNSs enable effective communication through a

175


Examining the Opportunities of Social Networking Adoption in the Health Care Systems

number of channels such as instant messaging,
video chat, and email. Furthermore, social media
have now been embedded in communication for
marketing purposes.
The digital age has changed the way we
communicate by making it possible for open
communication which strengthens relationships
and helps to overcome geographical boundaries
(Crockett & Gerhart, 2013). Liang, Barua, Lu,
Lin, and Shen (2012) states, SN become a primary
communication platform in the near future due
to its ease of use and accessibility. Social media
has been effectively used to address issues and
engage stakeholders due to its speed and extensive reach. It has become part of daily routine for
almost everyone.

2.1.2 Connection
The social networking platform has enabled
individuals and organizations to connect in new
ways (O’Brien, 2009). The creation of descriptive
personal profiles and linking with other members
has become common practice in SNSs, particularly
since the online world is now regarded as a social
space. Remaining connected via SNSs provides
several benefits in terms of offering people emotional and financial support, and providing information about jobs and the world at large. SNSs
also display a network of connections other than
names and photographs as integral information
about users (Donath & Boyd, 2004). SNSs connect people with people and it is also said that this
connection comes first before any conversation via
social media. The context of connections such as
their company, social status, political beliefs and
musical taste helps to provide information about
the users. Also, knowing that someone is connected to people you already know and trust helps
to establish trust in a new relationship. Through
the public display of connections, it will be easier
to identify the user (Donath & Boyd, 2004) and
there is less possibility of faking an identity. Fur-

176

ther, for those patients who are not comfortable
talking about their illness during face-to-face
consultation, social media is a convenient platform
(Britnell & Crowell, 2011). SNSs interconnects
people and so their health is interconnected also
(Smith & Christakis, 2008). People with mental
illness often refuse to seek appropriate treatment
because they feel uncomfortable or embarrassed;
instead, they often seek support from a community where they feel comfortable. Such problems
can be discussed via SNSs which enable them to
seek appropriate help anonymously and remain
connected to the community (Lauderdale, 2013).
SNSs facilitate connections among people sharing similar interests, old friends, and friends of
friends, school friends, classmate, community and
employees. Overall, they help to create a network
of connections through friends, dates and business
and professional connections. Networking helps
to establish connections or a chain of connections
which is one of the main purposes of SNSs.

2.1.3 Collaboration
Currently, the most popular collaboration tools
are wiki, blogs and social networks; further,
companies are increasingly turning to online
communities as a source of information; users
belonging to a community will gain insights
and experience of that community which can
be applied to improve individual and collective
collaboration (Ransbotham & Kane, 2011). Filesharing and task-management applications help
to create a collaborative work environment and
this has a significant effect on collaborative outcomes. An SNS is an online platform developed
for the purpose of mass collaboration (Huang &
Dunbar, 2013). SNSs provide an ideal platform
for health care professionals allowing them to
collaborate by sharing ideas, experiences, medical
journal article and medical cases with each other.
This helps to produce better patient outcomes as
health care professionals can conveniently acquire


Examining the Opportunities of Social Networking Adoption in the Health Care Systems

new knowledge in real-time and facilitate faster
adoption in practice (Britnell & Crowell, 2011).
Effective communication between patient and
health care professionals enables collaborative
decision-making (Sun, Wang, & Rodriguez, 2013).
Users learn from SNSs what steps to take if they
suddenly encounter a problem (Hackworth &
Kunz, 2011). It provides a mechanism for interaction with health care professionals and patients
and for exploring new ideas regarding health, and
enables medical follow-up via SNSs.

2.1.4 Coordination
Coordination is exchange of information verbally
or in written form. Active coordination among
health care professionals reduces unusual death
and wrong treatment (Uddin & Hossain, 2010).
Wohn, Lampe, Vitak, and Ellison (2011) states
that instant messaging and internal email enables
interaction and coordination at the individual
level whereas public communication channels
like interest-related groups allows them to share
information and enables coordination and interaction. According to Yates and Paquette (2011)
knowledge and action are the result of coordinated
which is done mostly through social network
and also social medial technology is capable of
strengthening information flows and further it is
flexible to keep up to the changing needs. Effective
collaboration is possible with use of information
and communication technology (Koschmider,
Oberweis, & Zhang, n.d). Coordination enables
to share common knowledge and to share workload through social media dashboard. Similarly,
coordination also helps to increase efficiency
and accuracy.

2.1.5 Social Knowledge
Social media technology provides a platform to
share knowledge, acquire information, solve problems, enhance learning and evaluate knowledge.
Moreover, engagement is the best way to gain value

from the knowledge exchanged in social media
(Bradley & McDonald, 2011; Cross, Parker, Prusak, & Borgatti, 2001). In term of time constraints,
having unlimited access to the information will
help to promote learning and creativity that is
possible only through the social media. Patients
are often in a better position to understand more
than physicians when it comes to their own health.
The health information obtained from SNSs helps
them to monitor their health conditions and understand medical options; it enables them to improve
their quality of life through suggestions received
online. Before they go to see doctors, they do an
online search for a self-diagnosis, and after seeing
a doctor, they may seek information to evaluate
the quality of service provided by the other health
care provider (Sun et al., 2013). Nowadays, almost
everyone searches online for information either
through YouTube, blogs or posts. Online search
is done whenever people need information on
anything such as products, places, people, jobs
and services and to confirm.
SNSs grab attention by providing interactive
discussions involving facts about health, motivational and inspiring stories, and real life cases
(Woolley & Peterson, 2012), overall helping users
to acquire knowledge and improve their health
care awareness. The capability of SNSs to reach
a wide audience in a matter of seconds has been
invaluable in cases of natural disasters such as
earthquakes and tsunamis and disease outbreaks.
Twitter was a lifesaver by providing updated medical information for the treatment of chronically
ill patients in 2011 tsunami, in Japan. Likewise,
during the outbreak of H1N1 flu in 2009, the possible prevention of the disease was followed by
50,000 users, and YouTube was viewed by over
2.6 million people (Britnell & Crowell, 2011).
SNSs are the most effective and fastest means of
disseminating information; they can be used as
mediums to update professional knowledge where
attendance at professional development activities such as medical seminars and workshops is
problematic.

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Examining the Opportunities of Social Networking Adoption in the Health Care Systems

2.1.6 Sustainability
The main contributors to the carbon footprint
are the energy consumption by PCs and monitors (40%) with 23% from data centers and 24%
from telecommunication. McKinsey Consultants
found that ICT application is the highest energy
consumption industry. E-waste disposal has also
been a major factor adding to carbon emission
(Dunn, 2010). The use of technology reduces
administrative tasks of record documentation
and simultaneously helps hospitals to function
in a paperless approach (Hawn, 2009). As stated
by Dunn (2010), information and communication
technology (ICT) in day-to-day activities can
help organizations to reduce carbon footprints
and promote a better environment. The carbon
footprint can be reduced by making use of cloud
computing services, server virtualization and
proper utilization of IT devices. A study conducted
by Patton, Poorsina, and Kahn (2011) shows that
Electronic Medical Records have the potential to
reduce carbon dioxide emission by 1.7 million
tons across the United States, saving up to 1,044
tons of paper annually. This shows that, overall,
the use of the social network will reduce carbon
emissions and e-waste, thereby contributing to
sustainable and greener health care systems (McNickle, 2011). The adoption of SNSs will help to
reduce global carbon emissions and help to reduce
costs associated with paper and telephone calls.
SNSs can reduce carbon emission produced by
the unnecessary travel made to health care centers
and also reduce e-waste generated from printed
prescriptions and medical records, which can be
made available electronically.
Figure 1 shows the overview of the opportunities of SNSs.

3. METHODOLOGY
The quantitative approach is the method chosen to
carry out this study since “Quantitative research

178

examines relationships between variables, which
are measured numerically and analysed using a
range of statistical techniques” (Saunders, Lewis,
& Thornhill, 2012, p. 162). It also involves the
collection of numerical data, which allows the
data to be analysed by using mathematically-based
methods or statistics (Sukamolson, n.d). Moreover
the quantitative method is considered to be more
reliable and objective, because it reduces complex
problems to a limited number of variables and the
sample is assumed to be representative of the targeted population. This study takes the objectivist
approach and the health care professionals in the
sample are social actors for the research. The data
and information was collected through an online
survey without physically participating or being a
member of the organization under study (Saunders,
Lewis, & Thornhill, 2007). The research does not
reveal social phenomena from the perceptions and
subsequent actions of social actors. In this study,
there is no social interaction within the research
environment and the researcher does not physically
take part in analysing the details of the situation
to understand and interpret events and the reality
working behind them (Saunders et al., 2007, p.
108). Moreover, the survey strategy is associated
with the deductive research approach which is
commonly used to answer questions like ‘what’,
‘who’, ‘when’, ‘how much’ and ‘how many’. The
target population for this study was the health care
professionals of Bhutan and included doctors,
physicians, specialists and nurses.

3.1 Scope of Study
This chapter focuses on examining the opportunities associated with the adoption of SN in the
health care system. The health care professionals
of Bhutan carried out the study. However, it also
covers whether SN will assist the health care sector in Bhutan to overcome the obstacles of harsh
geographical terrain, address the acute shortage
of health professionals, and enable the government to achieve its goal of providing ‘health for


Examining the Opportunities of Social Networking Adoption in the Health Care Systems

Figure 1. Opportunities associated with social networking– prepared by the author

everyone’. Bhutan may be able to address these
issues given the advantages that the adoption of
SNS will offer to the health sector. Furthermore
this chapter ascertains more precisely the attitudes
(i.e. willingness) of health care professionals to
the adoption of SNS by the health care system.
Moreover, it also provides insight into the anticipated benefits and improvements to the health care
system that the adoption of SN will bring in an
eco-friendly context.

3.2 Data Collection Method
Data was collected using “Qualtrics” online survey
software, the survey and a participant information
sheet were distributed through the social network
forum of the Bhutanese Doctor’s Association in
Facebook; also, text messages (WhatApp, Line,
WeChat) email letter and the web link requesting
doctors to participate in the survey were posted
on the Bhutan Medical Association website. The
data gathered from the survey was analysed using
the Statistical Package for the Social Sciences
(SPSS) software of IBM, Version 21. Two tests

179


Examining the Opportunities of Social Networking Adoption in the Health Care Systems

that were used to analyse and interpret the data are
factor analysis and reliability test using Cronbach’s
Alpha. The statistical test, factor analysis test was
applied in order to understand the relationship that
exists between the health care professionals and the
use of SNSs. Factor analysis test was conducted
to examine the risks and opportunities associated with using SNSs in the health care system
in Bhutan. The target population for this study
was the health care professionals of Bhutan. The
sample size for this study was 154, and survey
was completed by Bhutanese doctors, physicians,
specialists and nurses.

3.3 Survey Results
The minimum requirement for carrying out factor
analysis was satisfied with a valid sample size of
154 out of 166 respondents. Twelve data were
screened out due to missing values. The survey
consists of three sections: Demography, Risk and
Opportunities of SNS in relation to health care
system, but this chapter will focus on the demographic and concentrate more on opportunities
section. The demography section gathered personal information about health care professionals
such as gender, age, job title/designation, SNSs
they use mostly, number of hours they spent on
SN daily and the option to see whether they are
willing to use SNS as the mode of communication
with patients. Responses to this section indicated
that 63% are willing to use, and 37% of the health
care professionals in Bhutan are not willing to use,
social networking as a way to communicate with
the patients now and in the near future. According
to the data gathered, although a huge number of
health care professionals are willing to use social
networking as a mode of communication, but few
still think that it is not a feasible and satisfactory
way of delivering services.
Some of the justification provided by the doctors and the nurses of Bhutan for their willingness
to use social networks as a mode of communication were its accessibility, popularity, trend, con-

180

venience, time and cost saving, helps to reduce
the shortage of health care workers, reduces the
burden of travelling for consultation for minor
problems, educates people on emerging health
issues and reduces hospital waiting time. On the
other hand, the reasons given by doctors and nurses
for not adopting social networking as a mode for
communicating with patients now and in the future included: possible breaches of confidentiality
and security of the information, possibility of not
taking the medical case seriously, face-to-face
consultation or personal consultation is better
than online consultation, invasion of personal
life, increased workload, requirement of physical
examination before making diagnosis, less satisfaction, it is feasible only for a few patients since
some patients are illiterate. These were some of
the reasons given to show that the use of social
networking in the health care system of Bhutan is
not possible. From the survey result it was found
that 97% of the respondents stated that Facebook
is the most commonly-used social networking
site, whereas only 4% and 11% of respondent
uses LinkedIn and Twitter respectively. Twentyfive percent of participants use YouTube which
shows that it is the second most commonly-used
SNS by the health care professionals.

3.4 Data Reliability
The purpose of reliability testing in SPSS is to
determine whether or not the variables are strongly
correlated. Alpha above 0.70 is considered as reliable and alpha above 0.60 is considered as probably reliable but alpha below 0.59 is considered
not reliable (“Reliability Analysis:Cronbach’s
Alpha,” ; Tavkol & Dennick, 2011). The Alpha
values for the opportunities was greater than .70
which indicates reliability. The alpha value obtaine for the realibility test was .941 for 154 valid
respondand; N=17 variables, shown in Table 1.


Examining the Opportunities of Social Networking Adoption in the Health Care Systems

Table 1. Cronbach alpha (opportunities) - prepared by the author
Reliability Statistics
Cronbach’s Alpha

N of Items

.941

17

4. ANALYSIS
Factor analysis is a method commonly used for
interpreting self-reporting questionnaires and used
when it is desirable to reduce a large number of
variables to a smaller set of variables (factors), and
to determine the relationship between variables
(Costello & Osborne, 2005). From the KMO and
Bartlett’s Test table for Opportunity, the value for
Kaiser-Meyer-Olkin Measure of Sampling Adequacy is .901 which is higher than the combined

variable test; this shows that variance in the data
can be explained “marvelously” by the factors. In
addition to this, the Sig. value of Bartlett’s Test
of Sphericity is < than 0.05 indicating that the
data is suitable for factor analysis and moreover,
that there exist sufficient correlations among the
variables.
From the percentage of variance, the cumulative percentage of the total variance explained for
the three factors of the Opportunities is 70.2%
which is sufficient enough to explain the variance
of the extracted factors. As the sample size is 145
for this study, a factor loading of 0.45 will be
considered significant for interpretive purposes.
After the Varimax rotation, 6 of the 17 variables
have cross-loadings with few loadings having a
lower significance as shown in Table 2.

Table 2. Factors with cross-loadings - prepared by the author
Rotated Component Matrixa
Component
1

2

Communicate with patients frequently

.853

Collaborate with patients frequently

.836

Cooperate with patients frequently

.820

Connect with patients frequently

.807

Cooperate with doctors/nurses frequently

.703

Communication with doctors/nurses frequently

.620

Provide better and more reliable service to patients

.572

.509

Provide better and reliable service to doctors/nurses

.562

.361

Reduce number of patients visiting the hospital

.553

.531

.303
.351
.388
.611

Be more green in my activities

.870

Be more sustainable person

.780

Reduce carbon footprint produced by my activities

.699

Educate patients regarding treatment and medication/medical information

.455

3

.371

.523

Exchange information and knowledge with my colleagues

.846

Exchange information and knowledge via social networking websites

.820

Collaborate with doctors/nurses frequently

.594

.604

Connect with doctors/nurses frequently

.565

.594

181


Examining the Opportunities of Social Networking Adoption in the Health Care Systems

As seen in the 6 factors above, Communication with doctors/nurses frequently, Provide better and more reliable service to patients, Reduce
number of patients visiting the hospital, Educate
patients regarding treatment and medication/
medical information, Collaborate with doctors/
nurses frequently and Connect with doctors/nurses
frequently cross-load in two factors. Cross-loading
variables were deleted one by one and then a check
was done to determine whether or not this improved
the factor’s cross-loading. The following three
factors were generated: factor 1 was comprised
of 6 variables, factor 2 of 3 variables and factor
3 of 2 variables. The highest loading variable is
Collaborate with patients frequently followed by
Communicate with patient frequently, which shows
that the use of SNSs in the health care system will
mostly benefit the service provided to patients.

4.1 Factors Obtained
The factors generated were named accordingly,
Factor 1(Table 3) comprises of the following variables and it has been named as 4Cs, which stand
for Communication, Collaboration, Cooperation
and Connection. This study found that the adoption of SNSs would enable patients and doctors
to remain in contact with one another which in
turn help doctors/nurses to provide better and
more reliable service.
Factor 2 (Table 4) consists of 3 variables as
shown below and it has been named Green and
Sustainability as this study shows that the use of
SNSs will help to contribute to green awareness
and sustainability in their activities and also help
to reduce the carbon footprint generated mostly
from travelling to hospitals and from the health
care activities.

Table 3. Factor No. 1 for opportunities - prepared by the author
Variables

Factor Name

Collaborate with patients frequently
Communicate with patients frequently
Cooperate with patients frequently
Connect with patients frequently

4Cs

Cooperate with doctors/nurses frequently
Provide better and reliable service to doctors/nurses

Table 4. Factor No. 2 for opportunities - prepared by the author
Variables

Factor Name

Be more green in my activities
Be more sustainable person

Green and Sustainability

Reduce carbon footprint produced by my activities

Table 5. Factor No. 3 for opportunities - prepared by the author
Variables
Exchange information and knowledge with my colleagues
Exchange information and knowledge via social networking websites

182

Factor Name
Exchange Knowledge


Examining the Opportunities of Social Networking Adoption in the Health Care Systems

Factor 3 (Table 5) comprises of two variables:
Exchange information and knowledge with my colleagues and Exchange information and knowledge
via social networking websites. It has been named
as Exchange Knowledge since use of SNSs help in
obtaining knowledge and moreover, enabling the
exchange of information and acting as a source
of knowledge.

4.2 Discussion
From this study, three factors were generated for
Opportunities namely 4Cs, Green and Sustainability and Exchange Knowledge. In addition to the
existing opportunities, the Bhutanese health care
professionals strongly feel that the use of social
networking will introduce frequent collaboration,
connection, coordination and communication with
the patients and similarly, provide better and reliable service to doctors/nurses.
The data gathered from the health care professionals shows that 63% of them are willing to
adopt social networking as one of the modes for
communicating with patients and among health
care professionals themselves; 37% of health care
professionals feel that it is not feasible or beneficial to adopt social networking as a means of
communication. Further, the participants strongly
agree that there is a very significant relationship
between sustainability and social networking;
likewise, one of the factors that health care professionals strongly agree about is that the use of
social networking will help to reduce the carbon
footprint, and make them more sustainable and
green in their activities.
Three new factors were generated for opportunities: 4Cs, Green and Sustainability, and
Exchange Knowledge after analyzing the data using factor analysis. The new variables and factors
are shown in bold and capital letter as shown in
the figure below. All others are the opportunities
gathered from the literature review that use of
social networking will bring. Figure 2 shows the
opportunities associated with SNS’s.

4.3 Recommendations
Following are some of the recommendations
that will help to reduce the risks and increase the
benefits associated with social networking. The
recommendations are as follows:
1. Raise awareness of the benefits and opportunities of social networking and explain
how social networking will help to reduce
the number of patients visiting hospitals,
which in return reduces the workload of the
health care professionals (Hawn, 2009).
2. Health care professionals should be provided with the necessary facilities to sustain
their interest (Berge, 1995) and motivation
to adopt social networking as a means of
communicating among themselves and with
patients.
3. Explore different ways of reaching the illiterate population, such as visual or audio display. For example, use YouTube to educate
them about health awareness issues; present
images and provide audio recordings which
can be replayed multiple times.
4. Provide training for the health care professionals who lack computer skills and the
knowledge of social networking.
5. Ensure that social networking is made a
routine part of daily activities or at least
encourage health care professionals to use
it once a day initially.
6. From the literature review (Bertot, Jaeger,
& Hansen, 2012) to obtain all the potential
benefit from the use of social networking all
the issues related to social networking must
be addressed. These should cover issues of
security, privacy, information confidentiality, data accuracy, intellectual property rights
and HIPAA compliance.
7. It is very important for organisation to have
set of guidelines and policies applying to any
form of social media(Kaplan & Haenlein,
2010). Before adopting social networking,

183


Examining the Opportunities of Social Networking Adoption in the Health Care Systems

Figure 2. Opportunities of SNSs in health care system and additional factors obtained from the survey
undertaken by the health care professionals in Bhutan - prepared by the author

all the employees including health care professionals, ICT personnel and administrative
staff should be made aware of guidelines
and policy associated with the use of social
networking and measures should be put in
place to ensure that everyone abides by them.
8. Maximize the opportunities and benefits of
social networking and minimize the risks by
being careful and cautious. Patients must

184

give their consent for their information to
be communicated online.
9. It is recommended that social networking
be adopted in stages, rather than in one go,
so that it can be improved by learning from
feedback and mistakes.
10. It is crucial to choose the right medium
depending on the purpose since different
social media has different purpose (Kaplan


Examining the Opportunities of Social Networking Adoption in the Health Care Systems

& Haenlein, 2010). The organisation adopting social media should have a proper plan
including the kinds of services they will
provide through social media.

4.4 Research Limitations
This study fails to include patients’ opinions about
adopting social networking as a mode of communication with health care professionals, since the
survey covers only the latter although it is essential
to solicit patients’ viewpoints and opinions as they
are the users of the health services. Further, it is
equally important to have participation, communication, coordination and collaboration from all
the sides, since the success of adoption of social
networking as a mode of communication depends
both on the patients’ and health care professionals’ willingness to adopt it. The research should
be conducted including health care professionals
from all the districts, Basic Health Units and
Outreach Clinics, in addition to administrative
staff and ICT staff of the hospitals. Furthermore,
it should also include patients and their families
or next of kin for the survey so that wider picture
can be obtained on the opportunities of adopting
social networking in the health care system.

4.5 Theoretical Significance
This chapter will contribute to the literature of the
evolving topic of SN in the health care system. Academics, researchers and practitioners can use the
research outcomes in relation to the opportunities
of implementing social networking in the health
care system in general and more specific in Bhutan.
This study will add new theoretical significance
to the relationship between sustainability, green
IT and SN since the use of this technology will
reduce carbon emissions and e-waste generated
mainly by printing prescriptions and medical
reports (McNickle, 2011). This research will also
provide new concepts and insights into the use of
SN in the health care system in Bhutan.

5. CONCLUSION
The popularity of SN has increased rapidly since
it has led to positive changes in the way people
interact, communicate, cooperate, exchange
knowledge and share information. SNSs can also
enable users to better understand information and
enrich their knowledge. As a green initiative, the
use of SNSs will help to reduce carbon emissions and contribute to sustainability and green
awareness. The main aim of this chapter was to
ascertain whether the health care professionals
in Bhutan are willing to adopt social networking
as a mode for communicating with patients and
among health care professionals themselves in the
future. From the survey it was found that 63% of
health care professionals are willing to adopt social
networking as a mode of communication among
themselves and with patients now and in future.
One of the new factors generated was green
awareness and sustainability, indicating that Bhutanese health care professionals agree that there is
a strong relationship between social networking
and green awareness and sustainability. Moreover, they state that social networking will make
their activities sustainable and will contribute to
greener initiatives by reducing the carbon footprint
produced by travelling to a health care provider.
Three new factors for opportunities - 4Cs, Green
and Sustainability, and Exchange Knowledge from using social networking was generated from
this study.

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KEY TERMS AND DEFINITIONS
4Cs: Stands for Communication, collaboration,
cooperation and connection.


Examining the Opportunities of Social Networking Adoption in the Health Care Systems

Communication: Communicate with patients
and communicate among health care professionals
frequently through social network.
Collaborate: Social networking will enable
health care professionals to collaborate with
patients frequently.
Cooperate: Social networking will allow patients and health care professionals to cooperating
with one another frequently.

Connection: Social networking will help them
to remain connected with patients and among
health care professionals.
Exchange Knowledge: Exchange information and knowledge with the colleague via social
networking websites.
Sustainability: Making best use of existing
resources and facilities without causing damage and preserving the resources for the future
generations.

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Section 4

Web, Technology, and Social
Influence

191

Chapter 9

We Have Good
Information for You:

Cognitive Authority and Information
Retrieval on the Web
Filipe Roseiro Côgo
Federal University of Technology at Paraná (UTFPR), Brazil
Roberto Pereira
University of Campinas (UNICAMP), Brazil

ABSTRACT
Through the concept of Cognitive Authority, information relevance and quality have been related to the
expertise/skill of those who publish and share information on the Web. This chapter discusses how the
concept of cognitive authority can be used in order to improve the information retrieval on folksonomybased systems. The hypothesis is that a ranking scheme that takes into account the cognitive authority
of the information sources provides results of higher relevance and quality to users. To verify this hypothesis, the Folkauthority approach is adopted; a ranking scheme called AuthorityRank is proposed;
and an information retrieval system, named AuthoritySearch, is built. A real social network is used to
simulate the authority relationship among users, and the AuthorityRank scheme is compared with the
tf-idf scheme using the NDCG metric. The results indicate a statistically significant improvement in the
quality and relevance of the information obtained through the use of the AuthorityRank scheme.

INTRODUCTION
The consolidation of the Web 2.0 concept favored
the production and publishing of information in
an easier way, without rigid control or verification of quality (Murugesan, 2007). Therefore, the
production of information has taken place on a

large scale that generated the so-called information overload effect (Himma 2007), demanding
new ways and mechanisms for organizing and
retrieving information (Plale, 2013).
Traditionally, the information retrieval and
organization have been handled by using two
main approaches: i) classification/categorization

DOI: 10.4018/978-1-4666-7262-8.ch009

Copyright © 2015, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.


We Have Good Information for You

schemes, which often need to be elaborated by
experts; and ii) search engines, which are based
on the automatic indexing and retrieval of documents. Currently, search engines represent most
of the systems used for retrieving information on
the Web. However, they are not yet able to address
the issue of efficiently filtering its large amount
of information.
In this sense, a third approach, combining both
the categorization and the automatic indexing approaches, has emerged over the last decade. Named
tagging (Trant, 2009), this approach is characterized by the use of terms (tags) generated by the
users themselves to both describe and retrieve
information in an information retrieval system
(IRS). Web 2.0 applications like Delicious®,
Flickr® and Technorati® employ the folksonomy
approach, a tagging-based and collaborative way
for manually indexing the information.
The Folksonomy-based systems (FBS) benefit
from the social characteristics of Web 2.0 to improve the information organization, management
and retrieval. Some of the aspects discussed by
researchers as inherent benefits from folksonomies
are the sense of community generated by the use
of the technique, the explicit opinion from a set
of users about the available information, and the
possibility to reflect, almost in real time, changes
on the vocabulary utilized to express about the
resources (Golder and Huberman, 2006, Halpin
et al., 2007, Sen et al., 2006).
In a FBS, the categorization process (Trant,
2009) — (i.e., the assignment of tags to documents
for describing it or its meaning) — is held by the
users themselves. This approach makes the quality
of categorization directly dependent on who carried it out. By quality of categorization, we mean
the use of terms that describes the information
accordingly, making sense to a group of people
while avoiding unnecessary ambiguities. Thus,
getting better results in terms of organization and
indexing of documents in FBSs depends on the
knowledge and skills of users who are performing

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the categorization, raising the issue of identifying
the cognitive authority of the sources of information. Cognitive authority determines “who knows
what about what” (Wilson, 1983), being related
to the influence caused by someone in the way of
thinking of an individual, because this individual
judges him worthy of credit and trust.
The Folkauthority approach, proposed by
Pereira and da Silva (2008), tries to bring to the
information retrieval scenario the benefits of a
social strategy people use for obtaining information in their daily activities and situations: asking
for someone who may help them. Folkauthority
considers that, as each user (an entity) is able to
publish information (i.e., each user can be a source
of information), it is also able to categorize the
competence of an information source. Therefore,
it suggests attributing tags to these entities in
order to categorize each source of information
according to their cognitive authority, generating
a meta-categorization. The purpose of such tags,
however, is not to describe what an entity “is”, but
to specify what it “knows”, in what it is a reference, trustworthy, from the categorizer’s point of
view. Therefore, when retrieving information, it
is possible to identify and prioritize the information produced/shared by those who are experts in
a given subject for the person who is retrieving
the information.
In this chapter, we consider that a ranking
scheme that takes into account the cognitive
authority of the information sources provides
results of higher relevance and quality to users.
By adopting Folkauthority approach, it is possible to improve the relevance and quality of the
results of a query by given more importance to
certain sources of information when calculating
the ranking of the retrieved information. To verify
this hypothesis, the Folkauthority approach was
adopted; a ranking scheme called AuthorityRank
was proposed; and an information retrieval system, named AuthoritySearch, was built. Thus, this
research has the overall goal to analyze how the


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Folkauthority approach can be used for retrieving information in FBSs, and its effects on the
relevance and quality of the information retrieved.
The AuthoritySearch is an IRS that uses the
AuthorityRank. We designed the AuthoritySearch
system and simulated cognitive authority grant in
this system based on data obtained from the FBS
Delicious®: a FBS that allows users to categorize
and share their bookmarks. The ranking of the
retrieved information was determined based on
the cognitive authority of entities categorized
with the query terms, using a ranking scheme
called AuthorityRank (Côgo et al., 2012). This
scheme was compared to the traditional scheme
tf-idf (Manning et al., 2008) in order to evaluate its
ability to present documents of relevance and quality in the top results. The NDCG metric (Järvelin
and Kekäläinen, 2002) was used to quantitatively
assess the results of the experiments, sorting out
the data into groups according to: i) the criteria for
the evaluation, which was divided into relevance
and quality; and ii) the ranking schemes used to
classify the submitted documents. The averages
for the NDCG values generated in the evaluation
were analyzed statistically using Student’s t-test.
This chapter is organized as follows: Section 2
presents the Folkauthority approach and its relationship with the concept of cognitive authority,
suggesting its contributions to the information retrieval process; the documents’ scores calculation
through the AuthorityRank approach is also demonstrated. Section 3 presents the AuthoritySearch:
a FBS that uses the AuthorityRank, and introduces
the architecture and the data gathering for this
system. Section 4 presents the results obtained
by evaluating queries sorted by different ranking
schemes, both considering and not considering the
proposed schema. Section 5 points out limitations
of the work and directions for future research; and
Section 6 presents our final remarks.

FOLKAUTHORITY AND
INFORMATION RETRIEVAL
In this section, we discuss the relation of cognitive authority and IR on the Web 2.0, presenting the Folkauthority approach (Pereira and da
Silva, 2008), which allows to inquiry about the
relationship between the cognitive authority of
the information sources and the quality of their
information.
Wilson (1983) proposed a theory about cognitive authority that explains the nature of the
authority that people grant to an entity that has
knowledge about a particular topic. The cognitive
authority represents the influence that an entity
can cause on another individual in order to define
“who knows what about what”. Unlike “first hand”
knowledge, acquired from one’s own experience,
Wilson’s theory is related to the “second hand”
knowledge acquisition process, in which a person
uses an entity that has knowledge in order to acquire
information on a particular subject.
The Folkauthority approach takes into account
some relevant aspects of the Wilson’s cognitive
authority theory, as follows:
1. Cognitive Authority Always Involves at
Least Two Entities: The authority (i.e., an
individual, a book, an institution) and the
individual who recognizes that authority:
it depends on the recognition (or on the
grant, as it is referenced in the context of
Folkauthority approach) by someone, as a
person can have great knowledge about a
certain subject but, nevertheless, s/he may
not be recognized by others as a cognitive
authority;
2. Cognitive Authority Is Always Related to
Some Area of Interest: An entity can be
considered an authority on certain subjects,
while on others there is not the same level
of recognition;

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3. The Cognitive Authority has Levels of
Recognition: An entity can be recognized
with great or little cognitive authority.
The discussion on cognitive authority also
has direct relationship with the relevance and
quality of the information retrieved on the Web.
Information quality is a concept that refers to the
various dimensions by which information can be
evaluated, which include the value, completeness,
validity, reliability, credibility, relevance, trust and
authority of information, among others (Rieh,
2002). There are some initiatives in the literature
relating cognitive authority with the concept of
information quality, mainly in the context of the
Web (Fritch and Cromwell, 2001; Savolainen,
2007), and that guided the definition of the criteria
used to assess the ranking scheme proposed in this
paper. To obtain details about these initiatives, see
the Additional Reading Section of this chapter.
There is also a recognized relationship between
trust, credibility and the authority of information
that is retrieved, managed, exchanged and used
on the business and enterprise context. Hosmer
(1995) states that many researchers proposed that
trust is essential for understanding managerial effectiveness and economic exchange. Poston and
Speier (2005) claim that Knowledge Management
Systems allows an efficient and effective sharing
of intellectual resources on enterprise context.
The authors examine how content ratings and
credibility indicators affect search and evaluation
process, as well as the decision performance. The
Additional Reading Section exhibits literature
related to cognitive authority issues on business
context, e.g., see Medlin (2004), Ratnasingam
(2005), Batt (2003) and Dietz (2006). From this
literature, is possible to reflect on how to integrate
the Folkauthority concept as a global framework
for identify and manage relations regarding the
cognitive authority of any business entity, such
as stakeholders of business projects, employees,
managers or, even, the own enterprises. Also,
the proposed AuthorityRank ranking schema

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contributes in presenting a model to incorporate
information retrieval features in the Folkauthority
framework.
According to Pereira and da Silva (2008), a
FBS that implements the Folkauthority approach
generates a social network formed by the entities
that grant and receive cognitive authority — a
network of authorities. This network can be modeled as a weighted directed graph, where each
node represents an entity, and where each directed
edge represents a cognitive authority relation from
a node to another; the edge may have a tag (i.e.,
label) describing the subject on which the cognitive authority is granted, and a weight defining
the level of recognition of this authority.
Figure 1 illustrates a network of authorities,
in which it is possible to observe the granting of
authority from the entity “A” to the entity “B”
(with the pair tag/weight “information, 3”) that,
in turn, grants authority to entity “C” (tag/weight
“information, 2”). Furthermore, the figure highlights the granting of authority between entities
“A”, “B”, “C” and “D” in the same subject, i.e.,
the tag “information”. This highlight represents
a chain constrained by the tag “information”.
The notion of constrain by a given tag refers to
the consideration of only the grants of authority
performed by using such tag. Thus, in Figure 1,
the highlighted portion of the network coincides
with the chain constrained by the tag information.
There are different proposals in literature for
modeling a FBS. Szomszor et al. (2008) and
Hotho et al. (2006) use set theory, such that a
folksonomy is represented by the tuple F = (U,
T, R, Y), in which three non empty sets U, T and
R denote, respectively, users, tags and resources,
and the Cartesian product Y ⊆ U × T × R denotes
the categorizations in the system. In this way,
a FBS that uses the concept of Folkauthority
can be formalized by defining A ∪ E as the set
of categorized authorities, in which the set A
denotes the authorities, while the set E denotes
the entities. The set A is a subset of E, because
the authorities are the own entities of the system


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Figure 1. Network of authority highlighting the part of the network restricted by the tag “information”

and, therefore, includes the case where A is equal
to E (i.e., all entities are authorities). The set of
authority grants can be denoted by the function G:
(E × T × A) → P, were P = {1, 2, 3, 4, 5} is the
set of weights to be associated with the authorities. Thus, the function G(e, t, a) = p states that
an entity e considers another entity a a cognitive
authority on subject t weighing p, and restricts the
attribution of a single weight p for each subject t
related to each entity a.
Among the information that could be extracted
from a network of authorities for the purposes of
information retrieval, in this work we used the
calculation of the PageRank with Priors (White
and Smyth, 2003) in order to estimate the weight
that each authority exercises in the score of a retrieved document. When a PageRank with priors
algorithm is applied to a network of authorities, a
value that synthesizes a notion of global authority
of the network can be established, considering the
network topology, as well as the weights given to
the authorities. This measurement makes it possible to answer the question “what are the most
important entities (authorities) in the network in
relation to an individual or group of individuals?”
(White and Smyth, 2003). Based on the premise
that the information categorized/provided by
entities that are considered authorities on a given
subject is more important for those who recognize
their authorities (Pereira and da Silva, 2008), it
is possible to use such a measurement in order to

differentiate the ranking of documents categorized
by authorities.
For example, consider a system that uses the
Folkauthority approach and a query containing
the terms “information retrieval interaction”. The
first step for retrieving the relevant documents is to
calculate the value of tf-idf (Manning et al, 2008) of
the document set. Then, the network of authorities
must be constrained so that their topology reflects
the granting of authority containing tags with the
terms “information”, “retrieval” and “interaction” (currently, we consider only unit terms) in
accordance with the operators expressed in the
query. This restriction enables the calculation of
the PageRank with priors value of each authority
contained on the constrained network. The next
step is to combine these values to generate a final
value that will be used to rank the documents,
taking into account, therefore, the authorities
available on the network for each subject.
For the task of information retrieval using
Folkauthority, it is necessary that the ranking of
a document reflect its importance based on the
authorities that categorized this document. Such
requirement reveals some important design issues
for an IR system that adopts the Folkauthority
approach:
1. Ranking with Folkauthority: The contribution of each authority for the importance
of a document, given a query, must be

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based on the tags that the authority used
to categorize this document. For example,
an authority in “information” may possess
documents categorized with this tag. These
documents, as discussed, will have the value
of their ranking differentiated when a query
containing the term “information” is passed
to the system. Thus, it is necessary to have
a representation that can distinguish the importance of each term used by an authority
to describe a document, so that the distinction of importance can be evidenced on the
rank of each document. It means that, the
distinction of importance is given by some
tag of the document, not to the document as
a hole.
2. Indexing with Folkauthority: There is an
issue about when to perform the calculation
of the information related to the network of
authorities (e.g., the PageRank with priors
of each authority). One alternative is to calculate them at indexing time, such that the
importance of each authority for each term
is calculated offline and, then, assigned to
the documents in a form of a boost. This
approach has the disadvantage of requiring
the boost values to be updated when the
index is altered (e.g., when an authority, a
tag or a document is added/removed from
the system). However, it offers advantages in
the efficiency of query time, since the boost
is already calculated. Another alternative
is to calculate the information related to
the network of authorities (e.g., PageRank
with priors) at query time, such that the
importance of an authority for each term of
the documents must be stipulated for each
query performed on the system. This approach eliminates the necessity of constantly
update of boost values, but can compromise
the search time efficiency.

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For the first issue, one of the contributions of
this study is the AuthorityRank: a ranking schema
that uses a social metric value about the authorities
to improve the IR task (Côgo et al., 2012). For the
second issue, we present the indexing process of
the AuthoritySearch system, designed to evaluate
the AuthorityRank schema.

Ranking with Folkauthority:
The AuthorityRank
An information retrieval system usually needs to
use an indexing scheme that allows the creation
of a ranking that sorts its documents according to
their relevance to the users’ query. In our work, the
documents should be indexed taking into account
the cognitive authority of who created/categorized
them. Therefore, we adopted an approach in which
a boost factor is given to the terms that appear in
the documents categorized by an entity that is an
authority on this term, as can be seen in Equation
1. The value of boosti,j is computed at indexing
time, based on the authorities that categorized
the document dj and it matches the value of the
authorities PageRank with priors. This calculation
is shown in Equation 1, in which pri is the value
of the PageRank for an authority ai. In addition,
ej ∈ E, tk ∈ T and pm ∈ P refer, respectively, to
the entity which categorized the ai, the tag used
in this categorization and the weight assigned to
the ai by the entity ej.
Equation 1. Calculation of the PageRank with
Priors using the network of authorities

pri =

∑

G:E j ×Tk × Ai → pm

pm × ( prj / o j )

The component prj is the value of the PageRank with priors of the entity that categorized the
authority ai, while oj is the number of authorities
categorized by ej (i.e., the amount of node ej‘s
outgoing edges in the network of authorities).
In this way, the value of the PageRank for an


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authority ai in a term tk is based on the sum of the
PageRank of the authorities that have categorized
the authority ai. When a query is prepared and
represented in the form of a k-dimensional term
vector (with k being the total number of terms of
the set of documents), the process of ranking the
information uses the boost value calculated in the
indexing time, as shown in Equation 2.
Equation 2. Calculation of the score of a document, given a query, by the scheme AuthorityRank

s j ,q = ∑ ( tfi , j × idfi ) × boosti , j
ti ∈q

For each document dj that meets the query q,
a value sj,q, called score, is calculated to represent
the similarity between a query q and the document
dj. This value is calculated based on the terms ti
used in the query q. The result of a query consists
of the documents ordered by their respective score
values. The value tfi,j represents the importance
of a term present in a document dj, and the value
idfi refers to the importance of a particular term
to distinguish the documents in the index and is
equal to the log in base 10 of the total number of
documents in corpus divided by the number of
documents containing term ti. The value of tfi,j is
calculated by counting the occurrences of term ti
in document dj. If a retrieved document has been
categorized by an entity that is not an authority
on any of the query terms, the ranking scheme
must deal with this situation, so that, nevertheless, there is a definition of importance from the
document to the query. In this work, we used the
tf-idf scheme’s value of the score.
The possibility to reorder the n first results of
a query by using the calculated score sj,q is another
issue to be considered. Equation 2 presents two
distinct components: one related to the calculation
of tfi,j × idfi, and other related to the calculation
of boosti,j. Thus, it is possible to reorder the n
first results considering these components of
sj,q. For instance, considering a set of documents

ordered by the value of sj,q, one could calculate
the value of the component Σ(tfi,j × idfi) and
reorder the documents based on this value. We
call this approach AR/TF-IDF, since it uses the
AuthorityRank to retrieve the documents and the
tf-idf to reorder the retrieved documents. In this
case, the AuthorityRank scheme (sj,q) is responsible for selecting the n documents more similar
to the query, while the tf-idf performs the task of
ordering the documents (it is different of using
tf-idf or AuthorityRank to order all documents in
the index). In this chapter, we also compare this
approach to the performance of AuthorityRank
and tf-idf for the IR task.

Indexing with Folkauthority
In an IR system that adopts Folkauthority, the
documents are indexed considering who provided
them. Thus, a boost factor is given to terms that
appear in documents categorized by an authority
in this term. This boost factor is determined by the
popularity of this authority in the network of authorities, considering the weights and tags granted
to this authority. The boost factor is calculated at
indexing time and its values are indexed together
with the terms of the documents
Figure 2 shows the boost assignment for the
terms “information” and “quality”. This figure
illustrates the situation in which it was granted
authority to a user with the tags “information” and
“quality” (weights 3 and 2, respectively). Furthermore, this user has categorized documents D1 and
D2 with the tag “information”, the documents D3
and D4 with the tag “quality”, and the document
D5 with both tags. Thus, the boost factor to be calculated for the term “information” is proportional
to the weight given to the tag “information” when
the authority was granted (which is greater than
the weight given to the tag “quality”). In addition,
the document D5 accumulates boost coming from
the terms “information” and “quality”.
An important feature that was considered in the
boost calculation is the concept of authority propa-

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Figure 2. Process of boost assignment to terms on a document

gation, which refers to the fact that an authority in
some subject can categorize another authority on
the same subject. This case is illustrated in Figure
1, in which the user “A” considers the user “B” an
authority on “information”, and the user “B”, in
turn, considers the user “C” an authority on this
same subject. In such a macro vision, the user “C”
should contribute more for the calculation of the
boost given by the term “information” than if s/
he had not been categorized by another authority
on this subject. This feature is incorporated in the
AuthorityRank schema by the use of the PageRank
with priors value as a boost factor.
In order to exemplify the authority propagation,
it is possible to revisit Figure 2 and suppose the
network of authorities is constrained by the tag
“information”. If the authority has the value of

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PageRank with priors in the constrained network
of authorities equals to 1.5 then this will be the
value for the boost of the term “information” for
documents D1 and D2 (and also the value of contribution to document D5). These values are added
to the inverted index and are related as a boost to
each term on each document. The PageRank with
priors values were normalized between 1.0 and
2.0 to account for the terms in a document that
does not have a respective authority, in which case
the boost value will be equal to 1.0.

THE AUTHORITYSEARCH SYSTEM
To evaluate our approach, we developed the AuthoritySearch system based on the architecture


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presented on Figure 3, where the orientation of
the arrows indicates the provision of services from
one module to another. In this architecture, it is
possible to observe the personomy Delicious®
Data module (the individuals’ information), responsible for providing information about users’
categorization, as the assignment of tags to documents. It provides information for the Delicious®
Data Crawling module, which is used to gather
information from this system in order to populate
the folksonomy (a collection of personomies) and
simulate the authority granting process in the AuthoritySearch. The Boost Calculation module is
responsible to calculate the PageRank with priors
from the information gathered from the Delicious®
Data Crawling and the Data Indexing module
stores all this information on an inverted index.
In this architecture, data from the Boost Calculation module are indexed and stored together with
documents. All this modules perform their work
offline, i.e., previously to the user query. Nevertheless, it is notable the User Interface module,
where the user can perform a query on the system
and see the ordered list of documents that matches
the query. This is done by the Query Processing
module, which actually retrieves documents from
Index considering the authorities’ boost.
Because there is no FBS based on the concept
of Folkauthority whose data have been generated
from actual users activities engaged in the task
of granting authorities, we decided to simulate

the network of authorities. Golbeck and Hendler
(2006) argue that, as some social network formed
by real users is difficult to be obtained, simulating
data from a social network is a viable alternative for
a study/evaluation/validation of models, because
most of these networks have known topological
properties, which make network simulation easy.
In this way, the evaluation of the proposed
approach was based on the simulation of the
process of authority granting using data from Delicious® system (specifically, from the tool called
Network, which represents explicit relationships
among entities). Based on the instantiation of this
model, we sought to verify our hypothesis and to
identify solutions for issues related to the use of
the concept of Folkauthority for the information
retrieval in FBSs.

Getting Data to the
AuthoritySearch System
The Delicious® system was chosen in order to
get data for the AuthoritySearch system because
of its characteristics and because it represents a
database widely used by several other researchers
that investigate folksonomies, such as Körner and
Strohmaier (2010), Golder and Huberman (2006),
and Bao et al. (2007). Delicious® has a tool called
Network, which allows the user to add any other
user who has interesting bookmarks to his/her
network (i.e., similar to Twitter®’s following re-

Figure 3. AuthoritySearch architecture

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lationship). These relationships are important for
this work because they represent an appreciation
of the collection of bookmarks (i.e., the personomy) of a user by another one. Heuristically, we
considered that such relationships represent an
explicit grant of cognitive authority. Although this
heuristic is subject to errors — e.g., a user adds
another user to his/her network because s/he is a
critic of the resources of the latter, or only because
the person is in a higher hierarchical position —
we considered that these cases are sporadic and
that they do not interfere with the overall result
of the experiments. Thus, we considered that the
topology of the network of authorities would be
similar to the topology of the network formed by
using the Network tool, allowing, thus, the definition of which entities have granted authorities,
and which ones received such a grant.
Moreover, we also need to define which tags
and what weights were used to describe the cognitive skills of each authority. This task was accomplished through the simulation of a cognitive
authority grant process divided into three steps:
1. The definition of the topology of the network
of authorities; 2. The definition of the entities’
personomy; and 3. The instantiation of the network
of authorities.
In the first step, it was performed a crawling of the social network of contacts formed by
using the Network tool, in which identifiers for
each network entity were stored, as well as the
relationship of “who added who to his/her network”. From this information, we can instantiate
the sets E and A, which represent the entities
and authorities, respectively. In the second step
of the simulation, the sets T, R, and Y have been
instantiated, capturing the personomies of each
entity persisted in the previous step, which can
be retrieved directly from the FBS. Then, this
information is passed as input to the third step,
which simulates the grant of authority among the
members of the social network.
The simulation process of the authority grant
was conducted based on the premise that, given

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the granting from entity “A” to entity “B”, the
tags that could be used to describe the cognitive
authority of “B” are those present at the intersection between the set of tags used by the two
entities for categorizing their documents. If the
intersection of these sets is empty (i.e., the entities do not share a common vocabulary), then, no
granting of authority should occur. This heuristic
was used to specify that someone must share some
vocabulary with the other in order to be considered
a cognitive authority by the latter.
In order to improve the performance, in the
process of choosing candidate tags, 75% of the tags
used by the authority were disregarded, i.e., those
whose frequency of use was considered too low
when compared to the frequency of use of other
tags that are more frequent. This cut off heuristic
is based on the observance that the users’ tags frequencies are distributed over a “long tail” (Golder
and Huberman, 2006). Thus, the probability of a
tag being chosen was based on the frequency of
use of this tag by the entity recognized as authority. The number of tags to be used in the granting
of authority should be around 3, corresponding
to the average usage of tags in resources in FBSs
(Golder and Huberman, 2006; Wang and Davison,
2008). In this simulation, the number of tags was
randomly chosen between 2 and 4.
After selecting the tags for the edges of the
network of authorities, it was necessary to define
the levels of authority that accompanied the chosen
tags in order to assign a weight to each tuple in
the cartesian product (E × T × A), generating the
function G. The level to be assigned to a tag used
in granting authority should be proportional to
the frequency of use of that tag by the authority.
Thus, this assignment was based on the percentile
that the selected tag is located on the frequency
distribution of the use of tags that can be assigned to the authority (i.e., the remaining 25%
of the tags). In this way, the selected tag in the
first percentile of the most used tags (100-80%)
received a 5 weight, while a selected tag in the
second percentile (80-60%) received 4, and so on.


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THE EVALUATION OF
AUTHORITYSEARCH
The evaluation of the use of AuthorityRank in the
AuthoritySearch system was carried in an experimental setting that involved a database containing
59,924 documents, 21,484 tags and 7,210 grants
of authority. In this setting, we could verify the
performance of three different ranking schemas
to retrieve information, named AuthorityRank,
AR/TF-IDF and tf-idf. To achieve this goal, 7
(seven) everyday IRS’s users were asked to assess
the relevance and quality of the top 30 retrieved
documents, ordered by the three ranking schemas. The participants evaluated the top search
results according to different information needs,
expressed by 10 different queries distributed over
the 7 participants (3 of the 7 participants performed
2 queries on the system). For the elaboration
of the queries, the participants expressed their
information needs to the researchers and guided
the definition of terms for the queries. The participants were asked to define queries on topics
of real interest, mainly about topics related to
their work. Thus, each query was defined based
on a scenario that describes the participants’
information needs, prepared after a survey with
participants. All participants were informed about
the research and gave their ethical consent for the
use of their data for academic purposes.
In order to better control the evaluation method,
some literature about the factors that influences
the judgment of information quality and relevance
was consulted and the factors which relates to our
evaluation method was identified. Saracevic et al.
(1988) purpose a method to study this issue which
is as environmentally natural as possible, such as
asking to the participants judge information about
topics related to their work. This principle was also
adopted in this research, since participants had free
time to perform the search related to their work.
Harter (1996) and Schamber (1994) enumerate
factors that influence the judgment of information
relevance and quality. Considering the method

used in this research, the relevant factors pointed
by these authors are: 1.The formal experience/
knowledge related to the query (all participants of
this research had formal education about the query
topic), 2.The modal and style of documents (all
data presented to participants are web documents,
such as videos, photos, sites and text documents
and were published in Delicious® through years
2010 and 2011), 3.The interface available to
interact with information (the AuthoritySearch
interface provide a simple way to evaluating the
relevance/quality of information, through a form).
Also, the concepts of quality and relevance were
explained to the participants, and a reference to
these criteria definitions was available all the time.
For each query performed in the AuthoritySearch system, it was requested for the participants
to use two search methods (named “yellow” and
“green” for the participants), which ordered the
documents according to AuthorityRank and tf-idf
(in order to define the rank for AR/TF-IDF, the
top 30 documents ranked by AuthorityRank was
reordered by the tfi,j × idfi component of Equation 2). The participants assessed the quality and
the relevance of the documents in a 0-4 scale.
Some authors discuss about the quantity of levels
that should contain a scale to assess information
relevance and quality. For example, Yao (1995)
performs a review about this issue. Keen (1971)
and Saracevic (1975) utilizes in their research
a 4 and 3 points scale, respectively. Vakkari e
Sormunen (2004) also performed a research in
which they verified the effect of the gradation
of relevance in the results of IRSs’ evaluation.
However, as pointed by Yao (1995), there is yet
a lack of understanding on this issue.
For evaluating an IRS it is necessary to define
the criteria that will be employed to evaluate the
search results. We adopted the criteria of relevance
and quality that, according to several authors
(Saracevic et al., 1988; Barry, 1994; Knight and
Burn, 2005), are criteria used by users of IRSs for
judging information. In the information retrieval
field, the concept of relevance is the measurement

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by which information is transmitted by a document
given a query (Goffman, 1964), i.e., relevance is
evaluated according to a need for information, so
that a document is relevant if it contains information that satisfies this need. Lachica et al. (2008)
related the concept of relevance to the satisfaction
of the user’s need for information, while the quality
of information was related to the intrinsic value
that such information brings to the user.
Many authors agree that the notion of relevance
and quality is formed by various dimensions,
among which they include the measurement (or
degree) represented in terms of a comparative
judgment with an absolute judgment about the
importance/quality of information. As the criteria of relevance and quality are better expressed
by a relative metric (i.e., gradual), the notion of
measurement (or extension) of relevance/quality
was brought to this work, allowing the end user
to measure a degree of relevance/quality to each
document in the search results evaluation form.
Based on these definitions, and due to the
need for making these concepts understood by
common users of IRSs, the following notions of
relevance and quality were explained to the users
participating in the research:
•

•

Relevance: Is the extent to which the information contained in a document is useful
to the resolution of the problem(s) listed in
the proposed search scenario.
Quality: Is the extent to which the participant believes that the information contained in a document is comprehensive,
well presented, deep and thorough, considering a broader context than the search
scenario for such judgment.

In our experiment, only a portion of the resources present in the database was annotated
with a relevance level for each query. Therefore,
it was not possible to calculate the recall metric
for the retrieved information in the AuthoritySearch system, because this metric is based on the

202

total amount of documents present in the index
that are relevant to a query. The evaluation of all
the resources present in the index is a costly task
(Sanderson, 2010; Bailey et al., 2003), making it
unviable for real cases. Hence, we needed to use
a metric for the evaluation of IRSs that was based
on the evaluation of the relevance of the first n
results of a search. Thus, to quantitatively compare
the outcome of users’ evaluations, we chose the
NDCG-10 metric (Järvelin and Kekäläinen, 2002),
as it is appropriate to the circumstances in which
the experiments were carried out, in which only
top 30 documents of each query was assessed
by its’ relevance/quality level. NDCG stands for
Normalized Discounted Cumulative Gain and is
a metric that computes the relative-to-the-ideal
performance of IR techniques based on the cumulative gain they are able to yield until a given
ranked position (Järvelin and Kekäläinen, 2002).
Gain, for the considerations of our experiments,
is the level of relevance judgments.
In addition to the tf-idf and AuthorityRank
ranking schemas described earlier, we analyzed a
third approach, named AR/TF-IDF, that combines
both schemas: it uses the AuthorityRank in order
to retrieve the documents, and the tf-idf to reorder
the retrieved documents. An analysis of this third
ranking schema is interesting because, in the
considered experimental environment, we did not
know the recall level of each ranking schema. By
reordering the top 30 results of the AuthorityRank
schema with the tf-idf component of Equation 2,
it was important to know if this reordering could
produce significant differences from the original
schema, i.e., if it could move relevant documents
to the top results.
To carry out the comparison of the three different ranking schemas (AuthorityRank, tf-idf,
AR/TF-IDF) according to the criteria of relevance
and quality, the following steps were conducted:
i) the definition of a collection of queries based
on the need for information of the participating
users; and ii) the judgment of 30 documents re-


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trieved in each query according to the relevance
and quality criteria.
The averages of NDCG-10 values on the criterion of relevance, for the 10 executed queries, are
presented in Figure 4 and Table 1 — the biggest
NDCG-10 values for the 5, 10, 20 and 30 top
results are presented in bold.
Applying the one-tailed Student’s t-test, for a
95% level of significance (α = 0.05), to the NDCG10 values, we verified that the AuthorityRank
scheme, when compared with the tf-idf schema,
presented a statistically significant improvement
in average of 54.4% for the top 5 results, 54.0% for
the top 10 results, 34.64% for the top 20 results,
and 23.4% for top 30 results. Therefore, we can

conclude that a ranking scheme that considers the
approach of Folkauthority is capable of displaying search results more relevant to users when
compared to tf-idf. Also, when comparing the
AuthorityRank with the AR/TF-IDF, it is possible
to verify a statistically difference only in the top 5
and 10 results, but this difference is not verifiable
in the top 20 and 30 results. When comparing tf-idf
with AR/TF-IDF, the only range of results that is
not statistically significant is the top 30 results.
It means that, by using the AuthorityRank and
reordering the documents through the tf-idf, it
was possible to improve the position of relevant
documents, moving them to the top results.

Figure 4. Average NDCG-10 of the queries to the question: “To what extent is the content presented by
the resource relevant to your search interest?”

Table 1. Average of the NDCG-10 values, considering the criterion of relevance of documents
Rank

Average

Standard Deviation

AR

AR/TF-IDF

TF-IDF

AR

AR/TF-IDF

TF-IDF

Top 5 results

0.549

0.490

0.355

0.033

0.041

0.029

Top 10 results

0.591

0.523

0.384

0.050

0.044

0.040

Top 20 results

0.638

0.580

0.474

0.066

0.070

0.100

Top 30 results

0.707

0.650

0.573

0.116

0.123

0.168

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The averages of NDCG-10 values on the criterion of quality, for the 10 executed queries, are
presented in Figure 5 and Table 2 — the biggest
NDCG-10 values for the 5, 10, 20 and 30 top
results are presented in bold.
Applying the one-tailed Student’s t-test, for
a 95% level of significance (α = 0.05), to the
NDCG-10 values, we verified that the AuthorityRank scheme presented a statistically significant
improvement in average of 32.83% for top 5 results,
32.26% for the top 10, and 29.01% for the top 20,
when compared to tf-idf. Although the AuthorityRank was 14.86% better for the top 30 then the
tf-idf scheme, this difference is not statistically
significant with α = 0.05. Based on these results,

it is possible to perceive that the AuthorityRank
ranking scheme also ordered the search results so
that higher quality documents were presented in the
initial results. Moreover, comparing AR/TF-IDF
with AuthorityRank and tf-idf, it is only possible
to obtain statistically differences on NDCG-10
values for top 5 and 10 results, and for top 5, 10
and 20 results, respectively.
A notable result is that the difference of AR/
TF-IDF and tf-idf is significant for a large number of result ranges than the difference between
AR/TF-IDF and AuthorityRank, which suggests
that, considering the Equation 2, the tfi,j × idfi
component in the AuthorityRank equation is
responsible for retrieving a considerable number

Figure 5. Average NDCG-10 of the queries to the question: “How would you rate the quality (for completeness, depth and breadth) of the presented content?”

Table 2. Average of the NDCG-10 values, considering the criterion of quality of documents
Rank

Average

Standard Deviation

AR

AR/TF-IDF

TF-IDF

AR

AR/TF-IDF

TF-IDF

Top 5 results

0.797

0.722

0.600

0.049

0.040

0.013

Top 10 results

0.812

0.726

0.614

0.038

0.028

0.026

Top 20 results

0.816

0.762

0,661

0.027

0.045

0.056

Top 30 results

0.839

0.800

0.730

0.044

0.068

0.111

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of relevant documents, while the boosti,j component is able to reorder these relevant documents
presenting them in the top results. Observing the
experimental results, it is possible to confirm our
hypothesis — that by considering the cognitive
authority of the sources of information it is possible to retrieve information with different levels
of quality and relevance.

DIRECTIONS FOR
FUTURE RESEARCH
The experiments presented used a simulation of
the network of authorities, which allowed us to
validate our hypothesis about considering cognitive authorities for the IR task. Although this
simulation was based in a real social network and
well though parameters, it is important to observe
the dynamics of a network generated by the activity
of cognitive authority granting. This is useful to
confirm some assumptions and statements made
through the development of this research, such as
the distribution over the tags frequencies and the
topology of the network of authorities.
Many authors discuss the diversity of dimensions considered by the users when judging the
information retrieved by an IRS (Saracevic et al.,
1988; Barry, 1994; Knight and Burn, 2005), but
not all these dimensions were evaluated in this
research. Thus, there is a gap about the effect of
the Folkauthority approach over such dimensions,
which is pointed as a future research opportunity. Also, it is possible to expand the presented
evaluation by increasing the number of queries
performed by the participant users. This increase
may clarify the relation between the queries, the
tags given to the authorities, and the relevance/
quality of the information retrieved by each ranking
schema. For example, it is possible to verify the
relation between the PageRank with priors of each
authority and the NDCG values of the documents
available from each authority. It could be done in
order to understand better the relation between

the process of cognitive authority granting and
the relevance/quality criteria of the information.
Finally, the presented research was restricted
to the retrieval of documents only, but many of the
discussed issues about IR could be extended to the
retrieval of authorities in some subject. Despite
the use of the IR algorithms to retrieve authorities instead of documents (since the authorities
are also categorized with tags) is an expected
decision, the experimental results presented in
this chapter can not be extended to the authorities
retrieval, because the judgment of the relevance/
quality of a document may be quite different of
the judgment for an authority. Therefore, another
future research direction is the investigation about
the retrieval of authorities in the context of Folkauthority approach.

CONCLUSION
In this paper, we addressed the problem of relevance and quality of search results in FBSs. For
this, we set the hypothesis that the consideration of
the quality of a source of information can be used
to prioritize the results at the time of information
retrieval, thereby improving the relevance and
quality of the retrieved information.
To validate this hypothesis, a ranking scheme
called AuthorityRank was developed, based on
an approach that operationalizes the concept of
cognitive authority, called Folkauthority. This
approach creates a network of authority based on
the granting of authority between entities of the
network, which was used to calculate the pagerank
with priors of each entity, used in the definition
of the AuthorityRank. Due to the non-existence of
an IRS that implements directly the Folkauthority
approach, an IRS named AuthoritySearch was
simulated, using the AuthorityRank scheme for
indexing its documents.
Employing the AuthoritySearch, we carried
out tests with end users to compare the schemes
AuthorityRank and tf-idf (traditionally employed

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in the indexing of IRSs). In these tests, 7 users
performed 10 different queries, and assessed each
one of the 30 retrieved documents in accordance
with the criteria of relevance and quality. To
perform the evaluation of the AuthorityRank, we
compared it with the tf-idf (which is a well known
Web ranking schema) and employed the NDCG10 metric and the Student’s t-test to measure de
results of the experiments.
Aiming at assessing whether the use of AuthorityRank might be better (or worse) than the
traditional tf-idf scheme, the experiment indicated
that, with a confidence level of 95%, the AuthorityRank is better than the tf-idf in both the criteria
of relevance and quality, up to the top 20 results.
For the criterion of relevance, this assertion can
be extended to the top 30 results, but the same
does not occur for the quality criteria.
Nevertheless, the calculation of the documents’
score through the AuthorityRank also uses the
results of the calculation of the score by tf-idf. The
main difference in the calculation of these scores
lies in the addition of the boost from cognitive
authorities that have categorized the retrieved
documents. Our study actually measured the
difference between using the tf-idf in its original
form and using it with the boost of the authorities.
Because the introduction of this boost for ranking
the documents has improved the relevance and
quality of search results, we can also say that the
use of the concept of Folkauthority has beneficial
implications for the information retrieval in FBSs.
Although this study was restricted to document retrieval in the AuthoritySearch system, we
suggest the possibility to explore the retrieving
of cognitive authorities in any particular subject.
However, because judging the quality of information may be different from judging the quality of
an authority, the results presented in this chapter
cannot be generalized directly to the retrieval of
authorities, demanding further experiments and
analysis.

206

We hope this study could to contribute to further
discussions and studies about Social Information Retrieval on the Web, defining new forms
of using the knowledge generated by users, and
generating search results with greater relevance
and quality to them.

ACKNOWLEDGMENT
We especially thank our advisor and friend Sérgio
Roberto P. da Silva, who passed away last year,
for the support and orientation he gave us during
several years. This paper has a lot of his ideas
and thoughts.

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Vakkari, P., & Sormunen, E. (2004). The influence of relevance levels on the effectiveness of
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American Society for Information Science, 55(11),
963–969. doi:10.1002/asi.20046

KEY TERMS AND DEFINITIONS
Cognitive Authority: A concept that refers to
the knowledge acquisition through the consultation to another person. Represents the influence
someone can cause on the thoughts of others.
Folkauthority: Represents the categorization
of others’ cognitive authority through the use of
folksonomy.
Folksonomy: The collective results of individuals’ categorization of information by using tags.
Indexing: Refers to the construction of an inverted index, which is a data structure that allows
efficient retrieval based on the terms of documents.
Information Need: Refers to an individual
or groups’ desire to obtain information about
some subject.
Information Retrieval: Activity related to
obtaining information that is relevant to an information need.
Normalized Discounted Cumulative Gain:
Metric used to evaluate a ranking schema that
considers a discount in the gain cumulative knowledge as its ranking position grows.
Quality: Attribute of an information which
refers to its’ use suitability, such as the scope,
objectivity, integrity or validity of that information.
Ranking Schema: Refers to the algorithm used
to retrieve and order a set of documents.
Relevance: Attribute of an information which
refers to its’ capacity to satisfy an information need.

211

Chapter 10

A Web-Based Method for
Ontology Population
Hilário Oliveira
Federal University of Pernambuco, Brazil

Fred Freitas
Federal University of Pernambuco, Brazil

Rinaldo Lima
Federal University of Pernambuco, Brazil

Rafael Dueire Lins
Federal University of Pernambuco, Brazil

João Gomes
Federal University of Pernambuco, Brazil

Steven J. Simske
Hewlett-Packard Labs, USA

Marcelo Riss
Hewlett-Parckard do Brasil, Brazil

ABSTRACT
The Semantic Web, proposed by Berners-Lee, aims to make explicit the meaning of the data available
on the Internet, making it possible for Web data to be processed both by people and intelligent agents.
The Semantic Web requires Web data to be semantically classified and annotated with some structured
representation of knowledge, such as ontologies. This chapter proposes an unsupervised, domain-independent method for extracting instances of ontological classes from unstructured data sources available
on the World Wide Web. Starting with an initial set of linguistic patterns, a confidence-weighted score
measure is presented integrating distinct measures and heuristics to rank candidate instances extracted
from the Web. The results of several experiments are discussed achieving very encouraging results, which
demonstrate the feasibility of the proposed method for automatic ontology population.

INTRODUCTION
In the last decades the amount of information
generated in digital form and published on the
Web has been growing daily at a fast rate. Nowadays, the Web can be considered as the largest
DOI: 10.4018/978-1-4666-7262-8.ch010

information repository in the world, becoming a
“library” of unprecedented size in human history,
encompassing all domains of knowledge. Most of
such knowledge is represented in textual format,
written in natural language, and interpretable
only by humans. Despite the increasing volume
of data available on the Web, human capacity for
processing and absorption of information remains

Copyright © 2015, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.


A Web-Based Method for Ontology Population

constant and limited (Ben-Dov & Feldman, 2005).
In such context, it is of paramount importance to
have computational systems that would automatically process and classify the huge volume of data
available on the Internet.
Storing information in plain text format does
not allow document accessibility, since the semantic aspects of its content are not explicitly
expressed. The lack of some kind of structure
hinders the exploration and interpretation of
semantic information by computational agents
(Fensel et al., 2002). According to Feldman and
Sanger (2007), the Web is currently at a syntactic
level, i.e., its contents can be read by machines, just
considering keywords and combinations of these,
and not on a semantic level, in which computer
systems can interpret unambiguously the information available. This characteristic constitutes an
important limitation of the current Web. Thus, the
task of automatically finding relevant information
to specific needs, especially those that require
some level of semantic interpretation, becomes
arduous, costly, time consuming, and, in many
cases impractical.
To address those limitations, the Semantic
Web (Berners-Lee & Hendler & Lassila, 2001)
was proposed as a global initiative, defined what
would be the evolution of the current Web scenario. The main goal of the Semantic Web is to
make explicit the meaning of the content of the
data on the Web. Thus, it is possible that web data
be processed by both people and computational
agents which would have access to the semantic
aspects of the data. The Semantic Web is based
on a layered architecture in which each layer adds
a higher level of expressiveness and inference
(Koivunen & Miller, 2001) to the others. One
of the fundamental layers in the development of
the Semantic Web is composed by ontologies,
which are responsible for providing the necessary
expressiveness to the representation of relevant
knowledge about a domain (Freitas, 2003). Thus,
the first step to make the Semantic Web goals

212

achievable is the definition of appropriate semantic
structures for representing any possible domain
of knowledge, which implies in the development
of domain or task-specific ontologies. Once the
ontology for a specific domain is available, the
next step is to semantically annotate related web
resources. Thus, computers must have access to
ontologies that enable both the representation and
sharing of knowledge of different domains, and
a process for mapping the chosen ontologies to
the web content.
On the other hand, although domain or taskbased ontologies are recognized as essential resources for the Semantic Web, the development
of such ontologies relies on domain experts or
knowledge engineers that typically adopt a manual
construction process. Such manual construction
process is very time-consuming and error-prone
(Cimiano, 2006). An automated or semi-automated mechanism to convert the information contained
in existing web pages into ontologies is highly
desirable. Ontology-based Information Extraction
(OBIE) (Wimalasuriya & Dou, 2010), a subfield
of Information Extraction (IE), is a promising
candidate for such a mechanism. An OBIE system
can process unstructured or semi-structured natural language text through a mechanism guided by
ontologies to extract certain types of information,
and present the output using ontologies.
Trying to overcome such a problem, this chapter
proposes an unsupervised, automatic and domainindependent method able to assist the process of
Ontology Population. The proposed methodology is able of extracting instances of ontological
classes from unstructured sources of information
written in natural language available on the Web.
The method is driving by an input ontology that
defines concepts which must be populated, and
an initial set of linguistic patterns (seed patterns)
used to extract and classify candidate instances. It
is based on a Confidence-weighted Score function
(ConfScore), which integrates different measures
and heuristics to rank candidate instances.


A Web-Based Method for Ontology Population

ONTOLOGY-BASED
INFORMATION EXTRACTION
Information Extraction (EI) can be defined as the
automatic process of identifying and retrieving relevant information from texts in natural language,
presenting it in a structured format (Riloff, 1994).
Information Extraction Systems (IES) have a high
coupling with the target domain being analyzed.
This can be considered as a portability problem
being one of the major challenges for the field
(Yildiz & Miksch, 2007; Wimalasuriya & Dou,
2010). Despite the advances achieved in recent
years with the use of Machine Learning (ML)
techniques, many of the tasks performed by IES
have a strong dependence with the domain being
processed. Therefore, the portability onto new
domains is still an open problem (Petasis & Karkaletsis & Paliouras & Krithara & Zavitsanos, 2011).
From the computer science point of view, an
ontology can be considered a computational artifact used to represent consensual knowledge about
a given domain. In recent years, there has been an
increasing interest in ontologies as a means for
representing machine-readable semantic knowledge. EI and Ontologies can interact in a mutual
assistance process (Nedellec & Nazarenko, 2005):
•

•

IES requires a representation of the domain as part of the understanding of the
real world, guiding the process of extracting relevant information. The use of ontologies in information extraction can be
useful to support the extraction process,
providing a mechanism to represent different types of complex knowledge (Yildiz &
Miksch, 2007).
The corpus used in the information extraction process is an important source of
knowledge and may be used to enrich an
ontology. IES can be used to assist the tasks
of Ontology Construction (OC). Some of
these tasks are Ontology Learning (OL)
(Cimiano & Buitelaar, 2008), Ontology

Enrichment (OE) (Petasis et al., 2011),
Ontology Population (OP) (Petasis et al.,
2011). These tasks are usually performed
in a cyclical process, in which ontologies
are used to describe which information is
relevant to the domain in focus, i.e., they
assist in the interpretation of the corpus
in a suitable extraction task level. On the
other hand, the new knowledge extracted
can be used to update the input ontology.
Therefore, it is assumed that the task of extracting information can be seen as a process that can be driven by ontologies.
Recently, the integration of EI and Ontologies,
called Ontology-based Information Extraction
(OBIE), has emerged as a subfield of EI in which
ontologies are used to guide the information extraction process and the output is usually used to
enrich an ontology(Wimalasuriya & Dou, 2010).
OBIE is a multidisciplinary subfield of research
involving concepts from Semantic Web (BernersLee et al., 2001), Information Extraction (Riloff,
1994), Natural Language Processing (Barros &
Robin, 1996), Machine Learning (Mitchell, 1997),
among others.
It is well known that the manual construction
process of ontologies is very time-consuming and
error-prone (Cimiano, 2006). Thus, an automated
or semi-automated mechanism to convert the
information contained in unstructured or semistructured sources into ontologies is highly desired.
Since an OBIE system can process unstructured
or semi-structured natural language text guided by
ontologies to extract certain types of information,
and use it output to update or create ontologies
(Wimalasuriya & Dou, 2010), this kind of system
can play an important role for ontology field.
In fact, OBIE is closely related to the Ontology
Population task, since the latter can be performed
by OBIE systems (Petasis et al., 2011).
Over the years, several OBIE systems have been
proposed using different types of IE techniques
such as linguistic rules represented by regular

213


A Web-Based Method for Ontology Population

expressions (Yildiz & Miksch, 2007), gazetteer
lists (Buitelaar & Siegel, 2006), classification
techniques (Wu & Weld, 2007), construction of
partial parse trees (Maedche & Neumann & Staab,
2003), wrappers (Buitelaar & Siegel, 2006), and
web-based search (Cimiano & Handschuh &
Staab, 2004; Cimiano & Staab & Gimme, 2005a;
Etzioni et al., 2004a; Mcdowell & Cafarella, 2008;
Carlson et al., 2010). It is worth mentioning that
these techniques are not mutually exclusive, they
can be used in combination, or in a hybrid way.
The proposed method in this chapter focuses
on the task of Ontology Population (OP). OP is
the process of inserting new instances of classes,
properties and/or relations in an existing ontology
(Petasis et al., 2011). The Ontology Population
process does not alter the structure of the ontology,
i.e., no changes in the hierarchy of classes and/
or relationships are performed. The updating task
is restricted to the set of instances of concepts,
relationships and properties of an input ontology.
The Ontology Population task has a crucial
role for building and maintaining knowledge bases
(Maynard & Li & Peters, 2008). In addition, this
task allows relating the knowledge described in
natural language with ontologies, assisting the
process of generation of semantic content (Wimalasuriya & Dou, 2010). Finally, the populated
ontology can be used in several applications such
as content management, information retrieval, text
mining, and automatic reasoning, among others.
The Ontology Population task from textual
resources, such as the Web, has motivated several researchers (Etzioni et al., 2004a; Cimiano
& Schmidt-Thieme & Pivk & Staab, 2005b;
Geleijnse & Korst, 2006; McDowell & Cafarella,
2008; Carlson et al., 2010) to propose different
solutions to the problem of learning instances of
concepts and/or relations of an ontology using
the Web as big corpus.
Etzioni et al. (2004a) proposed a domainindependent system, called KnowItAll, which
extracts information from the Web in an automated,
open-ended manner. It extracts instances of both

214

classes and relations using a set of domainindependent patterns. For assessing candidate
instances, KnowItAll uses the Pointwise Mutual
Information (PMI), a statistical measure which
computes the relationship degree between two
terms, as features in a Naive Bayes classifier to
combine these features for achieving a rough
estimate of the probability that each candidate
instance is correct.
In reference (Cimiano et al., 2005b), the authors propose an approach to learning taxonomic
relationships (is-a) that explores different sources
of evidence. The first two sources of evidence
used are: (1) the linguistic patterns of (Hearst,
1992) applied to a local corpus; and (2) apply
the same patterns using the Web as corpus. The
results showed that using the local corpus was
possible to obtain a higher accuracy than using
the Web as corpus. Using the Web, however, it
was possible to achieve greater coverage. Another
source of evidence assessed was the WordNet as
a semantic source of information to classify the
candidate instances terms. The authors evaluated
two variations, one using all the senses of a given
term in WordNet, and other using only the most
used sense. Basically, this measure explores the
meanings of two terms present in WordNet seeking
to identify whether there is a taxonomic relationship between them. Finally, other evidence used
is a simple heuristic based on syntactic variations
between two terms to evaluate whether there is a
taxonomic relationship between them. The authors
concluded that the simple combination of different sources of evidence yielded better results in
terms of the measures of precision, recall, and
F-measure than using the evidences separately.
The OntoSyphon approach (McDowell &
Cafarella, 2008) is a domain-independent, ontology-driven system that extracts class instances
web-scale metrics to assess candidate instances.
The authors used the Web as a big source of information during the extraction and classification
process of candidate instances. They conducted
several experiments evaluating different formulas


A Web-Based Method for Ontology Population

for calculating the PMI measure in the classification of candidate instances extracted using a local
corpus, and then the Web as corpus.
The Never-Ending Language Learning (NELL)
(Carlson et al., 2010) is an intelligent agent that
works continuously in a cyclic manner aiming
to: (1) extract and process information from the
Web to populate a structured knowledge base
(ontology) with new instances of concepts and
relations; and (2) learn how to perform this task
better than in the previous iteration of the system.
For doing that, NELL has different modules that
use machine learning techniques, wrappers, and
specific linguistic patterns on the Web. Each
module is responsible for assigning a confidence
level for each extracted candidate instance. At the
end of its extraction process, NELL only promotes
a candidate as an actual instance if the candidate
has a high confidence value.
The method for ontology population proposed
here uses a set of domain-independent linguistic
patterns, and explores the Web as a big source of
textual information. However, inspired in previous work (Cimiano et al., 2005b), the technique
presented here integrates different sources of evidences to assess the extracted candidate instances.
This constitutes the main difference between the
proposed method and the aforementioned related
work. The research hypothesis assumed is that using web-scale statistics, semantic similarity measures and some heuristics as one single measure to
classify the candidate instances may improve the
quality of the overall extraction process of class
instances for the purposes of ontology population.

AN UNSUPERVISED METHOD
FOR ONTOLOGY POPULATION
FROM THE WEB
The Unsupervised Method for Ontology Population from the Web (UMOPOW) is an autonomous,
domain-independent method to extract instances of
concepts defined by a domain ontology (Oliveira &

Lima & Gomes & Freitas, 2012b; Tomaz & Lima
& Gomes & Freitas, 2012). The proposed approach
profits of the high redundancy present in the web
content, considering it as a large corpus. Several
authors pointed that out as an important feature
because of the amount of redundant information
can represent a measure of its relevance (Hearst,
1992; Etzioni et al., 2004a; McDowell & Cafarella,
2008). Furthermore, the portability issue is taken
into account, i.e., the approach has to be able to
perform independently of the domain ontology.
Having a domain ontology as input, the
method relies on a set of domain-independent
linguistic patterns adapted from (Hearst, 1992),
successfully used by other researchers (Cimiano
et al., 2004; Cimiano et al., 2005a; Etzioni et al.,
2004a; McDowell & Cafarella, 2008; Carlson et
al., 2010). These patterns are instantiated and
applied both as discriminators and extractors of
candidate instances from which extraction rules
are created for each selected class of an input
ontology. Actually, the input ontology guides
both the selection and the extraction process of
candidate instances. Also, the same ontology is
used as a repository for the extracted instances,
which characterizes a typical task of Ontology
Population (Petasis et al., 2011).
In this chapter, we described 3 main steps of
the UMOPOW as shown in Figure 1. It essentially
consists of an iterative cycle that is executed for
each selected class in the input ontology. It is
quite reasonable to accept that is not possible to
acquire all possible instances for a class in only
one iteration. Instead, the entire extraction process
is split up into several simpler steps, in which
each iteration receives the acquired knowledge
information from the previous one (Oliveira et
al., 2012b).
The CHOP (Combined Heuristics for Ontology
Population) system (Oliveira & Lima & Gomes &
Ferreira & Freitas & Costa., 2012a), a prototype
implementation for validating UMOPOW, uses
a shallow syntactic parser (in English) which
performs the preprocessing tasks of tokenization,

215


A Web-Based Method for Ontology Population

Figure 1. Overview of the UMOPOW process

sentence splitting, Part-of-Speech (POS) tagging,
stemming and identification of Noun Phrases
(NP). These NPs are extracted as candidate instances of ontological classes. Moreover, to assess
and rank candidate instances, a combination of
similarity semantic and web-scale statistics measures that explores different sources of evidence
was proposed (Oliveira et al., 2012a).

Extraction of Candidate Instances
The first step of the proposed methodology is to
select a class c in the input ontology. Then, using
a set of domain-independent linguistic patterns
adapted from (Hearst, 1992), some queries are
formulated and passed to a web search engine
which retrieves a set of relevant documents.
Table 1 presents the original linguistic patterns
in column (a) and the queries derived of them in
column (b) for the Country class selected in the
input ontology.
The linguistic patterns listed in Table 1, in
general, are preceded or followed by example(s)

of candidate instances of the selected class. The
queries are applied to a web search engine for
retrieving N relevant documents for each query
shown in Table 1. Each retrieved document is
preprocessed by the NLP tool; in this work the
OpenNLP1 was used. The method is looking for
sentences such as, “such countries as CANDIDATES” or “CANDIDATE is a country” where
CANDIDATE(S) denotes a single or a list of noun
phrases. For instance, in the sentence “Countries
such as Brazil, Argentina, and Chile are great
commercial partners”, the noun phrases Brazil,
Argentina, Chile and commercial partners are
extracted as candidate instances for the Country
class. In addition, each candidate instance keeps
a list containing the linguistic patterns that were
responsible to extract it. This step is iterated over
all linguistic patterns until all retrieved documents
have been processed. In Table 2 are presented two
examples of candidate instances for the Country
class extracted in this step. In each row, one has
the name of the candidate instance (column a) followed by the patterns that extracted it (column b).

Table 1. Linguistic patterns adapted from (Hearst, 1992) and it respective queries

216

Linguistic Patterns (a)

Queries (b)

(P1) CANDIDATE is ART CLASS

is a country

(P2) CLASS(s) such as CANDIDATES

countries such as

(P3) such CLASS(s) as CANDIDATES

such countries as

(P4) CLASS(s) and other CANDIDATES

and other countries

(P5) CLASS(s) or other CANDIDATES

or other countries

(P6) CLASS(s) especially CANDIDATES

countries especially

(P7) CLASS(s) including CANDIDATES

countries including


A Web-Based Method for Ontology Population

Table 2. Candidate instances extracted for the class Country
Candidate Instance (a)

List of Linguistic Patterns (b)

Brazil

CANDIDATE is ART CLASS
such CLASS(s) as CANDIDATES
CLASS(s) and other CANDIDATES
CLASS(s) especially CANDIDATES

South Africa

CLASS(s) such as CANDIDATES
CLASS(s) especially CANDIDATES

In order to avoid invalid and repeated candidate
instances, the method performs the following
filtering steps:
1. Removing all candidate instances that do
not contains at least one noun;
2. Deleting a candidate instance that is already
an instance of the class or an instance of a
disjunctive class of the ontology;
3. Using a stemming algorithm, the method
identifies syntactic variations of candidate
instances. In the case of two candidate instances to be syntactically equivalent, just
one of the syntactic forms is maintained and
the lists of linguistic patterns of both forms
are merged;
4. Two candidate instances can be syntactically
different, but semantically equivalent. For
example, the candidate instances “USA” and
“The United States of America” both refer
to the same instance of the Country class.
Aiming at identifying these cases among
the set of candidate instances, the system
retrieves a list of synonyms of the two candidates using the WordNet (Fellbaum, 1998).
Then in case of one candidate instance to be
a synonym of the other, they are considered
semantically equivalent. This semantic filtering is restricted only to candidate instances
having an entry in the WordNet. The list of
linguistic patterns is merged in analogous
way as in the previous action.

Classification of Candidate Instances
The previous step produces a list of candidate
instances. Now, it is necessary to decide which
candidate instance is an actual instance of a given
class in the input ontology. A combination of webscale statistics, semantic similarity metrics, and
heuristics is applied on each candidate instance
extracted by the previous step. Such combined
confidence metric should estimate the likelihood
that a candidate instance is an actual instance
member of the class.
Instead of using just one, (i.e., as it has been
done in some related work presented in Background Section), the approach followed here would
combine several metrics and heuristics aiming at
yielding better results than simply using one of
them separately. Hence, a confidence-weighted
metric based on Pointwise Mutual Information
(PMI), WordNet similarity measures, and some
heuristics to calculate a final confidence score
that can improve altogether the ranking of candidate instances extracted in previous step. How
the aforementioned elements are used in order to
calculate the final confidence score is explained
in the following.
Pointwise Mutual Information (PMI) (McDowell & Cafarella, 2008). The PMI metric gives the
number of hits for a pair (ci, c), corresponding
to a candidate instance ci and a class c, for each
linguistic pattern p belonging to the patterns P
listed in Table 1. There are some variants of PMI
in the literature (Etzioni et al., 2004a; Cimiano
et al., 2004; McDowell & Cafarella, 2008). The

217


A Web-Based Method for Ontology Population

Box 1.

PMI STR−INORM (c, ci ) =

∑

hits (c, ci, p)

pP

(

max hits (ci ),Cont25

)

variant in Equation 1was chosen here because it
achieved better performance in the experiments
carried out and described in the Experimental
Evaluation section (Oliveira et al., 2012a).
In the Equation 1, the sum of the hits(c, ci, p)
stands for the number of hits returned by a query
formed by a class c, a candidate instance ci and
a linguistic pattern p. The result of this sum is
normalized by a value determined by sorting the
set of candidate instances by hits (ci) and then
selecting the hit count that appears at the 25th
percentile (Count25). The interested reader may
refer to (McDowell & Cafarella, 2008) for further
details about this formula. The PMI calculated
by Equation 1 is normalized in function of the
highest PMI value found in the set of all candidate instances evaluated for a class c. Candidate
instances with PMI (c, ci) = 0 were eliminated.
The set of linguistic patterns are used to calculate the PMI value by formulating queries such as
“cities such as Barcelona and”. In this example,
the PMI score evaluates the candidate instance
Barcelona for the City class using the pattern P2
listed in Table 1. It is very important the presence
of the word and, either after or before a candidate
instance using the patterns P2 to P7. As pointed
out by (Geleijnse & Korst, 2006), the use of this
connecting word should avoid some misclassifications. For instance, if one had extracted the
candidate instance Las instead of Las Vegas, and
since this candidate had a high PMI value because
of the query hits for “cities such as Las” include
the hits count for the correct phrases “cities such
as Las Vegas”, this would lead to a misclassification. Thus, the presence of the word and would
alleviate this kind of problem.

218



(1)

WordNet Similarity Score (WSC). Semantic
similarity measures based on WordNet have been
widely used in NLP applications before (Pedersen,
2010), and they typically take into account the
WordNet structure to produce a numerical value
for assessing the degree of the semantic similarity
between two concepts. In this work we choose
two well-known similarity measures based on
WordNet proposed by Resnik (Resnik, 1995), and
Wu and Palmer (Wu & Palmer, 1994).
•

•

Resnik (Resnik, 1995). This measure calculates the similarity between two concepts
by computing the amount of information
shared between them. This amount of information shared is determined by measuring the Content Information (CI) of Most
Specific Concept in Common (MSCC) between the two concepts. To do this, definitions and synonyms present in WordNet
are used to calculate the similarity between
them.
Wu and Palmer (Wu & Palmer, 1994). This
similarity measure relies on finding the
most specific concept that subsumes both
the concepts under measurement. The path
length from the shared concept to the root
is scaled by the sum of the distances of the
concepts to the subsuming concept. This
measure has the advantage of both being
easy to implement and offering competitive performance against other similarity
measures.

In the method proposed here, these similarity
measures provide the degree of similarity between


A Web-Based Method for Ontology Population

Box 2.
WSC (c, ci ) = Wu _ and _ Palmer (c, ci ) + Resnik (c, ci )

the class c and the candidate instance ci. The sum
of the similarity measures by Resnik and Wu and
Palmer is used here as the semantic similarity
score as presented in Equation 2.
Extra Pattern Score (EPS): This heuristic
is based on the idea that if a candidate instance
is extracted by many linguistic patterns, then
this gives strong evidence that this candidate
instance is a valid instance of its class. Based on
this assumption, EPS was defined as the number
of linguistic patterns that extracted a particular
candidate instance.
Direct Matching Score (DMS): This last
heuristic is based on the idea of finding a partial
occurrence of the class into the candidate instance
(Monllaó, 2010). To this end, a classical stemming
algorithm was employed on both labels of the
class and the candidate instance. If they match,
then the value “1” is assigned, or “0” otherwise.
For example, one may consider the University
class and the candidate instance “University of
London”, in this case the DMS for this candidate
instance is 1.
At this point, one is finally able to define
the proposed Confidence-weighted Score function (ConfScore) (Oliveira et al., 2012a, 2012b;
Tomaz et al., 2012) presented in Equation 3. All
constituents (PMI, WSC, EPS and DMS) of the
ConfScore are normalized and their values range
between 0 to 1.
Based on (Cimiano et al., 2005b), the Equation
3 reflects the hypothesis of combining different
measures and heuristics to obtain better results
than using only one of them in isolation. The
parameters in Equation 3 control the influence of
each constituent on the ConfScore. Preferably for
facilitating the analysis, comparison and visualiza-

(2)

tion of the final score results, the restriction α +
β + γ + ∆ = 1 was imposed, making the final
score value range between 0 to 1.

Population of the Input Ontology
Typical noisy information found on the Web can
produce incorrect candidate instances. Such spurious candidate instances may also take place for
other reasons, such as incorrect parsing of noun
phrases, misspelled instance names, among others.
It is imperative to provide a reliable estimation
of the quality of the extracted instances. Consequently, candidate instances having a confidence
value below a given threshold are removed.
One should take into consideration two important aspects of the use of the Web as corpus:
(1) the large volume of information; and (2) the
unreliability of the information found on the
Web, the accuracy of the information extracted
was prioritized. Therefore, after each iteration of
the method only candidate instances with a high
ConfScore value are promoted as instances for
the selected class.
In general, the choice of the above threshold
is performed empirically, being promoted as instances only candidates that have a value greater
than the chosen threshold. Given that the measure
of PMI has values varying in different order of
magnitude depending on the selected class, the
candidate instance in analysis, and the linguistic
patterns used, the definition of a given threshold
can became very difficult. Instead, in each iteration, the best N candidate instances (Top N) sorted
according to theirs ConfScore were promoted as
a real instances.

219


A Web-Based Method for Ontology Population

Table 3.Experimental setup – parameters settings
ConfScoreVariant (CSV)

Parameters Settings

CSV1 (Equation7)

EPSMax = 7, WNSMax = 2, DMSMax= 1

CSV2 (Equation3)

α

= 0.4, β = 0.35, γ = 0.20 and

∆

= 0.05

CSV3 (Equation 3)

α

= 0.4, β = 0.25, γ = 0.30 and

∆

= 0.05

Box 3.
ConfScore (c, ci ) = PMI (c, ci, P ) × α +WSC (ci ) × β + EPS (c, ci ) × γ + DMS (c, ci ) × ∆

EXPERIMENTAL EVALUATION
This section describes the experiments conducted
to evaluate the proposed method. The experiments
were divided into two parts:
•

•

220

Experiment 1: This experiment aims to
evaluate three variations of PMI introduced by McDowell and Cafarella (2008),
and compare them against our proposed
confidence-weighted score (ConfScore).
By the end of this experiment one expects
to determine both the most accurate PMI
variation in the candidate instances classification task and the influence of this
PMI variation to our ConfScore. Reference
(Oliveira et al., 2012a) provides further details about this experiment.
Experiment 2: In this experiment the effectiveness of three versions of ConfScore
are evaluated. Based on the results archived in the Experiment 1, changes in the
ConfScore were performed and this new
version is evaluated in this experiment.
Further details about this experiment may
be found in (Oliveira et al., 2012b).

(3)

Experiment 1: Evaluation of PMI
variations and ConfScore
Dataset Description: The dataset used in this
experiment was collected using 6 linguistic patterns listed in Table 1. The pattern P1 was not
used. An input ontology containing 10 classes
(Mammals, Birds, Cities, Diseases, Foods, Fruits,
Movies, Sports, TV Series, and Universities) was
employed. These concepts were chosen because
they represent different areas and have been used
in other studies (Cimiano et al., 2004; Cimiano
et al., 2005a; Etzioni et al., 2004a; McDowell &
Cafarella, 2008). Using each linguistic pattern
500 web snippets were gathered, totalizing 3000
snippets for each class. Web snippets or just snippets, are fragments of text returned when queries
are applied to a web search engines. The snippets
were gathered using the Bing Search Engine Application Programming Interface2 (API).
Evaluation Measure: Aiming to evaluate the
effectiveness of the UMOPOW, at first the classification results at a given cut-off rank are analyzed,
considering only the Top N candidate instances
returned by the method. In the experimental setup
here, four cut-off points were determined corresponding to the top 10, 50, 100 and 300 in the
list of candidate instances. Thus, the Accuracy at


A Web-Based Method for Ontology Population

Box 4.
A (N ) =

numberofcorrectinstacesextracted

N

the Top N candidate instances, A(N), is defined
by Equation 4.
Three PMI variations: In this experiment,
three PMI variations presented in (McDowell &
Cafarella, 2008) were evaluated. They are defined
as follows:
•

•

PMIStrength. This variation is calculated by
the sum of all co-occurrence hits returned
by the query hits(c, ci, p). For this, queries
are formulated for the pair (c, ci) and each
linguistic pattern p belongs to the set of linguistic patterns P used in this experiment.
Equation 5 presents how to calculate it.
PMIStr-INorm. As pointed out in McDowell
and Cafarella (2008), the Strength variation is biased towards the more frequent
instances. In order to compensate this,
Strength variation is normalized by the
number of hits of the candidate instance
ci (hits(ci)). However, even using this
normalized version, one may be mislead
when the candidate instance is very rare or
misspelled. Thus, the normalization factor hits(ci) is modified to be constrained
to have at least a minimum value. This
value is determined by sorting the candidate instances by hits(ci) and then selecting Count25, the hit count that appears at

(4)

•

the 25th percentile (see Equation 1). As reported in (McDowell & Cafarella, 2008),
other percentiles could also work well for
addressing this problem.
PMIStr-ICNorm. Following the same idea of
the PMIStr-INorm, this variation combine normalization factors for both the candidate
instance ci and the class c at the same time.
The Equation 6 shows how to calculate it.

In this experiment, the first version of our
ConfScore was evaluated using each PMI variations described above. The ConfScore analyzed in
this experiment differs from the version presented
in Equation 3 by how it is calculated. The Equation 7 shows how the ConfScore evaluated in this
experiment is calculated.
Where:
•
•

•

PMI_Var denotes the PMI variation used;
EPS is the value of the Extra Pattern Score
heuristic. EPSMax represents the maximum
value that the measure can take. This value is the total of linguistic patterns used
(EPSMax = 6);
WSC denotes the value of the WordNet
Similarity Score. The WSCMax denotes the
maximum value that the measure can take,
(i.e., WSCMax = 2).

Box 5.
PMIStrength (c, ci) = ∑hits (c, ci, p)

(5)

pP

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A Web-Based Method for Ontology Population

Box 6.

PMI STR−ICNORM (c, ci ) =

∑ hits (c, ci, p)

max (hits (ci ),Cont ) × hits (c )
pP

(6)

25

Box 7.
ConfScore (c, ci ) =

•

EPS (ci ) + WSC (c, ci ) + DMS (c, ci ) + 1
EPS Max +WSC Max + DMS Max + 1

DMS (Direct Matching Score). This heuristic value can take is 1 if the match occurs, or 0 otherwise. Therefore, DMSMax =
1.

After running the Extraction of Candidate
Instances on the dataset collected, the first 300
candidate instances were selected sorted by confidence score in descending order for each pair
(class, confidence score). Finally, a considerable
annotation effort was necessary in which three

× PMI _Var (c, ci )

humans evaluators were in charge of manually
confirming the system predictions.
In the grouped bar graphs depicted in Figures
2-4 report the results of the experiment for the
10 classes in the input ontology. For all graphs,
the accuracy values of each PMI metric variant
(PMIStrength, PMIStr-INorm, and PMIStr-ICNorm) were
compared with its use to compose the ConfScore
metric, i.e., ConfScoreStrength, ConfScoreStr-INorm and
ConfScoreStr-ICNorm. For instance, considering the
first group in the Figure 2 which corresponds to

Figure 2. Accuracy results between PMIStrength and ConfScoreStrength

222

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A Web-Based Method for Ontology Population

Figure 3. Accuracy results between PMIStr-INorm and ConfScoreStr-INorm

the City class, the first black bar represents the
accuracy value obtained by the proposed weighted
version ConfScoreStrength metric, whereas the second gray bar represents the accuracy value of the
PMIStrength.
Analyzing the results shown in Figures 2-4,
one may notice that the method presented here was
able to successfully extract a considerable amount
of positive instances for most classes. The most
restrictive thresholds Top10 and Top50 showed a
higher precision, while in the broader thresholds
Top100 and Top300 presented a natural tendency to
lose accuracy. One may also observe a variation
in accuracy between the different classes, this
happened due to factors such as the complexity
of the domain and co-occurrence of the extracted
candidate instances with the linguistic patterns
used. Other important issue to be noted is the
lack of reliability of the information provided on
the Web. Most of this information is written by
people who are usually not experts in the field
addressed. The more complex is the analyzed
domain, the greater the chances of erroneous
information to be found.

Classes like City, TV Series and Movie always
had a high accuracy, obtaining a value of at least
60%. These classes are widely known and easy
to interpret, i.e., there were a low occurrence of
wrong information. However, the classes Mammal, Bird and Fruit did not obtain equivalent
results. For the Fruit class, for example, has been
identified a minimum accuracy of approximately
28% analyzing the Top300 threshold. Due to the
complexity of these domains, a lot of wrong information was found. For example, many snippets
analyzed contained errors, in which the authors
mistook certain species of Birds with species of
Mammal, and vice-versa. The same happened with
the Fruit class where many terms related to the
Food class were found. This error is even understandable because the Fruit class can be considered
a subclass of Food. The large co-occurrence of
erroneous information led the method to extract
false instances, due to the high PMI value that
these instances obtained.
The four major problems that had a negative
impact on the results obtained were:

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A Web-Based Method for Ontology Population

Figure 4. Accuracy results between PMIStr-ICNorm and ConfScoreStr-ICNorm

•

•

224

The first problem was generated by some
false candidates instances that have a high
co-occurrence value only with one or two
linguistic patterns and value zero in the
others. For instance, in the Disease class,
the candidate Multiple present in the Top50
archived a high value co-occurrence with
three patterns and zero in the others. Many
false candidate instances obtained high
PMI value because of this problem. One
possible solution being considered is to penalize each query hits(c, ci, p) that return
a zero.
The second problem is the presence of
incomplete candidate instances. For example, in the City class, was extracted the
candidate instance Los, where possibly the
correct would be the extraction of candidate instance Los Angeles. It was observed
that the co-occurrence value of the hits(c,
ci, p) of the incomplete candidate instance
Los contain the hits value of the candidate
Los Angeles, using the patterns: CLASS(s)
such as CANDIDATES, such CLASS(s)
the CANDIDATES, CLASS(s) especially
CANDIDATES and CLASS(s) including

•

•

CANDIDATES. This resulted in a high
PMI value for the candidate instance Los.
This problem is generated due to errors in
the noun phrases extraction or because of
incomplete text fragments.
The third problem was the presence of noisy
information as a hampering factor during
the extraction process. For example, for
the Disease class, the candidate instances
Heart and HIV (Human Immunodeficiency
Virus) represent relevant terms to the disease domain, but are not instances of this
class. However, because they are relevant
terms for the domain, they obtained a high
value on PMI, WSC and EPS. This problem led the method to promote false positive instances especially when using the
Top300 thresholds.
The fourth problem lies in extracting noun
phrases matching the pattern “NP and/or
NP”, because the sentences extracted by
this pattern usually represent a single noun
phrase, which, in most cases, generates
erroneous candidate instances. However,
during the evaluation phase, some of the
candidates extracted by the previous pat-


A Web-Based Method for Ontology Population

tern showed a high PMI value, resulting in
misclassification of such candidates usually putting them between the Top50 and
Top300. Yet, the simple separation of the two
noun phrases using the delimiter “and/or”
would result in an erroneous fragmentation
of some candidate expressed by compound
nouns. This happens, for instance, with the
candidate instance “Two and Half Men”
for the TV Series class, in which it would
result in two false candidates like “Two”
and “Half Men”. This problem is a limitation of the proposed approach and we intend to investigate how to solve it in future
work.
All the aforementioned problems have been
identified in three PMI variations and consequently
in the ConfScore. However, these problems have
a greater impact in the results archived using the
PMI isolated. Looking at the graphs shown in
Figures 2-4, one may observe that in most of the
comparisons independent of the threshold used
to ConfScore yields better accuracy than the PMI
variations used in isolation.
To assess if there was actually a significant difference between the results obtained by ConfScore,
the statistical significance test T-Student3 was
applied. This statistical test was performed in 12
comparisons, taking into account the four analyzed
thresholds (tops 10, 50, 100 and 300) and each of
the three PMI and ConfScore variations analyzed.
In all comparisons ConfScore got the best average
accuracy for the ten selected classes, and in five
of them, there was a statistically significant difference using a confidence level of 95% (α = 0.05).
Analyzing the average accuracy of the ten
selected classes for each TopN evaluated, the PMI
variation used isolated which obtained the best
results in this experiment was the PMIStr-INorm.
The two variations PMIStr-INorm and PMIStr-ICNorm
archived better results than variation PMIStrength,
showing that the normalization factor actually
had a positive impact on the accuracy. The good

results obtained by varying PMIStr-INorm also led
the variation ConfScoreStr-INorm to the best results
among the analyzed variations of ConfScore.

Experiment 2: Evaluation
of ConfScore Versions
Dataset Description: The dataset used for this
experiments present several modifications in relation to the used the Experiment 1. First, all the
linguistic patterns listed Table 1 were used. Five
new classes not used in the Experiment 1 were
included, thus now the input ontology contain 15
classes (Mammal, Amphibian, Reptile, Bird, Fish,
Insect, City, Country, River, Disease, Symptom,
Movie, Sport, TV Serie and University). Using each
linguistic pattern, 700 snippets were collected,
totalizing 4,900 snippets for each class. After the
first iteration of the method on this dataset, for each
class, the Top100 candidate instances were selected
and sorted by the ConfScore in descending order.
The main purpose of this experiment is to
provide a comparative analysis of three variants
of our ConfScore. The ConfScore Variant 1 (CSV1)
corresponds to the ConfScore presented in Equation 7 and used in Experiment 1. The Table 3
shows the parameters settings chosen for this
experiment. The tops 10, 50, and 100 thresholds
were considered in this evaluation. Analyzing the
results of Experiment 1, a higher weight to PMI
was given ( α = 0.4 ), because it presented the
best accurate results and tends to maintain the
precision even in less restrictive thresholds. Since
WSC and EPS presented similar behaviors in CSV2
more weight was given to WSC ( β = 0.35) , and
in CSV3 a more weight was given to EPS (
γ = 0.3). Due to its simplicity the DMS received
the lowest weight ( ∆ = 0.05).
Analyzing the comparative results of the
ConfScore’s versions presented in Figure 5, one
may observe that, in Top10, the best performance
was achieved by CSV2 with average accuracy of
91%, against 90% of CSV3, and 83% of CSV1.

225


A Web-Based Method for Ontology Population

Figure 5. Classification results in Top N (N = 10, 50, 100)

In Top50, the CSV2 and CSV3 presented a low
improvement on average: both 80% against 77%
of CSV1. However, when one looks at the Top100,
the CSV1 yielded a tiny average improvement
(72% average score against 71% of CSV2 and
70% of CSV3).
The average accuracy difference between CSV2
and CSV1 is statistically significant in Top10 (95%
confidence according to Student’s t-test). The same
can be said about CVS3 and CSV1. On the other
hand, though CSV2 and CSV3 had a low average
improvement in Top50 when compared to CSV1,
this difference is not statistically significant. The
experimental results suggest that the new variants of the ConfScore proposed achieved better
performance up to the Top50 candidate instances
assessed by the method.
The ConfScore version (CSV2 and CSV3) enhanced the insertion of correct instances in more
restrictive thresholds (Top10 and Top50), this happened because the distribution of weights for the
constituents. In this new version, the PMI value
is normalized, thus the contribution of other constituents in the final value of the trust is greater.

226

The constituents WSC and EPS produce good
results especially in more restrictive thresholds;
they influenced the increase of the confidence
value of some correct instances making them to
gain positions in the ranking of candidates for
most instances of the selected class.
In the CVS1 variation, the PMI value is not
standardized and all constituents act as weights
for this measure. Therefore, the impact of PMI
for the final confidence value is much higher
than the other constituents. The PMI measure is
able to maintain a balance in the results even on
the most comprehensive thresholds, a factor that
does not happen with the WSC, EPS and DMS,
which tend to have a greater loss in accuracy in
thresholds Top100. This characteristic influences
on the best results obtained by CVS1on Top100.
We decided to prioritize the accuracy of the
extracted instances, avoiding the maximum extraction of instances wrong method. Therefore, given
the results obtained by CVS2 on thresholds Top10
and Top50, we decided to consider this variation
as the best optimal parameter setting.


A Web-Based Method for Ontology Population

FUTURE RESEARCH DIRECTIONS
The main contribution in this chapter was a systematic analysis of a confidence weight-based
score applied on both identifying/extracting class
instances. Although the obtained results have
demonstrated the feasibility of such approach,
further research in the process of identifying an
instance in texts is needed, especially concerning
some issues strictly related to the bootstrapping
nature of the method proposed here.
A crucial issue to be considered for such algorithms is to avoid the introduction of too much
noise into further iterations of the algorithm.
Typically, this is achieved by applying appropriate
linguistic patterns and t-uple evaluation measures,
also called filtering functions, which select only the
most promising patterns and t-uples, allowing the
elimination of false candidate instances. Thus, one
expects to be able to obtain more accurate extraction results, especially in the implementation of a
linguistic pattern learning algorithm, by carrying
out an extensive comparison and evaluation of the
different filtering functions already proposed in
the literature (Weikum & Theobald, 2010).
The following issues are also expected to be
taken into account:
•

•

•

A detailed quantitative analysis of the actual contribution of each weight factor
would suggest the more reliable factors for
instance ranking.
The determination of the optimal parameters to be used. Analyzing the results
obtained, one may observe that each constituent of the ConfScore ranking score
depended of the domain of the class to be
selected.
Further work is needed to find out if other
sources of evidence, such as the Wikipedia,
could be integrated into the approach proposed here.

•

Considering domain-specific text corpora
instead of the Web, such as only web pages
related to a specific domain.

Finally, there are major trends to advance the
extraction of structured information to a more
expressive semantic level, as previously done
by recent endeavors including DBpedia (Auer et
al., 2007), NELL (Carlson et al., 2010), and the
YAGO-NAGA (Hoffart et al., 2011) projects.
These projects have demonstrated the positive
aspects of constructing and maintaining a comprehensive knowledge base of facts about named
entities, their semantic classes, and their mutual
relations as well as temporal contexts, with high
precision and high recall.

CONCLUSION
This work addressed the specific problem of
knowledge acquisition from unstructured texts
written in natural language found on the Web. The
relevance of this problem is due to the abundance
of non-organized, unlabeled or unstructured data
in the Internet, a library of unprecedented dimensions in the history of mankind that grows at a
fast pace. The need for autonomous methods for
extracting relevant information from this huge
source grows every day. The extracted information would boost Semantic Web applications by
enabling the enrichment or population of ontologies that can be used as knowledge bases in many
real-world applications.
In this context, this chapter presented an
unsupervised and domain-independent method
for populating ontologies from Web texts. The
proposed approach makes uses of an initial set of
surface patterns that, when combined with webscale statistics, semantic similarity measures and
some heuristics, provides means for effectively
ranking candidates to instances of classes of a domain input ontology. A prototype of the proposed
approach was implemented, reflecting the central

227


A Web-Based Method for Ontology Population

idea of decomposing the entire learning process
into several simpler steps performed within an
iterative cycle that is executed for each class of
the input ontology. Finally, the effectiveness of the
proposed approach was corroborated by several
experiments that have shown promising results.

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A Web-Based Method for Ontology Population

KEY TERMS AND DEFINITIONS
Confidence Score Measure: A measure to
estimate the trust of a candidate instance is a
positive instance to a related concept.
Information Extraction: A task of automatically identify and retrieval structured information
about pre-specified types of events, entities or relationships, from unstructured or semi-structured
sources.
Natural Language Processing: A subfield
of Artificial Intelligence (AI) whose goal is to
develop techniques that allows to interpret and
generate written texts or dialogues in natural
language.
Ontology Population: The task of updating
an input ontology with new instances of classes,
relations and properties.
Ontology: A computational artifact used to
represent different domains through its concepts
(classes), properties, relations, axioms, concept
hierarchy (taxonomy of concepts) and a hierarchy
of relations (taxonomy relations).

Ontology-Based Information Extraction:
A subfield of information extraction, in which
ontologies play a key role guiding the IE process, defining what is relevance on the analyzed
domains.
Semantic Web: Characterized as an evolution
of the current Web, it aims to make explicit the
semantics of the data available on their content,
creating an environment in which computational
agents and users can work collaboratively.

ENDNOTES



1
2



3

https://opennlp.apache.org/
http://www.bing.com/developers/s/APIBasics.html
http://en.wikipedia.org/wiki/Student’s_ttest

233

234

Chapter 11

User Engagement in
Feedback Sharing through
Social Influence
Agnis Stibe
University of Oulu, Finland
Harri Oinas-Kukkonen
University of Oulu, Finland

ABSTRACT
Organizations continuously strive to engage customers in the services development process. The Social
Web facilitates this process by enabling novel channels for voluntary feedback sharing through social
media and technologically advanced environments. This chapter explores how social influence design
principles can enhance the effectiveness of socio-technical systems designed to alter human behavior
with respect to sharing feedback. Drawing upon social science theories, this chapter develops a research
framework that identifies social influence design principles pertinent to persuasive systems that facilitate
user engagement in feedback sharing. The design principles are then implemented in an information
system and their effects on feedback sharing are explored in an experimental setting. The main findings
of this chapter contribute to research related to social influences on user behavior and to the practice
of designing persuasive information systems.

INTRODUCTION
The rapid evolution of information and communication technologies and the emergence of
the social web are continuously reshaping how
businesses engage customers. Ever-growing
connectivity not only provides new methods for
organizations to retain existing customer relationships, but also enables novel approaches to

providing rich customer engagement experiences
(Payne et al., 2008). At the same time, customers
are steadily developing an understanding of the
spectrum of opportunities provided by emerging
technologies. They acquire new habits of interaction and consumption, which then determine their
expectations about how services are designed
(Prahalad & Ramaswamy, 2003; Schlager et al.,
2013). Customers increasingly demand products

DOI: 10.4018/978-1-4666-7262-8.ch011

Copyright © 2015, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.


User Engagement in Feedback Sharing through Social Influence

and services that match their needs and preferences (Moeller et al., 2013). Therefore, businesses
seek opportunities to understand their customers’
expectations (Mangold & Faulds, 2009). In other
words, organizations need to reach their customers
proactively and collect their feedback, and they
need to provide ways for customers to interact
with them that are convenient and immediate
(Nambisan & Baron, 2009).
The Internet has become increasingly mobile
and social over the last decade. Social media has
rapidly expanded and businesses already use social
media to develop relationships with their customers (The Nielsen Company, 2012). Today, people
visit pages of organizations on Facebook or post
tweets containing specific usernames of organizations on Twitter to provide instant feedback
about their experiences with products and services
(Jansen, 2009; Gummerus, 2012). These developments influence various aspects of everyday life
by changing human behavior in both virtual and
physical space. For example, people use social
media more often through mobile devices. This
broadens the potential for businesses to establish
new forms of interaction with their customers as
they move around. In addition, the situated displays
that are often present in public places nowadays
attract peoples’ attention (Memarovic et al., 2012;
Huang et al., 2008), facilitate interaction with
them (Alt et al., 2013; Brignull & Rogers, 2003),
and alter their behavior (Dalsgaard et al., 2011;
O’Hara, 2003). This synthesis of social activity
and technologically advanced environments forms
an opportune channel for businesses to connect
with customers and collect their feedback almost
instantly. For example, organizations can post
questions and concerns on public displays and
people can use their social media accounts on
smartphones to respond. Earlier research from
similar environments has concentrated either
on social interaction through public and private
screens (Choi & Seeburger, 2011; Müller et al.,
2010) or on behavior change due to interactive
environments (Mathew, 2005; Jafarinaimi et al.,

2005). The main focus of this study is to examine
feedback-sharing behaviors facilitated through
situated displays. In this particular setting, businesses can engage customers more naturally,
as such interactions are completely voluntary
(Nambisan & Baron, 2009). However, for the
same reason, this setting also requires careful
consideration of the mechanisms that influence
people’s motivation to participate.
This chapter seeks to identify the design principles that can harness social influence to engage
people in sharing feedback. To accomplish that,
the relevant background is outlined and a review
of the related literature is provided. The primary
purpose of this review is to share knowledge about
the social influence principles that are relevant
in the context of this study and to develop a
theory-driven research framework. The secondary
purpose of this review is to discuss how the said
principles are interrelated and to what extent they
explain users’ perceptions about the effectiveness
of socio-technical systems. Thus, the following
research questions for this study are:
RQ1: Which social influence design principles
are relevant for fostering user engagement
in feedback sharing?
RQ2: How and to what extent do social influence
design principles explain users’ perceptions
about the effectiveness of feedback sharing
systems?
To answer these questions, this chapter presents a theoretical framework for identifying the
social influence design principles pertinent for
engaging people in feedback sharing, which further underpins a research model for assessing the
effectiveness of feedback-sharing systems. Next,
the chapter describes how the identified design
principles are implemented in an information
system, and their influence on feedback sharing
is empirically tested with 37 users. Users’ experiences with the system were measured through
an online questionnaire and were analyzed with

235


User Engagement in Feedback Sharing through Social Influence

two statistical data analysis methods. Finally,
the chapter concludes that the social influence
design principles of cooperation, competition,
recognition, social learning, and social facilitation have significant effects on user engagement
in feedback sharing.

BACKGROUND
Persuasive Technology
Fogg (2003) argues that, compared to humans,
computers can be more effective persuaders because of their capacity to maintain a high level
of interactivity and to adjust influence tactics
as situations develop. In addition, they can be
more persistent, offer greater anonymity, manage huge volumes of data, display information
in multiple ways, scale according to demand, and
be accessed from almost everywhere. Technologies can be designed to alter human behavior in
various contexts, such as health (Purpura et al.,
2011), energy efficiency (Froehlich et al., 2010),
the environment (Loock et al., 2011), learning
(Mintz & Aagaard, 2012), and business (Yu et
al., 2011). Technologies per se are not intended
to influence users, but, through services that can
be designed on top of them, they can facilitate
behavior change and simplify the behavior change
process (Lockton, 2012).
As an extension of Fogg’s (2003) work,
Oinas-Kukkonen and Harjumaa (2009) proposed
the Persuasive Systems Design model, which
described the key issues, the process model, and
28 design principles for developing and evaluating persuasive information systems. The model
has previously been examined in various contexts
(Oinas-Kukkonen, 2013) and findings have suggested that not all of the design principles should
always be applied, but their selection should be
based on a thorough understanding of a given
problem domain and the underlying theories.

236

Oinas-Kukkonen (2013) suggested designing
socio-technical systems that influence users’
behaviors and attitudes by building upon their
motivations or goals. Designing such systems
requires understanding not only software and
information systems, but also psychology.

Social Cognitive Perspective
Research in psychology suggests that human
beings can be proactive and engaged depending
largely on the social environments in which they
develop and function (Ryan & Deci, 2000). Human self-development, adaptation, and change are
embedded in social systems (Bandura, 2001). In
such systems, according to the social cognitive
theory, personal factors, behavioral patterns, and
environmental events all operate as interacting
determinants that mutually influence each other
(Bandura, 1986). In other words, there is an endless dynamic interaction between the person, the
behavior, and the environment in which a given
behavior is performed. This triadic reciprocal
determinism unfolds multiple pathways for studying behavioral change, including environmental
and personal change. Therefore, it is adapted in
this study to explore the effects of personal and
environmental determinants on user engagement
in sharing feedback (Figure 1).
The reciprocal interplay between personal
determinants (user factors) and behavioral determinants (user behavior) reflects the interaction
between what people think, believe, and feel, and
how they behave (Bandura, 1986). In the context of
social influence on peoples’ behaviors, the social
cognitive theory encourages us to look through
the lens of two key human capabilities: observation and self-regulation. The former indicates that
people are equipped with a capacity for vicarious
learning, which increases their behavioral knowledge and skills by observing others, thus exerting
a direct influence on their own behavioral intentions and subsequent behaviors (Bandura, 1977).
The latter indicates that successful, self-regulated


User Engagement in Feedback Sharing through Social Influence

Figure 1. Theoretical research framework

individuals have higher motivation, exploit better
behavioral strategies, and respond more appropriately to environmental influences (Bandura, 1991).
The further interplay between user factors
and software features portrays the interaction
among human beliefs, emotions, and cognitive
competencies, and how they are developed and
modified by social influences conveyed through
environmental factors (Bandura, 1986). Malone
and Lepper (1987) suggested that social environments foster three interpersonal motivating
factors: cooperation, competition, and recognition. The first two are driven by human nature
to cooperate and compete, and the third signals
peoples’ enjoyment of having their efforts and
accomplishments recognized and appreciated by
others. In many situations, these interpersonal
factors provide important intrinsic motivation that
would not be present in the absence of other people

(Malone and Lepper, 1987). Another significant
interpersonal motivating factor is the principle
of social facilitation described by Zajonc (1965),
who suggested that the role of social facilitation is
especially important to consider in social situations
because it highlights that peoples’ behavior can
be significantly affected by internal awareness of
being watched or evaluated by others.
To close the loop of triadic reciprocal causation, Bandura (1986) proposed that, in everyday
interactions, user behavior alters environmental
conditions and, in turn, is changed by the same
conditions that it creates. In addition, social
cognitive theory highlights the need to explore
aspects of social persuasion maintained by ambient environments. For that reason, the present
study applies the Persuasive System Design model
(Oinas-Kukkonen and Harjumaa, 2009) to identify
the corresponding design principles (i.e., features

237


User Engagement in Feedback Sharing through Social Influence

of persuasive software) for social influence on user
factors (i.e., self-regulation and observation) and
user engagement in feedback sharing.

Social Influence Design Principles
Five social influence design principles—cooperation, competition, recognition, social learning, and
social facilitation—were identified from the Persuasive System Design model (Oinas-Kukkonen
and Harjumaa, 2009) based on their conformity
to the previously described theoretical concepts.
The cooperation design principle can motivate
users to adopt a target attitude or behavior by
leveraging their natural impulse to cooperate, as
described by Malone and Lepper (1987) and others
(Bowles & Gintis, 2003; Okasha, 2013; Axelrod,
2000; Deutsch, 2011; Johnson & Johnson, 1989;
Mead, 1937; May & Doob, 1937). The competition
design principle can motivate users to adopt a target
attitude or behavior by leveraging their natural
drive to compete, as it is grounded in the judgment
process of the self-regulation concept suggested
by Bandura (1991), and described by Malone
and Lepper (1987), and others (Deutsch, 2011;
Johnson & Johnson, 1989; Rottiers, 2010; Mead,
1937; May & Doob, 1937). The judgment process
explains how people make judgments about their
own behaviors compared with traditional standards
or the behaviors of others. In a way, these judgments drive human behavior towards competition
with themselves or others. Public recognition of
an individual provided by a system can increase
the likelihood that the person will adopt a target
behavior, as it is grounded in the self-response
process of the self-regulation concept suggested
by Bandura (1991), and described by Malone
and Lepper (1987), and others (Rottiers, 2010;
Hernandez et al., 2011; Sundaram et al., 1998).
The self-response process explains how people
reward themselves for their good behaviors. In this
way, public recognition satisfies one’s desire for
rewarding self-responses and drives motivation
towards a target behavior.

238

The social learning design principle indicates
that users can be motivated to perform a target
behavior if they use a system to observe others
performing the behavior and learn from it. This design principle originates from the human capability
of observational learning (Bandura, 1977) that
has been previously studied in various contexts,
including social networks (Lamberson, 2010).
The social facilitation design principle holds
that users are more likely to perform a target
behavior if they perceive through a system that
others are performing the behavior along with
them. This design principle originates from the
social facilitation theory described by Zajonc
(1965) and Guerin & Innes (2009). In summary,
human behavior can be successfully altered by a
system that harnesses social influence through
specific design principles. This review identifies
five social influence principles for enhancing user
engagement within the setting of this study. Thus,
the following hypotheses are formulated:
An information system consisting of cooperation
(H1), competition (H2), recognition (H3), social
learning (H4), and social facilitation (H5) design
principles positively affects user engagement in
feedback sharing.
In the next section, the identified social influence design principles are discussed in detail to
investigate the potential interplay among them
as well as their collective power to explain users’
perceptions about the effectiveness of information
systems designed to encourage user engagement in
feedback sharing through situated displays that are
integrated with social media. The research model
for this study is also presented in the next section.

USER ENGAGEMENT IN
FEEDBACK SHARING
The previous section identified five social influence design principles—cooperation, competition,


User Engagement in Feedback Sharing through Social Influence

recognition, social learning, and social facilitation—that have the persuasive powers to alter
users’ behaviors towards more active participation in feedback sharing, thus strengthening the
effectiveness of such information systems.
Businesses increasingly look to collect greater
customer feedback, and the design principle of
cooperation is helpful in fostering feedback because it motivates people to collaborate to achieve
a shared goal (Malone & Lepper, 1987; May &
Doob, 1937), which is to generate more feedback
in this particular case. Therefore, cooperation
stands in the very center of the research model
depicted in Figure 2. The social facilitation design
principle is helpful in promoting cooperation, as
it indicates how many others are engaged at the
same time (Gasser et al., 2006; Zajonc, 1965),
thereby increasing peoples’ motivation to generate more feedback along with others. Therefore,
it is hypothesized that social facilitation has a
positive effect on cooperation (H6). The social
learning design principle is helpful in advancing
cooperation, as it provides a means for observing
the behaviors of other people and learning from
them (Bandura, 1977), which increases peoples’
capabilities to generate more feedback. Therefore,
it is hypothesized that social learning has a positive
effect on cooperation (H7). Finally, the competition and recognition design principles are helpful in
supporting cooperation, as they provide means for
observing one’s own performance in comparison
with that of others, which increases motivation to
produce additional feedback, which is driven by
a desire to achieve better results in competition
or to receive more recognition (Rottiers, 2010).
Therefore, it is hypothesized that competition and
recognition have a positive effect on cooperation
(H8). These three hypotheses characterize the
interplay of social influence design principles
within the context of this study.
To examine the power of these design principles
in explaining the effectiveness of information
systems, the literature on technology acceptance advises measuring the main predictor of

peoples’ behavioral intention to use technology,
namely its perceived usefulness (Venkatesh &
Bala, 2008; Venkatesh et al., 2012). Originally,
research on technology acceptance was developed
in an organizational context, where employees
were expected to use a new information system.
However, this study focuses on users’ voluntary
engagement; thus, the concept of perceived usefulness is adapted as perceived effectiveness (Lehto
& Oinas-Kukkonen, 2014), which is measured
as users’ perceptions about the effectiveness of
information systems for altering user behavior
towards engagement in feedback sharing. Therefore, it is hypothesized that cooperation has a
positive effect on perceived effectiveness (H9).
This completes the composition of the research
model. To empirically test the research model, an
information system is designed and implemented
in the next section of this chapter.

Feedback Collection System
For the purposes of this study, an information
system (hereinafter, the system) was developed
with an aim to engage people in sharing feedback.
The system was designed with the five previously
identified social influence design principles at
its core (hereinafter, features), adjusted for large
displays, and integrated with Twitter. Compared
to other social media, Twitter is convenient for a
fast feedback-sharing process because it restricts
the number of characters for each message to
140, thereby assisting users to describe their
concerns in a efficient way (Boyd et al., 2010).
This characteristic promotes Twitter as one of the
most suitable social media for the engaging with
the socio-technical systems previously described
in this chapter because people typically spend a
limited amount of time in particular public places
such as airports or other public transportation hubs.
Moreover, Twitter has been found to be effective for
user engagement (Junco et al., 2011), persuasion
(Young, 2010), and influencing actions outside
the virtual world (Stibe et al., 2011).

239


User Engagement in Feedback Sharing through Social Influence

Figure 2. Research model for this study

The system was designed to attract peoples’
attention by projecting questions at the top of a
display (Figure 3 and Figure 4), and people are
able to provide feedback using Twitter, that is,
by generating and sharing messages (tweets). As
people start using the system, it automatically
shows all updates on the display so that everyone
can follow their own actions and also what others
are tweeting.
Feedback provided by users is displayed in the
form of a newsfeed in the middle of the display
on the left side. This feature provides a means
for social learning (Bandura, 1977), as it allows
people to observe how others generate tweets and
to continuously learn from that. At the bottom
of the display, two social influence features are
implemented: social facilitation (on the left side)
and cooperation (on the right side). Displaying
the number of active participants allows people
to determine how many others are actually using the system along with them (Zajonc, 1965;
Guerin & Innes, 2009), while displaying the goal
of 100 tweets and the number of current tweets
allows people to experience cooperation towards
a common goal (Malone & Lepper, 1987). In the
middle of the display on the right side, the system

240

has either an implementation of the recognition
feature (Figure 3) or the competition feature
(Figure 4). They are purposefully separated in
order to perform an elaborate study of their effects because they both originate from the same
theoretical concept of self-regulation (Bandura,
1991), which is effective for influencing peoples’
motivation by emphasizing their individual performances in comparison with others (Festinger,
1954). The recognition feature assigns special titles
to active participants, which are then displayed
together with their pictures and usernames. The
competition feature displays the list of the most
active users, arranged by the number of tweets
they have each provided to the system.

Experiment
To empirically test the effects of the designed social
influence features, 37 participants used the system
simultaneously from two computer rooms. All participants in the study were international students of
computer science in a graduate program in Finland
and were enrolled in a course about information
and communication technologies and behavior
change. The participants were randomly divided


User Engagement in Feedback Sharing through Social Influence

Figure 3. Display of the system with the recognition (RE) feature

Figure 4. Display of the system with the competition (CE) feature

241


User Engagement in Feedback Sharing through Social Influence

into two groups, and each group was placed in a
separate computer room. One group, consisting
of 18 people, interacted with the implementation
of the system that emphasized the recognition
feature (RE), and the other group, consisting of
19 people, interacted with the implementation
that emphasized the competition feature (CT).
To make it seem realistic, participants were asked
to imagine that they were airline travelers waiting to depart at a gate in an airport setting. The
system was projected on a big display in front
of each group, and users generated their tweets
from Twitter on desktop computers and mobile
devices. The experiment lasted 30 minutes. Six
questions related to airline travel issues were
added to the system in pairs. At the beginning of
the study, two questions rotated in a loop on the
big display. After ten minutes, another two questions were added, and after another ten minutes,
the last two questions were added. The displayed
information was automatically refreshed every
15 seconds. Right after the interaction with the
system, all users were required to fill in an online
questionnaire containing demographic questions
and seven-point Likert-type scale indicators for
assessing their attitudes towards the system (Appendix) and their Twitter experiences.

The respondents consisted of 24 males (64.9%)
and 13 females (35.1%), mainly aged between 20
and 29 (86.5%), with positive attitudes towards
Twitter as an influential tool (70.3%), but with
less than six months of experience using Twitter
(73%) and tweeting either sometimes or never
(64.9%). Respondents all travel by air at least once
a year on average (78.4%). A detailed summary
is provided in Table 1.

ASSESSMENT OF THE SOCIAL
INFLUENCE EFFECTS
Nearly all respondents (91.9%) agreed that the
system was useful for feedback collection, and
the majority of the respondents (83.8%) thought
the system was effective for encouraging users
to participate. The same number of respondents
(83.8%) responded positively about the ability
of the system to increase user participation in
developing or improving services provided by
airports or airline companies. In addition, 73.0%
of the respondents believed that the system would
work well at an airport, to some degree.
Tweets provided by others on the big display encouraged many users (78.4%) to come

Table 1. Respondents’ characteristics
Demographics (N = 37)
Group
Gender
Age

Length of Twitter use
Frequency of tweeting

242

Value

Frequency

Percentage (%)

Recognition (RE)

18

48.6

Competition (CT)

19

51.4

Female

13

35.1

Male

24

64.9

20–24 years old

5

13.5

25–29 years old

21

56.8

Over 30 years old

11

29.7

Less than 6 months

27

73.0

More than 6 months

10

27.0

Never or sometimes

24

64.9

At least monthly

13

35.1


User Engagement in Feedback Sharing through Social Influence

up with their own tweets, and even more users
(81.1%) perceived the displayed number of how
many others were tweeting at the same time as
a positive motivator. Of the respondents, 70.3%
perceived the goal of 100 tweets as a group task
that required cooperation from all participants. The
same number of respondents (70.3%) believed, to
some degree, that Twitter is a powerful tool to call
for action outside the virtual world. Furthermore,
73.0% of participants saw themselves in the list of
top responders or recognized with special titles. A
relatively smaller number of participants (67.6%)
responded that the displayed list of top responders
or public recognition positively motivated them
to improve their performance.

Reliability and Validity
The research model was analyzed using partial
least squares structural equation modeling (PLSSEM) utilizing WarpPLS 4.0 software for data
analysis. WarpPLS is a component-based path
modeling software application that is appropriate to use when the purpose of the model is to
predict, rather than to test, established theories
(Hair et al., 2011). The statistical objective of
PLS-SEM is similar to that of linear regression;
that is, to demonstrate explained variance in the
latent variable as indicated by R-squared values,
to indicate the strength of the relationship between
latent variables in terms of β values, and to test
the significance of the relationship between latent
variables by reporting their p-values (Gefen et al.,
2011; Hair et al., 2011).
Overall, testing the model is carried out in two
steps: assessment of the reliability and validity
of the measurement model and assessment of
the structural model. The measurement model
includes the relationships between the constructs
(Table 2) and the indicators (Appendix) used to
measure them. The convergent and discriminant
validity of the research instrument is examined
in order to verify that the constructs’ measures
are valid and reliable before attempting to draw

conclusions regarding relationships among constructs (i.e., the structural model).
The indicators of the measurement instrument employed in this study were derived from a
number of sources to operationalize the constructs
(Appendix). The scales for measuring social facilitation (Zajonc, 1965; Guerin & Innes, 2009),
social learning (Bandura, 1977; 1986), cooperation
(Malone & Lepper, 1987; May & Doob, 1937),
competition (Malone & Lepper, 1987; Mead,
1937), and recognition (Malone & Lepper, 1987;
Baumeister, 1998) are self-developed because
there were no suitable existing scales for measuring these concepts. According to Boudreau et al.
(2001), the use of previously validated instruments
is efficient, but the fast pace of technological
change often prevents researchers from investing time in novel instrument development. The
scales for measuring perceived effectiveness are
derived from Venkatesh et al. (2008; 2012) and
Lehto and Oinas-Kukkonen (2014). Similar items
for measuring the aforementioned constructs
have been already tested (Stibe et al., 2013; Stibe
and Oinas-Kukkonen, 2014a; 2014b). Prior to
this study, the survey items were checked with
another scholar from the same field of research
to confirm that the scales demonstrate good face
and expert validity.
Each construct of the research model was
designed as reflective and was loaded with three
indicators. The properties of the scales were assessed in terms of indicator loadings, discriminant
validity, and internal consistency. Indicator loadings and internal consistencies greater than .70 are
considered acceptable (Fornell & Larcker, 1981).
The constructs in the model demonstrate good
internal consistency, evident from their composite
reliability scores, which range between .83 and
.89. Inspection of the latent variable correlations
and square roots of the average variance extracted
(AVE) in Table 2 demonstrate that all constructs
share more variance with their own indicators as
compared to other constructs. In addition, AVE
values of all the constructs were well above the

243


User Engagement in Feedback Sharing through Social Influence

Table 2. Latent variable correlations
CRA

COR

AVE

SF

SL

RE/CT

CR

SF

.78

.87

.69

.83

SL

.71

.84

.63

.17

.79

RE/CT

.82

.89

.73

.35

.09

.86

CR

.69

.83

.62

.42

.38

.43

.79

PE

.69

.83

.63

.17

.20

.33

.58

PE

.79

CRA = Cronbach’s Alpha; COR = Composite Reliability; Bolded diagonal = square root of Average Variance Extracted (AVE)

suggested minimum of .50 (Fornell & Larcker,
1981), thus demonstrating adequate internal
consistency. As recognition and competition in
this study were examined in separate groups, the
reliability of the representative construct (RE/CT)
was verified by inspecting them separately for each
group. The Cronbach’s Alpha levels of .82 for the
recognition group and .80 for the competition
group demonstrate that they do not significantly
differ from .81 for the unifying construct (Table
2), so RE/CT was used for further analysis.
Because all variables were measured using the
same instrument, common method variance posed
a potential threat to the validity of the results. To
test and possibly control for common method variance, Harman’s single-factor test was conducted
(Podsakoff et al., 2003). More than one factor
emerged to explain the variance in our analysis,
and the largest factor accounted for 31.3% of the
variance, which implies that common method
variance is unlikely to be a serious concern in
the present study.

Collaborative Engagement
The results of the PLS-SEM analysis are presented
in Figure 5. All hypotheses are supported and additional findings are presented with dashed lines
in the structural model. The β values next to the
arrows explain the strengths of the particular relationships, but the asterisks (*) mark their statistical
significance. Effect sizes (f2) determine whether

244

the effects indicated by path coefficients are small
(.02), medium (.15), or large (.35) (Cohen, 1988).
The results demonstrate that social facilitation,
social learning, and RE/CT jointly explain 57% of
variance in cooperation, which further explains
54% of variance in perceived effectiveness. Interestingly, an additional strong (.31), significant (p <
.01), and medium (.14) effect of social facilitation
on RE/CT was discovered.
After performing a more detailed analysis,
three controlling effects were found relevant to
the research model. First, a controlling effect of
whether users found themselves being recognized
or in competition (SAW) on their perceptions
about both recognition and competition (RE/CT)
features was found. This finding is significantly
relevant, as SAW has a strong (.57), significant
(p < .001), and large (.37) effect on users’ perceptions about the features. The indicator “I saw
myself recognized or in the list of top responders
on the big display” and a seven-point Likert-type
scale were used to measure SAW (Appendix).
Second, a strong (.42), significant (p < .001), and
medium (.17) controlling effect of frequency of
tweeting behavior (FREQ) on social facilitation
was found. To measure FREQ, the indicator “I
tweet on average” and the answer options “never
or sometimes,” “monthly,” “weekly,” and “daily”
were used. Third, a strong (.51), significant (p <
.01), and large (.35) controlling effect of users’
perceptions about Twitter being influential (INFL)
on the perceived effectiveness of the system was
found. The INFL factor was measured with the


User Engagement in Feedback Sharing through Social Influence

Figure 5. Results of PLS-SEM analysis

*** p < .001; ** p < .01; * p < .05; (R-squared contributions)

indicator “Twitter is a powerful tool to call for
action outside the virtual world” and a seven-point
Likert-type scale. More details about the total
effects and their sizes are presented in Table 3.
Additionally, the results of the PLS-SEM
analysis provide fit and quality indices that support the structural model (Kock, 2013). Besides
reporting the values of average path coefficient
(APC = .411, p < .001), average adjusted Rsquared (AARS = .417, p < .001), and average
block variance inflation factor (AVIF = 1.162),
the model demonstrates a large explanatory power
(GoF = .593) (Tenenhaus et al., 2005). Moreover,
both Sympson’s paradox ratio (SPR = 1.000) and
the nonlinear bivariate causality direction ratio

(NLBCDR = 1.000) provide evidence that the
model is free from instances of Sympson’s paradox (Pearl, 2009), and the direction of causality
is supported.
The results from the PLS-SEM analysis
demonstrate that all social influence features
examined in this study played an important role
in explaining the perceived effectiveness of the
system. Moreover, the RE/CT construct is found
to be the strongest predictor of user engagement
in collaborative content generation, that is, in the
actual feedback-sharing behavior. Therefore, more
detailed analysis is presented in the next section to
compare the recognition and competition features.

245


User Engagement in Feedback Sharing through Social Influence

Table 3. Total effects and effect sizes
SF

SL

RE/CT

CR

SAW

SF

FREQ

INFL

.42 ***
(.17)

RE/CT

.30 **
(.14)

CR

.45 ***
(.21)

.39 **
(.18)

.47 ***
(.25)

PE

.15 *
(.03)

.13 *
(.03)

.15 *
(.05)

.32 *
(.19)

.57 ***
(.37)

.13 *
(.14)

.27 **
(.11)

.19 **
(.00)

.09 *
(.03)

.06 *
(.03)

.51 **
(.35)

*** p < .001; ** p < .01; * p < .05; (f2) = Cohen’s f-squared

Recognition Outperforms
Competition
The normality of distribution throughout the dataset was verified using the stem-and-leaf method
provided by the Statistical Package for the Social
Sciences (IBM SPSS version 19). All questions
that failed to meet the normality requirements
were withdrawn from the dataset before conducting the subsequent analysis, which was carried
out using an independent samples t-test in SPSS.
The statistical objective of a t-test is to indicate
the significance of the difference in one factor or
dimension between means of two independent
groups by estimating t-values and reporting their
corresponding p-values.
When comparing both groups (Table 4), the
results demonstrate that the users of the system
with the recognition feature (RE) had stronger
beliefs about its effectiveness for user engagement
(PE) and its success in a real airport context (PE).
The RE group was also more willing to generate
more feedback when the total number of tweets
got closer to the goal of 100 tweets (CR).
Additional elaborate comparison (Table 5) of
users from the recognition group (RE) who saw
themselves (SAW) being recognized on a big
display (72%, n = 13) against other users (28%,
n = 5) revealed that the first subgroup maintained
stronger beliefs about the success of the system
in a real airport context (PE), and they were more

246

willing to produce more feedback when the total
number of tweets got closer to the goal of 100
tweets (CR). In contrast, more elaborate comparison of users from the competition group (CT) who
saw themselves (SAW) among top responders on
the big display (74%, n = 14) against other users
(26%, n = 5) revealed no significant differences
in any of the aforementioned dimensions.
The findings described in this section provide
support for the increased persuasive capacity of
cooperation (CR) when combined with recognition
(RE) rather than competition (CT) in such settings.
To uncover the effects of users’ previous Twitter
experience on their beliefs about the system, the
next section of this chapter presents additional
results of the t-test analysis.

Previous Twitter Experience
The additional t-test analysis revealed several
findings about the effect of Twitter experience
in other dimensions of the present study. The
comparison of the responses by users who had
been using Twitter for less than six months against
those with longer-term experience revealed three
significant differences (Table 6).
Users with less Twitter experience responded
more positively that the content tweeted by others
inspired them to create their own responses. This
finding explicitly conveys the basic idea of social
learning (SL), and it appears to be more salient for


User Engagement in Feedback Sharing through Social Influence

Table 4. T-test results: Recognition vs. competition
RE

CT

t-value

df

p

I believe that the system would work well in a real airport. (PE3)

5.56

4.32

2.937

35

.009 **

I felt more willing to post additional tweets as the total number of tweets got
closer to the goal of 100. (CR3)

5.17

3.84

2.680

35

.011 *

I think that the system is effective for encouraging users to participate. (PE2)

6.11

5.11

2.570

35

.015 *

RE = recognition group; CT = competition group; df = degrees of freedom; ** p < .01; * p < .05

Table 5. T-test results: Saw themselves recognized vs. others
I saw myself recognized on the big display. (SAW)

Yes

No

t-value

df

p

I felt more willing to post additional tweets as the total number of tweets got closer
to the goal of 100. (CR3)

5.77

3.60

3.401

16

.004 **

I believe that the system would work well in a real airport. (PE3)

5.85

4.80

2.927

16

.010 *

Yes = saw themselves; No = others; df = degrees of freedom; ** p < .01; * p < .05

users with less Twitter experience. Experienced
Twitter users were more inclined to agree that, on
average, they tend to tweet more often (FREQ)
than less experienced Twitter users.
When comparing users that tweet at least
monthly on average against those who tweet less
frequently, the results revealed four significant
differences between the groups (Table 7). Users
who tweet more often on average were significantly
more positive about Twitter being influential in
calling for action outside the virtual world (INFL);
they had believed more strongly that the dynamic
flow of tweets on the big display made them feel
like posting more tweets and that the display of
growing numbers of active participants encouraged them to be more active in tweeting. The
last two findings demonstrate significant effects
of social facilitation (SF) on frequent tweeters.

Users also reported significantly greater positive
responses about willingness to post additional
tweets when the total number of tweets got closer
to the goal of 100 tweets, which implies the principle of cooperation (CR).
The comparison of users (Table 8) who agreed
that Twitter is an influential tool (INFL) to call
for action outside the virtual world (70.3%, n =
26) against other users (29.7%, n = 11) reveals
that the first subgroup had stronger beliefs about
the success of the system in a real airport (PE),
and this subgroup was more willing to produce
more feedback when the total number of tweets
got closer to the goal of 100 tweets (CR).
In summary, the results of this section demonstrate that users’ previous Twitter experience
and their opinions about this medium could influence their beliefs about the designed system and

Table 6. T-test results: Length of Twitter use
Length of Twitter use.

<6m

>6m

t-value

df

p

I tweet at least monthly on average. (FREQ)

1.30

2.00

-2.845

35

.007 **

The content tweeted by others encouraged me to create my own responses.
(SL2)

4.78

3.20

2.747

35

.009 **

< 6 m = less than 6 months; > 6 m = more than 6 months; df = degrees of freedom; ** p < .01

247


User Engagement in Feedback Sharing through Social Influence

Table 7. T-test results: Frequency of tweeting
Frequency of tweeting. (FREQ)

ALM

NOS

t-value

df

p

Twitter is a powerful tool to call for action outside the virtual world. (INFL)

5.85

4.38

4.228

35

.000 ***

The dynamic flow of tweets on the big display made me feel like posting more
tweets. (SF3)

5.62

4.08

4.029

33

.000 ***

The displayed growing number of other active participants encouraged me to be
more active in tweeting. (SF1)

5.92

4.58

3.838

34

.001 **

I felt more willing to post additional tweets as the total number of tweets got
closer to the goal of 100. (CR3)

5.31

4.04

2.406

35

.022 *

ALM = at least monthly; NOS = never or sometimes; df = degrees of freedom; *** p < .001; ** p < .01; * p < .05

the implemented social influence features. This
means that previous experience using a particular social medium could play a significant role
in determining peoples’ attitudes and behaviors
towards using socio-technical systems such as
the one presented in this study. Therefore, the
experience of potential users should be carefully
considered when deciding which social media to
integrate for a particular context.

DISCUSSION
The results of this study provide evidence regarding the various positive effects of social influence
design principles on user behavior targeted to
feedback sharing. Almost all users considered the
system useful for collecting feedback. A majority
of participants agreed that the system could effectively encourage users to participate and could
engage users in developing or improving services
provided by airports or airline companies.
Initial data analysis revealed that tweets provided by others encouraged many users to come

up with their own. This finding implies the idea
of learning from observing others performing the
target behavior, thereby conveying the main idea
of the theoretical concept of vicarious learning
from the social learning theory (Bandura, 1977)
and providing support for hypothesis H4. Further,
even more users perceived the displayed number
indicating how many others were tweeting at the
same time as a positive motivator. This finding
reflects the theoretical concept of social facilitation (Zajonc, 1965; Guerin & Innes, 2009), thus
providing support for hypothesis H5.
Almost three-quarters of the respondents saw
themselves in the list of top responders or recognized with special titles, and more than two-thirds
responded positively that the displayed list of top
responders or public recognition motivated them
to improve their performance. These findings are
related to the interpersonal motivators suggested
by Malone and Lepper (1987) and the social cognitive theory of self-regulation (Bandura, 1991).
The judgment process supports the competition
feature, implemented here as the list of the top
responders, and the self-response process sup-

Table 8. T-test results: Influential vs. others
Twitter is a powerful tool to call for action. (INFL)

Yes

No

t-value

df

p

I felt more willing to post additional tweets as the total number of tweets got closer
to the goal of 100. (CR3)

5.00

3.27

3.344

35

.002 **

I believe that the system would work well in a real airport. (PE2)

5.35

4.18

2.721

35

.010 *

Yes = influential; No = others; df = degrees of freedom; ** p < .01; * p < .05

248


User Engagement in Feedback Sharing through Social Influence

ports the recognition feature (Oinas-Kukkonen
& Harjumaa, 2009), implemented here as the
public recognition with special titles. Thus, these
findings provide support for hypotheses H3 and
H2. Finally, more than two-thirds of respondents
perceived the goal of 100 tweets as a group task
that required cooperation from all participants.
This finding reflects the main idea of cooperation
described by Malone and Lepper (1987), thus
providing support for hypothesis H1.
The results from a rigorous PLS-SEM analysis
provide support for all hypotheses in the research
model. They demonstrate that competition, recognition, social learning, and social facilitation all
have strong, significant, and medium effects on
cooperation, and together they explain more than
half of the variance in it. These results provide
support for hypotheses H6, H7, and H8. Further,
cooperation has very strong, significant, and large
effect on perceived effectiveness, and explains
more than one third of the variance in it. This result provides support for the final hypothesis, H9.
Additionally, an effect of social facilitation on the
recognition/competition construct was discovered.
This implies that the presence of other users not
only has a direct effect on cooperation, but also
has an indirect effect on it through recognition
and competition. Thus, the more users were able
to perceive other participants along with them,
the more they perceived a sense of recognition
and competition.
In addition, three interesting controlling effects
were found during this data analysis. First, recognition and competition had a stronger influence on
those users who had seen themselves individually
recognized or listed among the top responders.
Compared to other features of this study, only these
two were designed to indicate users’ behaviors
based on their individual results, which enabled
users to compare their performances. According to the social comparison theory, people tend
to compare their behaviors with others to seek
inspiration when they are performing poorly
or to gratify themselves when they are doing

well (Festinger, 1954). This provides a potential
explanation for why both of these features had
stronger effects on those users who discerned
themselves through them compared to those who
did not. Second, social facilitation had a stronger
influence on users who tweet more frequently on
average. Presumably, frequent tweeters are more
aware of how to discern others and their activities
on Twitter (Honey & Herring, 2009); thus, they
are more equipped to experience this through a
system with a similar design. Third, users who
thought that Twitter is influential to call for action outside the virtual world had stronger beliefs
that the system is effective for user engagement
in feedback sharing.
Additional findings reveal that recognition
outperforms competition in influencing users’
willingness to generate more feedback and in
influencing their beliefs about the effectiveness
of the system. This pattern appears to be even
more salient for those users who saw themselves
recognized through the system. In addition, previous Twitter experience plays a substantial role in
predicting users’ perceptions about social influence features. Cooperation is more salient for users
who perceive Twitter as an influential tool, and,
together with social facilitation, is more salient for
frequent tweeters. As anticipated, social learning
had stronger effects on users with less Twitter
experience. Finally, users who perceive Twitter
as an influential tool believed more strongly that
the system would work well in a real airport.
To extend this discussion, potential future research directions and implications for practitioners
are highlighted in the next section of this chapter.

FUTURE RESEARCH DIRECTIONS
The main findings of this chapter provide implications for both further research related to social
influence on user behavior and for practitioners
designing current persuasive systems.

249


User Engagement in Feedback Sharing through Social Influence

Research Implications
Further research should focus on broadening the
research framework, extending the research model
with other social influence design principles, and
refining the design of the examined persuasive
software features. However, particular future studies could be focused not only on testing expanded
versions of the current research model, but they
could also break it down and test each social influence design principle separately or in various
combinations. Such studies would contribute to
the development of a more elaborate understanding of different social influence design principles
and their effects on user behavior when implemented as persuasive software features. Another
direction for further research would be to study
the design of particular social influence features.
The number of different implementations for the
same feature is limitless. Thus, further research
in this direction would reveal new design patterns
that have increased power to shape user behavior.
These designs can then be tested in the same or in
different contexts to find their best fit.

Managerial Implications
Practitioners can already design their own systems
based on the artifacts provided in this chapter, or
they can develop new approaches, for example
by redesigning some of the social influence features. Businesses can easily utilize the existing
infrastructure, that is, public screens, to establish
such systems on their premises and collect feedback from their customers immediately. Further,
organizations could launch such systems within
their work environment to facilitate internal discussions. For example, a screen in a coffee room
could potentially engage employees in sharing
feedback about concerns and ideas related to
their work. Any implementation of such systems
in actual places provides another opportunity for
researchers to test various designs of social influ-

250

ence features, thereby complementing the existent
body of knowledge.
In the future, when countless screens are increasingly appearing in public places, for example,
in supermarkets, movie theaters, museums, government offices, hospitals, schools, restaurants,
transportation spots, and even vehicles, such
socio-technical systems could gradually become
an integral part of these environments, thus becoming a seamless and natural channel for businesses
to engage with their customers wherever they currently are. These channels could play a significant
role in advancing customer relationships on the
one hand, and in increasing the amount of relevant
feedback for organizations on the other, because
they enable immediate interaction in the place
where customers acquire new experiences about
a certain service or product.

CONCLUSION
Studies such as the one presented in this chapter
are highly relevant, as they advance the design of
future information systems (Loock et al., 2011).
Along these lines, this chapter provides both researchers and practitioners with richer insights on
how social influence principles can be designed
as persuasive software features for information
systems aimed at facilitating behavior change
among users. Drawing upon the social cognitive
theory (Bandura, 1986), the social learning theory
(Bandura, 1977), the taxonomy of intrinsic motivations for learning (Malone and Lepper, 1987),
the social facilitation theory (Zajonc, 1965), and
interconnecting these theories through the Persuasive Systems Design model (Oinas-Kukkonen
and Harjumaa, 2009), this chapter has explored
the effects of social influence design principles
on altering user behavior towards engagement in
feedback sharing through social media integrated
with situated displays. A theory-driven research
framework was developed based on the relevant


User Engagement in Feedback Sharing through Social Influence

literature and a specific research model was proposed for further examination.
Five social influence design principles—cooperation, competition, recognition, social learning, and social facilitation—were indicated in the
research framework and then were designed as
persuasive software features in an information
system. This system was integrated with Twitter,
adjusted to large displays, and used by 37 participants. The perceptions of participants about the
system were measured using an online survey
instrument, and then were analyzed with two quantitative data analysis methods. The research model
was primarily tested using the partial least squares
structural equation modeling technique, followed
by more detailed analysis using the independent
samples t-tests. The results of the primary analysis provided substantial support for the research
model, and the subsequent t-tests enriched the
understanding of particularities associated with
the uncovered effects of social influence features
on users’ perceptions about the system.
The limitations of the study include the experimental setting based on a hypothetical scenario,
where users were able to watch others performing
the feedback-sharing behavior, and the narrow
sample of participants in terms of age and education. These limitations hold potential threats
to the validity and generalizability of the results
of this study. However, the developed research
framework, the proposed model, the reviewed
theoretical concepts, and the design of particular
social influence features could be applicable to
other settings and contexts.
Overall, this study provides valuable input
for further research related to social influence on
user behavior and it highlights several useful elements for the designers of persuasive information
systems. At the same time, businesses can gain
immediate benefits by designing and launching
such systems on their premises and collecting
feedback from their customers.

ACKNOWLEDGMENTS
The authors would like to thank Payam Hossaini,
Pasi Karppinen, Sitwat Langrial, Anssi Öörni, and
Seppo Pahnila, who helped with this research,
which is an extension of earlier research published in the proceedings of the 7th International
Conference on Persuasive Technology (Stibe and
Oinas-Kukkonen, 2012a) and in the proceedings
of the WWW/Internet 2012 Conference (Stibe
and Oinas-Kukkonen, 2012b) organized by the
International Association for Development of
the Information Society (IADIS). This is part of
the OASIS research group of Martti Ahtisaari
Institute, University of Oulu. The study was partly
supported by the Foundation of Nokia Corporation, as well as by the Someletti research project
on Social Media in Public Space (grant 1362/31)
and the SalWe Research Program for Mind and
Body (grant 1104/10), both provided by Tekes,
the Finnish Funding Agency for Technology and
Innovation.

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KEY TERMS AND DEFINITIONS
Competition (CT): The process of endeavoring to gain what others are endeavoring to gain at
the same time. For example, users could experience
competition if they are able to see themselves in
the list of top users of the same system, which are
ordered based on their performance.
Cooperation (CR): The process of striving
to achieve the same goals or working together.
For example, users could see the results of their
cooperative efforts through the same system.
Feedback Sharing: The process of generating and providing relevant information about
one’s experiences related to a product, service,
or brand. It is important for companies to collect
customers’ feedback in order to be able to improve
their offering so it will better match the needs of
their customers.
Persuasive Technology: Technology that is
intentionally designed to influence behaviors or
attitudes. Typically, such technologies are developed to target a specific behavior with an aim to
change it.

256

Recognition (RE): The value that one derives
from gaining acceptance and approval from others.
For example, users could receive public recognition in the form of special titles that are assigned
to them for their behaviors and displayed through
the same system.
Social Facilitation (SF): The influence on
one’s behavior when surrounded or watched by
others. For example, users could perceive others
using the same system along with them.
Social Influence: The influence on one’s
behavior by the actual, imagined, or implied
presence of others. People experience immediate
influences from others as soon as they occur in a
social environment.
Social Learning (SL): The process of acquiring new knowledge through observing the
behaviors of other people. For example, users
could observe others through the same system
and learn from them.
Twitter: The popular online micro-blogging
service for posting messages limited to 140 characters. Twitter was established in March 2006 and
it currently attracts more than 241 million monthly
active users (https://about.twitter.com/company).
User Engagement: The user experience that
combines psychological involvement and practical
participation in a target behavior. For example,
users can experience engagement in feedback
sharing if they actually do it and they believe
that such behavior is valuable, at the same time.

User Engagement in Feedback Sharing through Social Influence

APPENDIX
Table 9. Measurement items and combined loadings
Construct
Social Facilitation
(Zajonc, 1965;
Guerin & Innes,
2009)

Social Learning
(Bandura, 1977;
1986)

Recognition/
Competition
(Malone & Lepper,
1987; Mead, 1937)

Cooperation
(Malone & Lepper,
1987; May & Doob,
1937)

Perceived
Effectiveness
(Venkatesh et al.,
2003; 2012)

Indicator

Load

SF1

The displayed growing number of other active participants encouraged me to be more
active in tweeting.

.86

SF2

I perceived the displayed number of active participants as a positive motivator that
showed me how many others were tweeting at the same time.

.79

SF3

The dynamic flow of tweets on the big display made me feel like posting more tweets.

.84

SL1

Tweets provided by others on the big display encouraged me to come up with my own
tweets.

.78

SL2

The content tweeted by others encouraged me to create my own responses.

.85

SL3

From the tweets of others, I learned how to tweet myself.

.75

RE/CT1

The displayed [public recognition/list of top responders] motivated me to produce more
tweets.

.83

RE/CT2

The displayed [public recognition/list of top responders] helped me to monitor my
performance.

.88

RE/CT3

The displayed [public recognition/list of top responders] motivated me to improve my
performance.

.85

CR1

The displayed goal of 100 tweets and the adjacent counter stimulated me to produce
more tweets.

.86

CR2

I perceived the goal of 100 tweets as a group task that requires cooperation from all
participants including me.

.67

CR3

I felt more willing to post additional tweets as the total number of tweets got closer to
the goal of 100.

.82

PE1

I think that the system is effective for encouraging users to participate.

.65

PE2

I believe that the system would work well in a real airport.

.89

PE3

I would expect the system to increase user participation in the development or
improvement of services when provided by airports or airline companies.

.81

All indicators employed a seven-point Likert-type scale for assessing attitudes, with the following response options: 1) strongly disagree,
2) disagree, 3) disagree somewhat, 4) undecided, 5) agree somewhat, 6) agree, 7) strongly agree.

257

Section 5

Web and Education

259

Chapter 12

Creating a Community of
Practice in Learning
Ebenezer Uy
De La Salle – College of Saint Benilde, Philippines
Eusebio Yu
De La Salle – College of Saint Benilde, Philippines

ABSTRACT
Social media plays a huge part in Filipinos’ lives. In the area of learning, the proponents observed the
emergence of an online community of practice using Facebook groups that has over 350 members. The
aim of the chapter is to answer the question: How do online communities of practice engage students
to learn and build new knowledge? The objective is to propose a framework that will guide readers to
build their own online community of practice based on its learning context. To achieve the objective,
the proponents use the inductive approach of grounded theory using action research. Results show that
community members used different Facebook features to support their ongoing community of practice.
Further studies may also assess the applicability of the framework in other areas of development.

INTRODUCTION
Creating a Facebook group for every new class
is becoming a common practice. One of the top
reasons for doing so is because the students are
already on Facebook regularly. While being inside
Facebook, they could be notified of group posts.
Posts could include class announcements, lecture /
presentation materials, questions/comments from
students, links to videos and other resources for
the class, etc.
Usually, a new Facebook group is made for
every section for every subject. Let’s say one

professor is teaching 3 sections of the same subject. He will create 3 separate Facebook groups
for each of the sections, even if they’re all the
same subject.
It makes the groups very focused. The group
is applicable for a specific section and will probably be active for only a specific term. After the
term ends, the group will become inactive since
students are already done with that subject. In
addition, the interactions in the group are very
section-specific. A question raised in a particular
class may not be the same question raised in the
other classes.

DOI: 10.4018/978-1-4666-7262-8.ch012

Copyright © 2015, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.


Creating a Community of Practice in Learning

There is an alternative to the scenario above.
Instead of creating a new Facebook group for
every section, only one group is created for all
the sections of the same subject. And not only is
the group active for a specific term only, it will be
the same group that would be used for succeeding
terms that the subject would be offered. Because
of which, an online Community of Practice (CoP)
is formed for that particular subject. Students
who have already finished the course could still
participate and even serve as mentors for the
current students.

REVIEW OF RELATED LITERATURE
Communities of Practice
Every year, corporations spend millions of dollars on training and educating their employees.
According to the American Society for Training
and Development, these corporations spend approximately one thousand dollars per employee
per year in 2005. The investment in training and
education stems from the current trend that businesses are continuing to stay on the cutting-edge
to maintain their competitive advantage over
other organizations. Given that knowledge-based
organizations will continue to be the driving force
of economy, it is essential for organizations to
support the knowledge and information needs of
their workers (Hara, 2009).
In the past, professional development was discussed within the context of traditional learning.
However, traditional learning methods have been
criticized for focusing on transmission of explicit
knowledge. As a result, administrators have a
difficulty in determining the tangible impact of
traditional learning methods because of the inherent difficulties of applying knowledge learned
in a traditional setting to the work environment
(Hara, 2009).
Current research supports the assertion that
learning must take place within an organiza-

260

tional context for it to be considered useful.
Consequently, a shift from traditional methods to
a system of learning founded on collaboration and
experience should be considered. Such learning
can best be supported through communities of
practice (COPs) (Hara, 2009).

Concept of Communities of Practice
Communities of Practice (COPs) are “collaborative, informal networks that support professional
practitioners in their efforts to develop shared
understandings and engage in work-relevant
knowledge building” (Hara, 2009). In other words,
these are groups of people who share a common
goal, problems or passions about a given topic
and want to deepen their knowledge and expertise
in one area on an ongoing basis (Clark, 2006).
The ability of COPs to foster a friendly environment for discussing common subject matters
and concerns encourages the creation and transfer
of new knowledge. It also assists in connecting
experts and practitioners with a common professional interest and similar experiences and expertise. COPs use face to face meetings, workspaces,
maps and networks to promote peer interaction
and address the various needs in all levels of an
organization (Clarke, 2006).

Background on Communities
of Practice
The term, communities of practice, originated
from Lave and Wenger in 1991. Their original
definition emphasized “legitimate peripheral
participation”, a form of apprenticeship which
allows newcomers to participate while learning
the lingo and develop a shared identity of the
community. The end result of the process is the
assimilation into the community for the newcomer
(Hara, 2009).
The following is the original definition of
communities of practice as defined by Lave and
Wenger (2006):


Creating a Community of Practice in Learning

A community of practice is a set of relations
among persons, activity, and world, over time and
in relation with other tangential and overlapping
communities of practice. A community of practice
is an intrinsic condition for the existence of knowledge, not least because it provides the interpretive
support necessary for making sense of its heritage.
Thus, participation in the cultural practice in
which any knowledge exists is an epistemological
principle of learning. The social structure of this
practice, its power relations, and its condition for
legitimacy define possibilities for learning (i.e.,
legitimate peripheral participation).
Other definitions of communities are a derivation from the original definition given by Lave and
Wenger. Of course, not all scholars have reacted
positively to the concept of communities of practice. A handful like Henriksson (2000) criticized
that COPs may not represent organizational reality
and suggested that the concept should be kept in
alignment with existing organizational research.
In addition, Fox (2000) criticized the lack of
discussion about individuals or group abuse of
power in the research of communities of practice.
Regardless of the negative reactions, the concept
is still extremely useful for articulating collective
knowledge creation within organizations (Hara,
2009).

Attributes of Communities
of Practice
The following section discusses five attributes
of communities of practice. These are as follows: (1) a group of professional practitioners
(2) development of shared meaning (3) informal
social networks (4) supportive culture and (5)
engagement in knowledge building.
1. Group of Professional Practitioners: A
community, by definition, involves at least
two members. However, Orr (1996) pointed
out that professionals prefer autonomy. In

2.

3.

4.

5.

addition, studies showed that professionals will work in groups. It is essential that
professionals share knowledge collectively
if a community of practice is to be fostered.
Development of Shared Meaning: “As
people work together, they not only learn
from doing, they develop a shared sense
of what has to happen to get the job done”
(Hara, 2009). The information exchanged
in community of practice is communicated
among its members. This most basic social
process results in the development of shared
meanings. In order for the professionals to
communicate effectively, the development
of shared meaning and means to knowledge
exchange is essential (Wenger, 1998). Since
each communities of practice exists within
a certain context, the shared meanings that
evolved should also exist within the same
context (Hara, 2009).
Informal Social Network: Communities of
practice are sometimes difficult to identify
and isolate for a given study. They exhibit
organizational patterns that are not reflected
in traditional organizational charts. This is
because communities of practice are informal
networks that evolve organically. Wenger
(1992) supported the claim by stating that
“there is no distinction between learning
[within communities of practice] and social
participation” (Hara, 2009).
Supportive Culture: Communities of
Practice also foster a supportive culture.
More importantly, the members of the communities of practice should trust each other
– at the very least – on a professional level
(Hara, 2009).
Engagement in Knowledge Building:
Learning is a process of acquiring and identifying relationships between facts and ideas.
Communities of practice represent systems
whereby members may be able to acquire and
share information. It will thrive if the community is based on people caring about and

261


Creating a Community of Practice in Learning

taking responsibility for the other members
and for the well-being of other members of
the community. In other words, communities
of practice are based on the cultivation of
trust and mutual respect (Hara, 2009).

Practice that are linked through mergers and acquisitions (3) formal networks that span organizations but are not part of any formal relationships
and (4) self-organizing networks of individuals
(Archer, 2006).

In a related study, Wenger identified three dimensions of the relationship of practice as a source
of coherence of a community (Roberts, 2011).

1. Internal Communities of Practice: Internal
Communities of Practice are COPs that occur
entirely within the individual organization.
These communities handle explicit knowledge or intellectual capital, adopt a set of
rules for managing knowledge and provide
opportunities for sharing knowledge among
community members (Archer, 2006).
2. Communities of Practice in Network
Organizations: A network organization is
a relationship among independent organizations. Such networks have been growing
rapidly in number and scope with majority
of business organizations now belonging to
one type of network. Member organizations
in a network work in close collaboration and
continuous cooperation on certain projects
or processes, common products and even a
common strategy (Archer, 2006). Some of
the advantages of building network organizations include:
a. Faster time to market
b. Ability to concentrate on core
competencies
c. Increase in competencies due to networking with business partners
d. Need to guarantee availability of resources and materials
e. Risk and cost mitigations
f. Fresh insights derived from cross
boundary and cross organization
partnerships

1. First, community members interact with
one another, establishing relationships and
norms through mutual engagement;
2. Second, members are bound together by
a mutual understanding of a sense of joint
enterprise; and
3. Finally, members develop a shared repertoire
over time. This includes communal resources
such as include languages, routines, artifacts
and stories.
Wenger (2002), in addition, distinguishes the
three modes of belonging to a social learning
system like communities of practice.
1. First, engagement is achieved by doing
things together. An example will be taking
and producing artifacts.
2. Second, imagination is involved in creating
an image of the community, ourselves and
of the world in order to reflect on the current situation, orient ourselves and explore
possibilities.
3. Third, alignment is involved in making sure
that the local activities are aligned with other
processes so that they can be effective beyond
our own engagement.
These methods are displayed in the characteristics of communities of practice as stated by
Wenger in Table 1.
There are four classifications of Communities
of Practice that the study identified: (1) Internal
communities of practice (2) Communities of

262

From a communities of practice standpoint, an
important question to decide on is which organizations should be connected. Networks of practice
make it easier for inter-organizational exchanges
to happen and shared practice among the organiza-


Creating a Community of Practice in Learning

Table 1. Characteristics of communities of practice (Roberts, 2011), classification of communities of
practice
Sustained mutual relationships – Harmonious or conflictual
Shared ways of engaging in doing things together
The rapid flow of information and propagation of innovation
Absences of introductory preambles
Very quick setup of problem to be discussed
Substantial overlap in participants’ description of who belongs
Knowing what others know, what they can do, and how they can contribute to an enterprise
Mutually defining identities
The ability to assess the appropriateness of actions and products
Certain styles recognized as displaying memberships
A shared discourse reflecting a certain perspective on the world

tions provides a channel to share knowledge more
efficiently (Brown and Duguid, 1991). Learning
and knowledge exchange through networks may
serve as a resource generator to enhance learning.
Powell, Koput, and Smith-Doer (1996) suggested that the focal innovation of an industry
that is operating using complex processes will be
found in inter-organizational networks of learning
rather than within individual firms (Archer, 2006).
In a network organization, knowledge sharing is encouraged through a network agreement,
aided by knowledge transfer and learning through
various channels such as communities of practice.
With such networks, there is always a risk of
knowledge leakage to other competing organizations (Archer, 2006).
3. Network of Practice: Network of Practice
is an open activity system focused on work
practices and may exist primarily through
electronic communication. People participating in a network of practice normally work
on similar occupations and have similar interests. They gather to engage in knowledge
exchange about problems and issues that are
common to their occupations and shared
practice (Archer, 2006).

4. Formal Network of Practice: A formal
network of practice differs from network of
practice since the former has a membership
that is controlled by fees/ and or acceptance
through some central authority that also assist in organizing, facilitating and supporting
member communications, events and discussion topics. This is similar to a professional
or non-profit association, although they
are classified more as an affinity network
(Archer, 2006).
5. Self-Organizing Network of Practice:
A self-organizing network of practice is
a loosely organized and informal network
that has no central management authority
or sponsor. Membership is voluntary and
there is no explicit commitment. Most of
these types of networks operate virtually, so
communication strategy is primarily based
on knowledge codification. People participate in such networks due to their affiliation
with a profession rather than an organization.
A good example of such network is Usenet
groups (Archer, 2006).

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Creating a Community of Practice in Learning

Differences among the
Classifications
Major differences of note among the classifications were seen in the following characteristics:
(Archer, 2006)
a. The type of knowledge transferred and the
desired objective or outcome;
b. Funding;
c. Intellectual property;
d. Dispute Resolution;
e. Potential Knowledge contribution;
f. Professional expertise;
g. Potential problems including maintaining
interest and contribution; and
h. Remediation of operational problem

FRAMEWORK FOR USE OF ICT
IN COMMUNITIES OF PRACTICE
According to Wenger (1998), the purpose of the
existence of communities of practice is to create
a common area for individual meetings in order
to interact, exchange and assimilate experiences
around application areas with clearly defined objectives. These interactions lead to the innovation
and development of the core competencies of the
company (Campus et al, 2011).
The common area, therefore, should use the
cycle of knowledge reception, diffusion, assimilation and renovation in the organizational
database, structuring experience and facilitating
its members’ contributions. In this manner, we
may be able to apply to COP, as an agent, the
whole knowledge governance model based on the
seven strategies defined below: technology and
market watch, tacit knowledge management, communications model, individual and organizational
learning; quality and Research and development
(Campus et al, 2011).
On the other hand, COPs should also facilitate
the relation among community members beyond

264

just information exchange. The dynamic exchange
is only possible if the internalization of mission
and objective occurs within the context of the
community. This is because the internalization
would facilitate the flow of the interaction. As
a result, it will encourage cohesion amongst its
members (Campus et al, 2011).
COPs hinge on three pillars which is the basis
on the management framework and the necessary
tools to support it (Campus et al, 2011).
•
•

•

Technology provides COPs with the necessary tools to create effective areas of collaboration from the operational standpoint.
The necessary culture and environment to
meet the objectives of the community, the
organization and its individuals. The goal
is to achieve an identity and generate policies and appropriate management models grounded on training, awareness and
motivation
And the management model through which
the rules of the game are established, the
work processes, the role of actors, knowledge types and the associated taxonomy.

The creation of COP is linked to two approaches: push and pull (Campus et al, 2011).
The push approach, declared by the organization, is communities of practice that is decided
and chosen by the leaders based on the strategic
direction of the organization.
The pull approach, is based on providing
resources and support the group in developing
successful collaboration within the organization.
The challenge is to find a reasonably grounded
and practically applicable theoretical foundation
for developing and evaluating knowledge management process and information technology in the
area of volunteer sector. Research that focuses on
the pull approach is still lacking and important in
order to fully understand both approaches (Campus
et al, 2011)


Creating a Community of Practice in Learning

More recently, Dube, Bourhis and Jacob (2006)
developed a typology of online communities of
practice that has four dimensions: demographics,
organizational context, members and technological environments. However, the typology they
developed was developed independent of face –
to – face communities of practice (Hara, 2009).
When considering online communities of
practice, it is evident that there is a need to investigate how online COPs differ from face-to-face
communities of practice.
Another known framework to study online
knowledge sharing is Cyber Ba, Literally “cyber
place” (Nonaka and Konno, 1998). Basically,
Cyber Ba is an environment for distributing explicit knowledge to other members. It supports
internalization phase whereby focused training
with senior mentors and colleagues consists primarily of continued exercises that stress patterns
and working on those patterns. However, it does
not address the issues of collective learning and
identity formation (Hara, 2009).

IT plays an important role in supporting communities of practice. The proponents distinguish
three categories: (1) supporting social actions
inherent in COPs, (2) supporting different stages
of COPs’ lifecycles, and (3) adaptive use of collaborative technologies that assist knowledge
management issues and requirements.

Supporting Social Actions
Inherent in COPs
Ngwenyama and Lyytinen proposed a framework
that indicates four cluster areas that identifies
what type of ICT tool would be appropriate for
the tasks needed. The cluster areas are as follows:
Instrumental action or research tools (example:
document management system), communicative
action or communication tools (example: email),
discursive action or groupware tools (example:
online messaging) and strategic action (intelligent
agents). (DOTSIKA, 2006) Please refer to figure 2
on the next page for the structure of the framework.

Figure 2. Detailed research design and strategy

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Creating a Community of Practice in Learning

Figure 1. Supporting social actions supporting different stages of a COP’s lifecycle

Table 2. Wegner’s five stages of COP adaptive use of collaborative technologies
Stages

Main Functions

IT Enabling technologies

1

Connect, plan, commit

Email, e-conferencing, list servers, online forums, corporate intranet

2

Form framework, create context

Same as above, plus remote login, facilities transfer and info repositories

3

Operate, collaborate, grow

Same as above plus online directories, analytical tools, intelligent agents, feedback
facilities and portals

Sustain, renew, maintain, wind
down
4

Shut down

Knowledge repositories may remain for future communities

According to Wenger’s Communities Evolution model, five stages were identified: potential,
coalescing, active, dispersed and memorable. At
the end of the model, the community disappears
but the knowledge remains in stories and artifacts.
(Dotsika, 2006) Table 2 maps the five stages
with their main functions and possible relevant
technologies.
Another set of ICT used in support of COPs
are tools that usually support the above action
categories and different stages of the lifecycle.
The tools are as follows: knowledge management
suites, portals and collaboration tools or groupware
(Dotsika, 2006).
Frameworks identified for communities of
practice mainly adhere to the push approach. Two
such frameworks are used as basis for Systems

266

Defense and Engineering Firm (SDE) and the
Spanish nuclear power plants (Campus et al, 2011).

The Use of ICT in
Communities of Practice
According to Checkland and Holwell (1995),
the main role of an information system is that
of a support function that assists people in their
different activities of actions. However, many
of today’s information systems are difficult to
learn and awkward to use; the current information systems change the users’ activities in ways
that the users do not want. Therefore, the use of
information technology in communities of practice
must be flexible and employees should be able to
adapt technologies based on the local needs. Much
research examines the role of ICT in supporting


Creating a Community of Practice in Learning

work activity (e.g. Galegher, Kraut and Egido,
1990), the interest is mainly focused on how ICT
can support distributed communities of practice
which has grown with the widespread use of the
Internet (Hara, 2009).
Despite the enthusiasm of some scholars, online communities of practice have a tendency to
be a hit or miss proposition. Some are successful
while some are not. INDISCHOOL is one example
of the successful online communities of practice
that generated over 87000 teachers as of December
2005 (Hara, 2009).
There are also evidences that ICT-supported
strategies for COP development work better than
ICT-led strategies. (Kling and Courtright, 2003)
ICT moves from being an underlying infrastructure
to the linking mechanism. Giles Grant of BNFI
states that “IT should only be an enabler for sharing and collaboration. It isn’t the community. The
community is the people”. Therefore, the best
software to use, according to Nick Milton of Knock
(KMOnline, 2004), is the one that the community
is most familiar with and is most prepared to use
(Clarke, 2006).
1.
2.
3.
4.
5.
6.
7.
8.

Ongoing interactions;
Work;
Social structures;
Conversation;
Fleeting interactions;
Instruction;
Knowledge exchange; and
Documents

REVIEW OF RELATED SYSTEMS
These program suites include – Tomoye, community Zero, iCohere, and Communispace – were
strongly oriented towards fleeting interactions and
instructions. However, they have weak support
of social structures, knowledge exchange and
documents. It also includes a local search, an
expert’s database, discussion and events notification facility. None provided video and audio supported meetings or webinars. All, except Tomoye,
provided community governance and polls (See
Table 3 for the features of COP software).
The next section will showcase three different
Communities of Practice software packages and
its diverse features and offerings.

ICohere Communities of
Practice Software
ICohere provides a platform for engaging and
building sustainable collaborative communities
of practice. By creating focused connectivity
across geographic, business and cultures, COPs
use the collective knowledge to arrive at new ideas
to move organizations forward. (iCohere, 2011).
The web community software draws on four
key areas of strengths: Relationship building,
collaborative learning, collaborative knowledge
sharing and project collaboration. These areas
are integrated within a set of transformational
strategies and engaging face to face and online
facilitation. (iCohere, 2011) Please see Table 4.

Table 3. Features of COP software (Clarke, 2006)
Relationships
Member networking profiles;
Member directory with
“relationship-focused” data
fields; Subgroups that are
defined by administrators or
that allows members to selfjoin, Online meetings, online
discussions

Learning

Knowledge

Action

Recorded PowerPoint
presentations; e-learning
tools; Assessments; Web
Conferencing; Online meetings;
Online discussions; Web site
links

Structured databases; Digital
Stories; Idea banks; Web
conferencing; Online meetings;
Online discussions; Expert
database and search tools;
Announcements; web links

Project management; Task
management; Document
collaboration; File version
tracking; File check-in and
check-out; Instant messaging;
Web conferencing; ; Online
meetings; Online discussions;
Individual and group calendars

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Creating a Community of Practice in Learning

Table 4. iCohere process (iCohere, 2011)
Traditional Strategies

Engaging Group Processes and Facilitation

- Relationship Building
- Collaborative Learning

- Collaborative Knowledge Sharing
- Project Collaboration

ICohere platform combines traditional features like online discussions, instant messaging,
document management and searchable member
profiles with simple and powerful tools like web
conferencing and streaming that will sustain and
energize the community involved.(iCohere, 2011)

Communispace Software
Communispace is a communities of practice
software that helps organizations deeply engage
with their customers through delivering insights
and thus creating business results. The company
was started in 1999 and it offers full service community capabilities from customer insights, expert
facilitation, member recruitment and analysis reporting. Features that aid in ensuring full service
community are web 2.0, social networking and
online communities.

Community Zero
Community Zero is a web based platform that
allows organizations to build secure, scalable and
online communities to engage the customers and
improve business performance. With over a decade
of offering hosted service and used by more than
2 million users, community zero easily scales to
support multiple communities and addresses the
organization’s various collaboration and information requirements (Community Zero, 2011).
The features of Community Zero are as follows:
•
•

268

Collaboration through centralized calendars, polls and member directories;
Communication through email, polls and
RSS feeds;

•
•
•

Control and Customization through personalized site URL’s, community tools and
content moderation;
Reports and Analytics through Google analytics and detailed reports; and
Architecture and Security through SSL
certification and persistent storage.

Future Trends of
Communities of Practice
There is increasing evidence that COPs are being
formalized into organizational structures with
budgets, resources and tasks and thus become
more like project teams with an aim and strategy
(Hara, 2009).
There has also been significant growth in the
number of network organizations in their various
forms, due to a variety of influences. This is a trend
that will continue and communities of practice
encouraging learning and sharing knowledge
within and among firms are an important aspect
of success in this endeavors. All of these forms of
communities of practice will continue to grow in
order to encourage the application of knowledge
through sharing and collaboration (Hara, 2009).
In the next section, the proponents looked at
the area of social media in the field of education
and learning.

THE INTERNET AND SOCIAL MEDIA
Without a doubt, the Internet is impacting people
from all walks of life. There is a whole generation
today who have grown up being connected to the
Internet. Charles H. F. Davis calls them digital


Creating a Community of Practice in Learning

natives – people who have never experienced not
having the Internet. These are the men and women
who were born in the 1990’s where the popularity of the World Wide Web exploded. They are
now in their late teens to early 20’s and currently
university-level students.
Social networking or social media came in
the early 2000’s. Popular social networking sites
include: Friendster (2002), MySpace (2003),
LinkedIn (2003), Facebook (2004), Twitter (2006).
As of October 2012, there are 1 billion active
Facebook users worldwide (Dan Rohr, 2013).
The Philippines has 31 million Facebook users
which is equivalent to a 30.12% penetration rate
(Market Insight, 2013). According to a study done
by the Harvard Institute of Politics in 2011, 90%
of college students have profiles on Facebook.

Usage of Social Media
Social media refers to web-based and mobile applications that allow people to easily create content,
share information or resources, and to engage
with other people in online conversations. The
content can be in the form of plain text, images,
audio or video podcasts, and even live streaming
of audio or video.
Most usage of social media are people sharing
personal information about themselves, their family, work, or things that interest them. The social
media platform allows people to let others know
their connections with others (Boyd, 2012). These
connections could range anywhere from bosses,
work colleagues, friends, acquaintances, to close
family members such as siblings or spouse. In
Facebook, people regularly upload pictures with
their friends and re-live the experience that they
had when the picture was taken. Conversations that
begun during the offline experience are continued
online via comments made on the picture.
It is only in the last couple of years that businesses took notice that people were spending a
lot of time on social media. They realized that
their customers as well as prospective custom-

ers were on the social media platform and they
could actually engage with them personally. The
era of social media marketing came about with
companies putting up their own Facebook pages
left and right. Lots of books were written to give
companies tips and tricks on how to do social media
marketing (Safko, 2010). Even the traditional mass
media big-three of TV networks, radio stations,
and print (newspapers and magazines) are now
interacting with their audience on social media.
Live TV shows get immediate feedback and comments from people via Twitter & Facebook posts
and even broadcast them back on air.

Social Media and
Educational Institutions
Since businesses have jumped on the bandwagon
of social media, educational institutions are not to
be outdone. Universities and colleges have created
their own blogs to highlight stories of their current
students and graduates. The life and culture of the
school are shared through personal stories. This
helps prospective students decide whether or not
they want to enroll in that institution. The usage of
social media here is more for marketing purposes.
Educational institutions are also using social
media for broadcasting announcements. For
example, if there is a suspension of classes, the
administrator could simply post a status on their
Facebook page and the students who are online
would immediately see the announcement and
could share it with their friends in just a few
mouse clicks. In addition, reminders or upcoming events could also be advertised to the student
body. The usage of social media here is more for
notification purposes.
Social media technology is also being used to
bridge the gap between the academic and social
dimensions of a person (Lang, 2012). In particular,
when a student goes to a foreign country, he has
to make new social connections. Social media
technology can aid in this aspect and indirectly
impact the learning process of the student.

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Creating a Community of Practice in Learning

Social Media and Learning
The idea of using social media to aid in the learning
process has not been explored that much yet. One
obvious reason for such is that social networking
sites have only been around for just a little more
than a decade. Distance education and online
learning have been existing for a longer time but
they usually use systems which are separate from
social networking sites. Recent research is beginning to show how social media is helping adult
learners learn in an informal learning environment
(Heo, 2013).
A Facebook group was also used in supporting
a group of business education students undertaking teaching practicum. The study found that there
was a good amount of user engagement/posts in
the group (English, 2008).
But in general, there is still a lack of research
in the usage of social media for learning. Furthermore, there is a need to create a framework to
assess the impact of social media in IT Education.
(Davis, 2012).
Being teachers in a higher education institution,
this challenge led us to wonder if we could aid
the learning process by supplementing classroom
discussions with an online community via social
media. Will the students learn more? Will the
sharing of online resources motivate students to
learn the subject? Will peer-to-peer interaction
via social media encourage students not to get
passive or get stuck? These questions drove us
to create an online community of practice using
social media technology.

CREATING AN ONLINE COMMUNITY
OF PRACTICE USING SOCIAL
MEDIA TECHNOLOGY
The Facebook group was created way back in
2011. The subject was about web design technologies. It was initially created to allow the students
to have a medium to check requirements and

270

announcements. Term after term, students were
added to the group. It eventually developed into
a community composed of current students, past
students, alumni, and instructors. There are now
over 350 members in the group.
The instructors are the group administrators
and the prime movers of the group. They share
resources and encourage the students to contribute
and interact with each other. The members of the
group are motivated to share information about
web design voluntarily. The alumni are encouraged
to help the students who are just new to learning
web design. Professors and alumni help one another as peers in order to deepen their expertise
and knowledge of the subject areas.
Some of the knowledge shared include: technical information, technology trends and developments, Massive Online Open Courses (MOOC’s),
job and career opportunities, Youtube videos,
quizzes, and Student- Alumni coaching sessions.

ISSUES, CONTROVERSIES,
PROBLEMS
Although there is a clamour for social media in
education, literature has discovered gaps within
the system that may hinder collaboration and
knowledge sharing in the area of social media
•
•

Lack of Research in the area of social media and it’s use and purpose in postsecondary education (Davis, 2012)
Need for framework for evaluating impact
of Social Media in IT Education (Davis,
2012)

Given the research gaps, the proponents seek
to address the given research questions:
•

How do online communities of practice
engage students to learn and build new
knowledge?


Creating a Community of Practice in Learning

SOLUTIONS AND
RECOMMENDATIONS

DETAILED RESEARCH
DESIGN AND STRATEGY

In order to achieve its research objectives and
targets, the proponents adhere to the constructivist
approach in qualitative research, based on the systems thinking school. In addition, the research used
the hybrid approach based on Checkland’s soft
systems methodology and Strauss and Corbins’
version of grounded theory.
The hybrid methodology allows the proponent to develop a framework on communities of
practice that is acceptable to all the participants.
Therefore, the methodology used is both participant and researcher centered. In order to complement the research methodology, the proponent
used single case embedded design to analyze the
given phenomenon. Results are collected using
data triangulation in the form of interviews, focus
group discussions, and participant observations.
Using the approach, the proponents merged the
seven phases of the two methodologies into a fivestep process as used in the inductive-hypothetical
research strategy.
The next section shows the detailed research
design that the research project adhered to.

In order to create a framework that represents the
communities of practice that exist in the Facebook
group, the proponents used the hybrid methodologies of grounded theory and soft systems
methodology and did the following: 1. Assess
the existence of communities of practice using
codes identified 2. Identify common themes and
categories 3. Create a paradigm model to describe
the communities of practice and 4. Create a framework that guides educators in creating their own
communities of practice.

QUALITIES OF COMMUNITIES
OF PRACTICE
The first element that the proponents evaluated is
the existence of the communities of practice. In
order to evaluate if the communities of practice
existed in the Facebook group, the proponents
compared different definitions of communities
of practice with the codes identified in the web
design Facebook group.

Table 5. Comparison of definition of communities of practice
Author

Definition

Web Design Communities of Practice

Wenger

Groups of people that share a concern, a set of
problems, or a passion about a topic and who deepen
their knowledge and expertise in this area on an
ongoing basis

     √ The members of the community share the
common objective of learning more about web design
technologies and solving complex problems on
technology

Sergio Vasquez

Group of people linked by a common, recurring and
stable practice whereby they learn in this common
practice

     √ The web design community of practice is a
group that constantly and consistently engaged with its
members on the topic of web technologies

Lesser and Storck

A group whose members regularly engage in sharing
and learning based on their common interests

     √ The web design community of practice constantly
updates the Facebook group with relevant information
regarding web design and applications

John Brown

Group of people with different functions and
viewpoints, committed to joint work over a significant
period of time during which they construct objects,
solve problems, invent, learn and negotiate meaning
and develop a way of reading mutually.

      √ The web design community solves Web related
problems & shares opinions in solving complexproblems

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Creating a Community of Practice in Learning

Taking into consideration four different definitions of Communities of practice, it validates the
existence of the communities of practice in the
online web design group.
The next step taken by the proponent is to analyze the qualities of the communities of practice
and identify common themes from the codes taken
using data analysis.

Evaluation of the Online
Community of Practice
To evaluate the online community of practice, the
proponents performed interviews, focused group
discussions, participant observations, and surveys.
The proponents used the Grounded Theory by
Corbin and Strauss and performed Open Coding
and Axial Coding of the data we have gathered
from the surveys and interviews.
The main categories that came out of Open
Coding include the following:
1.
2.
3.
4.

Community Engagement
Effectiveness
Commitment to Sharing and Learning
Self-improvement

Figure 3. Themes of communities of practice

272

Community Engagement
Many of the members indicated that the online
community of practice motivated them to study
because there is a strong sense of belongingness
in the group. By knowing that other like-minded
peers are there, studying alongside you, and willing to help you out when you get stuck, they are
encouraged to push forward.
In addition, members are motivated to share
knowledge to other members since there is a
common topic and perhaps there is a “pay-itforward” effect of sharing. When one benefits
from a resource shared by another, that person
is motivated to share other resources that he has
also found to be helpful.
Finally, the shared resources serve as a form
of reference for the members. When members
encounter problems in doing their projects, they
can come back to the Facebook group, look at the
references, solve their own problems, and move
forward in completing their projects.

Effectiveness
The second category that was uncovered from the
data analysis is effectiveness. The surveyed mem-


Creating a Community of Practice in Learning

bers said that the online community of practice
was helpful for them. For the current students,
they had higher motivation to learn and work on
their projects compared to another class which
had their own Facebook group but didn’t have that
group serve as an online community of practice.
This effectiveness was driven by mutual trust
among members. Since they knew that they were
on this journey of learning web design together
and that they were there for each other, they were
motivated to study and learn. In addition to that,
they knew that those who went ahead of them including those who have passed the course already
or who have graduated and are already in the IT
industry, are also in the group willing to help them
anytime they had a hard time finishing their work.

Self-Improvement

Commitment to Sharing
and Learning

In order to properly explain the community of
practice that is present in the web design group, the
proponents used the model paradigm prescribed
by axial coding of Grounded theory to discuss the
phenomenon of community of practice.
The paradigm model is divided into five
conditions: causal conditions, the phenomenon,
context, intervening conditions and consequences.
The model allows the proponents to identify the
relationships between the categories and the
subcategories.
In addition, the reasons why the community
of practice is present are due to the continuous
community engagement and the commitment
to learning and sharing. The strategies that the
community used include encouraging members
to contribute by giving incentives and moderating
posts to ensure quality. Please see Figure 5 for the
paradigm model of the community of practice.
The Community of Practice framework takes
into account inputs, four main elements and
outputs. The inputs to the framework include
information that the members contribute to the
community. It also takes information that the
learners share on the topic of web design.

The third category that came out of the data
analysis is that there is a commitment to sharing
and learning within the Community of Practice.
Some members continue to share resources even
if they are not part of the ongoing class anymore.
These members include past students, present and
past faculty, and even those who have graduated
already.
One factor that has greatly contributed to this
is that the instructor himself exhibits an attitude
of continuous learning. Although he is already
a teacher, he also takes the role of a continuous
learner. The instructor actively contributes to the
group and as a result, encourages the students to
share as well.
On the part of the alumni, there is also a
willingness to share and mentor current students.
Being those who have been on the receiving end
of mentoring in the past, they now want to mentor
others in return.

Finally, a desire of self-improvement is evident
among the members of the group. This serves as
the underlying motivation why they want to learn
more. They want to learn more about web design
because ultimately, they improve in their skills and
would be more employable in the future.
Besides learning about web design, they also
learn about the latest job or career opportunities
and the latest trends / technologies of web design.
Finally, they are also able to build relationships
with peers who may become colleagues or even
possible business partners in the future.

Model Paradigm

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Creating a Community of Practice in Learning

Figure 5. Technology framework of community of practice

There are four elements that are integral in a
COP’s success. These elements are as follows:
area of responsibility/activities, types or modes
of knowledge passed, technology support features and actions that the COP’s do in the area
of learning.
The output contributes to the process of new
knowledge and best practices that may be useful
for other community members. Given the model
paradigm and themes identified, the proponents
were able to create a technology framework that
depicts the creation of the online community of
practice through social media.
Important aspects of the technology framework
include five components: (1) activities done in
the community (posts, videos, alumni-student

274

coaching, games, sharing best practices among
others), (2) technology support features (timeline,
files, likes, ask question feature among others),
(3) qualities of COP’s (community engagement,
effectiveness, self-improvement and commitment
to sharing and learning), (4) different knowledge
types and modes (socialization, externalization
and combination) and actions taken (communicate, collaborate and integrate knowledge). By
combining these elements, the community will be
able to gather best practices in web design, solve
problems and create new insights and knowledge.


Creating a Community of Practice in Learning

Figure 4. Paradigm model of communities of practice

FUTURE RESEARCH DIRECTIONS
The current research is a good initial study in the
area of communities of practice using social media.
The next step of the research includes validating
the results of the study with an external audience
and other experts in the field of communities of
practice and collaboration.
Subsequently, further studies may be conducted
to examine the impact of behavioral characteristics (power and position) in the dynamics of
collaboration inside the group. A comparative
study may also determine if the framework can be
similarly applied to other social media groups like
Google+, LinkedIn, or any similar social media
platform. Other similar sectors like training and
development or government agencies may also

adopt the framework and evaluate if it is effective
in knowledge sharing and innovation.

CONCLUSION
The communities of practice present in the Web
Design Facebook group is an informal group that
was formed due to the commitment of learners to
learning and acquiring new knowledge, effectiveness of collaborative activities and commitment
to sharing and self-improvement.
Based on the hybrid implementation of
grounded theory and soft systems methodology, the proponents were able to identify core
categories and best practices in the organization.
In order to establish the relationship between the

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Creating a Community of Practice in Learning

categories, the proponents were able to create
a model paradigm that effectively analyzes the
given phenomenon. Using these categories and
existing literature, the proponents were able to
create a technology framework that provides a
guide for communities of practice to collaborate
effectively using Facebook groups.
The framework shows that the communities of
practice inside the Web Design Facebook group
are composed of five different elements: qualities
of the communities of practice, areas and activities where knowledge building and collaboration
is delivered, types of knowledge generated and
the role of technology in supporting the different
activities of the communities of practice.

REFERENCES
Archer, N. (2006). A classification of Communities of Practice. In Encyclopedia of Communities of Practice in Information and Knowledge
Management (p. 21). London: Idea Group.
doi:10.4018/978-1-59140-556-6.ch005
Boyd, D. M., & Ellison, N. B. (2012). Part VIII:
Social Network Sites. Online Communication and
Collaboration. Reading (Sunderland).
Campos, et al. (2011). Sharing Knowledge through
Communities of Practice. Hershey, PA: IGI Global.
CheckLand. (1999). Systems Thinking Systems
Practice. Chichester, UK: John Wiley.
Clarke, C. a. (2006). The Concept of Communities of Practice. In Encyclopedia of Communities of Practice in Information and Knowledge
Management (p. 92). London: Idea Group.
doi:10.4018/978-1-59140-556-6.ch010
Clarke, E. C. (2006). Encyclopedia of Communities of Practice in Information and Knowledge
Management. Hershey, PA: Idea Publishing.

276

Communispace. (2011). Communispace Official
Website. Retrieved October 20, 2011 from http://
www.communispace.com/home.aspx
CommunityZero. (2011). Community Zero Official Website. Retrieved October 19, 2011 from
http://www.communityzero.com
Davis, C. (2012). Social Media in Higher Education: A Literature Review and Research Directions.
The Center for the Study of Higher Education at
The University of Arizona and Claremont Graduate University.
Dotsika, F. (2006). Article. In E. Coakes & S.
Clarke (Eds.), An IT Perspective on Supporting
Communities of Practice, Encyclopedia of Communities of Practice in Information and Knowledge
Management (pp. 257–263). Idea Group Inc.
English, R., & Duncan-Howell, J. (2008). Facebook© goes to college: Using social networking
tools to support students undertaking teaching
practicum. Journal of Online Learning and
Teaching, 4(4).
Hara, N. (2009). Communities of Practice Fostering Peer to Peer learning and Informal Knowledge
Sharing in the Workplace. Springer.
Heo, G. M., & Lee, R. (2013). Blogs and Social
Network Sites as Activity Systems: Exploring
Adult Informal Learning Process through Activity Theory Framework. Journal of Educational
Technology & Society, 16(4).
iCohere. (2011, August 24). Icohere information.
Retrieved August 24, 2011, from Icohere official
website: http://www.icohere.com/webcommunities.htm
Market Insight. (2013). Socialbakers. In Philippine
Facebook Statistics. Retrieved March 25, 2013,
from http://www.socialbakers.com/facebookstatistics/philippines


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Roberts, J. (2011). A Communities of Practice
Approach to Management Knowledge Dissemination. Hershey, PA: IGI Global. doi:10.4018/9781-60566-802-4.ch001
Safko, L. (2010). The Social Media Bible: Tactics.
Tools, and Strategies for Business Success.
Socialbakers. (2013). In Philippine Facebook
Statistics. Retrieved March 25, 2013, from http://
www.socialbakers.com/facebook-statistics/philippines
Vicedo, K. (2011). Proceedings of Knowledge
Management Workshop. Philippines: CNDR.
Wenger, E. (2002). Cultivating Communities of
Practice. Harvard Business Review Press.

KEY TERMS AND DEFINITIONS

open coding, by making connections between
categories.” They proposed a “coding paradigm”
that involved “conditions, context, action/ interactional strategies and consequences” (Strauss &
Corbin, 2008).
Communities of Practice: Collaborative,
informal network that support professional
practitioners in their efforts to develop shared
understandings and engage in work-relevant
knowledge building.
Formal Network of Practice: Network of
practice that requires membership that is controlled
by fees/ and or acceptance through some central
authority that also assist in organizing, facilitating
and supporting member communications, events
and discussion topics.
Open Coding: Includes labeling concepts,
defining and developing categories based on their
properties and dimensions (Khandkar, 2013).

Axial Coding: “A set of procedures whereby
data are put back together in new ways after

277

278

Chapter 13

Influence of Perceived
Quality of Official University
Websites to Perceived Quality
of University Education and
Enrollment Intention
Achmad Nizar Hidayanto
Universitas Indonesia, Indonesia
Fanny Rofalina
Universitas Indonesia, Indonesia
Putu Wuri Handayani
Universitas Indonesia, Indonesia

ABSTRACT
This chapter aims to analyze the impact of a university’s website quality to the intentions of prospective students to enroll at the university. The proposed model was tested by distributing questionnaires
to third-year senior high school students around Jakarta. Respondents were asked to follow a series of
instructions to access the websites of two universities, the University of Muhammadiyah Malang and the
Indonesian Islamic University. After completing the task, respondents were asked to complete a questionnaire to evaluate website quality. Based on the analysis of 117 valid questionnaires, it is concluded
that website quality influences the perception by prospective university students of quality university
education, which subsequently affects the intention of prospective students to enroll at the university.
The finding confirms that the quality of official websites can be used as an extrinsic attribute to signal
the quality of education at the university; thus, its optimal maintenance must be endeavoured.

DOI: 10.4018/978-1-4666-7262-8.ch013

Copyright © 2015, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.


Influence of Perceived Quality of Official University Websites

INTRODUCTION
The development of information technology has
rapidly revolutionized the world. The commercialization of the Internet, web technology, and
its application in the 1990s have brought about
the development of the phenomenon in businessto-consumer (B2C) electronic commerce (Chen,
Rungruengsamrit, Rajkumar, & Yen, 2013; Lin &
Lu, 2000; Scaglione, Schegg, & Murphy, 2009).
The emergence of electronic commerce or commonly referred to as e-commerce has provided
a powerful marketing channel for commercial
organizations (Maity & Dass, 2014; Ramanathan,
Ramanathan, & Hsiao, 2012; Samiee, 2008). Via
the web, organizations can engage consumers, and
not only provide information but also sell products
and services online.
However, e-commerce marketing channels
have their limitations. All trading activities
conducted through websites, as well as all interactions are mediated by technology offered on
websites (referred to as a technology-mediated
environment). All interactions are facilitated by
the website, leaving consumers unable to directly
evaluate products and services to be purchased
or traded as can be done in a conventional store.
Therefore, the ability of consumers to assess the
quality of traded products and services is reduced.
This channel limitation impacts product experience. Experience products are products whose
quality can be accurately evaluated only after the
products are purchased and used for a specific time
period that is relatively shorter than the products’
total life usage (Ford, Smith, & Swasy, 1988).
Examples of experience products include clothing,
food, electronics, and others. Consumers need to
interact directly with the product before they can
determine its quality, such as the need to be first,
felt, executed, and so on.
Services differ from products. Not like products
which can be measured quantitatively, services
are intangible, cannot be measured, counted, and
tested. Service is heterogeneous and its value

changes from one consumer to another (Bateson,
1977; Zeithaml, 1981). Most previous research
work investigated quality of services in a traditional situation, i.e., transactions occur in the real
world. Various models to measure service quality
have also been developed. These measurement
models typically use facilities, personnel quality,
service delivery, service providers’ management,
and others as indicators of service quality (Cheng
& Tam, 1997; Parasuraman, Zeithaml, & Berry,
1988; Shi, Prentice, & He, 2014; Vera & Trujillo,
2013). Many services are currently offered online
(e-services) for example translation services,
e-learning, e-government services, etc. Hence,
limitations encountered in e-commerce marketing channels are encountered in the context of
e-services.
Some previous researchers have investigated
how virtual product experience (VPE) available
via the web interface can deliver visual product
attributes better (Chen, Hsu, & Lin, 2010; Coyle
& Thorson 2001; Jiang & Benbasat, 2004; Lee,
Kim, & Fiore, 2010; Li, Daugherty, & Biocca,
2003; Liu, Li, & Hu, 2013; van Noort, Voorveld,
& van Reijmersdal, 2012). However, e-commerce
channels still have limitations in delivering experiential product attributes compared to physical
stores, especially the attributes of services. There
were some efforts to improve customer perception
of product quality, one of them through signaling
theory. Signal theory is effective to understand
signals that consumers use to make an accurate
assessment of quality when faced with limited
information about a product or service. The signals commonly used in the sale or transaction in
the real world or a conventional store are brand
(Erdem & Swait, 1998; Magnini, Karande, Singal,
& Kim, 2013; Rubio, Oubiña, & Villaseñor, 2014;
Wu, Yeh, & Hsiao, 2011), seller reputation (Chu
& Chu, 1994; Das, 2014), price (Dawar & Parker,
1994; Kukar-Kinney, Ridgway, & Monroe, 2012;
Marian, Chrysochou, Krystallis, & Thøgersen,
2014; White & Yuan, 2012), and store environment
or transaction place (Baker, Grewal, & Parasura-

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Influence of Perceived Quality of Official University Websites

man, 1994; Bao, Bao & Sheng, 2011; Wu, Yeh,
& Hsiao, 2011). Amongst these signals, store
environment or transaction place can be equated
with an e-commerce site (Watson, Berton, Pitt, &
Zinklan, 2000). Through signaling theory, we can
explore how the signal, such as website quality,
can be used to indicate the quality of the product
or service when product or service attributes cannot be directly evaluated.
Our research focused on educational services,
particularly in higher education. Almost all universities equip themselves with online marketing
channels, such as their official websites. The web’s
broad reach, range of interactive capabilities, and
the rapid development of web usage in the world
have made web technology more important to
universities for promotional and commercial
purposes (Djajadikerta & Trireksani, 2006). A
university’s official website is akin to a retail site
which sells products and services. In the context of
the university as an educational service provider,
prospective students hailing from different places
who wish to enroll at university are its customers.
Similar to the context of the product, prospective
students also have limitations in determining the
quality of education that a university offers as they
are not able to see first-hand how the teachinglearning process is carried out at the university, the
campus environment and facilities, the university’s
student life, and so on. Coupled with the challenge
of communicating the attributes of the service or
services in a technology-mediated environment,
the university website is a potential strong signal
to evaluate service quality.
A previous study has been conducted to investigate how to communicate the quality of their
products online using signal theory by Wells,
Valacich, & Hess (2011). Signaling theory in
general is a framework for understanding how
two parties convey hidden information during
interaction, transaction, or agreement (Spence,
1974). Wells, Valacich, & Hess (2011) used
quality of retail website as a signal to influence
consumer perceptions of the products sold on the

280

website. Their findings showed that perception of
product quality affects the purchase intentions of
the products. However, there has been no further
research on how to communicate the quality of
the service or e-services, especially educational
services offered by university. Through this research, we aim to contribute to the use of signaling
theory to predict how website quality affects the
perceived quality of university education, which
then affects the intentions of prospective students
to enroll at the university.

BACKGROUND
Various models have been developed to measure
the quality of educational institutions, especially
universities. For this paper, we used the model
of education developed by Cheng & Tam (1997)
to determine the attributes of quality university education services: Goal and specification
model, Resource-input model, Process model,
Satisfaction model, Legitimacy model, Absence
of problems model, and Organization learning
model. In the following subsections, we explain
some theories as the foundations of our research.

Information Asymmetry
Information asymmetry is a concept in economy.
Economic modeling often assumes the market is
perfect, all information needed is present, both
from a seller’s and buyer’s perspectives. However,
in reality, the market is imperfect; much information is hidden, especially in transactions.
Mankiw (2010) defines information asymmetry as different access to relevant knowledge. Based
on this definition, one party possesses relevant
information while others do not. Asymmetry of
information or information gap generally occurs
when a transaction takes place between two parties. This happens for several reasons. It can be
due to limited time, limited knowledge to properly
evaluate intrinsic attributes, or the unavailability of


Influence of Perceived Quality of Official University Websites

Table 1. Comparison of attributes for computers and university education
Computers

University Education

Extrinsic
Attributes

Brand, price, seller reputation and store environment

Brand, University image, Tuition fee, University location

Intrinsic
Attributes

Processor, RAM, Hard disk, Video graphic card,
Operating System

Indicators on seven models of education quality
introduced by Cheng & Tam (1997)

intrinsic attributes for evaluation. When consumers cannot evaluate intrinsic attributes properly,
the assessment of the quality of product/service
becomes less accurate.
This study focuses on information asymmetry
from a consumer’s perspective. Prior to purchasing
products/services, consumers want to make sure
of product quality. They evaluate and consider
two types of attributes of the product/service,
namely extrinsic and intrinsic attributes. Extrinsic
attributes are attributes that are not attached (not
inherent) to the products/services, any change in
these attributes will not change the nature of the
products/services (Lawley, Birch, & Hamblin,
2012; Richardson, Dick, & Jain, 1994; Veale &
Quester, 2009). Intrinsic attributes are attributes
that are attached to the products/services; if
changed, it would change the nature of products/
services (Lawley, Birch, & Hamblin, 2012; Richardson, Dick, & Jain, 1994; Veale & Quester,
2009). Examples and comparisons of extrinsic and
intrinsic attributes of computers as products and
university education as services are summarized
in Table 1. In this table, we utilize the education
quality model of Cheng & Tam (1997) to determine
the attributes of university education quality. We
focus on the attributes that prospective students
care for more and divided them into extrinsic and
intrinsic attributes.
Information asymmetry occurs when consumers cannot properly evaluate intrinsic attributes of a
product/service. This happens for several reasons.
It can be because customers have limited time
(Zeithaml, 1988), customers have limited knowledge to properly evaluate the intrinsic attributes,
or the intrinsic attributes are not available for

evaluation. When consumers cannot evaluate the
intrinsic attributes properly, the assessment of the
quality of product/service becomes less accurate.
This is relevant to the definition of intrinsic
attributes: internal components of a computer
are more predictive to determine computer quality. Novice users and expert users will arrive
at different assessments about the quality of a
computer. Common people and people who have
broad knowledge of the intricacies of university
education will arrive at different assessments of
university education quality. Different evaluations will also be formulated by people who can
directly evaluate products/services and by people
who can only access product/service information
through the internet. On the other hand, sellers/
service providers have a full understanding of
the intrinsic attributes of their products/services.

Signaling Theory
Spence (1974) introduced the concept of “signal”
to economics. Signaling theory is a framework
to understand how two parties convey concealed
information in the context prior to a deal (Spence,
1974). From a consumer’s point of view, signaling
theory is applied to understanding how consumers
assess product quality when faced with information asymmetry (Kirmani & Rao, 2000). From
the standpoint of a seller/provider, a signal is a
cue which can be used to convey reliable information regarding product quality that is not readily
observable to consumers. By this definition, the
purpose of signaling theory is the use of signals
to reduce information asymmetry between parties to a transaction. Through utilizing signals,

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Influence of Perceived Quality of Official University Websites

consumers are more confident that they picked
quality good s or quality services.
Sellers/providers of products/services typically
use extrinsic attributes as signals. Empirical studies show that consumers with low level of product
familiarity rely more on extrinsic attributes due to
their limited ability to assess intrinsic attributes
(Rao & Monroe, 1998). Thus, in cases when
intrinsic attributes are not available or the ability
of consumers to judge goods/service quality is
limited, consumers are likely to rely on extrinsic
attributes. Extrinsic attributes, such as brand,
reputation, price/fee, or store environment, are
usually easy to observe and are readily available
for observation. For these reasons, sellers use
extrinsic attributes as signals to convey product
quality.
E-commerce marketing channels indeed are
limited. Any trading activity is carried out through
a website, in a technology-mediated environment.
This prevents consumers from directly evaluating
products/services quality that are usually available
in a conventional store environment (i.e., direct
interaction between sellers/providers and products/
services trial is a tool to present intrinsic attributes
of a product’s or service’s quality to consumers in
conventional transactions). Current e-commerce
technology limits the ability of sellers to convey
intrinsic attributes of products (Grewal, Iyer, &
Levy, 2004). Consequently, a consumer’s ability
to properly judge the quality of product/service
is reduced.
E-commerce marketing channel limitations
equally hold true in service transactions. Unlike
goods that can be easily evaluated qualitatively,
services are intangible and heterogenic. Service
quality is quite relative amongst consumers
(Bateson, 1977; Zeithaml, 1981). Previous studies have investigated service quality in traditional
environment, where the transaction is carried out
in real life. Various models to measure service
quality have been developed. These models usually
use facility, personnel quality, service delivery,
provider management, and many more factors

282

as indicators of service quality (Cheng & Tam,
1997; Parasuraman, Zeithaml, & Berry, 1988). In
contrast, transactions in a technology-mediated
environment do not allow consumers to see, feel,
and directly observe those indicators in order to
determine quality of service offered online.
In the context of this paper, education provided by a university is a commercial service for
prospective students. Prospective students can
visit university directly, observe the facilities,
interact directly with university students and staff
members. However, the experience or information
presented in a conventional environment is not
properly available in a technology-mediated one,
namely the university website. Therefore, sellers/
providers using this e-commerce marketing channel need to leverage information or use signals to
facilitate the consumer’s ability to formulate an
accurate assessment of product/service quality
(Pavlou, Liang, & Xue, 2007).

Quality Dimensions of
University Website
Johns (1999) stated in his research that service
can be defined as performance, output, or process.
Service is intangible and the output itself is seen
as an activity. Furthermore, Lovelock & Wright
(2002) define service as an economic activity
that creates and delivers benefits to customers at
a particular time and fulfills desired changes on
behalf of the service recipient. How the service
is delivered creates customer value towards the
service. The concept of quality has a variety of
definitions as it highly depends on context and
personal perspective. Nashihuddin (2010) defines
quality as conformance to customer expectation.
Therefore, quality is not determined by criteria set
by an organization. It is determined by customer
assessment. Crossing the two concepts together,
service quality is an important tool for an organization to differentiate itself from its competitors
(Ladhari, 2008). In addition, Zeithaml (1988)
explains that service quality focuses on evaluation


Influence of Perceived Quality of Official University Websites

that reflects customer perception of the service.
Service quality hence is much related to customer
satisfaction.
Higher education institutions (i.e. university)
are examples of organizations that provide services to customers as its core business. It provides
education services to students (customers). Parasuraman, Zeithaml, & Berry (1985) equated university quality to the situation where educational
institution meets and/or exceeds its customers’
expectations. There were also some attempts to
define the quality attribute of education, among
them were Lee, Barker, & Mouasher (2013), Lee
& Min (2013), and Lupo (2013). There is yet no
consensus regarding the definition for university
quality even though each definition is highly
correlated. Therefore, a single definition of an
educational institution’s quality cannot at present
be derived. Education quality is more appropriate
to be defined based on stakeholder’s criteria. This
study focuses on the perspective of consumers
as stakeholders who evaluate the quality of the
university. Consumers for the study are prospective students who wish to enroll at a university.
Various models have been developed to
measure the quality of educational institutions,
particularly universities. In this study, we use the
education quality model developed by Cheng &
Tam (1997). Seven models of quality of education were introduced by Cheng & Tam (1997)
to illustrate the different conceptions of quality, comprised of goal and model specification,
resource-input models, process model, satisfaction
model, legitimacy model, absence of problems
model, and organizational learning model.
Many studies have been conducted to explore
consumers’ perceptions of website quality as a
medium to deliver service (Cebi, 2013; Hidayanto,
Mukhodim, Frisca, & Junus, 2013; Ho, Kuo, & Lin,
2012). One of the instruments commonly used to
measure website quality is WebQual (Loiacono,
2002). Khawaja & Bukhari (2010) extended
WebQual to measure the quality of university

websites, where the quality is measured through
9 dimensions, namely reliability, navigability,
responsiveness, efficiency, functionality, usefulness, ease of use, accuracy, and web appearance.
This study combines the education quality model
of Cheng & Tam (1997) and these 9 dimensions
in order to derive university website attributes
which can be used as a signal to promote university
quality as a whole. The following is a detailed
explanation of each variable:
•

•

•

•

•

Reliability: A website is reliable if the system can guarantee the completion of any
action by the user. There is no failure in
accessing the website. Website should also
always be available or accessible 24/7.
Responsiveness: Responsiveness is measured by response time. Response time refers to the time it takes to get a response
after an action is performed on the website.
Functionality: Based on the mapping of
10 university websites and the observation of information needed by prospective
students, features that are generally required by prospective students are online
admission, search box or Quick Access or
website directory, and FAQs about student
admission.
Ease of Use: This dimension requires a
website to contain information and design that are easy to read and understand.
Furthermore, the website should have an
intuitive navigation system, that is, it is
easy to move from one page to another,
easy to learn (Benbunan-Fich, 2001).
Information Quality: The quality of information contained on a website can be
measured by whether the provided information is accurate, current, appropriate,
and complete. In the context of universities, required information includes university profile, faculty profile, university
facilities, accreditation, quality, and uni-

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Influence of Perceived Quality of Official University Websites

•

versity’s achievements, campus life, address and contact information, schedule of
new student admission, admission requirements, admission procedures, and tuition
fee information.
Visual Appeal: According to this dimension, a website is of good quality if it uses
the latest pictures, does not overuse pictures and features that consume a lot of
memory and time to load, and is simple.
Website is also homogeneous; all pages
should follow a coherent design.

Conceptual Model of Research
As we explain in the previous section, there are
various intrinsic and extrinsic attributes which
affect product quality. Not all intrinsic attributes
can be directly evaluated by the consumers, unless
they are given the opportunity to try the products,
for example, when buying fruits in the traditional
market one is allowed to examine a specimen of
the fruit being bought. Many consumers then rely
on extrinsic attributes to evaluate the quality of
products (Richardson, Dick, & Jain, 1994).
For education services, one of the challenges
faced by service providers is the impossibility of
prospective students to experience the learning
process in real-time classes. One effort by the
education service provider is, for example, to
host an open house, where prospective students
can inspect the facilities, consider the curriculum,
and other supporting services. The limitations of
an open house lies in the fact that it is limited in
scale and limited by time; it can only be done at
certain times, and can only hold a few hundred
prospective students, when in fact the actual
number of prospective students could be in the
hundreds of thousands. In the end, prospective
students typically rely on various channels available on the Internet such as official university
websites, portal news websites, or even electronic
word-of-mouth (e-wom) which is commonly

284

found in social networks like Facebook or Twitter.
Through these extrinsic cues, consumers usually
infer the quality of education services, to replace
the intrinsic attributes that cannot be evaluated
directly (Zeithaml, 1988).
From the various channels of information
on the Internet, prospective students typically
refer to official university websites as their main
reference. Information from social media may
be used complementarily, given the information
presented in social media is not as complete nor
as comprehensive as the official website. Official university websites normally contain all the
information related to programs offered, which
is needed by prospective students to evaluate the
quality of education, such as: programs offered,
curriculum, qualifications of teaching staff, facilities, registration process, academic schedules,
admission requirements, and so on. Official university websites also contain information about
student activities and achievements, and career
path of alumni, as indicators of university quality. Although prospective students may not feel
directly the quality of teaching at the university,
these attributes are most easily evaluated, and assist
prospective students in assessing the quality of the
university and its suitability for the prospective
student. In this context, the university website
could be a reflection of how education services are
delivered at the university (e.g., Baker, Grewal, &
Parasuraman, 1994). Thus, the official university
website is one of the strong signals representing
quality of education at the university.
Most signaling theory studies about e-commerce have investigated how traditional signals
(reputation, warranty, advertising cost, etc.) affect
trust, risk, and purchase intentions for online vendor (Aiken & Boush, 2006; Kim, Kim, & Park,
2010). A website can be used to communicate
the intrinsic attributes of a product/service, such
as by writing features of the product/service,
facilities, images, and so on. Websites can also
communicate extrinsic attributes, such as price/


Influence of Perceived Quality of Official University Websites

fee, brand, and website quality attributes. Just like
a store or a transaction place (bank, service station, university), which has its own architecture or
interior design, a website also has such attributes
(e.g. visual appeal, information quality, ease of
use, etc.) that may affect perceived quality of
the product/service displayed on the site. These
website attributes can serve as signals.
Past studies have shown that consumers can
easily assess the quality of websites, as evidenced
in the measurement instrument WebQual (Loiacono, Watson, & Goodhue, 2007) and SiteQual
(Yoo & Donthu, 2001). Another study states that
consumers have high confidence in assessing
certain aspects of website quality, such as the
assessment of visual appeal in less than a second
(Lindgaard, Fernandes, Dudek, & Brown, 2006).
Study by Wells, Valacich, & Hess (2011) also
showed a positive correlation between perceived
website quality and perceived product quality.
Taking into account that prospective students
have limited information about a university, the
quality of university websites is expected to affect the perception of quality of university education. This is because the quality of university
websites is more available and easy to evaluate
for prospective students when they navigate the
website. Given that extrinsic attributes often acts
as a reflection of intrinsic attributes, we propose
following hypothesis:
H1: Perceived quality of university website
positively affects the perceived quality of
university education by prospective students.
In information systems studies, there are various theories that underlie human behavior to adopt
technology. Most studies on technology adoption
are rooted in behavioral intention, which say that
the user’s intention to adopt a new technology is
a conscious effort which can be explained and
predicted by their behavioral intentions. Examples
of well-established theories of technology adoption are Theory of Reasoned Action (Fishbein &

Ajzen, 1975), Theory of Planned Behavior (Taylor
& Todd, 1995), Technology Acceptance Model
(Davis, 1989), and Innovation Diffusion Theory
(Rogers, 1983). These theories were widely adopted in a variety of fields, such as e-commerce
to predict the purchase of products by consumers,
to predict web revisit behavior, to predict reuse
of online games, and so on.
According to these theories, human behavior
to adopt a technology is determined by attitude.
For example, in the Theory of Reasoned Action,
attitude (e.g. in the form of perceived value,
perceived product quality, perceived risks, etc.)
is a variable that drives consumers to perform a
particular behavioral action (e.g. purchase or web
revisit) and involves a certain object target (such
product or web) in a certain context and time frame
(e.g. online news portals, e-commerce) (Fishbein
& Ajzen, 1975). The same is also mentioned in
the Innovation Diffusion Theory which shows a
positive correlation between behavioral beliefs and
behaviors to adopt the technology. Several other
studies also showed a positive correlation between
the quality of the product and the intention to
buy (Boulding & Kirmani 1993; Chang & Wildt,
1994; Rao, Qu, & Ruekert, 1999; Toivonen, 2012;
Tsiotsou, 2006). This suggests that the perception
of the quality of the product will drive consumers’
willingness to buy the product. Likewise, in the
context of the quality of educational services, the
better the perceived quality of education services,
the more will prospective students be interested
to enrol at the university. Prospective students
certainly want a university with the best quality
to support their future careers. From this, we can
formulate the following hypothesis:
H2: Perceived quality of university education will
positively affect the enrollment intention of
prospective students at the university.
The theoretical model used in this research
can be described in Figure 1.

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Influence of Perceived Quality of Official University Websites

Figure 1. Research theoretical model

METHODOLOGY
Research Object Selection
First we selected 10 official university websites
in Indonesia. These 10 universities comprise of 5
state and 5 private universities in Indonesia. The
selection of these universities is done through filtering from top 30 Indonesian universities according Webometrics Ranking of World Universities
(2011) and A-accredited universities according
Badan Akreditasi Nasional-Perguruan Tinggi
(BAN-PT) Republik Indonesia (2011). BANPT is the national accreditation body for higher
education officially authorized by the Indonesian

Ministry of Education. We consider these ten
university websites as candidates of our research
objects. Table 2 summarizes the ranking of these
websites according to Webometric and BAN-PT.
Next we further filtered the candidate research
objects by considering the quality of the features
and dimensions of the ten websites. We avoided
choosing the university whose reputation is too
well-known and famous, so the respondents are
expected to give answers based on their observations of the actual university website. If we used
well-known universities as a case study, respondents will tend to be affected by the brand of the
university, so the answer is biased. For example,
if we used the website of UI, ITB, or UGM as

Table 2. Candidate research objects
University

Webometric Rank

Accreditation

Universitas Indonesia (UI)

1

A

http://ui.ac.id/

Institut Teknologi Bandung (ITB)

2

A

http://itb.ac.id/

Universitas Gadjah Mada (UGM)

3

A

http://ugm.ac.id/

Institut Pertanian Bogor (IPB)

5

A

http://ipb.ac.id/

Institut Teknologi Sepuluh Nopember (ITS)

9

A

http://its.ac.id/

Universitas Bina Nusantara (BINUS)

30

A

http://binus.ac.id/

Universitas Gunadarma (GUNDAR)

4

A

http://gunadarma.
ac.id/

Universitas Kristen Petra (PETRA)

7

A

http://petra.ac.id/

Universitas Islam Indonesia (UII)

17

A

http://uii.ac.id/

Universitas Muhammadiyah Malang (UMM)

8

A

http://umm.ac.id/

286

University Website


Influence of Perceived Quality of Official University Websites

Table 3. A quality dimension comparison between the UII and UMM websites
Dimension

UII

UMM

University profile

v

v

Faculty profile

v

v

Facilities

v

v

Accreditation information, quality, and performance

v

v

Campus life information

v

v

Contact information

v

v

Registration information (schedules, requirements, procedures)

v

v

Tuition fees information

-

v

Online registration

-

v

Online payment confirmation

-

-

Online registration document upload

-

-

Online registration download

-

v

Graduation check

-

v

Search feature or quick access

v

v

FAQ or Q & A forum for registration

-

-

our case study, the respondents are likely to give
a positive answer considering the favourable
brand name and reputation of these universities.
Therefore, to have an optimal application of signal
theory and avoid bias due to brand, we sought
universities with reputations that are less known
by respondents. As such, private universities were
chosen as our case study. Furthermore, we chose
universities which are not based in Jakarta. This
was done considering our respondents live in
Jakarta. This criteria left PETRA, UII, and UMM
as our choice. After trying to get feedback from
some students regarding their familiarity with
these three universities and due to the scarcity of
information amongst students about these three
universities, UII and UMM were determined as
our choice for case study.
We further compared the features available
on both university websites. The UMM website
is superior to the UII website in terms of website
quality. Based on our observation, the information
displayed on UMM website is more comprehensive
than that on the UII website. The UMM website

allows online registration of new students, which
is not available at the UII website. Table 3 summarizes the comparison of the quality dimensions
of both websites.

Data Collection Procedure
This research was conducted by sending questionnaires to respondents. In accordance with this
research topic, our respondents were prospective
students who wish to apply at universities. According to 2012 government data, the number of high
school graduates in Indonesia reached 1,517,125
(Pikiran Rakyat, 2012), although not all of them
continue to university.
The sample of the study was comprised of
prospective students who come from high schools
with grade 12 in Jakarta (capital of Indonesia) who
wish to graduate and pursue studies at university.
They usually start looking for universities which
offer quality programs and which match their interests. We chose convenience sampling by visiting
SMA 68 which is one of the more well-known

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Influence of Perceived Quality of Official University Websites

high schools in Jakarta and a tutoring agency
called Zenius Education in Jakarta. Completion
of the questionnaire was done online via http://bit.
ly/surveyfny. Respondents were given a series of
instructions to complete the questionnaire, which
included instructions to access the websites of
UMM and UII as their first tasks. After accessing
the websites, respondents were asked to fill in pairs
their perceptions of the quality of both websites,
the quality of the respective universities, and also
their intention to enroll at the two universities.

Research Instrument
The research instrument was prepared based on
university website quality dimensions and research
hypotheses. The instrument was made by forming
a closed-ended questionnaire which was directed
to measure these dimensions using a five-point
Likert scale, except for item RES1. The Likert
scale is used as a measurement tool because this
scale can measure a person’s perception or opinion on a given matter. Respondents were asked
to give value to a statement filed. The range of
values used was from 1 to 5, with a value of 1
= strongly disagree, 2 = disagree, 3 = neutral,
4 = agree, and 5 = strongly agree. Our research
instrument is detailed in Table 4.

RESULTS AND DISCUSSIONS
Respondents’ Demographics
We obtained 117 valid questionnaires. We asked
respondents basic information such as gender,
age and high school. Related to the responsiveness dimensions, we asked what kind of Internet
connection was used and its speed. We used this
information to see the correlation between the
internet access speed and respondent judgment on
the responsiveness dimensions. A total of 53.8%
of respondents were male and 46.2% were female.
In terms of respondent age, approximately 69.2%
were 17 years old, followed by respondents aged
16 years old approximately 17.1%, and aged over
18 years old of about 13.7%. Respondents came
from 47 high schools or their equivalent, spread
across Greater Jakarta. In terms of Internet connection, 39.3% of respondents used Wi-Fi internet
connection, 33.3% used Broadband (Speedy,
Fastnet, etc.); 15.4% used Mobile CDMA (AHA,
FlexiNet, Smartfren Connex, etc.), and 12% used
MobileGSM (Telkomsel Flash, Sympathy, XL,
IM3, 3, Axis, etc.). Table 5 summarizes respondent
demographics.

Table 4. Research instrument
Reliability (adapted from Khajawa & Bokhari, 2010; Djajadikerta & Trireksani, 2006; Pavlou, 2001)
Rel1

Every action is completed by the website

Rel2

I never fail to access the website

Res1

The average time to load or complete an action on the website (12, 9, 6, 3, & 0 secs)

Res2

When I use this website, there is a small waiting time between my actions and website’s response

Res3

Website loads quickly

Responsiveness (Loiacono, Watson, & Goodhue, 2007)

continued on following page

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Influence of Perceived Quality of Official University Websites

Table 4. Continued
Functionality (adapted from Khajawa & Bokhari, 2010; Loiacono, Watson, & Goodhue, 2007)
Fun1

It’s easy for me to find information on the website

Fun2

This website allows me to find specific information directly

Fun3

Website provides discussion forum or Frequently Asked Questions page about students admissions

Fun4

Website provides adequate features to facilitate online admissions
Ease of Use (Loiacono, Watson, & Goodhue, 2007)

Eas1

Information on website is easy to read

Eas2

Information on website is easy to understand

Eas3

It’s easy for me to navigate from one page to another

Eas4

Website navigation is easy to use and to understand

Inq1

University and campus profiles are well explained

Inq2

Faculty and course of study information are well explained

Inq3

University’s facilities information is well explained

Inq4

University’s accreditation, quality, and achievement are well explained

Inq5

Campus life information is well explained

Inq6

University address and contacts are well explained

Inq7

New student admissions process information are well explained

Inq8

Tuition fee information is well explained

Inq9

Website uses consistent language in every page

Inq10

Website provides complete information for prospective students

Inq11

Website provides information that is appropriate for prospective students

Inq12

Website provides recent information

Inq13

All links on the website are not broken

Inq14

Every link on the website has been well implemented or contains meaningful information

Information Quality (Djajadikerta & Trireksani, 2006)

Visual Appeal (Bernier et al., 2002)
Vis1

Website uses recent images

Vis2

I rate images displayed on this website as attractive

Vis3

Design used on every page on the website is homogeneous and coherent

Vis4

I don’t need to scroll down the page to read all information on one page

Vis5

I rate the overall website design as attractive
Website Quality (adapted from Everard & Galletta, 2005)

Kws1

I rate that this university website has high-quality
Education Quality (adapted from Boulding & Kirmani, 1993; Rao, Qu, & Ruekert, 1999)

Pkp1

I view that education of this university is well designed

Pkp2

I rate that this university education has high-quality
Enrollment Intention (adapted from Watson, & Goodhue, 2007)

Ip1

I want to enroll myself at this university

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Influence of Perceived Quality of Official University Websites

Table 5. Respondent demographics
Category

Frequency

Percentage

Male

63

53.8%

Female

54

46.2%

Sex

Frequency

Percentage

Broadband

39

33.3%

Mobile CDMA

18

15.4%

Mobile GSM

14

12.0%

Wi-Fi

46

39.3%

Connection Type

Age (year)
16

20

17.1%

17

81

69.2%

18

16

13.7%

Validity and Reliability Tests
of Research Instrument
Generally, validity and reliability tests are carried
out by using a quantitative approach. However, not
all variables in this research instrument allowed
for quantitative testing, i.e. variable Kws and Ip1,
because each of them only has one question item.
Thus, we decided to conduct a qualitative approach
for these two variables. Qualitative validity and
reliability tests were carried out by means of
construct validity (Singarimbun & Effendi, 2006).
Other variables were still tested quantitatively as
each of them consists of several question items.
The validity test was carried out by using corrected item-total correlation. The reliability test
was conducted using Cronbach’s alpha, by finding
the value of the instrument’s reliability coefficient
(α = Cronbach’s alpha).
Table 6 summarizes validity and reliability
testing results. Items ending in A indicate the
observation results for the UMM website, whereas
items ending in B indicate the observation results
for the UII website. Based on the corrected itemtotal correlation test, all instrument question items
have a correlation coefficient greater than 0.3.
Furthermore, the value of each item is greater
than the r-table value, which is 0.1816 (df = n-2;
n-117, significance 5%). Thus, question items
given in the questionnaire have been qualified as
valid. In addition, Cronbach’s Alpha values for

290

Category

all variables are above 0.6, hence all variables
are considered reliable.
Meanwhile, a construct validity test (qualitative
approach) was conducted on item variables Kws1
and Ip1. Both items followed 3 steps of construct
validity: search for operational definitions from
experts in literature, create own definition and
discuss it with other experts, ask prospective
respondents or people who have the same characteristics with prospective respondents directly
about the concept to be measured. Item Kws1
and Ip1 were qualified for each step. Therefore,
items Kws1 and Ip1 can be considered valid to
use in research.

Hypotheses Testing Results
Multiple linear regression analysis is performed
to analyze linear relationships between 6 variables
of quality dimensions of a university website
(divided into 32 items) with the variable of perceived website quality. Multiple correlation index
R shows the correlation between independent and
dependent variables. Values of R range from 0 to 1.
If R-value is closer to 1, the relationship between
the independent and dependent variable is high.
The results of the multiple correlation index shows
that the r-value for the UMM website is 0.835 and
the UII website is 0.929. This value indicates the
correlation between overall quality dimensions to
the perceived quality of the UMM and UII websites


Influence of Perceived Quality of Official University Websites

Table 6. Reliability and validity testing results
Item

Corrected ItemTotal

Cronbach’s Alpha

Item

Corrected ItemTotal

Cronbach’s Alpha

Rel1A

0.568

0.696

Rel1B

0.520

0.683

Rel2B

0.520

0.699

Res1B

0.448

Rel2A

0.568

Res1A

0.601

Res2A

0.681

Res2B

0.712

Res3A

0.339

Res3B

0.298

Fun1A

0.393

Fun1B

0.413

Fun2A

0.416

Fun2B

0.448

Fun3A

0.427

Fun3B

0.553

Fun4A

0.375

Fun4B

0.529

Eas1A

0.594

Eas1B

0.642

Eas2A

0.705

Eas2B

0.649

Eas3A

0.669

Eas3B

0.632

Eas4A

0.699

Eas4B

0.690

Inq1A

0.649

Inq1B

0.756

Inq2A

0.731

Inq2B

0.735

Inq3A

0.704

Inq3B

0.758

Inq4A

0.741

Inq4B

0.774

Inq5A

0.557

Inq5B

0.736

Inq6A

0.594

Inq6B

0.597

Inq7A

0.713

Inq7B

0.683

Inq8A

0.444

Inq8B

0.506

0.694

0.830

0.912

Inq9A

0.612

Inq9B

0.723

Inq10A

0.738

Inq10B

0.644

Inq11A

0.708

Inq11B

0.601

Inq12A

0.577

Inq12B

0.685

Inq13A

0.437

Inq13B

0.473

Inq14A

0.658

Inq14B

0.630

Vis1A

0.648

Vis1B

0.544

Vis2A

0.512

Vis2B

0.735

Vis3A

0.536

Vis3B

0.545

Vis4A

0.371

Vis4B

0.414

Vis5A

0.511

Vis5B

0.686

Pkp1A

0.765

Pkp1B

0.749

Pkp2A

0.765

Pkp2B

0.749

0.710

0.867

0.653

0.697

0.826

0.925

0.795

0.857

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Influence of Perceived Quality of Official University Websites

Table 7. Summary of statistical tests to website quality dimensions
Item

Reliability

Visual
appeal

ANOVA
F-Test

T-Test

UMM

UII

UMM

UII

UMM*

UII*

R 0.835
(high)

R 0.929
(high)

F-value
6.048

F-value
16.654

Part. insignificant

Part. insignificant

Part. insignificant

Part. insignificant

Res1

Part. insignificant

Part. insignificant

Res2

Part. insignificant

Part. insignificant

Rel1
Rel2

Ease of
use

Multiple Correlation
Index

Res3

Part. insignificant

Part. insignificant

Fun1

Part. insignificant

Part. insignificant

Fun2

Part. insignificant

Part. insignificant

Fun3

Part. insignificant

Part. insignificant

Fun4

Part. insignificant

Part. insignificant

Eas1

Part. insignificant

Part. significant

Eas2

Part. insignificant

Part. insignificant

Eas3

Part. insignificant

Part. insignificant

Eas4

Part. insignificant

Part. insignificant

Inq1

Part. insignificant

Part. significant

Inq2

Part. insignificant

Part. insignificant

Inq3

Part. insignificant

Part. significant

Inq4

Part. insignificant

Part. insignificant

Inq7

Part. insignificant

Part. insignificant

Inq8

Part. insignificant

Part. insignificant

Inq9

Part. insignificant

Part. insignificant

Inq10

Part. insignificant

Part. insignificant

Inq12

Part. insignificant

Part. insignificant

Inq13

Part. insignificant

Part. insignificant

Inq14

Part. significant

Part. insignificant

Vis1

Part. significant

Part. significant

Vis2

Part. insignificant

Part. significant

Vis3

Part. insignificant

Part. significant

Vis4

Part. insignificant

Part. insignificant

Vis5

Part. insignificant

Part. insignificant

*Part. Means Partially

is high. The R-Square (R2) for the UMM website
is 0.697- this means that the 6 variables of quality dimensions affect the perceived quality of the
UMM website as significant as 69.7%, whereas
the remaining 30.3% are influenced by other
variables not included in this research model. The

292

R-Square (R2) for the UII website is 0.864 - this
means that the 6 variables of quality dimensions
affect the perceived quality of the UII website as
significant as 86.4%, whereas the remaining 13.6%
are influenced by other variables not included in
this research model.


Influence of Perceived Quality of Official University Websites

ANOVA is used to test the significance of the
overall effect of quality dimensions altogether on
the perceived quality of a university website. Using 0.05 for the significance level, we compared
calculated F-values with values in the F-table. If
we look at the F-test results, these show that overall
quality dimensions are jointly significant (F-value
> F-table) in affecting perception of website quality for both university websites (UMM: 6.048 >
1.6; UII: 16.654 > 1.6). The statistical test results
are summarized in Table 7.
Moreover, based on coefficient analysis (Ttest), we can see items that have a direct effect
(partially affecting other items) on perceived
quality of university website:
•

•

The test shows that only variables Vis1
(the use of latest images) and Inq14 (all
links are well-implemented) have significant positive effects on the perceptions of
UMM’s website quality. These two variables have T-values larger than values in
the T-table, namely Inq14 = 3.144 (>1.99)
and Vis1 = 2.617 (>1.99). Regression
equations obtained are YUMM = (0.462)
Inq14 + (0.353) Vis1. This means that for
every 1% increased value of Inq14, this
will positively increase the perceived quality of the UMM website as significantly as
0.462. Same observation can be made for
Vis1.
The test shows that variables Fun3 (Q &
A Forum about student admission), Eas1
(information is easy to read), Inq1 (information of university profile), Inq3
(information of university facility), Inq4
(information of university accreditation
and achievement), Inq11 (information displayed meets prospective students’ need),
Vis1 (the use of latest images), Vis2 (the
use of attractive images), and Vis3 (website design homogeneity) have significant
positive effects on perceptions of UII’s
website quality. Regression equations ob-

tained are YUII = (0.146)Fun3 + (0.266)
Eas1 + (0.397)Inq1 + (0.388)Inq3 +
(0.261)Inq11 + (0.403)Vis1 + (0.246)
Vis2 + (0.269)Vis3. This means that for
every 1% increased value of Fun3, this will
positively increase the perceived quality
of UMM website as significantly as 0,146.
Same observation applies for Eas1, Inq1,
Inq3, Inq11, Vis1, Vis2, and Vis3.
Simple linear regression analysis is used to
analyze the linear relationship between a variable
of perceived quality of university website (Kws1)
to the variable of perceived quality of university
education. Perception of university education
quality is measured by 2 items, namely Pkp1 and
Pkp2. Given that simple regression involves only
a dependent and an independent variable, simple
regression testing was conducted separately for
Pkp1 and Pkp2.
The results of a multiple correlation index
of Kws1 and Pkp1 show that the r-value for the
UMM website is 0.574 and the UII website is
0.705, whereas the results of a multiple correlation
index of Kws1 and Pkp2 show that the r-value for
the UMM website is 0.632 and the UII website is
0.664. These values indicate the correlation between perceived quality of the UMM website to its
perceived quality of university education is fairly
high and for UII is high. R Square (R2) indicates
the coefficient of determination. Results show that
the percentage contribution of university website
quality perceptions (Kws1) to the perception of
high quality university education (Pkp1) is about
32.9% and 49.6% for UMM and UII respectively,
whereas the percentage contribution of the university website quality perceptions (Kws1) to the
perception of high quality university education
(Pkp2) is about 40.0% and 44.1% for UMM and
UII respectively. Furthermore, if we look at F-test
results, these show that website quality perception variable can be used to predict the perceived
quality of university education, as all F-values are
greater than those in the F-table (3.94).

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Influence of Perceived Quality of Official University Websites

Table 8. Summary of correlation tests between website quality perception and education quality perception
Independent
Variable

Kws1

Dependent
Variable

Multiple Correlation Index
UMM

F-Value

UII

T-Value

UMM

UII

UMM

UII

0.496

56.505

113.389

7.052
H1 accepted

7.113
H1 accepted

0.441

76.633

90.586

6.961
H1 accepted

6.762
H1 accepted

R

R2

R

R2

Pkp1

0.574.

0.329

0.705

Pkp2

0.632

0.400

0.664

Based on coefficient analysis (t-test), all Tvalues are greater than those in the T-table (1.98).
We know that variable Kws1 has a significant
positive effect on the perceptions of UMM education quality and UII education quality. Hence,
we can conclude that Hypothesis 1 is accepted.
A summary of testing results for this relationship
is in Table 8.
Multiple linear regression analysis was also
performed to analyze linear relationships between
perceived quality of university education with
university enrollment intention. The results of the
multiple correlation index shows that the r-value
for the UMM website is 0.727 and for the UII
website is 0.737. These values indicate the correlation between perceived quality of education at
UMM and UII to its enrollment intention is high.
The value of R2 for UMM is 0.528, meaning that
the percentage contribution of perceived quality
of university education to university enrollment
intention is 52.8%, while the remaining 47.2%
is influenced by other variables not included in
this model. A similar result for UII was observed,

where the value of R2 is 0.543, meaning that the
percentage contribution of perceived quality of
university education to university enrollment
intention is 52.8%. Furthermore, if we look to at
F-test results, it shows that F-values for UMM
and UII are 63.730 and 67.653 respectively with
a significance level at 0.0000; these F-values are
greater than those in the F-table (3.09). Taking
into account these results, it can be concluded that
quality perception variable can be used to predict
university enrollment intention.
Based on coefficient analysis (t-test), we know
that variables Pkp1 and Pkp2 have significant positive effects on university enrollment intention for
both UMM and UII. Hence, we can conclude that
Hypothesis 2 is accepted, i.e., this means that perceived quality of university education positively
influences enrollment intention. A summary of
the statistical test results for correlation between
perceived university quality and enrollment intention is in Table 9.

Table 9. Summary of correlation test between education quality perception and enrollment intention
Independent
Variable

Pkp1
Pkp2

294

Dependent
Variable

Ip1

Multiple Correlation Index
UMM

F-Value

UII

R

R

0.727

0.528

2

R

R

0.737

0.543

T-Value

UMM

UII

UMM

UII

63.730

67.653

4.587
H2 accepted

2.330
H2 accepted

3.134
H2 accepted

5.804
H2 accepted

2


Influence of Perceived Quality of Official University Websites

Discussions
One of our results showed the relationship among
the website quality dimensions and the perception of university website quality. We examined
six (6) website quality dimensions which are:
Reliability, Responsiveness, Functionality, Ease
of Use, Information Quality, and Visual Appeal.
Reliability measures the ability of a website
to meet every user action and to be available for
access on a 24/7 basis. Together with other quality
dimensions, Reliability (Rel1 and Rel2) affects the
perception of university website quality. However,
based on the results of the statistical test (T-test),
the Reliability variable does not partially affect
perceived website quality. This implies that if
the Reliability factor is not supported by other
dimensions (responsiveness, functionality, ease of
use, information quality, and visual appeal), this
variable will not help in boosting user perception
of university website quality.
User perception that a website can load fast
(Res3) is one of the factors that has a significant
positive correlation on the perception that a university’s website is of high quality. On the other hand,
users seem less aware of the specific perception
regarding website response time (Res1 and Res2)
because statistical tests show these items do not
have any significance. In addition, the dimension
of responsiveness does not have a partial effect
on perceived quality for both university websites.
According regression analysis results, together
with other dimensions of quality, Functionality
affects the perceived quality of the university
website. Specifically, the perception that a website
is of medium quality correlates with the ease of
finding information (Fun1 and Fun2). On the other
hand, respondents did not seem too concerned
about the Student Registration Feature and Q &
A Forum about student admissions (Fun3 and
Fun4) because correlation test results for these
items showed no significance. We suspect this
is because both university websites do not have

a significant quality difference with respect to
these two aspects.
The UMM website facilitates online registration, but not all processes can be done online.
The UMM website only facilitates registration,
the downloading of registration documents, and
acceptance checking. Other processes, such as
confirmation of payment and uploading of registration documents cannot be carried out online. On
the other hand, the UII website does not facilitate
those processes online. Although there are indeed
differences, the quality difference is not too extreme as the UMM website only facilitates few
admissions processes online and both websites
do not have Q & A forum about student admissions. Moreover, T-test results showed that the
Functionality dimension does not directly affect
the perception of university website quality.
Ease of Use dimension measures how well a
website presents itself so users require minimum
effort to use it. Based on statistical regression coefficient, the Ease of Use dimension has a positive
correlation to perceived quality of university website. However, this dimension does not partially
affect perceived quality of university website.
There are differences in Ease of Use dimension
between the UMM and UII websites. Differences
exist in Eas1 and Eas2 item, where the mean scores
for UMM is greater than that of UII. Based on
these results, respondents viewed that information
contained on the UMM website is easier to read
and understand than the UII website.
Information Quality is one of dimensions that
has a positive correlation to perceived quality
of university website. Partially, Inq1 (university
profile information), Inq3 (university facility
information), and Inq11 (suitability of the information supplied to the needs of prospective
students) affect perceived website quality of the
UII website. On the other hand, only Inq14 (all
links are well-implemented) that has a direct effect on perceived quality of the UMM website.
Difference on Information Quality dimension for
both UMM and UII websites exists in item Inq1,

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Influence of Perceived Quality of Official University Websites

Inq7, and Inq12. Mean scores in these items for the
UMM website are greater than UII’s. This means
respondents viewed information of university
profile and student admission as well as latest
information presentation to be better on the UMM
website than the UII website. One remark that
can be added is the gap of Inq1 for both websites.
The UII website provided a link for the university
profile at the footer. This certainly has a negative
effect on perceived quality because the user needs
to scroll down to the bottom of the website for
such an important piece of information.
The website design attractiveness (Vis5) has
a high correlation to perceived quality of university website. The use of latest images (Vis1)
partially has an influence on perceived quality
of the UMM website. On the other hand, the use
of latest images (Vis1), the attractiveness of the
images used on the website (Vis2), and website
design homogeneity partially affected perceived
quality of the UII website. Based on correlation
coefficient values, Visual Appeal dimension has
the highest correlation value compared to other
dimensions. Differences in the Visual Appeal
dimension for both UMM and UII websites exist
in items Vis1 and Vis2. Mean scores for those
items for the UMM website are greater than UII’s.
Respondents assessed that the use of latest images on the UMM website is better than the UII
website. In addition, respondents rated the images
used on the UMM website to be more attractive
than those on the UII website.
Based on manipulation check, there is perception difference of the overall website quality
between UMM and UII in the eyes of respondents.
UMM’s mean score is greater than UII’s. In
other words, respondents see that the quality of
the UMM website is better than the UII website
for the overall quality dimension for university
websites. Based on correlation coefficient values,
the Visual Appeal dimension has the highest correlation to perceived quality of university website
than other dimensions. In addition, looking at
linear regression equations YUMM = (0.462)

296

+ Inq14 (0353) Vis1 and YUII = (0.146) Fun3
+ (0.266) + Eas1 (0397) Inq1 + (0.388) Inq3 +
(0.261) Inq11 + (0403) Vis1 + (0246) Vis2 +
(0269) Vis3, it is clear that Visual Appeal has
a significant role influencing user perception of
university website quality.
Previous studies on the utilization of signals
theorized that an information signal which (1)
is easy to observe, and (2) which the user can
confidently evaluate it is the most influential
signal in quality evaluation (Richardson, Dick,
& Jain, 1994). A study on user perceptions of
a website shows that Visual Appeal can be reliably assessed within 50 milliseconds (Lindgaard,
Fernandes, Dudek, & Brown, 2006). Therefore,
previous studies are in line with the findings of
our research. Visual appeal refers to the aesthetic
quality of a website and aesthetics has been proven
to be a dominant component in website quality in
a more experiential context (Valacich, Parboteeah,
& Wells, 2007; Van der Heijden, 2004).
The research model of this study sought to confirm whether the perceived quality of university
websites positively affects the perceived quality
of university education through H1. The variable
for perceived university website quality (Kws1)
has a fairly high correlation with the variable for
perceived quality of university education (Pkp1
and Pkp2). Based on T-test results, the variable
for perceived university website quality positively
influences the perception of university education.
Therefore, research hypothesis H1 is accepted, and
it can be concluded that website quality is seen as
a signal for university quality. An explanation of
this is the quality of a website provides a signal
of the service quality provided by the university,
which then reflects the overall quality of the university. In our case, website quality dimensions that
can signal quality are reliability, responsiveness,
functionality, ease of use, information quality, and
visual appeal. These six dimensions of website
quality jointly affect perceived university website quality and hence, can act as good signals to
indicate the quality of education offered by the


Influence of Perceived Quality of Official University Websites

university whose website is being evaluated. This
is consistent with the findings of Wells, Valacich,
& Hess (2011) in the context of product, where
website quality can be a signal of product quality.
The research model ultimately sought to
confirm if perception of university education
quality (which is linearly influenced by perceived
university website quality) positively affects the
enrollment intention of prospective students.
The variable for perceived quality of university
education was found to have a high correlation
with university enrollment intentions. Based on
T-test results, the variable for perceived quality
of university education positively affects enrollment intention. Therefore, research hypothesis H2
is accepted. Moreover, there was no enrollment
intention difference between the two universities.

Implications
This study provides scientific contributions to
academic world as we conclude that website
quality (specifically university official website)
is theoretically eligible to be a signal of service
quality (specifically university education). From
an information systems perspective, we propose
that signaling theory provides a new and powerful
theoretical foundation to explain how and why
website quality with its related features influences
enrollment intentions of prospective students. This
study also contributes to the literature on signaling
theory by validating website quality as a signal,
which is different from existing signals, such as
university tuition fees and reputation.
Results of this study also have strategic implications for universities and other educational
institutions that use e-commerce marketing channels. First, this study provides an intuitive recommendation to educational institutions to own
and maintain a high quality official website as
prospective students will rely on the quality of
the website as an extrinsic signal. A university or
educational institution can signal quality education
services by maintaining a high quality institutional

website. It can be said that if prospective students
view the official university website to be of high
quality, then they will be more confident with the
quality of education offered by the said university. Consequently, this perception is expected to
positively affect the intentions of enrollment at
the university.
In addition, as a result of this study’s findings, educational institutions can prioritize the
improvement of the visual appeal of their websites. Attractive designs may give a halo effect
for website visitors. Next, educational institutions
can focus on information quality as prospective
students visit the university website to search for
information, particularly information related to
the university profile and university enrollment.
The university should furnish prospective students
sufficient information in accordance with their
needs as education service users.

FUTURE RESEARCH DIRECTIONS
This study only uses quality of official university
websites as a signal to indicate the quality of
educational services at a university. Subsequent
research could include information asymmetry
components and credibility signals in the research
model. Doing so will allow the formulation of a
more comprehensive signal theory perspective for
this study. In addition, as many universities also
utilize social network such as Facebook and Twitter
as a means of communicating with its customers,
the quality of information in social media may be
one of the signals of university quality. Moreover,
social media is one of the most effective forms of
media for electronic word of mouth (e-WOM). It
would be interesting to have future research assess
if the quality of information on social media has
become one of the signals of university quality.
Furthermore, research can also be undertaken to
see which one is a stronger attribute in signaling
the quality of the university, to influence which

297


Influence of Perceived Quality of Official University Websites

focus should be chosen to improve a university’s
image to its consumers.

THEORETICAL SIGNIFICANCE
Signaling theory is widely used to understand how
a consumer assesses the quality of a product or
service when customers cannot directly interact
and scrutinize products/services. Consumers
normally rely on extrinsic attributes, which are
expected to signal the quality of products/services.
In many studies, extrinsic attributes commonly
used to indicate the quality of products/services
are brand, price, quality of service, reputation of
the seller, as well as physical environment. This
study shows that in the context of e-commerce,
website quality is an extrinsic attribute that signals
service quality, which in this study is educational
service. This study reinforces the findings of Wells,
Valacich, & Hess (2011), which show that website
quality is a signal of product quality. Thus, it can
be concluded that the quality of a website can
signal the quality of both products and services.

CONCLUSION
University education can be viewed as a type of
commercial service. Similar to other products and
services transactions, information asymmetries
exist between a university (as a service provider)
and prospective students (prospective service user/
consumer) regarding the service being offered
(university education). This asymmetry may occur
when information about university is not properly
available to prospective students. Signaling theory
is used to reduce information asymmetry by signaling aspects of education quality to prospective
students. One form of such signals is the official
university website. Based on the empirical results
of this paper, the following conclusions were
reached: First, the quality of university websites
can serve as a signal of university education qual-

298

ity. Secondly, the perceived quality of university
education, which is based on signaling theory,
eventually influences the enrollment intention of
prospective students at the university. Considering
the results, universities should be more attentive
of their official websites to elicit more enrollment
interest from prospective students.

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KEY TERMS AND DEFINITIONS
Enrollment Intention: Intention of prospective students to register at a certain university.
Information Asymmetry: Information gap
between consumers.
Perceived Quality: Perception of website quality according to users after visiting the website.
Signal: Something that can be used by consumers to make an accurate assessment of the
product/service quality.
Signaling Theory: A theory for understanding
how two parties convey concealed information in
the context prior to a deal.
University Quality: Criteria that defines the
quality of education offered by a university.
Website Quality: Criteria that defines the
quality of a website, such as ease of use, information quality, etc.

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Chapter 14

Students as Customers:

Participatory Design for Adaptive Web 3.0
Lei Shi
University of Warwick, UK
Alexandra I. Cristea
University of Warwick, UK
Craig Stewart
Coventry University, UK

ABSTRACT
The World Wide Web is changing, from the early Web 1.0 to the Social Web 2.0 and beyond to Web 3.0
interfaces, but more importantly, the users of the Web are also changing, and their numbers are increasing
rapidly in line with this evolution. In e-Learning, it is essential to be able to keep up with these trends and
provide personalized social interaction. Here, our main customers are our students, but these customers
do not come unprepared: they already have a great deal of Web experience, especially in the areas of
Social Networking Sites (SNS) and online interaction. Thus, it is essential to improve approaches used
in the past, where learners were only involved in the receiving part of the delivery process. This chapter
therefore proposes and explores applying participatory design methodologies in the early stages of the
social adaptive educational hypermedia system design process, showing also its benefits for further
design, implementation, and usage.

1. INTRODUCTION
The Web of today looks totally different from
that of the past. Its main driving forces are less
the technologies and mechanisms, but its thriving user communities. There are over 2.4 billion
Web users in the world, according to KPCB Web
Trends (Meeker & Wu, 2013). Moreover, younger

generations have embraced the Web as a normal
part of their lives, on which they spend a great
amount of time. For instance, according to Everfi
(Everfi, 2013), 13% of the 5500 American young
teens surveyed admitted to spending more than
five hours a day online, 16% of them admitted to
spending 3-5 hours, and 40% of them admitted
to spending 1-3 hours.

DOI: 10.4018/978-1-4666-7262-8.ch014

Copyright © 2015, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.


Students as Customers

In education, e-Learning is flourishing, with
most universities and even schools having a clear
e-presence and a varying proportion of online materials, including usage of e-Learning systems and
learning management systems (such as MOOCS,
Moodle, or older systems such as Blackboard,
WebCT, etc.). However, e-Learning lags somewhat behind in embracing the new technologies,
techniques and interaction models, for instance
e-Learning in the business (through lifelong
learning) or mobile sectors (ubiquitous learning).
In this global context, there is already a good
body of research available to support the benefits
of personalized education, both offline and online.
Targeting the latter, the research area of Adaptive
Hypermedia (AH) and Adaptive Educational
Hypermedia (AEH) (Brusilovsky, 2001) has been
growing rapidly during the past 20 years. It has
resulted in a plethora of AEH systems (AEHS) to
support, verify and evaluate the newly proposed
models, system architectures and methodologies.
Researchers in this area have been focusing on
posing and answering the six major questions
that define the core of adaptation, initially introduced by Brusilovsky (1996), namely, 1) what
can we adapt? 2) what can we adapt to? 3) why
do we need adaptation? 4) where can we apply
adaptation? 5) when can we apply adaptation?
and 6) how do we adapt?. Asking (and answering) these questions enables researchers to define
adaptation process, in order to design an AEHS
that better identifies a learner’s knowledge level,
learning goal, preferences, stereotypes, cognitive
and learning styles, etc. (Brusilovsky, 2004) to
provide adaptive and adaptable learning content,
navigation, presentation and interaction. Whilst
researchers (and system designers) are of importance during the AEHS design process, the other
crucial role that has often been neglected is that
of the customer of an AEHS (such as the learner
or end-user).
Indeed, with the ever-increasing commoditization of learning, and the rise in fees (especially
for higher education), students tend to act more

like customers than passive recipients of knowledge, as they have often been considered in the
past. They also come normally with a very good
background on Web 2.0 (as in social) and some
Web 3.0 (as in both personalized and social) systems and platforms, albeit with less knowledge in
the area of e-Learning (including pedagogy and
meta-cognition of life skills such as Learning to
Learn). Indeed with the rise of this ‘student-asclient’ paradigm, the business of higher learning
has broken the bounds of the traditional university
structures and ‘exploded’ onto the Web. MOOCs
are an excellent example of this, with vast numbers of students (often 100,000+) being able
to access courses designed by leading teachers
and researchers. These courses, like all previous
non-AEH courses, fall into the ‘one-size-fits-all’
trap (Brusilovsky, 2012), in that delivery of these
learning materials are not personalized to the
learner in anything other than a superficial manner. Therefore AEH research and development
has a great deal to offer the business of education, especially in using MOOCs (and Learning
Management Systems (LMS) such as Moodle)
as a vehicle for delivering a personalized lesson
to a large scale audience over the course of their
working life.
Furthermore, in the Web 2.0 era, a growing
number of researchers have been exploring the
ways to facilitate adaptive e-Learning by introducing a social dimension and integrating various Web
2.0 technologies. This identifies the advantages
of providing social media tools and supporting
linking learners, e.g., inquiry-based collaboration
(McLoughlin, 2007). Learners have been found to
also be more motivated to contribute to creating
an effective learning environment and enriching
learning experiences, supported by collaboration
and feedback from their peers (Dabbagh, 2011),
which brings the benefits of not only engaging
creating and sharing information and knowledge
within a collaborative learning context, but also
enhancing adaptation by monitoring and analyzing
learners’ social learning behaviors and interactions

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Students as Customers

with each other (Brusilovsky & Henze, 2007;
Krause, et al., 2009; Magnisalis, et al., 2011; Shi,
et al., 2013).
Accordingly, the research focus has shifted
from an individual orientation, on a student and
his cognitive processes (Werner, 1986), to a social orientation. In comparison with AEHSs, the
social-AEHSs have been pushing the research area
of AH and AEH towards fostering diversification
of (explicit and implicit) user modeling (Barla,
2011), as well as richer user experience. Due to the
wide use and popularity of major social network
sites (SNS) such as Facebook, Renren, Weibo,
Tumblr, Pinterest, the new generation of learners have already been frequently using Web 2.0
functionalities and social apps, which makes the
social-AEH learning environment more familiar
to them, and subsequently increases the usability
of such an e-Learning environment (Shi, et al.,
2013b). The significant features of social-AEHS
make it more important than ever before to ensure
the learners’ participation during the AEHS design
process (Shi, et al., 2012a).
In the conventional research process of the
AH and AEH area, researchers normally took a
researcher-centered (or designer-centered) approach, while the learners were usually involved
only in the evaluation stage (Lohnes & Kinzer,
2007; Seale, 2009; Mulwa, et al., 2011). For instance, the researchers firstly built an AHES with
their hypothesis and several new features, and then
conducted experiments to collect learners’ usage
data and/or distributed questionnaires, in order to
evaluate the system’s usefulness, ease of use, ease
of learning, satisfaction, privacy and data sharing,
and so on. However, the researcher-centered approach has limited ability to cater for the learners’
real needs (Looi, et al., 2009), because researchers’
knowledge about the adaptation process does not
necessarily guarantee that they know about the
end-users’ needs from the system. Not only are
more time and effort needed in the initial design
process, but the researchers (or designers) may

308

also face costly redesigns if they want to improve
the system in the follow-up research (or design)
iterations. Therefore, the adoption of user-centered
design (UCD) (Norman & Draper, 1986), participatory design (PD) (Schuler & Namioka, 1993)
and the analysis of phenomena characterizing the
human-computer interaction (HCI) (Shneiderman
& Ben, 2003) process should be considered even
since the early design stages, in order to build
more usable systems (Valtolina, et al., 2011). If
the system were designed to provide its end-users
with exactly what they need, it would provide a
better user experience, as well as encourage users
to try features and contents, so that the system
would collect greater usage data, which could
eventually lead to a more useable system with
greater benefits for the learner.
In this chapter, we therefore illustrate how
the customers of e-Learning, the students (note
that in lifelong learning the ‘student’ is often also
the employee and as such this can have a direct
benefit for the business that employs them), can
be involved in the design process, by applying a
PD methodology in the early stage of designing
a social-AEHS. For this purpose, we report our
case study, which mimicked a large co-designer
experiment in a small format and extracted an
ordered list of initial application requirements,
aiming at exploring how to apply a PD methodology and gathering issues and initial preferences
for future studies. We further show how this student involvement has benefitted the later design,
development and usage of our adaptive, social
e-Learning system.

2. BACKGROUND:
TOWARDS SOCIAL AEH
Adaptive hypermedia (AH) is a field of research
at the crossroads of hypermedia and user modeling (Brusilovsky, 1996). The main goal of AH
research is to improve the usability of hyperme-


Students as Customers

dia applications, by making them adaptive and
adaptable. As the most popular research area of
AH, adaptive educational hypermedia (AEH)
combines adaptive hypermedia system (AHS) and
Intelligent Tutoring Systems (ITS), with the aim
of breaking away from the “one-size-fits-all” mentality (Brusilovsky, 2012). This means engaging
learner interaction as well as enabling e-Learning
systems to adapt to different learners’ specific
needs in a given context, and thereby providing a
personalized learning experience for each learner.
A lot of conceptual A(E)H frameworks have been
proposed since the early 2000s, aiming to simplify
the process of building adaptive systems. Wellknown frameworks include AHAM, proposed by
Wu (2002), XAHM, proposed by Cannataro et al.
(2002), LAOS, proposed by Cristea and De Mooij
(2003), the Munich model, proposed by Koch and
Wirsing (2006), GAF, proposed by Knutov (2008),
GAL proposed by Van Der Sluijs, et al. (2009)
and so on. Afterwards, some conceptual A(E)H
framework with social dimensions were proposed,
such as SLAOS proposed by Ghali and Cristea
(2009b) that extended from LAOS by adding a
collaboration mechanism, and ALEF proposed
by Šimko et al. (2010).
Prior (and partially concomitantly) to the
development of conceptual A(E)H frameworks,
a variety of AEH systems and AEH-based learning tools have also been researched. For example,
AHA! (De Bra, et al., 2003) was designed as
an adaptive hypermedia platform that delivers
XHTML pages as a series of concepts. Each
concept is recommended to the user according to
a predefined adaptation strategy. MOT (Cristea &
Kinshuk, 2003) is a web-based generic adaptive
hypermedia system based on the LAOS framework for authoring adaptive learning materials.
The GRAPPLE (De Bra, et al., 2013) project created the GALE (Smits & De Bra, 2011) delivery
engine, which extended the principles of AHA!,
in order to produce a more general purpose and
fully extendable delivery engine. As regards the
branch that the social dimension is introduced,

one of the first attempts was MOT 2.0 (Ghali and
Cristea, 2009a) that was developed based on the
SLAOS framework, introducing several social
facilities, such as the ability to hold a discussion
via chat tool, to rate, tag learning items, and get
recommendations of advanced learners to contact
(Cristea and Ghali, 2011). Progressor (Hsiao,
et al., 2013) is a web-based tool based on the
concepts of social navigation and open student
modeling (Mitrovic & Martin, 2007) that helps
students to find the most relevant resources in a
large collection of parameterized self-assessment
questions on Java programming. Topolor (Shi, et al,
2013c) is social adaptive personalized e-Learning
system that provides extensive social features and
personalized recommendations including learning
topic recommendation, learning path recommendation, learning peer recommendation and so on,
in a adaptive e-Learning environment with rich
social interactions.
Learning is intrinsically a social endeavor (Bandura, 1977; Zimmerman, 1989; Wenger, 2000).
Social facets of learning have been described in a
variety of theoretical frameworks about people and
their learning (e.g., (Vygotsky, 1978), (Wenger,
2009) and (Dabbagh & Kitsantas, 2012)). It is not
surprising that the AEH research area has shifted
to a social orientation. We believe that the investments and achievements in this social-AEH branch
are shaping the future of learning and learning as
a business, which is one of the reasons why we are
pursuing this particular research direction. AEHS
allows personalization of e-Learning, meanwhile
social medias enable learners to create, publish
and share content, facilitating interaction and collaboration. The integration of social media tools
into AEHS offers new ways for learner/customer
engagement and extended user modeling, thereby
creating the so-called social personalized adaptive e-Learning environments (SPAEE) (Shi, et
al., 2013d). Therefore our overall research aim
is to improve the (lifelong) learning experience
and learning efficiency in e-Learning via social
adaptive learning.

309


Students as Customers

3. PARTICIPATORY DESIGN AND
THE WE!DESIGN METHODOLOGY
As one of the most important User-centered
design (UCD) approaches, participatory design
(PD) places greater emphasis on allowing users
to make the decisions (Vink, et al., 2008). March
(2005) states “New and unexpected interactions
with the immaterial have expanded the design
territory to include people as designers”. Rather
than the traditional view that users (and customers) are not necessary to participate in the design
process before the requirement gathering phase,
PD requires designers and users to equally work
together to set design goals and plan prototypes,
and engages users as active members of the design
process (Muller, 2003). Researchers and system
designers who endorse PD approaches believe that
users are capable (with necessary knowledge and
skills) and should play a more active role during
the design process (Triantafyllakos, et al., 2008;
Shi, et al., 2012b). PD offers users opportunities
to participate during the design process so as to
increase the probability of a usable design. It provides a chance for system designers to work with
users so as to better understand users’ real needs.
It supplies a tool that helps to identify issues and
solutions (Rashidah, 2011).
The research on learners as co-designers of
educational systems has been increasingly appealing to researchers. Könings, et al. (2010)
assert PD can be “adapted for use in education
as a promising approach to better account for
students’ perspectives in the instructional design
process in different school subjects”. Seale (2009)
claims that participatory methods have “the potential to both empower students and increase the
possibility that teachers will respond to student
voices”. Many PD approaches introduce learners
as co-designers in the design process, and bring
together design techniques of needs assessment,
evaluation, brainstorming, prototyping, consensus
building and so on. However, most of the existing
PD methodologies have strict requirements, and

310

most of them are focused on learning content design only (Triantafyllakos, et al., 2008). Learners
are the core participants in an e-Learning process,
so it is essential for the system designers to take
into consideration the learners’ opinions. Involving learners in the design process brings benefits
not only for applications, but also for the learners
themselves, because it can help exchange knowledge between students and designers (Roda, 2004).
As one of the PD methodologies, We!Design
is student-centered and can be easily applied in
real educational contexts (Triantafyllakos, 2008).
It brings some merits compared to other PD
methodologies:
1. Conducts cooperation between students and
designers in a short period of time;
2. Supports a content-independent learning
process, including note-taking and assessment, and
3. Exploits the potential of highly computerliterate students who are driven to collaborate
in order to produce a description of needs,
task sequences and user interface prototypes
(Triantafyllakos, 2008).
For these reasons, we have opted to use the
We!Design methodology in our research for requirements analysis.
The We!Design methodology contains two
phases (see Figure 1).
In PHASE 1, several parallel design sessions
are conducted with small groups of students under
the supervision of coordinators, aiming at proposing a low-tech prototype and a requirements list.
The size of session groups is kept small, in order
to minimize conflict possibility between the students, reduce time cost, and establish a friendly
and informal atmosphere. Each session consists
of three stages, including needs collecting, tasks
sequencing and prototype designing. In the first
stage, needs collecting, students build a set of needs
based on their experience of using a similar system
and their expectations from a new system. In the


Students as Customers

Figure 1. The We!Design methodology (Triantafyllakos, 2008)

second stage, tasks sequencing, students design
task sequences to satisfy the previously built set
of needs. In the third stage, prototype designing,
students design a low-tech prototype application
to complete the designed task sequences.
During PHASE 2, the system designers analyze the requirements proposed in PHASE 1 and
synthesize them into a single application, with an
ordered requirements list. Initially, the designers
organize, group and rewrite the collected needs to
avoid overlapping. Next, these needs are ordered
based on the number of sessions that they are
proposed and their importance assessed by the
students. Finally, the designers compile the diverse
task sequences of each final need into one task

sequence, analyze the prototyped designed by
the students, and eventually synthesize the final
prototype application. In the next section, we
will present the detailed process of applying the
We!Design methodology, together with the actual
data collected from the performed case study.

4. APPLYING PARTICIPATORY
DESIGN FOR ADAPTIVE WEB 3.0
4.1 Setup
In our small-scale case study, 2 coordinators and
6 undergraduates participated. One coordinator

311


Students as Customers

was a computer science Ph.D. student from the
University of Nottingham, UK; the other one
was a computer science Ph.D. student from the
University of Warwick, UK .The 6 undergraduate
students were from the ‘Politehnica’ University of
Bucharest, Romania. They were 4th years computer science students, studying a course entitled
‘Semantic Web’.
A short seminar was delivered at the beginning
of the case study to introduce the experimental
process, explain the case study’s goals, and recall
the required background knowledge including
how to design a system and what an AEH system
is. Firstly, one coordinator presented the concept
of AH and AEH, followed by some case studies of AEH systems, including AHA! (De Bra,
2003), MOT 2.0 (Ghali, 2009) and LearnFit
(Essaid, 2010). Then, the coordinator introduced
the concept of social networking sites (SNS) to
the students. All the students were, as expected,
familiar with SNS, such as Facebook, Google+
and YouTube, etc. They were also familiar with
UML and UML-based design.
Thereafter the students could take upon themselves the main roles of discussing and presenting,
while the coordinators were in charge of time
controlling and summarizing. The seminar focused
on the features of the AEH systems and SNS, and
aimed to acquaint the students with both domains,
and encourage them to think deeply about these
two kinds of system, so they could integrate both
to design new social-AEH systems.

4.2 Phase I: Design
Session with Students
We conducted two parallel design sessions, each
of which consisted of 3 students, and lasted for
about 2.5 hours. The two coordinators supported
these sessions, without interfering unless they
considered it necessary to bring the students back
on track. One coordinator was a human computer
interaction (HCI) expert, whose role was that of
ensuring that students consider preserving the

312

usability of the system; the other coordinator was
an e-Learning system expert, whose task was to
be preventing the students from loosing track of
the system design goals.
For facilitating the work, students in a group
sat together. In front of them was a table with
pens and a big white paper to record their ideas
on, and eventually draw the user interface of the
prototype. The two design sessions were recorded
by a video camera, so the coordinators could focus on guiding the case study and solve current
issues, instead of noting the problems occurred
for further research.

Stage 1: Needs Collecting
In the needs collecting stage, the students were
asked to extract a set of needs that are currently
not met, according to their previous e-Learning
experience. The expectation was that these needs
could be addressed by using a social-AEH system.
The students contributed to the needs collection
by brainstorming and discussing ideas. Initially,
the students considered the main features that they
expected to be provided by such an e-Learning
system, as well as briefly discussed problems
that they encountered when using such systems
previously. All the students had opportunities to
present their own ideas. Turn taking in suggestions
was supported. Additionally, while one student
was presenting, the others were encouraged to
ask questions and provide suggestions and comments. Afterwards, the students summarized all
the ideas into an initial need list, and then continually elaborated, categorized and evaluated these
needs. As a result of this process, 97 ‘raw’ needs
were proposed and ordered into a requirement list,
according to their perceived importance.

Stage 2: Task Sequencing
In the task sequencing stage, personas and scenarios were adopted as a lightweight method to
capture the system requirements. Personas contain


Students as Customers

users’ background information and specific situation related to using the system (Cooper, 2007).
Four personas were created to outline the real
characteristics of the system’s end-users. Take
one for example:
Michael is a sophomore student, studying a course
of ‘Java Programming Language’. He has learned
PHP, and achieved higher scores than most of the
other students. He prefers to analyze examples,
and then design his own program to check whether
he’s learnt the constructs from the examples. He
likes to share and discuss with other students.
Scenarios, such as the one above, create a story
with settings, personas and a sequence of actions
and events (Carroll, 2000). One of the designed
scenarios was:
When Sam is debugging his program using the
programming tool provided by the system, he receives a message from his friend asking for help.
He preserves his work, and asks what this friend
exactly needs.
In this stage, personas and scenarios were used
to describe the interaction between the persona
and the potential application to fulfill the proposed
needs, and enable rapid communication about
usage possibilities that might satisfy the needs
proposed in STAGE 1.

Stage 3: Prototype Designing
This stage was a refinement process, asking the
students to convert the needs collected in STAGE
1 and the task sequences designed in STAGE 2
to concrete requirements, so as to design a lowtech prototype application. Firstly, the students
portrayed the final task sequences and visualized
the scenarios on the large shared white paper
with necessary notes to present the basic ideas
of the interaction process and user interface. For
instance, the students drew a dropdown list that

could be used as a menu to switch between different views of the concept structure. Secondly,
the students re-evaluated each component from
the user interface, and proposed new components
and/or re-organized existing components, to make
sure each proposed task sequence could be completed smoothly. Finally, a stereotypical end-user
role-play was conducted, to evaluate the usability
of the designed prototype.

4.3 Phase II: Application Synthesis
In PHASE II, the principal designers gathered
and analyzed the product designed in the first
phase to synthesize a single application. The
requirements were firstly grouped into 35 final
ones, by removing duplicates. Next, they were
ordered according to the estimated importance,
which was computed according to the number of
times the requirements appeared in the students’
suggestions, in one form or another. Then, these
requirements were categorized into four categories, which represented the main areas for which
features could be built in a system, according to
the designer, and which are as follows:
1. Learning: Here entered, for example, requirements such as using of multiple types
of files, including photos, videos, slides, etc.;
allowing for multiple files was considered of
high importance by students; other (optional)
requirements of lesser importance were, for
example, taking tests after learning a topic;
getting assessment and feedback from teachers; etc.
2. Social Networking: This category included
important requirements such as creating
groups that are registered for the same topic;
and, in decreasing order of priority, discussing the topic with other students; etc.
3. Adaptation: This category involved requirements such as recommending other topics
according to the current learning topic;
recommending topics according to student’s

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Students as Customers

knowledge level and other students’ rating;
etc.
4. Usability: This category listed requirements
such as visibility of the system status; instructions and tips; graphical user interfaces; etc.
The results of these phases are described in
section 4.4 below. However, before this
data-mashing phase, we have gathered
more information from students, as
follows.

4.4 Additional Quantitative
and Qualitative Feedback
Gathering: The Questionnaire
The students who participated in the design sessions were also invited to answer a questionnaire
with 28 questions. They were asked to evaluate
the e-Learning environments that they had used
in the past, and to elicit their extra expectations
for features of a new social-AEH system. As
the students already went through the introductory material and design sessions, their answers
were more informed, and were able to help the
designer understand the priorities students set on
the previously extracted requirements. Due to the
limited space, only selected results are shown in
this section.

Figure 2. The reasons for using e-Learning systems

314

Students’ Previous Experience
with E-Learning Systems
There were several reasons for students to use eLearning systems in the past, as shown in Figure
2. The most important reason they gave was to
‘Save Time and Effort’. This corresponded to
their answers in the open-ended questions part of
the questionnaire, where the students stated that
‘Availability 24/7, everything is organized in one
place’ as being some of the features of e-Learning
systems that they liked the most. Out of this clear
preference, one of the requirements would be to
provide a simple, constantly available ‘one stopshop’, where all the material and functionality is
present, and thus not increase the learning burden.
From the point of view of social websites used,
the questionnaire result also indicated that all the
students have experience of collecting learning
resource from Wikipedia (see Figure 3). Wikipedia is indeed the largest general reference on the
Web, offering more than 30 million articles (List
of Wikipedias, 2013). YouTube was mentioned as
the second most popular social networking website
to collect learning resources from, while the third
one was LinkedIn. In the case study, students
also mentioned the requirements of access to and
search for open learning resources from outside
of the system. Therefore access to open learning
resources such as Wikipedia, and searching for


Students as Customers

Figure 3. SNS websites for collecting learning resources

learning materials, should have a high priority
to be developed.
After finding out about the students’ experience with e-learning systems and social platforms,
we further asked about specific features, if they
should or not be included in the system.

Preferences for the New
System Features
In Figure 4, 67% of the students prefer courses to
be published by both teachers and students; while
the other 33% think that the courses can only be
published by teachers. Besides, more students
(83%) prefer topics to be recommended according
to students’ ratings rather than the count of visits.
Figure 5 shows that half of the students prefer that
learning paths are kept static from creation; while
the other half consider that learning paths should
be adapted to the learning context. Furthermore,
the same percentages of students agree that learning paths can be both designed by teachers and
calculated by data collected from other students’
behaviors. Figure 6 shows that 17% of the students
prefer asynchronous interaction with others in the
system (such as comments); while the other 83%
of the students prefer synchronous interaction such
as chat window. Figure 6 also shows that 33% of
the students hope to have all social interaction
tools when they begin to use the system; while the
other 67% of the students prefer to obtain more
social interaction tools when they move up to a
higher user-level.

Importance of the Selected
System Features
The students were further asked to rate the importance of a list of features pre-selected by the
system designers on a 1-5 scale (1 = not important
at all; 5 = very important). Table 1 displays the
means and standard deviations of the result. The
feature considered the most important by the
students is the ‘Exchange of knowledge and approaches’ with the maximum mean value (4.83)
and the minimum standard deviation (0.41). The
minimum ones were ‘Multimedia delivery’ and
‘Recommendation of groups and other students’,
with an average of 3.67 > 3 and a standard deviation of 0.82. However, some clear preferences
could be seen from the students’ responses, and
these were further processed towards the system
requirements in the following subsection.

Suggestions on Designing a
New E-Learning System
The questionnaire also contained some openended questions that allowed students to provide
unrestrained wide-range responses, which could
reveal originally unanticipated findings in the
questionnaire (Reja, 2003). The suggestions of the
students are summarized in the list below (ranked
by the implementation priority, and labeled with
the functionality aspects):

315


Students as Customers

Figure 4. Preferences for learning material

Figure 5. Preferences for learning path

316


Students as Customers

Figure 6. Preferences for interaction

Table 1. Allocated importance of the features of an adaptive social e-learning system
Feature

Scale of Importance
Mean (1-5)

Standard Deviation

Exchange of Knowledge and approaches

4.83

0.408

Feedback of learning process and results

4.67

0.516

Recommendation of learning path

4.67

0.516

Trust of group members

4.50

0.548

Share learning materials and experience

4.50

0.548

Revision exercises

4.33

0.516

Trust of user-generated learning contents

4.33

0.816

Recommendation of related topics

4.00

0.894

Collaborative learning and group activities

4.00

0.894

Interactions and tips

4.00

0.632

Interactive learning content

4.00

1.265

Multimedia delivery

3.67

0.816

Recommendation of groups and other students

3.67

0.816

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Students as Customers

S1. The recommendation of learning materials
for a particular student should be based on
her/his performance during learning, mixed
with results from the exercise/tests. – Personalization & Exercises;
S2. Students should be able to create their own
learning paths in the courses that they were
interested in, while other students could
provide suggestions or use these learning
paths for their own study. – Adaptability &
Open Student Models & Social Interaction;
S3. The system should provide an interface to
access online libraries for reference while
students are learning related topics, and
make is possible for the students to save
these references inside the system. – Open
corpus & Social Interaction;
S4. Exercise tools are essential, especially for
practice courses such as programming language. It would be better to learn by using
the knowledge rather than just reading some
chapters. – Exercises;
S5. The system should introduce some learning
aid for students to improve their learning
efficiency. – Usage Tutorials & Learning
aids.
S6. The user interface should be as simple as
possible, concentrating all needed resources
in one place (a ‘one stop-shop’: either physically - with all material in one place, or on
one server, or virtually - as in a portal to
all the needed information). – Portal & User
interface.

4.5 Requirement List
Finally, the designer merged the results from
PHASE 2 and the responses from the questionnaire
into a requirement list, ordered by their priority.
The latter was computed from the estimated importance of a requirement, as stated by the students,
and from the separate information on the number
of times a (version of the) requirement appeared
during the design sessions. The resulting list of

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the ordered requirements for social-AEH systems
is shown in Table 2.

5. DISCUSSION OF THE
CASE STUDY
In PHASE 1, the coordinators had to be very clear
in which situation they needed to intervene and to
what extent. In the needs collection stage, especially at the beginning, the students were always
impatient to start exploring solutions to satisfy the
proposed needs rather than focusing on collecting needs, so the coordinators had to stop them
in time. In the task sequencing stage, personas
and scenarios were used to capture the requirements of the system. One of the best practices is
to identify primary personas, ‘the individual who
is the main focus of the design’ (Cooper, 2007).
To be primary, a persona is ‘someone who must
be satisfied but who cannot be satisfied with an
interface designed for any other persona. An interface always exists for a primary persona.’ (Cooper,
1999) With regard to scenarios, storyboards or
customer journeys were used to test the validity
of design and assumptions. The students had to
design an appropriate level of detail, because of
the short period of time. In the prototype designing stage, some solutions were found flawed to
some extent, and the students might be unwilling
to fix flaws or they might need extra time. The
coordinators should encourage them to get the
solution as well as control the time, as even if
the work was incomplete, the highlighted issues
could still inspire the designers.
In PHASE 2, the designers arranged the requirements proposed by the students, the descriptions of content-based requirement. It is possible
for the designers to misunderstand the original
meaning intended by the students, so it is necessary to show the reorganized requirements to
the students, and ask them to check whether the
requirement list is consistent with their original
ideas. Still, even though the students confirmed


Students as Customers

Table 2. Ordered requirements list for a social-AEH system
Category
Learning

Social
Networking

Requirement

N1

I2

P3

Use multiple types of files, e.g. PDFs, photos, videos, slides, etc.

5 (q)

1

1

Take tests after learning a topic

4 (q)

3

2

Get assessment and feedback from teachers

5 (q)

4

3

View learning progress in percentage

5

7

4

Tag and flag up topics in the learning path

1

2

5

Access to open learning resource, e.g. Wikipedia

6

5

7

Search learning resource within and outside of the system

6

6

8

Use interactive learning content, e.g. debugging tools.

q

9

6

Contribute to learning materials by creating and uploading files

3

8

9

Choose to view the whole or partial learning path

1

10

10

Create groups that are registered for the same topic

3

1

1

Discuss the current learning topic with other students

6

4

2

Set access rights for learning materials

q

8

3

Set access rights for groups

q

9

4

Ask and answer questions of other students

5

3

5

Create groups that share common learning interests

4

10

6

Use feedback & questions forum at the end of each lesson

5

5

7

Share and/or recommend learning materials

2

2

8

4 (q)

6

9

Write comments/notions wherever and whenever they want

5

7

10

View history discussion when selecting a particular topic

1

11

11

Design and publish courses for others to use

q

12

12

Recommend other topics according to the current learning topic

5 (q)

2

1

Recommend topics according to student’s knowledge level

4 (q)

1

2

Recommend topics by referring to other students’ rating

2 (q)

3

3

Adapt learning path according to learning progress

2 (q)

4

4

Adapt learning tools according to student’s user-level

1

7

5

Adapt social interaction tools according to students user-level

q

8

6

Recommend other students according to the current topic

q

6

7

Recommend other groups according to student’s interests

q

5

8

View system status

2

3

1

Use graphical user interfaces

4

1

2

Get instructions and tips

3 (q)

2

3

Select full screen option

1

4

4

Set themes, layout, etc.

2

5

5

Use communication tools to chat and leave messages

Adaptation

Usability

1. N: the number of times the requirement appeared in the students’ suggestions, (q: from questionnaire results).
2. I: the average importance of the requirement proposed by the students from the two design sessions.
3. P: the final resulting priority of the requirement, according to the principal designers.

319


Students as Customers

the requirements, it would be still possible that
the designer deviates from their intended design.
Overall, the students willingly contributed to
generating the requirements, and they were satisfied with both the experiment and the knowledge
they acquired during the experiment. From the
system designer’s perspective, the requirements
obtained represents a generic level of detail into
the requirements definition, which is collected
as natural language statements describing what
services the system is expected to provide. Besides, these requirements create a common vision
between the students and the system designers,
to make sure the system that will be developed
is what the students really need. The next step
is to generate the requirements specification
(intermediate-detail) and then the application
specification (high-detail) (Sommerville, 1995).
The questionnaire results indicate that currently the students’ favorite equipment to access
e-learning system is the laptop. While Canalys
recently released the worldwide shipment estimation of equipment for Web access (Titcomb,
2013), which indicates mobile computing devices,
especially smartphones, tablets and phablets (a
cross between phones and tablets), have a much
greater potential. This means that cross-platform
compatibility, including adaptive layout and adaptive screen orientation (landscape or portrait), is
urgently needed.
Facebook is the largest SNS in the world and
has 1.19 billion monthly active users, and 728
million daily active users on average in September 2013 (Facebook Newsroom, 2013), but most
people use Facebook for entertainment (Tosun,
2012) rather than learning, which is why the
questionnaire result shows that only 16.7% of
the students chose that they have ever collected
learning resource from Facebook.
Another interesting result is that half of the
students chose ‘Compulsory to Use’ as a reason
to use an e-learning system. This may be because
the systems are hard to use, or the students are
not confident to use them. Therefore it is crucial

320

to evaluate and analyze existing systems to find
out how to improve them or how to design a
better new system. The opinions of the systems’
end-user, the students, are very important, and
many aspects (e.g., system usability, accuracy of
recommendation, intended learning outcomes,
learning context) of the systems need to be taken
into consideration. Therefore the evaluation should
be conducted using a multi-dimensional approach
(Ozkan, 2009).
The main difference of this case study from the
original We!Design methodology was that, all the
students who participated in the design sessions
were asked to answer a questionnaire for collecting
more information. Although the coordinators were
trying to avoid transferring their own opinions
in the design session, it remains possible that
they could still have influenced the students. In
contrast to the design sessions, the questionnaires
have uniform questions but no middleman bias,
and the research instrument does not interrupt
the students. Besides, the structured questionnaires enable the responses to be standardized,
hence easier to analyze. The questionnaires were
delivered after the application synthesis phase,
because on the one hand, as the designers have
already analyzed the requirement proposed by the
students, they will be able to asked pointed questions to further understand the students’ opinion;
and on the other hand, since the students have gone
through the design session, they may like to have
more chance of proposing extra expectations and
helping the designers understand the priorities of
the previously extracted requirements.
One issue to raise here is that although the
software engineering knowledge of the computer
science undergraduate students can help shorten
the design duration, as the author of the We!Design
methodology stated (Triantafyllakos, 2008), this
may also have limited their ability to create a
domain-independent e-learning system. For instance, they mentioned the importance of tools for
practice courses such as programming language
courses, but they did not consider multimedia


Students as Customers

delivery as highly important, when for instance,
for art and social science subjects, the quality of
multimedia transmission and presentation might
be very important.

6. THE RESULT: TOPOLOR
Based on the case study result (and also the literature review on e-Learning systems and social
networking sites), we further developed an overarching research hypothesis that extensive social
features (based on suggestions S2 and S3 above),
personalized recommendations (based on S1) and
Facebook-like appearance of a system (anticipated
to make the environment more familiar to learn-

ers), subsequently increases the usefulness and
usability of the system (S6).
To be able to validate this hypothesis, a first
version of a personalized social e-learning system,
Topolor1 (Shi, et al., 2013c), was built.
This first prototype provided a learning portal
(S6) with a Facebook-like appearance (Shi, et al.,
2013b) as shown in Figure 7, featuring the profile
avatar and learner information, the fixed-position
top menu and the left side bar for navigation, and
the information flow wall for social interaction,
etc. It supports learning content adaptation (S1),
learning path adaptation (S1), adaptation to test
results (S4), and peer adaptation (S1-S3), and
provides a social e-learning environment (S2, S3),
i.e., learners can comment on a topic, ask/answer
a question about a topic, create and share notes

Figure 7. Screenshot of Topolor (first prototype) home page

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Students as Customers

related a topic, etc. This represents thus a much
broader range of adaptation than in regular adaptive hypermedia. It has been used as an e-Learning
platform for MSc level students in the Department
of Computer Science, at the University of Warwick, and the usage data is being anonymously
collected for analysis.
In the last year and a half, Topolor has been
under iterative development and evaluation, aiming at testing the hypothesis stated above and
progressing towards achieving the overall research
aim, as mentioned in Section 2. By the time of
the writing of this chapter, we have finished the
first two iterations of system development, as
well as two rounds of evaluation. Following the
experimental study on applying PD methodologies in developing a social-AEH system reported
in this chapter, which has given us an excellent
starting point in the system design. We have also
conducted several other studies on, among others:
subjective assessments of Topolor’s usability (S6);
social interaction design in a social-AEH system
(S2, S3); Learning behavior pattern analysis in
Topolor (S1-S3); and building light gamification
upon social interactions (S3, S5, S6).
In the primary evaluation of Topolor, SUS,
a ten-item attitude Likert scale (Brooke, 1996)
questionnaire was used to obtain a global view
of subjective assessments of usability for Topolor (Shi, et al., 2013f). Topolor was used to
teach ‘Collaborative Filtering’ during a two-hour
lecture, after which the students were asked to
fill in an optional SUS questionnaire. 10 (out of
21) students’ responses were received. The SUS
score was 75.75 out of 100 (with 0 worst and 100
best score, and σ=12.36, median=76.25), and the
Cronbach’s alpha value of the questionnaire data
was 0.85 (>0.8). Therefore, we could claim that
the first prototype of Topolor’s usability meets our
initial expectations. Positive qualitative feedback
from the students supported this SUS result.
We have reported the evaluation of Topolor’s
social toolset on each feature’s usefulness and ease
of use, as well as the reliability of the results (Shi,

322

et al., 2013h). Topolor was designed to include a
wider range of social interaction features than previous adaptive educational hypermedia systems.
The evaluation results indicated students’ high
satisfactions on both usefulness and ease of use of
the various social features that Topolor provides,
with ‘excellent’ level of reliabilities (Cronbach
& Shavelson, 2004). The oral feedback was that
they would have wanted to have more lessons in
this e-Learning environment. Decisive in this,
we believe, was the fact that a lot of the social
features had a look and feel familiar to them that
was similar to the popular Facebook environment.
Such familiarity is essential to consider in designing such systems.
User modeling is a process where learner’s specific needs are built and maintained (Brusilovsky
& Millán, 2007), either by explicitly gathering or
implicitly obtaining user data during user-system
interaction, in order to provide personalized and
adaptive services. Using an implicit approach,
a social-AEHS can track learning behaviors
unobtrusively and ubiquitously, hence inferring
unobservable information from observable information about a learner. To provide suggestions
on the further development and improvement of
implicit user modeling in Topolor, we analyzed
learning behavior in the first prototype, using data
mining methods and visualization tools (Shi, et al.,
2013g; Shi, et al., 2013j). We explored learning
behaviors patterns in Topolor, focusing on the
analysis of action frequency and action sequence.
The results revealed some interesting individual
learning behaviors and some common learning
behavior patterns (e.g., allowed for identification
of the social learner, using social tools more than
learning, in contrast to the focused learner, using
learning content more, etc.), which suggested
possible directions both to improve implicit user
modeling for the next prototype of Topolor, and
to design user modeling for other social-AEHS.
The evaluation results of the social interaction
features in Topolor showed high students satisfaction (Shi, et al., 2013i), but we are still keen


Students as Customers

to improve these features to make Topolor more
engaging. Therefore, according to the analysis
on the usage of social interaction features, we
proposed three light gamification mechanisms
to build upon those identified social interaction
features with relatively lower rating. Gamification is implemented for creating more interest,
attention and interaction to make a system more
engaging (Deterding, et al., 2011). Light gamification mechanisms here literally mean that we
intend to introduce gamification as a solution to
symbiotically make Topolor easier to use and more
engaging, rather than replace its social learning
community (Shi, et al., 2013e). The proposed
three gamification mechanisms include: 1) tip
mechanism as packaged missions (Kim, et al.,
2009) to navigate students to use various features
in Topolor (S5); 2) badge mechanism to cultivate
an environment of collaborative and competitive
e-Learning (Domínguez, et al., 2013)(S5); and
3) peer-review mechanism to prevent learners

from abusing features in Topolor and improve the
quality of posts (S5).
Based on the studies mentioned above, the
second prototype of Topolor was developed (see
Figure 8 for its homepage screenshot). We have
improved various features provided in the first
prototype and introduced some new features such
as open student modeling (Mitrovic & Martin,
2007) (S2) and light gamification mechanisms
(Shi, et al., 2013e)(S5), aiming to further validate
our overall research hypothesis by testing the
improved features and newly introduced features,
e.g., social interactions and adaptation strategies.
The evaluations have started already, and we are
now in the data-gathering phase.

7. FUTURE RESEARCH DIRECTIONS
The participatory design methodology applied in
the experiment is effective and straightforward, as
expected. We believe the readers of this chapter

Figure 8. Screenshot of Topolor (second prototype) home page

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Students as Customers

can benefit from the showcase of the way of applying this methodology in the case study. In this
section, we would like to further suggest several
potential research directions, according to the
experience from this research.
Firstly, the We!Design methodology points out
that it is necessary to involve the students with
software engineering knowledge background in
the design sessions. We did observe its benefits.
For instance, it was effective to let them design
personas, scenarios and design a user interface.
But then we also noticed some shortcomings. For
one thing, their computer knowledge might limit
their ability to design a general e-Learning system, as mentioned in section 5; for another, they
might somehow think from a system developer/
designer’s point of view, rather than that of an enduser, a customer of the system. Therefore, one of
the potential research directions is to investigate the
balance of the different knowledge backgrounds
of the students who participate in the design sessions, and how to lead them to communicate and
cooperate smoothly and effectively.
Secondly, this methodology was applied in the
very beginning of the system design process to collect needs and prototype user interfaces. It would
be also valuable to explore its usage in an iterative
system development process. For example, at the
beginning of the second development iteration, the
design sessions can extract users’ opinions of their
experience of using the system, and collect their
needs for improving the existing features and their
expectations of new features for the next version,
because in this stage, they might have already had
deeper understanding about what the system does
and how the system works.
In using an iterative design methodology it is
also possible to refine the priority lists according to
more focused user groups. The work presented in
this chapter describes the first stage of the Topolor
design process, which focused on Higher Education students, but can also find applicability to the
customers to be found in the Lifelong Learning
arena. As in any business, modern educational

324

environments need to be aware of the degree of
customer satisfaction in the products that they
use, and the PD process has proved to be an ideal
avenue to creating a system that brings this aspect
into the ground level of system design.

8. CONCLUSION
The emergence of Web 2.0 and the developmental
trend towards Web 3.0 is changing many perspectives in people’s everyday life, especially the way
that they assimilate, create and share knowledge.
On the other hand, the evolution of the younger
generation’s preferences is pushing the features
and services provided by Web applications to be
social, adaptive and personalized. Learning, as
one of the most important ongoing activities in
daily life, essentially means that e-learning needs
to keep up with these trends, because the learners,
the customers of the global education market,
are not satisfied any more in being the passive
receivers of knowledge. However, the design
methodologies for adapting and personalizing
social e-Learning environment have not yet been
extensively researched. This chapter, therefore,
proposes and explores applying participatory
design methodologies in the early stages of the
social adaptive educational hypermedia system
design process, showing also its benefits for further
design, implementation and usage.
In this chapter, we have reported our case study
on applying a participatory design methodology,
(i.e., the We!Design methodology), in the early
stage of designing a social-AEHS. This study
has created a practical sketch of the participatory
design methodology. From this study, we have
achieved our goal to gather issues and initial
preferences for our follow-up research. The results
from the experiment have been used not only for
starting the initial implementation of Topolor, but
also guiding further development. Therefore, we
suggest that it is crucial to get the customers of
e-Learning, the learners, involved in the whole


Students as Customers

system design process, even in the very beginning, and allow them to make decisions on what
services the system should provide and how to
present these features. This is especially necessary in the areas of Web 2.0 and the emerging
Web 3.0, as the experience of these end-users in
using these technologies in other contexts outside
e-Learning is sizeable. Thus, e-learning providers
and implementers need to take into account this
wealth of knowledge, and this chapter illustrates
a simple and straightforward way of doing it, also
further justified by the results of the evaluations
of the implementations created on this basis.
This chapter also sheds some light into the
applicability of Web 2.0 and especially Web 3.0
technology and theory in e-learning, and the
necessity of bringing these fields together to
enhance the experience of our clients/customers,
here, the learners.

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KEY TERMS AND DEFINITIONS
Adaptive Educational Hypermedia System:
A system that applies adaptive hypermedia to the
domain of education. It tailors what the learner
sees to that learner’s goals, abilities, needs, interests, and knowledge of the subject, by providing
hyperlinks that are most relevant to the learner.
Adaptive e-Learning: The e-learning systems
that has adaptation features.

Adaptive Hypermedia: A disputed research
field where hypermedia is made adaptive according to a user model. It tailors what the user sees
to a model of the user’s goals, preferences and
knowledge.
AEHS 2.0: The adaptive educational hypermedia systems that have Web 2.0 and social features.
Participatory Design: An approach to design
attempting to actively involve all stakeholders
(e.g. employees, partners, customers, citizens,
end users) in the design process to help ensure
the result meets their needs and is usable.
Requirement Analysis: The tasks that go into
determining the needs or conditions to meet for
a new or altered product, taking account of the
possibly conflicting requirements of the various
stakeholders, analyzing, documenting, validating
and managing software or system requirements.
Social E-Learning: The e-learning systems
that has social features.
Web 2.0: The description of World Wide Web
sites that use technology beyond the static pages
of earlier Web sites (Web 1.0).

ENDNOTES


1

https://github.com/aslanshek/topolor

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332

Chapter 15

The Open Innovation Paradigm:
Can Digital Storytelling Generate
Value for the Educational Field?
Luca Ganzerla
Ca’ Foscari University of Venice, Italy
Cinzia Colapinto
Ca’ Foscari University of Venice, Italy
Elena Rocco
Ca’ Foscari University of Venice, Italy

ABSTRACT
The aim of this chapter is to shed light on an emerging educational and business paradigm, stemming
from the digital revolution and the opportunities disclosed by Open Innovation. The central idea behind
Open Innovation is that, in a world of widely distributed knowledge, companies cannot afford to rely
entirely on their own research but should instead buy or license processes or inventions from other actors. After pointing out the potential benefits of digital storytelling and of Web 2.0 and 3.0 for promoting
pedagogical and organizational innovation, the authors present an application of the Open Innovation
Paradigm in education: the Value Generating Framework. The chapter offers empirical evidence of the
benefits through an in-depth analysis of the alliance between the Italian Zoo “Parco Natura Viva” and
the Italian foundation “Radio Magica.” This knowledge-intensive, collaborative, value network paradigm
is more successful than the previous firm-centric paradigm.

INTRODUCTION
The experience of learning is the opposite of
fun for many children around the world, but new
technologies and the so-called web 2.0/3.0 have
turned learning into an imaginative, creative and
pleasurable activity. Nowadays one has to take

into account that receivers tend to be more active
and creative, thus the border between production
and reception is blurred, leading to an overlapping
between the roles of the producers and receivers.
The emergence of technologies such as the internet
and its interactivity are not unknown phenomena;
and concepts like convergence and networks are

DOI: 10.4018/978-1-4666-7262-8.ch015

Copyright © 2015, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.


The Open Innovation Paradigm

the main players in the construction of a new
communication paradigm in different industries.
In the last decade there has been an impressive
take off of social computing, from blogging to
social networking and social tagging. Researchers
have pointed out that digital technologies have
transformed learning patterns, coping with the
different learning styles of the younger generation made up of the digital natives (McLester,
2007). All these fast growing and fast evolving
digital experiences have radically transformed the
traditional approach, and have traced the route for
a new communication paradigm in (media) education. Digital natives are continuously connected
with their peers and “always on” (Olbinger &
Olbinger, 2005; Pedrò, 2006), thus their learning
styles are affected by ubiquity, accessibility and
ease of use of resources.
Educational agencies are at the crossroads
of a number of innovative trends characterized
by the use of the internet in the classroom and
at home. Tools such as e-books, e-learning software, e-libraries, and Wikipedia are dramatically
changing the traditional approach. Studying the
evolution of the child’s literacy in digital environment since the early ages is the core of a fast
growing scientific literature. When adults adopt
sound teaching methodologies, children can benefit from the availability of digital tools as they
expand the opportunities for learning by visual,
interactive, and narrative supports unforeseeable
a few years ago.
The aim of this chapter is to shed a light on
an emerging educational and business paradigm,
stemming from the digital revolution and the opportunities disclosed by Open Innovation. The
chapter is organized as follows. In the first session
the authors present the Open Innovation paradigm
that lies at the foundation of the emerging educational and business paradigm presented in the
following sessions. The second session focuses
on the evolution of media and media education,
emphasizing the effects at educational level of the
transition from traditional to social and semantic

media. Based on the identification of the four
educational challenges, the third session shapes
and describes the framework of intervention and
analysis: the Value Generating Framework, that
leverages the opportunities disclosed by the Open
Innovation paradigm through a “wise” application
of digital storytelling. The fourth session focuses
on the alliance between the Zoo “Parco Natura
Viva” and “Radio Magica”, a non profit organization devoted to children education. Finally, the
last session discusses the benefits of the application of the Open Innovation paradigm both in
profit and non-profit contexts, pointing out how
the digital revolution can create avenues for new
strategies and solutions in the formal and informal
educational realms.

OPEN INNOVATION PARADIGM
Nowadays a particular form of collaboration for
innovation has rapidly taken off, named Open
Innovation: it “is a paradigm that assumes that
firms can and should use external ideas as well
as internal ideas, and internal and external paths
to market, as the firms look to advance their technology” (Chesbrough, 2003). This scenario leads
to collaborations sharing risk and reward. The
boundaries between a firm and its environment
have become more flexible thus innovations can
easily transfer inward and outward. The rationale
of Open Innovation is that, in a world of widely
distributed knowledge, intellectual property must
be managed openly to encourage synergies among
firms. Firms typically protects through patents the
output of innovation. However, empirical evidence
shows that thousands of patents are stored and
never transformed into products as firms have
limited resources. Therefore, internal inventions
not being used in a firm’s business should be taken
outside the company (e.g. through licensing, joint
ventures or spin-offs). In addition, as companies
cannot afford to rely entirely on their own research,
they should buy or license processes or inventions

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The Open Innovation Paradigm

(i.e. patents) from other companies. The open innovation paradigm encourages a radical change in
the use, management, and employment of intellectual property. It is a systematic encouragement
and exploration of a wide range of internal and
external sources for innovative opportunities, the
integration of this exploration with firm capabilities and resources, and the exploitation of these
opportunities through multiple channels.
The ability to successfully leverage economies
of scale, diversity of platforms, and customization of content in service of sustainable corporate
expansion is determined by economies of synergy.
The configuration of the internal and external
network organization is critical. Networked forms
of organization within companies or strategic alliances rather than horizontal integration of properties appear to be the most successful business
model (Arsenault & Castells, 2008). The system
is based on “co-opetition rather than competition”.
In sum, a knowledge-intensive, collaborative,
value network paradigm has demonstrated to be
more successful than the previous firm-centric
paradigm.
Times are mature to expand the Open Innovation paradigm beyond the business world.
As networked mentality is embedded in digital
natives, one must design digital educational environments capable to optimize synergies among
external and internal school’s resources. The Open
Innovation paradigm can be extremely useful
to suggest new avenues of collaboration among
educational institutions. Schools boundaries, as
firms’ boundaries, must be permeable and flexible
to maximize innovative learning opportunities and
accelerate knowledge dissemination. In order to
allow new generations benefit from this educational paradigm shift, networks have to embrace
a multidisciplinary mindset and adopt rules for
participatory and synergic collaborations.

334

THE EVOLUTION OF MEDIA
EDUCATION: TOWARD
A NEW PARADIGM
The relationship between media and education has
been creating an area of fruitful studies (Media
Education) since the 1930s with the establishment
of traditional media. This relationship has been
affected by the technological evolution, sociocultural and geo-political changes, the growing
role of communication (Masterman, 1997) and
the new media development, with the increasing
educational and teaching opportunities offered by
Web 2.0 and the Semantic Web. Media education
is an ideal bridge between the formal education
system and all external organizations, between
communities and educational institutions (Buckingham, 2006).
In the mid-1990s media education was understood as a set of practices primarily focused on
the comprehension of media in order to reveal the
false naturalness and educate students to a critical
autonomy about the media (Masterman, 1997;
Buckingham2006). Today, this predominantly
defensive attitude is partially spotted towards new
media, both in formal and informal educational
contexts (Rivoltella & Ferrari, 2010).
Since the end of the 20th century, revolutionary
and interconnected phenomena such as economic
and socio-cultural globalization, the spread of
the Internet and the development of ICTs have
originated great changes in all areas, not least
in education. Today new media occupy most of
people’s leisure time as they act as primary source
of information and affect consumption and lifestyles, shaping the quality of democracy. Digital
technologies offer opportunities and set new
challenges to all educational agencies (Smeets,
2005; Turvey, 2006; Casey & Bertram, 2011;
Rivoltella & Ferrari, 2010). The need to move to a
new media education paradigm is growing. In the
new paradigm, media education should focus on
developing an active and creative participation and
production of media (Buckingham, 2006; Parola


The Open Innovation Paradigm

2008, 2012; Anderson & Dron, 2011; Gutiérrez &
Tyner, 2012), recognizing equal importance to the
two souls of media education (interpretation and
production). The educational perspective - based
on a more reflective and collaborative learning
process in terms of multimedia analysis and production - has to be student-focused and characterized by social constructivism and connectivism
(Buckingham, 2006; Rivoltella & Ferrari, 2010;
Anderson & Dron, 2011; Fedorov, 2011; Gutiérrez & Tyner, 2012; Vivanet & Vercelli, 2012).
In this new paradigm contributions from media
culture play an important role. Media education
aims to enable new generations to exploit their
digital skills in order to create critical people and
conscious creators of meanings (Tolić, 2011).
The birth of a new media education paradigm is
certainly a logical consequence of the emergence
of new media with their peculiar features and the
appearance of generations with new educational
needs (see next paragraph). Since the late 1980s
the concept of literacy has dramatically changed
because of the pervasive effect of ICTs. This
concept has moved from a “monoconceptual”
(one language, one culture, only one way to learn,
communicate and educate) to a “pluriconceptual”
nature (multilingualism, multiculturalism, multimedia,...) (New London Group, 1996; Coiro,
Knobel, et al., 2008; Banzato, 2011). Consequently
a new conception of literacy and the transition to
the concept of multiliteracies (New London Group,
1996), new literacies (Lankshear & Knobel, 2003)
or multiple literacies (Kellner, 2002) occur. It
is clear that such a heterogeneous phenomenon
lends to a multiplicity of approaches and to a great
variability in the definition (Cervetti, Damico, &
Pearson, 2006; Coiro, et al., 2008).
Recently, there has been a shift in favor of
digital literacy (Glister, 1997; Banzato, 2011;
Gutiérrez & Tyner, 2012; Ng, 2012), that is «the
awareness, attitude and ability of individuals to
appropriately use digital tools and facilities to identify, access, manage, integrated, evaluate, analyze
and synthesi ze digital resources, construct new

knowledge, create media expressions, and communicate with others, in the context of specific
life situations, in order to enable constructive
social action, and to reflect upon this process»
(Martin 2006). In this context, digital literacy
becomes a kind of “theoretical umbrella term”
which welcomes other literacies such as library
literacy, information literacy, computer literacy,
media literacy, pho-visual literacy, reproduction
literacy, social-emotional literacy and so on (EshetAlkalai, 2004; Banzato 2011). This definition
attributes the same importance to the acquisition
of skills and knowledge to decode the media and
the ability to take advantage of the new digital
tools (especially those of Web 2.0 and Web 3.0)
to produce multimedia products coherently with
the above media education paradigm.
Another unique aspect is that knowledge
derives from multiple sources and not just from
the Web (Glister, 1997). Indeed, the concept of
digital literacy refers to “participatory and convergence culture” (Jenkins 2007), “collective
intelligence” (Lévy, 1996) and the existence of
interactive technologies. It is a kind of literacy
naturally evolutionary and adaptive with respect
to the continuous changes of the new technologies. It is able to absorb and metabolize future
literacies as well.

New Media and Educational
Opportunities
For a full understanding of the challenges that
all educational agencies are called to grasp, it is
necessary to identify the characteristics of the
antecedents of this global revolution (new media)
and their natural recipients and consumers (the
younger generations).
New media are “technologies of meaning”
and they are rapidly changing the way people
think, communicate and collaborate (New London
Group, 1996). Digital media have some specific
characteristics (Ferri, 2004, 2011; Jenkins et al.,
2006; Buckingham, 2006; Myssika, 2007; Ban-

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zato, 2011; Rivoltella & Ferrari, 2010; Mingrino,
2010; Prunesti, 2013): the easy availability, the
richness of the content and the simplicity of its use;
the fluidity of the visual imagery; the transmedia
and the interactivity; new models of authorship;
the sense of play; the creation of a “full immersion” experience; a peculiar communicative logic,
as the centrality of the devices is replaced by the
centrality of the subjects; knowledge organization
according to the database and – in the Sematic
Web - through ontologies; the need for users to
acquire new skills, to develop a multi-sensory
approach, to develop multitasking skills and to
cover the dual role of consumer-producer. All of
these aspects can be found in the typical tools of
Web 2.0 and – in different terms – of Web 3.0.
Web 2.0 is an important and substantial conceptual change, though not radical (Alexander,
2008): it is «a platform of applications accessible
via the Web, based on interactive technologies
which enable the active participation of users and
permit a high level of interaction among the users
themselves to connect, communicate, share and
collaborate online» (Clerici, De Pra, & Salviotti,
2012, p. 3). Many Web 2.0 tools and applications have been considered valuable resources in
education. The four fundamental characteristics
of the Semantic Web are: intelligence; personalization; interoperability; and virtualization (Rajiv
and Manohar Lal, 2011). Among the Web 3.0
applications, tools and services, some of them
have implications for education and research,
such as 3d, 3d virtual worlds and avatars, online
3-D virtual labs, educational labs and simulations
or 3d web; intelligent search engines (Devedzic,
2004; Rajiv & Lal, 2011).
Teaching 2.0 reinterprets teaching methods and
implies the overcome of classic dichotomies as
theory or practice and new or traditional media; a
rethinking of the roles according to more flexible
dynamics, the recognition of a central role of communication and sociability and a co-construction
of knowledge through dialogue, participation,
collaboration and, therefore, a return to reflec-

336

tive practices and laboratories (Rivoltella, 2010).
However the future seems to belong to Web 3.0.
The coexistence of Web 2.0 and Web 3.0 leads
to the concept of Social Sematic Web, a digital
ecosystem in which one creates a profitable circular relationship between the wide accessibility
of contents and data and their continuous expansion generated by interactions among users. The
Sematic Social Web seems to be an ideal digital
learning environment, in a socio-constructivist
pedagogical perspective, where, at the same time,
some strengths are enhanced and some weaknesses minimized (Rajiv & Lal, 2012; Vivanet
& Vercelli, 2012).

Who Are the Digital
Natives/Millenials?
Digital natives are immersed in the new digital age
and have developed new perceptions, new ways
of experiencing the world (Ferri, 2011). To quote
the philosopher Serres «the young generations
live in the virtual [...], they are able to assimilate
a lot of information at the same time. Through the
phone, they can connect with everyone, [...] with
the Network they can have complete knowledge
[...].Not having the same mind of their parents,
he or she learns in a different way.» (2013, pp.
14-15). Digital natives have a communication
and learning style based on playing, focused on
the expression of self, highly personalized and at
the same time oriented to the constant sharing of
information with peers (Ferri, 2011; Ng, 2012).
In particular, they learn primarily through experiencing and “having a go”, treating information
in a discontinuous rather than a linear way (Pedrò,
2006; Buckingham, 2006; Ferri, 2011), taking
advantage of the contribution, support and help
of virtual communities to which they belong or
to which they refer.
In the co-construction of knowledge, Millenials
give preference to digital over paper sources, to
images and multisensory stimuli over text-only
sources (Pedrò, 2006). They learn starting from


The Open Innovation Paradigm

the horizontal skills that each generation naturally
accrues with respect to the media. And there is no
doubt that the horizontal skills of digital natives are
much more extensive (though often unconscious)
than the ones of the previous generations. Despite
this, one should avoid to overestimate these skills’
breadth and depth, otherwise one risks to ascribe
to the so-called “digital natives” skills and abilities away (Rivoltella, 2001; Buckingham, 2006;
Ferri, 2011).
They learn in a different way because they are
totally immersed in a “participatory culture” (Jenkins, 2007) where they are “prosumer”. Thanks
to new media, this informal culture is primarily
a sharing culture (Jenkins, 2007; Petrucco &
Rossi, 2009). Finally, the Millenials are immersed
in a world that is absorbed «in “make-believe”,
in which appearances are not just on the screen
through which the experience is communicated,
but they become part of the experience» (Castells,
1996). For digital natives the boundaries between
real and virtual are blurred: these two different
planes of reality frequently interact in a natural
way (Grollo & Nardo, 2007; Ferri, 2011).
Since digital natives acquire most of these
skills and knowledge on their own, they need a
“guide”, a “support” or a “facilitator” for learning how to use new technologies for meaningful
purposes. Although it may seem paradoxical, even
– indeed especially – the digital natives need to
be educated in order to become digital literate in
a full and broad sense (Ng, 2012).

Digital Literacy and Four
Educational Challenges
As mentioned above, digital literacy is a theoretical concept that includes many other literacies. In
a single framework, it integrates several registers
and semantic codes (Martin, 2006). Extending the
concept of digital literacy within the wider context
of participatory culture and trying to define its
content, we can identify some skills and abilities

which are crucial for the digital native (Jenkins
et al., 2006):
•
•
•
•
•
•
•
•
•
•
•

Play: The capacity to experiment with one’s
surroundings as a form of problem-solving;
Simulation: The ability to interpret and
construct dynamic models of real world
processes;
Performance: The ability to adopt alternative identities for the purpose of improvisation and discovery;
Appropriation: The ability to meaningfully sample and remix media content;
Multitasking: The ability to scan one’s
environment and shift focus onto salient
details on an ad hoc basis;
Distributed Cognition: The ability to interact meaningfully with tools that expand
our mental capacities;
Collective Intelligence: The ability to
pool knowledge and compare notes with
others towards a common goal;
Judgment: The ability to evaluate the reliability and credibility of different information sources;
Transmedia Navigation: The ability to
deal with the flow of stories and information across multiple modalities;
Networking: The ability to search for, synthesize, and disseminate information.
Negotiation: The ability to travel across
diverse communities, discerning and respecting multiple perspectives, and grasping and following alternative sets of norms.

Digital natives acquire some of these skills in
an unconscious way: just to name a few capturing
information, or locating using global positioning system and downloading/uploading music
and video files. Most digital literacy skills and
knowledge accrued outside the formal education
(Ito et al., 2008; Ng, 2012). As members of online communities, they are able to use different
language styles that every social network require.

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The Open Innovation Paradigm

We can identify three dimensions in digital
literacy: a technical dimension (technical and operational skills to use ICTs), a cognitive dimension
(the ability to think critically, evaluate and create
cycle of digital information; the knowledge of ethical, moral and legal issues associated with online
trading and content reproduction) and a socialemotional dimension (the use of Internet and Web
services and tools in a responsible way for communicating, socializing and learning according to
the “netiquette”). These abilities and knowledge
are essential for interacting in the Sematic Web,
but they are not enough. A crucial competence
is to learn to use images to think because digital
environments and the software interfaces are based
on the semantic graphic-visual communication.
It is also important to think in multi-domains or
transmedially, namely the ability to use software
applications across multiple devices and to develop
visual-interpretative skills in order to interact
with the media in an analytical way. Because of
the continuous increase of virtual communities,
discussion groups, and other collective learning
tools it becomes essential to know how to identify, understand, evaluate maps and networks of
social relations (Eshet-Alkalai, 2004). Finally, it
is essential the ability of manipulation and representation of the symbols in order to «give order
and meaning to the dialogue with unpublished text
and images» (Banzato, 2011, p. 161).
For all these aspects, digital literacy and its
instances are the coherent expression of the new
media education paradigm. The pedagogical references are socio-costructivism and connectivism.
The former refers to a cooperative and interactive
process, where educators act as facilitators in the
research of knowledge through a learning by doing
process (Dewey, 1999a, 1999b; Garrison, 1997;
Kanuka & Anderson, 1999; Anderson & Dron,
2011; Vercelli & Vivianet, 2012). Connectivism
is crucial to fully capture the learning revolution
produced by Web 2.0 and 3.0: knowledge is mainly
a process of building networks with different “virtual” communities, data, and resources (Vercelli

338

& Vivanet, 2012). Moreover connectivist learning
is based on both production and consumption of
educational content (Anderson & Dron, 2011).
Finally, in both pedagogical approaches students
are at the center with their own horizontal skills,
knowledge, life experiences, predilections and
expectations (Anderson & Dron, 2011; Vercelli
& Vivianet, 2012).
Now we can identify some of the main challenges for educational agents. A first challenge
is to identify which horizontal skills a digital native actually has (Rivoltella, 2001; Buckingham,
2006; Grollo & Nardo, 2007; Ng, 2012). There
is a clear risk in taking for granted embedded
skills. A second challenge is the fading distinction
between appearance and reality (Grollo & Nardo,
2007) with a more and more frequent overlap in
terms of attribution of value and meaning. This
situation has important educational implications,
as it generates a continuous acceleration and
compression of time and a sense of frustration
because the world is represented, as more attractive
and rewarding than it really is (Nardo & Grollo,
2007). This crucial challenge requires a responsive approach, such as creating paths and projects
shared by digital natives and educators in order to
provide digital products able to lead back to reality
in a new encounter with the natural world. The
third challenge concerns the role of prosumers.
Forming a conscious and critic author is without
a doubt one of the prerogatives of the education
in the twenty-first century (Rivoltella & Ferrari,
2010). This also means to breed future citizens
who are able to supervise the media, rather than to
be supervised by them (Buckingham, 2006; Grollo
& Nardo, 2007; Tòlic, 2011). A fourth challenge
refers to the different ways in which digital natives
learn within a participatory culture characterized
by the prospect of a collective (Lévy, 1994) and
connective (De Kerckhove, 1997) Intelligence. All
educators are called to give up the rigid pattern of
vertical distribution of knowledge and embrace a
relationship of shared learning (Grollo & Nardo,
2007): they harness the technologies that students


The Open Innovation Paradigm

use outside of school to engage them in finding
and analyzing resources themselves.

Storytelling and Digital Storytelling
Because of its breadth and depth of action, storytelling, in its digital evolution, is one of the
most interesting educational strategy (Bumgarner, 2012; Robin & McNeil, 2012; De Rossi &
Petrucco, 2013).
According to Barthes (1987), narration (although with different methods and languages) is
central and constitutive of human experience and
is represented in various forms (individual or collective), generating connective patterns of thought
and culture. When we tell a story, we have to collect
data and information, rework and consequently
activate processes of knowledge and learning that
allow us to shape our experiences (Kaneklin &
Scaratti, 1998; Malita & Martin, 2010; De Rossi,
2013). The narrative is thus the first “device of
interpretation and knowledge” used by humans to
give meaning to their life experience (Petrucco,
2013). The narrative stimulates «the ability to
relate internal states with external reality, to reconnect the past with the present in a projection into
the future [...]: substantially, the narrative would
help to make learning actually distributed and
located» (Petrucco & De Rossi, 2009). Our brain
seems to have evolved to transform information
into stories, to the point that the narratives would
have the same influence on our forma mentis as
personal experiences (Restak, 2004).
So, between storytelling and learning there is
«an inextricably intertwined because the process
of composing a story is also a process of meaningmaking» (Malita & Martin, 2010, p. 3061), that
encourages critical thinking, developing cognitive
skills and organizing knowledge (Bruner, 2002).
The natural transformative power of the narration has in the emotional engagement one of its
essential component for activating attention and
learning motivation. All “good” stories have the
incredible ability to provoke emotions and cogni-

tive participation, to facilitate understanding of
complex events, to stimulate reflection on own
our existential and open prospects for change
(Pennac, 2008; Petrucco & De Rossi, 2009; Blezza
Picherle, 2013; Gaeta et al., 2014).
These observations show the validity of this
methodology to grasp some of the educational
challenges of the twenty-first century. New
media lead to a further expansion of the possibilities of creation, dissemination and meeting
narratives (Robin, 2008; Petrucco & De Rossi,
2009; Schank, 2013; Gaeta et al., 2014). In the
participatory culture, the digital storytelling is as
a method to recreate ties, sense of community and
a kind of cultural glue among people (Lambert,
2006; Robin, 2008; Hall, 2012; Boscolo Nale &
Colombo, 2013).
The digital storytelling (hereafter DST) is «a
personal experience represented in narrative form.
A script, or the essence of the story, is extracted
from the narrative and the amplified by including video, music, still-frame imagery, and the
author’s voice. […] The inclusion of multimedia
makes the story come alive and takes the story
to a place that could not be achieved by writing
alone» (Jakes, 2007, p. 43). Given its undoubted
educational functions, the DST can be related
to different academic disciplines (Robin, 2008;
Robin & McNeil, 2012), can be fiction or (and)
no-fiction (Alexander & Levine, 2008), linear or
no-linear (Liua et al. 2010), adaptive/interactive
or mobile/ubiquitous (Gaeta et al., 2014). In all
these case, digital stories further co-constructed
learning in a multidisciplinary, interdisciplinary
and socio-constructivist prospective (Jenkins,
2007; Petrucco & De Rossi, 2009; Rivoltella &
Ferrari, 2010; Bumgarner, 2012; Dibattista &
Morgese, 2012; Parola, 2012; Yang & Wu, 2012;
De Rossi, 2013).
One of the most structured and articulated
model of DST is the five-step ADDIE (Analysis,
Design, Development, Implementation, Evaluation) Model. In the preliminary analysis phase
learners have to identify the topics, the main pur-

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pose, the script and the target audience of the story
(considering the age, gender, cultural background
and other defining features), the perspectives in
order to tell the story in an interesting way (Robin
& McNeil, 2012; Yang & Wu, 2012). There is
here a first moment of critical awareness on the
aspects of the production of a media text and on
the crucial role of the target audience of the story
(Buckingham, 2006; Bumgarner, 2012). In the
design phase, learners and educators complete
the script and storyboard. This is a very crucial
step that implies the elaboration and sharing of
new ideas, discussions on the storyboard and
the media organization (Robin, 2006; Liua et al.
2010; Yang & Wu, 2012). In the media education
prospective, participants learn to analyze the different media, their communicative potential and
how they can affect the story comprehension.
Because not all media support and strengthen the
digital stories in the same way, it is necessary to
create a “good” story. In the development phase,
the story is actually created. Selected elements
are linked and combined to form the digital story
(Robin, 2006, Shneiderman et al., 2006; Liua et
al, 2010; Yang & Wu, 2012). Learners are made
aware of the real problems of creating a media
product, the need to accept (sometimes) different
solutions because of external factors (Production),
to reflect on the effectiveness of selected media
and the opportunity of a more functional use of
media (Language and Representation). Sematic
Web apps and tools may be important especially
with regard to the story development in 3D. In the
implementation, the creators decide how to use
the multimedia product and if it is necessary to
create additional resources to improve and expand
the visual experience (Robin, 2006; Shneiderman
et al., 2006; Liua et al., 2010; Robin & McNeil,
2012). In the last phase, digital story creators use
different tools and applications to assess whether
they have achieved the communicative goals.
Obviously, the evaluation is carried on during all
stages (Robin & McNeil, 2012).

340

The features and advantages to use DST are
manifold. Studies demonstrate that DST activates
the attention of learners, motivates them to explore
new ideas, to develop proposals, engages them
in the learning and problem solving (Druin &
Solomon, 1996; Robin, 2008; Castoldi, 2013; De
Rossi, 2013). Through the different phases, the
learners become creative and productive (Robin,
2008; Malita & Martin, 2010) and think critically
(Malita & Martin, 2010; Yang & Wu, 2012). In
respect to the socio-costruttivist prospective, DST
is learner-centered (Boscolo Nale & Colombo,
2013) and the digital story creators are motivated
in a collaboration process for the co-construction
of social meaning, ideas, capabilities (Liua et al.,
2010), and of the reconstructing of their own beliefs thanks to intercultural and intergenerational
interactions (Malita & Martin, 2010; Hall, 2012;
Yang & Wu, 2012). DST is a reflective and active
method (De Rossi, 2013) that stimulates an open
and flexible behavior. Finally, DST is a bridge
between digital natives and immigrants/educators (Robin, 2008; Hall, 2012; Boscolo Nale &
Colombo, 2013), school and society, person and
community, her/himself and the others, among
different subjects and topics. For all these reasons, DST needs to go out of the classroom: for
opportunities (to get in touch with external reality
and to create a connection with the community
and explore issues and topics from other points
of view) (Petrucco, 2013) and coherence (the
increasing ubiquity and pervasiveness of new
media pushes towards a model of storytelling just
in time) (Schank, 2013).

Value Generation Framework
Compared to traditional methods, storytelling is
based on metaphors and emotions that enforce
the learning experience. Any learning activity can be enforced by storytelling. DST can
stimulate students’ attention, create curiosity
around, and trigger desire to deepen knowledge
through personal study. In our framework, digital


The Open Innovation Paradigm

storytelling is the core of four complementary
actions that generate value for the entire network
of involved institutions. Suppose, for instance,
a class is involved in a Roman Art project. The
network of possible institutions are museums,
both local and international, schools, libraries, and
non-profit organizations in the education field. A
museum may invest in digital storytelling to create
children dedicated learning paths. Such products
are outputs of a multidisciplinary creative team,
involving art experts, writers for children’s book,
multimedia productions’ professionals, and the
school itself, and they generate advantages at two
levels. First, children can augment their learning
experience through participation with the creative team in the co-creation of digital resources
based on a sound educational methodology, such
as storytelling. DST can enforce their learning
experience both before and after the visit. It also
generates a positive externality for those that, due
to geographical or other impediments, cannot visit
the museum. Secondly, DST might become a
driver for an innovative museum’s communication
strategy, with translation in different languages.
DST has the potential to attract to the museum
the broad segment of families with children. The
museum’s offer might include also labs and hands
on activities targeted to different ages. Worldwide
known museums such as Louvre, Guggenheim,
MOMA have invested in kids’ education since
a long time ago, contributing to revolutionize
the old fashion reputation of museums as places
available to selected persons. Digital media and
social platforms can now expand the potential
of museums’ communication, through on line
educational and informative resources that can
be created in participatory manners. The core of
our framework is the Open Innovation paradigm
applied to the co-creation of digital storytelling.
In figure 1, based on the amount of resources
available, a museum might invest in the production
of complementary documents (quadrant 2), such
as free, downloadable educational kid’s friendly
material, useful also for special needs (i.e., big

font for those with dyslexia), or video-storytelling
(quadrant 3). At the same time, museums and organizations collaborating with the creative team,
can exploit revenues from products and services
derived from the original storytelling. Labs and
training courses can be sold as a complement to
a specific learning project of the museum, while
DST might evolve into the production of books,
ebooks and merchandizing.

THE ALLIANCE BETWEEN A
ZOO AND A WEB RADIO
We consider how the new opportunities generated
by the digital media evolution, the drawing up of
a new media education paradigm and a specific
method (such as DST) can be applied in an unusual educational context such as a modern zoo
Our case study concerns a “new concept” zoo
(Parco Natura Viva, PNV) and a digital media
(the university spin off Radio Magica, RM), a
children web radio whose services are Web 2.0
oriented. The meeting of these two companies
allows us to develop a proposal for innovative,
integrated and diversified educational activities
in the Social Semantic Web. A proposal that
is able to create value for PNV, Radio Magica
and the communities involved. In particular, we
consider the contribution that the web radio can
provide to the park helping to overcome some of
its critical aspects (see the SWOT analysis) and
take advantage of its points of difference.

“Parco Natura Viva”
Parco Natura Viva is a 42 hectares zoological
garden created in 1969 by the architect Alberto
Avesani. It is located in Bussolengo, a little
town between Verona and Garda Lake, two important touristic sites in Northern Italy. PNV is
divided into two areas: the Safari Park and the
Fauna Park, which is for pedestrians only (www.
parconaturaviva.it). It has achieved high qual-

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Figure 1. The value generating framework

ity standards in various areas (conservation of
endangered species, research projects in-sintu
and ex-sintu, environmental education). PNV
has been admitted to the European Association
of Zoos and Aquaria (EAZA) and the World Association of Zoos and Aquaria (WAZA). PNV
pursues three goals: environmental education,
protection and breeding of endangered species, and
research. This threefold mission characterizes all
the modern zoos and implies a radical rethinking
of the organization and of the infrastructure (e.g.
building exhibits to ensure the welfare of animals
through environments similar to natural ones and
at the same time capable of allowing immersive
visits. Despite its scientific achievements, PNV
suffers from the large misperceptions caused by
being a zoo. The negative opinion and the lack of
a communication strategy capable to disseminate
correct information through traditional and new
media, have contributed to keep the number of
visitors quite low (under 500.000 visitors in the
last years).
PNV has grown to more than 140 employees/
external collaborators. It is a private company and
does not receive any public funding. Among the

342

Italian zoos, PNV presents the broader education
team with five employers (all part-time) with different school training (natural sciences, biology,
psychology and educational sciences). The offer
is wide and moves from activities and workshops
offered during the weekend to all kind of visitors,
guided tours for groups and schools within the
Park, to occasional events and ad hoc proposals
for people with disabilities, up to the weekly summer camps varied according to age groups. The
education team is also responsible for the communications tools inside the park, the production
of scientific documents offline and online and
scientific divulgation for children. Customers are
mainly people from local or neighbor regions; and
extra-regional audience relies on “word of mouth”
rather than through appropriate marketing and
communications strategies.
The qualitative and quantitative data collection for the SWOT analysis was based on primary
(participant observation and interviews) and secondary sources.
PNV presents the following points of difference: the location; the overall quality of the
structure (in particular the Fauna Park) in terms


The Open Innovation Paradigm

of care, cleanliness and professionalism of the
staff; the scientific authority; the wide variety of
animals, of which the most endangered, rare and
subject to conservation and reintroduction projects (strong element of differentiation); and the
emphasis on the welfare of animals (a sensitive
aspect). PNV has to involve the younger users, on
a wider, prolonged and deep level, enhancing the
extensive training opportunities through a more
focused and aware use of the resources and tools
offered by the Web 2.0 and 3.0. Referring to the
model of Open Innovation (Chesbrough, 2006,
2011) and the Blue Ocean Strategy (the simultaneous pursuit of differentiation and low-cost
to create new market space, Kim & Mauborgne,
2005), the Park could get out of the compelling
logic of a continuous renewal of the offer with
the creation and the opening of new exhibits and
attractions (typical of the competitors) in order to
focus on the existing resources and capabilities
intercepting new market segments. Moreover, it is
crucial to involve the audience, especially young
audience, before and beyond the experience of the
visit. This paradigm represents an optimal solution
to realize and support the mission of a modern
zoo, particularly environmental education, and to
respond creatively to the serious consequences of
the actual economic crisis.

“Radio Magica”
Radio Magica (RM) is a not profit foundation
born to support children education. Its educational services are mainly performed through a
web digital platform that supports a radio and
an on-line library for children and teens from 0
to 13. The web radio broadcasts 12 hours a day
and seven day a week music, stories and kids
programs that stir imagination and entertain the
young generations by stimulating listening skills
and fantasy. The editorial staff work side by side
with a Scientific Committee, composed by experts in developmental childhood (pediatricians,
neuropsychiatrists, teachers, psychologists, etc.)

in order to select contents for children of varying
ages and with different needs. RM is organized
as a Village with four houses, designed to allow
access to the content of increasing complexity,
but without a strict regard to age, because each
child goes through different personal development
stages. It begins with the Water house (birth)
symbolized by the Minnow, it follows the Earth
with Hedgehog (exploratory phase), the Air with
the little Eaglet (phase of abstract thought), and
finally to the Fire with the little Cub. A close look
at this library allows understanding part of this
radio’s mission. RM places at the centre of its philosophy and projects a culture of inclusion. Every
paperback book introduced into the digital library
might get an audio format for all but essential
for children who are blind or visually impaired;
a video narratives with Italian Sign Language
for hearing impaired children and with Symbols
Language for those who can not or is not able
to use the letters of ‘ alphabet i.e. children with
language problems connected to issue of autism,
for pre-school children who are learning to read
and for foreigners.
Finally, in the RM portal, a tool shed is designed to offer information, suggestions and good
practices related to childhood and special needs
to adults like teachers, educators, and families.
The mission of RM is to promote culture and the
pleasure of active listening among children, their
families, to sensibilise adults to the importance
of reading aloud, to instil strong listening habits
at an early age to all children and to collaborate
with institutions in order to promote initiates that
disseminates the benefits of good listening.

Managerial Implications
As DST is considered one of the best practices for
learning promoting, the collaboration with RM
could be a solution to appraise PNV’s cultural
and natural resources and to give more visibility
to its triple mission. The DST could realize these
goals in two ways.

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The Open Innovation Paradigm

The creation of a no-fiction and fiction stories
related to PNV, written by professional children
authors can stimulate the curiosity of the listener,
who can integrate and re-elaborate the digital
product. An activity that can be done freely and
independently by users or developed in an educational context with the support of RM media
experts. Already these types of intervention – properly calibrated, structured and organized –help to
broaden the experience of the park visit, creating
that bridge between school and society, person and
community that is a hallmark of DST and that is
one of the educational issues raised above (fourth
educational challenge). These digital products are
related to educational guides containing different
paths, materials and information for independent
use by the children, for children with special
needs, and for children supported by an adult.
Digital natives would be the “real” producers of
“authentic” User Generated Content because they
respect certain publication requirements, made a
creative effort and create outside of professional
routines. The digital product creation implies: the
development of critical thinking; the stimulation
of the interpretative attention; the acquisition of
skills to process remixes and mash-ups; and the
reflections on the problems of authorship (first
and third educational issues). Audios, photos
and videos can be taken from secondary sources
and from primary sources, for example, making
photos and videos directly on the place where
story is setting: the park. In this way one restores
clear boundaries between the real world and the
fiction (second educational question). The other
interesting aspect is that in this way the users
live, consciously and explicitly, a condition that
they are used to live in an unconscious way: the
double dimension of consumer and producer (third
educational question). In this case Radio Magica
creates a product (the audio storytelling) which
then can be appropriated by listeners who share,
process, and enrich it becoming co-creators and
innovators; it is a perfect example of the carrying
out of the Open Services Innovation paradigm

344

(Chesbrough, 2011). Finally, the resulting media
product represents a perfect fusion of the trinity
information-education-entertainment (the three
essential characteristics of the modern zoos) that
more and more in contemporary communication
merge and mingle with each other (de Maurissens, 2013).
A second option is the creation of a DST by a
group of digital natives in collaboration with the
educators, the RM digital media professionals
of and PNV staff. In this case, the educational
potential of DST would be fully realized, allowing to deal with the four educational issues in an
even more accomplished way. As Schank (1990)
said, human beings are naturally inclined to listen,
remember and tell stories, but the real problem is
to find interesting stories to tell. However, the park
contains a lot of fascinating and amazing stories to
tell. Because DST acts as «a form through which
the community represents its problems through the
eyes of the children, in an attempt to resolve them»
(Petrucco & De Rossi, pp. 90-91), the authors of
DST are not only part of a process of learning
and changing their false beliefs about the modern
zoo, but they become PNV spokespersons: they
sensitize visitors about situations of environmental
education. These latter aspects are central in the
“Strategic Plan 2013-2016. Eaza moving forward
in the a decade of biodiversity” (2013). Finally,
digital products created through the involvement
of multiple figures (children and teens, educators,
PNV and Radio Magica experts) is an expression
of a teaching that absorbs logics and dynamics of
participatory and sharing culture of the new digital
age. These digital products, once shared on social
networks and on the web, naturally lead the park
outside regional borders gaining a national and
international visibility.
RM roots on the culture of inclusion and modern zoological parks offer more and more places
with high educational potential for people with
special needs. There are sensory stimulation and
high emotional involvement that can arouse thanks
to animals. Transforming video storytelling into


The Open Innovation Paradigm

products for persons with different disabilities
would allow access in a different way to an audience that already offers proposals and specific
activities. Even for the disabled, therefore, the park
experience would extend, prolong, complement.
These products could become appbook, ebook,
and books supporting the work, activities and
projects of Radio Magica and PNV.
Thanks to Radio Magica, PNV will finally be
able to completely exploit the value of its product/
service, in particular by optimizing the performance and visibility of its events and educational
proposals. This would allow the park to fulfill the
“Global Strategy of the parks” (EAZA 2010-2012),
and the requirements of the European Directive
(1999/22/EC) and the Italian law (DL 73/2005)
about, the educational dimension of zoos.

Future Directions
Radio Magica represents a win-win situation: it
pursues educational goals by satisfying simultaneously the interests of a huge group of educators
in search of material for the emerging scholastic
needs, and the interests of content providers
that with a small economic effort can make this
material available on line receiving, in exchange,
a huge benefit in terms of visibility and, in the
long run, profit.
Different partners are offering contents tailored
for the RM multimedia platform, and receive in
exchange a new channel to develop more effective communication strategies, and, in this way,
increase their profits.
As at the beginning it is crucial to invest in a
critical mass of content in order to attract users,
PNV has to invest in regular updates as a key success factor for online learning environments. It is
well known that a highly relevant factor for the
success is the support of all organisations, in terms
of financing, equipment, personnel, or the readiness to adapt organisational structures (Normann,
1977). It has been important that both companies
are characterised by a flexible organisational

structure and a general openness to pedagogical
innovation. RM has invited PNV to become active member of the content broadcasted by the
Radio. The goal is to involve Parco Natura Viva
in producing the innovative products it is specialized to create (educational material for children,
virtual tours and explanations) and deliver them
through a multimedia platform. On its side, RM is
specialized in crafting communication strategies
to schools, parents and educators. PNV can gain
two types of benefits. First, general visitors will
develop a more realistic perception of the role of
the park. Second, the park will attract new visitors
beyond the local territory.
Thanks to the collaboration between Radio
Magica and PNV, new ways of collaborative
creation and exchange of content have been introduced. Moving away from traditional environments, where user activities are usually limited to
communication about the content, PNV communicate to users of Web 2.0-enriched environments,
able to work directly on the content itself.

CONCLUSION
This chapter tries to build bridges between Media
and Education, and could be a valuable contribution for the 21st century learning environments.
To gather evidence, we use a case study approach
focusing on Parco Natura Viva and Radio Magica.
While a number of barriers to full social media adoption across the education system were
discussed, what became clear was the potential
power that digital technologies have mainly: 1)
to empower teachers and students, 2) to act as
a tool through which learning occurs, and 3) to
democratise learning at a global level.
The peculiarity of Radio Magica is that it can
be defined as a “field experiment” that applies
the paradigm of Open Innovation to Education.
Indeed, Radio Magica’s goal is to exploit digital
storytelling and the new communication paradigm
to launch a collaborative platform: it provides ef-

345


The Open Innovation Paradigm

fective solutions for schools’ educational needs
(for students with and without special needs), and
allows educational content providers to develop
new communication strategies by making their
resources available to educational agencies and
increase profits. Therefore, this interesting case
study has allowed us to discuss the role of web in
relation to social entrepreneurship and education.

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ADDITIONAL READING

Schank, R. (2013). Metodologie narrative per la
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350

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The Open Innovation Paradigm

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351


The Open Innovation Paradigm

Mcdrury, J., & Alterio, M. (2003). Learning
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352

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KEY TERMS AND DEFINTIONS
Digital Native: Is a person who was born
during or after the general introduction of digital
technologies; this expression has been strongly
criticized for the deterministic implications on
both digital immigrants and digital natives generations (Jenkins, 2007; Rivoltella & Ferrari,
2010; Ng, 2012); however, the term has entered
common language.
Digital Storytelling: Merges traditional storytelling with digital tools and refers to emergent
new forms of digital narratives (from web-based
stories, to narrative computer games and podcasts).
In general digital stories are short and involve
interactivity.
Literacy: Is the ability to read and write.
Today, literacy has to rely on «a larger mindset
and the ability to continuously adapt to the new
literacies required by the new technologies that
rapidly and continuously spread on the Internet»
(Coiro, et al., 2008, pp.23-24). In this perspective
we should consider all different literacies such as
computer literacy, information literacy, internet
literacy, media literacy, and others, as the expressions of a phenomenon that can be traced back to
the same root: the face of new literacies.
Open Innovation: Is “…the use of purposive
inflows and outflows of knowledge to accelerate
internal innovation, and expand the markets for
external use of innovation respectively” (Chesbrough, 2006). Nowadays, a large number of
profit organizations (Procter&Gamble, Xerox,
STMicroelectonics, …) leverages the Open Innovation paradigm to create new products (often
the so-called customer centric products), shorten
the time to market and reduce risks.


The Open Innovation Paradigm

Participatory Culture: Is a neologism in
reference of a culture in which people do not act
as consumers only, but also as contributors or
producers (prosumers). The increasing access to
the Internet has come to play an integral part in
the expansion of participatory culture because it
increasingly enables people to work collaboratively; generate and disseminate ideas, and creative
works; and connect with people who share similar
goals and interests.
Web 2.0: Tim O ‘Reilly coined the term Web
2.0 as opposed to Web 1.0 in 2005; it refers to sites
that use technology beyond the static pages. In the
Web 2.0 ecosystem there are different tools and
applications such as Wiki-based platforms, blogs

and micro-blogs, instant messaging programs,
video/photo sharing systems, and social/professional networks.
Web 3.0 or Semantic Web: Is a new form
of web content, an environment where software
agents roaming from page to page can readily
carry out sophisticated tasks for users. We can
define it as an extension of the current web, in
which information is given well-defined meaning,
better enabling computers and people to work in
cooperation (Bearners-Lee, Hendler, & Lassila,
2001).The term “Web 3.0” appears on the pages of
the New York Times in an article by John Markoff
in 2006, however the term Semantic Web was used
for the first time by Tim Berners-Lee in 2001.

353

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396

About the Contributors

Pedro Isaias is an Associate Professor at the Universidade Aberta (Portuguese Open University) in
Lisbon, Portugal, responsible for several courses and director of the Master’s degree program in Management/MBA. He was director of the Master’s degree program in Electronic Commerce and Internet
since its start in 2003 until July 2014. He holds a PhD in Information Management (in the speciality of
information and decision systems) from the New University of Lisbon. He is the author of several books,
book chapters, papers, and research reports, all in the information systems area. He has headed several
conferences and workshops within the mentioned area. He has also been responsible for the scientific
coordination of several EU-funded research projects. He is also a member of the editorial board of several journals and program committee member of several conferences and workshops. At the moment,
he conducts research activity related to Information Systems in general, E-Learning, E-Commerce, and
WWW related areas. Pedro Isaias is an Adjunct Professor at School of Information Systems, Curtin
University, Australia.
Piet Kommers is associate professor at the University of Twente and honorary professor for UNESCO
Education in Eastern Europe. He was director of the NATO Advanced Research Workshop on Cognitive Learning Tools and leader in a number of research projects in the various stages of the European
5th, 6th, and 7th Framework Programs. As executive editor of the International Journal of Web-Based
Communities and chairman in a number of IADIS conferences, he is frequently involved in innovative
projects around the world. His specialties are educational policy development, international student
exchange, and corporate communication. In the fields of Networked Society and Project-Based Mobile
and Virtual Reality Learning, he is in the top of the citation indices. His recent interest is in “empirical
modelling” for enabling learners to build conceptual understanding and creative problem solving. His
website can be found at: http://pkommers.wix.com/piet-kommers.
Tomayess Issa is a senior lecturer at the School of Information Systems at Curtin University, Australia.
Tomayess completed her Doctoral research in Web Development and Human Factors. As an academic,
she is also interested in establishing teaching methods and styles to enhance the students’ learning experiences and resolve problems that students face. Tomayess Issa is the Conference and Program Co-Chair
of the IADIS International Conference on Internet Technologies and Society and IADIS International
Conference on International Higher Education. Furthermore. She initiated the IADIS Conference for
Sustainability, Green IT, and Education. Currently, she conducts research locally and globally in Information Systems, Human-Computer Interaction, Usability, Social Networking, Teaching and Learning,
Sustainability, Green IT, and Cloud Computing. Tomayess participated in a couple of conferences and

About the Contributors

published her work in several peer-reviewed journals, books, book chapters, papers, and research reports.
Tomayess Issa is a project leader in the international research network (IRNet-EU) (Jan. 2014 – Dec.
2017) for study and development of new tools and methods for advanced pedagogical science in the
field of ICT instruments, e-learning, and intercultural competences.
***
Leonelo Almeida is Professor at the Department of Informatics, UTFPR, Brazil. He received a MSc
in Informatics at UFPR, Brazil, and a PhD in Computer Science at UNICAMP, Brazil. His research
interests involve awareness in collaborative systems, Human-Computer Interaction, Universal Design,
CSCW, digital inclusion, and accessibility. He is engaged in multidisciplinary projects that investigate
the barriers faced by people while using computers considering scenarios of vast economic and social
diversity as the Brazilian. From the participation in those projects and from his doctoral research, he
recently published works in international conferences (e.g. International Conference on Informatics
and Semiotics in Organisations’09, HCI International’09 ‘14), in local and international journals (e.g.
Brazilian Computing in Education Magazine, International Journal of Information Systems and Social
Change), and book chapters focused on Web accessibility and awareness of others in collaborative systems.
Evelin Carvalho Freire de Amorim is currently PhD candidate in Universidade Federal de Minas
Gerais (UFMG). Her PhD plan is about mining social media, employing Machine Learning, in order to
monitorate stock Market. Before PhD, she was lecturer in Universidade Federal do Espírito Santo (UFES)
for three years. During her lecturer time, Evelin published some papers about automatic correction of
portuguese essays and semantic Web. Also she obtained her master degree in Pontificia Universidade
Católica do Rio de Janeiro (Puc-Rio). The master thesis was about Content Extraction in News Web
Pages. Research interesting of Evelin are Information retrieval, Social Media and Machine Learning.
Cecília Baranauskas is Professor at the Institute of Computing, UNICAMP, Brazil. She received
a BSc and MSc in Computer Science and a PhD in Electrical Engineering at UNICAMP, Brazil. She
spent a sabbatical year in the UK, as Honorary Research Fellow at the Staffordshire University and as a
Visiting Fellow at the University of Reading, working with Prof. Kecheng Liu’s Applied Informatics with
Semiotics Lab (2001-2002). She also received a Cátedra Ibero-Americana Unicamp-Santander Banespa
to study accessibility issues on software engineering at Universidad Politécnica de Madrid (2006-2007).
Her research interests have focused on HCI, particularly investigating different formalisms (including
Organizational Semiotics and Participatory Design) in the analysis, design, and evaluation of societal
systems. Former IFIP TC13 representative, she is associated to the BR-CHI (an ACM SIGCHI local
chapter) Executive Council and to the Special Committee for HCI at SBC (Brazilian Computing Society).
Carla D. M. Berkenbrock is Adjunct Professor of Computer Science at Santa Catarina State University (UDESC) at campus CCT/Joinville. She has PhD in Electrical and Computer Engineering from
Aeronautics Institute of Technology (ITA) (2009), Master’s in Computer Science from the Federal
University of Santa Catarina (UFSC) (2005), and Bachelors Degree in Computer Science from the State
University of Santa Catarina (UDESC) (2002). Her research interests include collaborative systems, mo-

397

About the Contributors

bile computing, distributed systems and cache coherency. Her official full CV for the Brazilian National
Research Council (CNPq) is available at Curriculum Lattes: http://lattes.cnpq.br/5460117776241230.
Filipe Roseiro Côgo is MSc. in Computer Science at Universidade Estadual de Maringá (UEM)
and BS in Computer Science at UEM. He is currently assistant professor at Universidade Tecnológica
Federal do Paraná (UTFPR) and is involved in teaching and research projects related to areas such as
human-computer interaction, information retrieval, collaborative systems, Social Web, and computational intelligence.
Cinzia Colapinto (PhD) is Assistant Professor of Management at Ca’ Foscari University of Venice.
She obtained a Master in Political Sciences and a PhD in Business History and Management at the University of Milan (Italy). She spent several periods as Visiting Researcher at the Department of Media and
Communications, London School of Economics and Political Science (United Kingdom), at the Communications and Media Policy Institute, University of Canberra (Australia), at the School of Commerce
and Administration, Laurentian University, Sudbury (Canada), and at the Institute for Creative Industries
and Innovation (CCI), Queensland University of Technology (Australia). Her main publications are in
the Journal of Knowledge Economy, International Journal of Technoenterpreneurship, Industry and
Higher Education, International Journal of Media Management, International Review of Economics.
She is the author of the monographs Open Your Mind: Il Potere Del Pensiero Critico e Creativo (Franco
Angeli, 2009) and Finestre Sull’Innovazione (Academia Universa Press, 2011).
Alexandra I. Cristea is Associate Professor (Reader), Chair of the Graduate Studies Committee
of the Faculty of Science, Director of Graduate Research, and Head of the Intelligent and Adaptive
Systems research group. Her research includes user modelling, personalisation, semantic and Social
Web, authoring over 200 papers (~2200 citations on Google Scholar). She has led various projects: EU
Minerva projects ALS (06-09), ADAPT (02-05); Warwick-funded project APLIC (11-12), and participated in BLOGFOREVER (11-13), GRAPPLE (08-11), PROLEARN (07), (BBC-featured) Assistance
Technologies (13-15). She has been organizer of workshops, co-organizer, panelist, and program committee member of various conferences in her research field (including, for example, UMAP, ED-MEDIA,
Hypertext, Adaptive Hypermedia, ICCE, ICAI). She has given invited talks in various countries. She
acted as UNESCO expert (Ministry of Education and Educational institutes) as well as EU expert for
FP6, FP7, eContentPlus. She is a BCS fellow and an IEEE and IEEE CS member.
Ruslan Rustamovich Fayzrakhmanov is a postdoctoral fellow at Vienna University of Technology in Austria. In 2014, he lectured there the course Applied Web Data Extraction and Integration.
He received his Master’s degree in Informatics and Computer Engineering from Perm State Technical
University, Russia, in 2008 and PhD in Computer Science from Vienna University of Technology in
January 2014. His research interests include Web information extraction, Web accessibility, machine
learning, and data mining.
Fred Freitas holds a diplom in Informatics from ITA (1984) and received his PhD in Electrical Engineering from the University of Santa Catarina, Brazil, in 2002. He is affiliated with the Informatics
Center of the Federal University of Pernambuco, Brazil, and is on a sabbatical leave at the University

398

About the Contributors

of Mannheim, Germany. Besides publishing extensively in qualified conferences and journals, he has
edited a number of special issues in periodicals and taken part in the organization of workshops and
conferences, always on ontology-related theoretical and practical themes. He was elected and served
for two years in the Brazilian committe for Artificial Intelligence (CEIA). He has or had collaborations
with the University of Paul Cezanne at Marseille and INRIA, Montbonnot, in France, and the Universities of Karlsruhe, Freiburg and Mannheim, in Germany. His interest areas today comprise ontologies,
the semantic Web, knowledge representation and reasoning (particularly over description logics), and
text mining.
Alona Forkosh-Baruch, PhD, is a senior faculty member at Levinsky College of education. She
heads the Authority for Supporting Teaching and Learning and the ICT in Education division. She is also
a researcher at Tel Aviv University School of Education. She has published dozens of academic papers,
books and book chapters, and papers in international conferences on systemic and learning aspects of
ICT in K12 and higher education, innovative pedagogical practices using technology, and social media
in education, among others. Her expertise in ICT implementation in the education system is recognized
nationally and internationally and is reflected in an academic record including extensive research experience in national as well as international research networks, consortiums, and international expert summits.
Luca Ganzerla (PhD) is Research fellow at Ca’ Foscari University of Venice. He graduated in Economics (University of Brescia) and in Education (University of Verona). He obtained a PhD in Education
and Lifelong Learning Science (University of Verona) with a PhD thesis about “The Complex Universe
of Narrative Picture-Books: International Critical Approaches and Historical Evolution – Anglo-Saxon
and French Perspectives.” He spent his annual research fellowship at the Italian zoo Natura Viva Park
and at the kids Web radio Radio Magica. His research interests are children’s literature (especially
picture-books, illustrated books, children and young-adults’ novels, and classic children’s novels) and
Pedagogy of reading. He collaborated on projects about reading education in different school grades.
Isabela Gasparini is a Professor of Computer Science at Santa Catarina State University (UDESC)
at campus CCT/Joinville. She has a PhD degree in Computer Science from Federal University of Rio
Grande do Sul (UFRGS) (2013). She has a MSc degree in Computer Science from UFRGS (2003) and
has graduated in Computer Science from Londrina State University (UEL) (1999). Currently, she is a
representative member of the southern region in the Special Commission of Human-Computer Interaction
(CEIHC) of the Brazilian Computer Society (SBC). Her research interests include Human-Computer
Interaction, user modeling, adaptive interfaces, and culturally aware systems. Her official full CV for
the Brazilian National Research Council (CNPq) is available at Curriculum Lattes: http://lattes.cnpq.
br/3262681213088048.
João Emanoel Ambrósio Gomes holds a Computer Science degree from the University Center of
João Pessoa (2010) and a Master’s degree in Computer Science from the Informatics Center of the Federal University of Pernambuco (CIN-UFPE) (2013). He is currently PhD student in Computer Science
from the Informatics Center of the Federal University of Pernambuco. He has experience in the fields of
Software Engineering and Artificial Intelligence, acting on the following topics: Social Network Analysis,

399

About the Contributors

Prediction of Links, Characterization of Social Communities, Ontologies, Semantic Web, Data Mining,
Opinion Mining, Distributed Software Development, and Digital Educational Games.
Putu Wuri Handayani, MSc, is a lecture in Faculty of Computer Science Universitas Indonesia.
She obtained her Master’s degree in Electronic Business from University of Applied Science Fulda,
Germany. She is currently pursuing her Doctoral study at the Faculty of Computer Science Universitas
Indonesia. Her research interest are related to information system/information technology, such as ecommerce, enterprise resource planning, supply chain manaegement, customer relationship management,
and healthcare information systems.
Arnon Hershkovitz, PhD, is a Senior Lecturer in Tel Aviv University’s School of Education. His
research and teaching are focused on the merging points of education and technology, with an emphasis
on affective and social points of view of learning (including non-formal) and teaching processes, and on
using various research methods (including Educational Data Mining and Learning Analytics). In recent
years, he has been promoting research on aspects of learning and teaching in today’s social media era,
and is interested in studying how knowledge is distributed, consumed, evaluated, and learned via these
platforms.
Achmad Nizar Hidayanto is Head of Information Systems/Information Technology Stream, Faculty
of Computer Science, Universitas Indonesia. He obtained his Master’s degree and PhD in Computer
Science from Universitas Indonesia. His research interests are related to information systems/information technology, e-learning, information systems security, change management, distributed systems, and
information retrieval.
Avanilde Kemczinski is a Professor of Computer Science at Santa Catarina State University (UDESC)
at campus CCT/Joinville. She has a Dr. degree in Production Engineering from Federal University of
Santa Catarina (UFSC) (2005). She has a MSc degree in Production Engineering from UFSC (2000)
and has graduated in Occupational Therapy from Santa Catarina Association of Education – Faculty of
Health Sciences of Joinville (ACE) (1992). She is the leader of the Research Group on Computers in
Education (GPIE) by CNPq/UDESC. Her research interests include Computers in Education, particularly educational technology, learning objects, human-computer Interaction. Her official full CV for
the Brazilian National Research Council (CNPq) is available at Curriculum Lattes: http://lattes.cnpq.
br/0048790978449306.
Marcos Hideshi Kimura is a Consultant and expert of design interaction at MoboUX. His current
research interests includes Human-Computer Interaction (HCI), Mobile Computing, Responsive Web
Design, Usability Evaluation, and Usability Engineering. His official full CV for the Brazilian National
Research Council (CNPq) is available at Curriculum Lattes: http://lattes.cnpq.br/8611592110268914.
Rinaldo José de Lima is a PhD student in Computer Science from the Federal University of Pernambuco (UFPE), Recife, Brazil. He received his MSc degree on Artificial Intelligence from the same
university in 2010. Since 2012, he has been working as a research assistant on several projects involving
Automatic Summarization at the Informatics Center, UFPE. He was a research fellow of the National

400

About the Contributors

Council for Scientific and Technological Development (CNPq) from 2010 to 2014. He has a relevant
five-year experience on Data Mining and Data Warehousing gained when he worked as a business
intelligence consultant for telecommunication companies. He published several papers in international
journals and conferences on Artificial Intelligence. His main research topics include Ontologies, Machine
Learning, Text Mining, and the Semantic Web.
Rafael Dueire Lins holds a BSc degree in Electrical Engineering (Electronics) from the Federal
University of Pernambuco, Brazil (1982), and a PhD degree in Computing from the University of Kent
at Canterbury, UK (1986). Lins published 10 books, amongst them the best-seller Garbage Collection:
Algorithms for Dynamic Memory Management (John Wiley & Sons, UK, 1996) translated into Chinese
(Mandarin) and published by ChinaPub in 2004, and over 200 papers in refereed journals and international conferences. His pioneering contributions encompass the creation of the Lambda-Calculus with
explicit substitutions, the first general and efficient solution to cyclic reference counting in sequential,
parallel, and distributed architectures. Lins was one of the pioneer researchers in document engineering
and digital libraries in Latin America. In this area, he was the first to address the problem of back-to-front
interference (bleeding) in documents. He is currently Full Professor at Federal University of Pernambuco,
Recife, Brazil, and vice-chair of TC-11 (Graphs Recognition) of IAPR (International Association for
Pattern Recognition).
Márcio José Mantau is a Professor of Computer Science at Santa Catarina State University (UDESC)
at campus CEAVI/Ibirama. He has a Master’s degree in Applied Computing from Santa Catarina State
University (UDESC) and has a Bachelor of Information Systems. His current research interests includes
Computer-Supported Cooperative Work (CSCW), Mobile Computing, and Human-Computer Interaction
(HCI). His official full CV for the Brazilian National Research Council (CNPq) is available at Curriculum
Lattes: http://lattes.cnpq.br/3410696560418245.
Harri Oinas-Kukkonen is Professor of information systems with special emphasis on information
systems’ use at the University of Oulu, Finland. His current research interests within human-computer
interaction and user behavior include behavior change, persuasive design, and the next generation of the
Web. He has been listed among the hundred most influential ICT experts in the country and a key person
to whom companies should talk to when developing their strategies for Web-based services. In 2005,
he was awarded The Outstanding Young Person of Finland award by the Junior Chamber of Commerce
for his achievements in helping industrial companies to improve their Web usability. Recently, he coauthored a book, Humanizing the Web: Change and Social Innovation, published by Palgrave Macmillan.
Hilário Tomaz Alves de Oliveira holds a BSc degree in Computer Science from the University
Center of João Pessoa (2010) and a MSc degree in Computer Science from the Informatics Center of
the Federal University of Pernambuco (CIN-UFPE) (2013). He has been working on several projects
involving universities and industry conducting research in the areas of Information Retrieval, Information Extraction, Text Mining, and Natural Language Processing. Currently, Hilário is a PhD student in
Computer Science from the Federal University of Pernambuco. Since 2011, his research areas include
Information Extraction, Information Retrieval, Ontology-Based Information Extraction, Opinion Mining, and Text Summarization.

401

About the Contributors

Peldon is an ICT officer in the Jigme Dorji Wangchuck National Referral Hospital, Thimphu, Bhutan.
She currently heads the ICT Unit and has wide experience in project management, Hospital Information
System and Rural Telemedicine system of Bhutan. Peldon completed her Master’s of Commerce (Information Systems) from Curtin University, Western Australia, Australia, and completed her Bachelor of
Computer Applications from Seshadripuram First Grade College in 2006. Peldon completed a Master’s
Dissertation titled “Examining the Risks and Opportunities of Social Network Adoption in the Health
Care System in Bhutan.”
Roberto Pereira is DSc in Computer Science at Universidade Estadual de Campinas, MSc in Computer Science at Universidade Estadual de Maringá (UEM), and BS in Information Systems at UNISEP.
He has experience on the management of research projects and on the design of computational solutions.
His main research interests are human-computer interaction, organizational semiotics, design, culture,
values, social software, and social web.
Marcelo Riss is a software architect at HP Brazil R&D division. Marcelo holds a Computing Science degree and a Master’s in Science degree on Production Engineering, specifically in the digital
systems research area. He is a software engineer and architect with more than 20 years of background
experience in software development, acting at the development from small to large software systems.
His experience was acquired along the years working in software development projects in several different areas as industrial and commercial automation, e-commerce, document workflow automation,
digital publishing, embedded systems, SOA systems, mobile applications, among others. Due his large
software development and architecture background, Marcelo is currently working as technical leader and
architect for HP Labs-related projects executed at image and printing lab, inside Brazil R&D Division,
where his main mission is use his software engineering background to help or accelerate the application
of research programs into new HP products.
Elena Rocco (PhD) is Assistant Professor at the University of Venice, Ca’ Foscari. She was a Fulbright Scholar at the University of California Los Angeles. After a postdoctoral degree in the School
of Information (Un. of Michigan), she went back to Italy. In 2010, she obtained a grant for a social
entrepreneuship project aimed to become a University spin off. In 2012, the project gave birth to Radio
Magica Foundation. In less than 2 years, the foundation has won three International awards. Her papers
and books have been presented in the Proceedings of the EIASM Workshops on Coopetition Strategy and
on Trust within and Between Organizations; European Academy of Management Annual Conferences;
CHI and CSCW; Economia & Management; International Studies for Management & Organization,
Research Management; Finanza, Marketing e Produzione. Her books are L’Organizzazione Della Fiducia (Carocci 2001), L’Architettura Delle Complementarità (Carocci 2008), and Coopetition Strategies:
Theory Experiments and Cases (Routledge 2011).
Fanny Rofalina, SKom, obtained her bachelor degree in Information System from the Faculty of
Computer Science Universitas Indonesia. Her research interests are related to information system/
information technology such as e-commerce and customer relationship management. Currently, she is
working in a private company as an analyst.

402

About the Contributors

Sonia San-Martín, PhD, is Associate Professor at the University of Burgos (Spain). She is lecturer
and researcher in Marketing and has been the Marketing Manager of the university for three years. Her
current research areas include contractual approach and marketing, relationship marketing, internal
marketing, international marketing, consumer behaviour, electronic commerce, and mobile commerce.
She has presented papers in national and international conferences organized by AEMARK, ACEDE,
IADIS, and EMAC, among others. She has written a book, some book chapters, and has published in
national and international journals such as the Journal of Retailing and Consumer Services, Journal of
Services Marketing, Journal of Service Research, Cyberpsychology and Behaviour, Personnel Review,
International Business Review, Online Information Review, Electronic Commerce Research and Applications, Industrial Management & Data Systems, and Psychology & Marketing, among others. She has
received several awards for her research from AEDIPE, ESIC, CES, and FEC.
Tanti Kartika Sari earned her Master’s of Commerce degree in Information Systems from Curtin
University in 2013. She was worked in one of top 10 bank in Indonesia developed her career in Consumer Banking, IT Project Management, and IT Business Analysis areas for more than 10 years prior
continued her study in Australia. Tanti has presented her paper, “Triple Bottom Line, Responsibility, and
Integrity: A New Sustainability Framework,” at IADIS Sustainability, Technology, and Education 2012.
This chapter is based on her Master’s dissertation in Curtin University. Her dissertation was supervised
by Dr. Tomayess Issa. Currently, she is serving as a casual staff for two schools in Curtin University,
Western Australia, as a Research Assistant for School of Information Systems and a Casual Academic
Assistant for School of Management.
Lei Shi was born in Qiqihar, China. He received the BSc degree in Network Engineering from Xidian University, Xi’an, China, in 2008, and the MSc degree in Digital Art and Design from Zhejiang
University, Hangzhou, China, in 2011. Since October 2011, he has been working as a PhD researcher
in the Intelligent and Adaptive Systems Research Group, at the Department of Computer Science, University of Warwick, United Kingdom. He holds the prestigious Chancellor’s International Scholarship
granted by Warwick Graduate School. His current research interests include Education Intelligence,
User Modelling, Adaptive Systems, Gamification, Social Computing, Human-Computer Interaction,
and User Experience Research.
Steven J. Simske is the Director and Chief Technologist for the Content Solutions Lab in HP Labs.
His research areas include image processing, image analysis, and document understanding technologies
ranging from automatic book digitization to and speech recognition. Steve developed the toolset for
architecting massive intelligent systems—meta-algorithmics—which affords the combination of two or
more intelligent systems to create more robust, accurate, and often faster larger systems, or “ecosystems.”
This culminated in the recent book, Meta-Algorithmics. Steve created the technologies for HP’s Security
Printing and Imaging program: image analysis, security, analytics and forensics to prevent counterfeiting, protect-branded products, and investigative support for anti-fraud. This led in part to his invitation
to participate as a Member of the World Economic Forum Global Agenda Council on Illicit Trade and
Organized Crime for the past four years. Steve is an HP Fellow and has more than 80 US Patents and
more than 300 peer-reviewed publications.

403

About the Contributors

Agnis Stibe, (PhD, MBA, MSc Comp) is a Postdoctoral Associate at MIT Media Lab. His research
is focused on studying socially influencing systems (SIS) and their capacity to facilitate user engagement through persuasive design. The foundation of this research is built on an extensive knowledge base
deriving from socio-psychological theories that describe various aspects of social influence. Over the
past 20 years, Agnis Stibe has gained professional experience in advertising, sales and marketing, Web
development and solutions, IT services and products, and customer relationship management. He has
worked for a number of multinational IT companies such as Hewlett-Packard, Oracle, and First Data
International. In the course of his career, Agnis Stibe has twice been awarded a recognition from the
Minister of Education and Science of Latvia for his long-term creative work. He has also received awards
from the Nokia Foundation and the Latvian Fund for Education.
Ebenezer Uy is currently a customer program and project manager at Hewlett Packard. He also concurrently serves as a part-time faculty member at De La Salle College of Saint Benilde. Ebenezer has
over 10 years of experience delivering quality outputs on time. He was recognized by Microsoft for best
practice in the field of Microsoft Sharepoint 2010. Subsequently, he’s a certified Project Management
Professional (PMP) with solid background in both technical (IBM Cognos and DB2 certified; CSCU [IT
security] certifications) and service management (ITIL Foundation 2011 certified) areas. He garnered
1st runner up in the Challenge Future’s Change the Biz World Global competition and is a champion
mentor of six different Microsoft Imagine Cup and Challenge Future global IT competition teams.
Craig Stewart has worked in the area of HCI, IT, and multimedia research and education for over
20 years. His academic background reflects this diversity with a PhD in Computer Science from the
University of Nottingham and an MSc in Molecular Genetics and a BSc in Genetics. He is the Lecturer
for the MSc in Information Technology and the MSc in Computing at Coventry University. Dr. Stewart’s
research interests include: eLearning, TEL, User Modelling, Adaptive Hypermedia, Intelligent Tutoring
Systems, Cultural Studies, and HCI and HF. His Doctoral research (titled “A Cultural Education Model:
Design and Implementation of Adaptive Multimedia Interfaces in eLearning”) examines the effect that
TEL is having on cultural education. By bringing a more unbiased and personal approach to HCI and
eLearning through the application of cultural variables to a user model and personalised interface, the
learner receives a lesson that minimises cultural bias.

404

405

Index

4Cs 126-128, 173, 182-183, 185, 188

A
Adaptive Educational Hypermedia System 306, 324,
331
Adaptive e-Learning 307, 309, 331
Adaptive Hypermedia 307-309, 322, 331
Adjusted Rand Index 102, 112, 115, 119
AEHS 2.0 331
Authoring Tools 53-54, 61, 63, 68, 72
Awareness of Others 52-54, 58-59, 64-66, 68, 72
Axial Coding 272-273, 277

B
BE Honeycomb 136, 144

C
Cognitive Authority 191-196, 200, 205, 210
Communities of Practice 163, 259-268, 271-272,
275-277
Competition (CT) 246, 256
Confidence Score Measure 233
Cooperation (CR) 246-247, 256
CSCW 65, 72

D
Default-Dependent Signals 4, 24
Default-Independent Signals 4, 24
Digital native 337-338, 352
Digital storytelling 332-333, 339-341, 345, 352

E
ease of use 176, 283, 285, 295-296, 305, 308, 322,
333
e-commerce 2, 5, 9, 64, 74, 100, 103, 106, 279-280,
282, 284-285, 297-298

Educational Policies 171
education quality 281, 283, 293-294, 297-298
EFA 121-122, 129-130, 133, 137-138, 144
Effectiveness 3, 27, 29, 32, 40, 74, 96-97, 194, 220,
228, 234-235, 238-239, 243-246, 249, 272-275,
340
Efficiency 29, 31, 42, 74, 96-97, 177, 236, 283, 309
Employee 121, 124-127, 134, 137-138, 144, 174,
260, 308
Enrollment Intention 278, 294, 297-298, 305
Entropy 105, 109, 114-115, 119
Exchange Knowledge 173, 175, 183, 185, 189, 310
Expert 96, 243, 267-268, 281, 312

F
Facebook-Era 171
Feedback Sharing 234-236, 238-239, 248-250, 256
Folkauthority 191, 193-197, 199, 203, 205-206, 210
Folksonomy 192, 194, 199, 210
Formal Network of Practice 277

G
Groupware 58, 65, 72, 265-266
Guidelines 9, 38, 52-54, 58-59, 63, 66, 68, 72, 76,
126-127, 137

H
Health Care System 175, 178-180, 182, 184-185
Heuristic Evaluation 73-74, 76-79, 81, 85, 89-92, 96
Honeycomb framework 121, 126-127, 136-138, 144
HTML Segmentation 99-109, 112-115, 119
Human-computer interaction (HCI) 97, 308

I
ICT 145, 156, 162, 171, 178, 185, 264-267
Indexing 192, 196-197, 199, 206, 210
Indonesia 286-287

Index

Information Asymmetry 1-2, 6, 14, 17, 280-281,
297-298, 305
Information Extraction 25, 27-29, 35, 39, 45, 49-50,
212-213, 233
Information Need 210
information quality 194, 201, 285, 295-297, 305
Information Retrieval 26, 28, 99-102, 104, 109, 113115, 119, 191-196, 199, 201, 205-206, 210, 214
Inspection method 76, 94, 96-97
intention to enroll 288

L
Likert’s Scale Agreed Level 144

M
Machine Learning 28, 101-105, 113-114, 119, 213,
215
Mobile-based Interface 78, 85-87, 89
Mobile Shopping 1-4, 24

N
Natural Language Processing 28, 104-106, 213, 233
Normalized Discounted Cumulative Gain 202, 210
Normalized Mutual Information 102, 112, 115, 119

O
Observational method 75, 97
Ontology 28, 35-39, 41-42, 45, 113, 211-217, 219220, 222, 225, 227-228, 233, 336
Ontology-based Information Extraction 212-213,
233
Open Coding 272, 277
Open innovation 332-334, 341, 343, 345, 352
Ownership and Authorization 136, 144

P
Paradigm Shift 146, 164, 171, 334
Participatory culture 337-339, 353
Participatory Design 306, 308, 310-311, 323-324,
331
Perceived Quality 278, 280, 285, 290, 292-298, 305
Personalization 2-7, 9, 14-15, 24, 74, 309, 336
Perspective 76, 122, 126, 148, 152, 236, 281-283,
297, 320, 335-336, 352
Persuasive Technology 236, 251, 256
Privacy Features 73-74, 77, 79, 94

406

R
Ranking Schema 194, 196, 202, 205-206, 210
Recognition (RE) 241, 246, 256
Recommendations 15, 53, 58-59, 65-66, 76, 105,
121-122, 137-138, 145, 149, 161, 163, 183,
271, 309, 321
Reputation 1, 3-6, 9, 11-12, 14-15, 17, 24, 126, 137,
279, 282, 284, 286-287, 297-298, 341
Requirement Analysis 64, 331
responsiveness 283, 288, 295-296
Rich Internet Application 72

S
Satisfaction 16, 74, 76-77, 79, 81, 97, 122, 124, 126,
134, 137, 148-149, 152-153, 156, 159, 161,
180, 202, 280, 283, 308, 322, 324
Secondary School 145, 161, 171
Semantic Web 26, 33-34, 58, 104, 113-114, 211213, 227, 233, 312, 334, 336, 341, 353
Signaling Theory 1, 279-281, 284, 297-298, 305
Silo Mentality 137, 144
Similarity Measures 215, 217-219, 227
SNS Apps 124-125, 137, 144
Social e-Learning 308, 321, 324, 331
Social Facilitation (SF) 247, 256
Social Influence 234-236, 238-240, 242, 245, 248251, 256
Social Learning (SL) 246, 256
Social Media 73-74, 104, 115, 122, 124, 126, 128,
130, 134, 146, 150, 157, 165, 175-177, 234235, 238-239, 248, 250-251, 259, 268-270,
274-275, 284, 297, 307, 309, 345
Social Network 73-74, 88, 94, 97, 122, 126, 171,
173-174, 177-179, 189, 191, 194, 199-200,
205, 297, 308, 337
Social Networking Site 171, 180
Sustainability 121, 173-175, 178, 182-183, 185, 189
Systematic Literature Review 52-53, 65, 68, 72

T
Tacit Knowledge 122, 137, 144, 264
Teacher-Student Relationships 148-151, 153, 161163, 171-172
Technology adoption 123, 285
Twitter 64, 74, 81, 150, 177, 180, 199, 235, 239240, 242-247, 249, 251, 256, 269, 284, 297

Index

U
University Quality 283-284, 294, 296-297, 305
university website 280, 282-284, 286, 288, 290, 293,
295-298
Usability problem 76, 81-87, 89, 92, 97
User Agent 62-64, 72
User Engagement 234, 236, 238-239, 245-246, 249,
256, 270
User Interface (UI) 53, 97

V
Value Generating Framework 332-333, 342
Vector Space Model 102, 119
visual appeal 285, 295-297
Visual Design 5, 9, 12, 14-15, 24, 31

W
Web 3.0 or Semantic Web 353
Web-based Interface 74, 78-79, 82-87, 89
Web Data Extraction System 50
Web Information Extraction 25, 27, 35, 45, 50
Web Page Processing 25-27, 29, 35-36, 39-40, 42,
45, 50
Web Page Understanding 25, 29, 34-35, 39, 45, 50
Website Quality 278, 280, 283, 285, 287-288, 290,
293, 295-298, 305

407