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Assessing Consumer Acceptance of Online Shopping: Examining Factors Affecting
Purchase Intentions

Dissertation
Submitted to Northcentral University
Graduate Faculty of the School of Business and Technology Management
in Partial Fulfillment of the
Requirements for the Degree of
DOCTOR OF PHILOSOPHY

by
KENNETH L. FLICK
Prescott Valley, Arizona
April 2009

UMI Number: 3353661
Copyright 2009 by
Flick, Kenneth L.

All rights reserved.

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Copyright 2009
Kenneth L. Flick

II

APPROVAL

Assessing Consumer Acceptance of Online Shopping: Examining Factors Affecting
Purchase Intentions
by
KENNETH L. FLICK
Approved by:

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Chair:

Bari Courts PhD

Date

Members: Efosa Osayamwen PhD
Scott Mensch PhD

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ABSTRACT
Assessing Consumer Acceptance of Online Shopping: Examining Factors Affecting
Purchase Intentions
By
KENNETH L. FLICK
Northcentral University, April 2009

This dissertation is a study in the area of online shopping acceptance. The purpose of this
study was to expand upon prior research and examine the correlations of many known
antecedents to online shopping acceptance to the dependent variable online purchase
intentions. The nature of the study is quantitative correlational by design. The gender of
the consumer was also examined as an influencing factor in online purchase intentions.
Perceived site quality was confirmed to be positively related to trust in the web site. The
importance of consumers' perceptions of web site quality was also affirmed.
Furthermore, consumers' attitude toward buying on the Internet was verified as being
positively related to online purchase intentions. The consumer's gender was not an
influencing factor with any of the antecedent factors of online shopping examined or to
the dependent variable online purchase intentions. Findings from this study reaffirm the
importance that consumers place on e-service quality.

IV

Acknowledgements
I would like to thank my committee for all of the valuable feedback and
inspiration that they provided. Dr. Bari Courts, my committee chair, was very helpful in
getting me through this process. Dr. Efosa Osayamwen's encouragement and suggestions
were very useful and helped me avoid many pitfalls. Both Dr. Scott Mensch and Dr.
Joseph Maggi, my external reviewer, provided encouragement, and suggestions that were
greatly appreciated.
I would like to especially thank Dr. James Neiman and Mr. Bud Jewell for
encouraging me to finish this task. Without their support, this accomplishment would not
have been possible. I also would like to thank my family and friends for putting up with
my lack of attendance at family events throughout the last four years. I would be
neglectful if I did not express my gratitude to Dr. Freda Turner and Northcentral
University for offering me this opportunity to pursue this degree.

v

Table of Contents
LIST OF TABLES

viii

LIST OF FIGURES

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CHAPTER 1: INTRODUCTION
Statement of the Problem and Purpose
Background and Significance of the Problem
Research Questions
Brief Review of Related Literature
Definition of Terms
Highlights and Limitations of Methodology
Summary and Conclusions

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CHAPTER 2: LITERATURE REVIEW
Prior Theories
Studies Focusing on Trust
Studies Exploring the Role of Consumer Privacy
Studies that Focus on Consumer Attitudes toward Online Shopping
Studies that Focus on Risk
Studies Examining the Role of e-Service Quality
Security for Web Shopping
Summary

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CHAPTER 3: METHODOLOGY
Overview
Restatement of the Problem and Purpose
Statement of Research Questions
Hypotheses
Description of Research Design
Operational Definition of Variables
Description of Materials and Instruments
Selection of Participants or Subjects
Procedures
Discussion of Data Processing
Methodological Assumptions, Limitations, and Delimitations
Ethical Assurances

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CHAPTER 4: ANALYSIS OF FINDINGS
Findings
Analysis and Evaluation of Findings
Summary

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CHAPTER 5: SUMMARY, CONCLUSIONS, AND RECOMMENDATIONS
Summary

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Conclusions

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Recommendations

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REFERENCES

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APPENDIXES
Appendix A: Cover Letter for Survey Participants
Appendix B: Informed Consent- 18 Years of Age and Older
Appendix C: Directions for Study Participation
Appendix D: Research Survey: Online Shopping Survey
Appendix E: Permission to Use Survey Instrument -1
Appendix F: Permission to Use Survey Instrument -2
Appendix G: Permission to Use Survey Instrument -3
Appendix H: Request for Participation Memo
Appendix I: Williamsburg Technical College Approval
Appendix J: Denmark Technical College Approval
Appendix K: K Research Question 1 Correlations
Appendix L: Research Question 2 Correlations
Appendix M: M Research Question 3 Statistics
Appendix N: Model Statistics

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LIST OF TABLES
Table 1 Scale Reliability
Table 2 Statistical Frequencies for Question 2 Gender
Table 3 Statistical Frequencies for Question 3 Age
Table 4 Statistical Frequencies for Question 16 Visually Appealing
Table 5 Statistical Frequencies for Question 17 Classy
Table 6 Statistical Frequencies for Question 14 Brand Equity 1
Table 7 Statistical Frequencies for Question 15 Brand Equity 2
Table 8 Statistical Frequencies for Question 11 Opportunism 1
Table 9 Statistical Frequencies for Question 12 Opportunism 2
Table 10 Statistical Frequencies for Question 13 Opportunism 3
Table 11 Statistical Frequencies for Question 8 Communication 1
Table 12 Statistical Frequencies for Question 9 Communication2
Table 13 Statistical Frequencies for Question 10 Communications 3
Table 14 Statistical Frequencies for Question 6 Trust 1
Table 15 Statistical Frequencies for Question 7 Trust 2
Table 16 Descriptive Statistics
Table 17 Statistical Frequencies for Question 18 Web Seal 1
Table 18 Statistical Frequencies for Question 19 Web Seal 2
Table 19 Statistical Frequencies for Question 20 Securityl
Table 20 Statistical Frequencies for Question 21 Security 2
Table 21 Statistical Frequencies for Question 22 Innovativeness 1
Table 22 Statistical Frequencies for Question 23 Innovativeness 2
Table 23 Statistical Frequencies for Question 29 Purchase Intentions
Table 24 Descriptive Statistics
Table 25 Hypothesis Testing at the Construct Level
Table 26 Research Question 1 Correlations
Table 27 Research Question 2 Correlations
Table 28 Gender and Age
Table 29 Gender and Age Crosstabulation
Table 30 Gender and Age Chi-Square Test
Table 31 Gender and Experience
Table 32 Gender and Experience Crosstabulations
Table 33 Gender and Experience Chi-Square Test
Table 34 Gender and Familiarity
Table 35 Gender and Familiarity Crosstabulations
Table 36 Gender and Familiarity Chi-Square Test
Table 37 Gender and Trust 1
Table 38 Gender and Trust 1 Crosstabulations
Table 39 Gender and Trust 1 Chi-Square Test
Table 40 Gender and Trust 2
Table 41 Gender and Trust 2 Crosstabulations
Table 42 Gender and Trust 2 Chi-Square Test
Table 43 Gender and Communication 1

vni

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Table 44 Gender and Communication 1 Crosstabulations
Table 45 Gender and Communication 1 Chi-Square Test
Table 46 Gender and Communication 2
Table 47 Gender and Communication 2 Crosstabulations
Table 48 Gender and Communication 2 Chi-Square Test
Table 49 Gender and Communication 3
Table 50 Gender and Communication 3 Crosstabulations
Table 51 Gender and Communication 3 Chi-Square Test
Table 52 Gender and Opportunism 1
Table 53 Gender and Opportunism 1 Crosstabulations
Table 54 Gender and Opportunism 1 Chi-Square Test
Table 55 Gender and Opportunism 2
Table 56 Gender and Opportunism 2 Crosstabulations
Table 57 Gender and Opportunism 2 Chi-Square Test
Table 58 Gender and Opportunism 3
Table 59 Gender and Opportunism 3 Crosstabulations
Table 60 Gender and Opportunism 3 Chi-Square Test
Table 61 Gender and Brand Equity 1
Table 62 Gender and Brand Equity 1 Crosstabulations
Table 63 Gender and Brand Equity 1 Chi-Square Test
Table 64 Gender and Brand Equity 2
Table 65 Gender and Brand Equity 2 Crosstabulations
Table 66 Gender and Brand Equity 2 Chi-Square Test
Table 68 Gender and Visually Appealing Crosstabulations
Table 69 Gender and Visually Appealing Chi-Square Test
Table 70 Gender and Classy
Table 71 Gender and Classy Crosstabulations
Table 72 Gender and Classy Chi-Square Test
Table 73 Gender and Web Seal 1
Table 74 Gender and Web Seal 1 Crosstabulations
Table 75 Gender and Web Seal 1 Chi-Square Test
Table 76 Gender and Web Seal 2
Table 77 Gender and Web Seal 2 Crosstabulations
Table 78 Gender and Web Seal 2 Chi-Square Test
Table 79 Gender and Security 1
Table 80 Gender and Security 1 Crosstabulations
Table 81 Gender and Security 1 Chi-Square Test
Table 82 Gender and Security 2
Table 83 Gender and Security 2 Crosstabulations
Table 84 Gender and Security 2 Chi-Square Test
Table 85 Gender and Innovativeness 1
Table 86 Gender and Innovativeness 1 Crosstabulations
Table 87 Gender and Innovativeness 1 Chi-Square Test
Table 88 Gender and Innovativeness 2
Table 89 Gender and Innovativeness 2 Crosstabulations
Table 90 Gender and Innovativeness 2 Chi-Square Test

IX

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Table 91 Gender and Usefulness 1
Table 92 Gender and Usefulness 1 Crosstabulations
Table 93 Gender and Usefulness 1 Chi-Square Test
Table 94 Gender and Usefulness 2
Table 95 Gender and Usefulness 2 Crosstabulations
Table 96 Gender and Usefulness 2 Chi-Square Test
Table 97 Gender and Usefulness 3
Table 98 Gender and Usefulness 3 Crosstabulations
Table 99 Gender and Usefulness 3 Chi-Square Test
Table 100 Gender and Ease of Use 1
Table 101 Gender and Ease of Use 1 Crosstabulations
Table 102 Gender and Ease of Use 1 Chi-Square Test
Table 103 Gender and Ease of Use 2
Table 104 Gender and Ease of Use 2 Crosstabulations
Table 105 Gender and Ease of Use 2 Chi-Square Test
Table 106 Gender and Purchase Intentions
Table 107 Gender and Purchase Intentions Crosstabulations
Table 108 Gender and Purchase Intentions Chi-Square Test
Table 109 Model Statistics

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LIST OF FIGURES
Figure 1. Research Architecture
Figure 2. Conceptual Model
Figure 4. Post-hoc Power Analysis

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XI

1
CHAPTER 1: INTRODUCTION
There are many challenges that must be overcome, if online shopping is to reach
its full potential. Bosworth (2006) stated, "consumers' increased wariness is costing
online businesses billions of dollars in lost revenue" (p. 1). Bosworth (2006) further
noted that online businesses lose $3.8 billion in revenue annually, due to a lack of
confidence on the part of consumers in current security measures provided for ecommerce.
Statement of the Problem and Purpose
Online shoppers are abandoning their shopping carts nearly 60% of the time.
Considering that an estimated $102 billion was spent online during 2006, shopping cart
abandonment cost online vendors $61 billion in lost sales revenues (Holland, 2006). In
consideration of this problem, the purpose of this quantitative research study was to
assess the factors that may cause consumers to accept or reject online shopping. A better
understanding of the factors that may affect online shopping acceptance or rejection will
provide online vendors with insights into what causes consumers to abandon or complete
transactions.
Background and Significance of the Problem
There is tremendous opportunity for improvement in the area of transaction
completion. "A major stumbling block for internet retail growth relates to consumer
distrust of shopping online-over concerns that are both real and imagined" (Arnold,
Landry, & Reynolds, 2007, p. 300). Zhou, Dai, and Zhand (2007) noted that the number
of aborted online transactions outnumbered completed transactions by four to one. Slyke,
Shim, Johnson, and Jiang (2006) cited concern for information privacy as an impediment

2

to consumer acceptance of online shopping. Valentine (2003) stated, "concerns about the
security of their credit card number prevent consumers from shopping online" (p. 39).
Valentine further elaborated, "according to Celent Communications, online payment
fraud is 30 times higher than payment fraud in the physical world" (p. 39).
The problem with lower than expected consumer acceptance of online shopping is
also linked to e-service quality (Lee & Lin, 2005). Aborted online transactions amounted
to a loss of an estimated $61 billion in sales revenues during 2006 (Holland, 2006). This
research project expands on prior research by including the possibility that the gender of
the consumer is a moderating factor in attitude towards the Internet, perceptions of web
site quality, and in the initial formation of trust.
It is hypothesized that these factors are correlated to the dependent variable
purchase intentions. With the information obtained from this research, web site
developers can potentially design online shopping experiences that are more conducive to
transaction completion. The results from this study may provide a more complete picture
of how consumers' attitudes, perceptions of web site quality, and gender ultimately
influence their purchase intentions.
Research Questions
Ql: To what extent, if any, is perceived site quality related to trust in the web
site?
Q2: To what extent, if any, is a consumer's attitude toward buying on the Internet
related to online purchase intentions?

3

Q3: To what extent, if any, is gender related to perceived web site quality, trust,
attitude toward buying on the Internet, and consequently online purchase
intentions?
BriefReview of Related Literature
There have been numerous studies of identifying factors that affect consumer
behaviors (Chan & Limayem, 2005; Chi, Lin, & Tang, 2005; Dillon & Reif, 2006;
Doolin, Dillon, & Corner, 2005; Shergill & Chen, 2005). Prior researchers have
attempted to identify factors that influence traditional consumer behavior and which also
may relate to consumer decision making and behavior in an online shopping environment
(So, Wong, & Sculli, 2005). These researchers have incorporated theoretical models such
as the technology acceptance model (TAM), the theory of reasoned action (TRA), and the
theory of planned behavior (TPB) (Klopping & McKinney, 2004).
Chi et al. (2005) reviewed several studies that identified different influences on
the formation of beliefs regarding the usefulness, ease of use, innovativeness, and
security. Chi et al. further noted, "Given the belief recurrence in theoretical models of
consumer behavioral intentions towards online purchases, additional work is necessary to
integrate these theories and compare the differences from a gender perspective" (p. 417).
While trust is an important antecedent to the acceptance of online shopping, it has
remained a very difficult concept to measure and define. The definition of trust has
frequently varied from study to study. One definition has been dominant, thus trust as
stated by Koufaris and Hampton-Sosa (2002a) is defined as:
the willingness of a party to be vulnerable to the actions of another party
based on the expectation that the other will perform a particular action

4

important to the trustor, irrespective of the agility to monitor or control
that party, (p. 1)
The initial formation of trust is dependent upon several antecedents including
consumers' perceptions of web site quality and structural assurances (Wakefield, Stocks
& Wilder, 2004). Risk perception is another antecedent to online shopping that is
frequently included in e-commerce studies. There are several risks that the consumer
could encounter when engaging in online commerce including personal risk, economic
risk, and privacy risk (Wakefield et al., 2004). Personal risk is defined as, "acquiring
potentially unsafe products or services or the seller not performing satisfactory" (Slyke,
Shim, Johnson & Jiang, 2006, pp. 422-423). In contrast, economic risk is the risk
associated with monetary loss.
Privacy risk is associated with potential for personal information to be disclosed
without the consumer's consent. This personal information could include the potential
buyer's social security number, credit card numbers, phone number, and address. Slyke et
al. (2006) noted, "Understanding how consumers' concerns for information privacy, or
their concerns about how organizations use and protect personal information impact
consumers' willingness to engage in online transactions is important to our knowledge of
consumer-oriented e-commerce" (p. 416). Slyke et al. (2006) focused on two research
questions: "How do consumers' concerns for information privacy affect their willingness
to engage in online transactions?" and "Does consumers' familiarity with a Web
merchant moderate the impact of concern for information privacy on risk and on trust?"
(p. 417). The major strength to this study was that the researchers used two data
collection efforts and large sample sizes to enhance external validity (Slyke et al., 2006).

5
Findings from this study confirmed that concern for information privacy (CFIP)
was important under specific conditions. For example, if the merchant was well known,
CFIP influenced consumers' perceptions of both risk and trust (Slyke et al., 2006). While
this was the case for well-known merchants, if the vendor was not well known, CFIP had
no effect on consumers' perceptions either of risk or of trust (Slyke et al, 2006). The next
significant finding from this study was that CFIP did not directly affect consumers'
willingness to transact, but rather was mediated by risk and trust (Slyke et al, 2006). The
final significant finding was, "that familiarity did not moderate the relationships between
CFIP and both trust and risk, although there were differences in the role of CFIP for more
well-known merchant (Amazon.com) and less well know merchant (Half.com)" (Slyke et
al., 2004, p. 433).
Within their exploratory study Drennan, Mort, and Previte (2006) focused on
'expert end users' online behaviors. While the introduction does state the purpose and
focus of the study, it does not include stated research questions. Contrary to the fact that
the study is focused on the expert end users, the authors provide a generic definition of
who the expert end users are.
The major weakness with the Drennan et al. (2006) study is the sample size of
only 76 respondents. This small sample size leads to external validity issues. Several
hypotheses from the Drennan et al. study are directly related to the proposed study. For
example hypothesis five, "the extent of end users' perceived risk will be related
negatively to online subscription and purchasing behavior" (Drennan et al., 2006, p. 8).
Findings from this study revealed the multi-dimensionality of e-privacy.

6
Other notable findings were in the areas of perceived risk and privacy active
behavior. Drennan et al. (2006) stated, "perceived risk was heightened by privacy
suspicion but not simply by privacy awareness" (p. 9.)- Further, "perceived risk was
found to increase levels of privacy active behavior and decrease online subscription and
purchasing behavior" (Drennan et al., 2006, p. 9). The researchers utilized expert end
users for this study. The population for the proposed study is college students in their
second year of studies at community colleges. This sample group should equate nicely
with expert end users. It is interesting to speculate on the possibility of the findings on
perceived risk holding for the proposed study.
Meinert, Peterson, Criswell II, and Crossland (2006) examined the role of web
site privacy policy statements and the formation of consumer trust. Meinert et al. quoted
Pennington, Wilcox, and Grover (2003) who found "evidence via an experimental design
that self-reported guarantees can influence system trust and indirectly influence consumer
purchase intentions" (p. 124). Furthermore, Meinert et al. referred to another study by
Meinert et al. (2006) in which the researchers stated, "the strength, or level of protection
guaranteed by the privacy policy statement influences consumer trust as measured by
willingness to provide personal information online" (p. 124).
Wakefield and Whitten (2006) examined the role of third-party organization
credibility and the creation of trust in e-retailers. The researchers examined the role of
web site assurance seals from companies such as Better Business Bureau (BBB) Online,
TRUSTe, Web Trust, and VeriSign. Wakefield and Whitten hypothesized, "the value of a
Web assurance seal is positively related to trust in the e-retailer" (p. 7). Other hypotheses
that are relevant to the proposed study included, "the perception of purchase risk is

7

negatively related to trust in the e-retailer, and "trust in the e-retailer is positively related
to purchase intentions" (Wakefield & Whitten, 2006, p. 8).
Wakefield and Whitten (2006) made a controversial statement when quoting from
Jarvenpaa and Todd (1996) who stated, "research demonstrates that attitudes toward
Internet shopping are not affected by demographic characteristics such as age" (p. 8).
This statement is contradicted by more recent studies (Sorce, Perotti, & Widrick, 2005;
Zhou et al., 2004). Findings from this study confirmed prior studies that indeed
institution-based assurances (assurance seals) elevate consumers' perceptions of trust
(Wakefield & Whitten, 2006).
Zhou et al. (2004) noted, "attitude is directly affected by users' belief about a
system, which consists of perceived usefulness and ease of use" (p. 50). There are several
accepted antecedents in the formation of a consumer's attitude towards the Internet in
general and specifically his/her online shopping intention. These factors may include
shopping motivation, innovativeness, perceived outcome, shopping orientation, and
normative beliefs (Zhou et al., 2004). Although each prior study focuses on specific
factors that influence consumer-buying behaviors, no prior research has correlated gender
with known antecedents to online shopping.
Sorce et al. (2005) conducted a study on how attitude and the age of the consumer
affect online buying behaviors. Unlike previous studies, the researchers surveyed students
and staff and compiled data on their buying experiences with over 17 products (Sorce et
al., 2005) The stated purpose for this study was, "to determine the impact of age and
attitude towards online shopping in predicting the likelihood to shop and buy online"
(Sorce et al., 2005, p. 122).

8

Quoting Dholakia and Uusitalo (2002), Sorce et al. (2005) noted, "that younger
consumers reported more hedonic and utilitarian benefits of online shopping than older
consumers" (p. 124). Further, "the relative impact of demographic factors has only been
addressed by a small number of studies" (Sorce et al., 2005, p. 124.). Moreover, quoting
Korgaonkar and Wolin (1999), Sorce et al. remarked, "that motivational factors as well as
age and gender impacted the likelihood of online purchasing" (p. 124). Both of these
statements confirm the need for additional research on how consumer attitudes and
demographics influence online purchase intentions.
The research questions are relative to this proposal. Research question one refers
to the impact of age on product search and online purchasing. The second research
question is in regard to online shopping attitudes and if there is any change according to
the age of the consumer. The final research question concerns the possible impact of age
and attitudes of consumers in predicting online shopping and purchase behavior (Sorce et
al., 2005). The methodology was mixed in that it included qualitative open-ended and
quantitative scaled items. The survey was distributed to undergraduate and graduate
students at a university in northeast United States. In addition, the researchers surveyed
staff, which allowed the inclusion of additional age groups not available with only a
student population (Sorce et al., 2005).
Results indicated that attitude had more of an impact on the likelihood of to shop
or search online than did age (Sorce et al., 2005). While younger consumers tended to
search for more products online than did older consumers, they did not necessarily
purchase more (Sorce et al., 2005). Further findings indicated that interests associated

9
with age influenced the products that were purchased in various product categories (Sorce
et al., 2005).
Definition of Terms
An understanding of terms that apply to this study is essential. Within this section,
complete definitions of all terms are provided. The source of all definitions is provided as
indicated with citations. Whenever possible the definitions were paraphrased in terms that
would be more understandable to persons not familiar with online shopping terminology.
Attitude toward buying on the Internet. For the purpose of this study, attitude
toward buying on the Internet is defined as, "a consumer's positive or negative feelings
about performing purchasing behaviors on the internet" (Chi et al., 2005, p. 419). This
variable affects a consumer's intention to make an online purchase. Privacy and Internet
trustworthiness are extraneous variables associated with attitude (Black, 2005).
Brand Equity. For the purpose of this study brand, equity is defined as, "the value
attributed to a certain brand when the consumer is familiar with that brand and holds
some favorable, strong, and unique brand associations in memory" (Wakefield et al.,
2004, p. 96).
Communication. For the purpose of this study, "communication is defined as the
formal and informal sharing of meaningful and timely information" (Wakefield et al.,
2004, p. 95).
Concern for Information Privacy (CFIP). For the purpose of this study, "concerns
about how organizations use and protect personal information" (Slyke et al., 2006, p.
416).

10
Opportunism. For the purpose of this study, opportunism is defined as, "a deceitoriented violation of implicit or explicit promises about a pre-determined role or
behavior" (Wakefield et al., 2004, p. 96).
Perceived Behavioral Control. In relation to the Theory of Planned Behavior
(TPB) is defined as a consumer's perceptions of his/her ability to perform a behavior
(Ajzen, 1991).
Perceived Site Quality. "Users' evaluation of a web site's features meeting users'
needs and reflecting overall excellence of the web site" (Aladwani & Palvia, 2002, p.
469).
Perceived Usefulness. For the purpose of this study, perceived usefulness is
defined as, "the prospective consumer's subjective probability that using the Internet will
efficiently facilitate his or her purchasing" (Chi et al., 2005, p. 420). This variable is
related to web site quality and trust in the web site (Wakefield et al., 2004).
Purchase Intentions. For the purpose of this study, a customer's likelihood of
buying from an online vendor (Lee & Lin, 2005).
Subjective Norms.. In relation to TPB, subjective norms are defined as how the
consumer feels that the action in question will be viewed by persons of importance to the
consumer (Ajzen, 1991).
Technology Acceptance Model (TAM). A model widely used in explaining
consumers' online behaviors. Regarding this model, Zhang, Prybutok, and Koh (2006)
stated: The original TAM has three constructs: (a) perceived ease of use, (b) perceived
usefulness, and (c) usage. Its primary objective is to predict and explain the use of
technology

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Theory of Planned Behavior (TPB). Theory of Planned Behavior (TPB) is an
extension of the original TRA. Ajzen (1991) noted, "the theory of planned behavior holds
that only specific attitudes toward the behavior in question can be expected to predict the
behavior" (p. 120).
Theory of Reasoned Action (TRA). Ajzen (1991) suggested that a person's
behavior is determined by his/her intention to perform the behavior and that this intention
is, in turn, a function of his/her attitude toward the behavior and his/her subjective norm.
Trust in the web site. In the context of this study, trust refers to cognitive-based
trust. "Cognition-based trust theory posits that initial trust relies on first impressions or
cognitive cues, as opposed to personal interactions with another party" (Wakefield et al.,
2004, p. 94). Furthermore, trust is defined as, "consumer trust in electronic transactions in
consumer-vendor relationships" (Chellappa, 2002, p. 15). Slyke et al. (2006) stated, "trust
in the Web merchant has been characterized as the belief that the merchant will not
behave opportunistically by taking advantage of the situation" (p. 421).
Highlights and Limitations of Methodology
This study was not intended to prove causation, but rather to examine the possible
correlation of consumers' attitudes, perceptions of web site quality and gender to the
initial formation of trust and subsequently to the dependent variable purchase intentions.
In addition, this study was designed to assess factors that contribute or deter consumers
from adopting online shopping. Furthermore, how gender potentially affects the
antecedents of online purchase intentions was examined.
Data for this quantitative correlational study was obtained with an online survey
instrument. Survey items were adapted from prior studies and were statistically proven to

12

measure the intended constructs. Study participants were directed to the survey
instrument and provided with complete instructions on how to submit their responses.
Targeted survey participants were in their second year of studies at community colleges
or from one of the two participating universities. Rationale for this choice of respondents
is, "college students are the greatest proportion of internet users" (Lee & Lin, 2005, p.
167). An additional reason for choosing this sample is, "online customers are generally
younger and better educated than conventional customers, meaning that student subjects
closely resemble the online customer population" (Lee & Lin, 2005, p. 167).
There were several limitations to this study to consider. Firstly, since respondents
were chosen from community colleges and two universities the results cannot be
generalized to the entire population. Secondly, the nature of this study was limited by
design to assess the correlation of antecedent factors to online shopping intention.
Correlation does not equate to causation (Trochim, 2001). Thirdly, the possibility that
answers provided by the respondents were unduly influenced by their peers if the survey
instrument was administered in a classroom setting. The length of duration for the study
was limited; therefore, long-term results were not available.
Summary and Conclusions
This research project expanded upon prior research by including the possibility
that the gender of the consumer is an influencing factor on the decision to complete or
abandon the transaction. In addition, how consumers' attitudes toward online shopping
and perceptions of web site quality influence the initial formation of trust and
consequently online purchase intentions was examined. The rationale for conducting this

13
study was to address the need for additional research in the area of online shopping
acceptance particularly as it relates to purchase intentions.

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CHAPTER 2: LITERATURE REVIEW
Contained within Chapter 1 was a statement of the problem, rationale for
conducting the research, the research questions, and the related hypotheses. The reader is
also provided concise definitions of key terms that are essential to this research study.
The brief review of literature section is followed with an overview that highlights the
chosen methodology and its limitations. The chapter is concluded with a restatement of
the problem, how this study expands the existing knowledge base and finally the rational
for this research project.
Chapter 2 extends the brief review of literature section from chapter 1. This
chapter provides a comprehensive overview of literature pertaining to this research study.
For the convenience of the reader and to aid readability, this chapter is organized in
subtopics. Within each subtopic section, an overview of studies that examined the subject
of online shopping acceptance/purchase intention with emphasis on different antecedent
constructs is presented. This chapter is concluded with a restatement of the problem and
the rationale for the need for this research project.
Prior Theories
There are several theories that have been incorporated into prior research studies
including: the Theory of Reasoned Action (TRA), Technology Acceptance Model
(TAM), Theory of Planned Behavior (TPB), Expectation-Confirmation Theory (ECT),
and Innovation Diffusion Theory (Cheung et al., 2005).
In the context of TRA, Ajzen (1991) suggested that a person's behavior is
determined by his/her intention to perform the behavior and that this intention is, in turn,
a function of his/her attitude toward the behavior and his/her subjective norm. The best

15
predictor of behavior is intention (Ajzen, 1991). Intention is the cognitive representation
of a person's readiness to perform a given behavior, and is considered an antecedent of
behavior. Three items determine intention: consumers' attitude toward the specific
behavior, subjective norms, and perceived behavioral control (Ajzen, 1991).
The TPB was developed as an extension of the original TRA. This theory is based
upon the belief that consumers' attitudes towards a behavior can be used to predict the
resulting behavior (Ajzen, 1991). There are three predictors included within TPB. In
addition to attitudes, consumers' subjective norms and behavioral control influence
intentions.
The TAM is widely used in explaining consumers' online behaviors. Included
within this model are three constructs perceived ease of use, perceived usefulness, and
usage. The expanded version of the TAM model includes the mediator variable intention
to use. Intention to use moderates the effect of perceived ease of use and perceived
usefulness on the dependent variable technology usage (Zhang et al., 2006).
Another theory that has been utilized in explaining online consumer behavior is
ECT. This consumer satisfaction theory is based upon a comparison of the level of
quality a consumer anticipates a product or service will deliver versus a post-purchase
evaluation of the product or service. Consumer satisfaction is largely dependent upon the
outcome of the post-purchase evaluation and how it compares with pre-purchase
expectations (Kim, Ferrin, & Rao, 2003).
An older but nonetheless accepted theory concerning the acceptance and diffusion
of new innovations is the Diffusion of Innovation Theory. Within this theory, Clark
(1983) attempted to explain how a new technological idea or a new use for an old one

16
migrates from its initial creation to adoption. The theory included five stages of
innovation adoption. The stages included knowledge, persuasion, decision,
implementation, and confirmation. In brief, consumers gain knowledge of the existence
of a product or service, they are persuaded to try the innovation, the actual decision or
commitment to the innovation is made, the innovation is used and confirmation for the
continuation of use is dependent upon positive outcomes (Clark, 1983).
Studies Focusing on Trust
Consumers' online shopping behaviors have been researched from a number of
different perspectives. Several studies have examined the role of consumers' perceptions
of trust (Gefen, Karahanna, & Straub, 2003; Koufaris & Hampton-Sosa, 2002a; Koufaris
& Hampton-Sosa, 2002b; McCloskey, 2006; Slyke, Belanger, & Comunale, 2002;
Wakefield, Morris, & Wilder, 2004). One study in particular focuses on how consumers'
perceptions of web site quality affect trust in the web site (Wakefield et al., 2004). The
authors also investigated how structural assurances (the value of web seals) affect trust in
the web site and in turn purchase intentions. Several antecedents to the initial formation
of trust were explored and verified with the survey instrument.
Responses submitted to questions on the survey instrument indicated that
communication has a positive relationship with initial trust in the web site. In contrast,
the perception of opportunism has a negative impact on the formation of initial trust in
the web site. Further findings indicated that product brand equity has a positive
relationship with initial trust in a web site. The hypothesis that web seal value has a
positive relationship with purchase intentions was not confirmed.

17
The authors also concluded that initial trust in a web site has a positive
relationship with purchase intentions (Wakefield et al., 2004). The findings from the
study are useful in explaining consumers' initial formation of trust resulting from their
perceptions of web site quality and web seal value. Since, this study was conducted with
the use of only one web site, the ability to generalize the findings is in question.
Moreover, while the formation of initial trust is linked to online purchase intentions, this
study is too narrow in focus to provide a comprehensive picture of why consumers
choose to purchase online or decline to do so.
Slyke et al. (2002) considered a consumer's trust in web merchants an important
determinant of the consumer's intentions to make purchases over the web, even when the
influences of perceived innovation characteristics are considered The perceive innovation
characteristics that were considered included relative advantage, complexity,
compatibility, result demonstrability, and visibility. The constructs result demonstrability
and visibility were rejected due to unacceptable Cronbach's alphas of under the
acceptable range of 0.70 and above.
Results from this study indicated that the independent variables explained 69.8%
of the variation in the dependent variable use (Slyke et al., 2002). There is one notable
contradiction in this study regarding the relationship of trust in web merchants and
intentions to shop online. "The major finding of this study is the significant relationship
of trust in Web merchants to intentions to use the Web for purchasing goods and
services" (Slyke et al., 2002, p. 11). In contrast to this statement, adding trust in web
merchants to the model only increased its explanatory power by less than one percent
(Slyke et al., 2002).

18
One notable problem with the Slyke et al. (2002) study is that the study did not
incorporate an actual web site. Respondents to the survey were only asked to answer
questions regarding perceptions of prior visits to web sites. Furthermore, trust in the web
merchant was not a significant factor in explaining the variation in the dependent variable
use. Trust has been proven in many studies to be an essential antecedent to purchase
intentions (Koufaris & Hampton-Sosa, 2002b; Wakefield, et al., 2004).
Koufaris and Hampton-Sosa (2002b) examined the role of consumers' initial
perceptions of a company's trustworthiness online. The authors sought answers to two
research questions. The first question related to whether customer experience with and
perceptions of the web site would have a strong influence on their perceptions of the
company's trustworthiness. The second area of inquiry was to determine the impact of
customer perceptions of three web site characteristics: usefulness, ease of use, and
security control. The constructs perceived usefulness and perceived ease of use are
adapted from the TAM (Koufaris & Hampton-Sosa, 2002b). The researchers separated
constructs into three groups to accommodate smaller sample sizes. These groups included
perceptions about the company's characteristics, perceptions about the company's
actions, and perceptions about the company's web site.
Results from the study indicated that perceptions about the company as well as
perceptions of web site could determine initial beliefs regarding trustworthiness (Koufaris
& Hampton-Sosa, 2002b). Moreover, intention to purchase is determined by perceived
company trustworthiness and perceived usefulness of the web site. Even though this
assertion was supported, the path between perceived ease of use and intention to purchase
was not confirmed (Koufaris & Hampton-Sosa, 2002b).

19
There are a few weaknesses evident in this study. The researchers only focused on
consumer perceptions of company trustworthiness, they did not explore the effects of
structural assurances afforded by third party web seals or stated privacy policies. In
addition, the researchers required that students make simulated online purchases. These
purchases were limited to two items one a service and the other a product. Additionally,
the subjects werefinanciallyrewarded for their participation in the study. Considering the
nature of the product selected for the simulated purchase a laptop computer, gender basis
may be a factor in the results.
In assessing the relationship of trust to online purchase intentions in another
recent study, Koufaris and Hampton-Sosa (2002a) four constructs relating to consumer
trust are indentified: disposition to trust, institutional-based trust, trusting beliefs, and
trusting intentions. The authors assessed the relationship of customer experience with the
web site and how prior experience with the web site affects customer trust in the
company (Koufaris & Hampton-Sosa, 2002a).
The researchers hypothesized the roles of several antecedents to trust including:
perceived usefulness of the web site, perceived ease of use, perceived control. Other
hypothesized relationships included how perceived control is related to perceived
usefulness and the relationship of enjoyment to perceived usefulness. The final
relationship hypothesized was how customer trust is associated with customer intention
to purchase (Koufaris & Hampton-Sosa, 2002a).
Results from the Koufaris and Hampton-Sosa (2002a) study confirmed the
positive relationships of perceived usefulness, perceived ease of use, and perceived
control. Furthermore, the relationship of enjoyment to perceived control was verified.

20

Koufaris and Hampton-Sosa (2002a) concluded, "that a positive experience with a web
site is important" (p. 12). The authors also noted, "If customers find a company's web
site easy to use and useful they will view the company more favorable" (Koufaris &
Hampton-Sosa, 2002a, p. 12).
Once again, there are several limitations to the study. The researchers utilized a
relatively small sample size of only 111 students. Some 80% of the respondents were in
the under 25-age group, so the ability to generalize the findings is in question. Moreover,
the researchers limited the study to perceptions of trust and the affect of prior experience
with the web site on purchase intentions. The researchers did not consider the potential
moderating affect of gender or attitudes towards the web and specifically online shopping
in conducting this research project.
Studies Exploring the Role of Consumer Privacy
There are several theories regarding privacy. One commonly held view is that
privacy is the right to be left alone (Gauzente, 2004). Another more contemporary view
is, "the right to privacy entitles one to exclude others from watching, utilizing, invading
his/her private realm" (Gauzente, 2004, p. 182). "Privacy is a primary factor of concern
among Internet users and may become one of the important barriers to e-service
development (Dinev & Hart, 2006, p. 27). Chellappa (2002) stated regarding the
importance of security and privacy, "Issues involving security and privacy have made
many consumers hesitant to transact online and in fact, over half of the respondents in a
sample of Americans nationwide, said that privacy and security are their biggest concerns
about electronic commerce" (p. 5). Furthermore, Slyke et al. (2006) stated,
"understanding how consumers' concerns for information privacy (CFIP), or their

21
concerns about how organizations use and protect personal information impact
consumers' willingness to engage in online transactions is important to our knowledge of
consumer-oriented e-commerce" (p. 416). Slyke et al. defined information privacy, "an
individual's ability to personally control information about his or herself (2006, p. 418).
Slyke et al. included four dimensions within the CFIP construct: collection concerns,
errors, secondary use of data, and improper use.
Findings from the Slyke et al. (2006) research study indicated that CFIP was
important with the well-known site Amazon.com and consequently affected both
perceptions of risk and trust. In contrast, if the site was relatively unknown, CFIP had no
affect on risk or trust (Slyke et al, 2006). "CFIP does not directly affect willingness to
transact, but instead is fully mediated by trust and risk" (Slyke et al., 2006, p. 417).
Contrary to the researchers' hypothesized relationships, familiarity did not have a
moderating affect on CFIP. Limitations to this study included the use of only one product
and only two web sites. This study was also limited by its rather narrow scope. In
addition, the study did not include several factors that were verified in other studies as
antecedents to online purchase intentions.
In a recent study on consumer privacy concerns, Dinev and Hart (2006) examined
intended e-service use at varying levels of information exchange. The authors identified
six levels of information exchange from low to high. The research questions that were
utilized to ground this research were: did consumers' privacy concerns influence
willingness to engage in e-services at varying levels of information exchange, and did
privacy concerns increase with the level of information exchange (Dinev & Hart, 2006).

22

The authors examined the relationships of privacy concerns and information
access, intended use, and information abuse at each level of information exchange. This
longitudinal study was conducted over a four-year period. The constructs measured were
privacy concern for information access (PCIA) and privacy concern for information
finding (PCIF) (Dinev & Hart, 2006). Findings from the study confirmed the
hypothesized relationships that as the level of information exchange increased so did the
level of concern for information privacy. Notable exceptions were that for level one of
information exchange (Internet browsing) consumers has little privacy concern whenever
they are browsing the Internet for accessing or finding information. While the results
were tabulated according to levels of information exchange and the two measured
constructs privacy concern for information access and privacy concern for information
finding, results were not correlated to demographic age groups or gender.
Consumers' perceptions of an environment being unduly risky may adversely
affect willingness to shop online (Milne & Culnan, 2004). "Consumers seek information
to reduce risk the risk of consuming a particular product or service where risk reflects
perceptions of the uncertainty and the adverse consequences of consuming a product or
service" (Milne & Culnan, 2004, p. 16). Milne and Culnan explored reasons why
consumers read or chose not to read online privacy notices. The authors further noted,
"that for privacy notices to build trust, the message should be informative and reassure
consumers that disclosing their personal information is a low-risk proposition" (Milne &
Culnan, 2004, p. 18). In addition, Milne and Culnan verified that consumers who
provided false information to a web site, or none at all, 63% of the surveyed respondents

23

stated that they would have provided the information if the web site had disclosed in a
privacy statement how the information would be used.
The researchers hypothesized that, the tendency to read online privacy notices is
positively associated with online privacy, concern for privacy is positively associated
with a tendency to read online privacy notices, andfinallythat concern for privacy is
negatively associated with trust of online privacy notices (Milne & Culnan, 2004). While
the Milne and Culnan study added to the knowledge base regarding online privacy
notices and in how consumers managed risk. The results from the Milne and Culnan
study are questionable. Milne and Culnan remarked in the discussion portion of the study
that the use of the Harris online sample might skew the results. "Clearly only individuals
who are comfortable disclosing personal information online are going to belong to such a
panel and take the time to fill out a survey online" (Milne & Culnan, 2004, p. 25).
Other researchers examined privacy from the perspective of consumers' stated
preferences vs. actual online behaviors (Berendt, Gunther, & Spiekermann, 2005). In an
experimental study, Berendt et al. utilized a web shopping experience that included an
anthropomorphic shopping bot. This electronic shopping assistant aided study
participants in selecting for two specific products. The primary objective of the study
was, "to investigate drivers and impediments of online interaction in general" (Berendt et
al., 2005, p. 102). Berendt et al. further stated, "Privacy concerns were suspected to be
one major impediment of truthful and deep online interaction" (p. 102).
Results from the Berendt et al. (2005) study contradicted findings from the Slyke
et al. (2006) and Milne and Culnan (2004)Slyke studies. The contradiction is in the area
of concern of information privacy. While results from these studies indicated that privacy

24

concerns and privacy statements affected consumer behavior, Berendt et al. (2005) stated,
"While many users have strong opinions on privacy and do state privacy preferences,
they are unable to act accordingly" (p. 105). Berendt et al. (2005) further noted, "Once
they are in an online interaction, they often do not monitor and control their actions
sufficiently; privacy statements seem to have no impact on behavior" (p. 105). Further
findings indicated, "that given the right circumstances, online users easily forget about
their privacy concerns and communicate even the most personal details without any
compelling reason to do so" (Berendt et al., 2005, p. 102).
The apparent reason for this contradiction is the nature of the methodology used
for the study. Results showed that respondents would divulge personal information
whenever they perceive that the online exchange is entertaining and the received benefits
are believed to be adequate (Berendt et al., 2005). The entertainment element was
provided by the interactive shopping bot and the benefit received was 60% off on
purchased merchandise. This is a huge incentive for participation in this study. How this
incentive skewed the results in unknown. Moreover, the experiment only included two
products cameras and coats, whether these findings are reproducible with other products
is in question.
Studies that Focus on Consumer Attitudes toward Online Shopping
Yang and Lester (2004) conducted research using a survey instrument that
measured consumers' attitudes toward buying online with 11 positive and 10 negative
features of online shopping. According to Yang and Lester (2004), most web sites are not
built to be user-friendly, but are built to provide information. The authors contended that
companies providing products and services online only consider users to be rational

25
customers. In doing so, individual differences in personalities are ignored. Yang and
Lester noted, "Researchers have demonstrated a link between personality and Internet
use" (p. 86). Further, Yang and Lester stated, "in a cross-cultural study of 12 countries, it
was found that web site quality, trust, and positive affect toward it were critical in
predicting both the shopper's purchase intentions and loyalty of visitors to the site" (p.
86).
Results from this research project indicated that online shoppers had higher mean
scores for the eleven positive features than non-shoppers. On the other hand, nonshoppers had higher mean scores for the ten negative features of online shopping (Yang
& Lester, 2004). The researchers expressed the need for additional research, "future
research is planned to explore the role of gender differences in attitudes toward online
shopping" (Yang & Lester, 2004, p. 90). Once again, the need for additional research in
how the gender of the consumer affects attitudes and consequently online shopping
intention was restated.
Dijst, Farag, and Schwanen (2005) enlarged the scope of the Extended
Model of Goal-directed Behavior (EMGM) in attempting to explain the
relationship of attitudes toward online shopping behaviors. The EMGM
model was expanded since, "since the original model pays little attention
to the operationalization of behavior, external variables were added to take
account of their importance". (Dijst, Farag & Schwanen, 2005, p. 5)
Prior researchers have criticized studies that were based upon the belief that
consumers' attitudes affect their revealed behaviors. Farag and Schwanen (2005)
suggested the possibility that consumers' choices are in part contingent upon factors that

26
frequently are not taken into consideration. Consequently, consumer attitudes could be
poor predictors of online shopping behaviors. To address the limitations of consumer
attitudes as predictors of behavior, a number of theories that include additional behavioral
factors have been developed. These behavioral based theories include the TPB (Ajzen,
1991) and the Extended Model of Goal-directed behavior (EMGB) (Perugini & Conner,
cited by Dijst et al., 2005).
The researchers defined two questions to guide this project. First, "to what extent
can the EMGB explain shopping behavior?" (Dijst et al., 2005, p. 8). The second
question was, "which determinants of behavioral desire in the EMGB are affected by
external variables? (Dijst et al., 2005, p. 8). Results from the study indicated, "no
significant effects of attitude on behavioral desire, while other psychological constructs
had statistically significant impacts" (Dijst et al., 2005, p. 11). Past behavior and
perceived behavioral control had a statistically significant on volition to shop online. The
researchers noted, "Allowing behavioral desire mediate the of attitude on volition and
behavior in studies of individual choice behavior therefore seems worthwhile" (Dijst et
al., 2005, p. 12).
The researchers only used the results from one product type (media) for this
project. Therefore, the ability to generalize the conclusions to other consumer products is
questionable. Furthermore, while demographic data was collected with the survey
instrument, how the gender or other demographic variables of the respondent influenced
online behavior is unknown.
Researchers conducting a study with New Zealand online shoppers identified four
factors that influenced consumers' perceptions of online shopping (Shergill & Chen,

27

2005). The four factors that were identified included web site design, web site
reliability/fulfillment, web site customer service, and web site security/privacy. In
addition, "four types of online New Zealand buyers; i.e., trail, occasional, frequent, and
regular online buyers; perceived the four web site factors differently" (Shergill & Chen,
2005, p. 79). The authors defined trial buyers as individuals who purchased online once
per year, occasional buyers purchased items online 2-4 times yearly, frequent buyers
purchase items online 5-10 times yearly, and regular online buyers make more than 10
purchases online per year (Shergill & Chen, 2005).
The stated purpose of the study was to identify key factors that influence New
Zealand consumers' online shopping behavior. Additionally, the researchers hoped to
determine if consumers in each of the defined online buyer categories perceived web site
factors and or web site elements differently, and finally how these perceptions affected
online buying behavior (Shergill & Chen, 2005). Seventeen items were utilized to
measure the four factors i.e. web site design, web site reliability/fulfillment, web site
customer service, and web site security/privacy.
Findings from the Shergill and Chen (2005) study revealed, "that each of the four
types of New Zealand buyers had different perceptions of each of the four factors" (p.
91). The researchers further stated, "regular online buyers were much more satisfied with
web site variables and web site factors than other online buyers" (Shergill & Chen, 2005,
p. 91). The finding of primary importance from this study is the fact that New Zealand
consumers were least satisfied with the level of web site security/privacy that was
provided (Shergill & Chen, 2005).

28

There were several apparent shortcomings with this study including a very low
sample size of 149 and only 102 usable surveys (Shergill & Chen, 2005). Furthermore,
the study was conducted in New Zealand and whether the findings will hold in other
geographical areas is in question. While demographic data gathered included the gender
and age of the respondents, this demographic information was not correlated with the
four factors. The potential influence of the gender of the respondent on the study
variables remains unknown.
Yang et al. (2007) investigated attitudes toward buying online as predictors of
online shopping. The unique element to this study was that the researchers compared
British online shoppers with American online shoppers. The researchers stated the
rationale for this study as, "the present study was designed to focus on the role of
attitudes toward online shopping, both positive and negative attitudes, in influencing
Internet purchases" (Yang et al., 2007, p. 198). Additionally, "while positive attitudes
capture the benefits of shopping online, the negative attitudes reflect the barriers and risks
associated with shopping online" (Yang et al., 2007, p. 199). Two positive factors
included access to products free of time and space constraints and ability to make
effective transactions were identified. On the other hand, three negative factors were also
identified; lack of security and privacy, lack of personal assistance including brand-name
recognition, and inability to touch product and lack of after-sales assistance (Yang et al.,
2007).
For the Yang et al. (2007) study the respondents were instructed to rate 21 items
of the attitude scale on afive-pointscale from 'a lot' to 'not much at all' (Yang et al.,
2007). Results from tabulated responses indicated that Americans scored higher on the

29
five positive items. In contrast, British respondents scored higher for seven of the eight
negative attitude items (Yang et al., 2007). Further findings indicated, "the predictors of
online shopping differed greatly by the specific product purchases, as well as by country"
(Yang et al., 2007, p. 201). For example, for Americans, positive attitudes were
significant predictors for four products, while negative attitudes were significant
predictors for five products (Yang et al., 2007).
Although Yang et al. (2007) were able to validate some predictors of online
shopping, thefindingsonly apply to a select few products. Once again, the sample size
utilized was relatively small for the number of items measured and the ability to
generalize the findings to the overall population is questionable. Although demographic
data was captured, it was not correlated to the findings from the individual items.
Heijden, Verhagen, and Creemers (2003) proposed a conceptual mode that
explained that online purchase intentions were dependent upon consumer attitudes
towards online purchasing. The model included antecedents from trust perspectives and
antecedents from technology. These antecedents were all hypothesized to affect attitudes
towards online purchasing and consequently online purchase intentions. Results from this
study partly supported other TAM studies. Heijden et al. noted, "the lack of perceived
usefulness and perceived ease-of-use on attitudes towards online purchasing" (p. 45). In
explaining these differences, Heijden et al. stated, "We believe it is conceivable that trust,
ease of use, and usefulness are 'threshold' variables" (2003, p. 45). Furthermore, Heijden
et al. noted, "this means that once a certain evaluation level is reached, the variable no
longer contributes to a favorable attitude" (2003, p. 46).

30

Wang, Chen, Chang, and Yang (2007) examined the effects of online shopping
attitudes, subjective norms, and control beliefs on online shopping intentions. The
researchers incorporated the theory of planned behavior into the model in explaining
online shopping intentions (Wang et al., 2007). The researchers proposed three distinct
hypotheses to explain behavioral intentions towards online shopping. Wang et al. stated,
"the more positive consumers' attitudes toward online shopping, the stronger will be their
behavior intentions toward online shopping" (p. 298). The second hypothesis concerned
consumers' perceptions of norms that govern behavior. For the final hypothesis the
researchers stated, "the more positive consumers' perceived behavior control beliefs
toward online shopping, the stronger will be their behavioral intentions toward online
shopping" (Wang et al., 2007, p. 298). Results from the Wang et al. (2007) study
confirmed that perceived control beliefs influence consumers' intentions to shop online
and that perceived control has a greater influence that the influence of consumers'
attitudes (Wang et al., 2007). In contrast, subjective norms had no influence on
consumers' intentions to shop online (Wang et al., 2007).
Studies that Focus on Risk
Perceived risk, "is commonly defined as a two-dimensional construct comprising
the uncertainty involved in a purchase decision and the consequences of taking an
unfavorable action" (Lwin & Williams, 2006, p. 239). Some research involving online
shopping suggests that consumers' perceptions of risk play a major role in determining
patronage decisions (Lwin & Williams, 2006). Moreover concerning perceived risk,
Naiyi (2004) stated, "perceived risk is a fundamental concept in consumer behavior that

31
implies that consumers experience pre-purchase uncertainty as to the type and degree of
expected loss resulting from the purchase and use of a product" (p. 177).
Lwin and Williams (2006) explored the affect of warranties on the reduction of
risk for consumers shopping online. The researchers further examined variables that were
affected by warranties and whether warranties would increase perceptions of product
quality and consequently purchase intentions (Lwin & Williams, 2006). The influence of
brand name and retailer reputation as risk reducers was also examined.
Within the context of the Lwin and Williams (2006) study, the researchers tested
six hypotheses. The first hypothesis was concerning online retailers with strong
reputations and consumers' perceptions of risk when a warranty was provided. The
second hypothesis concerned online retailers with strong reputations and consumer
perceptions of product quality whenever a warranty was provided. Furthermore, the
researchers hypothesized that consumer perceptions of product quality would not vary if
a warranty were provided or not if the retailer had a poor reputation. The third hypothesis
concerned consumer purchase intentions. In short, the researchers hypothesized that when
consumers dealt with retailers of strong reputation and a warranty was present online
purchase intentions are higher. In contrast, if the retailer had a poor reputation, a warranty
would not affect online purchase intentions (Lwin & Williams, 2006).
The final three hypotheses dealt with brands, product quality, and purchase
intentions. In stating the fourth hypothesis, Lwin and Williams (2006) hypothesized that,
whenever online retailers market well-known brands consumers will have lower
perceptions of risk whenever a warranty is provided. On the other hand, whenever online
retailers market unknown brands consumers' perceptions of risk will not vary regardless

32

of the presence of a warranty (Lwin & Williams, 2006). The researchers stated for the
fifth hypothesis,
.. .when dealing with online retailers who market well-known brands, consumers'
perceptions of product quality will be higher when a warranty is present than
absent. However, when dealing with retailers who market unknown brands,
consumers' perceptions of product quality will not vary with the presence or
absence of a warranty. (Lwin & Williams, 2006, p. 245)
Lwin and Williams (2006) noted in the final hypothesis,
.. .when dealing with online retailers who market a well-known brand, consumers'
purchase intentions will be higher when a warranty is present than absent.
However, when dealing with retailers who market unknown brands, purchase
intentions will not vary with the presence or absence of a warranty, (p. 245)
Results from the Lwin and Williams (2006) study indicated support for
hypotheses one through three. Therefore, whenever the online retailer's reputation was
considered strong, consumers perceived a lower level of purchase risk with the presence
of a warranty (Lwin & Williams, 2006). In dissimilarity to this finding, if the retailer's
reputation was considered weak, the presence or absence of a warranty had no influence
on consumers' perceived level of purchase risk (Lwin & Williams, 2006). Regarding
results in support of hypotheses four thru six the researchers stated, "No interaction for
perceptions of brand name and warranty information was found on perceptions of risk,
product quality, and purchase intentions (hence no support for H4 to H6)" (Lwin &
Williams, 2006, p. 246.).

33

Findings from this study may be useful for online retailers in reducing consumers'
perceptions of purchase risk, increasing perceptions of product quality, and in relieving
perceptions of risk resulting in an increase in purchase intentions (Lwin & Williams,
2006). However, the study falls short in that the researchers did not link demographic
information to any of the findings. Whether the ages of the study participants, levels of
income, or gender would moderate any of the findings is in question.
The main objective of another study was, "to discover whether consumers'
perceived risk of Internet shopping and the Internet shopping experience were associated
with online purchasing behavior" (Doolin, Dillion, Thompson & Corner, 2005, p. 67).
The researchers identified several types of perceived risk encountered when shopping
online including product risk, security risk, and privacy risk (Doolin et al., 2005). Doolin
et al. stated, "product risk is the risk of making a poor or inappropriate purchasing
decision" (p. 68). Further, "one aspect of product risk is the risk of a poor economic
decision through an inability to compare prices, being unable to return a product, or not
receiving a product paid for" (Doolin et al., 2005, p. 68). Other dimensions of risk are
related to the Internet as a medium of exchange rather than the perceptions of risk
associated in purchasing a product (Doolin et al., 2005). These perceptions are related to
consumers' held beliefs concerning the trustworthiness of the Internet as a channel for
shopping (Doolin et al., 2005).
Doolin et al. (2005) noted, "that security was a major factor in discriminating
between high and low intentions to purchase online" (p. 69). Furthermore, "apart from
concerns about the security of Internet transactions, Internet trustworthiness seems also to
relate to consumers' concerns about privacy" (Doolin et al., 2005, p. 69). The researchers

34

utilized three constructs for this study perceived risk, perceived benefits, and loss of
social interaction (Doolin et al., 2005). The notable dimensions of this study are the
inclusion of demographic variables in the analysis of the two dependent variables
frequency of online purchasing and amount of online purchasing. Results indicated that
demographic variables only contributed 14% of the total variation in online purchasing
behavior and 16% of the variation in frequency of Internet purchases (Doolin, et al.,
2005). Regarding the demographic variables included in this study only Internet
experience and gender had significant affects on the dependent variables (Doolin et al.,
2005).
This study was conducted in New Zealand and whether the findings will hold in
the United States is unknown. Moreover, the demographic variables were correlated with
frequency of Internet shopping and not with shopping intention (dependent variable used
in the proposed study). In addition, the demographic variables were not correlated with
attitude toward buying on the Internet and gender was not correlated with purchase
intention. These relationships are examined in the purposed study.
Studies Examining the Role ofe-Service Quality
Lee and Lin (2005) noted that e-service quality could be defined according to
consumer evaluations and judgments regarding the excellence and quality of e-service
delivery in the e-marketplace. The researchers stated two purposes for this study. The
first stated purpose was to, "derive the instrument dimensions of e-service quality based
on the SERVQUAL model and modify them following the reference to the related
literature on online shopping context" (Lee & Lin, 2005, p. 163). The second purpose

35
was, "to determine how the e-service quality dimensions affect overall service quality,
customer satisfaction and purchase intentions" (Lee & Lin, 2005, p. 162).
Lee and Lin (2005) examined five dimensions of e-service quality that included
web site design, reliability, responsiveness, trust, and personalization The five
dimensions of e-service quality were defined as, "web site design describes the appeal
that user interface design presents to customers" (Lee & Lin, 2005, p. 164). "Reliability
represents the ability of the web site to fulfill orders correctly, deliver promptly, and keep
personal information secure" (Lee & Lin, 2005, p. 164). In defining responsiveness the
Lee and Lin stated, "responsiveness describes how often an online store voluntarily
provides services that are important to its customers" (p. 165).
Trust was defined as "customer willingness to accept vulnerability in an online
transaction based on their positive expectations regarding future online store behaviors"
(Lee & Lin, 2005, p. 165). The final e-service quality dimension was personalization. The
researchers stated that, "personalization involves the individualized attention, personal
thank you notes from online stores, and the availability of a message area for customer
questions or comments" (Lee & Lin, 2005, p. 165).
Lee and Lin (2005) hypothesized that each e-service quality dimension has a
positive effect on overall service quality and customer satisfaction. Furthermore, the
researchers hypothesized that overall e-service quality has a positive effect on customer
satisfaction and customer purchase intentions. Furthermore, "customer satisfaction with
an online store positively influences purchase intentions" (Lee & Lin, 2005, p. 166).

Unlike the fore mentioned studies in this literature review, Lee and Lin did not utilize
online surveys but instead distributed 305 questionnaires. The survey sample consisted of

36
senior year undergraduate students who were enrolled in an e-commerce course. The
researchers noted a 100% response rate, which is not consistent with other research
projects regarding online shopping.
Results from the returned questionnaires indicated that trust had the strongest
affect not only on overall e-service quality, but also on customer satisfaction (Lee & Lin,
2005). The e-service quality dimension reliability was validated as a significant predictor
of overall service quality, customer satisfaction, and subsequent purchase intentions (Lee
& Lin, 2005). Contrary to the authors' expectations, web site design did not have a
significant effect on overall e-service quality, or on customer satisfaction (Lee & Lin,
2005). Further results indicated that personalization was not a significant predictor of
customer satisfaction, or overall e-service quality.
Collier and Bienstock (2006) posited that consumers judge the quality of an etailer according to five attributes. The five attributes that Collier and Bienstock included
ease of use, privacy, simple design, consistency and flexibility, and good information.
The researchers defined these attributes as, "in an online setting customers associate ease
of use with freedom from significant mental effort" (Collier & Bienstock, 2006, p. 36).
Regarding the privacy attribute Collier and Bienstock stated, "customers want to know
that their Web site interactions are private and information is safe" (p. 36).
Simple design according to Collier and Bienstock (2006) is, "visually pleasing but
not distracting" (p. 36). Moreover, the design does not include, "excessive use of
animation, flashing colors and scrolling words that can take away from the shopping
experience" (Collier & Bienstock, 2006, p. 36).sCollier and Bienstock further noted,
"reliability or consistency of a web site's function is necessary in order to build and

37

maintain an online customer base" (p. 36). Collier and Bienstock further commented, "a
web site's applications and links must work properly" (2006, p. 36).
Regarding the consistency aspect the researchers stated, "an online retailer needs
to be mindful of providing a consistent service that is flexible enough to appeal to what
may be widely differing types of customers in their target markets" (Collier & Bienstock,
2006, p. 37). The final attribute that customers judge an e-tailer on is good information.
Good information is information that is up to date and accurate (Collier & Bienstock,
2006). Providing good information is crucial for a web site, "no employees are readily
available to answer questions on a web site" (Collier & Bienstock, 2006, p. 37).
Results from the Collier and Bienstock (2006) study were based upon returned
surveys from 338 respondents. Survey participants were asked to evaluate their last
online retail shopping experience (Collier & Bienstock, 2006). The unique element to this
study was that it was conducted in one United States major city. Findings from the survey
indicated that over 50% of the respondents had experienced a problem or failure with
their last online transaction (Collier & Bienstock, 2006). Collier and Bienstock stated,
"though the interactivity of the web site is very important, the most important aspect of
the quality of the online retail experience is the delivery of the purchase" (p. 37). Another
major finding from the Collier and Bienstock study was in the area of the online retailer's
ability to address problems whenever they occur. The researchers noted, "being prepared
when a failure occurs is essential to building and maintaining customer relationships"
(Collier & Bienstock, 2006, p. 39). The only demographic information included in the
Collier and Bienstock (2006) study was the percentage of the respondents that were
female 52% and the average age of the respondents 25. While the researchers went to

detail in providing definitions for the five attributes, research results were concentrated
on outcomes from the shopping experience.
No results were provided for the measures of interactivity of the customer with
the e-tailer's web site (Collier & Bienstock, 2006). In concluding the research findings
Collier and Bienstock stated, "while web site interactivity or service recovery efforts
were significant predictors of customers' satisfaction, the outcome (delivery) of the
online transaction had the strongest impact on satisfaction and intentions to purchase
from an online retailer in the future" (p. 37).
The ability to generalize the findings from this study to the overall United States
population is questionable. This is because the demographics of each city are not known
since the name of each city was not provided and whether the city is a good
representation of the overall population of the United States is not known. Additionally,
the researchers stated that the survey was, "designed to assess customers' evaluations of
their last online retail experiences" (Collier & Bienstock, 2006, p. 37). The accuracy of
customers' memories concerning their last online retail experiences is questionable.
Furthermore, the authors did not examine how consumer demographic
information correlated with any of the measurement items. The reader only knows that
52% of the survey respondents were female. How consumer demographic data affected
the survey responses is unknown. For example, if gender had any effect on responses to
specific survey items is unknown. A better approach is to nonparametric tests with the
demographic data to examine its affect on individual item responses as in this study.
Another study on service quality conducted by Yang and Fang (2004) examined
the relationship of online service quality dimensions with customer satisfaction. Yang

39
and Fang examined e-service quality for online brokerage services. Eight factors were
chosen for this study. The eight factors were responsiveness, service reliability, ease of
use, competence, access, system reliability, timeliness, and security. These eight factors
were measured with specific items for each factor. For example, responsiveness was
measured with the following five items: prompt response to phone calls, prompt services,
prompt response to e-mails, prompt order execution, and prompt order confirmation
(Yang & Fang, 2004). These items were measured on a two- part scale either satisfied or
dissatisfied. This scale is highly unusual for studies that were included in this literature
review. In fact this is the only study reviewed that utilized a two-part scale. Moreover, in
sharp contrast to the other studies reviewed, the researchers did not utilize surveys, but
rather used third-party data (Yang & Fang, 2004). Regarding this approach to research,
Yang and Fang remarked, "an analysis of consumer compliments and complaints are
particularly useful in exploring the rich aspects of customer perceived service quality" (p.
310). The innovative approach used by Yang and Fang was the "netnography" defined as,
"a qualitative research technique which employs an ethnographic research method to
study online customers" (2004, p. 310). Further, Yang and Fang stated, "this
methodology uses the information publicly available in online forums to identify and
understand the needs and decision influences of relevant online consumer groups" (2004,
p. 310).
For data used in this study, Yang and Fang (2004) accessed two-customer review
web sites, Gomez and Epinions. Data was collected for a two-year period and 740
consumer comments were selected for analysis. E-service quality dimensions that lead to
consumer satisfaction mention quotients included responsiveness 35.8%, competence

40

16%, ease of use 10.2%, service reliability 9.1%, courtesy 7.5%, service portfolio 6.4%
and continuous improvement 6.4% (Yang & Fang, 2004). Yang and Fang (2004) noted,
"consistent with TAM, ease of use is a primary factor leading to customers' adoption of
and satisfaction with online services" (p. 317). In comparison to the consumer
satisfaction quotients, results indicated that the most commonly mentioned service
quality factors that lead to dissatisfaction included responsiveness 31%, service reliability
12%, ease of use 11.3%, competence 10.3%, access 9.6%, system reliability 7.8%,
timeliness 6.6%, and security/privacy 3.8%.
There are several readily apparent weaknesses to this study. Firstly, the
researchers utilized secondary data. The reliability of the secondary source data was
unknown. Secondly, the research study was conducted with only one service (online
brokerage services). If these results would hold for other services and or products offered
online is questionable. Thirdly, the ability to generalize these findings to the overall
population is questionable. Another marked difference in the Yang and Fang (2004)
study from the others analyzed in this review is the noted lack of demographic data. The
affect of demographic variables such as gender and age of respondents remains unknown.
Furthermore, the affect if any of consumers' attitudes toward web shopping was not
explored.
Security for Web Shopping
In defining information security, Iyengar (2004) stated, "information security is
about protecting three things: the confidentiality, the integrity, and the availability of
data" (p. 90). Iyengar further noted, "securing Internet Commerce is probably the biggest
challenge that information professionals have yet faced" (Iyengar, 2004, p. 90).

41
Regarding security provided for online shopping a survey of related literature revealed
two opinions that are in total disagreement on the issue. The first article entitled "The
Internet Is the Safest Channel" by Ivan Schneider (2005) supports this author's
contention that the Internet is indeed a safe channel for shopping. Schneider quoting
Parry stated, "although Internet fraud exists, it has a limited financial impact, with
adequate methods to contain the damage from identity thieves" (p. 12).
The author further noted Parry's assertion that, "a transaction-centered approach
to fraud prevention may be enough to protect both customers and shareholders" (Parry,
cited by Schneider, 2005, p. 12). In support of the contention that the Internet channel is
safe, Schneider further quoted Parry's comments, "Internet transactions have a built in
delay relative to other channels such as ATM, branch, or point of sale. Moreover,
Schneider quoting Parry continued, "the delay provides banks with an opportunity to
detect behaviors indicative of fraud" (2005, p. 12). According to Schneider, Parry
advocated a transaction-centered approach that focuses on actual consumer transactions
including routine change of address transactions (p. 12).
In contrast, Parent (2007) emphasized the fact that online shopping is still not
safe. To support this point of view he stated supporting facts from several sources. For
instance, according to the Privacy Rights Clearinghouse, during 2006 some 100 million
records that contained sensitive personal information involving 474 security breaches
were reported. Furthermore, during the same year, The Association of Certified Fraud
Examiners received 1,038 reported cases of internal theft, which included theft of
customer data. These reported security breaches and internal thefts amounted to an
average loss of $150,000 per company.

42

Parent (2007) surveyed the 50 top rated Internet retailers and discovered that, a
whopping 56%, or 28 out of 50 had no explicit policy statement on their web sites
regarding the internal and physical security of consumers' private information" (p. 10).
According to Parent (2007), information security contains six elements. Parent defined
confidentiality as, "information can only be read (but not modified or otherwise acted on)
by those with permission to do so" (2007, p. 11). Integrity is defined as, "information can
only be changed by those with permission to do so" (Parent, 2007, p. 11).
Parent (2007) defined availability as, "records can only be used (to compile lists
or other actions) by those with permission to do so" (p. 11). Access control was defined
by Parent as, "records can only be accessed by those with permission to do so - like
having a key to the locked filing cabinet containing all customer records" (p. 11). Further,
Parent defined authentication as, "those wanting to access records must prove, beyond
doubt, they are who they say they are (with passwords and the like)" (p. 12). The final
element the author defined was non-repudiation. Parent defined non-repudiation as,
"when records are accessed, the person who has accessed them cannot deny doing so (a
trail exists)" (2007, p. 12).
While both of these viewpoints have merit, Smith (2004) noted, "cybercrime is a
major problem in establishing and maintaining an online presence" (p. 225). In reference
to a recent study on cybercrime, Smith (2004) further stated, "although the study was
based on a six month period, it found a number of important trends that has significance
for e-commerce in general" (p. 225). Further, Smith (2004) noted, "an average of 32
attacks on each company per week was found, which represented a 64% annual growth
rate for such cyberattacks" (p. 225).

43

Summary
Contained within chapter 1 was a statement of the problem, rationale for
conducting the research, the research questions, and the related hypotheses. The reader
was also provided concise definitions of key terms that are essential to this research
study. The brief review of literature section was then followed with an overview that
highlighted the chosen methodology and its limitations. Chapter 1 was concluded with a
restatement of the problem, how this study expands the existing knowledge base and
finally the rationale for this research project.
Within chapter 2, the researcher extended the brief review of literature section
from chapter 1. This chapter included a comprehensive overview of literature pertaining
to this research study. For the convenience of the reader and to aid readability, this
chapter was organized in subtopics. Contained within each subtopic section, was an
overview of studies that examined the subject of online shopping acceptance/purchase
intention with emphasis on different antecedent constructs. This chapter then was
concluded with a restatement of the problem and the rationale for the need for this
research project. Few of the studies that were reviewed in the Literature Review
examined how gender of the consumer influenced attitudes toward the Internet,
perceptions of web site quality, or purchase intentions. While demographics were
included with a majority of the studies, demographic variables were not examined with
independent variables. By conducting an analysis of demographic variables with the
dependent variables, the influence that these variables have if any on the dependent
variables is determined.

44

The first section of chapter 3 includes a concise overview of the methodology
used for the research study. This section is followed with a restatement of the problem
and the purpose for undertaking the study is reviewed. The next section includes a
restatement of the research questions and the hypotheses resulting from the research
questions. The chapter is continued with a detailed description of the research design that
was chosen for this study. This section also includes operational definitions of the
variables both independent and dependent.
The section defined as description of materials and instruments includes the
proposed research instrument that will be utilized in collecting data for analyze from
respondents. The remaining sections of chapter 3 include a section on the selection of
participants, procedures that are followed for the study, a discussion on how the data will
be processed, and limitations to the study. The final section of chapter 3 includes a
discussion on ethical assurances for this study.

45

CHAPTER 3: METHODOLOGY
Overview
The purpose of chapter 3 is to provide a concise overview of the methodology that
was used for this study. The writer begins with a restatement of the problem addressed
within this research study. Following the restatement of the problem, the research
questions are defined and the hypotheses that were developed as a result of the research
questions are stated. The next section of chapter 3 includes a complete concise
description of the research design. The chapter is continued with a section that includes
operational definitions of the chosen variables. Immediately following the operational
definitions of the variables, a complete description of all materials and instruments that
were developed for the study is presented. In the next section, the selection of study
participants is discussed including a rationale for the section of specific populations. The
section on procedures includes a complete description on all procedures that were utilized
for this research study. Included within this section is a flow chart depicting how the
study was administered from start to finish.
Contained within the next section is a discussion on how data from response to
the survey instrument was analyzed and processed. The final sections of chapter 3 include
a section on methodological assumptions and limitations, and a section that includes a
discussion on the ethical assurances outlined for this study. Finally, chapter 3 is
concluded with a complete listing of all references that are cited.
Restatement of the Problem and Purpose
Holland (2006) determined that online shoppers are still abandoning their
shopping carts at a rate of nearly 60%. Considering that, an estimated $102 billion was

46
spent online during 2006, shopping cart abandonment cost online vendors $61 billion in
lost sales revenues In consideration of this problem, the focus of this research study was
to assess the factors that may cause consumers to accept or reject online shopping. A
better understanding of the factors that online shopping acceptance or rejection may
provide online vendors with insights into the possible causes of consumers' abandonment
or completion of online transactions. With more complete information on consumer
perceptions of constructs such as trust in the web site, perceived site quality and attitude,
web site designers can potentially design web sites that are more conducive to transaction
completion.
Statement of Research Questions
Ql: To what extent, if any, is perceived site quality related to trust in the web
site?
Q2: To what extent, if any, is a consumer's attitude toward buying on the Internet
related to online purchase intentions?
Q3: To what extent, if any, is gender related to perceived web site quality, trust,
attitude toward buying on the Internet, and consequently online purchase
intentions?
By answering these research questions with data drawn from responses to the
survey instrument, the possibility that web site quality, attitude, and gender influences
consumers' acceptance or rejection of online shopping was accessed. This study adds to
the existing body of knowledge regarding online shopping by examining gender first as a
potential influencing factor to other antecedents of online shopping acceptance and then
as an moderating factor to the dependent variable online purchase intention.

47

Hypotheses
Hlo". Perceived site quality is not correlated to the formation of trust in
the web site.
Hl a : Perceived site quality is correlated to the formation of trust in the web site.
H20: A consumer's attitude toward buying on the Internet is not correlated to
online purchase intentions.
H2a: A consumer's attitude toward buying on the Internet is correlated to online
purchase intentions.
H3o: Gender of the consumer is not associated to the formation of trust in the web
site, perceived site quality, a consumer's attitude toward buying on the Internet,
and online purchase intentions.
H3a: Gender of the consumer is associated to the formation of trust in the web
site, perceived site quality, a consumer's attitude toward buying on the Internet,
and online purchase intentions.
Description of Research Design
Correlational designs are utilized to measure the relationships between variables.
It should be noted that variables may be positively correlated, having a direct relationship
to one another, negatively correlated, that is as one variable increases in value the other
variable decreases in value, or have no relationship to one another equating to a 0
correlation between the variables (Wagner, 2007). Descriptive correlational designs are
strong when the researcher desires to describe an existing condition and to verify a
relationship among variables (Staff, 2003). The major weakness to using this type of

48
design is correlation does not equate to causation. Whenever the researcher is attempting
to prove causation, descriptive correlational designs are not appropriate (Staff, 2003).
A quantitative correlational design was appropriate for the research topic:
Assessing Consumer Acceptance of Online Shopping: Examining Factors Affecting
Purchase Intentions. The purpose of this research project was not to prove what factors
cause consumers to purchase online, but rather to provide an overview of prior research,
determine the relationship if any between the chosen independent variables and the
dependent variable online purchase intentions, and to conduct a correlational analysis of
various antecedents to the dependent variable. Furthermore, the possibility that the
gender of the consumer influences perceived site quality, attitudes toward buying on the
Internet, and purchase intentions was examined. The quantitative element to the design
refers to the collection of data from the survey instrument and analysis of the data to
validate or disprove the hypothesized relationships, and to examine the correlations if any
among variables, inter-item correlations, and correlations among constructs.
The research design was implemented according to the flow chart provided (see
Figure 1). The design for this study was developed as a result of a careful review of
literature in the field of online shopping. Following the analysis of current literature, a
verified problem was identified. Prior researchers did not consider the possibility that
perceived site quality is correlated with the formation of trust in the web site, the
possibility that a consumer's attitude toward buying on the Internet is correlated with
online purchase intentions, or the possibility that the gender of the consumer is associated
with antecedents to online shopping acceptance and ultimately online purchase

49
intentions. Furthermore, prior research did not examine gender as a possible moderating
variable to the antecedents to online purchase intentions.
The research questions were developed as a result of the identified problem and to
address this specific need for additional research. The next phase of the research design
involved selecting an appropriate design that would meet the overall purpose of the study.
In short, the purpose of this study is to assess how web site quality, attitude, and gender
influence consumers' acceptance or rejection of online shopping.
All research designs were considered for this study including quantitative,
qualitative, mixed, and descriptive. It was determined that a quantitative correlational
design would best match the requirements for this study. Rational for this selection was
that the purpose of the study is not to prove causation, but rather to examine the
relationships among variables and to validate or disprove the hypotheses. An exhaustive
evaluation of survey instruments from prior studies revealed that proven survey items
existed that was statistically verified to measure the intended constructs. Therefore, items
from prior studies were used to measure the constructs. Permission to use survey
questions from prior studies was obtained from various prior study authors (see Appendix
D) for the survey instrument. Where necessary, wording was changed to adapt the
question to this study.
The survey population chosen for this study consisted of students who are actively
enrolled in a marketing course or other business courses (due to availability at the time of
the study) at participating colleges of the South Carolina Technical College System and
other selected universities. These groups of participants were chosen since a nonmonetary incentive could be provided for their participation (extra-credit for the course).

50

Furthermore, faculty and staff from all participating Technical Colleges and universities
were invited to participate. Four of the 16 Technical Colleges chose to participate in the
study and two universities. By the inclusion of these groups, the study included a more
diversified demographic makeup.
Following approvals from both Northcentral University's Institutional Review
Board (IRB) and Technical College of the Lowcountry's (TCL) IRB, the survey
instrument was administered to a pilot group from TCL. In addition, IRB approval from
the other technical colleges and the remaining university that chose to participate was
granted. The pilot release included six to eight students and three to four faculty
members. Upon analysis of the results from the pilot study, the survey instrument was
adopted with only minor formatting modifications and placed online. The survey
instrument was then released to the other participating Technical Colleges and the two
participating universities. Data from the responses to the survey questions was compiled
from the online survey for further analysis and to answer the study questions. Findings
and conclusions from the data analysis are reported in chapters 4 and 5 of this
Dissertation.

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Operational Definition of Variables
The purpose of this section of chapter 3 is to provide operational definitions of the
variables that were used for this study, provide a conceptual model, describe the
instrumentation utilized to obtain the measurements of the variables, include a summary
of the reliability and validity analyses for existing instrumentation, and finally list a stepby-step description of how the instrumentation is implemented to obtain the measures of
the variables. Constructs were operationalized with proven items from prior studies). The
measures (survey questions) for the survey instrument are all based on the 7-point Likerttype scale: l=strongly disagree to 7=strongly agree.
Attitude toward buying on the Internet. Attitude toward the Internet is an
independent variable (Xi). This variable is also a dependent variable when associated
with hypothesis H3. The variables used to operationalize attitude toward buying on the
Internet include personal awareness of security and personal innovativeness. The survey
instrument includes six indicators that measure attitude, four items for measuring
personal awareness of security survey questions Q18, Q19, Q20, and Q21, and two items
for measuring personal innovativeness survey questions Q22 and Q23. The survey
questions that are included in the online instrument for these variables are adapted from
the Chi et al. (2005) study.
Gender. The independent variable gender (X2) refers to the sex of the study
participant and is operationalized with categorical data, having.the values m=male and
f=female. The survey instrument included one item to measure gender. For SPSS 15.0
analysis, the coding was changed to l=male and 2=female. Nominal data was collected
for gender with survey Q2.

53
Online purchase intention. The dependent variable (Y) is operationally defined as,
"measures of the strength of a consumer's intentions to perform a specified purchasing
behavior via the internet" (Chi et al., 2005, p. 419). Online purchase intention is
operationalized with several independent variables including: risk perception survey
items Ql 1, Q12, Q13 familiarity Q5. Familiarity with the web site was measured with
proven items adapted from the Slyke, Belanger, and Comunale (2002) study. Trust in the
web site Q6, Q7, and personal awareness of security Q20 and Q21. The dependent
variable online purchase intention is measured with Q29. Online purchase intentions are
also measured with proven items adapted from the Slyke et al. (2002) study. Ordinal data
was collected with these survey questions.
Perceived site quality. This independent variable (X3) and is also a dependent
variable associated with hypothesis H3 is defined as a consumer's perceptions of the web
site's overall appeal as it is measured by the web site's attractiveness survey items Q16
and Q17, brand equity survey items Q14 and Q15, opportunism survey items Ql 1, Q12,
Q13 and communication survey items Q8, Q9, and Q10. (Wakefield et al., 2004). How
consumers perceive a web site affects their likelihood of continuing the shopping
experience and making a purchase decision. Frequently, when consumers have a bad
initial perception of a web site's appeal they abandon the transaction (So et al., 2005).
Perceived site quality and its antecedents are measured with 10 items adapted from the
Wakefield et al. study. All chosen items were confirmed to load on the constructs that
they were intended to measure and scale reliability was confirmed using Cronbach's
alpha with all items above the accepted minimum of 0.70. Ordinal data was collected
with these survey questions.

54
Trust in the web site. The variable (X4) is a dependent variable when associated
with hypothesis HI and an independent variable when associated with hypothesis H3.
Trust in the web site is defined as, "expectation about the behavior of others in
transactions" (Kraeuter, 2002, p. 217.). The author noted several factors that influence
trust in the web site, "Individual characteristics of the trustor, basic willingness to trust,
his/her individual experience and the experiences of others with the trustworthiness of
another party or the safety of the system" (Kraeuter, 2002, p. 217). Trust in the web site
is operationalized with familiarity survey Q5 perceived usefulness survey items Q24,
Q25, Q26, perceived ease of use survey items Q27 and Q28. Perceived usefulness is
measured with three statistically proven measures adapted from the Koufaris and
Hampton-Sosa (2002a) study. The validity of the items adapted from this study were
tested using two-stage factor analysis using principal component analysis (PCA) and then
using structural equation modeling (SEM) in testing for convergent and discriminant
validity (Koufaris et al., 2002a). Web seal value was measured with Q18 and Q19. The
final item trust was measured with survey Q6 and Q7. Ordinal data was collected with
these survey questions. The items used to measure trust and the confounding variables
associated with trust have a Cronbach's alpha for reliability of 0.87. Acceptable item
reliability is ensured with Cronbach's alpha of 0.70 (Koufaris & Hampton-Sosa, 2002, p.
10).

55

Ttiotivp 7 Pnnrvrvhial M n H p l

Description of Materials and Instruments
The first item included in this study was the cover letter for survey participants
(see Appendix A). The cover letter included details on the study, the purpose for the
study and the declaration that participation is voluntary. The second item is the informed
consent document (see Appendix B). This document provides details on who may
participate in the study and information on the confidentiality of information and
responses provided by study participants. The third item is directions for participation in
the study (see Appendix C). This document includes complete directions that cover
exactly how respondents can successfully participate in this research study.
The next item is the actual survey instrument (see Appendix D). Included with the
survey instrument is an introduction that provides details on who is invited to participate
in the study, the number of questions involved, the expected time required to complete
the survey, a reminder that responses and information provided is held in a confidential

56
manner, and that a copy of the results at the conclusion of the study are available upon
request. The survey instrument was compiled from items (survey questions) adapted from
prior studies (Chi et al., 2005; Koufaris & Hampton-Sosa, 2002; Wakefield et al., 2004).
Permission to utilize survey questions from prior studies was obtained (see Appendixes
E, F, and G). For example, perceived site quality and its antecedent's communication,
opportunism, brand equity, and attractiveness are measured with 11-scaled items adapted
from the Wakefield et al. study. All chosen items for the survey instrument were
confirmed to load on the constructs that they were intended to measure and reliability was
confirmed using Cronbach's alpha with all items above the accepted minimum of 0.70.
The next appendixes contain documents used to request permission to use study questions
from prior studies (see Appendixes E, F, G). The next document is a memo requesting
participation from the Deans of Business Technologies at the 15 Technical Colleges in
South Carolina (see Appendix H).
Selection of Participants or Subjects
Participants for this study were chosen from four participating Technical Colleges
located in South Carolina and two universities headquartered in other regions of the
United States. One of the universities was in Pennsylvania and the other was an online
university headquartered in Arizona. The subject populations sampled from the Technical
Colleges and the Pennsylvania University were students who were actively enrolled in a
business-related course. In addition, faculty and staff were also encouraged to participate
in the study. All students were invited to participate in the study from the online
institution.

57
Rationale for this selection was that students could be offered a non-monetary
incentive for study participation and inclusion of faculty and staff provided more
diversified age groups. Inclusion of the online university also helped to potentially
mediate the bias introduced from convenience sampling. According to Yun and Good
(2007), "college students are important target consumers, particularly for online retailers,
since college students are generally comfortable with making purchases online and they
represent a lifetime of potential buying power and brand loyalties"(p. 1). Further
evidence that convenience sampling is appropriate for this study is provided by MartinezLopez, Luna, and Jose" Martinez (2005) quoting Peterson (2001), Lin and Lu (2000)
noted, "although the composition of the sample limits the generalization of the results,
due to the fact that university internet users only represent one part of the population of
uses, nevertheless the results from these samples can be significant and approximate" (p.
314).
Deans of Business Technology Divisions for 15 of the Technical Colleges in
South Carolina were contacted and asked for cooperation in identifying individual
Marketing and other Business instructors who would be willing to have their students
participate in this research project. Further, faculty and staff were requested to participate
at each of the 16 Technical Colleges in South Carolina. The Dean of the Business
department was contacted at the Pennsylvania university, and an online request for
participation was placed on the web site of the online school. All invited students had an
equal opportunity of participating.

58

Procedures
Following Internal Review Board (IRB) approvals, a pilot test of the survey
instrument was conducted with 16 students, faculty, and staff from the TCL to verify the
measurement items and readability of the consent form, study cover letter, and study
instructions. Due to comments received from the pilot test respondents, recommended
formatting modifications to aid readability were made to the survey instrument and it was
placed online and subsequently released to the participating colleges of the South
Carolina Technical College System and the two universities. Data from responses to the
questions on the survey instrument was compiled by www.surveymonkey.com
This study was conducted during a 4-month period beginning in September 2008
through the end of December 2008. This time period corresponded to the fall semesters at
these institutions. Estimated total potential student participants, faculty and staff, was
1,650. Of this potential number 340 surveys were received for a non-adjusted response
rate of 20.6%. Seventeen of the surveys were discarded due to missing answers resulting
in 323 usable surveys for an adjusted response rate of 19.6%. Survey data was processed
with SPSS 15.0 and AMOS 7.0.
Discussion of Data Processing
Data for this research project was collected with the use of an online survey
instrument). Research participants answered three demographic questions that were
specially coded for SPSS 15.0 software package analysis. The question used to measure
the independent variable gender is coded m=male and f=female. This coding was
changed for the actual survey release to l=male and 2=female. Statistics for this study
included bivariate correlation analysis, including Pearson Correlations, t-tests, item

59
means, standard deviations, and Cronbach's alpha tests. The statistical analysis for this
study was compiled using SPSS 15.0 with AMOS 7.0 software package. Hypotheses Hlo
and H2o were each tested with t-tests and Pearson Correlation coefficients statistics to
accept or reject the null hypotheses. Significance levels of 0.05 and 0.01 were utilized.
Hypothesis H3o incorporated a categorical variable gender, which was tested with
continuous variables. Gender is a nominal variable and according to Sims (2004) the
Pearson Chi-Square test is the appropriate statistical technique for testing nominal
variables (Sims, 2004, p. 7). The 0.05 level of significance was used to test hypothesis
H3o. According to Sims, to be a statistically significant Chi-Square test, the p value must
be below 0.05 for the 95% confidence level.
Methodological Assumptions, Limitations, and Delimitations
It was assumed that an adequate number of students, faculty, and staff from the 16
Colleges in the South Carolina Technical System would participate in this research study.
While all of the Technical Colleges were invited to participate only four chose to do so.
However, two universities were contacted one in Pennsylvania and one online university
headquartered in Arizona. It was estimated that nearly 1,700 respondents would be
available. It was also assumed that the instructors at the 16 Technical Colleges would
fully cooperate, provide extra credit as an incentive for students' participation, and ensure
that a sufficient number of students participate. Furthermore, it was assumed that if
participants were not familiar with either www.overstock.com or www.amazon.com, that
they would visit the site, conduct the search, and complete the mock purchase. This
assumption was based on the knowledge that students, faculty and staff were provided
complete directions on how to participate in the study.

60
This assumption was based on the fact that complete detailed study directions
were provided to all participants. Moreover, it was assumed that study participants would
answer the survey questions truthfully and submit the responses as requested. In addition,
it was also assumed that the company hosting the online survey would compile the
answers to the survey and that the survey would be available when requested by the
respondents. The survey company www.surveymonkey.com compiled the answers from
the online survey instrument.
Findings from this study may not be generalizable to the overall population. This
is due to the fact that study participants are from four participating colleges in the South
Carolina Technical College System and two universities. In addition, random sampling
was not possible for this study and convenience-sampling limitations apply. Furthermore,
it was impossible to quantify the population, therefore, only a best estimate of the total
number of potential participants was used. In addition, limitations from convenience
sampling also apply. The purpose of this study was to assess consumer perceptions and to
accept or reject the three stated hypotheses. Furthermore, the basic premise of this study
was not to prove causation, but to verify the extent of correlation among the independent
variables and the dependent variable online purchase intention. Additionally, the affect if
any of gender on the defined antecedents to online purchase intention and to the
dependent variable online purchase intention was assessed.
The literature review indicated that consumer acceptance of online shopping is a
very complex topic. It was not the intention of this researcher to assess all possible
factors involved in consumer acceptance of online shopping. The analysis of several prior

61
studies revealed a need for additional research involving the affect of gender, perceptions
of web site quality and attitudes towards shopping online.
Ethical Assurances
The first ethical problem involves the issue of informed consent. An online survey
instrument was used for gathering respondent data. Study participants had the right to
know the purpose of the study, the right to privacy, and the right to confidentiality
(Zikmund, 2003). The problem of informed consent was minimized with the use of an
informed consent document distributed to each potential study participant. This
researcher specified exactly what the purpose of the study was and how survey
information is used. Furthermore, the informed consent document ensured the privacy,
anonymity of study participants, and the confidentiality of information provided on the
survey instrument.
Regarding the anonymity of respondents, the survey instrument did not include
survey questions that could potentially be used to identify specific individuals. While
some demographic information was requested, this information refers to age groups,
gender, and experience with the Internet. Additionally, the responses to the survey items
were coded according to preferences on a 7-point Likert-type scale. Responses to the
gender question are coded l=male, 2=female.
Another area of concern involved the survey instrument. The survey instrument
contained questions from previous research studies. The rationale for this decision was
due to the fact that the chosen items were statistically proven to measure the intended
constructs. This ethical consideration was appropriately handled by obtaining permission
from the authors of the prior studies to utilize these questions in the proposed research

62
project. Consent for incorporation of prior study questions was confirmed before this
survey instrument was released for online use.
Another potential ethical issue concerned the use of TCL students as one of the
sample groups for the project. The fact that the researcher is currently employed at this
institution would appear to present an ethical dilemma. This potential ethical issue was
addressed by also administering the survey to the other three participating technical
colleges in South Carolina and the two participating universities. At the technical
colleges and the university in Pennsylvania the survey instrument was available to only
students actively enrolled in business related courses, faculty and staff.. The online
survey was available to all students enrolled at the online university. Additionally, the
fact that participation in the study was entirely voluntary was spelled out in the informed
consent document.
The nature of this study presented no potential for physical or psychological harm
to come to any of the study participants. The expressed purpose of the study was to
examine factors that influence online purchase intentions. The researcher assessed the
correlation of these factors with the dependent variable online purchase intentions and the
possibility that the gender of the respondent has a moderating affect on the factors that
influence online purchase intentions and ultimately on the actual purchase intention.

63
CHAPTER 4: ANALYSIS OF FINDINGS
Chapter 4 includes a restatement of the purpose of this quantitative correlational
study, findings, and evaluation of the findings, and concludes with a summary. This study
was not intended to prove causation, but rather to examine the possible correlation of
consumers' attitudes, perceptions of web site quality and gender to the initial formation
of trust and subsequently to the dependent variable purchase intentions. In addition, this
study was designed to assess factors that contribute or deter consumers from adopting
online shopping. Furthermore, how gender potentially affects antecedents to online
purchase intentions was examined.
Findings
For the study, four technical colleges from the South Carolina Technical College
System and two northern universities participated. The final number of study participants
was N=340 out of 1,650 potential respondents for a calculated response rate of 20.6%.
Seventeen of the returned surveys were incomplete and unusable. Therefore, the adjusted
response rate is N=323 out of 1,650 potential respondents for a response rate of 19.6%.
While all 16 of the Technical colleges were invited to participate, only four accepted the
invitation. Three research questions were examined for the study and three corresponding
hypotheses were developed as a result of the research questions.
The first chart displays reliability statistics for the scaled items from the survey
instrument, (see Table 17 Reliability Statistics) The Cronbach's Alpha is .902 for the 26
scaled items (excluding the demographic questions). There was a good balance regarding
gender of respondents. The breakdown of respondents according to gender included 155
or 48% were male and 188 or 52% were female.

64

Table 1
Scale Reliability
Cronbach's Alpha
Number of items

.902
26

Tables 2 and 3 refer to demographic information for the study.
Table 2
Statistical Frequencies for Question 2 Gender
Male
Female
Total

Frequency
155
188
323

Valid% Cumulative%
48.0
48.0
48.0
52.0
52.0
100.0
100.0 100.0

%

Table 3
Statistical Frequencies for Question 3 Age

18-25
26-35
36-45
46-55
56>
Total

Frequency
% Valid% Cumulative%
101
31.3
31.3
31.3
41
12.7
12.7
44.0
64
19.8
19.8
63.8
75
23.2
23.2
87.0
42
13.0
13.0
100.0
100.0 100.0
323

There was also an excellent dispersion of age groups represented in the study.
Segments based on age included 18-25 age group 101 respondents 31.3%, 26-35 age
group 41 respondents 12.7%, 36-45 age group 64 respondents 19.8%, 46-55 age group 75
respondents 23.2%, and persons 56 years of age or over amounted to 42 respondents and
13% of the total. This was expected since the study was open to students, faculty, and

65
staff. Inclusion of the faculty and staff enhanced the dispersion of age groups and the
ability to generalize the study results to the overall population.
To answer the first research question, the potential relationship of factors that may
determine perceived site quality and the formation of trust were examined. Perceived site
quality was measured with five dimensions including attractiveness, brand equity,
opportunism, communication, and trust in the web site. The related questions are visually
appealing Q16 and classy Q17 (see Tables 4 and 5), brand equity Q14 and Q15 see
(Tables 6 and 7), opportunism Ql 1, Q12, and Q13 (see Tables 8, 9, and 10).
Communication is measured with Q8, Q9, and Q10 see (Tables 11, 12, and 13). The
questions related to trust are Q6 and Q7 (see Tables 14 and 15). Questions 16 and 17
measure the attractiveness dimension (see Tables 4 and 5).
The first dimension of perceived site quality is web site attractiveness. This
dimension was measured with Q16 visually appealing and Q17 classy (see Tables 4 and
5). The majority of respondents 260 or 80.5% somewhat agreed to strongly agreed that
the web site they evaluated was visually appealing. Some 46 or 14.2% were unsure
(answered neutral), while the remainder somewhat disagreed 15 4.6% and two either
disagreed or strongly disagreed. Respondents to Q17 classy were not as confident in
stating that they felt the web site visited was classy. Only 59.4% rated the web site they
visited as classy. A rather large percentage of respondents 30.3% were neutral on the
question and the remainder somewhat disagreed 25 7.7%, disagreed 6 1.9% and strongly
disagreed two for 6%. To summarize this dimension of perceived site quality, a majority
of survey respondents felt the web site they visited was visually appealing, while fewer
respondents would rate the web site as being classy. The second question Q12 on

66
opportunism concerns perceptions of the vendor making empty promises on the
homepage. While respondents indicated a higher level of trust that the vendor would not
make promises on the homepage without keeping them 65.3%, 19.5% were not sure, and
15% somewhat agreed to strongly agreed that there is a likelihood of promises being
made and not kept. Analysis of this question also supports the findings of the Wakefield
et al. (2004) study.
The third and final question on this dimension Q13 concerns the possibility of the
vendor using the consumer's information without permission. Respondents to this
answered in a similar manner. Some 25.4% responded in the strongly agree to somewhat
agree range (meaning they felt that information they provided on the homepage would be
used without their permission). Moreover, 18% were not sure if their information would
be used without their consent. This means that over 43% of potential online shoppers
completing this study felt that their information would be used without their permission,
while 57% were confident that this would not occur (see Tables 8 and 9).
The second dimension of perceived site quality examined was brand equity. There
were two survey items used to measure brand equity brand equity one Q14 and brand
equity two Q15. The first question referred to whether the vendor offered excellent
quality brands. The majority of respondents to brand equity one 267 or 82.7% somewhat
agreed to strongly agreed that the brands offered for sale on the web site they evaluated
were of excellent quality. In contrast, 14.6% 47 respondents were unsure of the quality of
the items offered by the vendor. Six respondents 1.9% somewhat disagreed while the
remaining three respondents disagreed to strongly disagreed (felt the quality of the items
offered were of poor quality).

67
The second item used to measure brand equity was brand equity two. This
question is concerned with the respondents' prior knowledge of the brands offered for
sale by the vendor. A majority of respondents indicated that they had prior knowledge of
the brands offered by the vendor 261 80.8% somewhat agreed to strongly agreed. Only
19.2% of the respondents were neutral to strongly disagree that the vendor offered brands
well known (see Table 6 and Table 7). To summarize this dimension of perceived site
quality, a majority of survey respondents felt the vendor offered excellent quality brands
and that the brands offered were well known to them.
The third dimension of perceived site quality is opportunism. This dimension was
measured with three survey items. These questions refer to the likelihood of the vendor
taking advantage of the online shopper. The first question Ql 1 related to the ability of the
vendor to alter facts on the homepage to induce sales. This was a reverse worded question
meaning an answer more to the strongly disagree range meant the vendor was acting
ethically and not altering facts to make a sale. In agreement with the Wakefield et al.
(2004) study online shoppers' perceptions of opportunism is still a deterrent to online
sales. This study confirms this since 25.8% of respondents perceived that the online
vendor would alter facts to make a sale. When you add in the 22.6% who are unsure
48.4% or 158 respondents felt that the vendor would alter facts to make a sale. This
indicates that online vendors still have a long way to go in convincing potential shoppers
that the facts presented on their homepages are truthful.
The fourth dimension of perceived site quality is communication. This dimension
was measured with three survey items Q8 Communication 1, Q9 Communication 2, and
Q10 Communication 3 (see Tables 11, 12, and 13). The first question Q8 Communication

68
1 relates to the vendor's homepage providing excellent feedback options for the online
consumer. This time respondents perceived that the vendor did in fact provide excellent
feedback options. Over 73% responded in the somewhat agreed to strongly agreed range.
The remaining respondents were unsure (neutral) 18% or somewhat disagreed to strongly
disagreed 4.6%. Respondents to Q9 Communication 2 overwhelmingly perceived that the
vendor altered them to new product offerings or developments 80.2%.. The remaining
respondents were unsure (neutral) or felt that this service was not provided.
The final question for the communication dimension of perceived site quality is
Communication 3 Q 10. This question relates to the vendor seeking user feedback or
advice on what was offered for sale. Survey respondents to this question 68.1% indicated
that the vendor sought their advice or feedback. Some respondents were neutral (unsure)
17.6%, while the remaining 23.2% somewhat disagreed to strongly disagreed. This
analysis of the communication dimension of web site quality supports priorfindingsof
the Lee and Lin (2005) study.
The fifth dimension of perceived site quality is trust. This dimension is measured
with two survey items Q6 Trust 1 and Q7 Trust 2 (see Tables 14 and 15). The first that
measures this dimension Q6 Trust 1 refers to the consumer's perceptions of the vendor's
trustworthiness. Nearly 79% of survey respondents indicated that they felt the online
vendor was trustworthy. Survey respondents who were unsure (neutral) amounted to
17.3%.. Only 3.7% of total respondents somewhat disagreed to strongly disagreed. The
second question for this dimension Q7 Trust 2 relates to the consumer's perceptions of
the online vendor keeping his/her best interests in mind. Nearly 70% of respondents
believed that the online vendor keeps their best interests in mind. Some 23.8% were

69
neutral and the remaining 7.4% felt that online vendors do not keep the consumers' best
interests in mind. Resultsfromthis study further confirm the importance that Lee and Lin
(2005) placed on trust as a precedent to e-service quality and ultimately purchase
intentions.
The individual statistical frequencies are presented for review. To determine the
extent of the relationships among the factors and to accept or reject the null hypotheses
for RQ1 and RQ2 and related survey items bivariate correlational analysis was
conducted. For RQ3, a Chi Square test was conducted to determine the relationship
among factors if any and to accept or reject the null hypothesis. This non parametric test
was utilized since gender is a categorical variable.
Table 4
Statistical Frequencies for Question 16 Visually Appealing
Strongly Disagree
Disagree
Somewhat disagree
Neutral
Somewhat Agree
Agree
Strongly Agree
Total

Frequency
1
1
15
46
69
145
46
323

%

.3
.3
4.6
14.2
21.4
44.9
14.2
100.0

Valid%
.3
.3
4.6
14.2
21.4
44.9
14.2
100.0

Cumulative%
.3
.6
5.3
19.5
40.9
85.8
100.0

70
Table 5
Statistical Frequencies for Question 17 Classy

Strongly Disagree
Disagree
Somewhat Disagree
Neutral
Somewhat Agree
Agree
Strongly Agree
Total

Frequency
2
6
25
98
97
80
15
323

%
Valid% Cumulative%
.6
6
.6
1.9
1.9
2.5
7.7
10.2
7.7
30.3
30.3
40.6
30.0
30.0
70.6
24.8
95.4
24.8
4.6
4.6
100.0
100.0 100.0

Questions 14 and 15 measure the brand equity dimension of perceived site quality (see
Tables 35 and 36).
Table 6
Statistical Frequencies for Question 14 Brand Equity 1
Frequency %
Valid% Cumulative%
1
Strongly Disagree
.3
.3
.3
2
Disagree
.6
.6
.9
Somewhat Disagree
6
1.9
1.9
2.8
Neutral
47
14.6
14.6
17.3
61
18.9
18.9
36.2
Somewhat Agree
Agree
153
47.4
47.4
83.8
Strongly Agree
16.4
16.4
53
100.0
Total
, 323
100.0 100.0
Table 7
Statistical Frequencies for Question 15 Brand Equity 2

Strongly Disagree
Disagree
Somewhat Disagree
Neutral
Somewhat Agree
Agree
Strongly Agree
Total

Frequency
2
7
9
44
62
141
58
323

%
.6
2.2
2.8
13.6
19.2
43.7
18.0
100.0

Valid% Cumulative%
.6
.6
2.2
2.8
5.6
2.8
13.6
19.2
19.2
38.4
43.7
82.0
100.0
18.0
100.0

Questions 11, 12, and 13 refer to the opportunism dimension of perceived site quality
(see Tables 8, 9, and 10)

Table 8
Statistical Frequencies for Question 11 Opportunism 1
Frequency %
Valid% Cumulative%
Strongly Disagree
32
9.9
.9.9
.9.9
Disagree
83
25.7
25.7
35.6
Somewhat Disagree
52
16.1
16.1
51.7
Neutral
73
22.6
22.6
74.3
Somewhat Agree
50
15.5
15.5
88.8
Agree
27
8.4
8.4
98.1
Strongly Agree
6
1.9
1.9
100.0
Total
323
100.0 100.0
Table 9
Statistical Frequencies for Question 12 Opportunism 2
Frequency %
Valid% Cumulative%
Strongly Disagree
56
17.3
17.3
17.3
Disagree
108
33.4
33.4
50.8
Somewhat Disagree
47
14.6
14.6
65.3
Neutral
63
19.5
19.5
84.8
Somewhat Agree
10.8
10.8
95.7
35
Agree
12
3.7
3.7
99.4
Strongly Agree
2
100.0
.6
.6
Total
100.0 100.0

72

Table 10
Statistical Frequencies for Question 13 Opportunism 3
Frequency %
Strongly Disagree
Disagree
Somewhat Disagree
Neutral
Somewhat Agree
Agree
Strongly Agree
Total

42
118
24
58
56
18
7
323

13.0
36.5
7.4
18.0
17.3
5.6
2.2
100.0

Valid% Cumulative%
13.0
36.5
7.4
18.0
17.3
5.6
2.2
100.0

13.0
49.5
57.0
74.9
92.3
97.8
100.0

The communication dimension of perceived site quality was measured with questions 8,
9, and 10 (see Tables 11, 12, and 13).
Table 11
Statistical Frequencies for Question 8 Communication!
Frequency %
Valid% Cumulative%
Strongly Disagree
4
1.2
1.2
1.2
Disagree
4.3
10
3.1
3.1
Somewhat Disagree
4.6
4.6
9.0
15
Neutral
26.9
58
18.0
18.0
Somewhat Agree
24.5
51.4
79
24.5
Agree
109
33.7
33.7
85.1
Strongly Agree
14.9
100.0
48
14.9
Total
323
100.0 100.0

Table 12
Statistical Frequencies for Question 9 Communication!
Frequency %
Strongly Disagree
Disagree
Somewhat Disagree
Neutral
Somewhat Agree
Agree
Strongly Agree
Total

3
7
9
45
51
124
84
323

.9
2.2
2.8
13.8
15.8
38.4
26.0
100.0

Valid% Cumulative%
.9
2.2
2.8
13.8
15.8
38.4
26.0
100.0

.9
3.1
5.9
19.8
35.6
74.0
100.0

73

Table 13
Statistical Frequencies for Question 10 Communications 3
Frequency %
Valid% Cumulative%
Strongly Disagree
6
1.9
1.9
1.9
Disagree
17
5.3
5.3
7.1
Somewhat Disagree
23
7.1
7.1
14.2
Neutral
57
17.6
17.6
31.9
Somewhat Agree
65
20.1
20.1
52.0
Agree
111
34.4
34.4
86.4
Strongly Agree
44
13.6
13.6
100.0
Total
323
100.0 100.0
Trust in the web site was measured with Q6 and Q7 see (Tables 30 and 31)
Table 14
Statistical Frequencies for Question 6 Trust 1

Strongly Disagree
Disagree
Somewhat Disagree
Neutral
Somewhat Agree
Agree
Strongly Agree
Total

Frequency %
Valid% Cumulative%
3
.9
.9
.9
2
.6
.6
1.5
7
2.2
2.2
3.7
56
17.3
21.1
17.3
44
13.6
13.6
34.7
130
40.2
40.2
74.9
81
25.1
25.1
100.0
323
100.0 100.0

74

Table 15
Statistical Frequencies for Question 7 Trust 2
Frequency %
Strongly Disagree
Disagree
Somewhat Disagree
Neutral
Somewhat Agree
Agree
Strongly Agree
Total

1
10
13
77
69
112
41
323

.3
3.1
4.0
23.8
21.4
34.7
12.7
100.0

Valid% Cumulative%
.3
3.1
4.0
23.8
21.4
34.7
12.7
100.0

.3
3.4
7.4
31.3
52.6
87.3
100.0

The software package SPSS 15.0 was utilized to run bivariate correlational
analysis on the study data from the 323 respondents. The final chart for RQ1 provides
descriptive statistics for all survey items used to answer the question (see Table 16).

Table 16
Descriptive Statistics
Visually Appealing
Classy
Brand Equity 1
Brand Equity 2
Opportunism 1
Opportunism 2
Opportunism 3
Communication 1
Communication 2
Communication 3
Trust 1
Trust 2

Mean
5.48
4.80
5.59
5.51
3.41
2.87
3.15
5.22
5.61
5.07
5.63
5.18

Std.Deviation
1.096
1.130
1.055
1.199
1.546
1.442
1.610
1.316
1.287
1.457
1.215
1.247

N
323
323
323
323
323
323
323
323
323
323
323
323

To answer RQ2 survey items related to the formation of consumers' attitudes
toward the Internet and the potential correlations to online purchase intentions was

75

examined. Attitude toward buying on the internet is measured with two dimensions
personal awareness of security survey questions 18,19,20 and 21 (see Tables 17,18,19
and 20) and personal innovativeness measured with survey questions 22 and 23 (see
Tables 21 and 22). Purchase intention is measured with survey question 29 (see Table
23).
Attitude toward buying on the internet is measured with two dimensions personal
awareness of security and personal innovativeness. The first question for the personal
awareness of security dimension is question 18 Web Seal 1. Respondents to this question
answered 69.4% that the web seal on the homepage does matter to them. The next highest
number of respondents were neutral 21.4%, and the remaining 9.3% somewhat disagree
to strongly disagree (see Table 17). The next question for this dimension is Q19 Web
Seal 2.
Respondents to this question indicated that 10.8% found little to no value in a web
seal being displayed on the homepage of the vendor's web site. In contrast 67%
somewhat agree to strongly agree that a web seal displayed on the vendor's homepage
has value. The remainder of respondents to the question was neutral 22.3%, (see Table
18). The next question related to the personal awareness of security dimension is Q20
Security 1 (see Table 19). A majority of respondents to this question somewhat agree to
strongly agree 75.3%. The next highest percent is somewhat disagree to strongly disagree
15.2%.. Respondents who had no opinion (neutral) were 9.6% of the total. The final
question for the personal awareness of security dimension is Q21 Security 2 (see Table
20).

76
Regarding this question survey respondents answered 70% of the time somewhat
agree to strongly agree. Only 12.7% of respondents to this question were unsure (neutral),
and the remaining 17.3% somewhat disagree to strongly disagree. Compared to the
results from the Chellappa study (2002) consumers are a more positive perception of
online security for e-commerce.
The final dimension to attitude toward buying on the Internet is personal
innovativeness. There are two survey questions that relate to this dimension. Q22
Innovativeness 1 and Q23 Innovativeness 2 (see Tables 21 and 22).
Survey respondents to Q22 Innovativeness 1 answered somewhat agree to strongly agree
only 34.7% of the time. In contrast 43.2% somewhat disagree to strongly disagree. The
remainder of respondents answered neutral 223%.
The next question supporting this dimension is Q23 Innovativeness 2 (see Table
22). Respondents answered this question that 48.3% less than half are interested in trying
out new web sites. Some 28.5% of the respondents indicated that they somewhat disagree
to strongly disagree, and the remaining 23.2% were neutral on this question. These results
indicate that web site designers must have excellent advertising or an incentive to attract
persons to new web sites.
Purchase Intentions measured with Q29 (see Table 23) Regarding purchase
intentions the following responses were received 98 for 30.3% strongly agree, 123 for
38.1%o agree, 41 for 12.7% somewhat agree. In contrast 37 for 11.5% were neutral, 12 for
3.7% somewhat disagree, 9 for 2.8 disagree, and 3 for ..9% strongly disagree. For
descriptive statistics for RQ2 (see Table 24).

77
Table 17
Statistical Frequencies for Question 18 Web Seal 1

Strongly Disagree
Disagree
Somewhat Disagree
Neutral
Somewhat Agree
Agree
Strongly Agree
Total

Frequency
6
7
17
69
51
79
94
323

%

1.9
2.2
5.3
21.4
15.8
24.5
29.1
100.0

Valid%
1.9
2.2
5.3
21.4
15.8
24.5
29.1
100.0

Table 18
Statistical Frequencies for Question 19 Web Seal 2
Frequency %
Valid% Cumulative%
Strongly Disagree
6
1.9
1.9
1.9
Disagree
8
2.5
2.5
4.3
Somewhat Disagree
21
6.5
6.5
10.8
Neutral
72
22.3
22.3
33.1
Somewhat Agree
58
18.0
18.0
51.1
Agree
75
23.3
23.3
74.3
Strongly Agree
83
25.7
25.7
100.0
Total
323
100.0 100.0
Table 19
Statistical Frequencies for Question 20 Security 1
Strongly Disagree
Disagree
Somewhat Disagree
Neutral
Somewhat Agree
Agree
Strongly Agree
Total

Frequency %
Valid% Cumulative%
2.2
2.2
7
2.2
11
3.4
3.4
5.6
31
9.6
9.6
15.2
31
9.6
9.6
24.8
39
12.1
12.1
36.8
123
38.1
38.1
74.9
81
25.1
25.1
100.0
323
100.0 100.0

Cumulative%
1.9
4.0
9.3
30.7
46.4
70.9
100.0

78

Table 20
Statistical Frequencies for Question 21 Security 2
Frequency %
Valid% Cumulative%
Strongly Disagree
8
2.5
2.5
2.5
Disagree
19
5.9
5.9
8.4
Somewhat Disagree
29
9.0
17.3
9.0
30.0
Neutral
41
12.7
12.7
Somewhat Agree
47
14.6
14.6
44.8
Agree
83.9
127
39.3
39.3
Strongly Agree
52
16.1
16.1
100.0
Total
323
100.0 100.0

Table 21
Statistical Frequencies for Question 22 Innovativeness 1
Frequency
Valid%
%
Strongly Disagree
16
5.0
Disagree
64
19.8
Somewhat Disagree 59
18.3
Neutral
72
22.3
Somewhat Agree
15.8
51
Agree
45
13.3
Strongly Agree
18
5.6
Total
323 100.0

Cumulative%
5.0
5.0
19.8
24.8
43.2
18.3
22.3
65.2
15.8
81.1
94.4
13.3
5.6
100.0
100.0

Table 22
Statistical Frequencies for Question 23 Innovativeness 2

Strongly Disagree
Disagree
Somewhat Disagree
Neutral
Somewhat Agree
Agree
Strongly Agree
Total

Frequency %
Valid% Cumulative%
6
1.9
1.9
1.9
44
13.6
13.6
15.5
42
13.0
13.0
28.5
23.2
23.2
75
51.7
79
24.5
24.5
76.2
52
16.1
16.1
92.3
100.0
25
7.7
7.7
100.0
100.0 100.0

79
Table 23
Statistical Frequencies for Question 29 Purchase Intentions

Strongly Disagree
Disagree
Somewhat Disagree
Neutral
Somewhat Agree
Agree
Strongly Agree
Total

Frequency %
Valid% Cumulative%
3
.9
.9
.9
9
2.8
2.8
3.7
12
3.7
3.7
7.4
11.5
11.5
18.9
37
41
12.7
31.6
12.7
38.1
38.1
69.7
123
98
30.3
30.3
100.0
323
100.0 100.0

Table 24
Descriptive Statistics
WebSeallQ18
Web Seal 2 Q19
Security 1 Q20
Security 2 Q21
Innovativeness 1 Q22
Innovativeness 2 Q23
Purchase Intentions Q29

Mean
5.37
5.24
5.41
5.13
3.86
4.34
5.68

Std. Deviation
1.482
1.487
1.536
1.562
1.616
1.523
1.343

N
323
323
323
323
323
323
323

Analysis and Evaluation of Findings
Within this section of chapter 4, a comprehensive analysis of the findings of this
study is presented. Within the first section of this chapter a table is presented that
includes the constructs and statistical tests used to accept or reject each null hypothesis.
Immediately following construct level statistical table each individual research question
is provided and linked to the appropriate table. For RQ1 and RQ2 the tables contain
factor level correlations. For RQ3, non-parametric tests were performed since gender is a
categorical variable. The next section includes detailed information on how this research

80
study expands upon prior research. In concluding this chapter a summary is presented
with a brief overview of what has been discovered by this research project.
For a p-value to be statistically significant for a confidence level of 0.05 the
statistic must be less than p< 0.05, likewise for the p-value to have statistical significance
at the 0.01 confidence level the p- value must be p< 0.01 (Sims, 2004). Regarding
Pearson Correlation coefficients 0.10 is considered a small effect, 0.30 is considered a
medium effect, and .50 is considered to be a large effect (Trochim, 2001). For a ChiSquare test to indicate statistical significance at the 0.05 level of confidence the test value
must be below 0.05.
Table 25
Hypothesis Testing at the Construct Level
Hypothesis

Constructs

Hlo

Perceived Site
Quality, Trust
in the Web
Site

Appropriate Tests
Pearson
Correlation
.467

p<.01

Attitude
Pearson
toward buying Correlation
on the Internet,
.472
Online
Purchase
Intentions
Gender*,
Chi-square
H3o
Attitude
toward buying
on the Internet,
Perceived Site
Quality, Trust
in the Web
Site, Online
Purchase
Intentions
*Gender is a 1two value categorical variable.

p<.01

H20

Accept or
Reject
Reject null
hypothesis and
accept
alternative
hypothesis
Reject null
hypothesis and
accept
alternative
hypothesis

All Chi-square tests Accept the null
indicated no statistical hypothesis and
significance all values
reject the
above 0.05
alternative
hypothesis

81

To answer RQ1: To what extent, if any, is perceived site quality related to trust in
the web site and accept or reject the null hypothesis Hlo: Perceived site quality is not
correlated to the formation of trust in the web site bivariate correlational analysis was
conducted with SPSS 15.0. There is considerable support for rejecting the null hypothesis
and accepting the alternative hypothesis Hl a : Perceived site quality is correlated to the
formation of trust in the web site. All of the items that are factors of perceived site quality
are significantly correlated to Q6 Trust 1 and Q7 Trust 2 at the 0.01 level (2-tailed).
Pearson Correlation statistic is .467. As would be expected the questions related to
opportunism are negatively correlated to trust (see Table 26).
The next research question to be answered is RQ2: To what extent, if any, does a
consumer's attitude toward buying on the Internet influence online purchase intentions
and related hypotheses H2o: A consumer's attitude toward buying on the Internet is not
correlated to online purchase intentions and H2a: A consumer's attitude toward buying on
the Internet is correlated to online purchase intentions.
To adequately answer RQ2: To what extent, if any, does a consumer's attitude
toward buying on the Internet influence online purchase intentions and related hypotheses
H2o: A consumer's attitude toward buying on the Internet is not correlated to online
purchase intentions and H2a: A consumer's attitude toward buying on the Internet is
correlated to online purchase intentions bivariate correlational analysis with SPSS 15 was
conducted (see Table 27).
A significant correlation exists between each of the items used to measure both
dimensions of attitude and online purchase intentions. All correlations are positive and

82

are at the 0.01 (two-tailed level of significance). The Pearson Correlation statistic at the
construct level was .472. Innovativeness 1 Q22 and Innovativeness 2 Q23 have the
weakest correlations at .284 and .311 respectively. The null hypothesis can be rejected
with confidence and the alternative accepted.
The final research question of this study is the most comprehensive and entails all
of the independent variables measured so far. RQ3: To what extent, if any, does
gender influence perceived web site quality, trust in the web site, and attitude toward
buying on the Internet, and online purchase intentions. The related hypothesis is H3o:
Gender of the consumer is not associated to the formation of trust in the web site,
perceived site quality, a consumer's attitude toward buying on the Internet, and online
purchase intentions. The alternative hypothesis is H3a: Gender of the consumer is
associated to the formation of trust in the web site, perceived site quality, a consumer's
attitude toward buying on the Internet, and online purchase intentions.
The most feasible method to answer this research question and test the null
hypothesis is to run a analysis on gender with the other independent variables and the
dependent variable online purchase intentions (see Appendix: M). The demographic
variable gender is a categorical variable. The appropriate test to run for RQ3 is the
Pearson Chi-Square (Sims, 2004, p. 7). An independent Chi-Square test was conducted
for each variable with gender. Results from these tests indicated no statistical significant
association between gender and the other variables including the dependent variable
online purchase intentions. As a result of the findings from the Chi-Square tests, the null
hypothesis is accepted H3Q: Gender of the consumer is not associated to the formation of

83

trust in the web site, perceived site quality, a consumer's attitude toward buying on the
Internet, and online purchase intentions.
There are several important considerations from the cross tabulations that were
conducted for RQ3 (see Appendix: M). Female online shoppers tend to be more trusting
than male shoppers (see Table 38). Regarding communications with the online vendor,
female respondents placed more value on communications (see Tables 44, 47, 50).
Female respondents also indicated that they felt that the online vendor was more likely to
alter facts to make the sale (see Table 52). In contrast to this finding, more male
respondents indicated that they felt that the vendor was likely to make promises without
doing them (see Table 57). The final question regarding opportunism, more male
respondents indicated that the vendor was likely to use customers' information without
their permission (see Table 59).
The fact that the online vendor offered quality brands was of more importance to
female shoppers (see Table 62). Moreover, females recognized quality brands at a
slightly higher rate than males (see Table 65). The visual appearance of the web site was
of more importance to female shoppers than to males (see Tables 68 and 71). Female
respondents were more concerned with the web site having a security seal than were male
respondents (see Tables 74 and 77). In terms of online security, female shoppers are more
likely than males to feel secure in providing sensitive information online (see Tables 80
and 83).
Regarding innovativeness of online shoppers, females are somewhat more likely
to visit a new website (see Table 86). Moreover, female shoppers are more likely to try
out a new web site than are male shoppers (see Table 89). Both males and females

84
respondents agreed that the web site they visited could increase their shopping
effectiveness (see Tables 92). Furthermore, both genders were on agreement on the
usefulness of the web site visited (see Table 95). In terms of ease of use, both genders
agreed that the web site must be easy to use (see Tables 101 and 104). The final
conclusion from the cross-tabulation analysis was that 121 males and 123 females agreed
to strongly agreed that they would make a purchase from the web site visited (see Table
107).
While it was not the intent of the researcher to prove causation,
nevertheless, a step-wise regression analysis of highly correlated survey items with
the dependent variable online purchase intentions was conducted with SPSS 15.0.
A model (see Appendix N Table 28 Model Summary) was developed from this
analysis that has a high adjusted R square number meaning that there is significant
correlation of the independent variables with the dependent variable. The factors
chosen for the stepwise regression analysis for model formulation exhibited high
correlations with the dependent variable online purchase intentions.
A Post-hoc analysis was conducted for this study (see Figure 3)
Two-Tail Test (Hypothesis that Average is not equal to some test value)

Enter the average value for the sample and a value to compare it to. Also enter the
sample size and standard deviation for the sample or a rough estimates of them.
For reference, a 5-pt. scale may typically have a standard deviation of 0.8 to 1.2
and a 10-pt scale may have a standard deviation between 3.0 and 4.0 for most
items. The larger the standard deviation, the larger the sampling error.

85

Test Value:

I 3.5

' (Value to compare the sample
average to)

Sample Average:

I 4.41

i (Value measured from sample or
expected from sample)

Sample Size:

I 323

(Size of sample or desired number
of respondents)

Standard Deviation for Sample:

I 1.35

Alpha Error Level

I 5%

or Confidence Level:

TI (Probability of incorrectly rejecting
the null hypothesis that there is no
difference in the average values).
An Alpha of 5% corresponds to a
95% Confidence Interval.

Statistical Power: 100%

Figure 3. Post-hoc Power Analysis

86
Summary
The researcher began this chapter with a presentation of the findings from the
pilot study. The primary purpose of the pilot study was to test the effectiveness of the
study directions and the online survey instrument prior to the initiation of the primary
study. At the recommendation of faculty members who examined the initial survey
instrument a format change was made and the survey instrument was placed online for
the pilot study. An analysis of the findings from the study for answering RQ1 resulted in
rejecting the null hypothesis and accepting the alternative hypothesis Hl a : Perceived site
quality is correlated to the formation of trust in the web site. There was strong support for
rejecting the null hypothesis for RQ2 and accepting the alternative hypothesis H2a: A
consumer's attitude toward buying on the Internet is correlated to online purchase
intentions was presented.
The chapter was concluded with an analysis of the most comprehensive research
question of the three RQ3. A careful analysis of all Chi-Square tests for RQ3 resulted in
an acceptance of the null hypothesis and a rejection of the alternative. Gender has no
statistical significant affect on any of the independent variables and on the dependent
variable online purchase intentions. A new model was suggested (see Appendix: O Table
124) that has a statically significant adjusted R Squared value of 0.726. This model was
developed by using the stepwise regression method. The other six models that resulted
from the analysis were also included for comparison purposes.

87

CHAPTER 5: SUMMARY, CONCLUSIONS, AND RECOMMENDATIONS
Summary
Online shoppers are abandoning their shopping carts nearly 60% of the time.
(Holland, 2006). Considering that an estimated $102 billion was spent online during
2006, shopping cart abandonment cost online vendors $61 billion in lost sales revenues
(Holland, 2006). In consideration of this problem, the focus of this research study was to
assess the factors that may cause consumers to accept or reject online shopping. A better
understanding of the factors that online shopping acceptance or rejection will provide
online vendors with insights into what causes consumers to abandon or complete
transactions. Furthermore, the purpose of this study was not to prove causation, but rather
to examine the potential correlations of antecedents to online purchase intentions and
include the possibility that gender would have a moderating affect and potentially a
correlation with online purchase intentions.
As a result of the stated problem and purpose for the study the following research
questions were considered. RQ1: To what extent, if any, is perceived site quality related
to trust in the web site, research RQ2: To what extent, if any, does a consumer's attitude
toward buying on the Internet influence online purchase intentions, and RQ3: To what
extent, if any, does gender influence perceived web site quality, trust in the web site,
attitude toward buying on the Internet, and online purchase intentions. From these three
research questions three hypotheses were developed. Hypotheses Hlo." Perceived site
quality is not correlated to the formation of trust in the web site and hypothesis Hl a :
Perceived site quality is correlated to the formation of trust in the web site were
developed as a result of research question one. Hypotheses H20: A consumer's attitude

toward buying on the Internet is not correlated to online purchase intentions and H2a: A
consumer's attitude toward buying on the Internet is correlated to online purchase
intentions were developed as a result of research question two. The last two hypotheses
H3o: Gender of the consumer is not associated to the formation of trust in the web site,
perceived site quality, a consumer's attitude toward buying on the Internet, and online
purchase intentions, and H3a: Gender of the consumer is associated to the formation of
trust in the web site, perceived site quality, a consumer's attitude toward buying on the
Internet, and online purchase intentions were developed as a result of research question
three.
The initial research for this study included a comprehensive literature review of
over 68 current studies on the subject of online shopping acceptance. Within chapter 3,
the researcher provided details of the methodology chosen and how the study would be
conducted. After careful consideration of all research methods used for online shopping
research and the stated purpose of this study the quantitative correlational methodology
was chosen as the most appropriate.
The actual survey instrument was developed from items used in prior studies. The
items chosen were statistically proven to measure the intended constructs (see Appendix
D: Research Survey: Online Shopping Survey) The researcher first obtained permission
to adapt the questions from the original authors (see Appendixes F and G) Following the
development of the survey instrument, the writer conducted a Pilot study to verify the
suitability of the study directions and survey instrument. Once the survey questions and
directions were deemed to be correctly formatted and easily comprehended, the study
was initiated.

89
An e-mail correspondence was sent out to the presidents of all 16 Technical
Colleges of the South Carolina Technical College system (see Appendix H: Request for
Participation Memo). Full directions for study participation and the link to the online
survey hosted by www.surveymonkey.com were then e-mailed to the contact persons at
the institutions that agreed to participate in the study. When the researcher realized that
participation from the Technical Colleges would be less than anticipated he invited two
well known northern universities to also participate.
Furthermore, to gain an adequate number of survey respondents the online survey
was made available to faculty and staff from all six institutions. The estimated total
population was 1,650. The outcome of this effort to recruit participants was N= 340 a
calculated response rate of 20.6%. Seventeen of the surveys were unusable resulting in
323 usable surveys for an adjusted response rate of 19.6%. Within chapter 4, the
researcher presented study findings with complete analysis of each survey item. The
research findings were compared with prior research and found to be in support of at least
three prior studies the Wakefield et al. (2004), the Lee and Lin (2005) study, and the
Koufaris (2002a) study.
Conclusions
Findings from this study were in support of Koufaris and Hampton-Sosa's
(2002a) contention that ease of use is positively associated with trust and trust is
positively associated with online purchase intentions. In addition, findings from this
study support the prior work of Lee and Lin (2005) regarding the importance of web site
design whenever consumers are considering purchasing from an online vendor. This
study also confirms the contention of Wakefield et al. (2004) regarding the positive

90
association of brand equity, web seal value, and web site attractiveness in the initial
formation of trust and subsequently online purchase intentions.
In contrast to the findings of Zhou et al. (2007) that experience with the Internet
does not influence online purchase intentions, this study confirmed a positive correlation
between experience with the Internet and online purchase intentions. An additional
contradiction to prior findings occurred with Zhou et al. who found that males tend to
make more purchases online than females. Furthermore, findings indicated that gender
had no influence on the decision to purchase online. There was very strong support for
rejecting the null hypothesis and accepting the alternative hypothesis related to RQ1: To
what extent, if any, is perceived site quality related to trust in the web site Hlo: Perceived
site quality is not correlated to the formation of trust in the web site and accepting the
alternative hypothesis. Hl a : Perceived site quality is correlated to the formation of trust in
the web site.
In addition, there was considerable statistical support for rejection of the null
hypothesis related to RQ2: To what extent, if any, does a consumer's attitude toward
buying on the Internet influence online purchase intentions H2o: A consumer's attitude
toward buying on the Internet is not correlated to online purchase intentions and
accepting the alternative hypothesis H2a: A consumer's attitude toward buying on the
Internet is correlated to online purchase intentions.
Findings for RQ3: To what extent, if any, does gender influence perceived web
site quality, trust in the web site, attitude toward buying on the Internet, and online
purchase intentions required an acceptance of the null hypothesis H3o: Gender of the
consumer is not associated to the formation of trust in the web site, perceived site quality,

91
a consumer's attitude toward buying on the Internet, and online purchase intentions and
rejection of the alternative hypothesis. H3a: Gender of the consumer is associated to the
formation of trust in the web site, perceived site quality, a consumer's attitude toward
buying on the Internet, and online purchase intentions.
While it is apparent that this study furthered the work of several prior studies, the
most important aspect was the model that was developed from the step-wise regression
analysis. A model that has an adjusted R Square value of 0.726 which indicates 72.6% of
the variation in dependent variable online purchase intention is accounted for by the
model adds a significant contribution to the field of online shopping research. This model
emphasizes the importance that usefulness, security, ease of use, and attractiveness has in
the design of a successful web site. Moreover, the importance that familiarity and Internet
experience cannot be underestimated.
Recommendations
There were several limitations to this research project. The first notable one was
the availability of respondents to complete the online survey. Since only 323 completed
surveys were usable, the generalization of the findings is limited. Students' faculty and
staff at technical colleges and universities would not be representative of the overall
population. Furthermore, the geographical locations of this study were limited to South
Carolina, and Pennsylvania. This limitation was mediated with the inclusion of the online
university.
Several authors provided justification for using convenience sampling for this
study. According to Yun and Good (2007), "college students are important target
consumers, particularly for online retailers, since college students are generally

92
comfortable with making purchases online and they represent a lifetime of potential
buying power and brand loyalties" (p. 2). Further evidence that convenience sampling is
appropriate for this study is provided by Martinez-Lopez et al. (2005) quoting Peterson
(2001), Lin and Lu (2000) noted,
although the composition of the sample limits the generalization of the results,
due to the fact that university internet users only represent one part of the
population of uses, nevertheless the results from theses samples can be significant
and approximate, (p. 317)
Future research could expand this study by utilizing the existing survey
instrument and surveying a larger population. A longitudinal study could affect the
overall findings. Whether thefindingsfrom this study would hold true over an extended
period of time is a potential area for further inquiry. Another avenue for potential
expansion is with a sample population from another country. The survey instrument
could easily be translated into any foreign language and used to collect data from other
countries.
With the ever growing buying power of the Chinese people and the number of
Internet users expanding at an incredible yearly rate, conducting an online shopping
survey in China could potentially enable web site designers to specialize web sites to
meet the needs of this huge and diverse population. According to Edward (2001), "the
economy of the Chinese People's Republic with respect to the magnitude of the GNP
(according to the buying power) is located in the second place after the USA" (p25.).
Edward further noted,

93
if the hitherto trend of its development will be maintained, then about 2015-2020
it will overtake the economy of the USA However, due to the political system that
exists in China the feasibility of such a study must first be examined. This
researcher did not find one study on the online shopping habits of the Chinese
people, (p. 1)
The research from this study could also potentially be expanded by analyzing
specific demographic questions to survey items. For example, the survey responses could
be segmented by age groups and bivariate correlational analysis conducted to determine
the correlations among different age groups, and the factors that determine perceived site
quality. An interesting consideration for a future research study involves the potential
consideration of how different age groups perceive web site quality. Furthermore, a
potential area in which this study could be expanded upon is regarding attitudes towards
the Internet. Future research could be based upon an inquiry on the affect if any of the
consumer's age on his/her perceptions and attitudes towards shopping online. Other
researchers could expand upon this research in the area of perceptions of online security.
Another area of inquiry involves the possibility that the age of the respondent has an
effect on his/her perception of online security, and the potential ramifications related to
online purchase intentions.
In addition, future studies may focus attention on specific segments of our
population. For instance, the Hispanic population is one of the fastest growing population
segments in this country. The purchasing power of this minority segment is growing at a
rapid pace. Investigation into how best to design web sites that would entice this segment
would be an excellent area for future inquiry. Building upon the research from this

94
project and prior research studies a suitable survey instrument could be constructed to
evaluate the perceptions of potential Hispanic and other minority online shoppers.
Moreover, another overlooked population growing in size in this country is senior
citizens. During the primary literature review for this study, the researcher found only
one study that focused primarily on this market segment. Why do senior citizens choose
to shop online? What type of web sites attracts the most attention from seniors? This
segment has tremendous purchasing power and as more of the baby boomer generation
retires, there will be more need for the convenience that only online shopping can offer.
While this study confirmed that trust is an important factor in determining online
purchase intentions further inquiring into trust is needed.
While the researcher focused this study on factors influencing online purchase
intentions, further inquiry is needed on the factors that cause shoppers to return to the
online vendor's site. Furthermore, future research could examine factors that potentially
cause customers to return to the vendor's web site for additional purchases. Future
research could focus on how to establish and maintain customer loyalty. Other areas of
potential inquiry could center on the factors that may cause customers to return to the
vendor's web site. The importance of product pricing and how it affects the online
purchasing decision would be an interesting area for future study. In addition, the affect if
any of product pricing as the potential primary factor in driving consumers to the web site
could be examined. Future studies could focus on the importance of vendor follow up
after the online purchase. For instance, the percentage of online vendors who send
follow-up e-mail messages thanking the customer for the online purchase and inquiring
on how their services can be improved has not be examined. The researcher has found no

95
studies that are based on vendor follow-up after the sale and how vendor follow up
impacts customer loyalty and consequently return sales. In conjunction with the
preceding, the impact of a short questionnaire following the online purchase regarding
service quality, competitive pricing, and asking why the customer chose to shop with the
online vendor has not been explored to date.
Inquires on what drives consumers to vendor's web sites should be conducted.
The effectiveness of online advertising in promoting a vendor's web site could potentially
be the focus of a future study. The return on investment of online advertising including ad
banners and Google Adwords is an area for potential future studies. The researcher found
no studies that addressed the aforementioned areas. The impact of the online vendor's
refund policy as a contributing factor to online purchase intention should be examined.
During the review of literature, no studies were found that included the possible impact
of online vendors providing an online chat function to answer potential customers'
questions in real time. Since it has been statistically verified in this study that consumers'
perceptions of web site quality are an important factor influencing online purchase
intentions, the affect if any of an online chat function in elevating perceptions of web site
quality through improved communications is a potential subject for a future study. In
support of conducting research in this area of inquiry, a recent study conducted by Doolin
et al. (2006) confirmed that the loss of social interaction is an impediment to online
purchase behavior. Would inclusion of a real time chat function replace the loss of social
interaction?
In conclusion, while it was not the intention of this research to propose a new
predictive model for online purchase intention, a new model was developed that

96
confirmed the importance of web site usefulness, familiarity with the web site, ease of
use, Internet experience, the value of including a web seal on the homepage, and that the
web site should be visually appealing. Even though the predictive power of this model is
substantial with an adjusted R squared value of 0.726 could a model with higher
predictive power be constructed following a meta-analysis of current research?
It is apparent from this research project that in the area of online shopping
acceptance much work remains to be done. The factors that have been confirmed to be
antecedents to online shopping acceptance and conducive to online shopping may be
subject to change. For online vendors to be successful, they must have access to the most
current research, web sites should be designed to reinforce consumer perceptions of web
site quality, positively affect consumers' attitudes towards online shopping, and to meet
their specific needs.

97

REFERENCES
Aladwani, A & Palvia, P. (2002). Developing and validating an instrument for measuring
an instrument for measuring user-perceived web quality. Information and
Management, 39(6), 467-476. Retrieved January 7, 2006, from ProQuest
database.
Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human
Decision Processes, (50), 179-211. Retrieved January 1, 2005, from:
http://www.tcw.utwente.nl/theorieenoverzicht/Theory%20clusters/Health%20Co
mmunication/theory_planned_behavior.doc/
Arnold, T., Landry, T., & Reynolds, J. (2007). Retail online assurances: Typology
development and empirical analysis. The Journal of Marketing Theory and
Practice, 4(15), 299-313. Retrieved March 1, 2008, from ProQuest database.
Berendt, B., Gunther, O., & Spiekermann, S. (2005). Privacy in e-commerce: Stated
Preferences vs. actual behavior. Communications of the ACM, 48(A), 101-105.
Retrieved March 27, 2007,fromProQuest database.
Black, G. (2005). Predictors of consumer trust: Likelihood to pay online. Marketing
Intelligence & Planning, 23(6-7), 648-658. Retrieved November 24, 2007, from
ABI/INFORM Global database.
Bosworth, H. (2006). Consumers want a safer cyberworld. Consumer Affairs. Retrieved
October 22, 2006, from
http://www.consumeraffairs.com/news04/2006/05/cybersecurity.html
Chang, M., Cheung, W., & Lai, V. (2005). Literature derived reference models for the
adoption of online shopping. Information Management, 3(42), 543-559. Retrieved
March 12, 2006,fromProQuest database.
Chellappa, R. (2002). Consumers' trust in electronic commerce transactions: The role of
perceived privacy and perceived security. Atlanta, GA: Emory University,
Goizueta Business School. Retrieved May 2, 2007, from ProQuest database.
Cheung, C, Chan, G., & Limayem, M. (2005). A critical review of online consumer
behavior: Empirical research. Journal of Electronic Commerce in Organizations,
3(4), 1-18. Retrieved March 1, 2008, from ProQuest database.
Chi, Y., Lin, C, & Tang, L. (2005). Gender differs: Assessing a model of online purchase
intentions in e-tail service Jnternational Jounal of Service Industry Management,
16(5), 416-435.. Retrieved May 15, 2007, from ProQuest database.

98
Clarke, R. (1983). A primer in diffusion of innovation theory. Retrieved March 2, 2008,
from: http://www.anu.edu.au/people/Roger.Clarke/SOS/InnDiff.html
Collier, J., & Bienstock, C. (2006). How do customers judge quality in an e-tailer. MIT
Sloan Management Review, 48(1), 35-40. Retrieved October 20, 2006, from
ProQuest database.
Dijst, M., Farag. S., & Schwanen, T. (2005). Attitude theory applied to in-store and
online shopping. Retrieved January 24, 2008, from ProQuest database.
Dillon, T.W. & Reif, H.L. (2006). Identifying purchase perceptions that promote frequent
e-commerce buying. Int. J. Electronic Marketing and Retailing, 1(1), 48-66.
Retrieved November 7, 2007, from ProQuest database.
Dinev, T. & Hart, P. (2006). Privacy concerns and levels of information exchange: An
empirical investigation of intended e-services use. E-Service Journal 2006, 25-59.
Retrieved February 27, 2007, from ProQuest database.
Doolin, B., Dillon, S., Thompson, F., & Corner, J. (2005). Perceived risk, the Internet
shopping experience, and online purchasing behavior: A New Zealand
perspective. Journal of Information Management, 13(2), 66-88.
Drennan, J., Mort, G., & Previte, J. (2006). Privacy, risk perception, and expert online
behavior: An exploratory study of household end users. Journal of Organizational
and End user Computing, 18(\), 1-22. Retrieved October 18, 2006 from,
ProQuest database.
Edward, M. (2001). The economic position of the Chinese People's Republic. University
of Connecticut, Department of Economics web site. Retrieved February 14, 2009,
from http://ideas.repec.Org/a/eko/ekoeko/3_l 06.html
Gefen, D., Karahanna, E., & Straub, D. (2003). Trust and TAM in online shopping: An
integrated model. MIS Quarterly, 27(1), 51-90. Retrieved November 3, 2006,
from ProQuest database.
Gauzente, C. (2004). Web merchants' privacy and security statements: How reassuring
are they for consumers? A two-sided approach. Journal of Electronic Commerce
Research, 5(3), 181-198. Retrieved November 3, 2006, from
http://www.csulb.edu/web/journals/jecr/issues/20043/Paper4.pdf
Heijden, H., Verhagen, T., & Creemers, M. (2003). Understanding online purchase
intentions: Contributions from technology and trust perspectives. European
Journal of Information Systems, 12, 41-48. Retrieved February 17, 2006, from
ProQuest database.

99
Holland, A. (2006). Absolutely pitiful e-commerce shopping cart abandonment stats:
Four ways to improve yours. Chief Marketer. Retrieved February 10, 2008, from:
http://chiefmarketer.com/Channels/online/shopping_cart_abandonment_0904200
6/
Iyengar, J. (2004). A discussion of current and potential issues relating information
security for internet communications. Competitiveness Review, 14(1/2), 90-95.
Retrieved January 5, 2008,fromProQuest database.
Kim, D., Ferrin D., & Rao, H. (2003). A study of the of consumer trust on consumer
expectations and satisfaction: The Korean experience. ACM International
Conference Proceeding: Vol 50. Proceedings of the 5' international conference
on electronic commerce .(pp. 310-315). Pittsburgh: Pa.,Retrieved January 22,
2008, from ProQuest database.
Klopping, I., & McKinney, E. (2004). Extending the technology acceptance model and
the task-technology fit model to consumer e-commerce. Information Technology,
Learning, and Performance Journal, 22(1), 35-48. Retrieved December 2, 2006,
from ProQuest database.
Koufaris, M., & Hampton-Sosa, W. (2002a). Customer trust online: Examining the role
of the experience with the web site. CIS Working Paper Series, (CIS 2002-11).
New York:NY Department of Statistics & Computer Information Systems
Zicklin School of Business, Baruch College. Retrieved May 10, 2007, from
ProQuest database.
Koufaris, M., & Hampton-Sosa, W. (2002b). Initial perceptions of company
trustworthiness online: A comprehensive model and empirical test. CIS Working
Paper Series, (CIS 2002-05). New York:NY Department of Statistics &
Computer Information Systems Zicklin School of Business, Baruch College.
Retrieved May 10, 2007, from ProQuest database.
Kraeuter, S. (2002). The role of consumers' trust in online-shopping.. Journal ofBusiness
Ethics, 39(1-2), 43-50. Retrieved October 27, 2006, from
http://www.springerlink.com/content/kqmt06pyj 1 v0vkv2/
Lee, G. & Lin, H. (2005. Customer perceptions of e-service quality in online shopping.
International Journal of Retail and Distribution Management, 33(2/3) 161 -176
Retrieved November 10, 2007, from ProQuest database.
Lwin, M., Wirtz, J., & Williams, J. (2006). Promises, promises: How consumers respond
to warranties in internet retailing. The Journal of Consumer Affairs, 4(2), 236257. Retrieved February 2, 2007, from ProQuest database.
Martinez-Lopez, F., & Luna, P. (2005). Internet research, 15(3), 312-325. Retrieved
February 12, 2009 from ProQuest database.

100
McCloskey, D. (2006). The importance of ease of use, usefulness, and trust to online
consumers: An examination of the technology acceptance model with older
consumers. Journal of Organizational and End User Computing, 18(3), 47-65.
Retrieved November 1, 2006,fromProQuest database.
Meinert, D., Peterson, D., Criswell, J., & Crossland, M. (2006). Would regulation of web
site privacy Policy statements increase consumer trust? Informing Science
Journal, 9(4), Retrieved November 12, 2006, from ProQuest database.
Milne, G. & Culnan, J. (2004). Strategies for reducing online privacy risks: Why
consumers read or don't read online privacy notices. Journal ofInteractive
Marketing, J8(3), 15-29. Retrieved March 3, 2006, from ProQuest database.
Naiyi, Y. (2004). Dimensions of consumers' perceived risk in online shopping. Journal
of Electronic Science and Technology of China, 2(3), 177-182. Retrieved January
12, 2008,fromProQuest database.
Parent, M. (2007). The 6th and biggest lie of all: Lessons from a decade of e-tailing. Ivey
Business Journal. Retrieved March 5, 2008, from: E:\NCU\Research\Ivey
Business Journal - FEATURE ARTICLE.mht
Schneider, I. (2005). The Internet is the safest channel. Bank Systems & Technology,
¥2(8), 12. Retrieved October 27, 2006, from ProQuest database.
Shergill, G. & Chen, Z. (2005). Web-based shopping: Attitudes towards online shopping
in New Zealand. Journal of Electronic Commerce Research, 6(2), 79-92.
Retrieved January 24, 2008 from ProQuest database.
Sims, R. L. (2004). Bivariate data analysis. Hauppauge, NY: Nova Science Publishers.
Slyke, C, Belanger, F., & Comunale, C.L. (2002). Factors influencing the adoption of
web-based shopping: The impact of trust. Communications of the ACM 35(1.), 3249. Retrieved August 1, 2007,fromACM Digital Library.
Slyke, C, Shim, J., Johnson, R., & Jiang, J. (2006). Concern for information privacy and
online consumer purchasing. Journal of the Association for Information Systems,
7(6), 415-444. Retrieved May 17, 2007, from ProQuest database.
Smith, A. (2004). Cybercriminal impacts on online business and consumer confidence.
Online Information Review, 28(3), 224-234. Retrieved October 27, 2006, from
ProQuest database.
Sorce, P., Perotti, V., & Widrick, S. (2005). Attitude and age differences in online
buying. International Journal of Retail and Distribution Management, 33(2/3),
122-132. Retrieved November 27, 2007,fromProQuest database.

101
So, W.C., Wong, D.., & Sculli, D. (2005). Factors affecting intentions to purchase via the
Internet. Industrial Management & Data Systems, 10(9), 1225-1244. Retrieved
May 5, 2007, from ProQuest database
Staff. (2003). Descriptive and correlational designs. University of Illinois web site.
Retrieved August 27, 2007, from,
http://clcpages.clcillinois.edu/home/soc455/psycweb/research/descriptive.htm
Trochim, W.M. (2001). The research methods knowledge base (2nd ed.). Cincinnati, OH:
Atomic Publishing.
Tzortzatos, R., & Boulianne, E. (2005). Assurance seals on web sites aren't foolproof.
Bank Technology News 18(7), 44-45.. Retrieved November 1, 2006, from
ProQuest database.
Valentine, L. (2003). The fraudsters' playground. ABA Banking Journal, 95(8), 39-43.
Retrieved November 3, 2006, from ProQuest database.
Wagner, K. (2007). Introduction to research methods. Retrieved August 25, 2007, from
http://www.ask.com/bar?q=+Wagner+Introduction+to+Research+Methods&page
=1 &qsrc=2417&ab=0&u=http%3 A%2F%2Fpsychology.about.com%2Fod%2Fre
searchmethods%2Fss%2Fexpdesintro.htm
Wakefield, R., & Whitten, D. (2006). Examining user perceptions of third-party
organization Credibility and trust in an e-retailer. Journal of Organizational and
End User Computing, 18(2), 1-19. Retrieved November 30, 2006, from
ABI/INFORM Global database.
Wakefield, R., Stocks, M., & Wilder, W. (2004). The role of web site characteristics in
initial trust formation. The Journal of Computer Information Systems, 45(1), 94103. Retrieved November 30, 2006, from ABI/INFORM Global database.
Wang, M., Chen, C, Chang, S., & Yang, Y. (2007). Effects of online shopping attitudes,
subjective norms, and control beliefs on online shopping intentions: A test of the
theory of planned behavior. International Journal of Management, 24(2), 296302. Retrieved January 10, 2008, from ProQuest database.
Yang, B., & Lester, D. (2004). Attitudes toward buying online. CyberPsychology &
Behavior, 7(1), 85-90. Retrieved January 24, 2008, from ProQuest database.
Yang, B., & Lester, D. (2007). Attitudes toward buying online as predictors of shopping
online for British and American respondents. CyberPsychology & Behavior,
10(2), 198-203. Retrieved January 27, 2008, from ProQuest database.

102
Yang, Z., & Fang X. (2004). Online service quality dimensions and their relationships
with satisfaction: A content analysis of customer reviews of security brokerage
services. International Journal of Service Industry Management, 15(3/4), 302323. Retrieved November 30, 2006, from ProQuest database.
Yun, Z., & Good, L. (2007). Developing customer loyalty from e-tail store image
attributes. Managing Service Quality, 77(1), 1-2. Retrieved February 12, 2009
from ProQuest database.
Zhang, X. (2005). What do consumers really know? Communications of the ACM, August
2005, 48(9), 44-48. Retrieved November 2, 2006, from ACM Digital Library.
Zhang, X., Prybutok, V., & Chang, K. (2006). The role of impulsiveness in a tarn-based
online purchasing model. Information Resources Management Journal, 19(2), 5468. Retrieved January 15, 2008, from ProQuest database.
Zhou, L., Dai, L., & Zhang, D. (2007). Online shopping acceptance model: A critical
survey of consumer factors in online shopping. Journal of Electronic Commerce
Research, 8(\), 41-62. Retrieved June 5, 2007, from ProQuest database.
Zikmund, W.G. (2003). Business research methods. (7th ed.). Mason, OH: Southwestern Publishing.

APPENDIXES

104
Appendix A:
Cover Letter for Survey Participants
June 22, 2008
Dear Survey Participant:
I am a student at Northcentral University completing a doctoral degree in Business
Administration with a concentration in Management Information Systems. I am
conducting a research study entitled: Assessing Consumer Acceptance of Online
Shopping: Examining the Factors Affecting Purchase Intentions.
The purpose of this quantitative correlational study is to assess factors that pertain to
adoption or rejection of online shopping. A 7-point Likert-type-type scale instrument will
be used in this study to explore your perceptions of perceived site quality, attitudes
toward buying on the Internet, and purchase intentions. Your participation will involve
completing and submitting the online survey instrument available at www.
Flicksurvey.com. The time required to complete and submit the survey should only
require 20 -25 minutes of your time. Your participation in this study is voluntary and you
must be over 18 year of age to participate. If you choose not to participate or to withdraw
from the study at any time, you can do so without penalty or loss of benefit to yourself.
The results of the research study may be published but your name or any other
identifying data will not be used and only aggregate data will be used. Your specific input
will be maintained in confidence.

105
In this research, there are no foreseeable risks to you. The responses will be kept
confidential and you may terminate your participation at any time without penalty. Data
from all the responses will be automatically complied in a database, which precludes
human intervention or manipulation. This is done to protect your identity and ensure the
integrity of your responses. Upon completion of this study, upon your request, a copy of
the results will be emailed to you. There may be no direct benefit to you, other than extra
credit awarded by your Marketing instructor for completing and submitting this survey.
By signing this form, you acknowledge that you understand the nature of the study. These
forms and data will be retained electronically for three years in a secured file. Please
email this acknowledgement by clicking the - concur hyperlink. If you agree to
voluntarily participate in this study, please click this hyperlink to complete the 25-minute
survey. If you have any questions concerning the research study, please call me at (843)
987-0963 or (843) 525-0823. You may also reach me via email: [email protected]. Thank
you for your assistance and support in this research project.
Sincerely,
Kenneth L. Flick

106
Appendix B:
Informed Consent- 18 Years of Age and Older
I,

(type in Full Name) do hereby volunteer to

participate in the research project developed by Kenneth L. Flick to assess factors
influencing online shopping acceptance and purchase intentions. I understand that my
information and responses to the survey document will be held in confidence and will
only be used for this doctoral project. Results of this project may be published; however,
there will not be any connection attributable to me or the institution I represent.

107
Appendix C:
Directions for Study Participation
1. Please read the cover letter for survey participants.
2. Read and electronically sign the Informed Consent Document.
3. Please enter this address into your web browser overstock.com or amazon.com
if you entered overstock.com, please search for an alarm clock, and make a
mock purchase. Do not actually buy the item, as you will not be reimbursed for
the purchase. If you entered amazon.com search for any book you desire. Make
a mock purchase of the book. Do not actually buy the book, as you will not be
reimbursed for the purchase.
4. Please now go to the online survey at flicksurvey.com
5. Complete the survey and submit your responses. Directions on how to complete
the survey are included with the survey.
6. If you would like to be provided with results from this study, please submit
your request and current e-mail address to:
Kenneth L. Flick at this e-mail address: kflick(q),tcl.edu
Thank you for your participation.

108

Appendix D:
Research Survey: Online Shopping Survey

INTRODUCTION
This is a quantitative survey of marketing and business students at four colleges of the
Technical College System in South Carolina and participating universities. In addition,
faculty and staff at these institutions are encouraged to participate. It is conducted as part
of the doctoral study of Kenneth L. Flick student, at Northcentral University.. A 28question Likert-type-type questionnaire should take no more than 25 minutes of your
time to complete. There are 3 demographic questions and 25 questions pertaining to your
perceptions concerning online shopping. All information will be held in strict confidence.
A copy of the aggregate results of this survey will be made available to you upon request.
Thank you for your support in this research project.
Survey Instrument
2. What is your gender?*
Male
Female
3. What is your current age?*
18-25
26-35
36-45
46-55
56 and above
4. How much experience do you have with the Internet.*
Less than 1 year

109
2-5 years
More than 5 years
5. I am familiar with the web site I reviewed.
l=strongly disagree to 7=strongly agree
6. This company is trustworthy.
l=strongly disagree to 7=strongly agree
7. I trust this company keeps my best interests in mind
l=strongly disagree to 7=strongly agree
8. This vendor's homepage provides excellent feedback options.
l=strongly disagree to 7=strongly agree
9. This vendor's homepage alerts you to new product offering or developments.
l=strongly disagree to 7=strongly agree
10. This vendor's homepage seeks user advice or feedback on what they offer.
l=strongly disagree to 7=strongly agree
11. This vendor's homepage probably alters facts to make a sale.
l=strongly disagree to 7=strongly agree
12. This vendor's homepage is likely to promise things without actually doing them.
l=strongly disagree to 7=strongly agree
13. This vendor's homepage probably uses customer information without permission.

110
l=strongly disagree 4 to 7=strongly agree
14. This vendor's web site shows that it offers excellent quality brands.
l=strongly disagree to 7=strongly agree
15. This vendor's web site shows that it offers brands that are well-know to me.
l=strongly disagree to 7=strongly agree
16. The web site you reviewed is visually appealing.
l=strongly disagree to 7=strongly agree
17. The web site you reviewed is classy.
l=strongly disagree to '7=strongly agree
18. The security web seal on the homepage that you viewed matters to me.
l=strongly disagree to 7=strongly agree
19. The security web seal on the homepage that you viewed is valuable.
l=strongly disagree to 7=strongly agree
20. I would feel secure in providing sensitive information (e.g., credit card number)
purchasing online with this vendor.
l=strongly disagree to 7=strongly agree
21. It would be no security problem transmitting sensitive information online for
purchasing with this vendor.
l=strongly disagree to 7=strongly agree
22. When I hear about a new web site, I often find an excuse to go visit it.

Ill
l=strongly disagree to 7=strongly agree
23. In general, I am interested in trying out new web sites.
l=strongly disagree to 7=strongly agree
24.

Using this web site can improve my shopping performance.
l=strongly disagree to 7=strongly agree

25. I find this web site useful.
l=strongly disagree to 7=strongly agree
26. Using this web site can increase my shopping effectiveness.
l=strongly disagree to 7=strongly agree
27. My interaction with this web site is clear and understandable.
l=strongly disagree to 7=strongly agree
28. I find this web site easy to use.
l=strongly disagree to 7=strongly agree
29. I would use this web site to purchase products?
l=strongly disagree to 7=strongly agree

112

Appendix E:
Permission to Use Survey Instrument -1

Hi Kenneth,
Yes, feel free to adapt the study's items - be sure to cite as appropriate. Your study sounds
interesting.
Good luck.
Robin

From: Kenneth Flick [mailto:[email protected]]
Sent: Fri 3/28/2008 9:39 AM
To: Wakefield, Robin L.
Subject: Permission to use study questions
Dr. Wakefield:
I am currently pursuing a PhD in Business Administration with MIS Specialty from
Northcentral University, Prescott, Arizona. To fulfill the requirements for this degree, I
am conducting research on consumer acceptance of online shopping. For my research, I
need to compile a survey instrument that can be administered online. Therefore, I would
like to request your permission to use survey questions from your study entitled: The
Role of Web Site Characteristics in Initial Trust Formation. Please let me know if I may
adapt some of your questions for my research study.
Please contact me at: [email protected]
Thank you,
Sincerely,
Kenneth L. Flick
Division Dean
Business Technologies
Technical College of the Lowcountry
PO Box 1288
921 Ribaut Road
Beaufort, SC 29901

113
Appendix F:
Permission to Use Survey Instrument -2
Hi Kenneth,
of course you can adapt our questionnaire. All you need to do is cite our paper as your
source. Just so you know, you can do that with any published scale or other instrument.
You just need to cite the source.
Good luck
Marios
Marios Koufaris, Ph.D. email:[email protected]
Associate Professor http://cisnet.baruch.cuny.edu/koufaris
Coordinator, Information Systems Specialization, PhD Program in Business
Department of Statistics and Computer Information Systems
Baruch College, City University of New York
55 Lexington Ave., Box Bl 1-220
New York, NY 10010
Phone: (646)-312-3373
Fax: (646)-312-3351
"Kenneth Flick" <[email protected]> wrote:
To:
<[email protected]>, <[email protected]>
From: "Kenneth Flick" <[email protected]>
Date: 03/28/2008 02:10AM
Subject: Permission to use questions from your study
Dr. Koufaris and Dr. Hampton-Sosa,
I am currently pursuing a PhD in Business Administration with MIS Specialty from
Northcentral University, Prescott, Arizona.. To fulfill the requirements of this degree, I
am conducting research on consumer acceptance of online shopping. For my research, I
need to compile a survey instrument that can be administered online. Therefore, I would
like to request your permission to use survey questions from your study entitled:
Customer Trust Online: Examining the Role of Experience with the Web Site.
Please let me know if I may adapt some of your questions for my research study.
Please contact me at: [email protected]
Thank you,
Sincerely,
Kenneth L. Flick

Appendix G:
Permission to Use Survey Instrument -3
Dr. Tang
I am currently pursuing a PhD in Business Administration with MIS
Specialty from Northcentral University, Prescott, Arizona, USA. To fulfill
the requirements for this degree, I am conducting research on consumer
acceptance of online shopping. For my research, I need to compile a
survey instrument that can be administered online. Therefore, I would
like to request your permission to use survey questions from your study
entitled: Gender differs: assessing a model of online purchase intentions in e-tail
service. Please let me know if I may adapt some of your questions for
my research study. Also if you have current contact information for Dr. Chiu
and Dr. Lin, I would appreciate receiving it.
Please contact me at: [email protected]
Thank you,
Sincerely,
Kenneth L. Flick
Division Dean
Business Technologies
Technical College of the Lowcountry
PO Box 1288
921 Ribaut Road
Beaufort, SC 29901
843.525.8238
kflick@,tcl.edu
Dear Kenneth,
Thanks for your request about using our survey questions for your research.
It is fine with me for your request. I had forwarded your request to my colleague
(co-authors). If I get their feedback, I will inform you soon.
Best regards,
Ling-Lang Tang
Yuan Ze University

115
Appendix H:
Request for Participation Memo

OP T H E

LDWCDUNTRY

To: Dr.
FROM: Kenneth L. Flick
DATE: 6/7/08
SUBJECT: Participation in doctorial research project
I am pursuing my PhD in Business Administration with MIS Specialty at Northcentral
University. As a requirement for completion of this degree, I am conducting a research
project for my Dissertation entitled: Assessing Consumer Acceptance of Online
Shopping. The research project involves study participants filling out and submitting an
online survey instrument following a visitation to a selected web site. While some
demographic information is requested gender, age, and length of experience with the
Internet, absolutely no information will be required that could potentially be used to
identify any student, faculty, or staff that chooses to participate. I would like to involve
students who are actively enrolled in a Marketing class for participation in this study. If
possible, students should be awarded a few bonus points for study participation as an
incentive.
Additionally, I would like to invite the participation of all faculty and staff in this
research project.

116
The survey is completed following a short visitation to a selected web site
www.overstock.com or www.amazon.com . Study participants are asked to do a search
on the web site selected and to make a mock purchase of an alarm clock if
www.overstock.com is chosen or a book of their choosing if www.amazon.com is
chosen. Following this short exercise, participants will be directed to the online survey.
Complete directions will be e-mailed to you to forward to the selected Marketing
instructor. At the conclusion of this study, results will be available if requested. Please
simply e-mail your request to [email protected].
If you are willing to participate in this doctorial research project, please contact me with
the provided contact information.
Thank you,
Sincerely,
Kenneth L. Flick
Division Dean
Business Technologies
Technical College of the Lowcountry
PO Box 1288
921 Ribaut Road
Beaufort, SC 29901

117
Appendix I:
Williamsburg Technical College Approval
From: Brown, Eric [mailto:[email protected]]
Sent: Tuesday, May 13, 2008 10:36 AM
To: Kenneth Flick
Cc: Cox, Dr. Cleve
Subject: Dissertation request
Mr. Flick,
Your request to include Williamsburg Technical College students, faculty, and staff in
your study has been approved. We look forward to assisting you with your study. Please
forward details of your needs when available.
Dr. Eric Brown

118
Appendix J:
Denmark Technical College Approval
From:
Sent:
To:
Subject:

John Waddell [[email protected]]
Thursday, May 15, 2008 8:30 AM
Kenneth Flick
RE: Request for Assistance

Dear Kenneth:
I have been on travel recently.
You have my permission. Please contact Dr. Jackie Skubal, Vice-President for further
follow-up.
JKW
From: Kenneth Flick [mailto:[email protected]]
Sent: Friday, May 09, 2008 1:04 PM
To: John Waddell
Subject: Request for Assistance
Importance: High
Dr. Waddell,
Please see the attached memo.
Thank you,
Kenneth L. Flick
Division Dean
Business Technologies
Technical College of the Lowcountry
PO Box 1288
921 Ribaut Road
Beaufort, SC 29901
843.525.8238

119

Appendix: K
Research Question 1 Correlations
Table 26
Research Question 1 Correlations
Q16
1
Appealing
Q16

Sig.(2tailed)
N
Classy Q17 Pearson
Correlation
Sig.(2tailed)
N
Brand Equity Pearson
1Q14
Correlation
Sig.(2tailed)
N.
Brand Equity Pearson
2Q15
Correlation
Sig.(2tailed)
N
Opportunism 1 Pearson
Qll
Correlation
Sig.(2tailed)
N
Opportunism Pearson
2Q12
Correlation
Sig.(2tailed)
N
Opportunism Pearson
3Q13
Correlation
Sig.(2tailed)
N

Q17
Q14
Q15
QU
Q6
Q7
..543** .420** .536** -.186** ..543*" .431**
0.000

0.000

0.000

0.001

0.000 0.000

323

323

323

323

323

323 323

.543**

1

0.000
323

323

.420** ..429**
0.000

0.000

323

323

.429** ..440** -.188** ..433*" .457**
0.000

0.000

0.001

0.000 0.000

323

323

323

323 323

1

323

.536** ..440** ..635**
0.000

0.000

0.000

323

323

323

.635** -.215** ..545*" .513**
0.000

0.000

0.000 0.000

323

323

323 323

1

323

-.186** -.188** -.215** -.212**
0.001
323

0.001 0.000
323

323

-.212** ..591*" .483**
0.000

0.000 0.000

323

323 323

1

-.299*'-.329**

0.000
323

0.000 0.000
323

323 323

-.300** -.249** -.361** -.343** .643** -.402* -.446**
0.000

0.000

0.000

0.000

0.000

0.000 0.000

323

323

323

323

323

323 323

-.236** -.232** -.319** -.276** .534** -.320* -.400**
0.000

0.000

0.000

0.000

0.000

0.000 0.000

323

323

323

323

323

323 323

120
Table 26 continued:
Q16
CommunicatioPearson
n 1 Q8
Correlation
Sig- (2tailed)
N
Communicat- Pearson
ion 2 Q9
Correlation
Sig. (2tailed)
N

Communicat- Pearson
ion 3 Q10
Correlation
Sig. (2tailed)
N
trust 1 t^o
rearson
Correlation
f 1 ?"?"
tailed)
N
trustzv^/
rearson
Correlation
f1?'?"
tailed)
N
Visually
Appealing
Q16

Sig. (2tailed)
N
L-lassyl^l/ rearson
Correlation
f1?'?"
tailed)
N
Brand Equity Pearson
1Q14
Correlation
Sig. (2tailed)
N

Q17

Q14

Q15

Qll

Q6

Q7

504** ..422** .502** .560** -.223** ..601** .603**
0.000

0.000

0.000

0.000

0.000

323

323
323

323
323

323
323

323
323

0.000 0.000
323
323

323
323

.444** ..341** .526** .536** -.201** ..606** .570**
0.000

0.000

0.000

0.000

0.000

323

323

323

323

323

,43s**

364 **

385 **

0.000

0.000

0.000

323
543**

323
323
323
323
433** 545** 591** -299**

0.000

0.000

323
431**

323
323
323
323
457** 513** 483** -329**

323 323
674** 1

0.000

0.000

0.000

0.000

0.000

0.000 0.000
323

323

.495** ..217** ..503** .523**
0.000

0.000

0.000

0.000

0.000 0.000

323
1

0.000

0.000

323
674 * *
0.000

323
323
323
323
323
323 323
Q12
Q13
Q8
Q9
Q10
Q6
Q7
-.300** -.236** .504** ..444** .438** .543** .431**
Q.OOO 0.000
0.000 0.000 0.000 0.000 0.000
323
323
323
323
323
323
323
.249** -232** 422** 341** 364** 433** 457**
0.000

0.000

0.000

0.000

0.000

0.000

0.000

323
323
323
323
323
323
323
..351** -.319** .502** ..526** .385** .545** .513**
0.000

0.000

0.000

0.000

0.000

0.000

0.000

323

323

323

323

323

323

323

121
Table 26 continued:
Q16
Brand Equity Pearson
Correlation
2Q15
Sig.(2tailed)
N
Opportunism Pearson
1 Qll
Correlation
Sig.(2tailed)
N
Opportunism Pearson
Correlation
2Q12
Sig.(2tailed)
N
Opportunism Pearson
Correlation
3Q13
Sig.(2tailed)
N
Communicat- Pearson
ion 1 Q8
Correlation
Sig.(2tailed)
N
Communicat- Pearson
Correlation
ion 2 Q9
Sig.(2tailed)
N
Communicat- Pearson
ion 3 Q10
Correlation
Sig.(2tailed)
N
Trust 1 Q6
Pearson
Correlation
Sig. (2tailed)
N

Q17

Q14

Q15

Qll

Q6

Q7

-.343** -.276** .560** .536** .495** .591** .483**
0.000

0.000

0.000

0.000

0.000

0.000

0.000

323

323

323

323

323

323

323

643**

534** -223** -201** -217** -299** -329**

0.000

0.000

0.000

0.000

0.000

0.000

0.000

323

323

323

323

323

323

323

1

..600** -.332** -.397** -.302** -.402** -.446**
0.000

0.000

0.000

0.000

0.000

0.000

323

323

323

323

323

323

323

.600**

1

0.000
323

323

-.332** -.301**
0.000

0.000

323

323

-.301** -.296** -.334** -.320** -.400**
0.000

0.000

0.000

0.000

0.000

323

323

323

323

323

1

323

-.397** -.296** .647**
0.000

0.000

0.000

323

323

323

..647** .645** .601** .603**
0.000

0.000

0.000

0.000

323

323

323

323

1

323

.302** -.334** .645** .584**
0.000

0.000

0.000

0.000

323

323

323

323

.584** .606** .570**
0.000

0.000

0.000

323

323

323

1

323

.402** -.320** .601** .606** .503**
0.000

0.000

0.000

0.000

0.000

323

323

323

323

323

.503** .523**
0.000

0.000

323

323

1

.674**
0.000

323

323

122
Table 26 continued:
Q16
Trust 2 Q7

Q17

Q14

Q15

Qll

Q6

Pearson
.446** -.400** .603** .570** .523** .674**
Correlation
Sig.(20.000 0.000 0.000 0.000 0.000 0.000
tailed)
323
323
323
323
323
323
N
**. Correlation is significant at the 0.01 level (2-tailed)

Q7
1

323

Q21

Security 2

Security 1
Q20

Web Seal 2
Q19

Web Seal 1
Q18
Sig.(2tailed)
N
Pearson
Correlation
Sig.(2tailed)
N
Pearson
Correlation
Sig.(2tailed)
N
Pearson
Correlation
Sig.(2tailed)
N

Pearson
Correlation

Research Question 2 Correlations

Table 27

.000
323

323

323

.847**

..000

..467**

.000

.462**

323

1

323

323

323

323

.000

..000

.847**

323

.000

1

323

.000

.467**

323

.000

.462**

Q21

..533**

323

.000

.533**

323

323
1

.000

.511**

Q20

..000

..848**

Q19

.511**

323

.000

.848**

323

1

Q'18

Research Question 2 Correlations

Appendix L:

.000
323

322

.288**

323

.000

.287**

.000

.281**

322

.000

.325**

323

.000

.003
322

.257**

323

.000

.269**

Q23

.167**

322

.001

.181**

Q22

323

.000

..659**

323

.000

..726**

323

.000

..435**

323

.000

..419**

Q29

Pearson
Correlation
Sig.(2tailed)
N
Pearson
Correlation
Sig.(2tailed)
N
Pearson
Correlation
Sig.(2tailed)
N
322

322

..435**
..000
323

.419
.000
323

.659**
.000
323

.726**
.000
323

323

323

323

323

.000

.000

..000

.000

.288**

..257**

.269**

.287**

322

.000

.000

..003

.001
323

.281**

Q21

.325**

Q20

..167**

Q19

.181**

Q18

322

.000

.284**

322

.000

.704**

322

1

Q22

323

.000

.311**

323

1

322

.000

.704***

Q23

* Correlation significant at 0.01 level (two-tailed), *Correlation significant at 0.05 level (two-tailed)

Purchase
Intentions
Q29

Innovativeness 2
Q23

Innovativeness 1
Q22

Table 27 continued:

323

1

323

.000

..311**

322

.000

..284**

Q29

125

Appendix: M
Research Question 3 Statistics

Non- Parametric Tests
Table 28
Gender and Age

Gender* Age

Case Processing Summary
Cases
Valid
Missing
Total
N Percent N Percent N Percent
323 100% 0
.0%
323 100%

Table 29
Gender and Age Cross-tabulation
Age
18-25 26-35 36-45 46-55 56 and above Total
Gender Male
48
21
28
33
25
155
Female
53
20
36
42
17
168
Total
101
41
64
75
42
323
Table 30
Gender and Age Chi-Square Test
Value

df

Asymp. Sig.
(2-sided)
.500
.498

Pearson Chi-Square
3.358a
4
Likelihood Ratio
3.367
4
Linear-by-Linear
.239
Association
1
.625
N of Valid Cases
323
a. cells 0 cells (.0%) have expected count less than

Table 31
Gender and Experience

Gender*
Experience

Case Processing Summary
Cases
Valid
Missing
Total
N Percent N Percent N Percent
323 100% 0
.0% 323 100%

Table 32
Gender and Experience Cross-tabulations
Experience
Total
Less than 2-5 years More than
1 year
5 years
Gender Male
5
17
133
155
Female
9
22
137
168
Total
14
39
270
323
Table 33
Gender and Experience Chi-Square Test
Value

df Asymp. Sig.
(2-sided)
1.322a 2
.516
1.338 2
.512

Pearson Chi-Square
Likelihood Ratio
Linear-by-Linear
Association
1.307 1
.253
N of Valid Cases
323
a. 0 cells (.0%) have expected count less than

127
Table 34
Gender and Familiarity

Gender*
Familiarity

Case Processing Summary
Cases
Valid
Missing
Total
N Percent N Percent N Percent
323 100% 0
.0%
323 100%

Table 35
Gender and Familiarity Cross-tabulations
Familiarity
Gender Strongly Disagree Somewhat Neutral Somewhat Agree Strongly Total
Disagree
Agree
Disagree
Agree
Male
9
14
14
15
17
51
35
155
Female
7
14
6
16
25
51
49
168
Total
16
28
20
31
42
102
84
323

Table 36
Gender and Familiarity Chi-Square Test
df Asymp. Sig.
(2-sided)
6.827a 6
.337
6.928 6
.328
Value

Pearson Chi-Square
Likelihood Ratio
Linear-by-Linear
Association
2.281 1
.131
N of Valid Cases
323
a. 0 cells (.0%) have expected count less than

128
Table 37
Gender and Trust 1

Gender*
Trust 1

Case Processing Summary
Cases
Valid
Missing
Total
N Percent N Percent N Percent
323 100% 0
.0%
323 100%

Table 38
Gender and Trust 1 Cross-tabulations
Trust 1
Gender Strongly Disagree Somewhat Neutral Somewhat Agree Strongly Total
Disagree
Disagree
Agree
Agree
Male
1
2
3
32
19
58
40
155
Female
2
0
4
24
25
72
41
168
Total
3
2
7
56
44
130
81
323

Table 39
Gender and Trust 1 Chi-Square Test
Value

df

Asymp. Sig.
(2-sided)
.488
.399

Pearson Chi-Square 5.443a 6
Likelihood Ratio
6.928 6
Linear-by-Linear
Association
2.281
1
.469
N of Valid Cases
323
a. 6 cells (42.9%) have expected count less than

Table 40
Gender and Trust 2

Gender*
Trust 2

Case Processing Summary
Cases
Valid
Missing
Total
N Percent N Percent N Percent
323 100% 0
.0%
323 100%

Table 41
Gender and Trust 2 Cross-tabulations
Trust 2
Gender Strongly Disagree Somewhat Neutral Somewhat Agree Strongly Total
Disagree
Disagree
Agree
Agree
Male
0
7
9
37
33
52
17
155
Female
1
3
4
40
36
60
24
168
Total
1
10
13
77
69
112
41
323

Table 42
Gender and Trust 2 Chi-Square Test
Value

df

Asymp. Sig.
(2-sided)
.421
.369

Pearson Chi-Square 6.023a 6
Likelihood Ratio
6.502 6
Linear-by-Linear
Association
2.401
1
.121
N of Valid Cases
323
a. 3 cells (21.4%) have expected count less than

130
Table 43
Gender and Communication 1

Gender*
Communication 1

Case Processing Summary
Cases
Valid
Missing
N Percent N Percent N
323 100% 0
.0%
323

Total
Percent
100%

Table 44
Gender and Communication 1 Cross-tabulations
Communication 1
Gender Strongly Disagree Somewhat Neutral Somewhat Agree Strongly Total
Disagree
Agree
Disagree
Agree
Male
3
5
6
30
43
45
23
155
Female
1
5
9
28
36
64
25
168
Total
4
10
15
58
79
109
48
323

Chi-Square Test
Table 45
Gender and Communication 1 Chi-Square Test
Value

df

Asymp. Sig.
(2-sided)
.522
.515

Pearson Chi-Square 5.170a 6
Likelihood Ratio
5.230 6
Linear-by-Linear
.307
Association
1.044
N of Valid Cases
323
a. 3 cells (21.4%) have expected count less than

131
Table 46
Gender and Communication 2

Gender*
Communication 2

Case Processing Summary
Cases
Valid
Missing
N Percent N Percent N
323 100% 0
.0%
323

Total
Percent
100%

Table 47
Gender and Communication 2 Cross-tabulations
Communication 2
Gender Strongly Disagree Somewhat Neutral Somewhat Agree Strongly Total
Disagree
Disagree
Agree
Agree
Male
2
3
3
28
24
54
41
155
Female
1
4
6
17
27
70
43
168
Total
3
7
9
45
51
124
84
323

Chi-Square Test
Table 48
Gender and Communication 2 Chi-Square Test
Value

df

Asymp. Sig.
(2-sided)
.430
.424

Pearson Chi-Square 5.940a 6
Likelihood Ratio
5.990 6
Linear-by-Linear
Association
.614
1
.433
N of Valid Cases
323
a. 6 cells (42.9%) have expected count less than

132
Table 49
Gender and Communication 3

Gender*
Communication 3

Case Processing Summary
Cases
Valid
Missing
N Percent N Percent N
323 100% 0
.0%
323

Total
Percent
100%

Table 50
Gender and Communication 3 Cross-tabulations
Communication 3
Gender Strongly Disagree Somewhat Neutral Somewhat Agree Strongly Total
Disagree
Disagree
Agree
Agree
Male
2
9
8
38
28
49
21
155
Female
4
8
15
19
37
62
23
168
Total
6
17
23
57
65
111
44
323

Table 51
Gender and Communication 3 Chi-Square Test
df Asymp. Sig.
(2-sided)
11.544a 6
.073
11.703 6
.069
Value

Pearson Chi-Square
Likelihood Ratio
Linear-by-Linear
Association
.381
1
.537
N of Valid Cases
323
a. 2 cells (14.3%) have expected count less than

133
Table 52
Gender and Opportunism 1

Gender*
Opportunism 1

Case Processing Summary
Cases
Valid
Missing
N Percent N Percent N
323 100% 0
.0%
323

Total
Percent
100%

Table 53
Gender and Opportunism 1 Cross-tabulations
Opportunism 1
Gender Strongly Disagree Somewhat Neutral Somewhat Agree Strongly Total
Disagree
Disagree
Agree
Agree
Male
13
36
28
40
23
11
4
155
Female
19
47
24
33
27
16
2
168
Total
32
83
52
73
50
27
6
323

Table 54
Gender and Opportunism 1 Chi-Square Test
Value

df

Asymp. Sig.
(2-sided)
.549
.546

Pearson Chi-Square 4.959a 6
Likelihood Ratio
4.982 6
Linear-by-Linear
Association
.533
1
.465
N of Valid Cases
323
a. 2 cells (14.3%) have expected count less than

134
Table 55
Gender and Opportunism 2

Gender*
Opportunism 2

Case Processing Summary
Cases
Valid
Missing
N Percent N Percent N
323 100% 0
.0%
323

Total
Percent
100%

Table 56
Gender and Opportunism 2 Cross-tabulations
Opportunism 2
Total
Gender Strongly Disagree Somewhat Neutral Somewhat Agree Strongly
Disagree
Disagree
Agree
Agree
Male
23
49
27
31
18
6
1
Female
33
59
20
32
17
6
1
Total
56
108
47
63
35
12
2

Table 57
Gender and Opportunism 2 Chi-Square Test
Value

df

Asymp. Sig.
(2-sided)
.773
.772

Pearson Chi-Square 3.281a 6
Likelihood Ratio
3.290 6
Linear-by-Linear
Association
1.278
1
.258
N of Valid Cases
323
a. 2 cells (14.3%) have expected count less than

155
168
323

135

Table 58
Gender and Opportunism 3

Gender*
Opportunism 3

Case Processing Summary
Cases
Valid
Missing
N Percent N Percent N
323 100% 0
.0%
323

Total
Percent
100%

Table 59
Gender and Opportunism 3 Cross-tabulations
Opportunism 3
Gender Strongly Disagree Somewhat Neutral Somewhat Agree Strongly Total
Disagree
Disagree
Agree
Agree
Male
17
50
10
35
29
10
4
155
Female
25
68
14
23
27
8
3
168
Total
42
118
24
58
56
18
7
323

Table 60
Gender and Opportunism 3 Chi-Square Test
Value

df

Asymp. Sig.
(2-sided)
.290
.288

Pearson Chi-Square 7.344a 6
Likelihood Ratio
7.374 6
Linear-by-Linear
Association
4.603 1
.032
N of Valid Cases
323
a. 2 cells (14.3%) have expected count less than
5. The minimum expected count is 3.36.

136
Table 61
Gender and Brand Equity 1

Gender* Brand
Equity 1

Case Processing Summary
Cases
Valid
Missing
N Percent N Percent N
323 100% 0
.0% 323

Total
Percent
100%

Table 62
Gender and Brand Equity 1 Cross-tabulations
Brand Equity 1
Gender Strongly Disagree Somewhat
Disagree
Disagree
Male 0
1
3
Female 1
1
3
Total 1
2
6

Neutral Somewhat
Agree
27
33
20
28
47
61

Table 63
Gender and Brand Equity 1 Chi-Square Test
Value

df

Asymp. Sig.
(2-sided)
.545
.496

Pearson Chi-Square 4.989a 6
Likelihood Ratio
5.381 6
Linear-by-Linear
Association
1.393
1
.238
N of Valid Cases
323
a. 6 cells (42.9%) have expected count less than
5. The minimum expected count is .48.

Agree Strongly
Agree
66
25
87
28
153
53

Total
155
168
323

137

Table 64
Gender and Brand Equity 2

Gender* Brand
Equity 2

Case Processing Summary
Cases
Valid
Missing
N Percent N Percent N
323 100% 0
.0%
323

Total
Percent
100%

Table 65
Gender and Brand Equity 2 Cross-tabulations
Brand Equity 2
Gender Strongly Disagree Somewhat Neutral Somewhat Agree Strongly Total
Disagree
Disagree
Agree
Agree
Male
1
4
3
28
27
65
29
155
Female
1
3
6
18
35
76
29
168
Total
2
7
9
44
62
141
58
323

Table 66
Gender and Brand Equity 2 Chi-Square Test
Value

df

Asymp. Sig.
(2-sided)
.681
.677

Pearson Chi-Square 3.971a 6
Likelihood Ratio
3.996 6
Linear-by-Linear
Association
.187
1
.665
N of Valid Cases
323
a. 6 cells (42.9%) have expected count less than

138
Table 67
Gender and Visually Appealing

Gender* Visually
Appealing

Case Processing Summary
Cases
Valid
Missing
N Percent N Percent N
323 100% 0
.0%
323

Total
Percent
100%

Table 68
Gender and Visually Appealing Cross tabulations
Visually Appeal]ing
Gender Strongly Disagree Somewhat Neutral Somewhat Agree Strongly Total
Agree
Disagree
Disagree
Agree
Male
1
1
8
27
32
70
16
155
Female
0
0
7
19
37
75
30
168
Total
1
1
15
46
69
145
323
46

Table 69
Gender and Visually Appealing Chi-Square Test
Value

df

Asymp. Sig.
(2-sided)
.258
.677

Pearson Chi-Square 7.743a 6
Likelihood Ratio
8.579 6
Linear-by-Linear
Association
4.956 1
.026
N of Valid Cases
323
a. 6 cells (28.6%) have expected count less than
5. The minimum expected count is .48.

139

Table 70
Gender and Classy

Gender* Classy

Case Processing Summary
Cases
Valid
Missing
N Percent N Percent N
323 100% 0
.0%
323

Total
Percent
100%

Table 71
Gender and Classy Cross-tabulations
Classy
Gender Strongly Disagree Somewhat Neutral Somewhat Agree Strongly Total
Disagree
Agree
Disagree
Agree
Male
0
2
11
51
47
35
9
155
Female
2
4
14
47
50
45
6
168
Total
2
6
25
98
97
80
15
323

Table 72
Gender and Classy Chi-Square Test
Value

df

Asymp. Sig.
(2-sided)
.594
.493

Pearson Chi-Square 4.617a 6
Likelihood Ratio
5.403 6
Linear-by-Linear
Association
.216
1
.642
N of Valid Cases
323
a. 6 cells (28.6%) have expected count less than

Table 73
Gender and Web Seal 1

Gender* Web
Seall

Case Processing Summary
Cases
Valid
Missing
N Percent N Percent N
323 100% 0
.0%
323

Total
Percent
100%

Table 74
Gender and Web Seal 1 Cross-tabulations
Web Seal 1
Gender Strongly Disagree Somewhat Neutral Somewhat Agree Strongly Total
Disagree
Agree
Disagree
Agree
Male
3
4
8
36
24
36
44
155
Female
3
3
9
33
27
43
50
168
6
7
17
94
Total
69
51
79
323

Table 75
Gender and Web Seal 1 Chi-Square Test
Value

df

Asymp. Sig.
(2-sided)
.986
.986

Pearson Chi-Square
.990a 6
Likelihood Ratio
.990
6
Linear-by-Linear
Association
.468
1
.494
N of Valid Cases
323
a. 4 cells (28.6%) have expected count less than

141

Table 76
Gender and Web Seal 2

Gender* Web
Seal 2

Case Processing Summary
Cases
Valid
Missing
N Percent N Percent N
323 100% 0
.0% 323

Total
Percent
100%

Table 77
Gender and Web Seal 2 Cross-tabulations
Web Seal 2
Gender Strongly Disagree Somewhat Neutral Somewhat Agree Strongly Total
Disagree
Disagree
Agree
Agree
Male
2
3
7
43
26
35
39
155
Female
4
5
14
29
32
40
44
168
Total
6
8
21
72
58
75
83
323

Table 78
Gender and Web Seal 2 Chi-Square Test
Value

df

Asymp. Sig.
(2-sided)
.324
.317

Pearson Chi-Square 6.966a 6
Likelihood Ratio
7.037 6
Linear-by-Linear
Association
.007
1
.935
N of Valid Cases
323
a. 4 cells (28.6%) have expected count less than

142

Table 79
Gender and Security 1

Gender* Security 1

Case Processing Summary
Cases
Valid
Missing
N Percent N Percent
323 100% 0
.0%

N
323

Total
Percent
100%

Table 80
Gender and Security 1 Cross-tabulations
Security 1
Gender Strongly Disagree Somewhat Neutral Somewhat Agree Strongly Total
Disagree
Disagree
Agree
Agree
Male
2
3
16
16
22
54
42
155
Female
5
8
15
15
17
69
39
168
Total
7
11
31
31
39
123
81
323

Table 81
Gender and Security 1 Chi-Square Test
Value

df

Asymp. Sig.
(2-sided)
.459
.444

Pearson Chi-Square 5.690a 6
Likelihood Ratio
5.815 6
Linear-by-Linear
Association
.540
1
.462
N of Valid Cases
323
a. 2 cells (14.3%) have expected count less than

143

Table 82
Gender and Security 2

Gender* Security 2

Case Processing Summary
Cases
Valid
Missing
N Percent N Percent
323 100% 0
.0%

N
323

Total
Percent
100%

Table 83
Gender and Security 2 Cross-tabulations
Security 2
Gender Strongly Disagree Somewhat Neutral Somewhat Agree Strongly Total
Disagree
Disagree
Agree
Agree
Male
4
7
17
18
24
57
28
155
Female
4
12
12
23
23
70
24
168
Total
8
19
29
41
47
127
52
323

Table 84
Gender and Security 2 Chi-Square Test
Value

df

Asymp. Sig.
(2-sided)
.686
.684

Pearson Chi-Square 3.930a 6
Likelihood Ratio
3.948 6
Linear-by-Linear
Association
.058
1
.810
N of Valid Cases
323
a. 2 cells (14.3%) have expected count less than

144

Table 85
Gender and Innovativeness 1

Gender*
Innovativeness 1

Case Processing Summary
Cases
Valid
Missing
N Percent N Percent
323 100% 0
.0%

N
323

Total
Percent
100%

Table 86
Gender and Innovativeness 1 Cross-tabulations
Innovativeness 1
Gender Strongly Disagree Somewhat Neutral Somewhat Agree Strongly Total
Disagree
Disagree
Agree
Agree
Male
10
28
22
42
27
18
7
155
Female
6
36
37
29
24
25
11
168
Total
16
64
59
71
51
43
18
323

Table 87
Gender and Innovativeness 1 Chi-Square Test
Value

df Asymp. Sig.
(2-sided)
9.809a 6
.133
9.871 6
.130

Pearson Chi-Square
Likelihood Ratio
Linear-by-Linear
Association
.042 1
.838
N of Valid Cases
323
a. 0 cells (.0%) have expected count less than

145

Table 88
Gender andInnovativeness 2

Gender*
Innovativeness 2

Case Processing Summary
Cases
Valid
Missing
N Percent N Percent
323 100% 0
.0%

N
323

Total
Percent
100%

Table 89
Gender and Innovativeness 2 Cross-tabulations
Innovativeness 2
Gender Strongly Disagree Somewhat Neutral Somewhat Agree Strongly Total
Disagree
Disagree
Agree
Agree
Male
1
23
17
41
40
22
11
155
21
34
30
Female
5
25
39
14
168
Total
6
44
42
75
79
52
25
323

Table 90
Gender and Innovativeness 2 Chi-Square Test
Value

df

Asymp. Sig.
(2-sided)
.420
.393

Pearson Chi-Square 6.025a 6
Likelihood Ratio
6.275 6
Linear-by-Linear
Association
.017
1
.896
N of Valid Cases
323
a. 2 cells (14.3%) have expected count less than

146

Table 91
Gender and Usefulness 1

Gender*
Usefulness 1

Case Processing Summary
Cases
Valid
Missing
N Percent N Percent
323 100% 0
.0%

N
323

Total
Percent
100%

Table 92
Gender and Usefulness 1 Cross-tabulations
Usefulness 1
Gender Strongly Disagree Somewhat Neutral Somewhat Agree Strongly Total
Disagree
Disagree
Agree
Agree
Male
2
4
10
45
35
49
10
155
14
Female
2
6
14
46
42
44
168
77
93
24
Total
4
10
24
91
323

Table 93
Gender and Usefulness 1 Chi-Square Test
Value

df

Asymp. Sig.
(2-sided)
.907
.907

Pearson Chi-Square 2.130a 6
Likelihood Ratio
2.136 6
Linear-by-Linear
Association
.199
1
.655
N of Valid Cases
323
a. 3 cells (21.4%) have expected count less than

Table 94
Gender and Usefulness 2

Gender*
Usefulness 2

Case Processing Summary
Cases
Valid
Missing
N Percent N Percent
321 100% 0
.0%

N
323

Total
Percent
99.4%

Table 95
Gender and Usefulness 2 Cross-tabulations
Usefulness 2
Gender
Male
Female
Total

Strongly Somewhat Neutral
Disagree Disagree
1
8
29
1
7
22
2
15
51

Somewhat Agree
Agree
32
68
38
68
70
136

Table 96
Gender and Usefulness 2 Chi-Square Test
Value

df

Asymp. Sig.
(2-sided)
.325
.317

Pearson Chi-Square 5.812a
Likelihood Ratio
5.891
Linear-by-Linear
.098
Association
2.732
N of Valid Cases
321
a. 2 cells (16.7%) have expected count less than

Strongly Total
Agree
16
155
31
168
321
47

148

Table 97
Gender and Usefulness 3

Gender*
Usefulness 3

Case Processing Summary
Cases
Valid
Missing
N Percent N Percent
323 100% 0
.0%

N
323

Total
Percent
100%

Table 98
Gender and Usefulness 3 Cross-tabulations
Usefulness 3
Gender Strongly Disagree Somewhat Neutral Somewhat Agree Strongly Total
Disagree
Disagree
Agree
Agree
Male
2
2
10
37
42
48
14
155
Female
1
5
13
38
45
51
15
168
23
87
99
Total
3
7
75
29
323

Table 99
Gender and Usefulness 3 Chi-Square Test
Value

df

Asymp. Sig.
(2-sided)
.943
.939

Pearson Chi-Square 1.732a 6
Likelihood Ratio
1.779 6
Linear-by-Linear
Association
.103
1
.749
N of Valid Cases
323
a. 4cells (28.6%) have expected count less than

149

Table 100
Gender and Ease of Use 1

Gender* Ease of
Usel

Case Processing Summary
Cases
Valid
Missing
N Percent N Percent
322 100% 0 .003%

N
322

Total
Percent
99.6%

Table 101
Gender and Ease of Use 1 Cross tabulations
Ease of Use 1
Gender Strongly Disagree Somewhat Neutral Somewhat Agree Strongly Total
Disagree
Disagree
Agree
Agree
Male
1
3
6
19
33
75
17
154
1
2
14
37
76
38
168
Female
0
4
8
33
70
151
55
322
Total
1

Table 102
Gender and Ease of Use 1 Chi-Square Test
Value

df

Asymp. Sig.
(2-sided)
.053
.041

Pearson Chi-Square 12.426a 6
Likelihood Ratio
13.138 6
Linear-by-Linear
Association
8.950
1
.003
N of Valid Cases
322
a. 6 cells (42.9%) have expected count less than
5. The minimum expected count is .48.

Table 103
Gender and Ease of Use 2

Gender* Ease of
Use 2

Case Processing Summary
Cases
Valid
Missing
N Percent N Percent
322 100% 0 .003%

N
323

Total
Percent
100%

Table 104
Gender and Ease of Use 2 Cross tabulations
Ease of Use 2
Gender Strongly Disagree Somewhat Neutral Somewhat Agree Strongly Total
Disagree
Disagree
Agree
Agree
Male
1
2
7
19
33
64
29
155
Female
2
0
3
12
28
79
44
168
Total
3
2
10
31
61
143
73
323

Table 105
Gender and Ease of Use 2 Chi-Square Test
Value

df Asymp. Sig.
(2-sided)
10.073a 6
.122
10.920 6
.091

Pearson Chi-Square
Likelihood Ratio
Linear-by-Linear
Association
6.681
1
.010
N of Valid Cases
323
a. 5 cells (35.7%) have expected count less than

151
Table 106
Gender and Purchase Intentions
Case Processing Summary
Cases
Missing

Valid
Gender*Purchase
Intentions

N
323

Percent
100.0%

N
0

Percent
.0%

Total
N
323

Percent
100.0%

Table 107
Gender and Purchase Intentions Cross-tabulations
Purchase Intentions
Gender Strongly Disagree Somewhat Neutral Somewhat Agree Strongly Total
Disagree
Disagree
Agree
Agree
Male
1
3
7
23
23
58
40
155
Female
2
6
5
14
18
65
58
168
Total
3
9
12
37
41
123
98
323

Table 108
Gender and Purchase Intentions Chi-Square Test
Value

df Asymp. Sig.
(2-sided)
7.659 a 6
.264
7.717
6
.260

'earson Chi-Square
Likelihood Ratio
Linear-by-Linear
Association
2.010
1
.156
N of Valid Cases
323
4 cells (28.6%) have expected count less than 5.

152
Appendix N:
Model Statistics

Table 109
Model Statistics
Model

1
2
3
4
5
6
7

R

.704a
.823b
.836c
.847d
.851e
.853f
..856g

R
Adjusted St. Error
Change Statistics
Square R
of
R
F
dfl df2 Sig F Change
Square
Estimate Square Change
Change
.547
.677
.700
.717
.724
.728
.732

.546
.675
.697
.714
.720
.723
.726

.908
.767
.742
.721
.713
.709
.705

..547
..130
..022
..018
..007
..004
.004

384.185
127.910
23.385
19.592
8.121
4.311
4.987

L
11
1I
1I
1I
1I
I

318
317
316
315
314
313
312

.000
.000
.000
.000
.005
.039
.026

Dependent Variable for all Models = Purchase Intentions
a. Predictors: (Constant), Usefulness 2
b. Predictors: (Constant), Usefulness 2, Security 1
c. Predictors: (Constant), Usefulness 2, Security 1, Familiarity
d. Predictors: (Constant), Usefulness 2, Security 1, Familiarity, Ease of Use 1
e. Predictors: (Constant), Usefulness 2, Security 1, Familiarity, Ease of Use 1, Experience
f. Predictors: (Constant), Usefulness 2, Security 1, Familiarity, Ease of Use 1, Experience, Web
Seal 2
g. Predictors: (Constant), Usefulness 2, Security 1, Familiarity, Ease of Use 1, Experience, Web
Seal 2, Visually Appealing
Model 7 has the highest Adjusted R Squared value of .726 indicating that 72.6%
of the variation in the dependent variable purchase intentions is explained by the
independent variables Usefulness 2 Q25, Security 1 Q20, Familiarity Q5, Ease of Use 1
Q27, Experience Q4, Web Seal 2 Q19, and Visually Appealing Q16. The models were
determined thru the use of step-wise regression.

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