University Students Behavioral...

University students’ behavioral intention to use mobile
learning: Evaluating the technology acceptance model_1229 592..605
Sung Youl Park, Min-Woo Nam and Seung-Bong Cha
Sung Youl Park is a full-time professor in the Department of Educational Technology in Konkuk University in Seoul,
South Korea. His main research interest is e-learning for both formal and informal education in the vocational
educational field. Dr Min-Woo Nam is a full-time lecturer in Mokwon University. He is interested in constructing
learning management system. Seung-Bong Cha is a lecturer in the Department of Educational Technology in Konkuk
University. Address for correspondence: Professor Sung Youl Park, Department of Educational Technology, Konkuk
University, 1 Hwayang-dong, Gwangjin-gu, Seoul 143-701, Korea. Email: [email protected]
Abstract
As many Korean universities have recommended the implementation of mobile learning
(m-learning) for various reasons, the number of such tertiary learning opportunities has
steadily grown. However, little research has investigated the factors affecting university
students’ adoption and use of m-learning. A sample of 288 Konkuk university students
participatedintheresearch. Theprocess bywhichstudents adopt m-learningwas explained
using structural equation modeling technique and the Linear Structural Relationship
(LISREL) program.Thegeneral structural model basedonthetechnologyacceptancemodel
included m-learning self-efficacy, relevance for students’ major (MR), system accessibility,
subjective norm(SN), perceived usefulness, perceived ease of use, attitude (AT), and behav-
ioral intentiontousem-learning. Thestudyresults confirmedtheacceptabilityof themodel
to explain students’ acceptance of m-learning. M-learning AT was the most important
construct in explaining the causal process in the model, followed by students’ MRand SN.
Introduction
Korea remains one of the leading informationand communications technology (ICT) countries in
the Organization for Economic Cooperation and Development (OECD), even though her rank of
broadband use for high-speed Internet has recently dropped from the 1st in 2004 to 5th in 2010
in the world (OECD, 2010). Korea takes full advantage of ICT in supporting all levels of education
and human resource development, and e-learning is considered an important alternative in the
current knowledge-based society (Kim & Santiago, 2005). Diverse educational environments are
provided for various people with information technology (IT) (Um & Kim, 2007). Education in
Korea is now moving from e-learning to mobile learning (m-learning) as mobile technology
becomes popular in both formal and informal education in Korea (Jung, 2009).
While e-learning is based on the use of both wire and wireless Internet, in m-learning the
learner takes advantage of learning opportunities offered by mobile technologies such as cell
phones, smart phones, palmtops, tablet personal computers (PCs), personal digital assistants
(PDAs) and portable multimedia players (PMPs) (Kukulska-Humle & Traxler, 2005). M-learning
is a new and independent part of e-learning (Cho, 2007; Keegan, 2002). M-learning can be
defined as “any educational provision where the sole or dominant technologies are handheld or
palmtop devices.”
British Journal of Educational Technology Vol 43 No 4 2012 592–605
doi:10.1111/j.1467-8535.2011.01229.x
© 2011 The Authors. British Journal of Educational Technology © 2011 BERA. Published by Blackwell Publishing, 9600 Garsington Road, Oxford
OX4 2DQ, UK and 350 Main Street, Malden, MA 02148, USA.
The advantages of m-learning over e-learning are pushing its expansion. However, little research
has focused on how people adopt their m-learning and what factors affect m-learning compared
with e-learning. Furthermore, m-learning studies have investigated only educational efficacy by
using mobile devices (Jung, 2009; Kang, 2007; Yoon, 2007).
A recent trend is to adopt the technology acceptance model (TAM) as an explanatory tool in
investigating the e-learning process (Park, 2009). In terms of just m-learning outcomes in Korea,
a few studies have investigated mobile-based English learning and its satisfaction of PMP-based
learning. Therefore, m-learning research is restricted to use in particular fields (Jin, 2007; Jo,
2005; Um & Kim, 2007) and, consequently, not much research is conducted to identify the path
of how people adopt m-learning with TAM.
M-learning becomes popular with university students in Korea. The number of students who
have mobile devices is also growing. Furthermore, some universities provide students with smart
Practitioner notes
What is already known about this topic
• Technology acceptance model (TAM) is extensively used in various information and
communications technology (ICT) sectors to explain user’s intention to use new
technology.
• Mobile learning has become popular because of the low cost of telecommunication
and high quality of mobile devices.
• There is a need for research that focuses on how students adopt mobile learning in
university.
What this paper adds
• This study proposes and verifies the use of TAM to explain and predict students’
acceptance of mobile learning in university.
• External latent factors included in the general structural model such as mobile learn-
ing self-efficacy, major relevance, system accessibility and subjective norm were iden-
tified to have direct or indirect effects on behavioral intention to use mobile learning.
• Social motivational theory, which encompasses intrinsic and extrinsic motivational
factor, is a possible explanationto justify those factors’ influence onbehavioral intention.
Implications for practice and/or policy
• The general structural model enhances our understanding of students motivation of
using mobile learning. This understanding can aid our efforts when promoting mobile
learning. Educational providers should also endeavor to increase students’ positive
attitude toward m-learning.
• In terms of subjective norm, it is necessary for universities to put more emphasis on
mobile learning by offering a greater variety of mobile learning courses and advertis-
ing the benefits of mobile learning to attract students.
• Both on- and off-line support need to be provided to build up mobile learning self-
efficacy and mobile learning mentor systems and user-friendly learning management
systems could be good resources to increase self-efficacy.
• A high-quality wireless system accessibility environment needs to be constructed
and subsidies for mobile devices could be an extrinsic motivator to increase mobile
learning.
Factors related to use mobile learning 593
© 2011 The Authors. British Journal of Educational Technology © 2011 BERA.
phones for free and construct learning management systems (LMS) for m-learning. This trend is
expected to continue and expand as the price of smart phones and telecommunication costs has
decreased. Therefore, it is necessary to conduct research that deals more intensively with univer-
sity student’s intention to use m-learning in order to provide basic information for establishing
m-learning support systems for learners.
Objectives
This study used TAM as a theoretical framework of university students’ m-learning acceptance
and intention to use. The study objectives were to develop a general linear structural model of
m-learning acceptance of university students that would help school managers and educators
implement m-learning and analyze the relationship of university students’ behavioral intention
(BI) to use m-learning with selected factors such as their attitude (AT), perceived usefulness (PU),
perceived ease of use (PE), self-efficacy (SE) of m-learning, relevance for major (MR), system
accessibility (SA) and subjective norm (SN) within the model. In addition, some descriptive
statistics related to m-learning use and those selected factors were also determined.
Research hypotheses
According to the previously stated objectives, the following hypotheses were proposed:
H1: University students’ BI to use m-learning is related to their AT (H11), PU (H12), PE (H13),
m-learning SE (H14), MR (H15), SA (H16) and SN (H17).
H2: University students’ m-learning AT is related to their PU (H21), PE (H22), m-learning SE
(H23), MR (H24), SA (H25) and SN (H26).
H3: University students’ PU of m-learning is related to their PE (H31), m-learning SE (H32), MR
(H33), SA (H34) and SN (H35).
H4: University students’ PE of m-learning is related to their m-learning SE (H41), MR (H42), SA
(H43) and SN (H44).
Literature review
The TAM explains the use of IT and has been widely applied to various fields to understand the
personal acceptance of IT use after Davis’ (1989) proposal, which was related to Ajzen and
Fishbein’s (1980) theory of reasoned action. TAMproposes two concrete concepts (Davis, 1989):
the PU can be defined as the extent to which a university student believes using m-learning will
boost his or her learning, and PE as that to which one believes using m-learning will be free of
cognitive effort.
Previous research adopting TAM mainly investigated personal behavior to use new information
systems and technology in corporate environments (Abdul-Gader, 1996; Chin & Gopal, 1995;
Gefen & Straub, 1997; Igbaria, Gumaraes & Davis, 1995) and web shopping (Chang, Kim & Oh,
2002; Koo, 2003; Lederer, Maupin, Sena & Zhuang, 2000; Lin & Lu, 2000; Moon & Kim, 2001;
Pavlou, 2003; Shin & Song, 2000; Son & Lee, 2002; Teo, Lim & Lai, 1999).
Inthe educational field, TAMis alsousedas atool todetermine howstudents’ PUandPEaffect their
e-learning acceptance (Park, 2009; Park, Nam&Park, 2008). These two concepts were related to
factors such as ubiquity, motility, self-directed learning level, and enjoyment of m-learning and BI
to use m-learning (Jung, 2009). Because m-learning heavily depends on the use of IT such as
cellular phones, PMPs and PDAs, PU and PE may be affected by external factors such as personal
demographic situation, social atmosphere and organizational context. In addition, those two
concepts may affect AT toward m-learning and, in turn, finally affect BI to use. Hence, BI to use
mobile technology and devices is concerned with AT toward new technology and PU (Jin, 2007).
Several studies have investigated the intention to use m-learning by adopting TAM as the base
of research design. Phuangthong and Malisawan (2005) insisted that TAM was helpful to
594 British Journal of Educational Technology Vol 43 No 4 2012
© 2011 The Authors. British Journal of Educational Technology © 2011 BERA.
understand factors affecting m-learning adoption with 3rd generation mobile telecommunica-
tion (3G) technology. Jairak, Praneetpolgrang and Mekhabunchakij (2009) confirmed that the
unified theory of acceptance and use of technology as developed by Venkatesh, Morris, Davis
and Davis (2003), based upon TAM, was able to explain university students’ m-learning accep-
tance. They insisted that the university administration should emphasize a well fit design
m-learning system that is appropriate with student’s perception.
The previous literature about mobile media, mobile Internet and m-learning was analyzed. Gen-
erally, mobile media is characterized by integration of mobile communicating devices like cellular
phones and mobile information devices (MIDs) like PDA. However, cellular phones are now
adding wireless internet and computer abilities to their original voice-oriented functions, while
MIDs are adding voice message and date communication functions. Therefore, it is not meaning-
ful to distinguish between one and another. The various mobile devices are integrated and con-
sidered to be ICT devices as well as mobile devices.
Afewstudies have investigated the effectiveness of m-learning interms of learning achievements.
Learners in m-learning not only use text messages, images and movies but also communicate
among learners and teachers with mobile devices, thereby enhancing the learning efficiency
(Kim, 2006). Learning with PMP proved to be effective and efficient in terms of improving grade,
reducing private cost, managing time and student ATtoward learning (Lee, 2008). This may have
been because of the learning activities that adopted various multimedia through the m-learning.
M-learning works positively on many levels such as learning AT, improving educational interest
and concentration (Lee, Han & Lee, 2009). In general, teachers and students who use mobile
devices in teaching and learning tend to have positive responses toward using mobile devices
(Roach, 2002). Further, students and their parents showed positive cognitions about educational
usefulness by using tablet PC (Lee, 2005).
The motivation for using mobile devices consists of the following dimensions: social (sociality),
functional (immediateness, nobilities, information acquiring, time management), psychological
(relief) and cultural (decency/alignment, enjoyment/relaxation, ostentation, fashion/social
class). As university students perceive others according to cultural motivation, they adjust them-
selves to other friends because of their identities, social positions, displays of financial power and
communication styles (Lee, 2001). PU and PE meaningfully affect BI to use m-learning and
characteristics of mobile technology also significantly influence m-learning (Jung, 2009).
Methodology
Research design
A general structural model was developed based on the previous research. As explained in the
literature review, TAMcan be used to explain user behavior related to computing technology and
is still considered a good model to depict the pathof technology acceptance. Despite its widespread
use of TAM, its applications have remained limited. Aweakness of TAMis its exclusion of external
variables, whichmay affect users’ intentionto use technology (Legris, Ingham&Collerete, 2003).
In addition, Dishaw and Strong (1999) insisted on the need to examine the TAM under different
usage environments with a view to increasing the external validity of the TAM. Therefore, this
study adopted Park’s (2009) TAM as a baseline model in addition to the original TAM. He added
SE, SA and SN as individual, organizational and social factors, respectively, in the model.
Figure 1 represents the model to be tested and analyzed. It consists of both exogenous and
endogenous latent variables. Exogenous variable in causal modeling is the independent variable,
which is predetermined and given outside the model. Endogenous variable is determined by
the states of other variables in the model, contrasted with an exogenous variable. M-learning
SE, learning MR, SA and SN related to m-learning are included as exogenous variables and
Factors related to use mobile learning 595
© 2011 The Authors. British Journal of Educational Technology © 2011 BERA.
m-learning AT, PU, PE and intention to use m-learning as endogenous variables. Learning MR
was added to Park’s model because we believed that students with a major related to mobile
devices such as computer science and information management system have a greater desire to
use more mobile devices and adopt more m-learning. In the model, x and y represent the observed
exogenous and endogenous indicators, respectively. Delta and epsilon represent the error terms
for all observed indicators. The arrows linking latent variables specify the hypothesized causal
relationships in the direction of the arrows. The arrows between the latent variables and indica-
tors (observed variables) symbolize the measurement validity. PE and PU can be considered
cognitive constructs, AT an affective construct, and intention to use a behavioral construct.
Sample and procedure
The study population comprised university students taking e-learning courses at Konkuk Uni-
versity’s Seoul Campus. Normally, more than 8000 students of the 14 000 student body take at
least one e-learning course offered by the university every year. The whole number of the students
in the university is 15 000. This university was the first in Korea to construct a u-learning
environment. It has provided a wireless broadband (WiBro) service on the campus since 2008, so
the students can take e-learning courses by using mobile devices.
To use LISREL, a sample size of 200subjects would normally be anappropriate minimum(Marsh,
Balla & MacDonald, 1988). Similarly, Newcomb (1992) insisted that LISREL should not be used
with any fewer than 100 subjects. Considering those statements and the number of parameter
estimates, the number of sample subjects was set at 300.
After deciding the number of sample subjects, we adopted a cluster sampling method to choose
whole e-learning courses. Twenty e-learning courses were randomly selected from among the
Figure 1: General structural model to be tested
596 British Journal of Educational Technology Vol 43 No 4 2012
© 2011 The Authors. British Journal of Educational Technology © 2011 BERA.
e-learning courses offered by the university. Six hundred questionnaires were distributed to the
students with the aid of professors in charge of the selected courses during the orientation period
and collected immediately after orientation. Among the 600 respondents, there are 33 nonre-
sponses, giving a return rate of 94.5%. There was no incentive for participation in the survey. The
high response rate resulted from the active encouragement of each professor in the first class. Of
the 567 students, about half (288 students, 51%) were identified as having used mobile devices,
and these 288 were included in the study analysis. Students who had not experienced mobile
devices may not have understood the characteristics of m-learning. Because this study focuses on
m-learning, the students who had not used mobile devices were excluded. Therefore, the research
was limited to only those students who had experienced using mobile devices. Table 1presents the
demographic profile of the sample.
Instrumentation
The researchers developed the instrument based on the objectives of the study and a previous
literature review. Content validity was checked by pilot testing the instrument with 30 students in
the Department of Educational Technology at Konkuk University. The completed instrument was
Table 1: Demographic information of the sample
Variables Number (N) Percent (%)
School year
Freshman 69 23.96
Sophomore 48 16.67
Junior 94 32.64
Senior 77 26.74
Gender
Male 174 60.42
Female 114 39.58
Most commonly used mobile devices
Netbook 78 27.08
Portable multimedia player 86 29.86
iPod 41 14.24
PDA 3 1.04
Smart phone 6 2.08
Electronic dictionary 59 20.49
Others 15 5.21
Main method of mobile learning
Learning by downloading contents 187 64.93
Real-time video lectures using wireless broadband 77 26.74
Internal contents in mobile devices 23 7.99
Others 1 0.35
Most commonly used mobile learning contents
Major courses in university 33 11.46
Language study 167 57.99
Lectures for exam getting certifications 52 18.06
Lectures for getting a job 8 2.78
Others 28 9.72
Major place of mobile learning.
In the house 82 28.47
In the university 123 42.71
Traveling situation (in the subway or bus) 66 22.92
On the streets 3 1.04
Others 14 4.86
Total 288 100.00
Factors related to use mobile learning 597
© 2011 The Authors. British Journal of Educational Technology © 2011 BERA.
composed of four parts. Part I was designed to identify the demographic attributes of the respon-
dents, such as such as school year, gender, most commonly used mobile devices, main method of
m-learning, most commonly used m-learning contents and place of m-learning.
The questions in Parts II, III and IV were not only made based on Davis’s prior studies with
modifications to fit the specific context of the m-learning but were also mainly adapted from the
four prior studies for the study objectives: Park (2009), Ndubisi (2006), Lee, Cheung and Chen
(2005) and Malhotra and Galletta (1999). Part II consists of the following four subsections: PE,
PU, AT and BI. The questions in Part III were developed by the researchers to measure m-learning
SE. It was measured by two indicators: confidence in handling menu and software in mobile
devices and degree of necessary skills for using mobile devices.
The questions in Part IV were divided into three sections: learning MR, SAand SN. All constructs
were measured on 7-point Likert-type scales, from 1 = strongly disagree to 7 = strongly agree.
Statistical procedure
The data were coded first inanMS Excel programas soonas the questionnaires were collected and
later transferred to Statistical Analysis System (SAS), Windows version 9.3 (SAS Korea, Seoul,
South Korea). Before the analysis, a randomsample of 5%of the entered data was compared with
original questionnaire to check the coding accuracy. LISREL Windows version 8.3 (Scientific
Software International, Lincolnwood, IL) was used to test the hypotheses by structural equation
modeling (SEM). Descriptive statistical analyses such as mean, standard deviation, frequency,
percentage and correlation were also implemented using SAS.
Results
Analysis of measurement model
Both convergent and discriminant validity were checked in the measurement model. Convergent
validity implies the extent to which the indicators of a latent variable (factor) that are theoreti-
cally related should correlate highly. All factor loadings (lambda x and lambda y) exceeded 0.70,
which accounts for 50% of the variance. Considering the sample size of the study, these scores
were significant at a 0.05 significance level and a power level of 80% (Hair, Anderson, Tatham &
Black, 1998). Discriminant validity was confirmed by examining correlations among the con-
structs. As a rule of thumb, a correlation of 0.85 or larger indicates poor discriminant validity in
SEM(David, 1998). The results suggested an adequate discriminant validity of the measurement.
The correlation matrix between constructs is shown in Table 2.
Two reliability tests were carried out to secure accuracy and consistency: composite reliability (a)
and the variance extracted measure. For the former, all measures fulfilled the suggested levels
Table 2: A correlation matrix between constructs
Constructs BI AT PU PE SE MR SA SN
BI 1.000
AT 0.665 1.000
PU 0.626 0.767 1.000
PE 0.425 0.488 0.433 1.000
SE 0.443 0.440 0.343 0.547 1.000
MR 0.698 0.663 0.611 0.343 0.320 1.000
SA 0.561 0.396 0.368 0.439 0.455 0.471 1.000
SN 0.678 0.521 0.538 0.301 0.286 0.666 0.454 1.00
BI, behavioral intention; AT, attitude toward mobile learning; PU, perceived usefulness; PE, perceived ease of
use; SE, mobile learning self-efficacy; MR, major relevance; SA, system accessibility; SN, subjective norm.
598 British Journal of Educational Technology Vol 43 No 4 2012
© 2011 The Authors. British Journal of Educational Technology © 2011 BERA.
with composite reliability ranges from0.81 to 0.91. In general, a commonly used threshold value
for acceptable composite reliability is 0.70. For the latter, guidelines recommend that the variance
extracted value should exceed 0.50 for a construct. All measures exceeded these guidelines with
a range from 0.59 to 0.74. Table 3 shows the results of the confirmatory factor analysis and
reliability test with some descriptive statistics, mean and standard deviation. Figure 2 also graphi-
cally describes the relationships between the constructs and observed indicators, and presents the
loadings and residuals.
Table 4 summarizes the overall goodness-of fit measures of the model. The c
2
test result rejected
the model null hypothesis. Because c
2
test statistics are sensitive to the number of subjects and
require an assumption of multivariate normal distribution, other measures are better considered
in some cases as criteria for model fitting (Park, 2009). Actually, it can be difficult for a null
hypothesis to be accepted fromthe c
2
test result with a large sample size, even if the model fits the
collected data well (Kelloway, 1998).
In addition to c
2
statistics, the root mean squared residual (RMR), the root mean squared error of
approximation (RMSEA), goodness-of-fit index (GFI), adjusted goodness-of-fit index (AGFI) and
normal fit index (NFI) were examined. RMRis the difference betweenthe observed covariance and
predicted covariance. A value less than 0.08 is considered a good fit. RMSEA adjusts for the
complexity of the model and the size of the sample. A marginal value of RMSEA for acceptance
is 0.10. GFI, AGFI and NFI of the study approached the recommended values. GFI and AGFI are
affected by sample size and can be large for models that are poorly specified. The current consen-
sus rejects excessive reliance on GFI and AGFI. A value of NFI between 0.90 and 0.95 is accept-
able. A disadvantage of this measure of this measure is that it cannot be reduced by the addition
of more parameters to the model; therefore, it is not strongly recommended. Assessing all mea-
sures and considering the above statements, the full general structural model was accepted and
believed to be good enough to analyze the parameter estimates.
Hypothesis testing
In order to test the simple bivariate relationships between the latent variables, the general struc-
tural model was used and hypothesis testing was conducted within the context of the structural
model. This simplified the interpretation of the results because a relationship between two latent
variables could be examined while holding constant of all other constructs in the model.
When the parameter estimates of gamma (from an exogenous latent variable to an endogenous
latent variable) and beta (from an endogenous latent variable to an endogenous latent variable)
were statistically significant; those are denoted by asterisks. A t-value is defined as the ratio
between the parameter estimate and its standard error (Jöreskog & Sörbom, 1989). The t-value
was used as a criterion to test the significance of the parameters at the 0.05 level. T-values larger
than two in magnitude were judged to be significantly different from zero in this study. A t-value
larger than three is represented by two asterisks.
Hypothesis tests were conducted by confirming the presence of a statistically significant relation-
ship in the predicted direction. AT, SA and SN were significant for BI to use m-learning, while PU,
SE and MR were for AT, and PE, MR and SN were for PU. On the other hand, m-learning SE and
SAhad significant relationship with PE. The parameter estimates for the hypothesized paths their
t-values, and the hypothesis test results are summarized in Table 5.
Total, direct and indirect effects
Several trends were obvious inthe magnitude of the bivariate relationships proposed by the model
according to the results of total effects. In the context of BI, which was the key endogenous latent
variable of the study, AT, SA and SN were significant but other endogenous latent variables
such as PU and PE were not. Meanwhile, m-learning AT was affected by PU, and PU was in
Factors related to use mobile learning 599
© 2011 The Authors. British Journal of Educational Technology © 2011 BERA.
T
a
b
l
e
3
:
S
u
m
m
a
r
y
o
f
m
e
a
n
s
,
s
t
a
n
d
a
r
d
d
e
v
i
a
t
i
o
n
s
,
c
o
n
s
t
r
u
c
t
l
o
a
d
i
n
g
s
,
a
n
d
r
e
l
i
a
b
i
l
i
t
i
e
s
C
o
n
s
t
r
u
c
t
(
E
x
o
g
e
n
o
u
s
l
a
t
e
n
t
v
a
r
i
a
b
l
e
)
M
e
a
s
u
r
e
m
e
n
t
i
n
s
t
r
u
m
e
n
t
M
e
a
n
(
S
D
)
L
o
a
d
i
n
g
C
o
n
s
t
r
u
c
t
r
e
l
i
a
b
i
l
i
t
y
A
v
e
r
a
g
e
v
a
r
i
a
n
c
e
e
x
t
r
a
c
t
e
d
R
e
l
i
a
b
i
l
i
t
y
(
a
)
M
o
b
i
l
e
l
e
a
r
n
i
n
g
s
e
l
f
-
e
f
fi
c
a
c
y
I
h
a
v
e
t
h
e
n
e
c
e
s
s
a
r
y
s
k
i
l
l
s
f
o
r
m
o
b
i
l
e
l
e
a
r
n
i
n
g
(
S
1
)
.
5
.
0
0
(
1
.
2
6
)
0
.
7
9
0
.
9
0
0
.
6
9
0
.
9
0
I
a
m
a
s
k
i
l
l
f
u
l
u
s
e
r
i
n
m
e
n
u
o
r
s
o
f
t
w
a
r
e
f
o
r
m
o
b
i
l
e
l
e
a
r
n
i
n
g
w
i
t
h
m
o
b
i
l
e
d
e
v
i
c
e
s
(
S
2
)
.
4
.
8
4
(
1
.
3
3
)
0
.
8
8
I
h
a
v
e
c
o
n
fi
d
e
n
c
e
i
n
c
o
m
p
l
e
m
e
n
t
a
l
l
y
u
s
i
n
g
c
o
m
p
u
t
e
r
a
n
d
m
o
b
i
l
e
d
e
v
i
c
e
s
f
o
r
m
o
b
i
l
e
l
e
a
r
n
i
n
g
(
S
3
)
.
4
.
7
7
(
1
.
3
2
)
0
.
8
6
I
u
n
d
e
r
s
t
a
n
d
c
o
m
p
u
t
e
r
a
n
d
m
o
b
i
l
e
d
e
v
i
c
e
s
t
e
r
m
s
w
e
l
l
f
o
r
m
o
b
i
l
e
l
e
a
r
n
i
n
g
(
S
4
)
.
4
.
5
9
(
1
.
3
8
)
0
.
7
9
L
e
a
r
n
i
n
g
r
e
l
e
v
a
n
c
e
L
e
a
r
n
i
n
g
w
i
t
h
m
o
b
i
l
e
d
e
v
i
c
e
i
s
n
e
c
e
s
s
a
r
y
f
o
r
m
y
m
a
j
o
r
s
t
u
d
y
(
R
1
)
4
.
5
3
(
1
.
2
9
)
0
.
8
3
0
.
8
9
0
.
7
3
0
.
8
8
L
e
a
r
n
i
n
g
w
i
t
h
m
o
b
i
l
e
d
e
v
i
c
e
c
a
n
h
e
l
p
m
y
m
a
j
o
r
s
t
u
d
y
(
R
2
)
.
4
.
8
3
(
1
.
1
4
)
0
.
9
3
L
e
a
r
n
i
n
g
w
i
t
h
m
o
b
i
l
e
d
e
v
i
c
e
c
a
n
h
e
l
p
t
o
fi
n
d
a
j
o
b
i
n
t
h
e
f
u
t
u
r
e
(
R
3
)
.
4
.
7
9
(
1
.
1
9
)
0
.
8
0
S
y
s
t
e
m
a
c
c
e
s
s
i
b
i
l
i
t
y
I
c
a
n
e
a
s
i
l
y
g
e
t
i
n
f
o
r
m
a
t
i
o
n
o
r
c
o
n
t
e
n
t
s
f
o
r
m
o
b
i
l
e
l
e
a
r
n
i
n
g
(
A
1
)
.
4
.
6
8
(
1
.
2
1
)
0
.
8
0
0
.
8
1
0
.
5
9
0
.
8
1
M
o
b
i
l
e
d
e
v
i
c
e
s
h
a
v
e
g
o
o
d
c
o
m
p
a
t
i
b
i
l
i
t
y
w
i
t
h
o
t
h
e
r
c
o
m
p
u
t
e
r
d
e
v
i
c
e
s
(
A
2
)
.
4
.
7
3
(
1
.
2
4
)
0
.
7
3
I
t
i
s
e
a
s
y
t
o
a
c
c
e
s
s
i
n
t
e
r
n
e
t
a
n
d
s
e
a
r
c
h
f
o
r
m
o
b
i
l
e
l
e
a
r
n
i
n
g
(
A
2
)
.
4
.
6
8
(
1
.
3
0
)
0
.
7
7
S
u
b
j
e
c
t
i
v
e
n
o
r
m
M
o
b
i
l
e
l
e
a
r
n
i
n
g
i
s
s
i
g
n
i
fi
c
a
n
t
m
e
a
n
i
n
g
a
s
a
n
u
n
i
v
e
r
s
i
t
y
s
t
u
d
e
n
t
(
N
1
)
.
4
.
3
7
(
1
.
3
0
)
0
.
8
2
0
.
8
6
0
.
6
8
0
.
8
6
I
t
i
s
n
e
c
e
s
s
a
r
y
t
o
p
e
r
f
o
r
m
t
h
e
m
o
b
i
l
e
l
e
a
r
n
i
n
g
a
c
c
o
r
d
i
n
g
t
o
r
e
c
e
n
t
s
o
c
i
a
l
n
e
e
d
s
(
N
2
)
.
4
.
6
0
(
1
.
3
5
)
0
.
8
9
I
n
e
e
d
t
o
e
x
p
e
r
i
e
n
c
e
m
o
b
i
l
e
l
e
a
r
n
i
n
g
f
o
r
m
y
f
u
t
u
r
e
j
o
b
(
N
3
)
4
.
7
2
(
1
.
4
0
)
0
.
7
6
C
o
n
s
t
r
u
c
t
(
E
n
d
o
g
e
n
o
u
s
l
a
t
e
n
t
v
a
r
i
a
b
l
e
)
M
e
a
s
u
r
e
m
e
n
t
i
n
s
t
r
u
m
e
n
t
M
e
a
n
(
S
D
)
L
o
a
d
i
n
g
C
o
n
s
t
r
u
c
t
r
e
l
i
a
b
i
l
i
t
y
A
v
e
r
a
g
e
v
a
r
i
a
n
c
e
e
x
t
r
a
c
t
e
d
(
r
)
R
e
l
i
a
b
i
l
i
t
y
(
a
)
B
e
h
a
v
i
o
r
a
l
i
n
t
e
n
t
i
o
n
I
h
a
v
e
i
n
t
e
n
t
i
o
n
t
o
p
e
r
f
o
r
m
m
o
b
i
l
e
l
e
a
r
n
i
n
g
(
B
1
)
.
5
.
0
2
(
1
.
3
6
)
0
.
8
8
0
.
9
1
0
.
7
2
0
.
9
1
I
a
m
g
o
i
n
g
t
o
p
o
s
i
t
i
v
e
l
y
u
t
i
l
i
z
e
m
o
b
i
l
e
l
e
a
r
n
i
n
g
(
B
2
)
.
4
.
8
8
(
1
.
4
1
)
0
.
9
1
I
h
a
v
e
c
o
n
t
i
n
u
i
n
g
c
o
n
c
e
r
n
a
b
o
u
t
m
o
b
i
l
e
d
e
v
i
c
e
s
o
f
i
n
f
o
r
m
a
t
i
o
n
t
o
p
e
r
f
o
r
m
m
o
b
i
l
e
l
e
a
r
n
i
n
g
(
B
3
)
.
4
.
8
2
(
1
.
3
0
)
0
.
8
3
I
w
i
l
l
b
e
a
p
o
w
e
r
u
s
e
r
i
n
m
o
b
i
l
e
l
e
a
r
n
i
n
g
(
B
4
)
.
4
.
5
1
(
1
.
4
0
)
0
.
7
7
A
t
t
i
t
u
d
e
S
t
u
d
y
i
n
g
t
h
r
o
u
g
h
m
o
b
i
l
e
l
e
a
r
n
i
n
g
i
s
a
g
o
o
d
i
d
e
a
(
A
1
)
.
5
.
0
0
(
1
.
2
6
)
0
.
7
9
0
.
8
4
0
.
6
5
0
.
8
4
I
l
i
k
e
t
o
s
e
a
r
c
h
l
e
a
r
n
i
n
g
c
o
n
t
e
n
t
s
t
h
a
t
d
o
w
n
l
o
a
d
i
n
m
o
b
i
l
e
d
e
v
i
c
e
s
o
f
l
e
a
r
n
(
A
2
)
.
4
.
6
0
(
1
.
4
0
)
0
.
7
7
I
a
m
p
o
s
i
t
i
v
e
t
o
w
a
r
d
m
o
b
i
l
e
l
e
a
r
n
i
n
g
(
A
3
)
.
5
.
0
4
(
1
.
3
2
)
0
.
8
5
P
e
r
c
e
i
v
e
d
u
s
e
f
u
l
n
e
s
s
M
o
b
i
l
e
l
e
a
r
n
i
n
g
w
o
u
l
d
i
m
p
r
o
v
e
m
y
l
e
a
r
n
i
n
g
p
e
r
f
o
r
m
a
n
c
e
(
U
1
)
.
4
.
9
3
(
1
.
1
8
)
0
.
8
7
0
.
9
0
0
.
7
4
0
.
8
9
M
o
b
i
l
e
l
e
a
r
n
i
n
g
c
a
n
i
m
p
r
o
v
e
e
f
fi
c
i
e
n
c
y
o
f
l
e
a
r
n
i
n
g
(
U
2
)
.
4
.
9
8
(
1
.
2
1
)
0
.
8
8
M
o
b
i
l
e
l
e
a
r
n
i
n
g
g
i
v
e
s
m
e
h
i
g
h
e
f
f
e
c
t
s
o
f
l
e
a
r
n
i
n
g
(
U
3
)
.
4
.
7
8
(
1
.
2
3
)
0
.
8
3
P
e
r
c
e
i
v
e
d
e
a
s
e
o
f
u
s
e
I
t
i
s
e
a
s
y
t
o
d
o
w
n
l
o
a
d
a
n
d
s
a
v
e
l
e
a
r
n
i
n
g
c
o
n
t
e
n
t
s
w
i
t
h
m
o
b
i
l
e
d
e
v
i
c
e
s
(
E
1
)
.
5
.
1
6
(
1
.
4
0
)
0
.
7
3
0
.
8
7
0
.
7
0
0
.
8
7
I
t
i
s
e
a
s
y
t
o
u
s
e
m
e
n
u
o
f
m
o
b
i
l
e
d
e
v
i
c
e
s
a
n
d
s
o
f
t
w
a
r
e
(
E
2
)
.
5
.
1
1
(
1
.
2
1
)
0
.
8
6
I
t
i
s
e
a
s
y
t
o
u
s
e
m
o
b
i
l
e
l
e
a
r
n
i
n
g
c
o
n
t
e
n
t
s
(
E
3
)
.
5
.
0
9
(
1
.
2
5
)
0
.
9
1
A
l
l
l
o
a
d
i
n
g
s
w
e
r
e
s
i
g
n
i
fi
c
a
n
t
b
a
s
e
d
o
n
t
-
v
a
l
u
e
s
.
1
,
s
t
r
o
n
g
l
y
d
i
s
a
g
r
e
e
;
~
7
,
s
t
r
o
n
g
l
y
a
g
r
e
e
;
S
D
,
s
t
a
n
d
a
r
d
d
e
v
i
a
t
i
o
n
.
600 British Journal of Educational Technology Vol 43 No 4 2012
© 2011 The Authors. British Journal of Educational Technology © 2011 BERA.
turn influenced by PE. Therefore, the original TAM was good enough to explain the university
students’ m-learning acceptance. The strongest magnitude was found inthe relationship between
m-learning AT and BI (b
43
= 0.35), followed by SN (g
44
= 0.29).
In contrast, m-learning SE, MR and PU were significant in affecting user AT. In terms of PU, MR
(g
22
= 0.44) and SN (g
24
= 0.24) were significant. Meanwhile, m-learning SE (g
11
= 0.47) and SA
(g
13
= 0.18) were significantly related to PE. Therefore, all exogenous latent variables had signifi-
cant effects on at least two endogenous latent variables. According to the direct effect estimates,
PU was identified as the largest determinant to m-learning AT (b
32
= 0.59), and m-learning SE
had the largest effect on PE.
The total effect on a given variable is the sum of the respective direct and indirect effects. The
possible associations among the variables could be identified by the examination of indirect effect.
Most studies tend to concentrate on just direct effects in path analysis. However, causal relation-
ships should be identified by all possible effects such as direct, indirect, spurious and associational
effects. Because spurious and associational effects are difficult to find, direct and indirect effects
are enough to understand causal relationships (Bollen, 1989). The indirect effect of SAon BI was
Figure 2: Parameter estimates of general structural model
Table 4: Goodness-of-fit measures for structural equation modeling
Fit measures Values Recommended value
Chi-square 736.41 (p = 0.00) p > 0.05
Root mean squared residual 0.05* < 0.08
Root mean squared error of approximation 0.08* < 0.10
Goodness-of-fit index 0.84 > 0.90
Adjusted goodness-of-fit index 0.79 > 0.90
Normal fit index 0.88 > 0.90
*stands for fulfilling recommended value.
Factors related to use mobile learning 601
© 2011 The Authors. British Journal of Educational Technology © 2011 BERA.
0.231. PU had an indirect effect on BI of 0.207. According to the recommendation from Cohen
(1988), standardized path coefficients with values less than 0.1 are considered small, around 0.3
are medium and more than 0.5 are large. Therefore, PU and SA could be considered important
factors affecting BI, eventhough they did not have significant direct effects. Furthermore, PU had
a significant effect on m-learning AT.
These results revealed m-learning AT to be the most important variable among the endogenous
latent variables in influencing BI to use m-learning. However, PU and PE were also considered
important because PE affected PU, which in turn affected m-learning AT. In fact, all latent
variables in the structural model were significant in at least two relationships with each other.
Thus, the model specification was considered good.
Discussions and conclusions
The results of the present research supported the conclusion that the model well represented the
collected data according to the result of the goodness-of-fit test. Similar to earlier studies (Lee et al.,
2005; Saadé, Nebebe &Tan, 2007), this study confirmed the general structural model set up to be
a good model inhelping to understand and explainBI to use m-learning. One possible explanation
for this may be justified by including variables related to social and organizational contexts.
In general, variables related to the BI to use IT or to the actual use of IT could be grouped into four
categories: individual context, system context, social context and organizational context. While
social context means social influence on personal acceptance of IT use, organizational context
emphasizes any organization’s influence or support on one’s IT use (Park, 2009). This study
Table 5: Parameter estimates, t-value and result of hypotheses
Hypothesized path
Standardized estimate
Result of hypotheses Direct effect t-value Indirect effect Total
AT → BI (H11) 0.350 2.39* 0.350 Supported
PU → BI (H12) 0.034 0.31 0.207 0.241 Not supported
PE → BI (H13) -0.020 -0.36 0.088 0.068 Not supported
SE → BI (H14) 0.013 0.23 0.102 0.115 Not supported
MR → BI (H15) 0.162 1.85 0.231 0.393 Not supported
SA → BI (H16) 0.213 3.08** -0.049 0.164 Supported
SN → BI (H17) 0.285 4.07** 0.056 0.341 Supported
PU → AT (H21) 0.591 6.04** 0.591 Supported
PE → AT (H22) 0.90 1.58 0.138 0.228 Not supported
SE → AT (H23) 0.157 2.65* 0.143 0.300 Supported
MR → AT (H24) 0.340 3.08** 0.280 0.620 Supported
SA → AT (H25) -0.105 -1.57 -0.019 -0.124 Not supported
SN → AT (H26) -0.010 -0.15 0.147 0.137 Not supported
PE → PU (H31) 0.234 3.34** 0.234 Supported
SE → PU (H32) 0.062 0.88 0.109 0.171 Not supported
MR → PU (H33) 0.439 4.81** 0.021 0.460 Supported
SA → PU (H34) -0.103 -1.26 0.043 -0.060 Not supported
SN → PU (H35) 0.244 2.88* 0.003 0.247 Supported
SE → PE (H41) 0.467 6.26** 0.467 Supported
MR → PE (H42) 0.088 0.96 0.088 Not supported
SA → PE (H43) 0.183 2.10* 0.183 Supported
SN → PE (H44) 0.014 0.016 0.014 Not supported
*t > 2, **t > 3. AT, attitude; BI, behavioral intention; PU, perceived usefulness; PE, perceived ease of use; SE,
self-efficacy; MR, relevance for major; SA, system accessibility; SN, subjective norm.
602 British Journal of Educational Technology Vol 43 No 4 2012
© 2011 The Authors. British Journal of Educational Technology © 2011 BERA.
adopted SA as an organizational factor and SN as a social factor. In addition, m-learning SE (SA)
and MR as individual factors were included.
The study results also demonstrated TAM constructs had both direct and indirect effects on
university students’ BI to use m-learning.
An important study result was the finding that MR played a significant role in affecting
m-learning AT and PU. One possible explanation for this may be justified by motivational theory.
Major relevance may be considered an intrinsic motivational factor to affect AT and PU. Previous
study conducted by Park (2009) with TAM proposed SE is a powerful variable in explaining BI to
use e-learning. M-learning SE may be also considered an intrinsic motivational factor. According
to Bandura’s (1994) social motivational theory, higher SE induces a more active learning process.
In this study, m-learning SE affected both PE (PU) and m-learning AT.
On the other hand, SA and SN may be considered extrinsic motivational factors. Both variables
influenced BI to use m-learning. This result is similar to those of earlier studies and is connected
withm-learning (Beggs, 2000; Marcinkiewicz &Regstad, 1996; Park, 2009). InKorea, people are
encouragedtouseITineveryfieldtocatchupwiththerapidsocial changecausedbytheubiquitous
environment. University students may want to adopt e-learning or m-learning because they think
such experiences will be beneficial for future job preparation in the ubiquitous society.
SA as an organizational factor was one of the dominant exogenous constructs affecting BI to use
m-learning. It also affected PE. These may be natural results because m-learning requires a
wireless internet environment such as WiBro or wireless fidelity (Wi-Fi) compared with normal
internet. In fact, Konkuk University has already set up a ubiquitous learning infrastructure with
WiBro technology for e-learning.
In the context of endogenous constructs, neither PU nor PE had a significant direct effect on BI to
use m-learning. AT was identified as a determinant affecting BI to use m-learning. According to
the original TAM, PUis hypothesized to affect BI to use and PE is not hypothesized to directly affect
intention. Some parts of this research were consistent with previous research, whereas others
were contradictory. One possible clue is that all participants of the study conveniently used mobile
devices for learning. Therefore, those variables are not directly related to BI but rather may be
indirectly related to BI to use m-learning. Particularly, university students in Korea, the so called
M-generation, excel at using mobile devices and frequently access on wireless internet to get
necessary information.
Considering the above statements, there is potential for practical application in the development
and management of m-learning in university. The following recommendations are suggested
based on the study results. First, educators and managers should make an effort to boost univer-
sity students’ positive AT toward m-learning because AT has the largest direct effect on BI to use
m-learning. Second, as SN is also directly related to BI, the university should inform the students
that m-learning experience is necessary according to recent social needs. Third, a high-quality
wireless Internet environment and inexpensive mobile devices are necessary because SAis one of
the factors affecting directly BI to use m-learning. Wireless internet environments such as WiBro
or Wi-Fi zones should be constructed in the university. If the university provides students with
inexpensive price of mobile devices including smart phones or subsidies their purchase, then the
number of students taking m-learning will be increased. Fourth, m-learning SE affects both AT
and PE. PE affects PU, and PU affects AT in turn, which is a key endogenous variable to BI. Thus,
both on- and off-line support should be provided to build up m-learning SE and increase students’
positive AT toward m-learning. Practically, an online mentor system may be a good resource in
terms of support for SE and positive AT. If an online mentor system is set up in mobile LMS
constructed by the university, it will be helpful for students to take advantage of m-learning.
Factors related to use mobile learning 603
© 2011 The Authors. British Journal of Educational Technology © 2011 BERA.
Finally, as the research result was limited to only to those students who had experienced using
mobile devices, comparative research should be conducted to identify whether or not a difference
exists between mobile users and nonmobile users with TAM. In addition, this study focused only
on students. It is necessary to implement research with instructors and professors in university.
Their perception and adoption processes should be also taken into account in designing an
m-learning support program.
References
Abdul-Gader, A. (1996). The impact of user satisfaction on computer-mediated communication accep-
tance: a causal path model. Information Resources Management Journal, 9, 1, 17–26.
Ajzen, I. & Fishbein, M. (1980). Understanding attitudes and predicting social behavior. Englewood Cliffs, NJ:
Prentice-Hall.
Bandura, A. (1994). Self-efficacy. In V. S. Ramachaudran (Ed.), Encyclopedia of human behavior (pp. 71–81).
NewYork: Academic Press.
Beggs, T. A. (2000). Influences and barriers to adoption of instructional technology. Proceedings of the Mid-
south Instructional Technology Conference. (ERIC Document Reproduction Service No. ED 446 764).
Bollen, K. A. (1989). Sturctural equations with latent variables. NewYork: John Wiley & Sons.
Chang, H. S., Kim, J. K. & Oh, C. G. (2002). Technology acceptance model with web interactivity. The Journal
of MIS Research, 12, 4, 55–75.
Chin, W. W. & Gopal, A. (1995). Adoption intention in GSS: relative importance of beliefs. The Data Base for
Advances in Information Systems, 26, 2, 42–64.
Cho, S. K. (2007). Current status andfuture of MALL. Multimedia Assisted Language Learning, 10, 3, 197–211.
Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Erlbaum.
David, A. K. (1998). Multiple factor models. Retrieved May 14, 2007, from http://davidakenny.net/cm/
mfactor.htm
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information tech-
nology. MIS Quarterly, 13, 3, 319–339.
Dishaw, M. T. & Strong, D. M. (1999). Extending the technology acceptance model with task-technology fit
constructs. Information & Management, 36, 1, 9–21.
Gefen, D. & Straub, D. W. (1997). Gender differences in perception and adoption of e-mail: an extension to
the technology acceptance model. MIS Quarterly, 21, 4, 389–400.
Hair, J. F., Anderson, R. E., Tatham, R. L. & Black, W. C. (1998). Multivariate data analysis (5th ed.). Upper
Saddle River, NJ: Prentice Hall.
Igbaria, M., Gumaraes, T. & Davis, G. B. (1995). Testing the determinants of microcomputer usage via
structural equation model. Journal of Management Information Systems, 11, 4, 87–114.
Jairak, K., Praneetpolgrang, P. & Mekhabunchakij, M. (2009). An acceptance of mobile learning for higher
education students in Thailand. The Sixth International Conference of eLearning for Konwledge-Based
Society, 17–18 December 2009, Thailand.
Jin, D. W. (2007). An empirical study on the influencing factors of mobile internet service adoption. Doctoral
dissertation, Kwangwoon University, South Korea.
Jo, C. J. (2005). Astudy on the factors of affecting the intention to use of the mobile internet. Doctoral dissertation,
Chongju University, South Korea.
Jöreskog, K. G. &Sörbom, D. (1989). LISREL 7: a guide to the programs and applications. Chicago, IL: SPSS, Inc.
Jung, H. J. (2009). Determinants influencing mobile-learning for English learning. English21, 22, 2, 235–
255.
Kang, N. H. (2007). Developing the model of English teaching and learning using a mobile device in a blended
learning environment. Doctoral dissertation, Chungang University, South Korea.
Keegan, D. (2002). The future of learning: from elearning to mlearning. Hagen, Germany: Fern Universität.
Kelloway, E. K. (1998). Using LISREL for structural equation modeling: a researcher’s guide. Thousand Oaks, CA:
Sage.
Kim, C. & Santiago, R. (2005). Constructions of e-learning environments in Korea. ETR&D, 53, 4, 108–
114.
Kim, J. Y. (2006). A survey on mobile-assisted language learning. Modern English Education, 7, 2, 57–69.
Koo, D. M. (2003). An investigation on consumer’s internet shopping behavior explained by the technology
acceptance model. The Journal of MIS Research, 13, 1, 141–170.
Kukulska-Humle, A. & Traxler, J. (2005). Mobile learning: a handbook for educators and trainers. London:
Routledge.
604 British Journal of Educational Technology Vol 43 No 4 2012
© 2011 The Authors. British Journal of Educational Technology © 2011 BERA.
Lederer, A. L., Maupin, D. J., Sena, M. P. & Zhuang, Y. (2000). The technology acceptance model and the
World Wide Web. Decision Support Systems, 29, 3, 269–282.
Lee, C. H., Han, S. K. & Lee, S. H. (2009). Study on the analysis of effectiveness of the u-learning research
school. The Society of Korean Practical Arts Education, 15, 2, 312–332.
Lee, I. H. (2001). Motivations for mobile phone uses among collegiate undergraduates. Journal of Broadcast-
ing, 15, 3, 261–293.
Lee, N. S. (2008). The subjective study effectiveness of u-learning. Doctoral Dissertation, The Graduate School
of Mokwon University, South Korea.
Lee, M. K. O., Cheung, C. M. K. & Chen, Z. (2005). Acceptance of internet-based learning medium: the role
of extrinsic and intrinsic motivation. Information & Management, 42, 1095–1104.
Lee, Y. M. (2005). An analysis of the case study on tablet computer based mobile learning environment. The
Journal of Korean Association of Computer Education, 8, 1, 25–32.
Legris, P., Ingham, J. & Collerete, P. (2003). Why do people use information technology? A critical review of
the technology acceptance model. Information & Management, 19, 6, 751–765.
Lin, J. C. & Lu, H. (2000). Towards an understanding of the behavioral intention to use a web site. Interna-
tional Journal of Information Management, 20, 197–208.
Malhotra, Y. & Galletta, D. F. (1999). Extending the technology acceptance model to account for social influence:
theoretical bases and empirical validation. Proceedings of the 32nd Hawaii International Conference on
System Sciences.
Marcinkiewicz, H. R. & Regstad, N. G. (1996). Using subjective norms to predict teacher’ computer use.
Journal of Computing in Teacher Education, 13, 1, 27–33.
Marsh, H. W., Balla, J. R. & MacDonald, R. P. (1988). Goodness-of-fit indexes in confirmatory factor analysis:
the effect of sample size. Psychological Bulletin, 88, 245–258.
Moon, J. W. & Kim, Y. G. (2001). Extending the TAM for a World-Wide-Web context. Information & Manage-
ment, 38, 4, 217–230.
Ndubisi, N. O. (2006). Factors of online learning adoption: a comparative juxtaposition of the theory of
plannedbehavior andthe technologyacceptance model. International Journal onE-Learning, 5, 4, 571–591.
Newcomb, M. D. (1992). What structural equation modeling can tell us about social support. In I. G.
Sarason, B. R. Sarason &G. R. Pierce (Eds), Social support: an interactional view(pp. 26–63). NewYork: John
Wiley & Sons.
OECD (2010). OECD broadband portal. Fixed and wireless broadband subscriptions per 100 inhabitants
(Dec.2010). Retrieved January 20, 2011, from http://www.oecd.org/sti/ict/broadband
Park, S. Y. (2009). An analysis of the technology acceptance model in understanding university students’
behavioral intention to use e-learning. Educational Technology & Society, 12, 3, 150–162.
Park, S. Y., Nam, M. W. & Park, U. G. (2008). The structural model of factors affecting e-learning use of
teachers in vocational high school. Journal of Agricultural Education and Human Resource Development, 40,
2, 139–160.
Pavlou, P. A. (2003). Consumer acceptance of electronic commerce: integrating trust and risk with the
technology acceptance model. International Journal of Electronic Commerce, 7, 3, 69–103.
Phuangthong, D. & Malisawan, D. (2005). A study of behavioral intention for 3G mobile internet technology:
preliminary research on mobile learning. Proceedings of the Second International Conference on eLearning
for Knowledge-Based Society, August 4–7, 2005, Bangkok, Thailand.
Roach, R. (2002). Winston-Salem state experiments with handheld computers. Black Issues in Higher Edu-
cation, 18, 1, 45–57.
Saadé, R. G., Nebebe, F. & Tan, W. (2007). Viability of the technology acceptance model in multimedia
learning environments: comparative study. Interdisciplinary Journal of Knowledge and Learning Objects, 37,
175–184.
Shin, J. C. &Song, C. S. (2000). Study onthe adoptionof internet shopping: focused onthe role of innovation
characteristics and adoption barriers. Korean Journal of Marketing, 2, 2, 63–85.
Son, D. H. & Lee, H. S. (2002). The application of the TAM model towards the internet usage. Business
Management Review, 35, 2, 45–60.
Teo, T. S. H., Lim, V. K. G. & Lai, R. Y. C. (1999). Intrinsic and extrinsic motivation in internet usage. Omega
International Journal of Management Science, 27, 1, 25–37.
Um, M. Y. &Kim, M. R. (2007). Astudy on factors affecting users’ satisfaction level in using PMP for learning
purpose. The Journal of Korean Association of Computer Education, 10, 1, 77–88.
Venkatesh, V., Morris, M. G., Davis, G. B. & Davis, F. D. (2003). User acceptance of information technology:
toward a unified view. MIS Quarterly, 27, 3, 425–478.
Yoon, J. J. (2007). A study on a u-learning oriented model of English learning by utilizing mobile devices. Doctoral
dissertation, Chungang University, South Korea.
Factors related to use mobile learning 605
© 2011 The Authors. British Journal of Educational Technology © 2011 BERA.