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University students’ behavioral intention to use mobilelearning: 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 vocationaleducational field. Dr Min-Woo Nam is a full-time lecturer in Mokwon University. He is interested in constructinglearning management system. Seung-Bong Cha is a lecturer in the Department of Educational Technology in KonkukUniversity. Address for correspondence: Professor Sung Youl Park, Department of Educational Technology, KonkukUniversity, 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

participatedin theresearch.The process bywhich students adoptm-learningwas explained

using structural equation modeling technique and the Linear Structural Relationship

(LISREL)program.The generalstructuralmodel basedon the technologyacceptance model

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 intentionto usem-learning. Thestudy results confirmed theacceptability of 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’ MR and SN.

Introduction

Korea remains one of the leading information and 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 2010in 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-learningis 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 newtechnology.

• 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 explanation to justify those factors’ influence on behavioral 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.

ObjectivesThis 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. TAM proposes 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).

In the educational field, TAM is also used as a tool to determine how students’ PU and PE affect 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 twoconcepts 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




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 designm-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.

A few studies have investigated the effectiveness of m-learning in terms of learning achievements.

Learners in m-learning not only use text messages, images and movies but also communicateamong 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 AT toward 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 designA general structural model was developed based on the previous research. As explained in the

literature review, TAM can be used to explain user behavior related to computing technology and

is still considered a good model to depict the path of technology acceptance. Despite its widespread

use of TAM, its applications have remained limited. A weakness of TAM is its exclusion of external

variables, which may affect users’ intention to 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 andendogenous 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





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 procedureThe 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 200 subjects would normally be an appropriate minimum (Marsh,

Balla & MacDonald, 1988). Similarly, Newcomb (1992) insisted that LISREL should not be usedwith 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





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 1 presents 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 yearFreshman 69 23.96Sophomore 48 16.67 Junior 94 32.64Senior 77 26.74

GenderMale 174 60.42Female 114 39.58

Most commonly used mobile devicesNetbook 78 27.08Portable multimedia player 86 29.86iPod 41 14.24PDA 3 1.04Smart phone 6 2.08Electronic dictionary 59 20.49Others 15 5.21

Main method of mobile learning

Learning by downloading contents 187 64.93Real-time video lectures using wireless broadband 77 26.74Internal contents in mobile devices 23 7.99Others 1 0.35

Most commonly used mobile learning contentsMajor courses in university 33 11.46Language study 167 57.99Lectures for exam getting certifications 52 18.06Lectures for getting a job 8 2.78Others 28 9.72

Major place of mobile learning.In the house 82 28.47

In the university 123 42.71Traveling situation (in the subway or bus) 66 22.92On the streets 3 1.04Others 14 4.86

Total 288 100.00





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 thefour 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, SA and 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 in an MS Excel program as soon as the questionnaires were collected andlater transferred to Statistical Analysis System (SAS), Windows version 9.3 (SAS Korea, Seoul,

South Korea). Before the analysis, a random sample 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. Convergentvalidity 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 1.000AT 0.665 1.000PU 0.626 0.767 1.000PE 0.425 0.488 0.433 1.000SE 0.443 0.440 0.343 0.547 1.000

MR 0.698 0.663 0.611 0.343 0.320 1.000SA 0.561 0.396 0.368 0.439 0.455 0.471 1.000SN 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.





with composite reliability ranges from 0.81 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 from the 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) andnormal fit index (NFI) were examined. RMR is the difference between the 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 testingIn 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-valuewas 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

SA had 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 effectsSeveral trends were obvious in the 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





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 )

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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

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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 ) .

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0

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o f l e a r n

( A 2 ) .

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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 ) .

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( 1 . 3

2 )

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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 ) .

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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 ) .

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8

( 1 . 2

1 )

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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 ) .

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( 1 . 2

3 )

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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 )

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7 3

0 . 8

7

0 . 7

0

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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

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1 )

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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 ) .

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9

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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 .





turn influenced by PE. Therefore, the original TAM was good enough to explain the university

students’ m-learning acceptance.The strongest magnitude was found in the 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 SA on 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 > 0.05Root mean squared residual 0.05* < 0.08Root mean squared error of approximation 0.08* < 0.10Goodness-of-fit index 0.84 > 0.90Adjusted goodness-of-fit index 0.79 > 0.90Normal fit index 0.88 > 0.90

*stands for fulfilling recommended value.





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 in helping to understand and explain BI 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 hypothesesDirect effect t-value Indirect effect Total

AT → BI (H11) 0.350 2.39* 0.350 SupportedPU → BI (H12) 0.034 0.31 0.207 0.241 Not supportedPE → BI (H13) -0.020 -0.36 0.088 0.068 Not supportedSE → BI (H14) 0.013 0.23 0.102 0.115 Not supportedMR → BI (H15) 0.162 1.85 0.231 0.393 Not supportedSA → BI (H16) 0.213 3.08** -0.049 0.164 SupportedSN → BI (H17) 0.285 4.07** 0.056 0.341 SupportedPU → AT (H21) 0.591 6.04** 0.591 SupportedPE → AT (H22) 0.90 1.58 0.138 0.228 Not supportedSE → AT (H23) 0.157 2.65* 0.143 0.300 SupportedMR → AT (H24) 0.340 3.08** 0.280 0.620 Supported

SA →

AT (H25) -

0.105 -

1.57 -

0.019 -

0.124 Not supportedSN → AT (H26) -0.010 -0.15 0.147 0.137 Not supportedPE → PU (H31) 0.234 3.34** 0.234 SupportedSE → PU (H32) 0.062 0.88 0.109 0.171 Not supportedMR → PU (H33) 0.439 4.81** 0.021 0.460 SupportedSA → PU (H34) -0.103 -1.26 0.043 -0.060 Not supportedSN → PU (H35) 0.244 2.88* 0.003 0.247 SupportedSE → PE (H41) 0.467 6.26** 0.467 SupportedMR → PE (H42) 0.088 0.96 0.088 Not supportedSA → PE (H43) 0.183 2.10* 0.183 SupportedSN → 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.





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

with m-learning (Beggs, 2000; Marcinkiewicz & Regstad, 1996; Park, 2009). In Korea, people areencouragedtouseITineveryfieldtocatchupwiththerapidsocialchangecausedbytheubiquitous

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 touse m-learning. AT was identified as a determinant affecting BI to use m-learning. According to

the original TAM, PU is 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 suggestedbased 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 SA is 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.





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 anm-learning support program.

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