Description
This study show
that learners can judge m-learning by how well it meets their perceived compatibility of m-learning, and
they will regard m-learning as a useful, easy to use, and enjoyable tool if they can explore it themselves
through the content and interface screens over the mobile-based learning environments at any time in
any location, and these situations will further facilitate their intention to use m-learning. In conclusion,
the views of the extended TAM with the IDT provide clear expositions of learners' beliefs, which affect
their intention to use m-learning.
Towards an understanding of the factors affecting m-learning acceptance: Roles of
technological characteristics and compatibility
Yung-Ming Cheng
*
Department of Business Administration, Chaoyang University of Technology, Taichung City, Taiwan
a r t i c l e i n f o
Article history:
Received 26 October 2012
Accepted 24 April 2014
Available online 1 April 2015
Keywords:
Compatibility
Extended technology acceptance model
Innovation diffusion theory
M-learning acceptance
Technological characteristics
a b s t r a c t
To date, prior studies have placed considerably less emphasis on the determinants of learners' acceptance
of mobile learning (m-learning). Hence, this study's purpose was to combine the extended technology
acceptance model (TAM) with the innovation diffusion theory (IDT) to examine whether technological
characteristics (including navigation and convenience) and compatibility as the antecedents to learners'
beliefs affected their intention to use m-learning. Sample data for this study were collected from
Taiwanese mobile phone users; a total of 750 questionnaires were distributed, and 486 usable ques-
tionnaires were analyzed in this study, with a usable response rate of 64.80%. Collected data were
analyzed using structural equation modeling. This study showed that technological characteristics
(including navigation and convenience) and compatibility had signi?cant effects on perceived usefulness
(PU), perceived ease of use (PEOU), and perceived enjoyment (PE) of m-learning; besides, PU, PEOU, PE,
and compatibility, respectively, exhibited signi?cantly strong impacts on intention to use m-learning,
and PEOU indirectly affected intention to use m-learning via PU and PE. The results of this study show
that learners can judge m-learning by how well it meets their perceived compatibility of m-learning, and
they will regard m-learning as a useful, easy to use, and enjoyable tool if they can explore it themselves
through the content and interface screens over the mobile-based learning environments at any time in
any location, and these situations will further facilitate their intention to use m-learning. In conclusion,
the views of the extended TAM with the IDT provide clear expositions of learners' beliefs, which affect
their intention to use m-learning.
© 2015, College of Management, National Cheng Kung University. Production and hosting by Elsevier
Taiwan LLC. All rights reserved.
1. Introduction
Recently, mobile devices and ubiquitous computing technolo-
gies have created unprecedented opportunities for conducting
learning. Hence, mobile learning (m-learning) has increasingly
attracted the interest of educators, researchers, and companies that
publish learning materials and develop a seamless ubiquitous
learning environment that supports learning without constraints of
learning time and space (Cavus & Uzunboylu, 2009; Chen &Huang,
2012). M-learning is de?ned as a formof e-learning that speci?cally
uses mobile devices to integrate with ubiquitous computing
technologies to deliver learning contents and supports (Brown,
2005; Hwang & Chang, 2011; Muyinda, 2007), and it inherits
many advantages from e-learning. However, m-learning can
further extend the ?exibility of e-learning regardless of learners'
location using handheld mobile devices through wireless technol-
ogies (Hwang & Chang, 2011; Motiwalla, 2007). To date, mobile/
wireless technologies and applications have been rapidly and
widely developed for m-learning, but researchers have placed
considerably less emphasis on the determinants of learners'
acceptance of m-learning, which is an important topic for learners
if they are to use m-learning to help them continuously enhance
competencies and effectively solve problems.
Noteworthily, although m-learning is a relatively new tool,
which is more likely to be embraced by innovators or early adopters
(Alvarez, Alarcon, & Nussbaum, 2011; Martin et al., 2011), the
technological characteristics of this new information system (IS)/
information technology (IT) is not out of consideration, because
users tend to seek the technological bene?ts of using the new IS/IT
* Corresponding author. Department of Business Administration, Chaoyang Uni-
versity of Technology, Number 168, Jifeng East Road, Wufeng District, Taichung City
41349, Taiwan.
E-mail address: [email protected].
Peer review under responsibility of College of Management, National Cheng
Kung University.
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Asia Paci?c Management Review
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Asia Paci?c Management Review 20 (2015) 109e119
as compared with the traditional IS/IT to determine their attitude
toward the new IS/IT (Childers, Carr, Peck, & Carson, 2001). How-
ever, the empirical evidence on the role of technological charac-
teristics in explaining learners' acceptance of m-learning is less
well documented. Hence, this study conducted a survey to examine
whether technological characteristics as the antecedents to
learners' beliefs affected their intention to use m-learning. To date,
the technology acceptance model (TAM) is one of the most widely
applied models in a variety of domains including related IS/IT
acceptance studies (Lindsay, Jackson, & Cooke, 2011; Maditinos,
Chatzoudes, & Sarigiannidis, 2013; Wu, 2011), and thus it can be
used as the base for this study's research model. Furthermore, to
enhance the TAM's explanatory power, it should ?rst include the
intrinsic motivational perspective to extend its function (Davis,
Bagozzi, & Warshaw, 1992; Lee, Cheung, & Chen, 2005; Teo, Lim,
& Lai, 1999; Van der Heijden, 2004), and it may further be inte-
grated with the innovation diffusion theory (IDT) to address the
compatibility (Chen, Gillenson, & Sherrell, 2002; Ryu, Kim, & Lee,
2009; Tan & Chou, 2008; Tung & Chang, 2008; Wu & Wang,
2005). Thus, a hybrid model is developed for exploring learners'
intention to use m-learning. Based on the aforementioned state-
ment, this study's purpose was to combine the extended TAM with
the IDT to examine whether technological characteristics and
compatibility as the antecedents to learners' beliefs affected their
intention to use m-learning.
2. Literature review
2.1. The outline of m-learning
M-learning is de?ned as a form of e-learning that speci?cally
uses mobile devices [e.g., personal digital assistants (PDAs), cell
phones, smart phones, notebooks (NBs), or tablet personal com-
puters (PCs)] to deliver learning contents and supports (Brown,
2005; Hwang & Chang, 2011; Muyinda, 2007). Essentially, m-
learning is based on the use of mobile devices anywhere at any time
(Chen & Huang, 2012; Motiwalla, 2007), and the prevalent use of
portable technologies makes it easier for learners to learn when
and where they intend to access the learning materials (Evans,
2008). In this study, m-learning refers to IT for learning, which
employs the mobile devices to integrate with ubiquitous
computing technologies to support learners' learning activities
(Alvarez et al., 2011; Martin et al., 2011). In addition, it allows
learners to have access to learning contents (e.g., learning mate-
rials, tests, dictionaries) and conduct personalized curriculum
sequencing according to their learning needs (Chan, Leung, Wu, &
Chan, 2003; Chen & Hsu, 2008; Hwang & Chang, 2011; Lundin &
Magnusson, 2003).
Essentially, m-learning may play an extremely important role in
the ?eld of educationwhere it can make signi?cant contributions to
learners' learning performance (Fang, Huang, & Lu, 2007). To date,
Taiwan already has a very excellent mobile telecommunication
infrastructure, which is under continuous development due to the
strong commitment of the government (Chuang & Tsao, 2013; Fang
et al., 2007). With the use of innovative information and commu-
nication technologies, the mobile technology ?nds its way into the
?eld of education inTaiwan as well, and educational institutions are
picking up mobile learning services based on the highly developed
telecommunication infrastructure (Fang et al., 2007; Hwang &
Chang, 2011). Besides, with the development of new mobile de-
vices, m-learning has emerged as a prosperous trend in Taiwan
(Chuang & Tsao, 2013). Of these devices, the mobile phone is the
most widely used device, because Taiwan has approximately 29.5
million mobile phone subscribers in 2012, with a market penetra-
tion rate of approximately 127.6% (Commerce Industrial Services
Portal, Ministry of Economic Affairs, R.O.C., 2013). Hence, the mo-
bile phone has the promising potential to provide learning mate-
rials toTaiwan's learners (Chuang &Tsao, 2013). However, although
m-learning is a relatively new tool, which is more likely to be
embraced by learners in Taiwan, mobile device applications may
present some limitations such as the reduced screen size of mobile
devices and the requirement of being easy of using at any time in
any proper equipped location, and these may add to the problems
faced by learners (Chen & Huang, 2012; Hwang & Chang, 2011).
2.2. Theory of reasoned action
TRAoriginates fromthe ?eldof social psychology, andit has been
one of the most widely applied models in explaining individuals'
behavior (Cheung & Vogel, 2013; Hong et al., 2013; Lee, Qu, & Kim,
2007). To date, TRA has received substantial empirical supports by
several prior studies, and it has been applied to a wide range of
users' IS/IT acceptance (Cheung & Vogel, 2013; Hong et al., 2013).
From a theoretical viewpoint, TRA posits that an individual's
behavior is determined by the individual's intention to engage in a
given behavior, which in turn can be in?uenced by the individual's
attitude toward the behavior and subjective norm surrounding the
performance of the behavior (Ajzen & Fishbein, 1980; Cheung &
Vogel, 2013; Fishbein & Ajzen, 1975). Hence, the concept of TRA is
that individuals are usually rational and will consider the implica-
tions of their actions before they decide whether to performa given
behavior (Ajzen &Fishbein, 1980; Hong et al., 2013). Essentially, TRA
makes a major contribution to the prior attitude studies by pro-
posing the behavioral intention as the most key determinant of an
individual's behavior (Cheung & Vogel, 2013; Hong et al., 2013).
2.3. Extended TAM
Many theoretical models have been used to explain users' IS/IT
acceptance. Among them, TAM, proposed by Davis (1989) and
Davis, Bagozzi, and Warshaw (1989), is one of the most widely
accepted and applied models in a variety of domains including
related IS and IT acceptance studies (Lindsay et al., 2011; Maditinos
et al., 2013; Wu, 2011). TAM is adapted from the well-known TRA
(Ajzen & Fishbein, 1980; Fishbein & Ajzen, 1975), which is a model
used extensively for explaining technology acceptance and utili-
zation among users. In general, TAM proposes that two particular
beliefs, perceived usefulness (PU) and perceived ease of use (PEOU),
are the primary drivers for explaining user acceptance of speci?c
type of system (Davis et al., 1989). PU is de?ned as “the degree to
which a person believes that using a particular system would
enhance his/her job performance,” and PEOU is de?ned as “the
degree to which a person believes that using a particular system
would be free of physical and mental effort” (Davis, 1989, p. 320).
The external variables of the TAM can affect PU and PEOU, and both
PU and PEOU affect a person's attitude toward using the system,
and the attitude toward using the system determines behavioral
intention, which in turn leads to actual system use (Davis, 1989;
Davis et al., 1989). Essentially, previous studies have shown TAM
to be justi?ed both pragmatically and theoretically (Hossain & de
Silva, 2009), because it has reliable instruments with excellent
measurement properties (Chen, Fan, & Farn, 2007; Pavlou, 2003).
While TAM has been veri?ed as a valuable model in explaining
users' acceptance of technology in various contexts, cultures, and
usage dimensions (Chen et al., 2007; Chen &Tan, 2004; Ha & Stoel,
2009; Hern andez, Jim enez, & Martín, 2008; Hossain & de Silva,
2009; Lee, Li, Yen, & Huang, 2010; Pavlou, 2003; Polancic,
Hericko, & Rozman, 2010), it provides little assistance in
capturing the hedonic feature of a hedonic-oriented IS/IT in the
nonworking place (Kim, Choi, &Han, 2009; Van der Heijden, 2004).
Y.-M. Cheng / Asia Paci?c Management Review 20 (2015) 109e119 110
Thus, to enhance TAM's explanatory power, prior studies have
suggested that it should include the intrinsic motivator [i.e.,
perceived enjoyment (PE)] to extend its function (Davis et al., 1992;
Ha, Yoon, & Choi, 2007; Igbaria, Iivari, & Maragahh, 1995; Lee et al.,
2005; Teo et al., 1999; Van der Heijden, 2004). Based on TAM, Davis
et al. (1992) ascertained the importance of the role of PE in
explaining computer acceptance, and they found that PE and PU
mediated the in?uence of PEOU on intention to use the computer.
PE refers to the degree to which the activity of using a particular
systemis perceived to be personally enjoyable in its own right apart
from the instrumental value of the speci?c type of system (Davis
et al., 1992; Lee et al., 2005; Teo et al., 1999). Essentially, PU and
PEOU usually re?ect the extrinsic motivational aspect of speci?c
type of systemusage, whereas PE re?ects the intrinsic motivational
aspect of speci?c type of system usage (Davis et al., 1992). Subse-
quently, an extended TAM that integrates the PE construct into the
original TAM is proposed, and it posits that three particular beliefs
(PU, PEOU, and PE) are of primary relevance for IS/IT acceptance
behaviors (Ha et al., 2007; Igbaria et al., 1995; Kim et al., 2009; Lee
et al., 2005; Liu & Li, 2011; Park, Baek, Ohm, & Chang, 2014; Teo
et al., 1999; Van der Heijden, 2004).
2.4. IDT
IDT is the well-known theory proposed by Rogers (1962).
Innovation diffusion is a process in which information for an
innovation is communicated through certain channels over time
among members of a social system (Faiers, Neame, & Cook, 2007;
Rogers, 1995). IDT proposes ?ve attributes of an innovation,
namely, relative advantage, complexity, compatibility, trial ability,
and observability (Rogers, 1995, 2003), and previous studies (e.g.,
Agarwal & Prasad, 1998; Chen et al., 2002; Ryu et al., 2009; Wu &
Wang, 2005) have suggested that only the relative advantage,
complexity, and compatibility are consistently related to innovation
adoption. The three innovation attributes are further detailed as
follows. Relative advantage refers to the extent to which an inno-
vation provides bene?ts, image enhancement, convenience, and
satisfaction in comparison to traditional methods (Rogers, 1995,
2003). Basically, relative advantage is similar to the concept of PU
(Chen et al., 2002; Chen & Tan, 2004; Ryu et al., 2009; Tung &
Chang, 2008; Wu & Wang, 2005). Complexity refers to the extent
to which an innovation is perceived to be dif?cult to understand,
learn, or utilize (Rogers, 1995, 2003). In general, complexity is
inversely related to the concept of PEOU (Chen et al., 2002; Chen &
Tan, 2004; Ryu et al., 2009; Tung & Chang, 2008; Wu & Wang,
2005). Compatibility refers to the extent to which the innovation
is perceived to be consistent with the adopters' beliefs, lifestyle,
existing values, experience, and current needs, and high compati-
bility can result in preferable innovation adoption (Rogers, 1983,
1995, 2003).
2.5. Extended TAM with the IDT
To enhance extended TAM's explanatory power, it may further
be integrated with the IDT to address the compatibility (Chen et al.,
2002; Ryu et al., 2009; Tan & Chou, 2008; Tung & Chang, 2008; Wu
& Wang, 2005). Essentially, IDT and TAM have some obvious sim-
ilarities, that is, relative advantage is similar to the concept of PU
and complexity is inversely related to the concept of PEOU, and
thus, relative advantage and complexity can be respectively
replaced by PU and PEOU (Chen et al., 2002; Chen &Tan, 2004; Ryu
et al., 2009; Tung & Chang, 2008; Wu & Wang, 2005). Based on the
foregoing, this study integrates the PE construct into the original
TAM to constitute an extended TAM, and further combines the
extended TAM with the IDT to address the compatibility construct.
Accordingly, a hybrid model is developed for exploring learners'
intention to use m-learning.
2.6. Technological characteristics
Essentially, users tend to seek the technological bene?ts of using
the new IS/IT as compared with the traditional IS/IT to determine
their attitude toward the newIS/IT (Childers et al., 2001); therefore,
factors that contribute to the new IS/IT acceptance are likely to be
determined by the technological characteristics of this new IS/IT
(Yoon & Kim, 2007). Unquestionably, m-learning is an emerging
tool that uses the ability of mobile devices to integrate with ubiq-
uitous computing technologies to support learning activities
(Alvarez et al., 2011; Martin et al., 2011). As far as the technological
characteristics of m-learning are concerned, on the one hand,
delivering digital learning contents through mobile devices should
take into consideration the prerequisites for actual controlled
navigation that learners require to obtain related knowledge
(L opez, Royo, Laborda, & Calvo, 2009), and on the other hand,
convenience should be considered as a key factor of learners'
acceptance of m-learning mainly because ubiquitous computing
technologies are expected to give learners convenience through
their intelligence and intercommunication in life (Alvarez et al.,
2011; Martin et al., 2011; Yoon & Kim, 2007).
3. Hypotheses and research model
3.1. PU, PEOU, and PE from the extended TAM
Based on TAM, Davis et al. (1992) ascertained the importance of
the role of PE in explaining computer acceptance and usage, and
they found that PE and PU mediated the in?uence of PEOU on
intention to use the computer. The extended TAM indicates that
three particular beliefs, PU, PEOU, and PE, are of primary relevance
for IS/IT acceptance behaviors (Ha et al., 2007; Igbaria et al., 1995;
Kim et al., 2009; Lee et al., 2005; Liu & Li, 2011; Park et al., 2014;
Teo et al., 1999; Van der Heijden, 2004). In general, PU, PEOU, and
PE directly determine intention to use the IS/IT (Davis et al., 1992;
Van der Heijden, 2004); moreover, PU and PE mediate the in?u-
ence of PEOU on intention to use the IS/IT (Davis et al., 1992; Lee
et al., 2005; Van der Heijden, 2004). Hence, this study hypothe-
sizes the following:
H1: PEOU will positively affect PU of m-learning.
H2: PEOU will positively affect PE of m-learning.
H3: PU will positively affect intention to use m-learning.
H4: PEOU will positively affect intention to use m-learning.
H5: PE will positively affect intention to use m-learning.
3.2. Technological characteristic antecedents to user beliefs
M-learning refers to a form of learning tool, which employs the
mobile devices to integrate with ubiquitous computing technolo-
gies to support learning activities and deliver learning materials to
learners (Alvarez et al., 2011; Martin et al., 2011). Accordingly, the
navigation for mobile devices and the convenience provided by
ubiquitous computing technologies can be regarded as key factors
that re?ect the technological characteristics of m-learning (Alvarez
et al., 2011; L opez et al., 2009; Martin et al., 2011; Yoon & Kim,
2007). Based on the synthesized views of Childers et al. (2001)
and Yoon and Kim (2007), learners may tend to evaluate the
technological characteristics (including navigation and conve-
nience) of using m-learning as compared with e-learning to
determine their beliefs toward m-learning, because m-learning is
Y.-M. Cheng / Asia Paci?c Management Review 20 (2015) 109e119 111
an emerging IT in comparison with e-learning. Hence, this study
further infers whether the navigation and convenience as the an-
tecedents can affect PU, PEOU, and PE of m-learning. The discus-
sions are further detailed in the following sections.
3.2.1. Navigation
Navigation refers to the process of self-directed movement
through the media involving nonlinear search and retrieval process
that provides unlimited freedom of choice and greater control for
the users (Childers et al., 2001; Hoffman & Novak, 1996). The
?exibility of navigating through the interactive online environment
is the determinant of shoppers' perceptions of the ease of using the
interactive media, and the shoppers' enjoyment of using the
interactive media can also increase when they have an increased
navigating ability through the interactive online environment
(Childers et al., 2001). In the mobile context, navigation is the
process by which users explore all the levels of interactivity, all by
themselves, and through the content and interface screens (Tucker,
2008). Khalifa and Shen (2008) found that the navigational ef?-
ciency signi?cantly predicted the mobile users' PU. Although nav-
igation in mobile device applications presents some limitations
such as the reduced screen size of mobile devices and the
requirement of being easy of using (Avellis, Scaramuzzi, &
Finkelstein, 2004; Lee & Benbasat, 2003), a good navigation sys-
tem should leave the potential adopters of mobile technologies
with little question about where they are in the document and
where they can go fromthere (Khalifa & Shen, 2008; Tucker, 2008).
Based on the foregoing, if the mobile navigation devices can allow
users to self-explore through the content and interface screens,
then users may also regard the mobile applications as useful, easy
to use, and enjoyable. Thus, this study infers that navigation is
expected to positively affect learners' PU, PEOU, and PE of m-
learning. Hence, this study hypothesizes the following:
H6a: Navigation will positively affect PU of m-learning.
H6b: Navigation will positively affect PEOU of m-learning.
H6c: Navigation will positively affect PE of m-learning.
3.2.2. Convenience
Convenience refers to the extent to which the media make easier
for users to save their time and effort (Brown, 1990; Khalifa & Shen,
2008; Yoon & Kim, 2007). Shoppers can e-shop over the Internet at
any time in any proper equipped location; if they perceive the
interactive online environment as offering greater convenience, they
will be more likely to regard the media as both useful and easy to use
(Childers et al., 2001). Shoppers' perceived convenience of interac-
tive online media can reduce their time pressure and location re-
strictions, and this situation can make their interactive experience
more enjoyable (Childers et al., 2001). Besides, Liao and Cheung
(2002) proposed that users' perception of convenience positively
affected their PU of Internet-based e-retail banking. In the mobile
context, convenience refers to the extent to which users believe that
conducting their affairs through mobile commerce (m-commerce)
would be free of effort (Choi, Seol, Lee, Cho, & Park, 2008). Yoon and
Kim(2007) showed that users' perceived convenience had a positive
effect on their PU of wireless local area network. For learners, m-
learning can further extend the ?exibility of e-learning regardless of
their location using wireless technologies (Evans, 2008; Motiwalla,
2007). Thus, there is a tendency toward m-learning owing to the
convenience of mobile devices, and this convenience in mobile en-
vironments can increase learning effectiveness, ef?ciency, and
pleasure through the ability to learn anytime and anywhere (Evans,
2008; Kambourakis, Kontoni, Rouskas, & Gritzalis, 2007). Based on
the foregoing, this study infers that convenience is expected to
positively affect learners' PU, PEOU, and PE of m-learning. Hence,
this study hypothesizes
H7a: Convenience will positively affect PU of m-learning.
H7b: Convenience will positively affect PEOU of m-learning.
H7c: Convenience will positively affect PE of m-learning.
3.3. Compatibility and user beliefs
Compatibility refers to the extent to which the innovation is
perceived to be consistent with the adopters' beliefs, lifestyle, exist-
ing values, experience, and current needs, and highcompatibility can
result in preferable innovation adoption (Rogers, 1983, 1995, 2003).
Some studies (e.g., Chen et al., 2002; Ryu et al., 2009; Tan & Chou,
2008; Tung & Chang, 2008; Wu & Wang, 2005; Wu, Wang, & Lin,
2007; Xue et al., 2012) have combined the view of TAM with the
IDT to address the compatibility construct to explain users' IS/IT
acceptance, because such integration may be able to provide a
stronger model thanstanding alone. Among these studies, Chenet al.
(2002) examined consumers' acceptance of virtual store, Wu and
Wang (2005) investigated what would determine users' acceptance
of m-commerce, Tung and Chang (2008) explored what were the
important factors making students use online courses. These three
studies all found that compatibility directly affected PU and usage
intention. Wu et al. (2007) showed that if health-care professionals
regarded the mobile health-care systems (MHS) as being compatible
with their health-care practices, they would perceive the usefulness
of the MHS, prefer an easy-to-use MHS, and also enhance their
intention to use the MHS. Ryu et al. (2009) further found that when
elderly online users regardedthe video user-createdcontent (UCC) as
being compatible with their current usage and lifestyle, they would
be more likely to take the bene?t (i.e., usefulness) of the video UCC
into account; and they would also prefer an easy-to-participate (i.e.,
easy-to-use) video UCC in their current behaviors. Xue et al. (2012)
showed that if aging women regarded the Infohealth (i.e., a mobile
phone-based intervention) as being compatible with their current
usage habit, they would perceive that using Infohealth is bene?cial
and easy, and they would also have the intention to use Infohealth.
Besides, Tan and Chou (2008) used the view of the extended TAMto
explore user behavior in the context of mobile information and
entertainment services, and they further showed that users'
perceived technology compatibility affected their perceived play-
fulness. Based on the foregoing, this study infers that compatibility is
expected to positively affect learners' PU, PEOU, PE, and intention to
use m-learning. Hence, this study hypothesizes
H8a: Compatibility will positively affect PU of m-learning.
H8b: Compatibility will positively affect PEOU of m-learning.
H8c: Compatibility will positively affect PE of m-learning.
H8d: Compatibility will positively affect intention to use m-learning.
3.4. Research model
Based on the extended TAM with the IDT, this study's research
model presents technological characteristics (including navigation
and convenience) and compatibility that lead to learners' usage
intention of m-learning. The research model is depicted in Fig. 1.
4. Methodology
4.1. Measures
In this study, responses to the items in navigation, convenience,
compatibility, PU, PEOU, PE, and intention to use were measured on
a 7-point Likert scale from 1 (strongly disagree) to 7 (strongly
Y.-M. Cheng / Asia Paci?c Management Review 20 (2015) 109e119 112
agree) with 4 labeled as neutral. To ensure content validity of the
scales, the items must represent the concept about which gener-
alizations are to be made (Ong, Lai, &Wang, 2004). Items chosen for
the constructs in this study were adapted and revised from previ-
ous research. Further, to ensure the translation equivalence for the
original meaning of questionnaire, the standard backtranslation
procedure for the questionnaire was followed (Mullen, 1995;
Sperber, Devellis, & Boehlecke, 1994). The original questionnaire
was ?rst developed in English, and then the original questionnaire
was translated into Chinese by a bilingual Taiwanese MBA student,
and another bilingual Taiwanese MBA student backtranslated the
Chinese version of the questionnaire into English. Lastly, to ensure
the cross-cultural uniformity in translation (Parameswaran &
Yaprak, 1987; Sperber et al., 1994), two other bilingual Taiwanese
doctoral students provided independent checks on the back-
translation. The ?nal items are presented in Table 1 along with their
sources.
4.2. Pretest
Following a convenience sampling method, the questionnaire
was pretested on 35 Taiwanese mobile phone users with experi-
ence in browsing or purchasing the learning contents (e.g., digital-
game-based learning, e-books, e-dictionaries, e-magazines, or e-
tests) via mobile technology. Based on the feedback, the re-
spondents were asked to identify any ambiguities in the meanings,
and the questionnaire was revised based on their comments. The
instrument's reliability was evaluated, and the Cronbach’s a values
(ranging from 0.80 to 0.96) exceeded common requirements for
exploratory research, indicating a satisfactory level of reliability
(Hair, Anderson, Tatham, & Black, 1998; Nunnally, 1978). The ?nal
items are presented in Table 1 along with their sources. Those who
had participated in the pretest were excluded from the ?nal data
collection and subsequent study.
4.3. Sample size and data collection
Taiwan has demonstrated a strong demand for telecommuni-
cations services and has approximately 29.5 million mobile phone
subscribers, with a market penetration rate of approximately
127.6% (Commerce Industrial Services Portal, Ministry of Economic
Affairs, R.O.C., 2013). Hence, sample data for this study were
collected from Taiwanese mobile phone users, and the unit of
analysis was individual mobile phone users with experience in
browsing or purchasing the learning contents through mobile
technology. Following a convenience sampling method, the ques-
tionnaires were distributed to 75 mobile phone shops; each shop
was given 10 questionnaires and those were distributed to mobile
phone users who had experience in browsing or purchasing the
learning contents (e.g., digital-game-based learning, e-books, e-
dictionaries, e-magazines, or e-tests) through mobile technology.
A total of 750 questionnaires were distributed, and 516 (68.80%)
questionnaires were received; 30 of these received questionnaires
were discarded due to partial portions of missing values. Finally,
486 usable questionnaires were analyzed in this study, with a us-
able response rate of 64.80%. Besides, nonresponse bias is usually
tested by examining the differences in mean for demographic
variables between mobile phone users who responded to the sur-
vey and mobile phone users who did not. For lack of comparable
statistics from nonresponding mobile phone users, t tests were
used to test response bias between early and late wave returned
surveys, with the late wave respondents being treated as a proxy for
nonrespondents (Armstrong & Overton, 1977; Lambert &
Harrington, 1990; Oppenheim, 1966). In this study, 318 usable re-
sponses were received in the early wave and 168 in the late wave.
The mean differences between the two groups with respect to sex,
age, educational level, and the duration of usage (the respondents'
experience in browsing or purchasing the learning contents
through mobile technology) were tested using an unpaired t test.
No signi?cant differences were observed at the 0.05 level, indi-
cating no systematic differences between the two groups. Because
nonresponse bias does not appear to be a problem, the ?nal sample
of 486 usable responses can be regarded as representative of the
population.
4.4. Data analysis
The data analysis process of this study followed a two-step
approach for structural equation modeling (SEM) method recom-
mended by Anderson and Gerbing (1988). In the ?rst step, con?r-
matory factor analysis (CFA) was used to develop the measurement
model. In the second step, to explore the causal relationships
Convenience Perceived Ease of Use Intention to Use
Navigation
Compatibility
Perceived Usefulness
Perceived Enjoyment
H7c
H7a
H1
H2
H7b
H6a
H8c
H6b
H8b
H6c
H8a
H3
H5
H4
H8d
Fig. 1. The research model.
Y.-M. Cheng / Asia Paci?c Management Review 20 (2015) 109e119 113
among all constructs, the structural model for research model
depicted in Fig. 1 was tested using SEM. The statistical analysis
software packages used to perform these processes were AMOS 5.0
(SPSS, Inc., Chicago, IL, USA) and SPSS 8.0 (SPSS, Inc.).
5. Results
5.1. Descriptive characteristics of the usable respondents
A total of 486 usable questionnaires were analyzed in this study.
Among the usable respondents, 288 respondents (59.3%) were
men, and 198 respondents (40.7%) were women. The distribution of
age (in years) was as follows: under 21 (17.1%), 21e30 (56.8%),
31e40 (23.0%), 41e50 (2.9%), 51e60 (0.2%), and over 60 (0.0%).
Educational levels were generally high. Respondents who had
completed senior high school accounted for 4.1%, respondents who
had completed junior college numbered 19.5%, respondents who
had completed college/university numbered 52.7%, whereas re-
spondents who had completed graduate school comprised 23.7% of
the survey sample. In addition, the distribution of duration of usage
(the respondents' experience in browsing or purchasing the
learning contents through mobile technology) was as follows: un-
der 7 months (21.8%), 7e12 months (60.1%), 13e18 months (16.5%),
19e24 months (1.6%), and over 24 months (0.0%). The descriptive
characteristics of the usable respondents are depicted in Table 2.
5.2. Common method bias
When the research data were collected using self-reported
questionnaires, there was a concern that a common method bias
may occur (Malhotra, Kim, & Patil, 2006), which may affect the
empirical results. According to the views recommended by
Podsakoff, MacKenzie, Lee, and Podsakoff (2003), to prevent the
threat of common method bias, in this study, respondents were
assured that their participation and responses would be completely
anonymous, con?dential, and voluntary, they were noti?ed the
right to withdraw from their participation at any time, and they
were informed that there were no right or wrong answers to the
items and were requested to re?ect their true opinions on each
item as objectively as possible. Besides, a common method bias test
should be conducted, and a CFA approach to Harman's single-factor
test can be used to assess the common method bias (Sanchez,
Korbin, & Viscarra, 1995). This study employed CFA to test the ?t
of a single-factor model (where all items were loaded on a single
factor) and a seven-factor model. The results showed that the ?t
indices of the single-factor model [c
2
¼ 5080.861, df ¼ 230, c
2
/
df ¼ 22.091, p < 0.001, goodness-of-?t index (GFI) ¼0.487, adjusted
GFI (AGFI) ¼ 0.384, normalized ?t index (NFI) ¼ 0.372, Tuck-
ereLewis index (TLI) ¼ 0.318, comparative ?t index (CFI) ¼ 0.381,
and root mean square error of approximation (RMSEA) ¼ 0.209]
were worse than those of the seven-factor model (c
2
¼ 318.307,
df ¼ 209, c
2
/df ¼ 1.523, p < 0.001, GFI ¼ 0.948, AGFI ¼ 0.931,
NFI ¼0.961, TLI ¼0.983, CFI ¼0.986, and RMSEA ¼0.033). Thus, the
?t is considerably worse for the single-factor model than it is for the
multifactor model, which indicates that the common method bias
is not a problem for this study (Sanchez et al., 1995).
Table 1
Construct measurement and sources.
Construct Item Measure Source
Navigation (NAV) NAV1 Using m-learning allows navigation through the learning environment. Childers et al. (2001)
NAV2 Using m-learning allows me to explore the learning environment in a variety of ways. Tucker (2008)
NAV3 Using m-learning allows me to move ?uidly through the learning environment.
NAV4 Using m-learning allows ?exibility in tracking down information.
Convenience (CON) CON1 Using m-learning is convenient for me. Childers et al. (2001)
CON2 Using m-learning is a convenient way to learn. Liao and Cheung (2002)
CON3 M-learning allows me to learn whenever I choose. Yoon and Kim (2007)
CON4 M-learning allows me to learn wherever I choose.
Compatibility (COM) COM1 Using m-learning is compatible with most aspects of my learning. Agarwal and Prasad (1998)
COM2 Using m-learning ?ts well with the way I like to learn. Chen et al. (2002)
COM3 Using m-learning ?ts my learning style.
Perceived usefulness (PU) PU1 Using m-learning enhances my learning effectiveness. Davis (1989)
PU2 Using m-learning gives me greater control over learning. Ngai, Poon, and Chan (2007)
PU3 I ?nd m-learning to be useful in my learning.
Perceived ease of use (PEOU) PEOU1 Interacting with m-learning does not require a lot of my mental effort. Davis (1989)
PEOU2 My interaction with m-learning is clear and understandable. Ngai et al. (2007)
PEOU3 I ?nd m-learning to be easy to use.
Perceived enjoyment (PE) PE1 I ?nd using m-learning to be enjoyable. Davis et al. (1992)
PE2 The actual process of using m-learning is pleasant. Lee et al. (2005)
PE3 I have fun using m-learning.
Intention to use (ITU) ITU1 I will use m-learning on a regular basis in the future. Bhattacherjee (2001)
ITU2 I will frequently use m-learning in the future. Mathieson (1991)
ITU3 I will continue using m-learning in the future. Roca, Chiu, and Martínez (2006)
Table 2
Descriptive characteristics of the usable respondents.
Demographics Number Percentage
Gender
Male 288 59.3%
Female 198 40.7%
Age
<21 83 17.1%
21e30 276 56.8%
31e40 112 23.0%
41e50 14 2.9%
51e60 1 0.2%
>60 0 0.0%
Educational level
Senior high school 20 4.1%
Junior college 95 19.5%
College/university 256 52.7%
Graduate school 115 23.7%
Duration of usage (mo)
<7 106 21.8%
7e12 292 60.1%
13e18 80 16.5%
19e24 8 1.6%
>24 0 0.0%
Y.-M. Cheng / Asia Paci?c Management Review 20 (2015) 109e119 114
5.3. Results of structural modeling analysis
5.3.1. Measurement model
To assess the measurement model, three analyses were con-
ducted in this study. First, squared multiple correlation (SMC) for
each item, and composite reliability (CR) and average variance
extracted (AVE) for each construct were used in this study to test
the reliability of all constructs (Byrne, 2001; Hair et al., 1998;
Holmes-Smith, 2001; Nunnally, 1978). The results of CFA showed
that the SMC values for all items were greater than 0.5, which
indicated a good reliability level (Holmes-Smith, 2001). The values
of CR and AVE for all constructs exceeded the minimum acceptable
values of 0.7 and 0.5 (Hair et al., 1998; Holmes-Smith, 2001; Nun-
nally, 1978), indicating a good reliability level and subsequently
yielding very consistent results. Hence, the results of CFA demon-
strated an acceptable level of reliability for all constructs. Besides,
the reliability coef?cients of all constructs assessed by the Cron-
bach’s a value exceeded the 0.7 cutoff value as recommended by
Hair et al. (1998) and Nunnally (1978). The results of reliability test
are presented in Table 3.
Second, according to Anderson and Gerbing's (1988) rule, the
results of CFA showed that the t-value of every item exceeded 1.96
(p < 0.05), and therefore, the evidence of good convergent validity
was obtained as the items signi?cantly represented their con-
structs. The reports are listed in Table 3. Furthermore, to test for
discriminant validity, the procedure described by Fornell and
Larcker (1981) was used in this study. The results of CFA showed
that the AVE of each construct was greater than the squared
correlation for each pair of constructs, indicating that each
construct was distinct (Tables 3 and 4).
Third, the most common rules used to perform the CFA for
measurement model and testing the structural model include
stipulating that the GFI should be greater than 0.9, the AGFI should
be greater than 0.9, the NFI should be greater than 0.9, the TLI
should be greater than 0.9, the CFI should be greater than 0.9, the
RMSEA should be less than 0.08, and the c
2
/df should be less than 3
(Bagozzi &Yi, 1988; Bentler &Bonett, 1980; Byrne, 2001; Hair et al.,
1998). The overall ?t indices of measurement model were
c
2
¼ 318.307, df ¼ 209, c
2
/df ¼ 1.523, p < 0.001, GFI ¼ 0.948,
AGFI ¼ 0.931, NFI ¼ 0.961, TLI ¼ 0.983, CFI ¼ 0.986, and
RMSEA ¼ 0.033. The results of CFA showed that the indices were
over their respective common acceptance levels. Thus, the pro-
posed model generally ?ts the sample data well.
5.3.2. Structural model
The following step is to test the structural model for the research
model depicted in Fig. 1. The overall ?t indices for the structural
model were as follows: c
2
¼ 359.512, df ¼ 215, c
2
/df ¼ 1.672,
p < 0.001, GFI ¼ 0.942, AGFI ¼ 0.925, NFI ¼ 0.956, TLI ¼ 0.978,
CFI ¼0.982, and RMSEA ¼0.037. According to the views of previous
studies (e.g., Bagozzi & Yi, 1988; Bentler & Bonett, 1980; Byrne,
2001; Hair et al., 1998), the results of CFA showed that the ?t
indices for this structural model were quite acceptable.
5.3.3. Hypothesis testing
Properties of the causal paths, including standardized path
coef?cients (b), t values, and explained variances (R
2
), are shown
in Fig. 2. As for antecedents to learner beliefs and usage intention,
?rst, the effects of navigation on PU, PEOU, and PE were signi?cant
(b ¼ 0.211, 0.189, and 0.214, respectively; p < 0.001); hence, H6a,
H6b, and H6c are supported. Second, the effects of convenience on
PU, PEOU, and PE were signi?cant (b ¼ 0.124, 0.136, and 0.340,
respectively; p < 0.01); hence, H7a, H7b, and H7c are supported.
Third, the effects of compatibility on PU, PEOU, PE, and intention
to use were signi?cant (b ¼ 0.132, 0.163, 0.128, and 0.135,
respectively; p < 0.01); hence, H8a, H8b, H8c, and H8d are sup-
ported. As to relationships between learner beliefs and usage
intention, ?rst, the effects of PEOU on PU and PE were signi?cant
(b ¼ 0.363 and 0.238, respectively; p < 0.001); hence, H1 and H2
are supported. Second, the effects of PU, PEOU, and PE on intention
to use were signi?cant (b ¼ 0.399, 0.213, and 0.211, respectively;
p < 0.001); hence, H3, H4, and H5 are supported. The results of
hypothesis testing are shown in Table 5. In the following, the
explained variances (R
2
) of PU, PEOU, PE, and intention to use were
0.266, 0.081, 0.285, and 0.455, respectively. Further, using these
results, the direct and indirect effects between the constructs are
shown in Table 6. The results indicate that navigation,
Table 3
Results of con?rmatory factor analysis, validity analysis, and reliability test.
Construct
item
Estimate T-value Standardized
path coef?cients
SMC CR AVE Cronbach’s a
NAV 0.871 0.629 0.868
NAV1 1 d
a
0.862 0.741
NAV2 1.026 14.826 0.885 0.761
NAV3 1.050 15.277 0.934 0.813
NAV4 0.908 12.971 0.775 0.652
CON 0.925 0.757 0.909
CON1 1 d
a
0.790 0.667
CON2 1.059 22.246 0.888 0.818
CON3 1.094 23.164 0.922 0.871
CON4 0.920 19.008 0.787 0.661
COM 0.852 0.656 0.855
COM1 1 d
a
0.808 0.679
COM2 1.332 16.783 0.759 0.633
COM3 1.107 18.597 0.903 0.832
PU 0.941 0.842 0.960
PU1 1 d
a
0.924 0.859
PU2 1.073 19.419 0.947 0.901
PU3 1.057 20.032 0.952 0.909
PEOU 0.863 0.679 0.913
PEOU1 1 d
a
0.897 0.808
PEOU2 1.021 25.168 0.930 0.868
PEOU3 0.928 23.930 0.817 0.672
PE 0.934 0.824 0.904
PE1 1 d
a
0.876 0.779
PE2 1.116 25.586 0.902 0.823
PE3 1.053 22.708 0.823 0.690
ITU 0.918 0.789 0.932
ITU1 1 d
a
0.865 0.756
ITU2 1.050 23.904 0.946 0.899
ITU3 1.016 21.615 0.899 0.815
AVE ¼ average variance extracted; COM ¼ compatibility; CON ¼ convenience;
CR ¼ composite reliability; ITU ¼ intention to use; NAV ¼ navigation;
PE ¼ perceived enjoyment; PEOU ¼ perceived ease of use; PU ¼ perceived useful-
ness; SMC ¼ squared multiple correlation.
a
The loading was ?xed.
Table 4
Discriminant validity for the measurement model.
Construct NAV CON COM PU PEOU PE ITU
NAV 0.629
CON 0.062 0.757
COM 0.013 0.033 0.656
PU 0.107 0.064 0.058 0.842
PEOU 0.054 0.038 0.041 0.207 0.679
PE 0.122 0.189 0.061 0.101 0.135 0.824
ITU 0.101 0.080 0.106 0.349 0.246 0.198 0.789
Bold values along the diagonal line are the AVE values for the constructs, and the
other values are the squared correlations for each pair of constructs.
AVE ¼ average variance extracted; COM ¼ compatibility; CON ¼ convenience;
ITU ¼ intention to use; NAV ¼ navigation; PE ¼ perceived enjoyment;
PEOU ¼ perceived ease of use; PU ¼ perceived usefulness.
Y.-M. Cheng / Asia Paci?c Management Review 20 (2015) 109e119 115
convenience, and compatibility can indirectly make signi?cant
positive impacts on learners' usage intention of m-learning
through their PU, PEOU, and PE, whereas compatibility can also
directly make a signi?cant positive impact on learners' usage
intention of m-learning.
6. Discussion
Based on the extended TAM with the IDT, this study enhances
the understanding of the roles played by technological character-
istics and compatibility in the process of m-learning acceptance,
and thus offers relevant implications and suggestions for m-
learning providers wishing to realize learners' acceptance of m-
learning. The discussions are further detailed in the following
sections.
As this study's ?ndings present (Table 5), the effects of naviga-
tion and convenience on learners' intention to use m-learning are
fully mediated by the extrinsic motivators (i.e., PU and PEOU) and
intrinsic motivator (i.e., PE). The ?ndings are consistent with the
views of previous studies (e.g., Childers et al., 2001; Khalifa & Shen,
2008; Liao & Cheung, 2002; Yoon & Kim, 2007). The result impli-
cates that if learners can self-explore directly through the content
and interface screens over the mobile-based interactive learning
environments at any time in any location, they will be more likely
to regard m-learning as both useful and easy to use, and this situ-
ation will make their interactive experience more enjoyable, thus
learners' extrinsic motivators (PU and PEOU) and intrinsic moti-
vator (PE) will further facilitate their intention to use m-learning.
Accordingly, the technological characteristics such as navigating
?exibility and convenience of time, place, and execution should be
designed for m-learning applications. Navigation is the most key
antecedent that can make signi?cant impacts on learners' PU and
PEOU, and this has been con?rmed by the path analysis (Table 6),
Convenience
Perceived Ease of Use
[ R
2
= 0.081 ]
Intention to Use
[ R
2
= 0.455 ]
Navigation
Compatibility
Perceived Usefulness
[ R
2
= 0.266 ]
Perceived Enjoyment
[ R
2
= 0.285 ]
0.399
(9.118)
0.213
(4.753)
0.211
(5.047)
0.363
(7.965)
0.238
(5.141)
0.135
(3.295)
0.128
(2.746)
0.211
(4.514)
0.163
(3.239)
0.189
(3.701)
0.132
(2.897)
0.214
(4.451)
0.124
(2.903)
0.340
(7.382)
0.136
(2.839)
Fig. 2. Results of structural modeling analysis. Standardized path coef?cients are reported (t-values in parentheses). Absolute t-value > 1.96, p < 0.05; absolute t-value > 2.58,
p < 0.01; absolute t-value > 3.29, p < 0.001.
Table 5
Results of hypothesis testing.
Hypothesis Standardized path
coef?cients (b)
T-values Signi?cance Support
H1: PEOU /PU 0.363 7.965 p < 0.001 Yes
H2: PEOU /PE 0.238 5.141 p < 0.001 Yes
H3: PU /ITU 0.399 9.118 p < 0.001 Yes
H4: PEOU /ITU 0.213 4.753 p < 0.001 Yes
H5: PE /ITU 0.211 5.047 p < 0.001 Yes
H6a: NAV /PU 0.211 4.514 p < 0.001 Yes
H6b: NAV /PEOU 0.189 3.701 p < 0.001 Yes
H6c: NAV /PE 0.214 4.451 p < 0.001 Yes
H7a: CON /PU 0.124 2.903 p < 0.01 Yes
H7b: CON /PEOU 0.136 2.839 p < 0.01 Yes
H7c: CON /PE 0.340 7.382 p < 0.001 Yes
H8a: COM /PU 0.132 2.897 p < 0.01 Yes
H8b: COM /PEOU 0.163 3.239 p < 0.01 Yes
H8c: COM /PE 0.128 2.746 p < 0.01 Yes
H8d: COM /ITU 0.135 3.295 p < 0.001 Yes
COM ¼ compatibility; CON ¼ convenience; ITU ¼ intention to use;
NAV ¼ navigation; PE ¼ perceived enjoyment; PEOU ¼ perceived ease of use;
PU ¼ perceived usefulness.
Table 6
Direct and indirect effects between the constructs.
Construct PEOU PU PE ITU
DE InDE TE DE InDE TE DE InDE TE DE InDE TE
NAV 0.189 d 0.189 0.211 0.069 0.280 0.214 0.045 0.259 d 0.206 0.206
CON 0.136 d 0.136 0.124 0.049 0.173 0.340 0.032 0.372 d 0.177 0.177
COM 0.163 d 0.163 0.132 0.059 0.191 0.128 0.039 0.167 0.135 0.146 0.281
PEOU d d d 0.363 d 0.363 0.238 d 0.238 0.213 0.195 0.408
PU d d d d d d d d d 0.399 d 0.399
PE d d d d d d d d d 0.211 d 0.211
COM¼compatibility; CON¼convenience; DE ¼direct effects; InDE ¼indirect effects; ITU¼intention to use; NAV ¼navigation; PE ¼perceived enjoyment; PEOU¼perceived
ease of use; PU ¼ perceived usefulness; TE ¼ total effects.
Y.-M. Cheng / Asia Paci?c Management Review 20 (2015) 109e119 116
where navigation has a larger total impact on PU [total effect
(TE) ¼ 0.280] and PEOU (TE ¼ 0.189) than the total impact of
convenience on PU (TE ¼ 0.173) and PEOU (TE ¼ 0.136). This study
suggests that m-learning providers should try to develop friendlier
user interface by designing useful and easy-to-use features to
induce learners to use m-learning. Hence, some interesting ideas
from previous studies (e.g., Churchill & Hedberg, 2008; Motiwalla,
2007) for professionally designing the screen layouts for m-
learning services should be taken into consideration, such as
appropriate text formatting, full-screen presentation, minimized
scrolling, short contact time, interactivity over text, interactive
panels, interactive voice response. These enhancements are crucial
for assisting learners in improving their experience of using m-
learning. Moreover, convenience can have the greatest impact on
learners' PE, and this has also been con?rmed by the path analysis
(Table 6), where convenience has a larger total impact on PE
(TE ¼ 0.372) than the total impact of navigation on PE (TE ¼ 0.259).
Thus, this study suggests that m-learning providers should enhance
learners' perceived convenience of m-learning at any time in any
location to deliver pleasures to boost their usage intention of m-
learning.
As the testing results of compatibility-related hypotheses show
(Table 5), learners intend to use m-learning because they perceive it
to be more compatible with most aspects of their learning. The
result implicates that learners can judge m-learning by how well it
meets their perceived compatibility of m-learning. Thus, this study
suggests that m-learning providers should make their m-learning
services run compatibly with learners' existing values, needs, and
learning styles. In addition, the effects of compatibility on learners'
intention to use m-learning are also mediated by the extrinsic
motivators (PU and PEOU) and intrinsic motivator (PE). Hence,
compatibility can have direct and indirect effects on learners'
intention to use m-learning. The ?nding is also consistent with the
views of previous studies (e.g., Mallat, Rossi, Tuunainen, &
€
O€ orni,
2009; Wu & Wang, 2005; Wu et al., 2007; Xue et al., 2012) that
showcompatibility is an important determinant for usage intention
of the mobile technology. The foregoing results reveal that
compatibility exhibits stronger indirect impacts on learners'
intention to use m-learning than its direct impact (Table 6). Hence,
the exposition implicates that a successful m-learning design
should be developed to be widely compatible with learners'
behavior that is tailored to their existing values, needs, and learning
styles, so it can deliver the effectiveness, ef?ciency, and pleasure of
usage to learners and further boost their usage intention by
increasing the extent of their perceived compatibility of m-
learning. In general, m-learning has potential for providing a
mechanism where each learner will autonomously have their own
individualized learning paths based on their preferred learning
styles (Kinshuk & Lin, 2004; Yau & Joy, 2006). Hence, for m-
learning providers, creating adaptive m-learning environments
may be a good idea to ensure that m-learning can be compatible
with the vast majority of learners' learning needs and styles. Be-
sides, this study suggests that m-learning providers should rethink
how learners' learning needs and styles are expanded and enabled
with multifunctional mobile devices to tailor their m-learning
services to mobile learners.
With regard to the relationships between learners' beliefs and
their usage intention of m-learning (Table 6), learners intend to use
m-learning mainly because they perceive it to be easier to use
(TE ¼ 0.408) to their learning and secondarily because it is useful
(TE ¼ 0.399) and enjoyable (TE ¼0.211). Obviously, the ?ndings are
consistent with the views of previous studies (e.g., Cheong & Park,
2005; Kim et al., 2009; Nysveen, Pedersen, & Thorbjørnsen, 2005;
Park et al., 2014; Song, Koo, & Kim, 2007; Yang, 2007) that show
both extrinsic motivators (PU and PEOU) and intrinsic motivator
(PE) play important roles in affecting users' intention to use the
mobile technology. Furthermore, learners' PEOU (extrinsic moti-
vator) has positive and strong effects on their intention to use m-
learning, and their PU (extrinsic motivator) has a more powerful
effect on their intention to use m-learning than their PE (intrinsic
motivator). As advocated by prior research, inexperienced users'
PEOU (extrinsic motivator) has positive and strong effects on their
intention to use the particular technology (Venkatesh, 2000;
Venkatesh & Davis, 1996; Wu & Wang, 2005), and their PU
(extrinsic motivator) has a more positive effect on their intention to
use the particular technology than their PE (intrinsic motivator)
(Kim et al., 2009). In this study, most usable respondents had less
experience in using m-learning because 81.9% of usable re-
spondents had less than 1-year experience in browsing or pur-
chasing the learning contents through mobile technology, and thus,
the foregoing views addressed by prior research may be used to
explain this study's ?ndings. Hence, the results implicate that users
inexperienced in IT usage are motivated more extrinsically than
intrinsically at the initial adoption stage (Kim et al., 2009). This
study suggests that m-learning providers should assure learners of
browsing or purchasing the learning contents through mobile
technology in a more effective, more ef?cient, and pleasanter
manner to achieve their goals with a minimum of inconvenience,
and usefulness and ease of use should be especially thought of as
re?ecting the instrumental value on inexperienced learners' usage
intention of m-learning at the initial adoption stage.
7. Conclusions
Previous studies have only focused on the impacts of extrinsic
motivators such as PU and PEOU on IS/IT acceptance (Agarwal &
Karahanna, 2000; Lee et al., 2005). This study is one of the few
attempts to adopt the views of extrinsic motivation (PU and PEOU)
and intrinsic motivation (PE) to explain learners' usage intention of
m-learning. Besides, in this study, the views of the extended TAM
with the IDT provide clear expositions of learners' beliefs in
affecting their usage intention of m-learning. Hence, collectively
they have greater explanatory power than any single group of
factors in describing learners' principal beliefs in affecting their
usage intention of m-learning. Synthetically speaking, technolog-
ical characteristic antecedents (including navigation and conve-
nience) can fully indirectly have signi?cant impacts on learners'
intention to use m-learning through their extrinsic motivators (PU
and PEOU) and intrinsic motivator (PE); and compatibility can
make direct and indirect effects on learners' intention to use m-
learning. In brief, this study proposes a well-rounded theoretical
model (Fig. 1) that may act as an integrated base for the research of
m-learning acceptance, and this study's results for learners'
acceptance of m-learning are justi?ed both pragmatically and
theoretically. The following ?ndings are particularly worth
mentioning. First, navigation has the largest total impact on PU and
PEOU than the respective total impact of convenience and
compatibility on PU and PEOU (Table 6), and thus it is the most key
antecedent that can have signi?cant impacts on learners' PU and
PEOU, which jointly account for learners' intention to use m-
learning. Accordingly, m-learning providers should develop user-
friendlier interface by designing useful and easy-to-use screen
layouts recommended by learners to induce learners to use m-
learning. Next, convenience has the largest total impact on PE than
the respective total impact of navigation and compatibility on PE
(Table 6), so convenience can make the greatest impact on learners'
PE elicited by the m-learning use and further make them intend to
use m-learning. To boost learners' usage intention of m-learning,
m-learning providers should exert themselves to reduce learners'
time pressure and location restrictions within the mobile-based
Y.-M. Cheng / Asia Paci?c Management Review 20 (2015) 109e119 117
learning environments to cause more learners to pleasurably enjoy
interactive experiences in m-learning.
Several limitations should be noted in this study, and these
following suggestions for further research will be worth future ef-
forts in this ?eld. First, this study's ?ndings were based only on a
population of Taiwanese mobile phone users who had experience in
browsing or purchasing the learning contents through mobile
technology. Further research may generalize this study's sample to
the respondents who use different mobile devices (such as PDAs,
NBs, or tablet PCs). Second, this study did not investigate learners'
usage details of m-learning (e.g., types of mobile phone platforms,
types of technical speci?cations for mobile phones, browsing/pur-
chasing the learning contents and for what purposes, online/of?ine
with downloaded learning contents), which may have more im-
pacts on further analyses to support the quantitative ?ndings.
Further research may gather and take into account these usage
details of m-learning to obtain more comprehensive interpretations
to enrich the model of m-learning usage intention. Third, this study
focused on the understanding of the impacts of the technological
characteristic antecedents such as navigation and convenience
characterizing mobile-based interactive learning environments on
learners' perceptions of usefulness, ease of use, and enjoyment.
Further research may explore how the nontechnological factors
(such as course contents, interactive communication between in-
structors and learners, response attitudes) of mobile-based inter-
active learning environments affect learners' beliefs. Fourth,
respondents might usually display different relaxed feelings or
serious reactions for m-learning, depending onwhich situationthey
used it. Further researchin m-learning acceptance mayexamine the
different set of mechanisms between voluntary and mandatory
usage settings. Finally, this study was a cross-sectional analysis of
m-learning acceptance. It may be desirable to explore a complete
picture of the course of m-learning acceptance with learners'
increased experience in using m-learning. Further research may use
longitudinal analysis by taking into account the evolution of m-
learning acceptance over time.
Con?icts of interest
The author declares no con?icts of interest.
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doc_672153247.pdf
This study show
that learners can judge m-learning by how well it meets their perceived compatibility of m-learning, and
they will regard m-learning as a useful, easy to use, and enjoyable tool if they can explore it themselves
through the content and interface screens over the mobile-based learning environments at any time in
any location, and these situations will further facilitate their intention to use m-learning. In conclusion,
the views of the extended TAM with the IDT provide clear expositions of learners' beliefs, which affect
their intention to use m-learning.
Towards an understanding of the factors affecting m-learning acceptance: Roles of
technological characteristics and compatibility
Yung-Ming Cheng
*
Department of Business Administration, Chaoyang University of Technology, Taichung City, Taiwan
a r t i c l e i n f o
Article history:
Received 26 October 2012
Accepted 24 April 2014
Available online 1 April 2015
Keywords:
Compatibility
Extended technology acceptance model
Innovation diffusion theory
M-learning acceptance
Technological characteristics
a b s t r a c t
To date, prior studies have placed considerably less emphasis on the determinants of learners' acceptance
of mobile learning (m-learning). Hence, this study's purpose was to combine the extended technology
acceptance model (TAM) with the innovation diffusion theory (IDT) to examine whether technological
characteristics (including navigation and convenience) and compatibility as the antecedents to learners'
beliefs affected their intention to use m-learning. Sample data for this study were collected from
Taiwanese mobile phone users; a total of 750 questionnaires were distributed, and 486 usable ques-
tionnaires were analyzed in this study, with a usable response rate of 64.80%. Collected data were
analyzed using structural equation modeling. This study showed that technological characteristics
(including navigation and convenience) and compatibility had signi?cant effects on perceived usefulness
(PU), perceived ease of use (PEOU), and perceived enjoyment (PE) of m-learning; besides, PU, PEOU, PE,
and compatibility, respectively, exhibited signi?cantly strong impacts on intention to use m-learning,
and PEOU indirectly affected intention to use m-learning via PU and PE. The results of this study show
that learners can judge m-learning by how well it meets their perceived compatibility of m-learning, and
they will regard m-learning as a useful, easy to use, and enjoyable tool if they can explore it themselves
through the content and interface screens over the mobile-based learning environments at any time in
any location, and these situations will further facilitate their intention to use m-learning. In conclusion,
the views of the extended TAM with the IDT provide clear expositions of learners' beliefs, which affect
their intention to use m-learning.
© 2015, College of Management, National Cheng Kung University. Production and hosting by Elsevier
Taiwan LLC. All rights reserved.
1. Introduction
Recently, mobile devices and ubiquitous computing technolo-
gies have created unprecedented opportunities for conducting
learning. Hence, mobile learning (m-learning) has increasingly
attracted the interest of educators, researchers, and companies that
publish learning materials and develop a seamless ubiquitous
learning environment that supports learning without constraints of
learning time and space (Cavus & Uzunboylu, 2009; Chen &Huang,
2012). M-learning is de?ned as a formof e-learning that speci?cally
uses mobile devices to integrate with ubiquitous computing
technologies to deliver learning contents and supports (Brown,
2005; Hwang & Chang, 2011; Muyinda, 2007), and it inherits
many advantages from e-learning. However, m-learning can
further extend the ?exibility of e-learning regardless of learners'
location using handheld mobile devices through wireless technol-
ogies (Hwang & Chang, 2011; Motiwalla, 2007). To date, mobile/
wireless technologies and applications have been rapidly and
widely developed for m-learning, but researchers have placed
considerably less emphasis on the determinants of learners'
acceptance of m-learning, which is an important topic for learners
if they are to use m-learning to help them continuously enhance
competencies and effectively solve problems.
Noteworthily, although m-learning is a relatively new tool,
which is more likely to be embraced by innovators or early adopters
(Alvarez, Alarcon, & Nussbaum, 2011; Martin et al., 2011), the
technological characteristics of this new information system (IS)/
information technology (IT) is not out of consideration, because
users tend to seek the technological bene?ts of using the new IS/IT
* Corresponding author. Department of Business Administration, Chaoyang Uni-
versity of Technology, Number 168, Jifeng East Road, Wufeng District, Taichung City
41349, Taiwan.
E-mail address: [email protected].
Peer review under responsibility of College of Management, National Cheng
Kung University.
HOSTED BY
Contents lists available at ScienceDirect
Asia Paci?c Management Review
j ournal homepage: www. el sevi er. com/ l ocat e/ apmrv
http://dx.doi.org/10.1016/j.apmrv.2014.12.011
1029-3132/© 2015, College of Management, National Cheng Kung University. Production and hosting by Elsevier Taiwan LLC. All rights reserved.
Asia Paci?c Management Review 20 (2015) 109e119
as compared with the traditional IS/IT to determine their attitude
toward the new IS/IT (Childers, Carr, Peck, & Carson, 2001). How-
ever, the empirical evidence on the role of technological charac-
teristics in explaining learners' acceptance of m-learning is less
well documented. Hence, this study conducted a survey to examine
whether technological characteristics as the antecedents to
learners' beliefs affected their intention to use m-learning. To date,
the technology acceptance model (TAM) is one of the most widely
applied models in a variety of domains including related IS/IT
acceptance studies (Lindsay, Jackson, & Cooke, 2011; Maditinos,
Chatzoudes, & Sarigiannidis, 2013; Wu, 2011), and thus it can be
used as the base for this study's research model. Furthermore, to
enhance the TAM's explanatory power, it should ?rst include the
intrinsic motivational perspective to extend its function (Davis,
Bagozzi, & Warshaw, 1992; Lee, Cheung, & Chen, 2005; Teo, Lim,
& Lai, 1999; Van der Heijden, 2004), and it may further be inte-
grated with the innovation diffusion theory (IDT) to address the
compatibility (Chen, Gillenson, & Sherrell, 2002; Ryu, Kim, & Lee,
2009; Tan & Chou, 2008; Tung & Chang, 2008; Wu & Wang,
2005). Thus, a hybrid model is developed for exploring learners'
intention to use m-learning. Based on the aforementioned state-
ment, this study's purpose was to combine the extended TAM with
the IDT to examine whether technological characteristics and
compatibility as the antecedents to learners' beliefs affected their
intention to use m-learning.
2. Literature review
2.1. The outline of m-learning
M-learning is de?ned as a form of e-learning that speci?cally
uses mobile devices [e.g., personal digital assistants (PDAs), cell
phones, smart phones, notebooks (NBs), or tablet personal com-
puters (PCs)] to deliver learning contents and supports (Brown,
2005; Hwang & Chang, 2011; Muyinda, 2007). Essentially, m-
learning is based on the use of mobile devices anywhere at any time
(Chen & Huang, 2012; Motiwalla, 2007), and the prevalent use of
portable technologies makes it easier for learners to learn when
and where they intend to access the learning materials (Evans,
2008). In this study, m-learning refers to IT for learning, which
employs the mobile devices to integrate with ubiquitous
computing technologies to support learners' learning activities
(Alvarez et al., 2011; Martin et al., 2011). In addition, it allows
learners to have access to learning contents (e.g., learning mate-
rials, tests, dictionaries) and conduct personalized curriculum
sequencing according to their learning needs (Chan, Leung, Wu, &
Chan, 2003; Chen & Hsu, 2008; Hwang & Chang, 2011; Lundin &
Magnusson, 2003).
Essentially, m-learning may play an extremely important role in
the ?eld of educationwhere it can make signi?cant contributions to
learners' learning performance (Fang, Huang, & Lu, 2007). To date,
Taiwan already has a very excellent mobile telecommunication
infrastructure, which is under continuous development due to the
strong commitment of the government (Chuang & Tsao, 2013; Fang
et al., 2007). With the use of innovative information and commu-
nication technologies, the mobile technology ?nds its way into the
?eld of education inTaiwan as well, and educational institutions are
picking up mobile learning services based on the highly developed
telecommunication infrastructure (Fang et al., 2007; Hwang &
Chang, 2011). Besides, with the development of new mobile de-
vices, m-learning has emerged as a prosperous trend in Taiwan
(Chuang & Tsao, 2013). Of these devices, the mobile phone is the
most widely used device, because Taiwan has approximately 29.5
million mobile phone subscribers in 2012, with a market penetra-
tion rate of approximately 127.6% (Commerce Industrial Services
Portal, Ministry of Economic Affairs, R.O.C., 2013). Hence, the mo-
bile phone has the promising potential to provide learning mate-
rials toTaiwan's learners (Chuang &Tsao, 2013). However, although
m-learning is a relatively new tool, which is more likely to be
embraced by learners in Taiwan, mobile device applications may
present some limitations such as the reduced screen size of mobile
devices and the requirement of being easy of using at any time in
any proper equipped location, and these may add to the problems
faced by learners (Chen & Huang, 2012; Hwang & Chang, 2011).
2.2. Theory of reasoned action
TRAoriginates fromthe ?eldof social psychology, andit has been
one of the most widely applied models in explaining individuals'
behavior (Cheung & Vogel, 2013; Hong et al., 2013; Lee, Qu, & Kim,
2007). To date, TRA has received substantial empirical supports by
several prior studies, and it has been applied to a wide range of
users' IS/IT acceptance (Cheung & Vogel, 2013; Hong et al., 2013).
From a theoretical viewpoint, TRA posits that an individual's
behavior is determined by the individual's intention to engage in a
given behavior, which in turn can be in?uenced by the individual's
attitude toward the behavior and subjective norm surrounding the
performance of the behavior (Ajzen & Fishbein, 1980; Cheung &
Vogel, 2013; Fishbein & Ajzen, 1975). Hence, the concept of TRA is
that individuals are usually rational and will consider the implica-
tions of their actions before they decide whether to performa given
behavior (Ajzen &Fishbein, 1980; Hong et al., 2013). Essentially, TRA
makes a major contribution to the prior attitude studies by pro-
posing the behavioral intention as the most key determinant of an
individual's behavior (Cheung & Vogel, 2013; Hong et al., 2013).
2.3. Extended TAM
Many theoretical models have been used to explain users' IS/IT
acceptance. Among them, TAM, proposed by Davis (1989) and
Davis, Bagozzi, and Warshaw (1989), is one of the most widely
accepted and applied models in a variety of domains including
related IS and IT acceptance studies (Lindsay et al., 2011; Maditinos
et al., 2013; Wu, 2011). TAM is adapted from the well-known TRA
(Ajzen & Fishbein, 1980; Fishbein & Ajzen, 1975), which is a model
used extensively for explaining technology acceptance and utili-
zation among users. In general, TAM proposes that two particular
beliefs, perceived usefulness (PU) and perceived ease of use (PEOU),
are the primary drivers for explaining user acceptance of speci?c
type of system (Davis et al., 1989). PU is de?ned as “the degree to
which a person believes that using a particular system would
enhance his/her job performance,” and PEOU is de?ned as “the
degree to which a person believes that using a particular system
would be free of physical and mental effort” (Davis, 1989, p. 320).
The external variables of the TAM can affect PU and PEOU, and both
PU and PEOU affect a person's attitude toward using the system,
and the attitude toward using the system determines behavioral
intention, which in turn leads to actual system use (Davis, 1989;
Davis et al., 1989). Essentially, previous studies have shown TAM
to be justi?ed both pragmatically and theoretically (Hossain & de
Silva, 2009), because it has reliable instruments with excellent
measurement properties (Chen, Fan, & Farn, 2007; Pavlou, 2003).
While TAM has been veri?ed as a valuable model in explaining
users' acceptance of technology in various contexts, cultures, and
usage dimensions (Chen et al., 2007; Chen &Tan, 2004; Ha & Stoel,
2009; Hern andez, Jim enez, & Martín, 2008; Hossain & de Silva,
2009; Lee, Li, Yen, & Huang, 2010; Pavlou, 2003; Polancic,
Hericko, & Rozman, 2010), it provides little assistance in
capturing the hedonic feature of a hedonic-oriented IS/IT in the
nonworking place (Kim, Choi, &Han, 2009; Van der Heijden, 2004).
Y.-M. Cheng / Asia Paci?c Management Review 20 (2015) 109e119 110
Thus, to enhance TAM's explanatory power, prior studies have
suggested that it should include the intrinsic motivator [i.e.,
perceived enjoyment (PE)] to extend its function (Davis et al., 1992;
Ha, Yoon, & Choi, 2007; Igbaria, Iivari, & Maragahh, 1995; Lee et al.,
2005; Teo et al., 1999; Van der Heijden, 2004). Based on TAM, Davis
et al. (1992) ascertained the importance of the role of PE in
explaining computer acceptance, and they found that PE and PU
mediated the in?uence of PEOU on intention to use the computer.
PE refers to the degree to which the activity of using a particular
systemis perceived to be personally enjoyable in its own right apart
from the instrumental value of the speci?c type of system (Davis
et al., 1992; Lee et al., 2005; Teo et al., 1999). Essentially, PU and
PEOU usually re?ect the extrinsic motivational aspect of speci?c
type of systemusage, whereas PE re?ects the intrinsic motivational
aspect of speci?c type of system usage (Davis et al., 1992). Subse-
quently, an extended TAM that integrates the PE construct into the
original TAM is proposed, and it posits that three particular beliefs
(PU, PEOU, and PE) are of primary relevance for IS/IT acceptance
behaviors (Ha et al., 2007; Igbaria et al., 1995; Kim et al., 2009; Lee
et al., 2005; Liu & Li, 2011; Park, Baek, Ohm, & Chang, 2014; Teo
et al., 1999; Van der Heijden, 2004).
2.4. IDT
IDT is the well-known theory proposed by Rogers (1962).
Innovation diffusion is a process in which information for an
innovation is communicated through certain channels over time
among members of a social system (Faiers, Neame, & Cook, 2007;
Rogers, 1995). IDT proposes ?ve attributes of an innovation,
namely, relative advantage, complexity, compatibility, trial ability,
and observability (Rogers, 1995, 2003), and previous studies (e.g.,
Agarwal & Prasad, 1998; Chen et al., 2002; Ryu et al., 2009; Wu &
Wang, 2005) have suggested that only the relative advantage,
complexity, and compatibility are consistently related to innovation
adoption. The three innovation attributes are further detailed as
follows. Relative advantage refers to the extent to which an inno-
vation provides bene?ts, image enhancement, convenience, and
satisfaction in comparison to traditional methods (Rogers, 1995,
2003). Basically, relative advantage is similar to the concept of PU
(Chen et al., 2002; Chen & Tan, 2004; Ryu et al., 2009; Tung &
Chang, 2008; Wu & Wang, 2005). Complexity refers to the extent
to which an innovation is perceived to be dif?cult to understand,
learn, or utilize (Rogers, 1995, 2003). In general, complexity is
inversely related to the concept of PEOU (Chen et al., 2002; Chen &
Tan, 2004; Ryu et al., 2009; Tung & Chang, 2008; Wu & Wang,
2005). Compatibility refers to the extent to which the innovation
is perceived to be consistent with the adopters' beliefs, lifestyle,
existing values, experience, and current needs, and high compati-
bility can result in preferable innovation adoption (Rogers, 1983,
1995, 2003).
2.5. Extended TAM with the IDT
To enhance extended TAM's explanatory power, it may further
be integrated with the IDT to address the compatibility (Chen et al.,
2002; Ryu et al., 2009; Tan & Chou, 2008; Tung & Chang, 2008; Wu
& Wang, 2005). Essentially, IDT and TAM have some obvious sim-
ilarities, that is, relative advantage is similar to the concept of PU
and complexity is inversely related to the concept of PEOU, and
thus, relative advantage and complexity can be respectively
replaced by PU and PEOU (Chen et al., 2002; Chen &Tan, 2004; Ryu
et al., 2009; Tung & Chang, 2008; Wu & Wang, 2005). Based on the
foregoing, this study integrates the PE construct into the original
TAM to constitute an extended TAM, and further combines the
extended TAM with the IDT to address the compatibility construct.
Accordingly, a hybrid model is developed for exploring learners'
intention to use m-learning.
2.6. Technological characteristics
Essentially, users tend to seek the technological bene?ts of using
the new IS/IT as compared with the traditional IS/IT to determine
their attitude toward the newIS/IT (Childers et al., 2001); therefore,
factors that contribute to the new IS/IT acceptance are likely to be
determined by the technological characteristics of this new IS/IT
(Yoon & Kim, 2007). Unquestionably, m-learning is an emerging
tool that uses the ability of mobile devices to integrate with ubiq-
uitous computing technologies to support learning activities
(Alvarez et al., 2011; Martin et al., 2011). As far as the technological
characteristics of m-learning are concerned, on the one hand,
delivering digital learning contents through mobile devices should
take into consideration the prerequisites for actual controlled
navigation that learners require to obtain related knowledge
(L opez, Royo, Laborda, & Calvo, 2009), and on the other hand,
convenience should be considered as a key factor of learners'
acceptance of m-learning mainly because ubiquitous computing
technologies are expected to give learners convenience through
their intelligence and intercommunication in life (Alvarez et al.,
2011; Martin et al., 2011; Yoon & Kim, 2007).
3. Hypotheses and research model
3.1. PU, PEOU, and PE from the extended TAM
Based on TAM, Davis et al. (1992) ascertained the importance of
the role of PE in explaining computer acceptance and usage, and
they found that PE and PU mediated the in?uence of PEOU on
intention to use the computer. The extended TAM indicates that
three particular beliefs, PU, PEOU, and PE, are of primary relevance
for IS/IT acceptance behaviors (Ha et al., 2007; Igbaria et al., 1995;
Kim et al., 2009; Lee et al., 2005; Liu & Li, 2011; Park et al., 2014;
Teo et al., 1999; Van der Heijden, 2004). In general, PU, PEOU, and
PE directly determine intention to use the IS/IT (Davis et al., 1992;
Van der Heijden, 2004); moreover, PU and PE mediate the in?u-
ence of PEOU on intention to use the IS/IT (Davis et al., 1992; Lee
et al., 2005; Van der Heijden, 2004). Hence, this study hypothe-
sizes the following:
H1: PEOU will positively affect PU of m-learning.
H2: PEOU will positively affect PE of m-learning.
H3: PU will positively affect intention to use m-learning.
H4: PEOU will positively affect intention to use m-learning.
H5: PE will positively affect intention to use m-learning.
3.2. Technological characteristic antecedents to user beliefs
M-learning refers to a form of learning tool, which employs the
mobile devices to integrate with ubiquitous computing technolo-
gies to support learning activities and deliver learning materials to
learners (Alvarez et al., 2011; Martin et al., 2011). Accordingly, the
navigation for mobile devices and the convenience provided by
ubiquitous computing technologies can be regarded as key factors
that re?ect the technological characteristics of m-learning (Alvarez
et al., 2011; L opez et al., 2009; Martin et al., 2011; Yoon & Kim,
2007). Based on the synthesized views of Childers et al. (2001)
and Yoon and Kim (2007), learners may tend to evaluate the
technological characteristics (including navigation and conve-
nience) of using m-learning as compared with e-learning to
determine their beliefs toward m-learning, because m-learning is
Y.-M. Cheng / Asia Paci?c Management Review 20 (2015) 109e119 111
an emerging IT in comparison with e-learning. Hence, this study
further infers whether the navigation and convenience as the an-
tecedents can affect PU, PEOU, and PE of m-learning. The discus-
sions are further detailed in the following sections.
3.2.1. Navigation
Navigation refers to the process of self-directed movement
through the media involving nonlinear search and retrieval process
that provides unlimited freedom of choice and greater control for
the users (Childers et al., 2001; Hoffman & Novak, 1996). The
?exibility of navigating through the interactive online environment
is the determinant of shoppers' perceptions of the ease of using the
interactive media, and the shoppers' enjoyment of using the
interactive media can also increase when they have an increased
navigating ability through the interactive online environment
(Childers et al., 2001). In the mobile context, navigation is the
process by which users explore all the levels of interactivity, all by
themselves, and through the content and interface screens (Tucker,
2008). Khalifa and Shen (2008) found that the navigational ef?-
ciency signi?cantly predicted the mobile users' PU. Although nav-
igation in mobile device applications presents some limitations
such as the reduced screen size of mobile devices and the
requirement of being easy of using (Avellis, Scaramuzzi, &
Finkelstein, 2004; Lee & Benbasat, 2003), a good navigation sys-
tem should leave the potential adopters of mobile technologies
with little question about where they are in the document and
where they can go fromthere (Khalifa & Shen, 2008; Tucker, 2008).
Based on the foregoing, if the mobile navigation devices can allow
users to self-explore through the content and interface screens,
then users may also regard the mobile applications as useful, easy
to use, and enjoyable. Thus, this study infers that navigation is
expected to positively affect learners' PU, PEOU, and PE of m-
learning. Hence, this study hypothesizes the following:
H6a: Navigation will positively affect PU of m-learning.
H6b: Navigation will positively affect PEOU of m-learning.
H6c: Navigation will positively affect PE of m-learning.
3.2.2. Convenience
Convenience refers to the extent to which the media make easier
for users to save their time and effort (Brown, 1990; Khalifa & Shen,
2008; Yoon & Kim, 2007). Shoppers can e-shop over the Internet at
any time in any proper equipped location; if they perceive the
interactive online environment as offering greater convenience, they
will be more likely to regard the media as both useful and easy to use
(Childers et al., 2001). Shoppers' perceived convenience of interac-
tive online media can reduce their time pressure and location re-
strictions, and this situation can make their interactive experience
more enjoyable (Childers et al., 2001). Besides, Liao and Cheung
(2002) proposed that users' perception of convenience positively
affected their PU of Internet-based e-retail banking. In the mobile
context, convenience refers to the extent to which users believe that
conducting their affairs through mobile commerce (m-commerce)
would be free of effort (Choi, Seol, Lee, Cho, & Park, 2008). Yoon and
Kim(2007) showed that users' perceived convenience had a positive
effect on their PU of wireless local area network. For learners, m-
learning can further extend the ?exibility of e-learning regardless of
their location using wireless technologies (Evans, 2008; Motiwalla,
2007). Thus, there is a tendency toward m-learning owing to the
convenience of mobile devices, and this convenience in mobile en-
vironments can increase learning effectiveness, ef?ciency, and
pleasure through the ability to learn anytime and anywhere (Evans,
2008; Kambourakis, Kontoni, Rouskas, & Gritzalis, 2007). Based on
the foregoing, this study infers that convenience is expected to
positively affect learners' PU, PEOU, and PE of m-learning. Hence,
this study hypothesizes
H7a: Convenience will positively affect PU of m-learning.
H7b: Convenience will positively affect PEOU of m-learning.
H7c: Convenience will positively affect PE of m-learning.
3.3. Compatibility and user beliefs
Compatibility refers to the extent to which the innovation is
perceived to be consistent with the adopters' beliefs, lifestyle, exist-
ing values, experience, and current needs, and highcompatibility can
result in preferable innovation adoption (Rogers, 1983, 1995, 2003).
Some studies (e.g., Chen et al., 2002; Ryu et al., 2009; Tan & Chou,
2008; Tung & Chang, 2008; Wu & Wang, 2005; Wu, Wang, & Lin,
2007; Xue et al., 2012) have combined the view of TAM with the
IDT to address the compatibility construct to explain users' IS/IT
acceptance, because such integration may be able to provide a
stronger model thanstanding alone. Among these studies, Chenet al.
(2002) examined consumers' acceptance of virtual store, Wu and
Wang (2005) investigated what would determine users' acceptance
of m-commerce, Tung and Chang (2008) explored what were the
important factors making students use online courses. These three
studies all found that compatibility directly affected PU and usage
intention. Wu et al. (2007) showed that if health-care professionals
regarded the mobile health-care systems (MHS) as being compatible
with their health-care practices, they would perceive the usefulness
of the MHS, prefer an easy-to-use MHS, and also enhance their
intention to use the MHS. Ryu et al. (2009) further found that when
elderly online users regardedthe video user-createdcontent (UCC) as
being compatible with their current usage and lifestyle, they would
be more likely to take the bene?t (i.e., usefulness) of the video UCC
into account; and they would also prefer an easy-to-participate (i.e.,
easy-to-use) video UCC in their current behaviors. Xue et al. (2012)
showed that if aging women regarded the Infohealth (i.e., a mobile
phone-based intervention) as being compatible with their current
usage habit, they would perceive that using Infohealth is bene?cial
and easy, and they would also have the intention to use Infohealth.
Besides, Tan and Chou (2008) used the view of the extended TAMto
explore user behavior in the context of mobile information and
entertainment services, and they further showed that users'
perceived technology compatibility affected their perceived play-
fulness. Based on the foregoing, this study infers that compatibility is
expected to positively affect learners' PU, PEOU, PE, and intention to
use m-learning. Hence, this study hypothesizes
H8a: Compatibility will positively affect PU of m-learning.
H8b: Compatibility will positively affect PEOU of m-learning.
H8c: Compatibility will positively affect PE of m-learning.
H8d: Compatibility will positively affect intention to use m-learning.
3.4. Research model
Based on the extended TAM with the IDT, this study's research
model presents technological characteristics (including navigation
and convenience) and compatibility that lead to learners' usage
intention of m-learning. The research model is depicted in Fig. 1.
4. Methodology
4.1. Measures
In this study, responses to the items in navigation, convenience,
compatibility, PU, PEOU, PE, and intention to use were measured on
a 7-point Likert scale from 1 (strongly disagree) to 7 (strongly
Y.-M. Cheng / Asia Paci?c Management Review 20 (2015) 109e119 112
agree) with 4 labeled as neutral. To ensure content validity of the
scales, the items must represent the concept about which gener-
alizations are to be made (Ong, Lai, &Wang, 2004). Items chosen for
the constructs in this study were adapted and revised from previ-
ous research. Further, to ensure the translation equivalence for the
original meaning of questionnaire, the standard backtranslation
procedure for the questionnaire was followed (Mullen, 1995;
Sperber, Devellis, & Boehlecke, 1994). The original questionnaire
was ?rst developed in English, and then the original questionnaire
was translated into Chinese by a bilingual Taiwanese MBA student,
and another bilingual Taiwanese MBA student backtranslated the
Chinese version of the questionnaire into English. Lastly, to ensure
the cross-cultural uniformity in translation (Parameswaran &
Yaprak, 1987; Sperber et al., 1994), two other bilingual Taiwanese
doctoral students provided independent checks on the back-
translation. The ?nal items are presented in Table 1 along with their
sources.
4.2. Pretest
Following a convenience sampling method, the questionnaire
was pretested on 35 Taiwanese mobile phone users with experi-
ence in browsing or purchasing the learning contents (e.g., digital-
game-based learning, e-books, e-dictionaries, e-magazines, or e-
tests) via mobile technology. Based on the feedback, the re-
spondents were asked to identify any ambiguities in the meanings,
and the questionnaire was revised based on their comments. The
instrument's reliability was evaluated, and the Cronbach’s a values
(ranging from 0.80 to 0.96) exceeded common requirements for
exploratory research, indicating a satisfactory level of reliability
(Hair, Anderson, Tatham, & Black, 1998; Nunnally, 1978). The ?nal
items are presented in Table 1 along with their sources. Those who
had participated in the pretest were excluded from the ?nal data
collection and subsequent study.
4.3. Sample size and data collection
Taiwan has demonstrated a strong demand for telecommuni-
cations services and has approximately 29.5 million mobile phone
subscribers, with a market penetration rate of approximately
127.6% (Commerce Industrial Services Portal, Ministry of Economic
Affairs, R.O.C., 2013). Hence, sample data for this study were
collected from Taiwanese mobile phone users, and the unit of
analysis was individual mobile phone users with experience in
browsing or purchasing the learning contents through mobile
technology. Following a convenience sampling method, the ques-
tionnaires were distributed to 75 mobile phone shops; each shop
was given 10 questionnaires and those were distributed to mobile
phone users who had experience in browsing or purchasing the
learning contents (e.g., digital-game-based learning, e-books, e-
dictionaries, e-magazines, or e-tests) through mobile technology.
A total of 750 questionnaires were distributed, and 516 (68.80%)
questionnaires were received; 30 of these received questionnaires
were discarded due to partial portions of missing values. Finally,
486 usable questionnaires were analyzed in this study, with a us-
able response rate of 64.80%. Besides, nonresponse bias is usually
tested by examining the differences in mean for demographic
variables between mobile phone users who responded to the sur-
vey and mobile phone users who did not. For lack of comparable
statistics from nonresponding mobile phone users, t tests were
used to test response bias between early and late wave returned
surveys, with the late wave respondents being treated as a proxy for
nonrespondents (Armstrong & Overton, 1977; Lambert &
Harrington, 1990; Oppenheim, 1966). In this study, 318 usable re-
sponses were received in the early wave and 168 in the late wave.
The mean differences between the two groups with respect to sex,
age, educational level, and the duration of usage (the respondents'
experience in browsing or purchasing the learning contents
through mobile technology) were tested using an unpaired t test.
No signi?cant differences were observed at the 0.05 level, indi-
cating no systematic differences between the two groups. Because
nonresponse bias does not appear to be a problem, the ?nal sample
of 486 usable responses can be regarded as representative of the
population.
4.4. Data analysis
The data analysis process of this study followed a two-step
approach for structural equation modeling (SEM) method recom-
mended by Anderson and Gerbing (1988). In the ?rst step, con?r-
matory factor analysis (CFA) was used to develop the measurement
model. In the second step, to explore the causal relationships
Convenience Perceived Ease of Use Intention to Use
Navigation
Compatibility
Perceived Usefulness
Perceived Enjoyment
H7c
H7a
H1
H2
H7b
H6a
H8c
H6b
H8b
H6c
H8a
H3
H5
H4
H8d
Fig. 1. The research model.
Y.-M. Cheng / Asia Paci?c Management Review 20 (2015) 109e119 113
among all constructs, the structural model for research model
depicted in Fig. 1 was tested using SEM. The statistical analysis
software packages used to perform these processes were AMOS 5.0
(SPSS, Inc., Chicago, IL, USA) and SPSS 8.0 (SPSS, Inc.).
5. Results
5.1. Descriptive characteristics of the usable respondents
A total of 486 usable questionnaires were analyzed in this study.
Among the usable respondents, 288 respondents (59.3%) were
men, and 198 respondents (40.7%) were women. The distribution of
age (in years) was as follows: under 21 (17.1%), 21e30 (56.8%),
31e40 (23.0%), 41e50 (2.9%), 51e60 (0.2%), and over 60 (0.0%).
Educational levels were generally high. Respondents who had
completed senior high school accounted for 4.1%, respondents who
had completed junior college numbered 19.5%, respondents who
had completed college/university numbered 52.7%, whereas re-
spondents who had completed graduate school comprised 23.7% of
the survey sample. In addition, the distribution of duration of usage
(the respondents' experience in browsing or purchasing the
learning contents through mobile technology) was as follows: un-
der 7 months (21.8%), 7e12 months (60.1%), 13e18 months (16.5%),
19e24 months (1.6%), and over 24 months (0.0%). The descriptive
characteristics of the usable respondents are depicted in Table 2.
5.2. Common method bias
When the research data were collected using self-reported
questionnaires, there was a concern that a common method bias
may occur (Malhotra, Kim, & Patil, 2006), which may affect the
empirical results. According to the views recommended by
Podsakoff, MacKenzie, Lee, and Podsakoff (2003), to prevent the
threat of common method bias, in this study, respondents were
assured that their participation and responses would be completely
anonymous, con?dential, and voluntary, they were noti?ed the
right to withdraw from their participation at any time, and they
were informed that there were no right or wrong answers to the
items and were requested to re?ect their true opinions on each
item as objectively as possible. Besides, a common method bias test
should be conducted, and a CFA approach to Harman's single-factor
test can be used to assess the common method bias (Sanchez,
Korbin, & Viscarra, 1995). This study employed CFA to test the ?t
of a single-factor model (where all items were loaded on a single
factor) and a seven-factor model. The results showed that the ?t
indices of the single-factor model [c
2
¼ 5080.861, df ¼ 230, c
2
/
df ¼ 22.091, p < 0.001, goodness-of-?t index (GFI) ¼0.487, adjusted
GFI (AGFI) ¼ 0.384, normalized ?t index (NFI) ¼ 0.372, Tuck-
ereLewis index (TLI) ¼ 0.318, comparative ?t index (CFI) ¼ 0.381,
and root mean square error of approximation (RMSEA) ¼ 0.209]
were worse than those of the seven-factor model (c
2
¼ 318.307,
df ¼ 209, c
2
/df ¼ 1.523, p < 0.001, GFI ¼ 0.948, AGFI ¼ 0.931,
NFI ¼0.961, TLI ¼0.983, CFI ¼0.986, and RMSEA ¼0.033). Thus, the
?t is considerably worse for the single-factor model than it is for the
multifactor model, which indicates that the common method bias
is not a problem for this study (Sanchez et al., 1995).
Table 1
Construct measurement and sources.
Construct Item Measure Source
Navigation (NAV) NAV1 Using m-learning allows navigation through the learning environment. Childers et al. (2001)
NAV2 Using m-learning allows me to explore the learning environment in a variety of ways. Tucker (2008)
NAV3 Using m-learning allows me to move ?uidly through the learning environment.
NAV4 Using m-learning allows ?exibility in tracking down information.
Convenience (CON) CON1 Using m-learning is convenient for me. Childers et al. (2001)
CON2 Using m-learning is a convenient way to learn. Liao and Cheung (2002)
CON3 M-learning allows me to learn whenever I choose. Yoon and Kim (2007)
CON4 M-learning allows me to learn wherever I choose.
Compatibility (COM) COM1 Using m-learning is compatible with most aspects of my learning. Agarwal and Prasad (1998)
COM2 Using m-learning ?ts well with the way I like to learn. Chen et al. (2002)
COM3 Using m-learning ?ts my learning style.
Perceived usefulness (PU) PU1 Using m-learning enhances my learning effectiveness. Davis (1989)
PU2 Using m-learning gives me greater control over learning. Ngai, Poon, and Chan (2007)
PU3 I ?nd m-learning to be useful in my learning.
Perceived ease of use (PEOU) PEOU1 Interacting with m-learning does not require a lot of my mental effort. Davis (1989)
PEOU2 My interaction with m-learning is clear and understandable. Ngai et al. (2007)
PEOU3 I ?nd m-learning to be easy to use.
Perceived enjoyment (PE) PE1 I ?nd using m-learning to be enjoyable. Davis et al. (1992)
PE2 The actual process of using m-learning is pleasant. Lee et al. (2005)
PE3 I have fun using m-learning.
Intention to use (ITU) ITU1 I will use m-learning on a regular basis in the future. Bhattacherjee (2001)
ITU2 I will frequently use m-learning in the future. Mathieson (1991)
ITU3 I will continue using m-learning in the future. Roca, Chiu, and Martínez (2006)
Table 2
Descriptive characteristics of the usable respondents.
Demographics Number Percentage
Gender
Male 288 59.3%
Female 198 40.7%
Age
<21 83 17.1%
21e30 276 56.8%
31e40 112 23.0%
41e50 14 2.9%
51e60 1 0.2%
>60 0 0.0%
Educational level
Senior high school 20 4.1%
Junior college 95 19.5%
College/university 256 52.7%
Graduate school 115 23.7%
Duration of usage (mo)
<7 106 21.8%
7e12 292 60.1%
13e18 80 16.5%
19e24 8 1.6%
>24 0 0.0%
Y.-M. Cheng / Asia Paci?c Management Review 20 (2015) 109e119 114
5.3. Results of structural modeling analysis
5.3.1. Measurement model
To assess the measurement model, three analyses were con-
ducted in this study. First, squared multiple correlation (SMC) for
each item, and composite reliability (CR) and average variance
extracted (AVE) for each construct were used in this study to test
the reliability of all constructs (Byrne, 2001; Hair et al., 1998;
Holmes-Smith, 2001; Nunnally, 1978). The results of CFA showed
that the SMC values for all items were greater than 0.5, which
indicated a good reliability level (Holmes-Smith, 2001). The values
of CR and AVE for all constructs exceeded the minimum acceptable
values of 0.7 and 0.5 (Hair et al., 1998; Holmes-Smith, 2001; Nun-
nally, 1978), indicating a good reliability level and subsequently
yielding very consistent results. Hence, the results of CFA demon-
strated an acceptable level of reliability for all constructs. Besides,
the reliability coef?cients of all constructs assessed by the Cron-
bach’s a value exceeded the 0.7 cutoff value as recommended by
Hair et al. (1998) and Nunnally (1978). The results of reliability test
are presented in Table 3.
Second, according to Anderson and Gerbing's (1988) rule, the
results of CFA showed that the t-value of every item exceeded 1.96
(p < 0.05), and therefore, the evidence of good convergent validity
was obtained as the items signi?cantly represented their con-
structs. The reports are listed in Table 3. Furthermore, to test for
discriminant validity, the procedure described by Fornell and
Larcker (1981) was used in this study. The results of CFA showed
that the AVE of each construct was greater than the squared
correlation for each pair of constructs, indicating that each
construct was distinct (Tables 3 and 4).
Third, the most common rules used to perform the CFA for
measurement model and testing the structural model include
stipulating that the GFI should be greater than 0.9, the AGFI should
be greater than 0.9, the NFI should be greater than 0.9, the TLI
should be greater than 0.9, the CFI should be greater than 0.9, the
RMSEA should be less than 0.08, and the c
2
/df should be less than 3
(Bagozzi &Yi, 1988; Bentler &Bonett, 1980; Byrne, 2001; Hair et al.,
1998). The overall ?t indices of measurement model were
c
2
¼ 318.307, df ¼ 209, c
2
/df ¼ 1.523, p < 0.001, GFI ¼ 0.948,
AGFI ¼ 0.931, NFI ¼ 0.961, TLI ¼ 0.983, CFI ¼ 0.986, and
RMSEA ¼ 0.033. The results of CFA showed that the indices were
over their respective common acceptance levels. Thus, the pro-
posed model generally ?ts the sample data well.
5.3.2. Structural model
The following step is to test the structural model for the research
model depicted in Fig. 1. The overall ?t indices for the structural
model were as follows: c
2
¼ 359.512, df ¼ 215, c
2
/df ¼ 1.672,
p < 0.001, GFI ¼ 0.942, AGFI ¼ 0.925, NFI ¼ 0.956, TLI ¼ 0.978,
CFI ¼0.982, and RMSEA ¼0.037. According to the views of previous
studies (e.g., Bagozzi & Yi, 1988; Bentler & Bonett, 1980; Byrne,
2001; Hair et al., 1998), the results of CFA showed that the ?t
indices for this structural model were quite acceptable.
5.3.3. Hypothesis testing
Properties of the causal paths, including standardized path
coef?cients (b), t values, and explained variances (R
2
), are shown
in Fig. 2. As for antecedents to learner beliefs and usage intention,
?rst, the effects of navigation on PU, PEOU, and PE were signi?cant
(b ¼ 0.211, 0.189, and 0.214, respectively; p < 0.001); hence, H6a,
H6b, and H6c are supported. Second, the effects of convenience on
PU, PEOU, and PE were signi?cant (b ¼ 0.124, 0.136, and 0.340,
respectively; p < 0.01); hence, H7a, H7b, and H7c are supported.
Third, the effects of compatibility on PU, PEOU, PE, and intention
to use were signi?cant (b ¼ 0.132, 0.163, 0.128, and 0.135,
respectively; p < 0.01); hence, H8a, H8b, H8c, and H8d are sup-
ported. As to relationships between learner beliefs and usage
intention, ?rst, the effects of PEOU on PU and PE were signi?cant
(b ¼ 0.363 and 0.238, respectively; p < 0.001); hence, H1 and H2
are supported. Second, the effects of PU, PEOU, and PE on intention
to use were signi?cant (b ¼ 0.399, 0.213, and 0.211, respectively;
p < 0.001); hence, H3, H4, and H5 are supported. The results of
hypothesis testing are shown in Table 5. In the following, the
explained variances (R
2
) of PU, PEOU, PE, and intention to use were
0.266, 0.081, 0.285, and 0.455, respectively. Further, using these
results, the direct and indirect effects between the constructs are
shown in Table 6. The results indicate that navigation,
Table 3
Results of con?rmatory factor analysis, validity analysis, and reliability test.
Construct
item
Estimate T-value Standardized
path coef?cients
SMC CR AVE Cronbach’s a
NAV 0.871 0.629 0.868
NAV1 1 d
a
0.862 0.741
NAV2 1.026 14.826 0.885 0.761
NAV3 1.050 15.277 0.934 0.813
NAV4 0.908 12.971 0.775 0.652
CON 0.925 0.757 0.909
CON1 1 d
a
0.790 0.667
CON2 1.059 22.246 0.888 0.818
CON3 1.094 23.164 0.922 0.871
CON4 0.920 19.008 0.787 0.661
COM 0.852 0.656 0.855
COM1 1 d
a
0.808 0.679
COM2 1.332 16.783 0.759 0.633
COM3 1.107 18.597 0.903 0.832
PU 0.941 0.842 0.960
PU1 1 d
a
0.924 0.859
PU2 1.073 19.419 0.947 0.901
PU3 1.057 20.032 0.952 0.909
PEOU 0.863 0.679 0.913
PEOU1 1 d
a
0.897 0.808
PEOU2 1.021 25.168 0.930 0.868
PEOU3 0.928 23.930 0.817 0.672
PE 0.934 0.824 0.904
PE1 1 d
a
0.876 0.779
PE2 1.116 25.586 0.902 0.823
PE3 1.053 22.708 0.823 0.690
ITU 0.918 0.789 0.932
ITU1 1 d
a
0.865 0.756
ITU2 1.050 23.904 0.946 0.899
ITU3 1.016 21.615 0.899 0.815
AVE ¼ average variance extracted; COM ¼ compatibility; CON ¼ convenience;
CR ¼ composite reliability; ITU ¼ intention to use; NAV ¼ navigation;
PE ¼ perceived enjoyment; PEOU ¼ perceived ease of use; PU ¼ perceived useful-
ness; SMC ¼ squared multiple correlation.
a
The loading was ?xed.
Table 4
Discriminant validity for the measurement model.
Construct NAV CON COM PU PEOU PE ITU
NAV 0.629
CON 0.062 0.757
COM 0.013 0.033 0.656
PU 0.107 0.064 0.058 0.842
PEOU 0.054 0.038 0.041 0.207 0.679
PE 0.122 0.189 0.061 0.101 0.135 0.824
ITU 0.101 0.080 0.106 0.349 0.246 0.198 0.789
Bold values along the diagonal line are the AVE values for the constructs, and the
other values are the squared correlations for each pair of constructs.
AVE ¼ average variance extracted; COM ¼ compatibility; CON ¼ convenience;
ITU ¼ intention to use; NAV ¼ navigation; PE ¼ perceived enjoyment;
PEOU ¼ perceived ease of use; PU ¼ perceived usefulness.
Y.-M. Cheng / Asia Paci?c Management Review 20 (2015) 109e119 115
convenience, and compatibility can indirectly make signi?cant
positive impacts on learners' usage intention of m-learning
through their PU, PEOU, and PE, whereas compatibility can also
directly make a signi?cant positive impact on learners' usage
intention of m-learning.
6. Discussion
Based on the extended TAM with the IDT, this study enhances
the understanding of the roles played by technological character-
istics and compatibility in the process of m-learning acceptance,
and thus offers relevant implications and suggestions for m-
learning providers wishing to realize learners' acceptance of m-
learning. The discussions are further detailed in the following
sections.
As this study's ?ndings present (Table 5), the effects of naviga-
tion and convenience on learners' intention to use m-learning are
fully mediated by the extrinsic motivators (i.e., PU and PEOU) and
intrinsic motivator (i.e., PE). The ?ndings are consistent with the
views of previous studies (e.g., Childers et al., 2001; Khalifa & Shen,
2008; Liao & Cheung, 2002; Yoon & Kim, 2007). The result impli-
cates that if learners can self-explore directly through the content
and interface screens over the mobile-based interactive learning
environments at any time in any location, they will be more likely
to regard m-learning as both useful and easy to use, and this situ-
ation will make their interactive experience more enjoyable, thus
learners' extrinsic motivators (PU and PEOU) and intrinsic moti-
vator (PE) will further facilitate their intention to use m-learning.
Accordingly, the technological characteristics such as navigating
?exibility and convenience of time, place, and execution should be
designed for m-learning applications. Navigation is the most key
antecedent that can make signi?cant impacts on learners' PU and
PEOU, and this has been con?rmed by the path analysis (Table 6),
Convenience
Perceived Ease of Use
[ R
2
= 0.081 ]
Intention to Use
[ R
2
= 0.455 ]
Navigation
Compatibility
Perceived Usefulness
[ R
2
= 0.266 ]
Perceived Enjoyment
[ R
2
= 0.285 ]
0.399
(9.118)
0.213
(4.753)
0.211
(5.047)
0.363
(7.965)
0.238
(5.141)
0.135
(3.295)
0.128
(2.746)
0.211
(4.514)
0.163
(3.239)
0.189
(3.701)
0.132
(2.897)
0.214
(4.451)
0.124
(2.903)
0.340
(7.382)
0.136
(2.839)
Fig. 2. Results of structural modeling analysis. Standardized path coef?cients are reported (t-values in parentheses). Absolute t-value > 1.96, p < 0.05; absolute t-value > 2.58,
p < 0.01; absolute t-value > 3.29, p < 0.001.
Table 5
Results of hypothesis testing.
Hypothesis Standardized path
coef?cients (b)
T-values Signi?cance Support
H1: PEOU /PU 0.363 7.965 p < 0.001 Yes
H2: PEOU /PE 0.238 5.141 p < 0.001 Yes
H3: PU /ITU 0.399 9.118 p < 0.001 Yes
H4: PEOU /ITU 0.213 4.753 p < 0.001 Yes
H5: PE /ITU 0.211 5.047 p < 0.001 Yes
H6a: NAV /PU 0.211 4.514 p < 0.001 Yes
H6b: NAV /PEOU 0.189 3.701 p < 0.001 Yes
H6c: NAV /PE 0.214 4.451 p < 0.001 Yes
H7a: CON /PU 0.124 2.903 p < 0.01 Yes
H7b: CON /PEOU 0.136 2.839 p < 0.01 Yes
H7c: CON /PE 0.340 7.382 p < 0.001 Yes
H8a: COM /PU 0.132 2.897 p < 0.01 Yes
H8b: COM /PEOU 0.163 3.239 p < 0.01 Yes
H8c: COM /PE 0.128 2.746 p < 0.01 Yes
H8d: COM /ITU 0.135 3.295 p < 0.001 Yes
COM ¼ compatibility; CON ¼ convenience; ITU ¼ intention to use;
NAV ¼ navigation; PE ¼ perceived enjoyment; PEOU ¼ perceived ease of use;
PU ¼ perceived usefulness.
Table 6
Direct and indirect effects between the constructs.
Construct PEOU PU PE ITU
DE InDE TE DE InDE TE DE InDE TE DE InDE TE
NAV 0.189 d 0.189 0.211 0.069 0.280 0.214 0.045 0.259 d 0.206 0.206
CON 0.136 d 0.136 0.124 0.049 0.173 0.340 0.032 0.372 d 0.177 0.177
COM 0.163 d 0.163 0.132 0.059 0.191 0.128 0.039 0.167 0.135 0.146 0.281
PEOU d d d 0.363 d 0.363 0.238 d 0.238 0.213 0.195 0.408
PU d d d d d d d d d 0.399 d 0.399
PE d d d d d d d d d 0.211 d 0.211
COM¼compatibility; CON¼convenience; DE ¼direct effects; InDE ¼indirect effects; ITU¼intention to use; NAV ¼navigation; PE ¼perceived enjoyment; PEOU¼perceived
ease of use; PU ¼ perceived usefulness; TE ¼ total effects.
Y.-M. Cheng / Asia Paci?c Management Review 20 (2015) 109e119 116
where navigation has a larger total impact on PU [total effect
(TE) ¼ 0.280] and PEOU (TE ¼ 0.189) than the total impact of
convenience on PU (TE ¼ 0.173) and PEOU (TE ¼ 0.136). This study
suggests that m-learning providers should try to develop friendlier
user interface by designing useful and easy-to-use features to
induce learners to use m-learning. Hence, some interesting ideas
from previous studies (e.g., Churchill & Hedberg, 2008; Motiwalla,
2007) for professionally designing the screen layouts for m-
learning services should be taken into consideration, such as
appropriate text formatting, full-screen presentation, minimized
scrolling, short contact time, interactivity over text, interactive
panels, interactive voice response. These enhancements are crucial
for assisting learners in improving their experience of using m-
learning. Moreover, convenience can have the greatest impact on
learners' PE, and this has also been con?rmed by the path analysis
(Table 6), where convenience has a larger total impact on PE
(TE ¼ 0.372) than the total impact of navigation on PE (TE ¼ 0.259).
Thus, this study suggests that m-learning providers should enhance
learners' perceived convenience of m-learning at any time in any
location to deliver pleasures to boost their usage intention of m-
learning.
As the testing results of compatibility-related hypotheses show
(Table 5), learners intend to use m-learning because they perceive it
to be more compatible with most aspects of their learning. The
result implicates that learners can judge m-learning by how well it
meets their perceived compatibility of m-learning. Thus, this study
suggests that m-learning providers should make their m-learning
services run compatibly with learners' existing values, needs, and
learning styles. In addition, the effects of compatibility on learners'
intention to use m-learning are also mediated by the extrinsic
motivators (PU and PEOU) and intrinsic motivator (PE). Hence,
compatibility can have direct and indirect effects on learners'
intention to use m-learning. The ?nding is also consistent with the
views of previous studies (e.g., Mallat, Rossi, Tuunainen, &
€
O€ orni,
2009; Wu & Wang, 2005; Wu et al., 2007; Xue et al., 2012) that
showcompatibility is an important determinant for usage intention
of the mobile technology. The foregoing results reveal that
compatibility exhibits stronger indirect impacts on learners'
intention to use m-learning than its direct impact (Table 6). Hence,
the exposition implicates that a successful m-learning design
should be developed to be widely compatible with learners'
behavior that is tailored to their existing values, needs, and learning
styles, so it can deliver the effectiveness, ef?ciency, and pleasure of
usage to learners and further boost their usage intention by
increasing the extent of their perceived compatibility of m-
learning. In general, m-learning has potential for providing a
mechanism where each learner will autonomously have their own
individualized learning paths based on their preferred learning
styles (Kinshuk & Lin, 2004; Yau & Joy, 2006). Hence, for m-
learning providers, creating adaptive m-learning environments
may be a good idea to ensure that m-learning can be compatible
with the vast majority of learners' learning needs and styles. Be-
sides, this study suggests that m-learning providers should rethink
how learners' learning needs and styles are expanded and enabled
with multifunctional mobile devices to tailor their m-learning
services to mobile learners.
With regard to the relationships between learners' beliefs and
their usage intention of m-learning (Table 6), learners intend to use
m-learning mainly because they perceive it to be easier to use
(TE ¼ 0.408) to their learning and secondarily because it is useful
(TE ¼ 0.399) and enjoyable (TE ¼0.211). Obviously, the ?ndings are
consistent with the views of previous studies (e.g., Cheong & Park,
2005; Kim et al., 2009; Nysveen, Pedersen, & Thorbjørnsen, 2005;
Park et al., 2014; Song, Koo, & Kim, 2007; Yang, 2007) that show
both extrinsic motivators (PU and PEOU) and intrinsic motivator
(PE) play important roles in affecting users' intention to use the
mobile technology. Furthermore, learners' PEOU (extrinsic moti-
vator) has positive and strong effects on their intention to use m-
learning, and their PU (extrinsic motivator) has a more powerful
effect on their intention to use m-learning than their PE (intrinsic
motivator). As advocated by prior research, inexperienced users'
PEOU (extrinsic motivator) has positive and strong effects on their
intention to use the particular technology (Venkatesh, 2000;
Venkatesh & Davis, 1996; Wu & Wang, 2005), and their PU
(extrinsic motivator) has a more positive effect on their intention to
use the particular technology than their PE (intrinsic motivator)
(Kim et al., 2009). In this study, most usable respondents had less
experience in using m-learning because 81.9% of usable re-
spondents had less than 1-year experience in browsing or pur-
chasing the learning contents through mobile technology, and thus,
the foregoing views addressed by prior research may be used to
explain this study's ?ndings. Hence, the results implicate that users
inexperienced in IT usage are motivated more extrinsically than
intrinsically at the initial adoption stage (Kim et al., 2009). This
study suggests that m-learning providers should assure learners of
browsing or purchasing the learning contents through mobile
technology in a more effective, more ef?cient, and pleasanter
manner to achieve their goals with a minimum of inconvenience,
and usefulness and ease of use should be especially thought of as
re?ecting the instrumental value on inexperienced learners' usage
intention of m-learning at the initial adoption stage.
7. Conclusions
Previous studies have only focused on the impacts of extrinsic
motivators such as PU and PEOU on IS/IT acceptance (Agarwal &
Karahanna, 2000; Lee et al., 2005). This study is one of the few
attempts to adopt the views of extrinsic motivation (PU and PEOU)
and intrinsic motivation (PE) to explain learners' usage intention of
m-learning. Besides, in this study, the views of the extended TAM
with the IDT provide clear expositions of learners' beliefs in
affecting their usage intention of m-learning. Hence, collectively
they have greater explanatory power than any single group of
factors in describing learners' principal beliefs in affecting their
usage intention of m-learning. Synthetically speaking, technolog-
ical characteristic antecedents (including navigation and conve-
nience) can fully indirectly have signi?cant impacts on learners'
intention to use m-learning through their extrinsic motivators (PU
and PEOU) and intrinsic motivator (PE); and compatibility can
make direct and indirect effects on learners' intention to use m-
learning. In brief, this study proposes a well-rounded theoretical
model (Fig. 1) that may act as an integrated base for the research of
m-learning acceptance, and this study's results for learners'
acceptance of m-learning are justi?ed both pragmatically and
theoretically. The following ?ndings are particularly worth
mentioning. First, navigation has the largest total impact on PU and
PEOU than the respective total impact of convenience and
compatibility on PU and PEOU (Table 6), and thus it is the most key
antecedent that can have signi?cant impacts on learners' PU and
PEOU, which jointly account for learners' intention to use m-
learning. Accordingly, m-learning providers should develop user-
friendlier interface by designing useful and easy-to-use screen
layouts recommended by learners to induce learners to use m-
learning. Next, convenience has the largest total impact on PE than
the respective total impact of navigation and compatibility on PE
(Table 6), so convenience can make the greatest impact on learners'
PE elicited by the m-learning use and further make them intend to
use m-learning. To boost learners' usage intention of m-learning,
m-learning providers should exert themselves to reduce learners'
time pressure and location restrictions within the mobile-based
Y.-M. Cheng / Asia Paci?c Management Review 20 (2015) 109e119 117
learning environments to cause more learners to pleasurably enjoy
interactive experiences in m-learning.
Several limitations should be noted in this study, and these
following suggestions for further research will be worth future ef-
forts in this ?eld. First, this study's ?ndings were based only on a
population of Taiwanese mobile phone users who had experience in
browsing or purchasing the learning contents through mobile
technology. Further research may generalize this study's sample to
the respondents who use different mobile devices (such as PDAs,
NBs, or tablet PCs). Second, this study did not investigate learners'
usage details of m-learning (e.g., types of mobile phone platforms,
types of technical speci?cations for mobile phones, browsing/pur-
chasing the learning contents and for what purposes, online/of?ine
with downloaded learning contents), which may have more im-
pacts on further analyses to support the quantitative ?ndings.
Further research may gather and take into account these usage
details of m-learning to obtain more comprehensive interpretations
to enrich the model of m-learning usage intention. Third, this study
focused on the understanding of the impacts of the technological
characteristic antecedents such as navigation and convenience
characterizing mobile-based interactive learning environments on
learners' perceptions of usefulness, ease of use, and enjoyment.
Further research may explore how the nontechnological factors
(such as course contents, interactive communication between in-
structors and learners, response attitudes) of mobile-based inter-
active learning environments affect learners' beliefs. Fourth,
respondents might usually display different relaxed feelings or
serious reactions for m-learning, depending onwhich situationthey
used it. Further researchin m-learning acceptance mayexamine the
different set of mechanisms between voluntary and mandatory
usage settings. Finally, this study was a cross-sectional analysis of
m-learning acceptance. It may be desirable to explore a complete
picture of the course of m-learning acceptance with learners'
increased experience in using m-learning. Further research may use
longitudinal analysis by taking into account the evolution of m-
learning acceptance over time.
Con?icts of interest
The author declares no con?icts of interest.
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