University students' behavioral intention to use mobile learning: Evaluating the technology acceptance model

As many Korean universities have recommended the implementation of mobile learning (m-learning) for various reasons, the number of such tertiary learning opportunities has steadily grown. However, little research has investigated the factors affecting university students’ adoption and use of m-learning. A sample of 288 Konkuk university students participated in the research.The process by which students adopt m-learning was explained using structural equation modeling technique and the Linear Structural Relationship (LISREL) program.The general structural model based on the technology acceptance model included m-learning self-efficacy, relevance for students’ major (MR), system accessibility, subjective norm (SN), perceived usefulness, perceived ease of use, attitude (AT), and behavioral intention to use m-learning.The study results confirmed the acceptability of the model to explain students’ acceptance of m-learning. M-learning AT was the most important construct in explaining the causal process in the model, followed by students’ MR and SN. Introduction Korea remains one of the leading information and communications technology (ICT) countries in the Organization for Economic Cooperation and Development (OECD), even though her rank of broadband use for high-speed Internet has recently dropped from the 1st in 2004 to 5th in 2010 in the world (OECD, 2010). Korea takes full advantage of ICT in supporting all levels of education and human resource development, and e-learning is considered an important alternative in the current knowledge-based society (Kim & Santiago, 2005). Diverse educational environments are provided for various people with information technology (IT) (Um & Kim, 2007). Education in Korea is now moving from e-learning to mobile learning (m-learning) as mobile technology becomes popular in both formal and informal education in Korea (Jung, 2009). While e-learning is based on the use of both wire and wireless Internet, in m-learning the learner takes advantage of learning opportunities offered by mobile technologies such as cell phones, smart phones, palmtops, tablet personal computers (PCs), personal digital assistants (PDAs) and portable multimedia players (PMPs) (Kukulska-Humle & Traxler, 2005). M-learning is a new and independent part of e-learning (Cho, 2007; Keegan, 2002). M-learning can be defined as “any educational provision where the sole or dominant technologies are handheld or palmtop devices.” British Journal of Educational Technology Vol 43 No 4 2012 592–605 doi:10.1111/j.1467-8535.2011.01229.x © 2011 The Authors. British Journal of Educational Technology © 2011 BERA. Published by Blackwell Publishing, 9600 Garsington Road, Oxford OX4 2DQ, UK and 350 Main Street, Malden, MA 02148, USA. The advantages of m-learning over e-learning are pushing its expansion. However, little research has focused on how people adopt their m-learning and what factors affect m-learning compared with e-learning. Furthermore, m-learning studies have investigated only educational efficacy by using mobile devices (Jung, 2009; Kang, 2007; Yoon, 2007). A recent trend is to adopt the technology acceptance model (TAM) as an explanatory tool in investigating the e-learning process (Park, 2009). In terms of just m-learning outcomes in Korea, a few studies have investigated mobile-based English learning and its satisfaction of PMP-based learning. Therefore, m-learning research is restricted to use in particular fields (Jin, 2007; Jo, 2005; Um & Kim, 2007) and, consequently, not much research is conducted to identify the path of how people adopt m-learning with TAM. M-learning becomes popular with university students in Korea. The number of students who have mobile devices is also growing. Furthermore, some universities provide students with smart Practitioner notes What is already known about this topic • Technology acceptance model (TAM) is extensively used in various information and communications technology (ICT) sectors to explain user’s intention to use new technology. • Mobile learning has become popular because of the low cost of telecommunication and high quality of mobile devices. • There is a need for research that focuses on how students adopt mobile learning in university. What this paper adds • This study proposes and verifies the use of TAM to explain and predict students’ acceptance of mobile learning in university. • External latent factors included in the general structural model such as mobile learning self-efficacy, major relevance, system accessibility and subjective norm were identified to have direct or indirect effects on behavioral intention to use mobile learning. • Social motivational theory, which encompasses intrinsic and extrinsic motivational factor, is a possible explanation to justify those factors’ influence on behavioral intention. Implications for practice and/or policy • The general structural model enhances our understanding of students motivation of using mobile learning. This understanding can aid our efforts when promoting mobile learning. Educational providers should also endeavor to increase students’ positive attitude toward m-learning. • In terms of subjective norm, it is necessary for universities to put more emphasis on mobile learning by offering a greater variety of mobile learning courses and advertising the benefits of mobile learning to attract students. • Both onand off-line support need to be provided to build up mobile learning selfefficacy and mobile learning mentor systems and user-friendly learning management systems could be good resources to increase self-efficacy. • A high-quality wireless system accessibility environment needs to be constructed and subsidies for mobile devices could be an extrinsic motivator to increase mobile learning. Factors related to use mobile learning 593 © 2011 The Authors. British Journal of Educational Technology © 2011 BERA. phones for free and construct learning management systems (LMS) for m-learning. This trend is expected to continue and expand as the price of smart phones and telecommunication costs has decreased. Therefore, it is necessary to conduct research that deals more intensively with university student’s intention to use m-learning in order to provide basic information for establishing m-learning support systems for learners. Objectives This study used TAM as a theoretical framework of university students’ m-learning acceptance and intention to use. The study objectives were to develop a general linear structural model of m-learning acceptance of university students that would help school managers and educators implement m-learning and analyze the relationship of university students’ behavioral intention (BI) to use m-learning with selected factors such as their attitude (AT), perceived usefulness (PU), perceived ease of use (PE), self-efficacy (SE) of m-learning, relevance for major (MR), system accessibility (SA) and subjective norm (SN) within the model. In addition, some descriptive statistics related to m-learning use and those selected factors were also determined. Research hypotheses According to the previously stated objectives, the following hypotheses were proposed: H1: University students’ BI to use m-learning is related to their AT (H11), PU (H12), PE (H13), m-learning SE (H14), MR (H15), SA (H16) and SN (H17). H2: University students’ m-learning AT is related to their PU (H21), PE (H22), m-learning SE (H23), MR (H24), SA (H25) and SN (H26). H3: University students’ PU of m-learning is related to their PE (H31), m-learning SE (H32), MR (H33), SA (H34) and SN (H35). H4: University students’ PE of m-learning is related to their m-learning SE (H41), MR (H42), SA (H43) and SN (H44). Literature review The TAM explains the use of IT and has been widely applied to various fields to understand the personal acceptance of IT use after Davis’ (1989) proposal, which was related to Ajzen and Fishbein’s (1980) theory of reasoned action. TAM proposes two concrete concepts (Davis, 1989): the PU can be defined as the extent to which a university student believes using m-learning will boost his or her learning, and PE as that to which one believes using m-learning will be free of cognitive effort. Previous research adopting TAM mainly investigated personal behavior to use new information systems and technology in corporate environments (Abdul-Gader, 1996; Chin & Gopal, 1995; Gefen & Straub, 1997; Igbaria, Gumaraes & Davis, 1995) and web shopping (Chang, Kim & Oh, 2002; Koo, 2003; Lederer, Maupin, Sena & Zhuang, 2000; Lin & Lu, 2000; Moon & Kim, 2001; Pavlou, 2003; Shin & Song, 2000; Son & Lee, 2002; Teo, Lim & Lai, 1999). In the educational field,TAM is also used as a tool to determine how students’ PU and PE affect their e-learning acceptance (Park, 2009; Park, Nam & Park, 2008). These two concepts were related to factors such as ubiquity, motility, self-directed learning level, and enjoyment of m-learning and BI to use m-learning (Jung, 2009). Because m-learning heavily depends on the use of IT such as cellular phones, PMPs and PDAs, PU and PE may be affected by external factors such as personal demographic situation, social atmosphere and organizational context. In addition, those two concepts may affect AT toward m-learning and, in turn, finally affect BI to use. Hence, BI to use mobile technology and devices is concerned with AT toward new technology and PU (Jin, 2007). Several studies have investigated the intention to use m-learning by adopting TAM as the base of research design. Phuangthong and Malisawan (2005) insisted that TAM was helpful to 594 British Journal of Educational Technology Vol 43 No 4 2012 © 2011 The Authors. British Journal of Educational Technology © 2011 BERA. understand factors affecting m-learning adoption with 3rd generation mobile telecommunication (3G) technology. Jairak, Praneetpolgrang and Mekhabunchakij (2009) confirmed that the unified theory of acceptance and use of technology as developed by Venkatesh, Morris, Davis and Davis (2003), based upon TAM, was able to explain university students’ m-learning acceptance. They insisted that the university administration should emphasize a well fit design m-learning system that is appropriate with student’s perception. The previous literature about mobile media, mobile Internet and m-learning was analyzed. Generally, mobile media is characterized by integration of mobile communicating devices like cellular phones and mobile information devices (MIDs) like PDA. However, cellular phones are now adding wireless internet and computer abilities to their original voice-oriented functions, while MIDs are adding voice message and date communication functions. Therefore, it is not meaningful to distinguish between one and another. The various mobile devices are integrated and considered to be ICT devices as well as mobile devices. A few studies have investigated the effectiveness of m-learning in terms of learning achievements. Learners in m-learning not only use text messages, images and movies but also communicate among learners and teachers with mobile devices, thereby enhancing the learning efficiency (Kim, 2006). Learning with PMP proved to be effective and efficient in terms of improving grade, reducing private cost, managing time and student AT toward learning (Lee, 2008). This may have been because of the learning activities that adopted various multimedia through the m-learning. M-learning works positively on many levels such as learning AT, improving educational interest and concentration (Lee, Han & Lee, 2009). In general, teachers and students who use mobile devices in teaching and learning tend to have positive responses toward using mobile devices (Roach, 2002). Further, students and their parents showed positive cognitions about educational usefulness by using tablet PC (Lee, 2005). The motivation for using mobile devices consists of the following dimensions: social (sociality), functional (immediateness, nobilities, information acquiring, time management), psychological (relief) and cultural (decency/alignment, enjoyment/relaxation, ostentation, fashion/social class). As university students perceive others according to cultural motivation, they adjust themselves to other friends because of their identities, social positions, displays of financial power and communication styles (Lee, 2001). PU and PE meaningfully affect BI to use m-learning and characteristics of mobile technology also significantly influence m-learning (Jung, 2009).