Investigating the determinants and age and gender differences in the acceptance of mobile learning

With the proliferation of mobile computing technology, mobile learning (m-learning) will play a vital role in the rapidly growing electronic learning market. M-learning is the delivery of learning to students anytime and anywhere through the use of wireless Internet and mobile devices. However, acceptance of m-learning by individuals is critical to the successful implementation of m-learning systems. Thus, there is a need to research the factors that affect user intention to use m-learning. Based on the unified theory of acceptance and use of technology (UTAUT), which integrates elements across eight models of information technology use, this study was to investigate the determinants of m-learning acceptance and to discover if there exist either age or gender differences in the acceptance of m-learning, or both. Data collected from 330 respondents in Taiwan were tested against the research model using the structural equation modelling approach. The results indicate that performance expectancy, effort expectancy, social influence, perceived playfulness, and self-management of learning were all significant determinants of behavioural intention to use m-learning. We also found that age differences moderate the effects of effort expectancy and social influence on m-learning use intention, and that gender differences moderate the effects of social influence and self-management of learning on m-learning use intention. These findings provide several important implications for m-learning acceptance, in terms of both research and practice. British Journal of Educational Technology Vol 40 No 1 2009 92–118 doi:10.1111/j.1467-8535.2007.00809.x © 2007 The Authors. Journal compilation © 2007 Becta. Published by Blackwell Publishing, 9600 Garsington Road, Oxford OX4 2DQ, UK and 350 Main Street, Malden, MA 02148, USA. Introduction The use of information and communication technology (ICT) may improve learning, especially when coupled with more learner-centred instruction (Zhu & Kaplan, 2002). From notebook computers to wireless phones and handheld devices, the massive infusion of computing devices and rapidly improving Internet capabilities have altered the nature of higher education (Green, 2000). Mobile learning (m-learning) is the follow up of e-learning, which for its part originates from distance education. M-learning refers to the delivery of learning to students anytime and anywhere through the use of wireless Internet and mobile devices, including mobile phones, personal digital assistants (PDAs), smart phones and digital audio players. Namely, m-learning users can interact with educational resources while away from their normal place of learning— the classroom or desktop computer. The place independence of mobile devices provides several benefits for e-learning environments, such as allowing students and instructors to utilise their spare time while traveling in trains or buses to finish their homework or lesson preparation (Virvou & Alepis, 2005). If e-learning took learning away from the classroom, then m-learning is taking learning away from a fixed location (Cmuk, 2007). Motiwalla (2007) contends that learning on mobile devices will never replace classroom or other e-learning approaches. Thus, m-learning is a complementary activity to both e-learning and traditional learning. However, Motiwalla (2007) also suggests that if leveraged properly, mobile technology can complement and add value to the existing learning models, such as the social constructive theory of learning with technology (Brown & Campione, 1996) and conversation theory (Pask, 1975). Thus, some believe that m-learning is becoming progressively more significant, and that it will play a vital role in the rapidly growing e-learning market. Despite the tremendous growth and potential of the mobile devices and networks, wireless e-learning and m-learning are still in their infancy or embryonic stage (Motiwalla, 2007). While the opportunities provided by m-learning are new, there are several challenges facing m-learning, such as connectivity, small screen sizes, limited processing power and reduced input capabilities. Siau, Lim and Shen (2001) also note that mobile devices have ‘(1) small screens and small multifunction key pads; (2) less computational power, limited memory and disk capacity; (3) shorter battery life; (4) complicated text input mechanisms; (5) higher risk of data storage and transaction errors; (6) lower display resolution; (7) less surfability; (8) unfriendly user-interfaces; and (9) graphical limitations’ (p. 6). Equipped with a small phone-style keyboard or a touch screen, users might require more time to search for some information on a page than they need to read it (Motiwalla, 2007). These challenges mean that adapting existing e-learning services to m-learning is not an easy work, and that users may be inclined to not accept m-learning. Thus, the success of m-learning may depend on whether or not users are willing to adopt the new technology that is different from what they have used in the past. While e-learning and mobile commerce/learning has received extensive attention (Concannon, Flynn & Campbell, 2005; Davies & Graff, 2005; Govindasamy, 2002; Harun, 2002; Ismail, 2002; Luarn & Lin, 2005; Mwanza & Engeström, 2005; Motiwalla, 2007; Pituch & Lee, 2006; Selim, 2007; Shee & Wang, in Determinants and age and gender in mobile learning 93 © 2007 The Authors. Journal compilation © 2007 Becta. press; Ravenscroft & Matheson, 2002; Wang, 2003), thus far, little research has been conducted to investigate the factors affecting users’ intentions to adopt m-learning, and to explore the age and gender differences in terms of the acceptance of m-learning. As Pedersen and Ling (2003) suggest, even though traditional Internet services and mobile services are expected to converge into mobile Internet services, few attempts have been made to apply traditional information technology (IT) adoption models to explain their potential adoption. Consequently, the objective of this study was to investigate the determinants, as well as the age and gender differences, in the acceptance of m-learning based on the unified theory of acceptance and use of technology (UTAUT) proposed by Venkatesh, Morris, Davis and Davis (2003). The remainder of this paper is organised as follows. In the next section, we review the UTAUT and show our reasoning for adopting it as the theoretical framework of this study. This is followed by descriptions of the research model and methods. We then present the results of the data analysis and hypotheses testing. Finally, the implications and limitations of this study are discussed. Unified Theory of Acceptance and Use of Technology M-learning acceptance is the central theme of this study, and represents a fundamental managerial challenge in terms of m-learning implementation. A review of prior studies provided a theoretical foundation for hypotheses formulation. Based on eight prominent models in the field of IT acceptance research, Venkatesh et al (2003) proposed a unified model, called the unified theory of acceptance and use of technology (UTAUT), which integrates elements across the eight models. The eight models consist of the theory of reasoned action (TRA) (Fishbein & Ajzen, 1975), the technology acceptance model (TAM) (Davis, 1989), the motivational model (MM) (Davis, Bagozzi & Warshaw, 1992), the theory of planned behaviour (TPB) (Ajzen, 1991), the combined TAM and TPB (C-TAM-TPB) (Taylor & Todd, 1995a), the model of PC utilisation (MPCU) (Triandis, 1977; Thompson, Higgins & Howell, 1991), the innovation diffusion theory (IDT) (Rogers, 2003; Moore & Benbasat, 1991) and the social cognitive theory (SCT) (Bandura, 1986; Compeau & Higgins, 1995). Based on Venkatesh et al’s (2003) study, we briefly review the core constructs in each of the eight models, which have been theorised as the determinants of IT usage intention and/or behaviour. First, TRA has been considered to be one of the most fundamental and influential theories on human behaviour. Attitudes toward behaviour and subjective norms are the two core constructs in TRA. Second, TAM was originally developed to predict IT acceptance and usage on the job, and has been extensively applied to various types of technologies and users. Perceived usefulness and perceived ease of use are the two main constructs mentioned in TAM. More recently, Venkatesh and Davis (2000) presented TAM2 by adding subjective norms to the TAM in the case of mandatory settings. Third, Davis et al (1992) employed motivation theory to understand new technology acceptance and usage, focusing on the primary constructs of extrinsic motivation and intrinsic motivation. Fourth, TPB extended TRA by including the construct of perceived behavioural control, and has been successfully applied to the 94 British Journal of Educational Technology Vol 40 No 1 2009 © 2007 The Authors. Journal compilation © 2007 Becta. understanding of individual acceptance and usage of various technologies (Harrison, Mykytyn & Riemenschneider, 1997; Mathieson, 1991; Taylor & Todd, 1995b). Fifth, C-TAM-TPB is a hybrid model that combines the predictors of TPB with perceived usefulness from TAM. Sixth, based on Triandis’ (1977) theory of human behaviour, Thompson et al (1991) presented the MPCU and used this model to predict PC utilisation. MPCU consists of six constructs, including job fit, complexity, long-term consequences, affect towards use, social factors and facilitating conditions. Seventh, Moore and Benbasat (1991) adapted the properties of innovations posited by IDT and refined a set of constructs that could be used to explore individual technology acceptance. These constructs include relative advantage, ease of use, image, visibility, compatibility, results demonstrability and voluntariness of use. Finally, Compeau and Higgins (1995) applied and extended SCT to the context of computer utilisation (see also Compeau, Higgins & Huff, 1999). Their model consists of five core constructs: outcome expectations–performance, outcome expectations–personal, self-efficacy, affect and anxiety. Venkatesh et al (2003) conducted an empirical study to compare the eight competing models and then proposed a unified model, UTAUT, which contains four core determinants of IT use behaviour, and up to four moderators of key relationships (see Figure 1). UTAUT posits that performance expectancy, effort expectancy, social influence and facilitating conditions are determinants of behavioural intention or use behaviour, and that gender, age, experience and voluntariness of use have moderating effects in the