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Top1. Introduction
Advances in mobile technologies over the years have opened up a wide range of services and information that can be accessed through different kinds of mobile applications for use in various domains. One such domain that has seen unprecedented improvements is the mobile learning (m-learning) domain. As such, there has been increasing interest from researchers in understanding the acceptance of m-learning applications (Al-Emran, Mezhuyev, Kamaludin, 2018; Milošević, Živković, Manasijević & Nikolić, 2015). M-learning can be broadly defined as the use of mobile technologies to acquire knowledge and skills (Liu, Li & Carlsson, 2010). M-learning is quite popular because it provides several benefits to users including portability, ubiquity and mobility (i.e. learning not constrained by time and location), increased engagement with learning content, ability to enhance knowledge retention, cost-effectiveness, and the rapid development of the capabilities of mobile devices (Milošević et al., 2015; Peng, Su, Chou & Tsai, 2009).
Despite the benefits and growing capabilities of m-learning applications (henceforth referred to simply as m-learning apps), many researchers have argued that much is still not known about the factors that influence the acceptance of m-learning apps (Al-Emran et al., 2018; Liu et al., 2010; Milošević et al., 2015; Sabah, 2016). This could possibly be explained by the over-reliance on the technology acceptance model (TAM) as the majority of m-learning studies to date have focused on the TAM as their fundamental theoretical model (Al-Emran et al., 2018; Poong, Yamaguchi & Takada, 2017; Sánchez-Prieto, Olmos-Migueláñez & García-Peñalvo, 2017). In fact, continuous validation of the TAM in the m-learning context has provided mixed findings. For example, while some researchers have found ease of use to positively influence m-learning adoption (Poong et al., 2017), the association was non-significant in other studies (Liu et al., 2010). As such, instead of simply extending the TAM with other constructs as suggested in prior studies (Al-Emran et al., 2018), new insights on the factors that influence m-learning acceptance might be gained by developing and testing other theoretical models. This is particularly important as Benbasat and Barki (2007) argued that over-reliance on the TAM in any domain might put blinders on researcher’s abilities to unearth other valuable constructs, theories, and models that could explain technology acceptance in the domain (i.e. m-learning in the context of the present study). Additionally, although m-learning apps are widely used outside of academia (Liu et al., 2010), the systematic review by Al-Emran et al. (2018) showed that many of the prior studies on m-learning adoption have mostly focused on students. As such, it is imperative to also evaluate m-learning acceptance by other groups of users. Lastly, existing technology acceptance measurements have been criticized for not considering outcome factors by examining whether or not the use of the adopted technologies provide any known benefits to the users (Oliveira, Thomas, Baptista & Campos, 2016). The above discussion clearly suggests the existence of important gaps that need to be addressed in the m-learning adoption literature.