The benefits of information technology (IT) on individuals (Bernard, 2004; Leidner & Elam., 1993-94), groups(Leidner & Fuller, 1997) and organizations usage (Devaraj & Kohli., 2003) have driven information systems (IS) research for more than two decades. Numerous studies and articles have been published each year about individual acceptance of IT in different contexts. This important stream of research examines factors related to, among others, adoption, use, adaptation, diffusion, and appropriation of information technologies.
Over the decades, various theories and paradigms have been proposed in order to get better understanding of individual acceptance behavior on IT. The technology acceptance model (TAM) one of the intention models, is perhaps the most widely applied theoretical model in IS research. The core formulation of TAM argues that an individual's system usage is determined by behavioral intention, which is, in turn, influenced by two beliefs: perceived ease of use and perceived usefulness.
Another dominant research model to understand user acceptance of technology is the theory of planned behavior (TPB). According to the TPB, behavioral intentions to perform a behavior are jointly determined by three factors: attitude toward the behavior, subjective norms (SN), and perceived behavioral control (PBC). Grounded in social psychology, TPB has been widely applied to diverse disciplines such as leisure behavior, marketing/consumer behavior and medicine (Ajzen, 1991). In the IS filed, researchers also found empirical support for predicting the intention on the adoption of new technologies by using TPB (e.g. Harrison, Mykytyn, & Riemenschneider, 1997; Taylor & Todd, 1995; Yi, Jackson, Park, & Probst, 2006).
As research mainstream, substantial empirical studies have been conducted using this beliefs-attitudes-intention paradigm of human behavior extended from Fishbein and Ajzen's (1975) the theory of reasoned action (TRA). Although TAM has been extensively used, very few studies (if any) have attempted to examine the model by using "actual system usage" rather than self-reported use. For instance, in the studies reported by Legris, Ingham, and Collerette (2003), only one study measures actual system usage (Taylor & Todd, 1995). The research of TPB also confronts a similar situation (e.g. Carswell & Venkatesh, 2002; Yi et al., 2006). Since most of the studies use self-reported usage, it can be argued that the power of TAM has yet to extend beyond the conceptual perceptions domain. In this study, we aim to explore the TAM with actual use as a construct. This is done within the context of e-learning. In order to get a holistic overview, we integrate the constructs of TAM and TPB, and include actual system usage records in our research model.
Research Model and Hypotheses
It is not surprising that a long list of theories and models that IS researchers have used to predict or explain factors influencing individual acceptance of information technologies. The TAM and the TPB are two of the most widely applied theoretical models in addressing concerns on individual decisions of IT adoption (Legris et al., 2003). Indeed, researchers also introduce effects of the holistic and positive user experience to expand the view on individual behavior towards learning activities (Choi, Kim, & Kim, 2006; Saade & Bahli, 2005).
Figure 1 presents the research model for this study. Although several studies using TAM suggest that exclusion of attitude from the model (Venkatesh, 1999; Venkatesh & Davis, 2000; Yi et al., 2006), attitude towards the system has been identified as an essential determinant to behavioral intention, as described in the TPB. Indeed, in recent studies, attitude shows its effect on individual online acceptance behavior (Dinev & Hu, 2007; Heijden, 2003; Hsu & Lu, 2004; Lee, Cheung, & Chen, 2005; Moon & Kim, 2001; Saade, Nebebe, & Tan, 2007). …