The role of moderating factors in user technology acceptance

Abstract

Along with increasing investments in new technologies, user technology acceptance becomes a frequently studied topic in the information systems discipline. The last two decades have seen user acceptance models being proposed, tested, refined, extended and unified. These models have contributed to our understanding of user technology acceptance factors and their relationships. Yet they have also presented two limitations: the relatively low explanatory power and inconsistent influences of the factors across studies. Several researchers have recently started to examine the potential moderating effects that may overcome these limitations. However, studies in this direction are far from being conclusive. This study attempts to provide a systematic analysis of the explanatory and situational limitations of existing technology acceptance studies. Ten moderating factors are identified and categorized into three groups: organizational factors, technological factors and individual factors. An integrative model is subsequently established, followed by corresponding propositions pertaining to the moderating factors.

Introduction

Driven by market competitiveness, business enhancement, service improvement and work efficiency, organizations have invested heavily in information technology with the likelihood of continuing this investment pattern into the foreseeable future (Chau and Hu, 2002). Some estimates show that since the 1980s, 50% of all new capital investment in organizations has been in information technology (Venkatesh et al., 2003). Understanding the factors that influence user technology acceptance and adoption in different contexts continues to be a focal interest in information systems (IS) research.

Several models and theories have been developed to explain user technology acceptance behavior. However, these models have some limitations. The first limitation concerns the explanatory power of the models. Most of the existing studies account for less than 60% of variance explained, especially those using field studies with professional users. Although there may be many other factors that are beyond researchers’ reach, the differences in explanatory power between laboratory studies and field studies, and between studies using students and using professionals, imply some complex contextual factors in the real world that should be taken into account (e.g., the influence of organizational factors such as the voluntariness of IT usage). The second limitation of these models is the inconsistent relationships among constructs, making researchers question the generalizability of these models across differing contexts (e.g., Lee et al., 2003; Legris et al., 2003). These limitations call for improvement and refinement of existing studies.

Moderating factors may account for both the limited explanatory power and the inconsistencies between studies. In an early study, Adams et al. (1992) called for more examination of moderating factors. Several recent studies continue to call for the inclusion of some moderating factors (e.g., Lucas and Spitler, 1999; Venkatesh et al., 2003). Agarwal and Prasad (1998) explicitly criticized the absence of moderating influences in technology acceptance model (TAM), and called for more research to investigate moderating effects. Venkatesh et al. (2003) tested eight models and found that the predictive validity of six of the eight models significantly increased after the inclusion of moderating variables. Furthermore, they argued, “it is clear that the extensions (moderators) to the various models identified in previous research mostly enhance the predictive validity of the various models beyond the original specifications” (Venkatesh et al., 2003, p. 21). In addition, Chin et al. (2003) empirically examined and confirmed the significant influence of moderating factors in existing models of user technology acceptance.

While stating that “the extensive prior empirical work has suggested a large number of moderators”, Venkatesh et al. (2003, p. 21) included only four in their study: experience, voluntariness, gender and age. Based on a careful literature review, we believe that there are more moderating factors with empirical evidence than the four studied. For example, the nature of the tasks may affect users’ acceptance of technology, as does the nature of the technology. Few of these moderators were examined either conceptually or empirically in recent efforts. A systematic examination of significant moderating factors should contribute to our better understanding of the dynamics of the user technology acceptance phenomenon.

This study examines the moderating effects in user technology acceptance. It adds to the few studies that take into account the individual and contextual factors in technology acceptance (i.e., Igbaria et al., 1997). The objectives of this paper are three-fold. It first provides an overview of prior literature to disclose the limitations of explanatory powers and the inconsistencies between prior studies. Then the paper highlights the moderating factors that account for both the limitations of the explanatory power and the inconsistencies. Ten moderating factors that have strong empirical evidence are identified and categorized into three groups: organizational factors, technological factors and individual factors. And, finally, the paper proposes a new model with propositions pertaining to the effects of the moderating factors. Readers interested in other aspects of user technology acceptance research summaries, such as research emphases and evolutions, empirical sample sizes and characteristics, most influential authors, and critical comments from several major researchers, are encouraged to read a recent meta analysis by Lee et al. (2003), which lacks discussion of the effects of the moderating factors.

This study calls for more research attention to individual and contextual factors that are often neglected in technology acceptance studies but can be critical in applying theoretical models to specific situations in organizations. The study also provides a basis for further empirical investigation in this research area.