نقش عوامل تعدیل کننده در قبول واقعیت فناوری کاربران

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.

Conclusions Although they have received considerable empirical validation and confirmation, existing user acceptance models still have room for improvement. Their limited explanatory power and inconsistent relationships call for taking additional factors into account. Researchers have suggested models be tested in field settings with organizational and technological factors considered (Lucas and Spitler, 1999; e.g., Sun and Zhang, 2004). This present study is an attempt to move in this direction. By including the moderators in user acceptance models, we hope to lessen the limitations of low explanatory power and inconsistencies existing in prior studies. It is noteworthy that the influence of including moderating factors on R2R2 is statistically limited (Chin et al., 2003). However, by taking moderating factors into account, we are more confident in explaining and describing the meanings of existing models. This paper draws several implications for both researchers. First, this study suggests that research on moderating factors is of great value. This is consistent with suggestions from existing studies that contexts could play an important role in user technology acceptance (Davis et al., 1989; Taylor and Todd, 1995a; Szajna, 1996). It is noteworthy that the major function of moderating factors is explaining the inconsistencies by identifying the situational differences. Its effect in enhancing R2R2 is modest. This observation is consistent with prior empirical study (Chin et al., 2003). Second, research should pay more attention to less studied issues. For instance, few studies have empirically examined cultural issues associated with user technology acceptance. The mechanisms through which the culture exerts its influence are still unclear (e.g., Straub et al., 1997). Therefore, future research may focus on “how” questions by identifying the major cultural dimensions and their corresponding relationships with user technology acceptance. Third, compared to the moderating effects of individual factors, those effects of organizational factors such as the nature of tasks, and technological factors such as technology complexity, have not received sufficient attention so far and thus leave room for further investigation. We should also notice the interactions among these moderating factors. We cannot simply say that women always pay attention to the influence of SNs. For women who have a lot of experience with the technology of interest, it may not be true. Therefore, we should consider all the major moderating factors simultaneously. It is, however, too early to reach any conclusions about which effects are more robust. More empirical tests are needed to address the interactions among these moderating factors. Finally, from a methodological perspective, studies of user acceptance may need a methodological shift in order to gain richer understanding of less studied factors. So far, almost all the prior studies use quantitative research methodology and usually from a positivist perspective. Qualitative methodology, especially from an interpretive perspective, however, is informative and may be another useful alternative that can give researchers new insights (Lee et al., 2003). Among these methodologies, a good example is grounded theory (Glaser and Strauss, 1967), which allows a focus on contextual and process elements as well as the action of key players (users) associated with contextual change (Orlikowski, 1993). Although successfully used in IS in general, these methodologies, such as grounded theory, are rarely used in research on user technology acceptance. In addition, the nature of user technology acceptance calls for periodic examinations of the determining factors along with the development of information technology. New technologies often involve factors that have rarely been considered before. For example, trust, which is not a traditionally considered factor, may influence users’ intentions to use on-line shopping (Gefen et al., 2003) or mobile commerce (Siau et al., 2003). These methodological perspectives can help us identify the potential factors inductively. For practitioners, this research also has several implications in that the findings and propositions can be easily translated into practice. First, practitioners should pay particular attention to the inclusion of individual and contextual factors when using these models to predict user acceptance of technologies. Practitioners should realize that existing models are conditional and therefore simply provide a basis for understanding user technology acceptance. To predict user acceptance of a specific system, individual and contextual factors should be taken into account. Second, the findings have implications for designing training programs. Training programs should highlight the influence of individual and contextual factors. For example, for men, the training program should emphasize usefulness; while for women, ease of use and SNs (such as peer influence) should be emphasized. Further, trainers should pay attention to the evolution of trainees’ perceptions and the influence of SNs. Specifically, in the early stage of the system use, ease of use and SN is more important, especially for women. Therefore, trainers can develop specific tactics such as focusing on how to use the system and encouraging communication among female users Realizing that once users are no longer newcomers to the system, and thus focusing on usefulness, the training program should accordingly focus on usefulness, exploring the functional potentials of the system of interest. This strategy can also be applied for users with different levels of prior experience. Other potential factors include voluntariness, the nature of the tasks and the professions, and technological factors as suggested earlier in the paper. By taking these factors into account, practitioners can take corresponding measures to predict or promote user technology acceptance more effectively and efficiently. There are some limitations in this study. One is the limited number of articles reviewed. Even though they are considered representative, only 54 articles are included in this study. The results, therefore, could be biased to some extent. On the bright side, our results show great consistency with several other meta-analysis results (Lee et al., 2003; Legris et al., 2003; Ma and Liu, 2004). The second limitation is that the relationships between moderating factors are not under consideration and therefore the proposed model may need further refinement. For example, Chau and Hu (2002) argued that the subjects used in their research, physicians, had more “power of expertise”, and more autonomy over their work, and therefore were less likely to be influenced by “administrative and managerial decisions”, which were usually mandatory. Their arguments suggest a relationship between two moderators, voluntariness and profession autonomy. The research on interactions among factors and relationships within the integrated model can add more practical values to the model by finding more explicit factors that are easy to use. These limitations will be addressed in future research. It is noteworthy that we realize a balance between a comprehensive and a barebones model. The inclusion of moderating factors is assumed to enhance explanatory power while lowering the model's elegance. In this study, we emphasize enhancing explanatory power, while leaving parsimony to future research, since low explanatory power seems more salient to date (Lee et al., 2003).