Based on a critical review of the Unified Theory of Acceptance and Use of Technology (UTAUT), this study first formalized an alternative theoretical model for explaining the acceptance and use of information system (IS) and information technology (IT) innovations. The revised theoretical model was then empirically examined using a combination of meta-analysis and structural equation modelling (MASEM) techniques. The meta-analysis was based on 1600 observations on 21 relationships coded from 162 prior studies on IS/ IT acceptance and use. The SEM analysis showed that attitude: was central to behavioural intentions and usage behaviours, partially mediated the effects of exogenous constructs on behavioural intentions, and had a direct influence on usage behaviours. A number of implications for theory and practice are derived based on the findings.

We present a review and analysis of the rich body of research on the adoption and diffusion of IT-based innovations by individuals and organizations. Our review analyzes 48 empirical studies on individual and 51 studies on organizational IT adoption published between 1992 and 2003. In total, the sample contains 135 independent variables, eight dependent variables, and 505 relationships between independent and dependent variables. Furthermore, our sample includes both quantitative and qualitative studies. We were able to include qualitative studies because of a unique coding scheme, which can easily be replicated in other reviews. We use this sample to assess predictors, linkages, and biases in individual and organizational IT adoption research. The best predictors of individual IT adoption include Perceived Usefulness, Top Management Support, Computer Experience, Behavioral Intention, and User Support. The best predictors of IT adoption by organizations were Top Management Support, External Pressure, Professionalism of the IS Unit, and External Information Sources. At the level of independent variables, Top Management Support stands as the main linkage between individual and organizational IT adoption. But at an aggregate level, two collections of independent variables were good predictors of both individual and organizational IT adoption. These were innovation characteristics and organizational characteristics. Thus, we can consistently say that generic characteristics of the innovation and characteristics of the organization are strong predictors of IT adoption by both individuals and organizations. Based on an assessment of the predictors, linkages, and known biases, we prescribe 10 areas for further exploration.

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Despite considerable empirical research, results on the relationships among constructs related to information system (IS) success, as well as the determinants of IS success, are often inconsistent. A comprehensive understanding of IS success thus remains elusive. In an attempt to address this situation, which may partly be due to the exclusion of potentially important constructs from prior parsimonious models of IS success, we present and test a comprehensive theoretical model. This model explains interrelationships among four constructs representing the success of a specific IS (user satisfaction, system use, perceived usefulness, and system quality), and the relationships of these IS success constructs with four user-related constructs (user experience with ISs, user training in ISs, user attitude toward ISs, and user participation in the development of the specific IS) and two constructs representing the context (top-management support for ISs and facilitating conditions for ISs). To test the model, we first used meta-analysis to compute a correlation matrix for the constructs in the model based on 612 findings from 121 studies published between 1980 and 2004, and then used this correlation matrix as input for a LISREL analysis of the model. Overall, we found excellent support for the theoretical model. The results underline the importance of user-related and contextual attributes in IS success and raise questions about some commonly believed relationships.information technology, information systems, information systems success, structural equation modeling, meta-analysis