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Testing The Technology Acceptance Model

 

            
             In recent years, the technology acceptance model (TAM) has been widely used by IS researchers in order to gain a better understanding of the adoption and use of information systems. While TAM has been widely applied and tested in North America, there have been no attempts to extend this work to other regions of the world. Given the globalization of business and systems, there is a pressing need to understand whether TAM applies in other cultures. This study compares the TAM model across three different countries: Japan, Switzerland, and the United States. The study was conducted by administering the same instrument to employees of three different airlines, all of whom had access to the same information technology innovation, in this case, E-Mail. The results indicate that TAM holds for both the U.S. and Switzerland, but not for Japan, suggesting that the model may not predict technology use across all cultures. The implications of these findings are discussed.
             Keywords: Technology Acceptance Model; cross-cultural studies; E-Mail; IT acceptance; IT diffusion; IT adoption; IT use.
             1. Introduction.
             Recently, there has been a highly prolific stream of theoretical research on the acceptance and use of information technology (IT). In this work, it has been argued that users develop perceptions about the usefulness and ease-of-use of various technologies and that these, in turn, influence actual system use. This model, known as the technology acceptance model (TAM), is widely regarded as a relatively robust theoretical model for explaining IT use. From a practitioner perspective, TAM is useful for predicting if users will adopt new information technologies.
             Replication of the original work by Davis [10, 11, 12] suggests that TAM may hold across technologies as well as across persons, settings, and times, the latter being a requirement for robust theories [4]. Reinforcing this robustness, TAM has also been shown to demonstrate good predictive validity [38].


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