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Technology acceptance modelThe Technology Acceptance Model (TAM) is one of the most influential extensions of Ajzen and Fishbein’s theory of reasoned action (TRA) in the information systems literature. Developed by Fred Davis and Richard Bagozzi (Bagozzi et al., 1992; Davis et al., 1989). TAM replaces many of TRA’s attitude measures with two technology acceptance measures—ease of use, and usefulness. TRA and TAM, both of which have strong behavioural elements, assume that when someone forms an intention to act, that they will be free to act without limitation. In the real world there will be many constraints, such as limited ability, time constraints, environmental or organisational limits, or unconscious habits which will limit the freedom to act (Bagozzi et al., 1992).
Bagozzi Davis and Warshaw say:
Several researchers have replicated Davis’s original study (Davis, 1989) to provide empirical evidence on the relationships that exist between usefulness, ease of use and system use (Adams, Nelson & Todd, 1992; Davis et al., 1989; Hendrickson, Massey & Cronan, 1993; Segars & Grover, 1993; Subramanian, 1994; Szajna, 1994). Much attention has focused on testing the robustness and validity of the questionnaire instrument used by Davis. Adams et al (1992) replicated the work of Davis (1989) to demonstrate the validity and reliability of his instrument and his measurement scales. They also extended it to different settings and, using two different samples, they demonstrated the internal consistency and replication reliability of the two scales. Hendrickson et al (1993) found high reliability and good test-retest reliability. Szajna (1994) found that the instrument had predictive validity for intent to use, self-reported usage and attitude toward use. The sum of this research has confirmed the validity of the Davis instrument, and to support its use with different populations of users and different software choices.
Segars and Grover (1993) re-examined Adams et al’s (1992) replication of the Davis work. They were critical of the measurement model used, and postulated a different model based on three constructs: usefulness, effectiveness, and ease-of-use. These findings do not yet seem to have been replicated.
Mark Keil and his colleagues have developed (or, perhaps rendered more popularisable) Davis’s model into what they call the Usefulness/EOU Grid, which is a 2x2 grid where each quadrant represents a different combination of the two attributes. In the context of software use, this provides a mechanism for discussing the current mix of usefulness and EOU for particular software packages, and for plotting a different course if a different mix is desired, such as the introduction of even more powerful software (Keil, Beranek & Konsynski, 1995).
Adams, D. A., Nelson, R. R., & Todd, P. A. (1992). Perceived usefulness, ease of use, and usage of information technology: A replication. MIS Quarterly, 16, 227-247.
Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting social behaviour. Eaglewood Cliffs, NJ: Prentice-Hall.
Bagozzi, R. P., Davis, F. D., & Warshaw, P. R. (1992). Development and test of a theory of technological learning and usage. Human Relations, 45(7), 660-686.
Bass, F. M. (1969). A new product growth model for consumer durables. Management Science, 15, 215-227.
Bass, F. M. (1986). The adoption of a marketing model: Comments and observations. In V. Mahajan & Y. Wind (Eds.), Innovation Diffusion Models of New Product Acceptance. Cambridge, Mass.: Ballinger.
Budd, R. J. (1987). Response bias and the theory of reasoned action. Social Cognition, 5, 95-107.
Czaja, S. J., Hammond, K., Blascovich, J. J., & Swede, H. (1986). Learning to use a word processing system as a function of training strategy. Behaviour and Information Technology, 5, 203-216.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340.
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35, 982-1003.
Hendrickson, A. R., Massey, P. D., & Cronan, T. P. (1993). On the test-retest reliability of perceived usefulness and perceived ease of use scales. MIS Quarterly, 17, 227-230.
Keil, M., Beranek, P. M., & Konsynski, B. R. (1995). Usefulness and ease of use: field study evidence regarding task considerations. Decision Support Systems, 13(1), 75-91.
Segars, A. H., & Grover, V. (1993). Re-examining perceived ease of use and usefulness: A confirmatory factor analysis. MIS Quarterly, 17, 517-525.
Stewart, T. (1986). Task fit, ease-of-use and computer facilities. In N. Bjørn-Anderson, K. Eason, & D. Robey (Eds.), Managing computer impact: An international study of management and organizations (pp. 63-76). Norwood, NJ: Ablex.
Subramanian, G. H. (1994). A replication of perceived usefulness and perceived ease of use measurement. Decision Sciences, 25(5/6), 863-873.
Szajna, B. (1994). Software evaluation and choice: predictive evaluation of the Technology Acceptance Instrument. MIS Quarterly, 18(3), 319-324.
Tornatzky, L. G., & Klein, R. J. (1982). Innovation characteristics and innovation adoption-implementation: A meta-analysis of findings. IEEE Transactions on Engineering Management, EM-29, 28-45.
Wildemuth, B. M. (1992). An empirically grounded model of the adoption of intellectual technologies. Journal of the American Society for Information Science, 43(3), 210-224.
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