The Technology Acceptance Model (TAM) is an information systems theory that models how users come to accept and use a technology. The model suggests that when users are presented with a new software package, a number of factors influence their decision about how and when they will use it, notably:
- Perceived usefulness (PU) - This was defined by Fred Davis as "the degree to which a person believes that using a particular system would enhance his or her job performance".
- Perceived ease-of-use (EOU) Davis defined this as "the degree to which a person believes that using a particular system would be free from effort" (Davis, 1989).
History
The technology acceptance model is one of the most influential extensions of Ajzen and Fishbein’s theory of reasoned action (TRA) in the literature. It was developed by Fred Davis and Richard Bagozzi (Bagozzi et al., 1992; Davis et al., 1989). TAM replaces many of TRA’s attitude measures with the 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:
- Because new technologies such as personal computers are complex and an element of uncertainty exists in the minds of decision makers with respect to the successful adoption of them, people form attitudes and intentions toward trying to learn to use the new technology prior to initiating efforts directed at using. Attitudes towards usage and intentions to use may be ill-formed or lacking in conviction or else may occur only after preliminary strivings to learn to use the technology evolve. Thus, actual usage may not be a direct or immediate consequence of such attitudes and intentions. (Bagozzi et al., 1992)
Earlier research on the adoption of innovations also suggested a prominent role for perceived ease of use. Tornatzky and Klein (1982) analysed the relationship between the characteristics of an innovation and its adoption, finding that compatibility, relative advantage, and complexity had the most significant relationships with adoption across a broad range of innovation types. Eason studied perceived usefulness in terms of a fit between systems, tasks and job profiles, using the terms "task fit" to describe the metric (quoted in Stewart, 1986).
Usage
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 2×2 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).
Venkatesh and Davis extended the original TAM model to explain perceived usefulness and usage intentions in terms of social influence and cognitive instrumental processes. The extended model, referred to as TAM2, was tested in both voluntary and mandatory settings. The results strongly supported TAM2 (Venkatesh and Davis, 2000).
In an attempt to integrate the main competing user acceptance models, Venkatesh et al. formulated the Unified Theory of Acceptance and Use of Technology (UTAUT). This model was found to outperform each of the individual models (Adjusted R square of 69 percent) (Venkatesh et al., 2003).
See also
References
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.
Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, (46:2), 186-204.
Venkatesh, V., Morris, M. G., Davis, G.B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, (27:3), 425-478.
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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