Using perceived ease of use and perceived usefulness to predict acceptance of the World Wide Web

Tino Fenech

Queensland University of Technology

The Technology Acceptance Model (TAM) behavioural constructs of perceived usefulness and perceived ease of use were tested for predicting user acceptance of the World Wide Web (Web). The results indicate a poor fit for the model until the introduction an additional construct, computer self-efficacy.

Adoption; TAM; Self-efficacy

1. Study

Little research has been conducted to identify the factors that facilitate user adoption of the Web. For the Web to be used effectively for research, retailing, gaming and other purposes "...there must be a fit between technology and task and between individual characteristics and the technology" [8]. In an attempt to model this user adoption, this study began with a test of the principal behavioural constructs of Davis' [3] Technology Acceptance Model (TAM). The model, shown in Fig. 1, was originally developed the to understand the causal link between external variable and user acceptance of PC-based applications [5].

Fig. 1. Technology Acceptance Model.

A prospective user's overall attitude toward using a given system, such as the World Wide Web, is shown as a function of the belief constructs in the TAM: Perceived Usefulness (the degree to which a person believes that using a particular system would enhance his or her job performance) and Perceived Ease of Use (the degree to which a person believes that using a particular system would be free of effort) [4, p. 320].

The current study was conducted with 150 consenting students (gender split almost even) in their final semester of an undergraduate business studies course at an Australian university. All students had Internet access but were not necessarily users. A questionnaire was applied to gauge their beliefs and attitudes towards using the World Wide Web. Results were processed using the AMOS 3.6 [1] structural equation-modelling program. The original TAM as tested by Hubona and Kennick [7] indicated poor predictive value for Web user adoption.

To improve the TAM's predictive value for the Web, an additional behavioural construct, Computer Self-Efficacy [2,9] was included in the model. The measures of good fit [6] presented in Table 1 were most encouraging.


Measure of fit

Recommended values

Revised TAM

Chi-squared divided by degrees of freedom (chi2/df)

Less than 3.0


Non-Normed Fit Index (NNFI)

Greater than 0.9


Comparative Fit Index (CFI)

Greater than 0.9


Goodness of Fit Index (GFI)

Greater than 0.9


Adjusted Goodness of Fit Index (AGFI)

Greater than 0.8


Root Mean Square Residual (RMSR)

Less than 1.0


Table 1. Goodness of fit measures and results for the final TAM

2. Results

The intention of this study was to test the behavioural constructs of perceived usefulness and perceived ease of use as predictors of usage acceptance of World Wide Web. The study concludes that these constructs are inadequate for that purpose with the adjusted goodness of fit no better than 0.556 through application of the Technology Acceptance model. The secondary purpose of the study was to test the inclusion of the behavioural construct of computer self-efficacy within TAM for application to the World Wide Web. This later test was successful with the TAM adjusted goodness of fit of 0.890 once the additional construct, computer self-efficacy, was included in the Technology Acceptance Model.


While we have never met or spoken, thanks to the Geoffrey Hubona and Viswanath Venkatesh for their e-mails and suggestions. Their guidance was invaluable in studying the Technology Acceptance Model. To Chad Perry my thanks for his direction and subtle encouragement.


[1] Arbuckle, J.L., Amos User's Guide Version 3.6, SaltWaters Corporation, Chicago, IL, 1997.

[2] Compeau, D.R. and C.A. Higgins, Computer self-efficacy: development of measure and initial test, MIS Quarterly, 19(2): 189–211, June 1995.

[3] Davis, F.D., A technology acceptance model for empirically testing new end-user information systems: theory and results, Doctoral dissertation, MIT Sloan School of Management, Cambridge, MA, 1986.

[4] Davis, F.D., User acceptance of information technology: system characteristics, user perceptions and behavioral impacts, International Journal of Man-Machine Studies, 38: 475–487, 1993.

[5] Davis, F.D. and V. Venkatesh, A critical assessment of potential measurement biases in the technology acceptance model: three experiments, International Journal of Human–Computer Studies, 45: 19–45, 1996.

[6] Hartwick, J. and H. Barki, Explaining the role of user participation in information system use, Management Science, 40(4): 440–465, April, 1994.

[7] Hubona, G.S. and E. Kennick, The impact of external variables on information technology usage behavior, in: Proc. of the 29th Annual Hawaii International Conference on System Science 1995}, January 1996.

[8] Hubona, G.S. and S. Geitz, External Variables, beliefs, attitudes and information technology usage behavior, in: Proc. of the 30th Annual Hawaii International Conference on System Sciences. IEEE Computer Society Press, Los Almitos, CA, 1997.

[9] Yi, M.Y. and V. Venkatesh, Role of computer self-efficacy in predicting user acceptance and use of information technology, in: Proc. Conference for the Association of Information Systems (AIS), July 1996.