Extend of TAM Model with Technology anxiety and Self-Efficacy to Accept Course websites at University Canada West

Saleh Ali NuriAbdalla


Learning technology is the use of technology to support the learning process - widely known as e-learning. In higher education, this term refers to educational web sites such as online courses. The acceptance of technology in the learning process depends on some crucial factors. This research paper investigated the relationship among several variables that are related to educational technology performance based on the technology acceptance modal (TAM).  The respondents were 61 students who are studying in the University Canada West (both undergraduate and postgraduate). Descriptive, correlation and multiple regressions were conducted to date. The results of the investigation showed that there was a positive correlation relationship among variables except of one variable, technology anxiety that was not correlated with the others. The multiple regressions resulted that two of independent variables, perceived of ease and self-efficacy, had a significant positive effect on the intention of use.

Full Text:



Agarwal, R., & Karahanna, E. (2000). Time flies when you’re having fun: Cognitive absorption and beliefs about information technology usage. MIS Quarterly 24(4), 665-694.

Al-Adwan, A., & Smedley, J. (2013). Exploring students acceptance of e-learning using Technology Acceptance Model in Jordanian universities Amer Al-Adwan Applied Science University, Jordan. International Journal of Education and Development Using Information and Communication Technology, 9(2), 4–18.

Ariff, M. S. M., Yeow, S. M., Zakuan, N., Jusoh, A., & Bahari, A. Z. (2012). The Effects of Computer Self-Efficacy and Technology Acceptance Model on Behavioral Intention in Internet Banking Systems. Procedia - Social and Behavioral Sciences, 57, 448–452. https://doi.org/10.1016/j.sbspro.2012.09.1210

Chen, Y.-L. (2014). A Study on Student Self-efficacy and Technology Acceptance Model within an Online Task-based Learning Environment. Journal of Computers, 9(1). https://doi.org/10.4304/jcp.9.1.34-43

Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology, MIS Quarterly, 13, pp. 319-340.

Davis, F. D., Bagozzi, R. P., and Warshaw, P. R. (1989). User Acceptance of Computer Technology: A Comparison of Two Theoretical Models, Management Science, 35, pp. 982-1003.

Davison, AJ, Burgess, S & Tatnall, A. (2003) Internet technologies and business, Data Publishing Pty Ltd, Heidelberg, Vic.

Fauziah Adli, Nikos Joshua H.S, Vera Pujani, and M. (2014). E-Travel Model: An Empirical Study of Technology Acceptance Model and Self Efficacy. In International Conference on Business, Management & Corporate Social Responsibility (ICBMCSR’14) Feb. 14-15, 2014 Batam (Indonesia). https://doi.org/10.15242/icehm.ed0214038

Hall, B. C. (2008). Investigating the relationships among computer self-efficacy, professional development, teaching experience, and technology integration of teachers. Doctoral Dissertation, University of Cincinnati, Cincinnati, Ohio.

Hanif, A., Jamal, F. Q., & Imran, M. (2018). Extending the Technology Acceptance Model for Use of e-Learning Systems by Digital Learners. IEEE Access, 6, 73395–73404. https://doi.org/10.1109/ACCESS.2018.2881384

Hoong, A. L. S., Thi, L. S., & Lin, M.-H. (2017). Affective Technology Acceptance Model: Extending Technology Acceptance Model with Positive and Negative Affect. In Knowledge Management Strategies and Applications. InTech. https://doi.org/10.5772/intechopen.70351

Ignatius, J. and Ramayah, T. (2005). An Empirical Investigation of the Course Website Acceptance Model (CWAM). International Journal of Business and Society, Vol. 6, Iss. 2, pp. 69-82

Johnson, R.D. and Marakas, G.M. (2000) The Role of Behavior Modeling in Computer Skill Acquisition - Toward Refinement of the Model, Information Systems Research (11), pp. 402-417.

Marangunić, N., & Granić, A. (2015). Technology acceptance model: A literature review from 1986 to 2013. Universal Access in the Information Society, 14(1), 81–95.

Martin, E.W.E.; Brown,C.V, DeHayes, D.V., Hoffer , J.A. ,Perkin, W.C.(2001). Managing Information Technology. Prentice Hall; 4 edition (Sep 10 2001).

Masrom, M. (2007). Technology Acceptance Model and E-learning. 12th International Conference on Education, Sultan Hassanal Bolkiah Institute of Education Universiti Brunei Darussalam

Petrides, L. A. (2002). Web-bsed technologies for distributed (or distance) learning: Creating learning-centered educational experiences in the higher education classroom. International Journal of Instructional Media, 29(1), 69-77.

Ramayah, T. & Ignatius, J. (2005). Impact of Perceived Usefulness, Perceived Ease of Use and Perceived Enjoyment on Intention to Shop Online. ICFAI Journal of Systems Management (IJSM), Vol. III, No. 3, pp. 36-51.

Saadé, R. G., & Kira, D. (2009). Computer Anxiety in E-Learning: The Effect of Computer Self-Efficacy. Journal of Information Technology Education (Vol. 8). Retrieved from http://www.jite.org/documents/Vol8/JITEv8p177-191Saade724.pdf

Saade, Raafat & Kira, Dennis. (2006). Emotional State of Technology Acceptance Model. Issues in Informing Science and Information technology. 3. 403-409.

Salloum, S. A., & Shaalan, K. (2018, September). Factors affecting students’ acceptance of e-learning system in higher education using UTAUT and structural equation modeling approaches. In International Conference on Advanced Intelligent Systems and Informatics (pp. 469-480). Springer, Cham.

Simonson, M. R. Maurer, M. Montag-Torardi, M. and Whitaker, M. (1987). Development of a Standardized Test of Computer Literacy and a Computer Anxiety Index Journal of Educational Computing Research, 3(2), 231-247.

Sylvia, C., & Abdurachman, E. (2018). E-LEARNING ACCEPTANCE ANALYSIS USING TECHNOLOGY ACCEPTANCE MODEL (TAM) (CASE STUDY: STMIK MIKROSKIL). Journal of Theoretical and Applied Information Technology, 15, 19. Retrieved from www.jatit.org

Tatnall, A, Paull, S, Burgess, S & Davey, B 2003, Business information systems, Data Publishing, Heidelberg, Vic.

Wang, Alvin & H Newlin, Michael. (2002). Predictors of web-student performance: The role of self-efficacy and reasons for taking an on-line class. Computers in Human Behavior. 18. 151-163. 10.1016/S0747-5632(01)00042-

Yujong Hwang and Mun Y. Yi (2002). Predicting the use of Web-Based information systems: intrinsic motivation and self-efficacy. Eighth Americas conference on information system, P:1076-1081