Modelling the acceptance and use of electronic learning at the University of Zululand

Neil Davies Evans, Jerry le Roux


We present a new and compelling method to help understand some of the important needs, perceptions and expectations of users of existing electronic learning (e-learning) resources at the University of Zululand by contextualising the pedagogic place in this blended tertiary learning environment of e-learning resources and confirming their acceptance by both academic staff and students. Predicting their acceptance was achieved conceptually by adopting the Unified Theory of Acceptance and Use of Technology (UTAUT) model and statistically validating its application to predict the behavioural intentions and usage behaviour of the primary users towards e-learning using a positivist epistemological belief and deductive reasoning. This paper also embraces an interpretive research paradigm to include the researchers’ views on the topic. Partial Least Squares structural equation modelling and inferential statistics predicted the level of acceptance of e-learning by academic staff (adjusted R2 = 0.41) and students (adjusted R2 = 0.39) and illustrated the strengths and significances of the postulated UTAUT relationships and their moderating effects. Academic performance gains proved to be the strongest significant influence on both sets of primary users’ intentions to use e-learning. Although the results may not be generalised to other institutions, they do contribute to UTAUT’s theoretical validity and empirical applicability to the management of e-learning-based initiatives. We argue that the high predictive accuracies found in Venkatesh et al. (2003) could be obtained if significant moderators contextualised to the education sector were added to the structural equation model, although cognisance of maintaining a parsimonious structural equation model should also be taken into consideration before inflating the coefficient of determination (R2), which is a measure of how well a data set fits a statistical model (in this case UTAUT). A R2 value of 1 indicates a perfect fit – with the observed outcomes being replicated in the model – while a R2 value of 0 indicates that the data set does not fit the model at all. R2 values closer to 1 allow more predictable future outcomes, which in this study was the acceptance of existing e-learning resources by the primary users.


e-learning, Unified Theory of Acceptance and Use of Technology (UTAUT), Partial Least Squares Structural Equation Modeling (PLS-SEM), University of Zululand, South Africa

Full Text:



Agarwal, R. and Prasad, J. 1997. The role of innovation characteristics and perceived voluntariness in the acceptance of information technologies. Decision Sciences, 28(3): 557-582.

Agyei, V. 2007. From libraries to e-learning centres: a South African library experience. World Library and Information Congress: 73rd IFLA General Conference and Council. 19-23 August 2007. Durban, South Africa.

Ajzen, I. 2008. Icek Ajzen: Homepage. [Online]. (3 July 2015).

Backhouse, J. 2013. What makes lecturers in higher education use emerging technologies in their teaching? Knowledge Management & E-learning, 5(3): 345-358.

Boere, I. and Kruger, M. 2008. Developmental study towards effective practices in technology-assisted learning: third combined report from fifteen participating South African universities. Johannesburg: University of Johannesburg.

Brown, J.D. 2011. Likert items and scales of measurement? SHIKEN: JALT Testing & Evaluation SIG Newsletter. (15)1: 10-14. [Online]. (24 November 2015).

Council on Higher Education. 2014. Framework for institutional quality enhancement in the second period of quality assurance. [Online]. (31 January 2016).

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)8: 982-1003. [Online]. (31 January 2016).

Dillon, A. 2001. User acceptance of information technology. In Encyclopedia of Human Factors and Ergonomics. W. Karwowski, Ed. London: Taylor and Francis.

Dillon, A. and Morris, M. 1996. User acceptance of new information technology: theories and models. In Annual Review of Information Science and Technology. V. 31. M. Williams, Ed. Medford NJ: Information Today. 3-32. [Online]. (8 November 2015).

E-learning Africa. 2012. E-learning Africa Report. [Online]. (23 January 2016).

E-learning Africa. 2013. E-learning Africa Report. [Online]. (30 January 2016).

Evans, N.D. 2013. Predicting user acceptance of electronic learning at the University of Zululand. [Online]. (10 November 2015).

Hair, J.F., Black W.C., Babin, B.J. and Anderson, R.E. 2010. Multivariate data analysis: a global perspective. 7th ed. New Jersey: Pearson.

Hair, J.F., Ringle, C.M. and Sarstedt, M. 2011. PLS-SEM: indeed a silver bullet. Journal of Marketing Theory and Practice, 19: 139-151. [Online]. (13 November 2015).

Hair, J.F., Hult, G.T.M., Ringle, C.M. and Sarstedt, M. 2014. A primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). London: Sage Publications.

Hayes, A.F. 2013. Introduction to mediation, moderation, and conditional process analysis: a regression-based approach. New York: Guilford.

Mansell, R. and Tremblay, G. 2013. Renewing the Knowledge Societies vision: towards knowledge societies for peace and sustainable development. [Online]. (15 November 2015).

Moses, J.W. and Knutsen, T.L. 2012. Ways of knowing. 2nd ed. New York: Palgrave Macmillan.

Muller, W. and Evans, N.D. 2009. University of Zululand E-Learning Implementation Strategy and Plan. [Online]. (20 November 2015).

Ravjee, N. 2007. The politics of e-learning in South African higher education. International Journal of Education and Development using Information and Communication Technology, 3(4): 27-41. [Online]. (23 January 2016).

Ringle, C., Wende, S. and Will, A. 2004. SmartPLS Software Version 2.0.M3. [Online]. (9 August 2013).

Siemens, G. 2004. Connectivism: A learning theory for the digital age. [Online]. (22 January 2016).

Sturges, P. 2006. Finding new ways of serving real needs: the future of information services. (Lecture presented at University of Zululand). [Online]. (15 December 2015).

Taiwo, A.A. and Downe, A.G. 2013. The theory of User Acceptance and Use Of Technology (UTAUT): a meta-analytic review of empirical findings. Journal of Theoretical and Applied Information Technology, 49(1).

Thompson, R. L., Higgins, C. A., and Howell, J. M. 1991. Personal computing: toward a conceptual model of utilization. MIS Quarterly, 15(1): 124-143.

University of Zululand. 2004. Teaching and Learning Policy. (Unpublished).

University of Zululand. 2013. Teaching Development Proposal. (Unpublished).

Urbach, N. and Ahlemann, F. 2010. Structural equation modeling in information systems research using Partial Least Squares. Journal of Information Technology Theory and Application (JITTA), 11(2). [Online]. (18 September 2013).

Venkatesh, V., Morris, M., Davis, G., and Davis, F. D. 2003. User acceptance of information technology: toward a unified view. MIS Quarterly, 27(3): 425-478. [Online]. (04 February 2016).

Zinn, S. 2009. Readiness to adopt e-learning: pioneering a course in school librarianship education. South African Journal Library and Information Science, 75 (2), 159–169.



  • There are currently no refbacks.

ISSN 2304-8263 (online); ISSN 0256-8861 (print)
Powered by OJS and hosted by Stellenbosch University Library and Information Service since 2012.


This journal is hosted by the SU LIS on request of the journal owner/editor. The SU LIS takes no responsibility for the content published within this journal, and disclaim all liability arising out of the use of or inability to use the information contained herein. We assume no responsibility, and shall not be liable for any breaches of agreement with other publishers/hosts.

SUNJournals Help

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.