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

Abstract

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.