Developing an expanded Technological Acceptance Model for evaluating e-Learning in the Sub-Saharan African environment

The Technological Acceptance Model was originally developed in the United States of America, which is culturally different, from Sub-Saharan Africa. Applying the existing Technological Acceptance Model to evaluate technological applications intended for the SubSaharan African environment, is likely to give inaccurate results because of the cultural dissimilarities and the diverse socio-cultural composition of Sub-Saharan Africa. As a way to improve accuracy of results, this research reviewed relevant literature and applied a mixed methodology to gather data from 308 students from five public universities in five countries across the five Sub-Saharan African regions (North, South, East, West and Central) on the use of e-learning in universities. Upon analyses of the data through Cronbach‘s α measure, Kaiser-Meyer-Olkin‘s measure, Bartlett‘s test of Sphericity, confirmatory factor analysis and descriptive statistics, an extension of the original technology acceptance model was developed. The extended model has seven constructs: Perceived Ease of Use, Perceived Usefulness, Perceived Performance, Perceived Benefits, External Factors, Behavioural Intention, and Technological Acceptance. Four of these constructs (Perceived Ease of Use, Perceived Usefulness, Perceived Performance and Perceived Benefits) directly influence Behaviour Intention. In consonance with previous findings in literature findings, Perceived Usefulness rated higher than Perceived Ease of Use. Perceived Benefit rated the lowest among the four constructs. The research further confirms previous findings that Perceived Ease of Use influences Perceived Usefulness. Additionally, this study found that External Factors directly influence Perceived Usefulness, Perceived Ease of Use, Perceived Performance and Perceived Benefit. Amongst these, External Factors influence Perceived Benefit most, followed by Perceived Ease of Use, Perceived Performance, and lastly Perceived Usefulness. Last, but not least, the research further found that Behaviour Intention influences Technological Acceptance positively. Considering that this research collected data from only five countries in Sub-Saharan Africa to develop and test the model, caution needs to be taken when generalising the research findings beyond the said population and technology considered in the research. Future research on technological acceptance may refine the suggested expanded model to explain further, the variance in students‘ Behaviour Intention, Perceived Ease of Use, Perceived Benefit, Perceived Usefulness and Perceived Performance and also to examine the performance of the suggested expanded model to explain the different technology acceptance behaviours in the information technology field