E-Learning Adoption and Use in Higher Education: Evidence from Zimbabwe

E-Learning adoption and use by university students have become prevalent worldwide, but developing nations still need to catch up. This study aims to establish critical paths amongst determinants of “behavioural Intention” and “use behaviour” in eLearning use and adoption in Higher Education in Zimbabwe using the modified Unified Theory of Acceptance and Use of Technology. The PLS-SEM method was used to evaluate the modified unified theory of acceptance and use of technology path model. A sample of 520 university students was used to collect data using an online survey created on Google Forms. The findings show that “Habit” had the most influence (0.804) on “Behavioural Intention,” followed by “Performance Expectancy” (0.319) and “Effort Expectancy” (0.270). Behavioural Intention had a significant influence (0.831) on “Use Behaviour.” The path model explains 88.8% of “Behavioural Intention" and 76.1% of “Use Behaviour” variances. Though limited, this study is significant to students in higher Education, policymakers and researchers, given the importance of technology in the education sector.

Publication Details

Journal
Open Access Journals at BC (Boston College)
Published
2025-06-08
Primary Topic
Technology Adoption and User Behaviour
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article

E-Learning Adoption and Use in Higher Education: Evidence from Zimbabwe

Open Access Journals at BC (Boston College)
Technology Adoption and User Behaviour
article

E-Learning Adoption and Use in Higher Education: Evidence from Zimbabwe

article en

Abstract

E-Learning adoption and use by university students have become prevalent worldwide, but developing nations still need to catch up. This study aims to establish critical paths amongst determinants of “behavioural Intention” and “use behaviour” in eLearning use and adoption in Higher Education in Zimbabwe using the modified Unified Theory of Acceptance and Use of Technology. The PLS-SEM method was used to evaluate the modified unified theory of acceptance and use of technology path model. A sample of 520 university students was used to collect data using an online survey created on Google Forms. The findings show that “Habit” had the most influence (0.804) on “Behavioural Intention,” followed by “Performance Expectancy” (0.319) and “Effort Expectancy” (0.270). Behavioural Intention had a significant influence (0.831) on “Use Behaviour.” The path model explains 88.8% of “Behavioural Intention" and 76.1% of “Use Behaviour” variances. Though limited, this study is significant to students in higher Education, policymakers and researchers, given the importance of technology in the education sector.

Open Access Journals at BC (Boston College)
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Technology Adoption and User Behaviour
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