Factors influencing behavioral intention to use e-learning in higher education during the COVID-19 pandemic: A meta-analytic review based on the UTAUT2 model

Abstract

Abstract Amidst the COVID-19 pandemic, the e-learning demand among in tertiary education sector has surged, which has produced prolific research on factors influencing students’ and faculties e-learning adoption. Anchored in the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) framework, this study employed a meta-analytic approach to investigate the effects of seven key antecedents (i.e., Performance Expectation, Effort Expectation, Social Influence, Facilitating Conditions, Hedonic Motivation, Price Value, and Habit) and possible moderators on Behavioral Intention (BI) towards using e-learning. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, the study identified 91 empirical studies involving 37,910 participants including both university faculties and students. The results show that Habit was the most influential antecedent on BI. Apart from Habit, Hedonic Motivation, Price Value, Performance Expectation, and Facilitating Conditions were strongly correlated with BI towards using e-learning, whereas Effort Expectation, Social Influence, and BI had moderate relations with BI. The moderation analyses demonstrate that the variables of gender, user type, region, cultural orientation, and income level all significantly moderated the relations between various antecedents and BI. The study results provide some practical implications on how e-learning providers or institutions may more effectively improve e-learning adoption among faculties and students. Possible strategies may include designing strategies to enhance habit formation of users, leveraging hedonic motivation by incorporating interactive and engaging contents, and offering technical support and cost-effective e-learning platforms. Furthermore, strategies which are designed to foster positive e-learning adoption should also be tailored to accommodate diverse learner profiles by taking the moderating factors of gender, cultural backgrounds, and economic disparities, ultimately leading to more equitable and inclusive e-learning in higher education.

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