Antecedents of students’ e-learning continuance intention during COVID-19: An empirical study

• Published in: E-Learning and Digital Media Vol. 20; no. 3; pp. 224 - 254
• Main Authors: Al Amin, Md, Razib Alam, Md, Alam, Mohammad Zahedul
• Format: Journal Article
• Language: English
• Published: London, England SAGE Publications 01.05.2023
• Subjects: COVID-19; Electronic Learning; Fear; Intention; Original; Pandemics; Predictor Variables; Social Isolation; Structural Equation Models; Student Satisfaction; Usability; social isolation; government supports; e-learning; cyberchondria; Fear of academic irregularity; institutional supports; digital discrimination
• ISSN: 2042-7530; 2042-7530
• DOI: 10.1177/20427530221103915

This study aims at exploring the underlying determinants influencing students' continuance intention to use an e-Learning platform during the COVID-19 pandemic. Based on the technology acceptance model and expectation-confirmation model, the study investigated the role of contextual (i.e., social isolation), psychological (academic year loss and cyberchondria), and student support-related (government and institutional supports) determinants on students' continuance intention to use an e-Learning platform during the pandemic. The study collected data from 440 respondents and analyzed those with Structural Equation Modeling. The findings showed that an e-Learning continuance intention during the pandemic is affected by usefulness, ease of use, attitudes, and intention to use the e-Learning platform; while the behavioral intention is influenced by usefulness, ease of use, attitudes, contextual, psychological, and student support-related determinants; and attitudes are impacted by usefulness and ease of use. Moreover, usefulness is predicted by confirmation of expectation; e-satisfaction is forecasted by usefulness and confirmation of expectation; whereas, cyberchondria is influenced by social isolation; fear of academic year loss is influenced by cyberchondria. Finally, intention to use mediated the impact of usefulness, ease of use, attitudes, contextual, psychological, and student support-related determinants on continuance intention. The study contributes to e-Learning literature incorporating contextual, psychological, and student support-related determinants into the technology acceptance model and expectation-confirmation model, which guide policymakers to understand how all levels of students can be brought into the e-Learning platforms that eventually help to eliminate digital discrimination barrier in the academia during any emergency. The policymakers must be careful in designing eLearning platforms since students' e-learning continuance intention may vary due to unprecedented crises, such as COVID-19.