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

连续性 心理学 结构方程建模 可用性 技术接受模型 社会心理学 焦虑 计算机科学 人机交互 精神科 机器学习
作者
Md. Al Amin,Md Razib Alam,Mohammad Zahedul Alam
出处
期刊:E-learning and Digital Media [SAGE]
卷期号:20 (3): 224-254 被引量:24
标识
DOI:10.1177/20427530221103915
摘要

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.

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