Predicting the determinants of online learning adoption during the COVID-19 outbreak: a two-staged hybrid SEM-neural network approach

Abstract

Purpose This study aims to study the adoption of online learning in higher education through the perspective of the readiness of the following factors: self-directed learning (SDL), motivation for learning (ML), online communication self-efficacy (OCE) and learner control (LC). This was an empirical study in the context of developing countries, specifically Thailand. Design/methodology/approach This research applied a quantitative study method by collecting data from 605 higher education students in autonomous government institutions. The data analysis applied a structural equation model (SEM) to identify the significant determinants that affected the adoption of online learning. Moreover, this study applied a neural network model to examine the findings from the SEM. Findings From the data analysis using the SEM and neural network model, the results matched each other. The results of the empirical study were firm and supported that the readiness factors of students had statistical significance in the following order: SDL, OCE, LC and ML. Practical implications The study results showed an operational perspective to be prepared for online teaching, both for the related department of the Ministry of Education to support the infrastructure for online learning and for universities and instructors to create learning conditions and design teaching processes consistently with the online learning context. Originality/value Since the learning management in the 21st century is focused on student-centred learning, the empirical results obtained from this study presented the view of learners’ readiness that would influence the acceptance of online learning. In addition, this research presented the challenges and opportunities of online instruction during the COVID-19 pandemic.