An Integrated Model to Predict Students' Online Learning Behavior in Emerging Economies: A Hybrid SEM-ANN Approach

Purpose The online learning environment is a function of dynamic market forces constantly restructuring the e-learning landscape's complete ecosystemcape. This study aims to propose an e-learning framework by integrating the Technology Acceptance Model (TAM) and Theory of Planned Behaviour (TPB) to predict students' Online Learning Readiness and Behaviour. Design/methodology/approach A structured questionnaire was used to collect data from 406 students through a survey. The data were analysed using two-stage structural equation modelling and artificial neural network (ANN). Findings The study's results revealed that perceived ubiquity (PUB) positively influences perceived ease of use, usefulness and attitude. Similarly, perceived mobility significantly influences perceived ease of use and attitude. Furthermore, attitude, subjective norms, perceived behavioural control and perceived usefulness significantly influence readiness to learn online, which further influences students' online learning behaviour. The root-mean-square error (RMSE) values obtained from the ANN analysis indicate the models' predictive solid accuracy. Originality/value The study contributes to the existing literature by proposing an Online Learning Behaviour Model by integrating the TAM and the TPB frameworks in association with two additional constructs, PUB and Perceived Mobility. Secondly, this study proposes a unique triangulation framework of recommendations for learners, educators and policymakers.