Integrating MOOC service quality as a second-order factor in learner engagement, satisfaction and retention model: a moderated sequential multiple mediation model

Purpose This work presents massive open online course service quality (MOOCSQ) as a second-order factor and ascertains its influence on learner engagement, satisfaction and retention using a hybrid approach. Design/methodology/approach The research framework presents a moderated sequential multiple mediation in between MOOCSQ and learner retention, with mediators as learner engagement and satisfaction and moderator as technostress. The hybrid approach comprising partial least squares structural equation modeling (PLS-SEM) and artificial neural network (ANN) performed analysis on 751 responses to test the proposed hypotheses. Findings The outcome of the PLS-SEM suggests that MOOCSQ has a positive and significant impact on retention of learners. Massive open online course (MOOC) learner engagement and satisfaction mediate the association between MOOCSQ and MOOC learner retention (MLR). Leveraging a non-linear and non-compensatory ANN approach alongside PLS-SEM analysis yields deeper insights and a more comprehensive understanding of the subject. This approach illuminates the interrelationship between MOOCSQ and MLR with predictive accuracy of 75.8%. Originality/value It also underscores the pivotal role of MOOCSQ dimensions for sustainable superior learning experience. Further, insightful directives are offered for policymakers and educators to bolster MOOCs in hyper-competitive e-learning market.