Will users keep using generative AI An integrated theoretical approach

This research investigated the key facilitators and inhibitors shaping users' attitudes and continuance intention towards Generative Artificial Intelligence. Drawing on the 'Elaboration Likelihood Model' (ELM) and 'Status Quo Bias' theory, the study developed an integrated conceptual framework. The proposed model was empirically validated using 'partial least squares structural equation modeling (PLS-SEM)'. The outcomes showed that 'perceived anthropomorphism', 'perceived credibility', 'perceived interaction quality', 'peer endorsement', and 'expert endorsement' exerted substantial positive effects on continuance intention, whereas 'perceived inertia', 'perceived threat', and 'perceived regret avoidance' had significant negative effects. By incorporating both enabling and barrier perspectives, this research extends the current literature on Generative AI continuance and provides managerial insights for platform developers, business organisations, practitioners, and policymakers seeking to foster sustained user engagement of Generative AI.