Examining generative AI user continuance intention based on the SOR model

The purpose of this research is to examine generative artificial intelligence (AI) user continuance intention based on the stimulus-organism-response model.

We adopted a mixed method of structural equation modeling and fuzzy-set qualitative comparative analysis to conduct data analysis.

The results found that generative AI content quality (perceived personalization, perceived accuracy and perceived credibility) and system quality (perceived interactivity, perceived anthropomorphism and perceived intelligence) affect sense of empowerment and satisfaction, both of which further determine continuance intention.

Extant research has identified the effect of flow, trust and parasocial interaction on generative AI user continuance, but it has seldom disclosed the internal decisional process of generative AI user continuance intention. This research tries to fill this gap, and the results enrich the extant research on generative AI user continuance.