Understanding health information disclosure intention on AI-generated content platforms: a cognition-affect-conation framework

Purpose Based on the cognition-affect-conation (C-A-C) framework, this study aims to examine the dual effect of both enablers and inhibitors on user health information disclosure intention on AI-generated content (AIGC) platforms. Design/methodology/approach The authors adopted a mixed method of structural equation modeling and fuzzy-set qualitative comparative analysis to analyze data. Findings The results show that information quality, privacy assurance and perceived empathy affect perceived trust, which promotes disclosure intention. In contrast, information overload and low transparency lead to cognitive dissonance, which inhibits disclosure intention. Research limitations/implications The results imply that AIGC platforms need to build user trust and mitigate cognitive dissonance to promote user disclosure intention and ensure sustainable development. Originality/value Prior research has often used the privacy calculus theory to investigate user disclosure intention, emphasizing a rational cognitive perspective (the trade-off between benefits and risks) while neglecting the role of affective factors in behavioral decision. Drawing on the C-A-C framework, this study reveals the formation mechanism of AIGC user health information disclosure intention.