Self-Disclosure Intention in Human-AI Co-creation: The Dual Drivers of Self-Congruence and AI Attachment

This study employs a mixed-methods design - two 2 × 2 between- subjects experiments and a PLS-SEM analysis (N = 639) to examine the dual pathways driving user self-disclosure in Human-AI co-creation. Results indicate that Self-congruence and AI attachment not only directly promote disclosure intention but also exert an indirect mediating effect by enhancing perceived value and reducing perceived privacy risks. The study further found that self-congruence has an inverted U-shaped effect on perceived value, and that privacy self-efficacy positively moderates the relationship between self-congruence/AI attachment and perceived value, while negatively moderating the relationship between self-congruence/AI attachment and perceived privacy risk. This study integrates Privacy Calculus, Self-congruence, and Atachment Theory in Human-AI co-creation scenarios, revealing the “over-personalization paradox” and deepening our understanding of privacy tradeoffs in Human-AI collaboration. It also provides empirical evidence and practical implications for personalized adaptation, the establishment of user trust, and privacy feature development in AI collaborative design.