Key points are not available for this paper at this time.
Purpose As generative AI technologies increasingly mediate user interactions and services, trust in these systems has become a key determinant of user engagement and data-sharing behavior. This study investigates trust formation mechanisms driving users’ willingness to disclose personal information to generative AI, identifying key trustworthiness dimensions and examining their impact on perceived trust and disclosure intention. Design/methodology/approach Two empirical studies were conducted. Study 1 employed exploratory and confirmatory factor analyses (N = 557) to develop a multidimensional trustworthiness scale, while Study 2 applied structural equation modeling (N = 251) to assess how these factors affect trust in AI and subsequent disclosure intention. Findings Six trustworthiness factors were identified under competence (intelligence, company reputation and technical security) and warmth (psychological security, responsibility and comfort) dimensions. Psychological security, responsibility and technical security significantly predicted users’ trust in generative AI, which strongly influenced intention to disclose personal information. Originality/value By focusing on the multidimensional nature of AI trustworthiness, this study identifies key antecedents and psychological mechanisms underlying users’ trust in generative AI and disclosure behavior. It contributes to the literature by developing and validating a reliable scale for measuring the trustworthiness of generative AI systems. The findings highlight that trust is shaped not only by technical factors such as technical security but also by perceived moral dimensions, including psychological security and responsibility, offering practical implications for designing AI systems that are both functionally robust and ethically reassuring. Peer review The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-07-2025-0550
Kim et al. (2026) studied this question.