Risk perception and trust mechanisms in AIGC technologies: evidence from a Bayesian SEM analysis

Purpose This study aims to explore the mechanisms underlying public risk perception and trust in artificial intelligence-generated content (AIGC) technologies. It seeks to clarify how these factors influence behavioral intention and risk prevention sensitivity, thereby informing responsible governance of emerging digital technologies. Design/methodology/approach Guided by the UTAUT2, SARF and TPB frameworks, a conceptual model was developed to examine the interrelations among risk perception, system trust, degree of risk trust, behavioral intention and risk prevention sensitivity. A Bayesian structural equation modeling (BSEM) was used to analyze data collected from 1,185 respondents in four cities across Jiangsu Province, China. Findings The results reveal that increased risk perception can enhance public trust in governance systems, especially when supported by technical transparency and institutional safeguards. However, higher risk prevention sensitivity may inhibit the intention to adopt AIGC technologies. The study emphasizes the importance of a governance framework incorporating transparency, adaptive regulation and cross-sector collaboration. Originality/value This study establishes a detailed paradigm for Bayesian structural equation modeling in socio-technical research and provides empirical evidence to support transparent and trustworthy governance. It contributes to the development of a multi-stakeholder model for the responsible advancement of AIGC technologies.