Determinants of information verification behaviour among generative AI users: an empirical analysis based on SEM and fsQCA

Purpose Against the backdrop of the widespread application of generative artificial intelligence (AI) and the resulting challenges of information authenticity, this study aims to focus on users’ information verification behaviour when facing AI-generated content. It explores the influencing factors and underlying mechanisms of such behaviour, which is of great significance for enhancing public information literacy, improving the information ecology of AI platforms and fostering healthier information environments. Design/methodology/approach A mixed-methods approach is adopted, combining structural equation modelling (SEM) to test the net effects of individual variables and fuzzy-set Qualitative Comparative Analysis (fsQCA) to identify complex, synergistic configurations leading to high verification behaviour. Findings The SEM results indicate that self-efficacy, risk perception, algorithmic literacy, technological facilitation and subjective norms have significant positive effects on information verification behaviour, while information quality and platform reputation exert significant negative effects. The effects of information presentation features and algorithmic transparency are not significant. Furthermore, fsQCA identifies six high-coverage configurational paths, which can be categorised into three types: cognitive-technology enhancement, composite synergy-driven and norm-risk guided. Originality/value For the first time, this study combines information ecology theory with mixed-methods (SEM + fsQCA). Breaking through traditional single-factor analysis, it deepens our understanding of the synergistic mechanisms among information, humans, technology and environment, demonstrating both theoretical and methodological innovation.