This study investigates the behavioral patterns, key influencing factors and configurational mechanisms of researchers' information verification behavior toward Generative AI-generated content in human-AI interaction contexts.
A mixed-methods design was adopted. First, qualitative interviews and grounded theory identified influencing factors and constructed a theoretical framework. Then, hypotheses were proposed based on coding results and tested using partial least squares structural equation modeling on 506 valid questionnaires. Finally, fuzzy-set qualitative comparative analysis identified multiple paths triggering high-level verification behavior from a configurational perspective of antecedent conditions.
The study revealed three patterns of information verification behavior: verification via authoritative sources, platform comparison and interactive optimization verification and social network verification. Structural Equation Modeling showed that information quality, perceived risk, cognitive evaluation, AI literacy, task importance and algorithmic transparency significantly affect verification behavior. Qualitative comparative analysis further identified five equivalent driving paths, with cognitive evaluation and AI literacy as core conditions across all paths. The study also found that social influence, platform reputation and interactive design limitations, while not having significant independent effects, emerged as key configurational elements, highlighting the complexity of multi-factor concurrent effects.
The widespread use of Generative AI has triggered a severe information credibility crisis, making researchers' verification behavior crucial. This study addresses this challenge, fills the gap in research on AIGC as a “black-box information source” and provides evidence for building trustworthy AI systems and usage guidelines.
