Experimental Dataset on Generative AI Risk disclosure and User Adoption

As generative artificial intelligence (AI) accelerates travel-planning efficiency and model innovation, the latent risks embedded in AI-generated content have become a pivotal determinant of user trust and adoption. This research zeroes in on how proactive AI risk disclosure shapes user behavior. We test our predictions through four studies. Study 1 isolates the main effect: the mere presence (vs. absence) of AI risk disclosure on adoption intention. Study 2 manipulates the framing of the AI risk disclosure and examines a chained mediation—perceived accuracy risk → ability-based trust → adoption behavior. Studies 3 and 4 unpack boundary conditions, showing how uncertainty avoidance (Study 3) and explainability (Study 4) moderate the disclosure→adoption linkage. Studies 1–4 comprise raw data; the first row of each table lists the questionnaire items.