Survey on the Experience of Tourism Planning with Generative AI (AIGC)

This study, titled "The Impact Mechanism of Generative AI (AIGC) on Tourist Travel Decision-Making and Experience Reshaping," employs the Stimulus-Organism-Response (SOR) framework, integrated with Task-Technology Fit (TTF) and Trust Transfer theories, to investigate the mechanisms through which generative artificial intelligence influences travel decision-making. Research Hypotheses: A multi-layered hypothesis model was constructed. The H1 series proposed that AIGC's technological features—specifically, perceived anthropomorphism and task fit—positively influence tourists' psychological perceptions (cognitive trust, value co-creation experience, and perceived decision efficiency). The H2 series posited direct positive effects of these psychological perceptions on behavioral intention. The H3 series examined the mediating roles of these psychological variables between technological features and behavioral intention. Finally, the H4 series introduced technology readiness as a moderator for specific paths. Data Content and Collection: Data were collected via a scenario-based survey, yielding 374 valid responses. The questionnaire, designed using a 7-point Likert scale, measured 7 core constructs through 20 items. The sample is characterized by a young (82.1% aged 18-34), highly educated (68.2% hold a bachelor's degree or above), and technologically adept profile (80.5% are frequent AI users), enhancing its relevance for the research context. The survey employed a scenario simulation method, where respondents engaged with a "5-day complex travel planning" task, improving ecological validity. Key Findings: All main effect hypotheses (H1, H2) were supported. The mediating roles of psychological variables (H3) were confirmed, validating the "technology feature → psychological perception → behavioral intention" pathway. A significant and counterintuitive finding emerged in the moderation analysis: technology readiness negatively moderated the relationship between task fit and perceived decision efficiency. This suggests a technology compensation effect, where AIGC provides stronger efficiency gains for users with lower digital literacy. Data Interpretation and Usage: Analysis was conducted using Structural Equation Modeling (AMOS 28.0). Path coefficients, Bootstrap mediation tests, and interaction term analysis serve as the primary basis for interpretation. This dataset is valuable for researchers examining the psychological mechanisms of AIGC in tourism, validating theoretical constructs like anthropomorphism and value co-creation in smart tourism contexts, and exploring the potential of AI to bridge the digital divide. Caution is advised when generalizing findings due to the sample's demographic skew; the complex moderating role of technology readiness warrants further investigation.