The impact mechanism of Generative AI on tourists' travel decision-making: An integrated SOR-TTF-trust transfer model

Abstract

This study aims to examine how Generative Artificial Intelligence (AIGC) influences tourists' travel decision-making by investigating the impact of AIGC's technological characteristics on behavioral intentions through psychological mediating mechanisms, while exploring the boundary role of technology readiness. Adopting long-distance and complex travel planning as a simulated decision-making scenario, this study constructs an integrated Stimulus–Organism–Response (SOR), Task–Technology Fit (TTF), and Trust Transfer theoretical framework. A scenario-based simulation survey was conducted among mainstream AIGC tool users, yielding 374 valid responses. Structural equation modeling was employed to test the hypothesized relationships among Perceived Anthropomorphism, Information Quality and Task Fit, Cognitive Trust, Value Co-creation Experience, Perceived Decision Efficiency, Technology Readiness, and Behavioral Intention. The findings reveal that Perceived Anthropomorphism and Information Quality and Task Fit are core antecedents stimulating tourists' positive psychological responses. Cognitive trust, value co-creation experience, and perceived decision efficiency significantly mediate the relationships between technological characteristics and behavioral intentions, confirming tourists' role shift from passive information receivers to active itinerary co-designers. Notably, technology readiness negatively moderates the Information Quality and Task Fit → Perceived Decision Efficiency path, confirming a technology compensation effect whereby AIGC more effectively empowers groups with lower technology readiness. These findings offer actionable guidance for online travel platforms in designing anthropomorphic, task-fitted AIGC tools and leveraging the technology compensation effect to bridge the digital divide for digitally disadvantaged tourist groups.