Form AI-generated information to tourist booking intention: an integrated TAM-SOR perspective

Abstract

The rapid development of Generative Artificial Intelligence (GenAI) is transforming how travelers seek information and make travel decisions. Despite its growing adoption, limited research explains how AI-generated information translates into actual booking behavior in tourism. This study investigates the effects of GenAI search tools on tourists’ booking intentions through an integrated framework combining the Technology Acceptance Model (TAM), Stimulus-Organism-Response (S-O-R) paradigm, and Cognitive Load Theory (CLT). Using a quantitative research design, data were collected through an online survey of 357 Vietnamese tourists who use GenAI for travel purposes. Partial Least Squares Structural Equation Modeling (PLS-SEM) was employed to test the proposed framework and hypotheses. The findings indicate that Perceived AI Intelligence and Cognitive Load Reduction do not directly influence booking intentions. Instead, Perceived Value represents a central psychological mechanism, fully mediating the effect of AI intelligence and partially mediating the effect of perceived ease of use on booking intentions. Task Complexity also negatively moderates the relationship between cognitive load reduction and booking intentions. These results suggest that technological sophistication and cognitive relief alone are insufficient to stimulate booking behavior. Tourism marketers should therefore effectively emphasize meaningful value, actionable AI-generated information, and human support for complex and high-stakes travel planning decisions.