How does AI chatbot-generated information influence destination trust and purchase decisions in tourism?

Purpose This study examined the influence of AI chatbot-generated information in tourist purchase decisions (PDs). Grounded in the Stimulus–Organism–Response theory and the Elaboration Likelihood Model (ELM), this study highlights the roles of perceived value (PV), adoption, authenticity and accessibility in shaping destination trust (DT) and PDs. Design/methodology/approach Data were gathered through online and in-person surveys using purposive sampling of AI chatbot users. The study employed PLS-SEM to comprehensively analyze key variable relationships. Findings The findings reveal that the PV, adoption, authenticity and accessibility of AI chatbot-generated information positively impact DT, which in turn strengthens tourists' PDs. This highlights the critical role of chatbot effectiveness in shaping consumer confidence and travel choices. Practical implications The findings offer valuable insights for tourism marketers, destination managers and AI developers in optimizing chatbot design and communication strategies to enhance consumer engagement and influence tourist decision-making. Originality/value This study examines how AI chatbot-generated information influences tourists' decision-making, focusing on its PV, adoption, authenticity and accessibility, and how these factors relate to DT and PDs. Grounded in the Stimulus-Organism-Response theory and the ELM, it addresses the research gap in understanding AI chatbots' role in shaping travel PDs amidst the tourism industry's shift toward technology-driven solutions.