Use AI chatbots towards smart tourism

Purpose This study aims to explore tourists’ intentions to use artificial intelligence chatbots in tourism by integrating the Stimulus–Organism–Response (S-O-R) framework with affordance theory and the theory of consumption values. It conceptualizes chatbot affordances as stimuli that influence perceived functional, social and emotional values, which, in turn, affect usage intention. Design/methodology/approach A mixed-method approach was used, integrating partial least squares structural equation modeling (PLS-SEM) and fuzzy-set qualitative comparative analysis (fsQCA). Data were collected from 401 Taiwanese tourists via a structured online survey. Findings PLS-SEM results show that visibility, interactivity, information association and privacy/security affordances significantly enhance perceived values, which all positively influence usage intention. Notably, interactivity does not affect social value. fsQCA reveals multiple causal pathways to high or low usage intentions, with information association consistently emerging as a core condition in high-intention configurations. These findings have strategic implications for user segmentation in chatbot design: immediacy and interactivity attract younger users, while privacy and security appeal to risk-averse ones, guiding tailored affordances. Originality/value This study offers theoretical and methodological innovation by extending the S-O-R model with affordance and value theories and using both symmetrical (PLS-SEM) and configurational (fsQCA) analyses. The findings contribute to smart tourism literature and provide practical insights for chatbot design and strategic deployment in the tourism sector.