Travelers’ intentions to adopt travel AI chatbot services: an integrated multi-theory and multi-method analysis

Purpose This study aims to explore factors influencing travelers’ intentions to adopt artificial intelligence (AI) chatbots, focusing on both individual effects and configurational patterns. Design/methodology/approach An integrated framework combining the unified theory of acceptance and use of technology (UTAUT), affordance theory and self-determination theory (SDT) was tested using partial least squares structural equation modeling (PLS-SEM), necessary condition analysis (NCA) and fuzzy-set qualitative comparative analysis (fsQCA). Findings PLS-SEM indicates that performance expectancy, effort expectancy, interactivity, credibility, anthropomorphism and autonomy significantly influence adoption intention. NCA identifies performance expectancy, effort expectancy and anthropomorphism as necessary conditions. fsQCA reveals multiple sufficient configurations leading to high or low intention. Originality/value To the best of the authors’ knowledge, this study is the first to integrate autonomy and competence from SDT with UTAUT and affordance theory in travel AI chatbot adoption, applying NCA and fsQCA to reveal necessary and configurational conditions, thereby offering richer theoretical and practical insights.