Quality, norms and privacy in AI chatbot adoption: a multigroup analysis

Purpose This study aims to examine the factors influencing travelers’ adoption of AI chatbots in the tourism and hospitality sector by positioning system quality dimensions as antecedents of behavioral intention (BI). Design/methodology/approach A theoretical framework was developed by integrating the DeLone and McLean Information Systems (IS) success model with an extended theory of planned behavior (TPB) that incorporates personal norms and privacy concerns. Survey data were collected from 738 travelers with recent chatbot interaction experience and analyzed using partial least squares structural equation modeling (PLS-SEM) and multigroup analysis (MGA). Findings The results indicate that information quality service quality and system quality each significantly positively affect perceived behavioral control perceived behavioral control (PBC) attitudes and subjective norms. Among these predictors PBC emerges as the strongest determinant of BI followed by attitude and subjective norms whereas personal norms demonstrate a relatively modest effect. Privacy concerns function as a significant boundary condition negatively moderating the relationships between PBC and intention as well as between attitude and intention whereas normative influences remain unaffected. MGA further revealed that gender age and usage frequency meaningfully shape key adoption pathways. Practical implications Tourism and hospitality providers should prioritize usability and control-enhancing features to strengthen PBC, alongside strategic investments in information, service and system quality. Transparent privacy mechanisms and segment-specific engagement strategies are essential for mitigating privacy concerns and promoting chatbot adoption across diverse user groups. Originality/value This study advances technology adoption research by synthesizing IS quality dimensions with the TPB in the context of AI chatbot adoption. It offers novel insights into how privacy concerns and demographic factors condition user behavior, providing actionable guidance for the design of user-centric and trustworthy conversational AI systems in tourism and hospitality.