Abstract
As artificial intelligence (AI) technologies proliferate across the hospitality sector, urban boutique hotels face the distinct challenge of automating guest communications while preserving their hallmark personalized, high-touch brand identity. While prior research predominantly examines AI chatbots during the pre-purchase and booking stages, limited empirical focus has been placed on the post-purchase phase. This study evaluates the effectiveness of AI-driven chatbots on post-purchase service quality, guest satisfaction, and brand loyalty in urban boutique hotels. Employing Partial Least Squares Structural Equation Modeling (PLS-SEM) on survey data collected from 412 international guests across boutique properties in major Western European metropolitan areas, we test a structural model incorporating functional reliability, responsiveness, personalization, and perceived empathy. The findings indicate that functional reliability and responsiveness are the strongest direct predictors of post-purchase service quality, whereas perceived empathy exerts a minor influence. Furthermore, post-purchase service quality significantly mediates the relationship between chatbot performance dimensions and long-term brand loyalty. These results provide practical strategies for boutique hotel managers aiming to deploy hybrid digital-human service models without compromising boutique brand equity.
