As Artificial Intelligence (AI) technologies increasingly mediate service encounters in tourism, understanding how AI-enabled interactions shape tourist trust has become a pressing issue. Drawing on social exchange theory and justice theory, this study investigates the mechanisms and boundary conditions through which AI service quality influences tourist trust. Using the AI digital human Wang Bo at the Tengwang Pavilion scenic area as an empirical case, the research develops and tests a model in which perceived personalization and interactional justice act as parallel mediators, while privacy sensitivity and brand reputation serve as moderators. Results from structural equation modeling show that AI service quality significantly enhances both perceived personalization and interactional justice, which in turn foster tourist trust. Perceived personalization and interactional justice each play significant mediating roles, indicating that tourists’ trust in AI arises not merely from technical performance but from perceptions of individualized care and fair treatment. Among the moderating effects, privacy sensitivity attenuates the relationship between AI service quality and interactional justice but does not affect the link between AI service quality and perceived personalization. Brand reputation strengthens the impact of perceived personalization on trust but does not significantly moderate the relationship between interactional justice and trust. Overall, the findings reveal dual pathways—personalization-based and fairness-based—through which AI service quality builds trust, highlighting the interplay between technological excellence, human-like social attributes, and contextual factors in shaping tourists’ trust toward AI-enabled services. This study advances theoretical understanding of AI-mediated service relationships and offers practical insights for designing trustworthy AI experiences in tourism contexts.
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Li et al. (2026) studied this question.
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