Purpose This study aims to systematically review empirical research on how artificial intelligence (AI) shapes customers’ emotional experiences in hospitality and tourism. It integrates psychological, technological and social perspectives to address disjointed insights and develop a holistic understanding of AI-mediated emotions. Design/methodology/approach Following the preferred reporting items for systematic reviews and meta-analyses protocol, this review analyzes 159 empirical studies on emotional responses to AI in hospitality and tourism. It examines the theoretical frameworks, measurement methods and contextual factors used to explain customer emotions in AI interactions. Findings Customers’ emotional experiences with AI arise from the interplay among interface design, individual traits and service context. The literature remains constrained by methodological uniformity, limited field-based data and insufficient attention to cultural and contextual variation. Theoretical perspectives are often applied in isolation, underscoring the need for integrative models that link cognition, AI features and social interaction. Practical implications Designing emotionally intelligent AI requires enhancing user control, ensuring transparency and adapting to cultural and situational cues to strengthen trust. Social implications Emotionally responsive AI can enhance customer hedonic well-being by creating more pleasurable service experiences that generate positive emotional value. Originality/value This paper provides an interdisciplinary integration bringing together theoretical and empirical insights on customer emotions in AI-mediated services. It organizes findings into a conceptual model and introduces a fuzzy emotion approach capturing the fluid, context-sensitive nature of emotion. By identifying research gaps, it offers a roadmap for future theoretical advancement and the development of emotionally responsive AI systems.
Lytle et al. (Mon,) studied this question.