What the paper studied
As AI-based services proliferate in hospitality and tourism (chatbots, service robots, AI assistants), service failures — and how AI systems recover from them — have become a critical concern. Systematic reviews of AI-based service recovery in this space, however, remain scarce. Following PRISMA guidelines, the authors screened 78 empirical studies from 2018 to 2025 published in SSCI/SCI-indexed or top-tier hospitality and tourism journals, and applied the TCCM (Theory, Context, Characteristics, Methodology) framework to synthesize their theoretical foundations, research contexts, focal variables, and methodological designs.
Key findings
- Research on AI-based service recovery is fragmented across contexts, constructs, and methodological designs.
- The literature is dominated by scenario-based experiments, with less real-world field evidence.
- The TCCM synthesis surfaces both what is well studied and clear understudied areas that need targeted exploration.
Why it matters for hospitality and tourism
AI is now front-line: chatbots handle bookings and complaints; service robots deliver food and check guests in. When those systems fail, how they recover shapes customer trust, repurchase intent, and word-of-mouth. This review gives operators and researchers a map of what the evidence base actually supports — and where practitioners are still flying blind because the research is fragmented or lab-bound.
Practical takeaways
- Treat AI service recovery as a design discipline, not an afterthought — the review shows recovery strategy meaningfully shapes customer response.
- Be cautious generalizing from scenario-based studies to live operations; field evidence is thinner than the volume of published research suggests.
- Use scenario-specific recovery playbooks (chatbot vs. service robot vs. voice assistant; hotel vs. restaurant vs. travel agency) rather than a one-size-fits-all recovery flow.