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<p>The growing deployment of AI-based services in hospitality and tourism inevitably entails service failures, making service recovery a critical concern. Yet systematic reviews on AI-based service recovery remain scarce in this rapidly evolving field. Following PRISMA guidelines, we screened 78 empirical studies (2018–2025) on AI-based service recovery published in SSCI/SCI-indexed or top-tier hospitality and tourism journals, and applied the TCCM framework to systematically synthesize the included literature’s theoretical foundations, research contexts, focal variables and methodological designs. Our analysis shows that research on AI-based service recovery remains fragmented across contexts, constructs and methods, and is dominated by scenario-based experiments. This study provides a comprehensive overview of the field, proposes TCCM-informed future research directions, offers practical guidance for implementing scenario-specific AI service recovery strategies, and calls for targeted exploration of understudied areas.</p>

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A Systematic literature review of AI-based service failure and service recovery in hospitality and tourism industries using the TCCM framework

  • 期刊论文

  • 发表日期：

    2026年7月27日

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  • Yu Zhang,
  • Yue Yuan

摘要

The growing deployment of AI-based services in hospitality and tourism inevitably entails service failures, making service recovery a critical concern. Yet systematic reviews on AI-based service recovery remain scarce in this rapidly evolving field. Following PRISMA guidelines, we screened 78 empirical studies (2018–2025) on AI-based service recovery published in SSCI/SCI-indexed or top-tier hospitality and tourism journals, and applied the TCCM framework to systematically synthesize the included literature’s theoretical foundations, research contexts, focal variables and methodological designs. Our analysis shows that research on AI-based service recovery remains fragmented across contexts, constructs and methods, and is dominated by scenario-based experiments. This study provides a comprehensive overview of the field, proposes TCCM-informed future research directions, offers practical guidance for implementing scenario-specific AI service recovery strategies, and calls for targeted exploration of understudied areas.