Mapping generative AI research in tourism and hospitality

Abstract

Generative AI is reshaping the tourism and hospitality (T&H) industry. This study synthesises existing research, identifies key trends and challenges, and proposes a framework to guide future research. Using the SPAR-4-SLR protocol, we triangulate a systematic literature review with bibliometric mapping, qualitative content analysis, and lexicometric analysis of 83 peer-reviewed articles. We introduce and apply an extended ABCDE + D–TCM framework, integrating Antecedents, Barriers, Drivers, Decisions, and Effects with Theories, Contexts, and Methods to provide a structured, theory-grounded analysis. Findings reveal five dimensions of GenAI adoption in T&H: antecedents (e.g. personalisation demand), barriers (e.g. trust issues), drivers (e.g. perceived usefulness), decisions (stakeholder adoption), and effects (e.g. enhanced experiences). Research is largely customer-focused, quantitative, and concentrated in the United States, China, and South Korea, with limited attention to employees, organisations, and underrepresented regions. This is the first study to propose and apply the extended ABCDE + D–TCM framework, and offers a structured, multi-dimensional lens to assess how GenAI is understood and applied across tourism and hospitality research.