Bibliometric analysis of scientific articles on artificial intelligence and tourism: Compliance with Bradford, Lotka and Zipf s Laws

Journal of Gastronomy Hospitality and Travel (JOGHAT), cilt.9, sa.3, ss.1418-1440, 2026 (TRDizin)

Purpose: This study aims to examine the development of scientific research on artificial intelligence and tourism, focusing on

publication patterns, collaboration structures, thematic trends and the conceptual structure of the field.

Design/methodology/approach: Bibliographic data were retrieved from the Scopus database. A total of 1,089 English-language

journal articles published between 2001 and 2025 were analysed using Microsoft Excel and Bibliometrix/Biblioshiny. The study

included performance analysis, author, institution and country-level collaboration analyses, keyword co-occurrence analysis,

thematic and conceptual mapping, three-field analysis and historical citation analysis. In addition, publication sources, author

productivity and keyword frequencies were examined according to Bradford’s, Lotka’s and Zipf’s laws, respectively.

Results: The findings reveal rapid growth in scientific output, particularly in recent years, together with a concentration of

publications in a relatively small group of journals. Bradford zoning indicates a clear core–periphery structure in publication sources,

while the Lotka analysis shows a substantially higher proportion of single-publication authors than predicted by the classical

distribution. Keyword and thematic analyses identify artificial intelligence, tourism, ChatGPT, machine learning, generative

artificial intelligence and related digital technologies as prominent elements of the field’s conceptual structure. The results also

indicate increasing thematic diversification and the growing visibility of technology-oriented research topics.

Discussion: By integrating classical bibliometric laws with contemporary science-mapping techniques, this study provides a

comprehensive assessment of the structural and conceptual characteristics of artificial intelligence and tourism research. The

findings also highlight under-explored areas and offer a basis for future studies examining the evolution of this rapidly expanding

research domain.