Open Access Article

A Scientometric Review of Generative Artificial Intelligence Research in Tourism

Zhiyue Chen (1) , Yu Mu (2)
1. Sichuan Agricultural University image/svg+xml
2. Sichuan Agricultural University image/svg+xml
https://doi.org/10.36923/imf.v1i1.407

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Issue
Vol. 1 No. 1 (2026): Continuous
Submitted
October 10, 2025
Accepted
December 1, 2025
Published
December 11, 2025
Keywords:
    Generative Artificial Intelligence, Tourism, CiteSpace, HistCite, Pajek, Bibliometric Analysis, Technology Adoption
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Abstract

Background: With the rapid and widespread adoption of generative artificial intelligence (GenAI) in the tourism sector, scholars have examined this technology from multiple perspectives. However, a comprehensive and systematic overview of GenAI research in tourism remains lacking. Objective: This study aims to map the current research landscape, identify research hotspots, and trace the developmental trajectory of this emerging field using visualization-based scientometric tools. Methods: Relevant literature indexed in the core collection of the Web of Science was selected as the data source. CiteSpace was used to analyse collaboration networks and keyword co-occurrence, HistCite was applied to construct citation chronologies, and Pajek was employed to identify main research paths. Results: A total of 111 publications on GenAI in tourism (2023–2025) were retrieved. The findings reveal an exponential growth in the research output and demonstrate active collaboration among authors, institutions, and countries. Existing studies are mainly focused on GenAI’s technical characteristics, acceptance of technology, and practical applications. The literature exhibits strong cross-citation patterns, and a clear developmental path of knowledge evolution has been identified. Conclusion: Research interest in GenAI within tourism is expected to continue to increase. Nevertheless, cross-institutional and interdisciplinary collaboration requires further strengthening, and the scope of research topics and perspectives needs to be broadened as the field matures. Implications: This study fills an important gap by providing the first visualization-based scientometric review of GenAI research in tourism, offering valuable insights for scholars and practitioners seeking to understand and advance this rapidly evolving field.

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Authors

Zhiyue Chen
https://orcid.org/0009-0008-6528-168X
Yu Mu
https://orcid.org/0000-0002-2341-5802
muyushelley@hotmail.com (Primary Contact)
Author Biographies

Zhiyue Chen

Zhiyue CHEN is a young researcher in the College of Management at Sichuan Agricultural University, China. Her research focuses on service innovation. She is the principal investigator for funded projects supported by the Innovation Training Planning Project of Sichuan University Students (Grant No. S202510626100) and Sichuan Agricultural University (Grant No. 20251765).

Yu Mu

Yu MU is an associate professor in the College of Management at Sichuan Agricultural University, China. She received two PhD degrees, respectively, in Science, Business and Innovation from Vrije Universiteit Amsterdam, the Netherlands, and in Business Administration from Donghua University, China. Her main research interests include service innovation and artificial intelligence, with a special focus on innovation management in healthcare and tourism. Her work appears in journals including Annals of Tourism Research, The Service Industries Journal, Total Quality Management & Business Excellence, and the Journal of Hospitality and Tourism Technology. 

Chen, Z., & Mu, Y. (2025). A Scientometric Review of Generative Artificial Intelligence Research in Tourism. Innovation Management Frontiers, 1(1), 1-16. https://doi.org/10.36923/imf.v1i1.407
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Chen, Z., & Mu, Y. (2025). A Scientometric Review of Generative Artificial Intelligence Research in Tourism. Innovation Management Frontiers, 1(1), 1-16. https://doi.org/10.36923/imf.v1i1.407

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