Document Type

Article

Abstract

Machine learning techniques have gained increasing prominence in tourism and hospitality research for data analysis and business strategy formulation. This study systematically reviews 406 peer-reviewed articles published between 2007 and 2023, highlighting the growth of machine learning studies, the expansion of academic journal coverage, and the adoption of diverse data modalities employed in this field. More importantly, it discusses emerging research topics and associated machine learning approaches across different data modalities (i.e., numerical, textual, and image data). This study further addresses key issues and challenges in current applications and outlines future research directions in this domain. Overall, it offers an in-depth understanding and assessment of the current machine learning techniques within the tourism and hospitality literature.

Digital Object Identifier (DOI)

https://doi.org/10.1177/00472875251401912

Rights

© The Author(s) 2025. 

This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage). Request permissions for this article.

APA Citation

Ningqiao Li, Xiaoyi Liu, and Fang Meng. (2025). Machine learning techniques in tourism and hospitality research: A critical assessment. Journal of Travel Research, 65(7), 1899–1920.https://doi.org/10.1177/00472875251401912

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