TY - JOUR
T1 - Artificial Intelligence Research in Tourism and Hospitality Journals
T2 - Trends, Emerging Themes, and the Rise of Generative AI
AU - To, Wai Ming
AU - Yu, Billy T.W.
N1 - Publisher Copyright:
© 2025 by the authors.
PY - 2025/6
Y1 - 2025/6
N2 - This study examined the trends and key themes of artificial intelligence in the field of tourism and hospitality research. On 5 March 2025, a search was performed using “artificial intelligence” and related terms in the “Title, Abstract, and Keywords”, focusing on tourism and hospitality journals indexed in Scopus. The identified documents were subjected to performance analysis and science mapping techniques. The search yielded 921 documents, comprising 882 articles and 39 reviews. The number of documents increased from 3 in 1987 to 277 in 2024. R. Law from the University of Macau was the most prolific author, while the Hong Kong Polytechnic University recorded the highest publication count. Chinese researchers produced the most documents, totaling 262 articles and reviews. A keyword co-occurrence analysis revealed four key themes: “machine learning and sentiment analysis of online reviews”, “adoption of AI including robots and ChatGPT in the hospitality industry”, “artificial neural networks for tourism management and demand analysis”, and “random forest models in travel”. Additionally, the study noted a shift in research focus from tourism demand forecasting and sentiment analysis to using service bots and applying artificial intelligence to enhance service quality, with a recent emphasis on generative AI tools like ChatGPT.
AB - This study examined the trends and key themes of artificial intelligence in the field of tourism and hospitality research. On 5 March 2025, a search was performed using “artificial intelligence” and related terms in the “Title, Abstract, and Keywords”, focusing on tourism and hospitality journals indexed in Scopus. The identified documents were subjected to performance analysis and science mapping techniques. The search yielded 921 documents, comprising 882 articles and 39 reviews. The number of documents increased from 3 in 1987 to 277 in 2024. R. Law from the University of Macau was the most prolific author, while the Hong Kong Polytechnic University recorded the highest publication count. Chinese researchers produced the most documents, totaling 262 articles and reviews. A keyword co-occurrence analysis revealed four key themes: “machine learning and sentiment analysis of online reviews”, “adoption of AI including robots and ChatGPT in the hospitality industry”, “artificial neural networks for tourism management and demand analysis”, and “random forest models in travel”. Additionally, the study noted a shift in research focus from tourism demand forecasting and sentiment analysis to using service bots and applying artificial intelligence to enhance service quality, with a recent emphasis on generative AI tools like ChatGPT.
KW - artificial intelligence
KW - bibliometric analysis
KW - hospitality
KW - Scopus
KW - tourism
UR - http://www.scopus.com/inward/record.url?scp=105008907240&partnerID=8YFLogxK
U2 - 10.3390/tourhosp6020063
DO - 10.3390/tourhosp6020063
M3 - Review article
AN - SCOPUS:105008907240
SN - 2673-5768
VL - 6
JO - Tourism and Hospitality
JF - Tourism and Hospitality
IS - 2
M1 - 63
ER -