ARTIFICIAL INTELLIGENCE AND COMPETITIVENESS IN THE TOURISM BUSINESS: A BIBLIOMETRIC ANALYSIS OF RESEARCH
Abstract
The purpose of this study is to conduct a bibliometric analysis of existing research on the impact of artificial intelligence (AI) on the competitiveness of tourism enterprises. Using data from Web of Science and Scopus, this study examines trends in publication activity, the geographical distribution of research, citation levels of key authors, and major thematic directions. The findings indicate that leading research centres in AI applications within tourism are concentrated in China, Spain, India, the USA, the United Kingdom, and Portugal, highlighting the extensive academic interest in this field. The bibliometric analysis reveals that existing research primarily focuses on the application of machine learning for service personalization, the use of big data for forecasting tourism flows, the automation of customer service processes, and the development of smart tourism. Recent studies have also placed significant emphasis on generative artificial intelligence, particularly ChatGPT, examining its potential to influence digital tourism services, reshape business models, enhance operational efficiency, and redefine customer experiences. Furthermore, the reviewed literature highlights the role of AI in market intelligence, real-time analytics, and decision support systems, which allow businesses to adapt to evolving consumer demands and competitive landscapes. The increasing integration of AI in tourism is reflected in studies on chatbots, virtual assistants, and predictive analytics, which collectively contribute to personalized and seamless travel experiences. Despite the noted advancements, the bibliometric analysis identifies key challenges frequently discussed in the literature, including ethical concerns, data security issues, regulatory barriers, and the demand for a specialized workforce to implement AI solutions effectively. Addressing these challenges is essential for the sustainable integration of AI in the tourism sector. The results of this bibliometric study provide valuable insights into the evolving landscape of AI applications in tourism, offering a foundation for future research. The study underscores the importance of continuous academic investigation, interdisciplinary collaboration, and strategic policymaking to ensure the responsible and effective deployment of AI-driven innovations in tourism.
References
Buhalis D. et al. Technological disruptions in services: lessons from tourism and hospitality. Journal of Service Management. 2019. Vol. 30. No. 4. P. 484–506. DOI: https://doi.org/10.1108/josm-12-2018-0398
Pillai R., Sivathanu B. Adoption of AI-based chatbots for hospitality and tourism. International Journal of Contemporary Hospitality Management. 2020. Vol. 32. No. 10. P. 3199–3226. DOI: https://doi.org/10.1108/ijchm-04-2020-0259
Tussyadiah I. A review of research into automation in tourism: Launching the Annals of Tourism Research Curated Collection on Artificial Intelligence and Robotics in Tourism. Annals of Tourism Research. 2020. Vol. 81. DOI: https://doi.org/10.1016/j.annals.2020.102883
Zeng Z., Chen P.-J., Lew A. A. From high-touch to high-tech: COVID-19 drives robotics adoption. Tourism Geographies. 2020. Vol. 22. No. 3. P. 724–734. DOI: https://doi.org/10.1080/14616688.2020.1762118
Bulchand-Gidumal J. et al. Artificial intelligence’s impact on hospitality and tourism marketing: exploring key themes and addressing challenges / Current Issues in Tourism. 2023. P. 1–18. DOI: https://doi.org/10.1080/13683500.2023.2229480
Uchida Y., Ono T. Generational conflict and education politics: Implications for growth and welfare. Journal of Macroeconomics. 2021. Vol. 69.. DOI: https://doi.org/10.1016/j.jmacro.2021.103315
Aria M., Cuccurullo C. bibliometrix : An R-tool for comprehensive science mapping analysis. Journal of Informetrics. 2017. Vol. 11. No. 4. P. 959–975. DOI: https://doi.org/10.1016/j.joi.2017.08.007
R: The R Project for Statistical Computing. R: The R Project for Statistical Computing. URL: http://www.R-project.org/
RStudio Team. RStudio: Integrated development for R. RStudio, PBC, Boston, MA. URL: http://www.rstudio.com/
Gursoy D., Li Y., Song H. ChatGPT and the hospitality and tourism industry: an overview of current trends and future research directions. Journal of Hospitality Marketing & Management. 2023. P. 1–14. DOI: https://doi.org/10.1080/19368623.2023.2211993
Tourism's Digitalization as a Tool for Development in Network Economy Conditions. Професійна підготовка фахівців туристично-рекреаційної сфери: сучасний стан і шляхи вдосконалення у післявоєнний період: колективна монографія. Ч.1. / наук. ред. Г.А. Богатирьова. Переяслав : УГСП, 2024. C. 20–49. DOI: https://doi.org/10.5281/zenodo.14265481
Buhalis D. et al. (2019). Technological disruptions in services: lessons from tourism and hospitality. Journal of Service Management, vol. 30 no. 4, pp. 484-506. DOI: https://doi.org/10.1108/JOSM-12-2018-0398
Pillai R. and Sivathanu B. (2020). Adoption of AI-based chatbots for hospitality and tourism. International Journal of Contemporary Hospitality Management. vol. 32 no. 10, pp. 3199-3226. DOI: https://doi.org/10.1108/IJCHM-04-2020-0259
Tussyadiah I. (2020). A review of research into automation in tourism: Launching the Annals of Tourism Research Curated Collection on Artificial Intelligence and Robotics in Tourism. Annals of Tourism Research, no. 81. DOI: https://doi.org/10.1016/j.annals.2020.102883
Zeng Z., Chen P. J., & Lew A. A. (2020). From high-touch to high-tech: COVID-19 drives robotics adoption. Tourism Geographies, no. 22(3), pp. 724–734. DOI: https://doi.org/10.1080/14616688.2020.1762118
Bulchand-Gidumal J., William Secin E., O’Connor P., & Buhalis D. (2023). Artificial intelligence’s impact on hospitality and tourism marketing: exploring key themes and addressing challenges. Current Issues in Tourism, no. 27(14), pp. 2345–2362. DOI: https://doi.org/10.1080/13683500.2023.2229480
Uchida Y., & Ono T. (2021). Generational conflict and education politics: Implications for growth and welfare. Journal of Macroeconomics, no. 69. DOI: https://doi.org/10.1016/j.jmacro.2021.103315
Aria M., & Cuccurullo C. (2017). bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, no. 11(4), pp. 959–975. DOI: https://doi.org/10.1016/j.joi.2017.08.007
R Core Team (2014). R: A language and environment for statistical computing. R foundation for statistical computing, Vienna, Austria. Available at: http://www.R-project.org/
RStudio Team (2020). RStudio: Integrated development for R. RStudio, PBC, Boston, MA. Available at: http://www.rstudio.com/
Gursoy D., Li Y., & Song H. (2023). ChatGPT and the hospitality and tourism industry: an overview of current trends and future research directions. Journal of Hospitality Marketing & Management, no. 32(5), pp. 579–592. DOI: https://doi.org/10.1080/19368623.2023.2211993
Ivanova N. (2024). Tourism's Digitalization as a Tool for Development in Network Economy Conditions. Professional training of specialists in the tourism and recreation sector: Current state and ways of improvement in the post-war period (Part 1, pp. 20–49). Pereiaslav: UGSP. DOI: https://doi.org/10.5281/zenodo.14265481
