Journal of Smart Tourism

  • 제4권2호
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  • Pages.5-14
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  • 2024
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  • 2765-2157(pISSN)
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  • 2765-7272(eISSN)
경희대학교 스마트관광연구소 (Smart Tourism Research Center, Kyung Hee University)
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From Machine Learning Algorithms to Superior Customer Experience: Business Implications of Machine Learning-Driven Data Analytics in the Hospitality Industry

  • Egor Cherenkov (Conrad N. Hilton College of Global Hospitality Leadership, University of Houston) ;
  • Vlad Benga (Conrad N. Hilton College of Global Hospitality Leadership, University of Houston) ;
  • Minwoo Lee (Conrad N. Hilton College of Global Hospitality Leadership, University of Houston) ;
  • Neil Nandwani (Conrad N. Hilton College of Global Hospitality Leadership, University of Houston) ;
  • Kenan Raguin (Conrad N. Hilton College of Global Hospitality Leadership, University of Houston) ;
  • Marie Clementine Sueur (Conrad N. Hilton College of Global Hospitality Leadership, University of Houston) ;
  • Guohao Sun (Conrad N. Hilton College of Global Hospitality Leadership, University of Houston)
  • 투고 : 2024.01.17
  • 심사 : 2024.07.01
  • 발행 : 2024.06.30
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초록

This study explores the transformative potential of machine learning (ML) and ML-driven data analytics in the hospitality industry. It provides a comprehensive overview of this emerging method, from explaining ML's origins to introducing the evolution of ML-driven data analytics in the hospitality industry. The present study emphasizes the shift embodied in ML, moving from explicit programming towards a self-learning, adaptive approach refined over time through big data. Meanwhile, social media analytics has progressed from simplistic metrics deriving nuanced qualitative insights into consumer behavior as an industry-specific example. Additionally, this study explores innovative applications of these innovative technologies in the hospitality sector, whether in demand forecasting, personalized marketing, predictive maintenance, etc. The study also emphasizes the integration of ML and social media analytics, discussing the implications like enhanced customer personalization, real-time decision-making capabilities, optimized marketing campaigns, and improved fraud detection. In conclusion, ML-driven hospitality data analytics have become indispensable in the strategic and operation machinery of contemporary hospitality businesses. It projects these technologies' continued significance in propelling data-centric advancements across the industry.

키워드

과제정보

The leading author mainly reviewed, revised, and finalized the manuscript. The rest of the authors have made equal contributions and their order of authorship is alphabetical based on their last name.

참고문헌

  1. Alam, M. R., Sadri, A. M., & Jin, X. (2021). Identifying public perceptions toward emerging transportation trends through social media-based interactions. Future Transportation, 1(3), 794-813. https://doi.org/10.3390/futuretransp1030044
  2. Batrinca, B., & Treleaven, P. C. (2015). Social media analytics: A survey of techniques, tools and platforms. AI & Society, 30, 89-116. https://doi.org/10.1007/s00146-014-0549-4
  3. Berner, E. S., & Ozaydin, B. (2017). Benefits and risks of machine learning decision support systems. JAMA, 318(23), 2353-2354. https://doi.org/10.1001/jama.2017.16619
  4. Buhalis, D., Harwood, T., Bogicevic, V., Viglia, G., Beldona, S., & Hofacker, C. (2019). Technological disruptions in services: Lessons from tourism and hospitality. Journal of Service Management, 30(4), 484-506. https://doi.org/10.1108/JOSM-12-2018-0398
  5. Buhalis, D., Leung, D., & Lin, M. (2023). Metaverse as a disruptive technology revolutionising tourism management and marketing. Tourism Management, 97, 104724.
  6. Choi, J., Yoon, J., Chung, J., Coh, B.-Y., & Lee, J.-M. (2020). Social media analytics and business intelligence research: A systematic review. Information Processing & Management, 57(6), 102279.
  7. Hu, B., & Yan, B. (2022). Analysis system of MICE tourism economic development strategy based on machine learning algorithm. Mobile Information Systems, 2022, Article ID 1283040.
  8. Huang, L., & Zheng, W. (2021). Novel deep learning approach for forecasting daily hotel demand with agglomeration effect. International Journal of Hospitality Management, 98, 103038.
  9. Jeong, M., Shin, H. H., Lee, M., & Lee, J. (2023). Assessing brand performance consistency from consumer-generated media: The US hotel industry. International Journal of Contemporary Hospitality Management, 35(6), 2056-2083. https://doi.org/10.1108/IJCHM-12-2021-1516
  10. Karmalkar, V. (2021). Twego Trending: Data analytics based search engine using Elasticsearch. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 12(1S), 246-251.
  11. Kwon, W., Lee, M., & Back, K.-J. (2020). Exploring the underlying factors of customer value in restaurants: A machine learning approach. International Journal of Hospitality Management, 91, 102643.
  12. Kwon, W., Lee, M., Back, K.-J., & Lee, K. Y. (2021). Assessing restaurant review helpfulness through big data: Dual-process and social influence theory. Journal of Hospitality and Tourism Technology, 12(2), 177-195. https://doi.org/10.1108/JHTT-04-2020-0077
  13. Law, R., Li, G., Fong, D. K. C., & Han, X. (2019). Tourism demand forecasting: A deep learning approach. Annals of Tourism Research, 75, 410-423. https://doi.org/10.1016/j.annals.2019.01.014
  14. Lee, M., Kwon, W., & Back, K-J. (2021) Artificial intelligence for hospitality big data analytics: Developing a prediction model of restaurant review helpfulness for customer decision making. International Journal of Contemporary Hospitality Management, 33(6), 2117-2136. https://doi.org/10.1108/IJCHM-06-2020-0587
  15. Lee, M., Sisson, A., Costa, R., & Bai, B. (2023) Examining disruptive technologies and innovation in hospitality: A computer-assisted qualitative data analysis approach. Journal of Hospitality & Tourism Research, 47(4), NP47-NP61.
  16. Lee, M., Song, Y., Lee, K. Y., Li, L., & Yang, S-B. (2022) Detecting fake reviews with supervised machine learning algorithms, The Service Industries Journal, 42(13-14), 1101-1121. https://doi.org/10.1080/02642069.2022.2054996
  17. Li, Y., Zeng, F., Zhang, N., Chen, Z., Zhou, L., Huang, M., Zhu, T., & Wang, J. (2023). Multitask learning using feature extraction network for smart tourism applications. IEEE Internet of Things Journal, 10(21), 18790-18798. https://doi.org/10.1109/JIOT.2023.3281329
  18. Muehlenbein, H. (2006). Artificial intelligence and neural networks The legacy of Alan Turing and John von Neumann. In V. Golovko (Ed.), Heinz Muehlenbein // International Conference on Neural Networks and Artificial Intelligence: Proceedings, Brest, 31 May - 2 June, 2006 (pp. 8-17). Brest: BSTU.
  19. Nazareth, N., & Ramana Reddy, Y. V. (2023). Financial applications of machine learning: A literature review. Expert Systems with Applications, 219, 119640.
  20. Omidvar-Tehrani, B., Amer-Yahia, S., & Borromeo, R. M. (2019). User group analytics: Hypothesis generation and exploratory analysis of user data. The VLDB Journal, 28(2), 243-266. https://doi.org/10.1007/s00778-018-0527-4
  21. Park, H., Lee, M., & Back, K.-J. (2023). A critical review of technology-driven service innovation in hospitality and tourism: Current discussions and future research agendas. International Journal of Contemporary Hospitality Management, 35(12), 4502-4534. https://doi.org/10.1108/IJCHM-07-2022-0875
  22. Park, H., Lee, M., Back, K.-J., & DeFranco, A. (2022). Is hotel technology a double-edged sword on customer experience? A mixed-method approach using big data. Journal of Hospitality & Tourism Research, 48(5), 881-894. https://doi.org/10.1177/10963480221132758
  23. Puh, K., & Bagic Babac, M. (2022). Predicting sentiment and rating of tourist reviews using machine learning. Journal of Hospitality and Tourism Insights, 6(3), 1188-1204. https://doi.org/10.1108/JHTI-02-2022-0078
  24. Rahman, M. S., & Reza, H. (2022). A systematic review towards big data analytics in social media. Big Data Mining and Analytics, 05(03), Figure 5.
  25. Rodri guez, C. P., Ovseiko, P., Palomar, M. F., Kumpulainen, K., & Ramis, M. (2021). Capturing emerging realities in citizen engagement in science in social media: A ML Driven Analytics in Social Media Platforms protocol for the Allinteract study. International Journal of Qualitative Methods, 20.
  26. Schilling, A. T., Shah, P. P., Feghali, J., Jimenez, A. E., & Azad, T. D. (2022). A brief history of machine learning in neurosurgery. In V. E. Staartjes, L. Regli, & C. Serra (Eds.), Machine learning in clinical neuroscience (pp. 245-250). [location]: Springer International Publishing.
  27. Sebei, H., Hadj Taieb, M. A., & Ben Aouicha, M. (2018). Review of social media analytics process and Big Data pipeline. Social Network Analysis and Mining, 8(1), 30.
  28. Shinde, P. P., & Shah, S. (2018). A review of machine learning and deep learning applications. In 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA), Pune, India, 2018, pp. 1-6. Pune, India: IEEE.
  29. Viverit, L., Heo, C., Pereira, L. N., & Tiana, G. (2023). Application of machine learning to cluster hotel booking curves for hotel demand forecasting. International Journal of Hospitality Management, 111, 103455.
  30. Witten, I. H., Holmes, G., McQueen, R. J., Smith, L., & Cunningham, S. J. (1993). Practical machine learning and its application to problems in agriculture.
  31. Zingg, R., Andermatt, P., Mazloumian, A., & Rosenthal, M. (2021). Smart food waste management-Embedded machine learning vs cloud based solutions. In E. Mugellini &E. Carpanzano (Eds.), Proceedings of the 2nd FTAL Conference 2021 Sustainable Smart Cities and Regions, FTAL Conference 2021 - Sustainable smart cities and regions, Lugano, Switzerland, 28-29 October 2021. Switzerland: CEUR Workshop Proceedings.