A SYSTEMATIC NARRATIVE REVIEW (SNR) OF ARTIFICIAL INTELLIGENCE (AI) APPLICATIONS IN TOURISM

Abstract

Although numerous review articles exist on various applications of artificial intelligence (AI) in the tourism and hospitality sector, there is a gap in evaluating the effectiveness of AI methods and algorithms applied to specific applications and various multimodal data domains. Therefore, this study aims to comprehensively examine and analyze established AI methods in the hospitality/tourism industry, encompassing demand forecasting, destination analysis, behavioral patterns, and data modeling for improved customer service and experience. This methodology examines the relationship between AI methods and their applications in hospitality/tourism as a systematic narrative review (SNR), based on a comprehensive literature review from 2010-2023. This review provides new insights into the selection of AI methods adapted to specific environments and various multimodal datasets prevalent in the hospitality and tourism sectors. Furthermore, methods that could potentially drive advancements in the tourism/hospitality sector are identified. Additionally, this study presents a pioneering approach to developing personalized AI models targeting intelligent tourism platforms to accurately predict tourism preference behavior patterns. This comprehensive SNR (Strategic Realization) examines the effectiveness of AI methods in the tourism and hospitality sectors, addressing specific applications and various multimodal data domains, and shedding light on theoretical and practical advancements in data collection, analysis, and modeling through AI-powered technology. Furthermore, this study advances the theoretical and practical aspects of AI-powered technology in the field by offering a pioneering approach to developing personalized AI models for smart tourism platforms.

Keywords

• Artificial intelligence (AI)
• Hospitality/tourism
• AI methods/algorithms

TURİZMDE YAPAY ZEKA (YZ) UYGULAMALARINA DAİR SİSTEMATİK BİR ANLATIMSAL İNCELEMESİ (SAİ)

Abstract

Turizm ve konaklama sektöründe yapay zekânın (YZ) çeşitli uygulamalarına dair çok sayıda inceleme makalesi bulunmasına rağmen, belirli uygulamalar ve çeşitli çok modlu veri alanları için uygulanan YZ yöntemlerinin ve algoritmalarının etkinliğinin değerlendirilmesinde bir boşluk bulunmaktadır. Bu nedenle, bu çalışma, talep tahmini, destinasyon analizi, davranış kalıpları ve geliştirilmiş müşteri hizmetleri ve deneyimi için veri modellemesini kapsayan, konaklama/turizm endüstrisi alanında yerleşik YZ yöntemlerini kapsamlı bir şekilde incelemeyi ve analiz etmeyi amaçlamaktadır. Bu metodoloji, 2010-2023 yılları arasındaki kapsamlı bir literatür taramasına dayanarak, YZ yöntemleri ile konaklama/turizmdeki uygulamaları arasındaki ilişkiyi sistematik anlatı incelemesi (SAİ) olarak incelemektedir. Bu inceleme, konaklama ve turizm sektörlerinde yaygın olan belirli ortamlara ve çeşitli çok modlu veri kümelerine uyarlanmış YZ yöntemlerinin seçimine ilişkin yeni bilgiler sağlamaktadır. Ayrıca, turizm/konaklama sektöründe ilerlemeleri potansiyel olarak sağlayabilecek yöntemler de belirlenmiştir. Ek olarak, bu çalışma, turizm tercih davranış kalıplarını doğru bir şekilde tahmin etmek için akıllı turizm platformlarını hedefleyen kişiselleştirilmiş YZ modelleri geliştirmeye yönelik öncü bir yaklaşım sunmaktadır. Bu kapsamlı SAİ, turizm ve konaklama sektörlerinde YZ yöntemlerinin etkinliğini, belirli uygulamaları ve çeşitli çok modlu veri alanlarını ele alarak yapay zekâ destekli teknoloji aracılığıyla veri toplama, analiz ve modelleme alanlarındaki teorik ve pratik gelişmelere ışık tutmaktadır. Bu çalışma ayrıca, akıllı turizm platformları için kişiselleştirilmiş yapay zekâ modelleri geliştirme konusunda öncü bir yaklaşım sunarak, yapay zekâ destekli teknolojinin alandaki teorik ve pratik yönlerini ilerletmektedir.

Keywords

• Yapay zekâ (YZ)
• Otelcilik/Turizm
• YZ yöntemleri/algoritmaları

References

1. Asimov, I. (1942). Runaround. In Astounding science fiction John W. Campbell (Ed.), (pp. 94–103). Street & Smith Publications
2. Atsız, O. (2021). Virtual reality technology and physical distancing: A review on limiting human interaction in tourism. Journal of Multidisciplinary Academic Tourism, 27–35. https://doi.org/10.31822/jomat.834448
3. Aydın, B. (2016). Restaurant Image in Social Media: The Case of Tripadvisor. Journal of Multidisciplinary Academic Tourism, 1(1), 13–30. https://doi.org/10.31822/jomat.287998
4. Buhalis, D., & O'Connor, P. (2005). Information communication technology revolutionizing tourism. Tourism recreation research, 30(3), 7-16. https://doi.org/10.1080/02508281.2005.11081482
5. Campbell, C., Sands, S., Ferraro, C., Tsao, H. Y. J., & Mavrommatis, A. (2020). From data to action: How marketers can leverage AI. Business horizons, 63(2), 227-243. https://doi.org/10.1016/j.bushor.2019.11.002
6. Claveria, O., Monte, E., & Torra, S. (2015). Tourism demand forecasting with neural network models: different ways of treating information. International Journal of Tourism Research, 17(5), 492-500. https://doi.org/10.1002/jtr.2016
7. Derra, N. D. (2021). Machine Learning and Technostress as Important Aspects for Improving the Performance of Data Scientists in Contemporary Marketing Contexts (Doctoral dissertation).
8. Doborjeh, Z., Hemmington, N., Doborjeh, M., & Kasabov, N. (2022). Artificial intelligence: a systematic review of methods and applications in hospitality and tourism. International Journal of Contemporary Hospitality Management, 34(3), 1154-1176. https://doi.org/10.1108/IJCHM-06-2021-0743

9. Donaire, J. A., Camprubí, R., & Galí, N. (2014). Tourist clusters from Flickr travel photography. Tourism management perspectives, 11, 26-33. https://doi.org/10.1016/j.tmp.2014.03.003
10. Dopico, M., Gómez, A., De la Fuente, D., García, N., Rosillo, R., & Puche, J. (2016). A vision of industry 4.0 from an artificial intelligence point of view. In Proceedings on the international conference on artificial intelligence (ICAI) (p. 407). The Steering Committee of The World Congress in Computer Science, Computer Engineering and Applied Computing (WorldComp).
11. Duan, Y., Edwards, J. S., & Dwivedi, Y. K. (2019). Artificial intelligence for decision making in the era of Big Data–evolution, challenges and research agenda. International journal of information management, 48, 63-71. https://doi.org/10.1016/j.ijinfomgt.2019.01.021
12. Duarte Pimentel, T., & Conceição de Paula, S. (2019). Performance assessment protocol: notes towards an academic management tool based on a Brazilian tourism course. Journal of Multidisciplinary Academic Tourism, 1(4), 15–35. https://doi.org/10.31822/jomat.543115
13. Elliott, A. (2019). The culture of AI: Everyday life and the digital revolution. Routledge.
14. Fernandes, T., & Oliveira, E. (2021). Understanding consumers’ acceptance of automated technologies in service encounters: Drivers of digital voice assistants adoption. Journal of Business Research, 122, 180-191. https://doi.org/10.1016/j.jbusres.2020.09.057
15. Filieri, R., D’Amico, E., Destefanis, A., Paolucci, E., & Raguseo, E. (2021). Artificial intelligence (AI) for tourism: an European-based study on successful AI tourism start-ups. International Journal of Contemporary Hospitality Management, 33(11), 4099-4125. https://doi.org/10.1108/IJCHM-02-2021-0142
16. Giglio, S., Bertacchini, F., Bilotta, E., & Pantano, P. (2020). Machine learning and points of interest: typical tourist Italian cities. Current Issues in Tourism, 23(13), 1646-1658. https://doi.org/10.1080/13683500.2019.1577717
17. Golmohammadi, A., Shams Ghareneh, N., Keramati, A., & Jahandideh, B. (2011). Importance analysis of travel attributes using a rough set‐based neural network: The case of Iranian tourism industry. Journal of Hospitality and Tourism Technology, 2(2), 155-171. https://doi.org/10.1108/17579881111154289
18. Grewal, D., Guha, A., Satornino, C. B., & Schweiger, E. B. (2021). Artificial intelligence: The light and the darkness. Journal of Business Research, 136, 229-236. https://doi.org/10.1016/j.jbusres.2021.08.008
19. Gronroos, C. (2008). Service logic revisited: who creates value? And who co‐creates?. European business review, 20(4), 298-314. https://doi.org/10.1108/09555340810886585
20. Guo, M., Zhang, C., & Wang, F. (2017, December). What is the further evidence about UML?-A systematic literature review. In 2017 24th Asia-Pacific Software Engineering Conference Workshops (APSECW) (pp. 106-113), Nanjing, China. IEEE. https://doi.org/10.1109/APSECW.2017.28
21. Haynes, N. C. (2020). Robots, artificial intelligence and service automation in travel, tourism and hospitality. Journal of Tourism Futures, 6(2), 191-192. https://doi.org/10.1108/JTF-12-2019-0133
22. He, K., Ji, L., Wu, C. W. D., & Tso, K. F. G. (2021). Using SARIMA–CNN–LSTM approach to forecast daily tourism demand. Journal of Hospitality and Tourism Management, 49, 25-33. https://doi.org/10.1016/j.jhtm.2021.07.002
23. Höpken, W., Müller, M., Fuchs, M., & Lexhagen, M. (2020). Flickr data for analysing tourists’ spatial behaviour and movement patterns: A comparison of clustering techniques. Journal of Hospitality and Tourism Technology, 11(1), 69-82. https://doi.org/10.1108/JHTT-02-2018-0018
24. Iftikhar, P., Kuijpers, M. V., Khayyat, A., Iftikhar, A., & De Sa, M. D. (2020). Artificial intelligence: a new paradigm in obstetrics and gynecology research and clinical practice. Cureus, 12(2). https://doi.org/10.7759/cureus.7204
25. Jarrahi, M. H. (2018). Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making. Business horizons, 61(4), 577-586. https://doi.org/10.1016/j.bushor.2018.03.007
26. Kalyanathaya, K. P., Akila, D., & Rajesh, P. (2019). Advances in natural language processing–a survey of current research trends, development tools and industry applications. International Journal of Recent Technology and Engineering, 7(5C), 199-202.
27. Kong, H., Yuan, Y., Baruch, Y., Bu, N., Jiang, X., & Wang, K. (2021). Influences of artificial intelligence (AI) awareness on career competency and job burnout. International Journal of Contemporary Hospitality Management, 33(2), 717-734. https://doi.org/10.1108/IJCHM-03-2020-0182
28. Kumar, V., Rajan, B., Venkatesan, R., & Lecinski, J. (2019). Understanding the role of artificial intelligence in personalized engagement marketing. California Management Review, 61(4), 135-155. https://doi.org/10.1177/0008125619859317
29. 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. https://doi.org/10.1016/j.ijhm.2020.102643
30. Lado-Sestayo, R., & Vivel-Búa, M. (2020). Hotel profitability: a multilayer neural network approach. Journal of Hospitality and Tourism Technology, 11(1), 35-48. https://doi.org/10.1108/JHTT-10-2018-0098
31. Li, Y., & Cao, H. (2018). Prediction for tourism flow based on LSTM neural network. Procedia Computer Science, 129, 277-283. https://doi.org/10.1016/j.procs.2018.03.076
32. Liu, X. (2011, July). Tourism demand forecasting in Weifang based on artificial neural network. In 2011 International Conference on Control, Automation and Systems Engineering (CASE) (pp. 1-4). IEEE. https://doi.org/10.1109/ICCASE.2011.5997574
33. Long, E., Lin, H., Liu, Z., Wu, X., Wang, L., Jiang, J., ... & Liu, Y. (2017). An artificial intelligence platform for the multihospital collaborative management of congenital cataracts. Nature biomedical engineering, 1(2), 0024. https://doi.org/10.1038/s41551-016-0024
34. Lusch, R. F., Vargo, S. L., & O’brien, M. (2007). Competing through service: Insights from service-dominant logic. Journal of retailing, 83(1), 5-18. https://doi.org/10.1016/j.jretai.2006.10.002
35. Mariani, M., & Borghi, M. (2021). Customers’ evaluation of mechanical artificial intelligence in hospitality services: a study using online reviews analytics. International Journal of Contemporary Hospitality Management, 33(11), 3956-3976. https://doi.org/10.1108/IJCHM-02-2021-0198
36. McCarthy, J., Minsky, M. L., Rochester, N., & Shannon, C. E. (2006). A proposal for the dartmouth summer research project on artificial intelligence, august 31, 1955. AI magazine, 27(4), 12-12. https://doi.org/10.1609/aimag.v27i4.1904
37. Meloni, A., Angioni, S., Salatino, A., Osborne, F., Recupero, D. R., & Motta, E. (2023). Integrating Conversational Agents and Knowledge Graphs Within the Scholarly Domain. IEEE Access, 11, 22468-22489. https://doi.org/10.1109/ACCESS.2023.3244904
38. Mingotto, E., Montaguti, F., & Tamma, M. (2021). Challenges in re-designing operations and jobs to embody AI and robotics in services. Findings from a case in the hospitality industry. Electronic Markets, 31, 493-510. https://doi.org/10.1007/s12525-020-00439-3
39. Mitchell, R., Michalski, J., & Carbonell, T. (2013). An artificial intelligence approach. Machine learning. Berlin, Heidelberg: Springer.
40. Moher, D., Liberati, A., Tetzlaff, J., & Altman, D. G. (2009). Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. The Bmj, 339. https://doi.org/10.1136/bmj.b2535
41. Neuburger, L., Beck, J., & Egger, R. (2018). The ‘Phygital’tourist experience: The use of augmented and virtual reality in destination marketing. In Tourism planning and destination marketing (pp. 183-202). Emerald Publishing Limited. https://doi.org/10.1108/978-1-78743-529-220181012
42. Pai, P. F., Hong, W. C., & Lin, C. S. (2005, December). Forecasting tourism demand using a multifactor support vector machine model. In International Conference on Computational and Information Science (pp. 512-519). Berlin, Heidelberg: Springer Berlin Heidelberg. https://doi.org/10.1007/11596981_76
43. Parvez, M. O. (2020). Use of machine learning technology for tourist and organizational services: high-tech innovation in the hospitality industry. Journal of Tourism Futures, 7(2), 240-244. https://doi.org/10.1108/JTF-11-2019-0126
44. Păvăloaia, V. D., Teodor, E. M., Fotache, D., & Danileţ, M. (2019). Opinion mining on social media data: sentiment analysis of user preferences. Sustainability, 11(16), 4459.https://doi.org/10.3390/su11164459
45. Prahalad, C. K., & Ramaswamy, V. (2004). Co-creation experiences: The next practice in value creation. Journal of interactive marketing, 18(3), 5-14. https://doi.org/10.1002/dir.20015
46. Prentice, C., Dominique Lopes, S., & Wang, X. (2020). The impact of artificial intelligence and employee service quality on customer satisfaction and loyalty. Journal of Hospitality Marketing & Management, 29(7), 739-756. https://doi.org/10.1080/19368623.2020.1721393
47. Rahman, A., Hossain, M. S., Muhammad, G., Kundu, D., Debnath, T., Rahman, M., & Band, S. S. (2023). Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues. Cluster computing, 26(4), 2271-2311. https://doi.org/10.1007/s10586-022-03672-5
48. Ramos- Henríquez, J. M., Gutierrez-Tano, D., & Diaz-Armas, R. J. (2021). Value proposition operationalization in peer-to-peer platforms using machine learning. Tourism Management, 84, 104288. https://doi.org/10.1016/j.tourman.2020.104288
49. Rathore, B. (2023). Integration of Artificial Intelligence& It’s Practices in Apparel Industry. ugc approved research journals in india| UGC Newly Added Journals|(IJNMS), 10(1), 25-37.
50. Ruel, H., & Njoku, E. (2021). AI redefining the hospitality industry. Journal of Tourism Futures, 7(1), 53-66. https://doi.org/10.1108/JTF-03-2020-0032
51. Samala, N., Katkam, B. S., Bellamkonda, R. S., & Rodriguez, R. V. (2020). Impact of AI and robotics in the tourism sector: a critical insight. Journal of tourism futures, 8(1), 73-87. https://doi.org/10.1108/JTF-07-2019-0068
52. Sánchez-Medina, A. J., & Eleazar, C. (2020). Using machine learning and big data for efficient forecasting of hotel booking cancellations. International Journal of Hospitality Management, 89, 102546. https://doi.org/10.1016/j.ijhm.2020.102546
53. Sati, V. P. (2019). Potential and Forms of Sustainable Village Tourism in Mizoram, Northeast India. Journal of Multidisciplinary Academic Tourism, 1(4), 49–62. https://doi.org/10.31822/jomat.527278
54. Schmitt, M. (2023). Big Data Analytics in the Metaverse: Business Value Creation with Artificial Intelligence and Data-Driven Decision Making. Available at SSRN 4385347. https://doi.org/10.2139/ssrn.4385347
55. Seyitoğlu, F., & Ivanov, S. (2022). Understanding the robotic restaurant experience: a multiple case study. Journal of Tourism Futures, 8(1), 55-72. https://doi.org/10.1108/JTF-03-2021-0066
56. Shrivastava, R., & Mahajan, P. (2016). Artificial intelligence research in India: a scientometric analysis. Science & Technology Libraries, 35(2), 136-151. https://doi.org/10.1080/0194262X.2016.1142304
57. Silva, E. S., Hassani, H., Heravi, S., & Huang, X. (2019). Forecasting tourism demand with denoised neural networks. Annals of Tourism Research, 74, 134-154. https://doi.org/10.1016/j.annals.2018.10.004
58. Singh, J., Sajid, M., Gupta, S. K., & Haidri, R. A. (2022). Artificial Intelligence and Blockchain Technologies for Smart City. Intelligent Green Technologies for Sustainable Smart Cities, 317-330. https://doi.org/10.1007/978-3-030-90920-2_17
59. Sloman, A. (2011). What's information, for an organism or intelligent machine? How can a machine or organism mean?. In Information and computation: Essays on scientific and philosophical understanding of foundations of information and computation (pp. 393-438). https://doi.org/10.1142/9789814324359_0015
60. Stafford, I. S., Kellermann, M., Mossotto, E., Beattie, R. M., MacArthur, B. D., & Ennis, S. (2020). A systematic review of the applications of artificial intelligence and machine learning in autoimmune diseases. NPJ digital medicine, 3(1), 30. https://doi.org/10.1038/s41746-020-0229-0
61. Tuomi, A., Tussyadiah, I., & Stienmetz, J. (2020). Service robots and the changing roles of employees in restaurants: a cross cultural study. E-review of Tourism Research (eRTR), 17(5).
62. Turow, J. (2021). The voice catchers: How marketers listen in to exploit your feelings, your privacy, and your wallet. Yale University Press.
63. Vargo, S. L., & Lusch, R. F. (2004). The four service marketing myths: remnants of a goods-based, manufacturing model. Journal of service research, 6(4), 324-335. https://doi.org/10.1177/1094670504266131
64. Vives, A., & Jacob, M. (2020). Dynamic pricing for online hotel demand: The case of resort hotels in Majorca. Journal of Vacation Marketing, 26(2), 268-283. https://doi.org/10.1177/1356766719850133
65. Walls, A. R., Okumus, F., Wang, Y. R., & Kwun, D. J. W. (2011). An epistemological view of consumer experiences. International journal of hospitality management, 30(1), 10-21. https://doi.org/10.1016/j.ijhm.2010.03.010
66. Xiao, Y. & Watson, M. (2019). Guidance on conducting a systematic literature review. Journal of Planning Education and Research, 39(1), 93–112. https://doi.org/10.1177/0739456X17723971
67. Xie, G., Qian, Y., & Wang, S. (2021). Forecasting Chinese cruise tourism demand with big data: An optimized machine learning approach. Tourism Management, 82, 104208. https://doi.org/10.1016/j.tourman.2020.104208
68. Xu, L., Sanders, L., Li, K., & Chow, J. C. (2021). Chatbot for health care and oncology applications using artificial intelligence and machine learning: systematic review. JMIR cancer, 7(4), e27850. https://doi.org/10.2196/27850
69. Zhang, B., Li, N., Shi, F., & Law, R. (2020). A deep learning approach for daily tourist flow forecasting with consumer search data. Asia Pacific Journal of Tourism Research, 25(3), 323-339. https://doi.org/10.1080/10941665.2019.1680680
70. Zhang, C., & Zhang, J. (2014). Analysing Chinese citizens' intentions of outbound travel: a machine learning approach. Current Issues in Tourism, 17(7), 592-609. https://doi.org/10.1080/13683500.2012.720997
71. Zhu, L., Lim, C., Xie, W., & Wu, Y. (2018). Modelling tourist flow association for tourism demand forecasting. Current Issues in Tourism, 21(8), 902-916. https://doi.org/10.1080/13683500.2017.1329282