Customised Holiday Experiences through Artificial Intelligence: Case Studies from the Aviation and Hospitality Sectors

Customised Holiday Experiences through Artificial Intelligence: Case Studies from the Aviation and Hospitality Sectors

This article explores the impact of artificial intelligence (AI) on the aviation and hospitality industries, both of which are rapidly evolving due to technological advancements. It aims to understand the increasing importance of artificial intelligence by examining the various ways in which it is used in these sectors through qualitative research. The research included an analysis of online sources such as airport and hotel websites, booking platforms, and social media accounts of travel-related businesses. This comprehensive data collection provides insight into the various applications of artificial intelligence in tourism. Thematic analysis was then used to categorise the data according to similar uses, providing a detailed understanding of the role of AI in these areas. It compares and examines artificial intelligence applications adopted by aviation and hospitality organisations, evaluating their effectiveness and differences. The study reveals the various ways in which AI is being integrated into these industries and highlights its significant contributions across various dimensions. It also highlights how AI can deliver competitive advantage, improve customer experiences, and introduce innovative paradigms to the aviation and hospitality industries. One important aspect of the research is its ability to provide a deeper understanding of emerging AI trends in these sectors and lay a strong foundation for future research. Ultimately, this study provides valuable insight to stakeholders in aviation and hospitality, equipping them with an informed perspective on leveraging AI for growth and long-term sustainability in their respective industries

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  • Abeyratne, R., & Abeyratne, R. (2017). Artificial Intelligence and Air Transport. Megatrends and Air Transport: Legal, Ethical and Economic Issues, 173-200.
  • Akcay, S., & Breckon, T. (2022). Towards automatic threat detection: A survey of advances of deep learning within X-ray security imaging. Pattern Recognition, 122, 108245.
  • Aparicio, D., & Misra, K. (2023). Artificial intelligence and pricing (Vol. 20, pp. 103-124). Emerald Publishing Limited.
  • Benckendorff, P. J., Xiang, Z., & Sheldon, P. J. (2019). Tourism information technology. Cabi.
  • Bhatia, A., Roy, B., & Kumar, A. (2022). A review of tourism sustainability in the era of Covid-19. Journal of Statistics and Management Systems, 25(8), 1871-1888.
  • Carrier, E., & Fiig, T. (2018). Future of airline revenue management. Journal of Revenue and Pricing Management, 17, 45-47.
  • Çankaya, D. (2020). Artificial Intelligence, API and Big Data Based Solutions Becoming Widespread in Aviation. Academic Perspective Procedia, 3(1), 465-473.
  • Çolakoğlu, A. A. (2020). Analysis of European airports with machine learning algorithms (Master's thesis, Pamukkale University Institute of Social Sciences).
  • 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.
  • Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K. & Wright, R. (2023). “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. International Journal of Information Management, 71, 102642.
  • Ercan, F. (2020). Use of artificial intelligence technologies in tourism marketing and application examples. Ankara Hacı Bayram Veli University Faculty of Tourism Journal, 23(2), 394-410.
  • Garcia, A. B., Babiceanu, R. F., & Seker, R. (2021). Artificial intelligence and machine learning approaches for aviation cybersecurity: An overview. In 2021 Integrated Communications Navigation and Surveillance Conference (ICNS) (pp. 1-8). IEEE.
  • Gössling, S., & Lane, B. (2015). Rural tourism and the development of Internet-based accommodation booking platforms: a study in the advantages, dangers and implications of innovation. Journal of Sustainable Tourism, 23(8-9), 1386-1403.
  • Gövce, M. (2020). The role of e-lifestyle in mobile tourism purchasing behaviour. Journal of International Social Research, 13(70).
  • Gupta, D. G., & Jain, V. (2023). Use of Artificial Intelligence with Ethics and Privacy for Personalized Customer Services. In Artificial Intelligence in Customer Service: The Next Frontier for Personalized Engagement (pp. 231- 257). Cham: Springer International Publishing.
  • Gündüz, C., & Gündüz, S. (2017). An application on identifying organizational stress sources of airport employees and methods to combat stress. TİDSAD, 11, 187-199.
  • Gündüz, C., & Topaloğlu, C. (2021). Identification of Porter's generic competitive strategies in halal hotels: A research on managers. Balıkesir University Social Sciences Institute Journal, 24(45), 557-579.
  • Gündüz, C., Rezaei, M. Pironti, M. (2023). The Use of Artificial Intelligence Technologies in the Tourism Sector and Application Examples. In: USBK 3. Proceeding Book, Ufuk University, İstanbul.
  • Huang, H., & Zhu, J. (2021). A short review of the application of machine learning methods in smart airports. In Journal of Physics: Conference Series (Vol. 1769, No. 1, p. 01, 2010). IOP Publishing.
  • Ivanov, S., & Webster, C. (2019). Conceptual framework of the use of robots, artificial intelligence and service automation in travel, tourism, and hospitality companies. Robots, artificial intelligence, and service automation in travel, tourism and hospitality, 7-37.
  • Iranmanesh, M., Ghobakhloo, M., Nilashi, M., Tseng, M. L., Yadegaridehkordi, E., & Leung, N. (2022). Applications of disruptive digital technologies in hotel industry: A systematic review. International Journal of Hospitality Management, 107, 103304.
  • Ilgar, S. C., & Ilgar, M. Z. (2014). Using computer programs in qualitative data analysis. IZU Journal of Social Sciences.
  • Knani, M., Echchakoui, S., & Ladhari, R. (2022). Artificial intelligence in tourism and hospitality: Bibliometric analysis and research agenda. International Journal of Hospitality Management, 107, 103317.
  • Koroniotis, N., Moustafa, N., Schiliro, F., Gauravaram, P., & Janicke, H. (2020). A holistic review of cybersecurity and reliability perspectives in smart airports. IEEE Access, 8, 209802-209834.
  • Kumar, K., & Thakur, G. S. M. (2012). Advanced applications of neural networks and artificial intelligence: A review. International journal of information technology and computer science, 4(6), 57.
  • Le Clainche, S., Ferrer, E., Gibson, S., Cross, E., Parente, A., & Vinuesa, R. (2023). Improving aircraft performance using machine learning: a review. Aerospace Science and Technology, 108354.
  • Li, M., Yin, D., Qiu, H., & Bai, B. (2021). A systematic review of AI technology-based service encounters: Implications for hospitality and tourism operations. International Journal of Hospitality Management, 95, 102930.
  • Lv, H., Shi, S., & Gursoy, D. (2022). A look back and a leap forward: a review and synthesis of big data and artificial intelligence literature in hospitality and tourism. Journal of Hospitality Marketing & Management, 31(2), 145-175.
  • Limna, P. (2022). Artificial Intelligence (AI) in the hospitality industry: A review article. Int. J. Comput. Sci. Res, 6, 1- 12.
  • Manigandan, R., & Raghuram, N. V. (2022). Artificial Intelligence (AI) In Hotel Industry and Future Development: an Extensive In-Depth Literature Review and Bibliometric Analysis. International Journal of Intelligent Systems and Applications in Engineering, 10(4), 664-676.
  • Molchanova, K. (2020). A Review of Digital Technologies in Aviation Industry. Logistics and Transport, 47(3-4), 69-77.
  • Murty, M. N., & Devi, V. S. (2015). Introduction to pattern recognition and machine learning (Vol. 5). World Scientific.
  • Nam, K., Dutt, C. S., Chathoth, P., Daghfous, A., & Khan, M. S. (2021). The adoption of artificial intelligence and robotics in the hotel industry: prospects and challenges. Electronic Markets, 31, 553-574.
  • Nannelli, M., Capone, F., & Lazzeretti, L. (2023). Artificial intelligence in hospitality and tourism. State of the art and future research avenues. European Planning Studies, 1-20.
  • Pérez-Campuzano, D., Ortega, P. M., Andrada, L. R., & López-Lázaro, A. (2021). Artificial Intelligence potential within airlines: a review on how AI can enhance strategic decision-making in times of COVID-19. Journal of Airline and Airport Management, 11(2), 53-72.
  • Pinheiro, A. B., Pinto, A. S., Abreu, A., Costa, E., & Borges, I. (2021). The impact of artificial intelligence on the tourism industry: a systematic review. Advances in Tourism, Technology and Systems: Selected Papers from ICOTTS20, Volume 1, 458-469.
  • Popesku, J. (2019). Current applications of artificial intelligence in tourism and hospitality. In Sinteza 2019- International Scientific Conference on Information Technology and Data Related Research (pp. 84-90). Singidunum University.
  • Popkova, E. G., Sergi, B. S., Rezaei, M., & Ferraris, A. (2021). Digitalisation in transport and logistics: A roadmap for entrepreneurship in Russia. International Journal of Technology Management, 87(1), 7-28.
  • Rawal, Y. S., Soni, H., Dani, R., & Bagchi, P. (2022, July). A review on service delivery in tourism and hospitality industry through artificial intelligence. In Proceedings of Third International Conference on Computing, Communications, and Cyber-Security: IC4S 2021 (pp. 427-436). Singapore: Springer Nature Singapore.
  • Rezaei, M., Giovando, G., Rezaei, S. and Sadraei, R. (2022), "What are the fundamental knowledge-sharing drivers of small family businesses in the restaurant and fast-food industry?” British Food Journal, Vol. 124 No. 7, pp. 2149-2178.
  • Ritchie, J., & Lewis, J. (2003). The applications of qualitative methods to social research (pp. 24-46). London.
  • Samara, D., Magnisalis, I., & Peristeras, V. (2020). Artificial intelligence and big data in tourism: a systematic literature review. Journal of Hospitality and Tourism Technology, 11(2), 343-367.
  • Saydam, M. B., Arici, H. E., & Koseoglu, M. A. (2022). How does the tourism and hospitality industry use artificial intelligence? A review of empirical studies and future research agenda. Journal of Hospitality Marketing & Management, 31(8), 908-936.
  • Shin, H., & Perdue, R. R. (2022). Hospitality and tourism service innovation: A bibliometric review and future research agenda. International Journal of Hospitality Management, 102, 103176.
  • Singh, P., Elmi, Z., Lau, Y. Y., Borowska-Stefańska, M., Wiśniewski, S., & Dulebenets, M. A. (2022). Blockchain and AI technology convergence: Applications in transportation systems. Vehicular Communications, 100521.
  • Soori, M., Arezoo, B., & Dastres, R. (2023). Artificial intelligence, machine learning and deep learning in advanced robotics, A review. Cognitive Robotics.
  • Sridhar, B., & Bell, D. (2022). Sustainable Aviation Operations and the Role of Information Technology and Data Science: Background, Current Status and Future Directions. In AIAA Aviation 2022 Forum (p. 3705).
  • Tepylo, N., Straubinger, A., & Laliberte, J. (2023). Public perception of advanced aviation technologies: A review and roadmap to acceptance. Progress in Aerospace Sciences, 138, 100899.
  • Terry, G., Hayfield, N., Clarke, V., & Braun, V. (2017). Thematic analysis. The SAGE handbook of qualitative research in psychology, 2, 17-37.
  • Thums, J., Künzel, L., Klumpp, M., Bardmann, M. M., & Ruiner, C. (2023). Future air transportation and digital work at airports–Review and developments. Transportation Research Interdisciplinary Perspectives, 19, 100808.
  • Wimmer, R. D., & Dominick, J. R. (2013). Mass media research. Cengage learning.
  • Yang, L., Henthorne, T. L., & George, B. (2020). Artificial intelligence and robotics technology in the hospitality industry: Current applications and future trends. Digital transformation in business and society: Theory and cases, 211-228.