Keywords
hotel and restaurant business
digital platforms
tourism
transformation
innovation
How to Cite
Abstract
The article is devoted to the analysis of the transformational impact of the sharing economy on the traditional hotel and restaurant business in the context of the development of digital platforms, in particular Airbnb and Uber. The study reveals key changes in the structure of the tourism market caused by the spread of peer-to-peer services that provide access to housing and transport beyond the boundaries of classic business models. The modern tourism industry is undergoing significant changes under the influence of digital transformation and the development of the sharing economy, which is based on new forms of consumption, access to resources, rather than their ownership. One of the most striking manifestations of this transformation is the rapid growth of the influence of such digital platforms as Airbnb and Uber, which radically change traditional ideas about the functioning of the hotel and transport sectors in tourism. In the context of constantly growing demand for individualized, flexible and affordable tourism services, sharing platforms provide a high level of convenience, democratization of access to the market for both consumers and service providers. The main objective of the study is to identify strategic challenges and opportunities for traditional tourism industry entities in the context of competition from digital platforms and to formulate directions for their adaptation to new market conditions. The scientific work used an interdisciplinary approach, which includes the analysis of scientific sources, market analytics, expert interviews with representatives of the tourism business, as well as case methods to study the experience of adapting hotels and restaurants in different countries. The results of the study indicate that an effective response of the traditional hotel and restaurant sector to the challenges of digitalization is possible provided that innovative services are introduced, service personalization is increased, digital integration of booking processes and communication with customers is achieved, as well as the formation of flexible models of interaction with sharing economy platforms. The need to improve regulatory and legal regulation in the context of ensuring equal conditions for competition is also emphasized. The conclusions obtained can be used to develop strategies for the development of tourism enterprises in the digital era and improve state policy in the field of tourism.
References
1. Chen, M., Mao, S., & Liu, Y. (2014). Big Data: A Survey. Mobile Networks and Applications, 19(2), 171–209. https://doi.org/10.1007/s11036-013-0489-0
2. Gandomi, A., & Haider, Z. (2015). Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management, 35(2), 137–144. https://doi.org/10.1016/j.ijinfomgt.2014.10.007
3. Marr, B. (2016). Big Data in Practice: How 45 Successful Companies Used Big Data Analytics to Deliver Extraordinary Results. Wiley. https://doi.org/10.1002/9781119278825
4. Katal, A., Wazid, M., & Goudar, R. H. (2013). Big data: Issues, challenges, and technologies. In 2013 International Conference on Emerging Trends and Applications in Computer Science (ETACS) (pp. 404–409). https://www.stat.purdue.edu/~doerge/BIOINFORM.D/SPRING16/KatalWazidGoudar_2013.pdf
5. Galetsi, P., Katsaliaki, K., & Kumar, S. (2020). Big data analytics in health sector: Theoretical framework, techniques and prospects. International Journal of Information Management, (50), 206–216. https://doi.org/10.1016/j.ijinfomgt.2019.05.003
6. Wulff, A., & Wunck, C. (2016). Integration of business process management and Big Data technologies. In International Conference on Industrial Engineering and Operations Management (pp. 8–10). https://ieomsociety.org/ieom_2016/pdfs/61.pdf
7. Mohamed, A., Najafabadi, M. K., Wah, Y. B., Zaman, E. A. K., & Maskat, R. (2020). The state of the art and taxonomy of big data analytics: view from new big data framework. Artificial intelligence review, (53), 989–1037. https://doi.org/10.1007/s10462-019-09685-9
8. Batini, C., Cappiello, C., Francalanci, C., & Maurino, A. (2009). Methodologies for data quality assessment and improvement. ACM computing surveys (CSUR), 41(3), 1–52. https://doi.org/10.1145/1541880.1541883
9. Naeem, M., Jamal, T., Diaz-Martinez, J., Butt, S. A., Montesano, N., Tariq, M. I., ... & De-La-Hoz-Valdiris, E. (2022). Trends and future perspective challenges in big data. In Advances in Intelligent Data Analysis and Applications: Proceeding of the Sixth Euro-China Conference on Intelligent Data Analysis and Applications, 15–18 October 2019, Arad, Romania (pp. 309–325). Springer Singapore. https://doi.org/10.1007/978-981-16-5036-9_30
10. Chakraborty, D., Rana, N. P., Khorana, S., Singu, H. B., & Luthra, S. (2023). Big data in food: Systematic literature review and future directions. Journal of Computer Information Systems, 63(5), 1243–1263. https://doi.org/10.1080/08874417.2022.2132428
11. Zikopoulos, P., & Eaton, C. (2011). Understanding big data: Analytics for enterprise class hadoop and streaming data. McGraw-Hill Osborne Media. https://dl.acm.org/doi/abs/10.5555/2132803
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