Artificial intelligence as a basis for innovation management in tourism

Authors

  • Svetlana R. Muminova Financial University under the Government of the Russian Federation (Moscow, Russia)
  • Natalia G. Tomashevskaya Russian State University of Tourism and Service (Moscow, Russia)

DOI:

https://doi.org/10.24412/1995-042X-2022-2-94-100

Keywords:

innovation, neural network, machine learning, tourism, computer vision, big data analysis

Abstract

The paper overviews theoretical researches and practical applications related to implementation of artificial intelligence (AI) in tourism. Recently, much attention is given to the machine learning algorithms, neural networks and computer visions as promising tools of the digital transformation of tourist industry. Prognostic and classification models build by means of them allow all stakeholders of tourist industry to move to a new level of decision-making process and thus to improve the quality of the service. In particularly, AI-based software enables local authorities not only to measure anthropogenic load in some area, to perform ecologic monitoring of recreation territories and to model their sustainable development, but also to increase safety level for tourists. Transport companies could optimize tourist itineraries and study behavior models of the clients at the moment of buying tickets and hotel and restaurant owners would get more efficient tools for determining preferences of the consumers, the degree of their satisfaction and that would lead to constructing much more efficient relations with them. Another important issue is that neural networks are capable to resolve the problem of fake reviews. Undoubtedly, that will rise the credibility of the information available on internet. Summarizing, AI is becoming a new technological paradigm on the basis of which an innovative management processes in tourism will be designed soon.

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Author Biographies

Svetlana R. Muminova, Financial University under the Government of the Russian Federation (Moscow, Russia)

PhD in Engineering, Associate Professor

Natalia G. Tomashevskaya, Russian State University of Tourism and Service (Moscow, Russia)

Senior lecturer

References

Muminova, S. R., Feoktistova, V. M., & Vagina, U. V. (2018). Innovation in tourism based on in-formation technology. Servis v Rossii i za rubezhom [Services in Russia and Abroad], 12(1), 6-15. doi: 10.24411/1995-042X-2018-10101. (In Russ).

Li, H., Hu, M., & Li, G. (2020). Forecasting tourism demand with multisource big data. Annals of Tourism Research. doi: 10.1016/j.annals.2020.102912.

Su, X. (2020). Simulation of economic development of tourism industry based on FPGA and ma-chine learning. Microprocessors and Microsystems. doi: 10.1016/j.micpro.2020.103523.

Luo, Y., He, J., Mou, Yu., Wang, J., & Liu, T. (2021). Exploring China's 5A global geoparks through online tourism reviews: A mining model based on machine learning approach. Tourism Manage-ment Perspectives. doi: 10.1016/j.tmp.2020.100769.

Bi, J.-W., Liu, Y., & Li, H. (2020). Daily tourism volume forecasting for tourist attractions. Annals of Tourism Research. doi: 10.1016/j.annals.2020.102923.

Almukhamedova, O. A. (2021). Applying the artificial intelligence neural network systems in achieving sustainable tourism development. Servis v Rossii i za rubezhom [Services in Russia and Abroad], 15(3), 7–17. doi: 10.24412/1995-042X-2021-3-7-17. (In Russ.).

Khorsand, R., Rafiee, M., & Kayvanfar, V. (2020). Insights into TripAdvisor's online reviews: The case of Tehran's hotels. Tourism Management Perspectives. doi: 10.1016/j.tmp.2020.100673.

Giglio, S., Pantano, E., Bilotta, E., & Melewar, T. C. (2020). Branding luxury hotels: Evidence from the analysis of consumers’ “big” visual data on TripAdvisor. Journal of Business Research. doi: 10.1016/j.jbusres.2019.10.053.

Budhi, G. S., Chiong, R., Wang, Z., & Dhakal, S. (2021). Using a hybrid content-based and behav-iour-based featuring approach in a parallel environment to detect fake reviews. Electronic Com-merce Research and Applications. doi: 10.1016/j.elerap.2021.101048.

Wang, N., Yang, J., Kong, X., & Gao, Y. (2022). A fake review identification framework considering the suspicion degree of reviews with time burst characteristics. Expert Systems with Applications. doi: 10.1016/j.eswa.2021.116207.

Gómez, D., Salvador, P., Sanz, J., & Casanova, J. L. (2021). A new approach to monitor water quality in the Menor sea (Spain) using satellite data and machine learning methods. Environmen-tal Pollution. doi: 10.1016/j.envpol.2021.117489.

Zhang, K., Lin, Zh., & Zhang, J. (2021). Tourist gaze through computer vision: Differences be-tween Asian, North American, and European tourists. Annals of Tourism Research. doi: 10.1016/j.annals.2020.103039.

Payntar, N. D., Hsiao, W.-L., Covey, R. A., & Grauman, K. (2021). Learning patterns of tourist movement and photography from geotagged photos at archaeological heritage sites in Cuzco, Pe-ru. Tourism Management. doi: 10.1016/j.tourman.2020.104165.

Zhang, Y., Yang, H., & Wang, G. (2021). Monitoring and management of high-end tourism in pro-tected areas based on 3D sensor image collection. Displays. doi: 10.1016/j.displa.2021.102089.

Lin, Y. (2020). Automatic recognition of image of abnormal situation in scenic spots based on In-ternet of things. Image and Vision Computing. doi: 10.1016/j.imavis.2020.103908.

Abkarian, H., Tahlyan, D., Mahmassani, H., & Smilowitz, K. (2022). Characterizing visitor engage-ment behavior at large-scale events: Activity sequence clustering and ranking using GPS tracking data. Tourism Management. doi: 10.1016/j.tourman.2021.104421.

Barnes, S. J., & Kirshner, S. N. (2021). Understanding the impact of host facial characteristics on Airbnb pricing: Integrating facial image analytics into tourism research. Tourism Management. doi: 10.1016/j.tourman.2020.104235

Sengupta, P., Biswas, B., Kumar, A., Shankar, R., & Gupta, S. (2021). Examining the predictors of successful Airbnb bookings with Hurdle models: Evidence from Europe, Australia, USA and Asia-Pacific cities. Journal of Business Research. doi: 10.1016/j.jbusres.2021.08.035.

Sánchez-Medina, A. J., & C-Sánchez, E.(2020). Using machine learning and big data for efficient forecasting of hotel booking cancellations. International Journal of Hospitality Management. doi: 10.1016/j.ijhm.2020.102546.

Webb, T., Schwartz, Z., Xiang, Zh., & Singal, M. (2020). Revenue management forecasting: The resiliency of advanced booking methods given dynamic booking windows. International Journal of Hospitality Management. doi: 10.1016/j.ijhm.2020.102590.

Huang, L., & Zheng, W. (2021). Novel deep learning approach for forecasting daily hotel demand with agglomeration effect. International Journal of Hospitality Management. doi: 10.1016/j.ijhm.2021.103038.

Al Shehhi, M., & Karathanasopoulos, A. (2020). Forecasting hotel room prices in selected GCC cit-ies using deep learning. Journal of Hospitality and Tourism Management. doi: 10.1016/j.jhtm.2019.11.003.

Gaur, L., Afaq, A., Solanki, A., Singh, G., Sharma, S., Jhanjhi, N. Z., My, H. T., & Le, D.-N. (2021). Capitalizing on big data and revolutionary 5G technology: Extracting and visualizing ratings and reviews of global chain hotels. Computers & Electrical Engineering. doi: 10.1016/j.compeleceng.2021.107374.

Zhang, C., Xu, Z., Gou, X., & Chen, S. (2021). An online reviews-driven method for the prioritiza-tion of improvements in hotel services. Tourism Management. doi: 10.1016/j.tourman.2021.104382.

Ivanko, D., Sørensen, Å. L., & Nord, N. (2020). Selecting the model and influencing variables for DHW heat use prediction in hotels in Norway. Energy and Buildings. doi: 10.1016/j.enbuild.2020.110441.

Tanizaki, T., Hoshino, T., Shimmura, T., & Takenaka, T. (2020). Restaurants store management based on demand forecasting. Procedia CIRP. doi: 10.1016/j.procir.2020.05.101.

Lu, J., Meng, Y., Timmermans, H., & Zhang, A. (2021). Modeling hesitancy in airport choice: A comparison of discrete choice and machine learning methods. Transportation Research Part A: Policy and Practice. doi: 10.1016/j.tra.2021.03.006.

Gunter, U., & Zekan, B. (2021). Forecasting air passenger numbers with a GVAR model. Annals of Tourism Research. doi: 10.1016/j.annals.2021.103252.

Khan, W. A., Ma, H.-L., Chung, S.-H., & Wen, X. (2021). Hierarchical integrated machine learning model for predicting flight departure delays and duration in series. Transportation Research Part C: Emerging Technologies. doi: 10.1016/j.trc.2021.103225

Zhu, X., & Li, L. (2021). Flight time prediction for fuel loading decisions with a deep learning ap-proach. Transportation Research Part C: Emerging Technologies. doi: 10.1016/j.trc.2021.103179.

Yuan, Y., Yang, M., Feng, T., Rasouli, S., Li, D., & Ruan, X. (2021). Heterogeneity in passenger sat-isfaction with air-rail integration services: Results of a finite mixture partial least squares model. Transportation Research Part A: Policy and Practice. doi: 10.1016/j.tra.2021.03.003.

Kumova, D. M. (2021). The use of artificial intelligence-based platforms in tourism. Servis v Rossii i za rubezhom [Services in Russia and Abroad], 15(3), 18–26. doi: 10.24412/1995-042X-2021-3-18-26. (In Russ.).

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Published

2022-04-11

How to Cite

Muminova, S. R., & Tomashevskaya, N. G. (2022). Artificial intelligence as a basis for innovation management in tourism. Services in Russia and Abroad, 16(2/99), 94–100. https://doi.org/10.24412/1995-042X-2022-2-94-100

Issue

Section

STATE, MUNICIPAL AND CORPORATE GOVERNANCE IN SERVICES SECTOR: CURRENT ISSUES