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http://dx.doi.org/10.22937/IJCSNS.2021.21.7.2

Formation of Scenarios for The Development of The Tourism Industry of Ukraine With The Help of Cognitive Modeling  

Shelemetieva, Tetiana (National University (Zaporizhzhia polytechnic))
Zatsepina, Nataly (National University (Zaporizhzhia polytechnic))
Barna, Marta (Lviv University of Trade and Economics)
Topornytska, Mariia (Lviv University of Trade and Economics)
Tuchkovska, Iryna (Lviv University of Trade and Economics)
Publication Information
International Journal of Computer Science & Network Security / v.21, no.7, 2021 , pp. 8-16 More about this Journal
Abstract
The tourism industry is influenced by a large number of factors that affect the development scenarios of the tourism in different ways. At the same time, tourism is an important component of the national economy of any state, forms its image, investment attractiveness, is a source of income and a stimulus for business development. The aim of the article is to conduct an empirical study to identify the importance of cognitive determinants in the development of tourism. The study used general and special methods: systems analysis, synthesis, grouping, systematization, cognitive modeling, cognitive map, pulse method, predictive extrapolation. Target factors, indicators, and control factors influencing the development of tourism in Ukraine are determined and a cognitive model is built, which graphically reflects the nature of the influence of these factors. Four main scenarios of the Ukrainian tourism industry are established on the basis of creating a matrix of adjacency of an oriented graph and forecast modeling based on a scenario approach. The practical significance of the obtained results lies in the possibility of their use to forecast the prospects of tourism development in Ukraine, the definition of state policy to support the industry that will promote international and domestic tourism.
Keywords
tourism; methodology; cognitive modeling; factors of influence; cognitive map; development scenarios;
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1 Jiao EX, Chen JL. (2019), Tourism forecasting: A review of methodological developments over the last decade. Tourism Economics, 25(3):469-492. DOI: https://doi.org/10.1177/1354816618812588   DOI
2 Mai, T. and Smith, C. (2018), Scenario-based planning for tourism development using system dynamic modelling: a case study of Cat Ba Island, Vietnam, Tourism Management, Vol. 68 No. 1, pp. 336-54.   DOI
3 Nor, M.E., Khamis, A., Saharan, S., Abdullah, M.A.A.,Sall eh, R.M., Asrah, N.M. and Lee, M.H. (2016), Malaysia tourism demand forecasting by using time series approaches, Social Science (Pakistan), Vol. 11 No. 12, pp. 2938-45.
4 Park, S, Lee, J, Song, W (2017) Short-term forecasting of Japanese tourist inflow to South Korea using Google trends data. Journal of Travel and Tourism Marketing 34(3): 357-368.   DOI
5 Zhertovskaja E., Yakimenko M. (2020) Cognitive Modeling of the Development of Tourist Recreational Cluster in the Imperatives of Innovative Development of the Territory, in New Silk Road: Business Cooperation and Prospective of Economic Development. DOI: https://doi.org/10.2991/aebmr.k.200324.137
6 World Travel & Tourism Council (2021). WTTC calls on UK's leadership to safely restore international mobility. Retrieved from: https://wttc.org/News-Article/WTTC-callson-UKs-leadership-to-safely-restore-international-mobility.
7 Petrevska, B. (2017), Predicting tourism demand by ARIMA models, Economic research-Ekonomska istrazivanja, Vol. 30 No. 1, pp. 939-50.   DOI
8 Solonina E. (2020). Domestic tourism in Ukraine is on the rise through COVID-19. How long? Retrieved from: https://www.radiosvoboda.org/a/vnytrishniy-turyzm-vukraini-covid19/30726545.html
9 Ukrinform (2020). "Domestic" tourist season 2020: cheap, but hard to get without a car. Retrieved from: https://www.ukrinform.ua/rubric-society/3052126-vnutrisnij-turisticnij-sezon-2020-desevo-ale-distatisa-bezavto-vazko.html
10 Assaf AG, Li G, Song H, Tsionas MG. (2019). Modeling and Forecasting Regional Tourism Demand Using the Bayesian Global Vector Autoregressive (BGVAR) Model. Journal of Travel Research, 58(3):383-397. DOI: https://doi.org/10.1177/0047287518759226   DOI
11 Farrell, S., Lewandowsky, S. (2018). Computational Modeling of Cognition and Behavior. Cambridge: Cambridge University Press. doi:10.1017/CBO9781316272503.
12 Department of Strategic Planning and Macroeconomic Forecasting (2020). Consensus forecast "Ukraine in 2020-2021: the consequences of the pandemic", no. 51. pp.31.
13 Hirashima, A, Jones, J, Bonham, CS. (2017), Forecasting in a mixed up world: nowcasting Hawaii tourism. Annals of Tourism Research, 63: 191-202. DOI: https://doi.org/10.1016/j.annals.2017.01.007.   DOI
14 Mei, L. (2015), Tourism demand forecasting by improved SVR model, International Journal of u-and e-Service, Science and Technology, Vol. 8 No. 5, pp. 403-12.
15 Papadopoulou G. (2020) Management Models of Tourism Industry: The Case of Greece. Open Science Journal, 5(1). DOI: https://doi.org/10.23954/osj.v5i1.2354   DOI
16 Anokhina, M., Golubev, A. Kondrashina, O. (2019) Cognitive Modeling in the Management of Economic Growth of the Agriculture in Russia. Journal of Environmental Management and Tourism, v. 10, n. 1, p. 119- 134, DOI: https://doi.org/10.14505//jemt.v10.1(33).12.   DOI
17 Chen, J.S. (1998), The tourists' cognitive decision making model, The Tourist Review, Vol. 53, No. 1, 4-9. DOI: https://doi.org/10.1108/eb058263   DOI
18 United Nations World Tourism Organization (2021). Tourist Numbers Down 83% But Confidence Slowly Rising. Retrieved from: https://www.unwto.org/taxonomy/term/347
19 Morales F.C. Tourism models: the differential key to economic success. Retrieved from: https://www.globaltourismforum.org/blog/2020/02/12/tourism-models-economic-success/
20 Shelemetieva T., Hres-Yevreinova S., Mamotenko D. (2020). Analytical tool of strategic management: use of pestanalysis in tourism, Black Sea Economic Studies, 53, 38-46, DOI: 10.32843/bses.53-6   DOI
21 Heathcote, A.; Brown, S.; Wagenmakers, E. (2015). An Introduction to Good Practices in Cognitive Modeling. In An Introduction to Model-Based Cognitive Neuroscience; Forstmann, B.; Wagenmakers, E., Eds.; Springer: New York and NY.
22 Ghalehkhondabi, I., Ardjmand, E., Young, W.A. and Weckman, G.R. (2019), A review of demand forecasting models and methodological developments within tourism and passenger transportation industry, Journal of Tourism Futures, Vol. 5 No. 1, pp. 75-93. DOI: https://doi.org/10.1108/JTF-10-2018-0061.   DOI