Current State and Prospects of Russian Outbound Tourism

  • Dmitry Aleksandrovich KOZLOV Plekhanov Russian University of Economics, Russian Federation

Abstract

The main aim of this paper is to analyze the Russian outbound tourism flow: problems of Russian tourist market, causes and consequences of repeated crisises. According to key findings, Russian tourist market is influenced by several factors. Developed equation of multiple variable regression model indicates that the main factors are wage of Russian’s converted in Euro and gross domestic product per capita. Regression model has great quality parameters and may be used to predict future condition of Russian outbound tourism. Russian Government makes all efforts to reorient own population to domestic tourism cutting off access to the rich world heritage. The decline in real incomes, the lack of regulation of tourist activities, the shortcomings of legislation, the lack of responsibility to customers are led to the decreasing of Russian outbound and domestic tourisms.

References

[1] Chen, K.Y., and Wang, C.H. 2007. Support vector regression with genetic algorithms in forecasting tourism demand. Tourism Management, 28(1): 215-226. DOI: https://doi.org/10.1016/j.tourman.2005.12.018.
[2] Dzhandzhugazova, E.A. et al. 2018. Entrepreneurial Clusters as a Model of Spatial Development of Russian Tourism. Journal of Environmental Management and Tourism, 4(28): 757-765. DOI:1 https://doi.org/0.14505/jemt.v9.4(28).09.
[3] Frechtling, D.C. 2001. Forecasting tourism demand: methods and strategies. Butterworth Heinemann, Oxford, 274 p.
[4] Frechtling, D.C. 2013. The Economic Impact of Tourism: Overview and Examples of Macroeconomic Analysis. UNWTO Statistics and TSA Issue Paper Series STSA/IP/2013/03 (Online), Available at: http://statistics.unwto.org/en/content/papers
[5] Kosheleva, A.I., Gareev, R.R., Valedinskaya, E.N., and Astafeva O.A. 2018. Professional Competency and Cross-Cultural Potential: a Study of Frontline Employees in Russian Non-Chain Hotels before the 2018 FIFA World Cup. Astra Salvensis, 6(S):511-520.
[6] Kozlov, D.A. 2016. Modelling and forecasting of Russian outbound tourism. Actual Problems of Economics, 181(7): 446-7.
[7] Kozlov, D.A., and Popov, L.A. 2016. Prognozirovanie v turizme. Uchebnik [Forecasting in tourism. Textbook]. Plekhanov Russian University of Economics. pp.320.
[8] Lin, C.J., Chen, H.F., and Lee, T.S. 2011. Forecasting tourism demand using time series, artificial neural networks and multivariate adaptive regression splines: evidence from Taiwan. International Journal of Business Administration, 2(2): 14-24. DOI: https://doi.org/10.5430/ijba.v2n2p14.
[9] Long, W., Liu, C., and Song, H. 2018. Pooling in Tourism Demand Forecasting. Journal of Travel Research. DOI: https://doi.org/10.1177/0047287518800390.
[10] Miloradov, K., and Eidlina, G. 2018. Analysis of tourism infrastructure development projects in the context of “Green economy”. European Research Studies Journal, 21(4): 20-30.
[11] Official documents for excluding tour operators from official state registry. Available at: https://www.russiatourism.ru/content/2/section/126/detail/3590/]
[12] Outbound Travel From Russia. 2018. IPSOS publication. Available at: https://www.ipsos.com/ipsos-comcon/ru-ru/analiz-vyezdnogo-potoka-rossiiskih-turistov
[13] Song, H., Dwyer, L., Li, G., and Cao, Z. 2012. Tourism economics research: A review and assessment. Annals of Tourism Research. 39(3): 1653-1682. DOI: https://doi.org/10.1016/j.annals.2012.05.023.
[14] Song, H., Li, G., Witt, S.F., and Athanasopoulos G. 2011. Forecasting Tourist Arrivals Using Time-Varying Parameter Structural Time Series Models. International Journal of Forecasting, 27 (3): 855-869. DOI: https://doi.org/10.1016/j.ijforecast.2010.06.001.
[15] Song, H., and Witt, S.F. 2000. Tourism Demand Modelling and Forecasting: Modern Econometric Approaches. In Advances in Tourism Research series. Taylor & Francis.
[16] Song, H., Witt, S.F., and Li, G. 2009. The Advanced Econometrics of Tourism Demand. In Routledge Advances in Tourism (Book 13). New York: Routledge. 234 p.
[17] Song, H., Witt, S.F., and Qiu, R.T. 2017. Can Bagging Improve the Forecasting Performance of Tourism Demand Models? In: Kreinovich V., Sriboonchitta S., Huynh VN. (eds) Robustness in Econometrics. Studies in Computational Intelligence, vol 692. Springer, Cham. DOI: https://doi.org/10.1007/978-3-319-50742-2_25
[18] Strong outbound tourism demand from both traditional and emerging markets in 2017. UNWTO Press Release. Available at: http://media.unwto.org/press-release/2018-04-23/strong-outbound-tourism-demand-both-traditional-and-emerging-markets-2017
[19] Valedinskaya, E.N., and Astafeva, O.A. 2018. Innovative methods for demand stimulation in tourism industry. Astra Salvensis, 6(S): 613-626.
[20] Volchek, K., Liu, A., Song, H., and Buhalis, D. 2018. Forecasting tourist arrivals at attractions: Search engine empowered methodologies. Tourism Economics. DOI: https://doi.org/10.1177/1354816618811558.
[21] Yang, X., Pan, B., and Evans, J.A. 2015. Forecasting Chinese tourist volume with search engine data. Tourism Management, 46: 386-397. DOI: https://doi.org/10.1016/j.tourman.2014.07.019.
Published
2019-02-04
How to Cite
KOZLOV, Dmitry Aleksandrovich. Current State and Prospects of Russian Outbound Tourism. Journal of Environmental Management and Tourism, [S.l.], v. 9, n. 6, p. 1263-1276, feb. 2019. ISSN 2068-7729. Available at: <https://journals.aserspublishing.eu/jemt/article/view/2670>. Date accessed: 22 aug. 2019. doi: https://doi.org/10.14505//jemt.9.6(30).16.