Do Different Settings Matter in the Economically Sustainable Tourism Approach? A Comparative Study of Serbia, Kazakhstan, and Hungary

Marko D. Petrović (), Tamara Gajić, Shakhislam Laiskhanov (), Milan M. Radovanović, Željko Anđelković, Emin Atasoy and Dariga M. Khamitova
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Marko D. Petrović: Geographical Institute “Jovan Cvijić” SASA, 11000 Belgrade, Serbia
Tamara Gajić: Geographical Institute “Jovan Cvijić” SASA, 11000 Belgrade, Serbia
Shakhislam Laiskhanov: Department of Science and Innovation, International Educational Corporation, Almaty 050043, Kazakhstan
Milan M. Radovanović: Geographical Institute “Jovan Cvijić” SASA, 11000 Belgrade, Serbia
Željko Anđelković: Department of Geography and Tourism, Faculty of Science and Mathematics, University of Niš, 18000 Niš, Serbia
Emin Atasoy: Department of Social Sciences, Faculty of Education, Uludag University, 16059 Bursa, Turkey
Dariga M. Khamitova: Department of Art and Art Management, Kazakh National Academy of Choreography, Astana 010000, Kazakhstan

Sustainability, 2025, vol. 17, issue 11, 1-35

Abstract: This study explores residents’ perceptions of tourism development with a particular emphasis on the economic dimension of sustainability, focusing on how economic benefits, costs, and related factors shape local support in Serbia, Kazakhstan, and Hungary. By analyzing perceived advantages and disadvantages, the study aims to assess the extent of local support for tourism and the moderating effects of travel frequency and contact with tourists. In parallel, tourist arrival forecasts for 2025–2030 provide context on the anticipated dynamics of tourism growth, with Hungary showing the highest projected increase. Using advanced statistical techniques, including Multi-Group Analysis (MGA), structural equation modeling (SEM), and machine learning methods, key factors driving tourism support were identified. Positive perceptions of economic benefits and cultural identification significantly enhance support for tourism, while perceived costs act as inhibitors. The application of Random Forest and XGBoost (version 1.7.x) models improved predictive accuracy, while K-means clustering and principal component analysis (PCA) clarified relationships among constructs. The findings provide actionable insights for developing sustainable tourism strategies that prioritize economic outcomes and community engagement, particularly in culturally and economically diverse settings.

Keywords: economic benefits; tourism development; perceived benefits; cultural identification; predictions (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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