Akademik Veri Yönetim Sistemi
Sustainability (Switzerland), cilt.17, sa.11, 2025 (SCI-Expanded)
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.