Yelyzaveta Saihak

The effects of the economic recession and the COVID-19 crisis call for more active support for the tourism industry. To pursue a supranational tourism policy and create a favorable marketing environment at the national level, it is necessary to consider the objective differences between member states and their characteristics in the field of tourism. This study aims to highlight the main factors that characterize the asymmetry of the tourism industry in the EU countries, which allows ensuring the competitiveness of national tourism companies through the formation of an appropriate marketing strategy. The research methodology includes calculation of the asymmetry coefficient and cluster and classification analysis based on Eurostat data.
At the first stage, 27 indicators were selected that characterize the structural proportions of the tourism industry and the intensity of tourism in the EU countries. Based on the calculation of the asymmetry coefficient, a high level of heterogeneity of the tourism industry parameters in the EU countries for each of the indicators was demonstrated. At the second stage, clustering (algorithm – k-means, metric – Euclidean distance) of the EU countries was carried out according to the selected indicators. As a result, eight clusters were obtained, which showed asymmetry in developing national tourism sectors in the EU. At the third stage, as a result of classification (method – decision trees), seven combinations of indicators were identified, which completely distinguish the resulting clusters of the EU countries. The parameters included in these combinations are, in fact, the main factors of the asymmetry in the development of the EU tourism industry.
Based on the analysis of the asymmetric development of the tourism industry by country, it is possible to determine its growth points and competitiveness drivers in the EU internal market and identify marketing strategies.

Nowadays, there is uncertainty in the external environment of tourism infrastructure companies. Such a situation necessitates modeling their behavior in the strategic perspective and identifying key development factors that will minimize the impact of adverse socioeconomic indicators, thus minimizing the likelihood of negative financial outcomes. This is taking place in conditions of development of tourism infrastructure companies in Ukraine and intensified competition with the absence of an appropriate management system in the development, production, and sale of tourism products and services. Thus, this study is highly relevant. The aim of this paper is to model the strategic development of tourism infrastructure using the SWOT methodology and correlation-regression analysis. Based on the diagnosis of trends in tourism infrastructure companies of Ukraine and their environment, an approach to the development strategy of tourism infrastructure companies is proposed. First, market trends in the tourism industry, which is a crucial component of the development strategy of tourism infrastructure, are considered. The essence of the proposed approach is to use the SWOT methodology. The determination of the most significant factors of companies’ internal and external environment is based on multifactor regression-correlation modeling. It is concluded that the general growth of tourism in Ukraine is due solely to the growing demand among the citizens of Ukraine for outbound tourism.

Type of the article: Research Article

 Abstract
The growing pressure on tourism systems caused by overtourism, environmental degradation, and uneven spatial distribu-tion of tourist flows highlights the need for adaptive and sustainability-oriented management approaches. This study develops and tests an AI-driven eco-routing and tourist behavior modelling framework (ER-TBM) as a choice-architecture tool for sustainable destination marketing. The framework combines a fixed graph representation of a synthetic destination, Markov decision states, tabular Q-learning, and congestion-sensitive recommendations. To address reproducibility, the benchmark specifies ten points of interest, 22 undirected edges, all edge attributes, tourist preference and fatigue distributions, Poisson arrivals, recommendation compliance, congestion and emission functions, training settings, stopping rules, and random seeds. Five baselines and ER-TBM were evaluated under normal, peak, and event-demand conditions using 30 paired independent runs per scenario with 120, 180, and 240 agents, respectively. Relative to Dijkstra routing, ER-TBM reduced mean congestion exposure by 5.1-5.3% and increased tourist utility by 0.6-0.7%, while mean CO2 remained statistically indistinguishable and monetary route cost was 9.4-9.8% higher. The simulation-based Tourism Sustainability Index ranged from 0.712 to 0.732 for ER-TBM and did not show practically meaningful superiority over optimized baselines. The findings indicate that behavior-aware eco-routing can redistribute simulated visitor flows without a statistically significant increase in emissions, although this benefit is accompanied by higher monetary route costs. From a sustainable destination marketing perspective, the proposed framework represents a computational choice-architecture proposition. Its potential to influence actual tourist behavior, recommendation acceptance, destination image, satisfaction, and revisit intention requires further field-based validation.

Acknowledgment
The research was conducted within the framework of the prospective development plan for the scientific field “Social Sci-ences” No. BF/25-2026 on the topic “Formation of a Competitive Development Model for the Ukrainian Economy: Human Capital, Financial Stability, and the Development of Innovative Industries amid War-Related Challenges and the Emergence of the Sixth Technological Paradigm”.