Tetiana Riabovolyk

The russian aggression against Ukraine underscores the need to reassess regional strategies for digitalization and inclusivity. The study aims to identify strategies for enhancing these areas during wartime. Taxonomy and cluster and factor analysis methods have shown that regions with a higher level of digitalization have lower levels of poverty and unemployment. Specifically, regions in the top quartile of the digitalization index reported, on average, 12% lower unemployment rates compared to those in the bottom quartile. The analysis identifies distinct regional groupings: areas such as Zaporizhzhia, Kherson, Donetsk, and Luhansk are partially occupied and exhibiting low digitalization and inclusivity, in contrast with Mykolaiv and Kirovohrad that show moderate progress. Rural regions face a significant digital divide, with only 60% of rural households having stable internet access compared to 90% in urban areas. Factor analysis confirms that wartime conditions have accelerated digital transformation, evidenced by a 42% increase in Diia app usage from 2021 to 2023 and a rise in internet penetration from 62% in 2019 to 78% in 2023. Additionally, IT sector export revenues grew by 20% in 2022, and technology startups doubled between 2019 and 2023. The study proposes a strategic framework for regional adaptation: intensive digitalization and inclusivity for Zaporizhzhia, Kherson, Donetsk, and Luhansk regions; digitalization for Mykolaiv region; inclusivity for Kirovohrad and Zakarpattia regions; and balanced adaptation for Chernivtsi and Khmelnytskyi regions. Recommended measures include modernizing digital infrastructure, expanding educational opportunities, supporting startups, and aligning digital and social initiatives to foster regional resilience and development.

Dividend policy, as part of corporate governance, is largely dependent on the institutional environment in which companies operate. The study aims to determine factors affecting dividend policy in the conditions of the Ukrainian underdeveloped stock market, legal insecurity of minority shareholders, high cost and concentration of capital. For this purpose, hypotheses about the impact of a company’s financial state, size, business risk, and ownership structure on dividend payments were tested using a sample of 58 Ukrainian non-financial public joint-stock companies and applying Interactive tree classification techniques (C&RT). The resulting classification model for predicting dividend decisions correctly classifies 92.86% of companies that paid dividends and 93.3% of companies that did not. The findings, based on the classification tree and importance scale, prove the hypothesis that companies in which individuals and institutional investors have a controlling interest are more likely to pay dividends than other non-state companies. The financial indicators accurately classify only those firms that do not pay dividends, and business risk does not affect classification accuracy at all. The paper substantiates the ways of using the study findings for economic regulation, protection of minority shareholders’ rights, and proliferation of modern corporate governance practices.