Tetyana Nestorenko

The ongoing Russia-Ukraine war has profoundly disrupted the higher education landscape, compelling numerous institutions to adapt to unprecedented challenges. This study investigates the resilience and adaptive strategies of relocated higher education institutions under martial law, focusing on Berdyansk State Pedagogical University. The analysis emphasizes the critical role of digital transformation in sustaining academic operations amidst displacement. Methodologically, the study integrates qualitative interviews and quantitative analysis, exploring how cloud technologies, learning management systems, and AI-driven chatbots contributed to continuity in education. The results reveal that digital platforms ensured accessibility to educational resources, increased student engagement, and enhanced institutional resilience. Over 85% of surveyed participants identified learning management systems’ platforms as pivotal in maintaining educational quality, while AI chatbots were instrumental during crises, offering real-time communication and support even during power outages. Additionally, cloud-based solutions enabled the preservation of critical data and ensured uninterrupted access to academic resources, facilitating smooth transitions for both faculty and students. The findings underline that digital transformation not only mitigates immediate disruptions but also fosters long-term innovation in higher education institutions operating in war zones. This study offers valuable insights into how relocated institutions can leverage digital tools to build resilience, sustain educational quality, and adapt to evolving challenges in war-affected regions.

Acknowledgments
We are grateful to the Armed Forces of Ukraine for allowing us to engage in scientific research. This work was supported by the project “Supporting the cooperation of the University of Opole with Ukrainian universities within the FORTHEM Alliance 2024.”

Type of the article: Research Article

Abstract
This study examines the association between multidimensional digital transformation and national startup ecosystem performance across 23 European Union economies, comparing EU11 with 12 selected EU15 economies from 2017 to 2024. The balanced panel contains 184 country-year observations. Principal component analysis is used to construct three composite indicators: the Digital Services Index, Digital Connectivity Index, and Digital Human Capital Index. The indices demonstrate satisfactory factorial adequacy, internal consistency, and one-component structures supported by parallel analysis. Their associations with startup ecosystem performance are estimated using two-way fixed-effects models. Because the panel contains only 23 country clusters, the principal inference uses CR2 bias-reduced country-clustered standard errors with Satterthwaite-adjusted degrees of freedom. In the full-sample specification, DSI has a positive but only marginally significant association with startup ecosystem performance (β = 0.0834, p = 0.082), while DCI, DHCI, and e-government are statistically insignificant. R&D intensity has a negative contemporaneous coefficient that is also marginally significant (β = −0.0145, p = 0.053). Regional heterogeneity is jointly significant and is concentrated primarily in digital connectivity: DCI is negatively associated with startup ecosystem performance in the EU11 group, whereas its total slope is approximately zero in the selected EU15 group. DHCI has a positive total slope within the selected EU15 group, although the difference between the EU15 and EU11 slopes is not statistically significant. A one-year-lagged specification produces a positive but marginal DSI coefficient and does not identify statistically significant associations for the remaining predictors. These estimates should be interpreted as conditional associations rather than causal effects.