Assessing the effectiveness of digital transformation of regional economic systems

In the modern economic paradigm, digital transformation has established itself as a key driver of socio-economic development in territories. However, its outcomes demonstrate significant variability, driven by disparities in the availability of financial and technological resources, differences in the institutional environment, and the quality of human capital. This heterogeneity creates risks of increasing regional development disparities, necessitating the development of reliable tools for the comparative assessment of their effectiveness in the context of digital transformation. The study aims to develop and practically test a comprehensive methodological approach for the comparative assessment of the effectiveness of regional systems within the framework of digital transformation. The empirical basis for testing the methodology is data from the regions of the Volga Federal District (VFD). The methodology is based on a Data Envelopment Analysis (DEA) model, adapted to the specifics of regional systems. This allowed for a quantitative assessment of the relative efficiency of each region’s use of its digital potential. Another stage of the research involved the classification of regional systems using cluster analysis. Calculations based on data from 2016 and 2023 recorded positive dynamics, manifested in an increase in the average level of digital transformation efficiency in the VFD regions. Cluster analysis revealed a stable stratification, distributing all regions of the district into three distinct groups corresponding to high, medium, and low levels of digital transformation development. The developed methodological approach is of high practical value, as its results can be used by regional authorities to formulate targeted strategic decisions in the field of digital transformation, tailored to specific local conditions. A promising direction for subsequent scientific inquiry is the expansion of the analysis timeframe. This would allow for not only tracking long-term dynamics, but also analyzing the trajectories of regions transitioning between the identified clusters.