Here, we explore the determinants and territorial heterogeneity of regional innovation development across Russian regions, employing the Russian Regional Innovation Index (RRII) and indicators of Gross Regional Product (GRP). The empirical database comprises 1363 small innovation enterprises (SMEs) spun-off from budgetary and research organizations and universities, specifically 34 flagship universities, 28 innovation clusters, 156 technology parks, and 15 science and technology innovation centers, along with indicators of the infrastructure–institutional environment, innovation–investment activity, scientific–educational potential, and human–social characteristics. Regression analysis enabled the identification of major factor groups that strongly effect regional innovation development, with infrastructure–institutional and innovation–investment indicators being the most significant. Cluster analysis of RRII and GRP delineated three groups of regions, (1) leaders with high innovation activity and substantial economic potential, (2) intermediate regions with moderate innovation activity and varying economic capacity, and (3) regions with high economic capacity but low innovation activity, exhibiting structural disparities between the economy and innovation. By combining regression and cluster analyses, we provide a comprehensive assessment of regional innovation ecosystems, reveal spatial imbalances, and identify priority areas for regional innovation policy. The study contributes to the theory of regional innovation systems and offers practical recommendations for strategic planning and optimizing the allocation of resources among key elements of innovation infrastructure.
Sysoeva et al. (2026) studied this question.