Authors:

I.A. Solovyova, M.V. Podshivalova, E.D. Akhmadeev

South Ural State University, Chelyabinsk, Russia

Abstract:

The key shortcomings of the Russian innovation system are weak commercialization, infrastructural constraints, and underdeveloped network interactions. This paper aims to design a new digital tool for assessing the effectiveness of regional innovation policy, namely an interactive map of the innovation landscape, using the Chelyabinsk region (Russia) as a case study. The methodological framework draws on the concept of network capital, the theories of innovation systems and infrastructure, innovation gaps, and the spatial approach to analysing the regional innovation landscape. The methods employed include interactive mapping, digital platform design, programming using Python and HTML, CSS, JavaScript technologies, and the matrix-based approach for formulating governance recommendations. The empirical foundation is built on data from companies operating in the Chelyabinsk region (the SPARK Interfax, the Federal Institute of Industrial Property (FIPS)), as well as from official websites of universities and support foundations (totalling more than 5,000 entries). The study offers a theoretical and methodological justification for a visualization tool designed to foster cooperation among innovation actors and track the effectiveness of regional innovation policy. Its pilot implementation using data from the Chelyabinsk region reveals that the regional center concentrates 80% of university intellectual property, 59% of industrial companies holding patents, and 51% of R&D centers. At the same time, regional policy exhibits characteristics of both “network radiation” and “the crowding effect”. The latter is associated with significant disparities in the infrastructural support for innovation activities across the region’s municipalities. To address the identified gaps, three types of cities are distinguished – “innovation core”, “untapped potential”, and “innovation periphery” – and possible development strategies for each are proposed. The interactive map and the decision matrix used by government authorities in establishing innovation policy can reduce innovation imbalances within the region.

Keywords: innovation landscape; innovation policy; regional policy; mechanism for stimulating commercialization; innovation infrastructure; interactive map; innovation gaps.

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For citation: Solovyova I.A., Podshivalova M.V., Akhmadeev E.D. (2026). Assessing the effectiveness of regional innovation policy based on interactive mapping. Upravlenets / The Manager, vol. 17, no. 5, pp. 52–68. DOI: 10.29141/2218-5003-2026-17-5-4. EDN: PNOEDH.