Innovative Development of Regions: An Integrated Analysis of Infrastructure, Investment, and Human Capital

Abstract

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.

1. Introduction

In state-of-the-art academic and applied research, innovation development is regarded as one of the key factors (i) facilitating economic growth, (ii) enhancing territorial competitiveness, and (iii) fostering long-term sustainable socio-economic systems. Amid increasing global market segmentation, accelerated technological renewal, and structural economic transformations, the ability of regions to generate, disseminate, and implement innovations becomes a strategic resource shaping their trajectories of economic and social development. Regions with developed innovation infrastructure, stable scientific and educational linkages, and effective technology transfer mechanisms possess substantial competitive advantages, both within the national economy and in the international economic environment. These advantages manifest themselves in higher economic growth rates, diversification of the industrial structure, increased labor productivity, and greater investment attractiveness. Conversely, limitations in the innovation environment reduce regional potential, exacerbating spatial socio-economic differentiation.

In Russia, issues of innovation development are outlined in a number of strategic and program documents, including the Strategy for Scientific and Technological Development, the national project “Science and Universities,” as well as regional programs supporting innovation activities, technological clusters, technology parks, and innovation science and technology centers. The implementation of these initiatives is aimed at fostering a favorable innovation environment, stimulating research and development, facilitating knowledge commercialization, and enhancing the competitiveness of regional economies. However, despite the availability of institutional and financial support from the state, pronounced differentiation among Russian regions in terms of innovation development persists. A number of constituent entities of the country demonstrate consistently high innovation activity and intensive adoption of new technologies, while other regions face constraints that hinder the implementation of innovation processes. This asymmetry underscores the need for a comprehensive analysis of the factors underlying differences in regional innovation development within the country.

The relevance of this study is determined by several factors. First, the innovation activity of regions is directly linked to the economic resilience, productivity, and overall competitiveness of the national economy. Second, a high level of innovation development fosters the emergence of new industries, the creation of high-skilled jobs, and growth in value added. Third, contemporary external and internal challenges, including global economic crises, pandemic-related restrictions, and geopolitical instability, amplify the significance of innovation development as a tool for enhancing the technological resilience and strategic autonomy of regions and countries with different economic models. Despite a broad body of research, the scientific literature remains heterogeneous in interpreting the factors underlying innovation activity, as well as in approaches to their systematization and empirical assessment. Domestic and international studies tend to focus either on (i) analyzing specific aspects of innovation activity, such as scientific–educational potential or regional investment activity (Pyo & Choi, 2025; Y. Zhang, 2023; Li & Wang, 2024), or (ii) on examining innovation processes within individual regions or limited territorial groups, which complicates the generalization and cross-regional comparison of results (Toma & Laurens, 2024). Consequently, a research gap remains associated with the insufficient development of comprehensive cross-regional models for assessing the relative contribution of various factors affecting differences in the innovation development of regions.

The objective of this study is to identify and empirically evaluate the factors influencing the innovation development of regions in Russia by grouping them into broad factor categories and applying methods of regression and cluster analysis.

The novelty of the study stems from the development of an original model of innovation development factors consolidated into four groups: (1) the scientific–educational factor group, encompassing indicators of scientific and educational activity aimed at shaping the innovation potential of regions, (2) the innovation–investment factor group, reflecting the level of adoption of new technologies, investment activity, development of innovation projects, and attraction of venture capital, (3) the infrastructure–institutional factor group, accounting for the availability of innovation infrastructure, state support institutions, technology parks, clusters, as well as the regulatory and legal framework and mechanisms for governing innovation activity, and (4) the human–social factor group, characterizing human capital, workforce qualifications, working conditions, social factors, and mechanisms for stimulating innovation activity.

This study makes a methodological contribution to the investigation of regional innovation development, using the example of Russia, by proposing an integrative factor model that enables a systemic assessment of innovation activity in regions with different economic structures and levels of institutional development, thereby making it applicable in an international context. The results may have potential applications for public authorities, development institutions, business entities, and scientific organizations in formulating and adjusting regional strategies and programs for innovation development. The Section 2 and Section 3 examine theoretical approaches to studying the innovation development of Russian regions and the major determinants shaping their innovation activity. The Section 4 describes the research methodology and the methods of regression and cluster analysis employed. The Section 5 and Section 6 are devoted to the empirical analysis of factors of innovation development in Russian regions and their typologization, taking into account the level of innovation and economic development. The Section 7 presents the findings of the study and recommendations for enhancing regional innovation activity.

The present study is restricted to the data of 2023, the latest one available for current official statistics.

2. Literature Landscape

2.1. Theoretical and Empirical Foundations for Innovation Development in Regions

2.1.1. Concept of Regional Innovation System

In recent decades, the innovation development of regions has taken a central place in research on global and regional economics, management, and innovation studies, being regarded as one of the key factors of territorial competitiveness and long-term sustainable growth. The dominant theoretical framework in this area is the concept of RIS, interpreted as a set of stable and institutionalized interactions between universities, research organizations, industrial enterprises, and public institutions that facilitate the creation, dissemination, and use of knowledge and technologies at the regional level (Lundvall, 2010; Cooke et al., 1997; Asheim & Isaksen, 2002). Within Lundvall’s evolutionary approach, innovations are viewed as the outcome of socially and institutionally embedded learning processes, which complement the institutional–organizational interpretation of RIS and help explain the persistence and spatial differentiation of regional innovation dynamics. Subsequent research confirms that the RIS concept retains its status as a primary analytical tool for studying regional innovation development in the context of globalization and the growing complexity of innovation processes (Asheim et al., 2011; Lundvall, 2007).

2.1.2. Institution Environment and Spatial Heterogeneity of Innovations

Contemporary studies building on this theoretical tradition have expanded the analysis beyond purely economic factors and emphasize the institutional and social conditions of innovation activity. Empirical and review works in recent years demonstrate that differences in institutional quality, the level of cooperation, and the structure of innovation infrastructure largely determine the spatial heterogeneity of regional innovation development (Fitjar & Rodríguez-Pose, 2013). In this context, cross-country studies highlight the importance of national institutional settings and public policy in shaping conditions for knowledge transfer and innovation activity, underscoring that the efficiency of innovation systems stems not only from resource availability but also from coherence between regulatory and governance mechanisms (Wright, 2025). Similar conclusions regarding the high importance of institutional quality, education, and innovation for ensuring sustainable economic growth have been drawn for G20 countries, confirming the universality of these factors while preserving regional specificity (Dogan, 2025).

2.1.3. Role of Universities in Regional Innovation Systems

A substantial body of scientific publications is dedicated to analyzing the role of universities as key actors in regional innovation systems. Universities are viewed not only as producers of knowledge and human capital but also as active participants in innovation ecosystems, engaging in cooperation with business, building entrepreneurial networks, and establishing innovation-based enterprises. For instance, Suorsa (2007) offers a critical analysis of the transformational role of universities, emphasizing the need for their active institutional embedding in regional innovation processes, which contributes to strengthening the innovative dynamics of regional systems. The works by González-López and Asheim (2020) show that university participation in “smart specialization” strategies promotes the diversification of regional knowledge and the development of sustainable innovation clusters. The analysis of the evolution of university ecosystems also indicates that strengthening the entrepreneurial and innovation functions of universities enhances their contribution to regional innovation development (Ranga & Etzkowitz, 2013; Autio et al., 2014).

The current empirical studies further develop these findings by focusing on specific mechanisms of university–business interaction. For example, Davey et al. (2025) indicate that the intensity and quality of university–business cooperation significantly affect the founding of academic spin-offs, with the regional context and institutional environment determining the sustainability of such companies. In turn, Kirihata (2024) demonstrates that the activity of technology transfer offices can reduce the bankruptcy risk of university spin-offs, although the effect varies considerably depending on the level of development of the regional innovation infrastructure. Complementing these results, García-Machado et al. (2021) confirm, via PLS-SEM modeling, that joint university–business R&D projects positively affect firms innovation activity, thereby strengthening regional innovation linkages.

These findings are systematically summarized in several reviews dedicated to university ecosystems and innovation (for instance, Marchant-Pérez & Ferreira, 2025) and are supported by empirical assessments of the impact of university activity on regional innovation indicators (Natário & Oliveira, 2025). In particular, it is emphasized that sustainable interaction between universities and industry accelerates technology transfer, fosters entrepreneurship, and promotes the commercialization of scientific developments, thereby shaping resilient regional innovation circuits.

2.1.4. Innovation Infrastructure and Agglomeration Effects

Alongside universities, the literature extensively analyzes the role of innovation infrastructure, including clusters, technology parks, innovation centers, and other forms of spatial concentration of innovation activity. Research shows that the development of such elements contributes to the growth of innovation activity among firms, strengthens inter-organizational linkages, and fosters stable partnerships between science and industry, in line with global perspectives on the functioning of innovation ecosystems (Kastryulina, 2012). Additional empirical evidence on the spatial dimension of innovation development is provided in the work by Hu et al. (2025), where the integration of higher education strategies and the development of urban and regional agglomerations are shown to contribute substantially to the formation of stable innovation clusters and the strengthening of agglomeration effects.

2.1.5. Digitalization and Sustainability of Innovative Development

Another research direction relates to the influence of digitalization and digital technologies on regional innovation development. Numerous systematic reviews demonstrate that digital platforms and tools expand opportunities for knowledge exchange, accelerate learning and technology-transfer processes, and create new models of interaction among actors in regional innovation systems (Khizar et al., 2025). Within more comprehensive frameworks, digital transformation is considered in close connection with sustainable development. For instance, the Digital Sustainability Ecosystem approach highlights the potential of digital technologies in achieving sustainability goals by improving resource efficiency, fostering digital cooperation, and engaging multi-level actors (Florek-Paszkowska & Ujwary-Gil, 2025). Several studies confirm a positive correlation between the innovative and digital development of regions and their economic growth. Empirical data indicate a correlation between the level of adoption of innovations and digital tools and indicators of GRP, investment activity, and social development, which is of fundamental importance for regional policymaking (Dzhalalov et al., 2025). In this context, research conducted for various countries and regions shows that investments in R&D act as a significant driver of economic growth, but their effect is amplified in the presence of effective institutions and governance mechanisms (Gutiérrez-Sánchez & Benéitez-Andrés, 2025; Singh et al., 2025).

2.1.6. Institutional Mechanisms and the Role of Human Capital

An important line of inquiry involves analyzing the institutional environment for innovations, including public strategies, support instruments, venture financing, and public–private partnership mechanisms. Some empirical reports indicate that the efficiency of innovation development is largely determined by the coherence of institutional incentives and coordination mechanisms between public and private actors (Shatskaya, 2022). Complementing this approach, Mirahmadi et al. (2025) demonstrate through the application of machine learning methods that removing institutional and regulatory barriers is a critical condition for the advancing of startup ecosystems in developing countries. Numerous studies also emphasize the crucial role of education and human capital as foundational components of innovation development, especially in the context of digital transformation and the growing demand for new competencies (Dospanov, 2025).

2.1.7. Networking and International Integration of Innovations

The significance of inter-regional and international interaction, knowledge and practice exchange, and participation in networked and transnational projects is widely considered a factor in strengthening the innovation potential of regions and countries (Ballesteros-Ballesteros & Zárate-Torres, 2025). It is emphasized that engaging regions in multi-level cooperation networks expands access to external sources of knowledge, technologies, and resources, and enhances the learning and adaptive capacity of regional innovation systems. For example, Loučanová et al. (2026) show that trade openness and regional participation in supranational clusters contribute to increased innovation activity in EU countries, thereby intensifying spatial differentiation effects and generating additional competitive advantages.

2.1.8. Current Methodological Approaches and Research Gaps

It is worth noting that there is growing interest in the literature to the application of bibliometric and systematic reviews aimed at identifying major research trends, the evolution of scientific approaches, and future prospects in the field of innovation ecosystems (Pilelienė & Jucevičius, 2023). Such studies help to structure the accumulated empirical and theoretical knowledge, reveal dominant themes and methodological approaches, and identify existing research gaps. Contemporary works are increasingly focused on developing integrated methodological approaches that combine factor, cluster, and systemic models of analysis. This provides a deeper understanding of the interrelations between elements of regional innovation development and allows for assessing their relative significance in different institutional and spatial contexts.

Thus, the state-of-the-art literature indicates that the innovation development of regions represents a complex, multifactorial, and systemic process in which university ecosystems, innovation infrastructure, digital technologies, institutional mechanisms, and human capital interact. The combination of these elements creates the conditions for generating, disseminating, and commercializing knowledge and determines a region’s capacity for sustainable economic growth and adaptation to technological and institutional changes. However, despite a significant body of theoretical and empirical research, gaps remain related to assessing the role and relative significance of individual factors in regional innovation development, as well as to identifying variations in their influence depending on institutional and spatial context. In particular, questions regarding the interaction of the major components of innovation ecosystems and their combined effect on regional innovation dynamics remain insufficiently studied. This necessitates a more detailed and comprehensive analysis of the core elements of innovation ecosystems as factors of regional innovation development, which defines the logic and structure of the present study.

2.2. Developing Hypotheses

Accounting for the presented theoretical analysis and the identified theoretical gaps, it seems appropriate to empirically assess the relative importance of various groups of factors in the regional innovative development and the nature of their interaction. The study tests the following hypotheses:

Hypothesis 1 (H1).

Infrastructure–institutional and innovation–investment factors exert a more significant influence on the innovation development of regions compared to scientific–educational and human–social factors.

Hypothesis 2 (H2).

Quantitative indicators of scientific–educational and human–social development of regions do not provide a direct positive effect without considering institutional and infrastructure conditions.

3. Theoretical Framework

3.1. The Role of Universities in Regional Innovation Development: Approaches and Russian Practice

In the economic literature, universities are regarded as key institutional actors in regional innovation development, fulfilling the functions of knowledge generation, human and intellectual capital formation, and the translation of scientific results into the regional economy. Unlike the traditional model of the university as a primarily educational institution, current research emphasizes its active role in shaping innovation ecosystems and regional innovation systems (Cooke, 2001; Etzkowitz, 2008). According to a large body of research, universities serve not only as sources of qualified personnel but also as centers for research activity, entrepreneurial initiatives, and network interactions with business and the state. For instance, the Triple Helix model (Etzkowitz & Leydesdorff, 2000) conceptualizes the interaction of science, industry, and government as a fundamental source of regional innovation dynamics. The further development of this concept in works by Carayannis and Campbell (2009) and Carayannis et al. (2012) incorporates societal and cultural factors through the Quadruple and Quintuple Helix models, enhancing the understanding of universities as structural elements of innovation ecosystems. Empirical data confirm that regions with strong university centers exhibit higher innovation activity, greater resilience to economic shocks, and an enhanced capacity for structural transformation (Audretsch & Feldman, 2004). Cross-regional studies in EU and OECD countries show a statistically significant correlation between university densities, the intensity of university–industry interaction, and levels of patent activity and technological entrepreneurship (Guerrero et al., 2016; Perkmann et al., 2021). According to Florida (2003), universities serve as critical anchors for regional development, attracting talent and fostering an innovation-oriented environment. The most well-known examples include the Silicon Valley ecosystem, which formed with significant involvement from Stanford University, as well as innovation clusters around Massachusetts Institute of Technology in the USA, University of Cambridge in the UK, and Technical University of Munich in Germany. In these cases, universities act as the core of innovation networks uniting startups, research centers, venture capital, and regional governance bodies (Saxenian, 1994; Casper, 2013).

A distinct line of research focuses on differentiating universities based on their functionality in innovation development. Several reports distinguish between research universities, regionally oriented universities, and applied universities whose contributions to regional innovation differ in scale and impact mechanisms (Benneworth & Charles, 2005). It is emphasized that regionally oriented universities, in particular, play a system-shaping role in the development of peripheral and industrially transforming regions while adapting global knowledge to local conditions (Huggins & Johnston, 2009). The research underscores that regionally oriented universities are best suited for inclusive innovation development, especially in regions with medium to low levels of economic diversification (Benneworth et al., 2016).

In Russian academic and policymaking practice, a similar logic is implemented through support for a network of so-called “anchor universities”, intended to function as centers of socio-economic and innovation development in the constituent entities of the country. The concept of anchor universities was institutionally established as part of state policy for modernizing higher education and regional development, primarily in documents of the Russian Ministry of Science and Higher Education and university strategic development programs (Guseva et al., 2024). Anchor universities were established on the basis of regional higher education institutions with substantial educational, scientific, and human capital potential, aiming to concentrate resources and enhance their contribution to the regional economy. The importance of anchor universities for regional innovation processes is confirmed by research showing that universities participating in state strategic academic leadership programs such as “5-100” and “Priority-2030” have shifted their institutional missions and strategic models toward strengthening their innovation, research, and regional roles (Tsvetkova, 2025). Currently, anchor universities include, for instance, Don State Technical University, Samara National Research University, Tyumen Industrial University, Siberian State Industrial University, Belgorod State Technological University, and a number of other institutions located in major industrial and agglomeration centers of the country.

The role of anchor universities in advancing innovation in Russian regions manifests in several interconnected areas: (i) training personnel for priority sectors of the regional economy, (ii) developing applied research and experimental work, (iii) building up SIEs, (iv) participating in cluster formation, and (v) implementing projects for the technological and social development of territories (Berestov et al., 2020). In a number of regions, anchor universities initiate cooperative projects with industrial enterprises, shared-use centers, and regional authorities (Ilyina, 2020). Some empirical reports indicate that the presence of an anchor university correlates with enhanced regional innovation activity, expanded interaction networks between science and business, and improved quality of human capital (Myzrova et al., 2023). However, it is also noted that the effectiveness of this model significantly depends on the institutional environment, regional policy, and the degree of university involvement in real economic processes.

Thus, we can see that universities play an important role within regional innovation ecosystems, although the forms and scale of their influence may vary significantly depending on the institutional context, the model of university development, and the nature of interaction with other actors. This underscores the need for further consideration of other elements of regional innovation ecosystems, namely clusters and SIEs built on the basis of scientific and educational organizations.

3.2. Clusters as a Factor in the Innovative Development of Regions

The concept of a “cluster” in economic theory dates at least back to the works of Marshall (1890), who, analyzing the spatial organization of industry, highlighted the advantages to geographically concentrating companies within the same industry driven by agglomeration effects, labor specialization, and the informal exchange of knowledge. According to Marshall, the co-location of companies fosters a specific productive environment in which knowledge and skills diffuse more rapidly than under conditions where economic agents are spatially dispersed. The further development of the cluster concept is linked to the theory of competitive advantage, primarily in the works by M. Porter (Porter, 1990, 1998), who regarded clusters as a key factor in enhancing competitiveness and fostering the innovative growth of regional economies. In the conventional interpretation, a cluster is a geographically concentrated group of interconnected companies, specialized suppliers, service organizations, and associated institutions—such as universities, research centers, public authorities, and infrastructure organizations. Their interaction creates conditions for strengthening innovation dynamics, increasing productivity, and achieving sustained competitive advantage at both regional and global levels (Veselovsky et al., 2021).

Most reports dedicated to regional innovation development emphasize that clusters serve as a valuable mechanism for stimulating the innovation activity of economic agents by fostering dense network interactions, accelerating knowledge exchange, and reducing transaction costs. Building on Porter’s ideas, scholars from neoclassical and evolutionary perspectives view clusters as institutionally and spatially embedded environments for the generation and diffusion of knowledge where spillover effects occur—the inter-firm and inter-organizational transfer of technologies, competencies, and managerial practices. The presence of such effects, as theoretical and empirical studies show, positively impacts the innovation performance and competitiveness of companies participating in clusters (Bittencourt et al., 2018; Kim et al., 2023; Kowalski & Hegerty, 2025; Chetia et al., 2025). At the same time, contemporary research points to potential limitations and negative effects of cluster development. In particular, it is noted that excessive spatial concentration of companies can lead to “local lock-in”, excessive resource duplication, intensified competition for limited factors of production, as well as risks of uncontrolled knowledge leakage and weakened incentives for radical technological change. Such effects are especially characteristic for mature clusters and require institutional mechanisms for renewal and diversification (Boshma, 2005; Cassiman & Veugelers, 2006).

A significant strand of cluster analysis is the institutional approach, where clusters are considered not only as a result of market coordination but also as active institutional formations. Under this view, clusters can function as institutional entrepreneurs, initiating changes in the formal and informal rules governing participant interactions, lowering institutional barriers, and shaping a favorable environment for innovation activity in regions. They are increasingly seen as a special form of meta-organization capable of coordinating strategies and interactions among enterprises, scientific and educational institutions, and state structures (Lupova-Henry et al., 2021; Radziwon, 2023), and influencing the configuration of the regional innovation system (Shirasawa & Seo, 2025). Numerous empirical studies confirm a positive relationship between the development of clusters and the level of regional innovation activity. In particular, they highlight the role of clusters in forming stable mechanisms of inter-firm cooperation, developing collaboration with universities, and integrating regional actors into global research and development networks. The most well-known examples, such as the Silicon Valley innovation cluster and the Boston biotechnology cluster, demonstrate the significant contribution of cluster structures to the creation of innovative products, the establishment of technological standards, and the attraction of venture and corporate capital (Engel, 2015; Giroud et al., 2024). In the Russian academic literature, particular attention is paid to empirical assessments of the impact of cluster initiatives on the innovation potential of regions, taking into account features of the national institutional environment. Studies dedicated to the development of biomedical (Malyshev & Kozina, 2020; Islankina et al., 2019), industrial (Zhambrovskij et al., 2020; Andreeva, 2020), and high-tech (Nosonov, 2023) clusters reveal both successful practices of cluster cooperation and institutional constraints associated with the dominant role of the state and a prevailing “top-down” approach to initiating cluster projects (Kutsenko et al., 2017; Abashkin et al., 2017). The reports indicate that the growth of innovation activity within cluster associations in Russian regions is accompanied by strengthened network interaction, the formation of collective competencies, and an increased role of local innovation agents, including universities and scientific organizations (Mottaeva, 2025).

Thus, the academic literature confirms that clusters are one of the key factors of regional innovation development, providing an institutional and organizational environment for the concentration of knowledge, accelerated information exchange, and cooperation among different types of actors.

3.3. SIEs Formed on the Basis of Universities and Research Institutes as a Factor in the Innovative Development of Regions

Built on universities and research institutes, SIEs are regarded as an important element of regional innovation ecosystems, facilitating the diffusion of technologies, the commercialization of knowledge, and the creation of sustainable innovation networks. This phenomenon is actively studied within the frameworks of technological and/or university entrepreneurship and cluster development, where SIEs serve as a link between the scientific sphere and the regional economy. Within the Triple Helix model, accounting for university–industry–government interaction, developed by Etzkowitz and Leydesdorff (1999), the transformation of universities into active participants in the innovation economy is accompanied by the development of entrepreneurial functions and the creation of spin-offs and startups emerging at the intersection of scientific research and market opportunities. Empirical research confirms that university spin-offs contribute to the technological renewal of regional economies while strengthening interaction between science, industry, and venture capital (Rothaermel et al., 2007). Similarly, Guerrero et al. (2016) note that university-based SIEs form an institutional and organizational “bridge” between academic knowledge and commercial applications, stimulating inter-organizational cooperation and the development of innovation ecosystems at the regional level. A significant contribution to understanding the functioning mechanisms of SIEs was made by Cohen and Levinthal (1990), who proposed the concept of absorptive capacity, interpreted as a company’s ability to recognize the value of external knowledge, assimilate it, and apply it in innovation and commercial activities. This concept has gained wide acceptance in studies of university entrepreneurship, as it helps explain why the mere existence of scientific developments does not guarantee an innovation effect without organizations’ ability to adapt knowledge to market requirements. In turn, Leitão et al. (2022) emphasize that the successful development of SIEs largely depends on the presence of institutional and organizational support structures, including business incubators, accelerators, technology parks, and professional networks. Furthermore, the works by Shane (2004) and Vohora et al. (2004) provide a detailed analysis of the entrepreneurial activity of scientists in the university environment and identify factors for the creation and development of successful spin-offs such as (i) researchers’ entrepreneurial experience, (ii) institutional support, (iii) access to financial resources, and (iv) quality of management decisions. These studies highlight that university-based SIEs are not passive by-products of scientific activity but emerge as a result of the interaction of scientific, entrepreneurial, and institutional potentials.

Contemporary research focuses on the problem of transitioning from entrepreneurial intentions to the practical implementation of innovation projects. For instance, Kitić et al. (2025) show that the contribution of innovative SIEs to regional development is determined not only by their creation but also by the conditions for their subsequent growth and scaling. The authors demonstrate that the gap between entrepreneurial intentions and actual action is largely due to the employed financial models, access to external financing, and institutional characteristics of the regional environment, especially in countries with emerging ecosystems. These findings underscore the importance of the financial and institutional context for realizing the innovation potential of university-based SIEs. Other studies indicate that startups and SIEs play a critical role in the dynamics of regional technological modernization. For example, Amini Sedeh et al. (2022) analyze mechanisms for stimulating entrepreneurship in developed and developing economies and show that university-based SIEs are an important element of structural transformations in the regional economy. The authors link the growth of SIEs to the quality of the business environment, the development of support infrastructure, and the availability of qualified human capital. On the other hand, Gregorio and Shane (2003) and Galloway and Brown (2002) emphasize the role of educational programs and academic training in evolving high-tech startups and stimulating scientists’ entrepreneurial activity.

It is also worth noting that research on the network aspects of innovation development has significantly contributed to the study of SIEs. Thus, Granovetter (1985), within the theory of social embeddedness, indicates that innovative enterprises develop more effectively when integrated into stable social and professional networks. This proposition was further developed in the works by Bathelt et al. (2002) concerning cluster and regional innovation systems, where the role of local and global network connections in shaping firm behavior is emphasized. In the digital economy, research by Fini et al. (2011) demonstrates that digital platforms, online services, and digital financing tools such as crowdfunding or ICOs expand the potential of university SIEs via facilitating access to markets, investors, and international partnership networks. This is particularly relevant for regions with limited resources, where digital channels partially compensate for structural limitations on innovation development.

Russian research also actively develops the topic of university-based SIEs, focusing on the institutional conditions for their emergence, functioning, and interaction with regional innovation ecosystems. For instance, Kuznetsova and Shmakova (2022a, 2022b) examine the potential and profile of SIEs in Russian universities by analyzing their role in the development of the regional innovation economy and identifying sectoral and territorial features of their operation. Turko et al. (2023) investigate legal and organizational issues related to the creation and state registration of SIEs in universities and scientific organizations. A later work by Turko et al. (2024) supplements these conclusions with a systematic analysis of the economic activity of SIEs operating in the Russian scientific and educational sphere, allowing for an assessment of their development dynamics, challenges, and opportunities for state support. Podshivalova and Almrshed (2021) study the management of the innovation potential of SIEs in high-tech industries, highlighting the importance of support structures including business incubators and accelerators. Prokopchuk et al. (2021) additionally analyze the mechanisms of state registration and the regulatory framework for SIE operations, helping to evaluate their effectiveness and degree of integration into regional innovation ecosystems. The financial dimension is also considered a critical factor for the successful development of SIEs. For example, Gavrilina (2022) analyzes global and national trends in the financing infrastructure for SIEs, demonstrating the influence of the availability of financial instruments on regional innovation activity. In another work, Kokhno (2021) examines the opportunities provided by business incubators for SIE development, emphasizing their role in shaping an entrepreneurial environment and supporting startups. Furthermore, Langa et al. (2025) and Bawono et al. (2026) consider the concept of open innovation as a driver of industrialization in developing economies, directly linking it to the creation and scaling of university startups. The important role of technology transfer in shaping entrepreneurial potential is noted by Paredes-Leon et al. (2023), who emphasize an efficiency of knowledge-transfer mechanisms in promoting the sustainable development of SIEs and regional innovation infrastructure. Thus, Russian research also contributes to the analysis of foundations in which the institutional, organizational, financial, and infrastructural aspects of SIE operation are viewed as interconnected elements ensuring their sustainable development and fostering innovation development of regions and the national economy as a whole.

An important research direction is the analysis of technology-transfer mechanisms that enable the movement of scientific results into the commercial sphere. The works by Bozeman and Boardman (2014a, 2014b) examine institutional barriers and incentives affecting the efficiency of technology transfer, including issues of intellectual property, licensing agreements, and legal regulation. These aspects are of fundamental importance for university-based SIEs because technology transfer connects the academic environment and the market. Additionally, several comparative studies show that regions and countries with well-developed support systems for university startups demonstrate higher levels of innovation activity. For instance, research on Germany, the USA, and Northern Europe (Clarysse et al., 2005; Bercovitz & Feldman, 2008) reveals that the presence of business incubators, accelerators, and specialized financial institutions significantly increases the survival and growth rates of university SIEs. Moreover, beyond economic and institutional effects, the social impact and diffusion of innovations at the societal level play an important role. For example, Vasina and Sysoeva (2024) shows that the “Patent Factory” model acts as a factor in increasing a societal innovation activity by enhancing the diffusion of innovations and unlocking inventive potential. This amplifies the impact of university-based SIEs on a regional development, demonstrating that their contribution extends beyond direct economic benefits and supports the formation of an innovation culture and socio-economic transformation of regions. It is also worth noting that SIEs perform an important social function by creating new jobs, increasing the mobility of qualified youth, and strengthening local innovation communities. Studies within the concept of entrepreneurial ecosystems (Leal et al., 2023; Audretsch et al., 2021) indicate the integration of university startups into regional innovation systems contributes not only to economic growth but also to the socio-cultural transformation of territories.

Thus, the literature supports the view that SIEs formed on the basis of universities and research organizations constitute an independent and significant factor in regional innovation development. Their contribution is determined by a combination of scientific potential, entrepreneurial activity, institutional environment, financial mechanisms, and network interactions, which justifies their separate consideration in the empirical part of the present study.

3.4. Further Elements of Regional Innovation Development

In addition to universities, clusters, and SIEs, the literature identifies a number of additional factors exerting a substantial influence on regional innovation development. These include digital infrastructure and digitalization, institutional conditions and public policy, financing mechanisms, human and social capital, specialized innovation support infrastructure, as well as international and inter-regional linkages. The combined action of these factors creates a favorable environment for innovation, enhances cross-factor synergies, and increases the resilience of regional innovation ecosystems.

Digital infrastructure and digitalization. Digital transformation is now viewed as a primary driver for innovation development, contributing not only to strengthening of regional technological competencies but also to transforming the kinds of interaction among innovation actors. Research by Nambisan et al. (2017), for instance, shows that digital platforms, big data, and artificial intelligence constitute the core of innovation networks, accelerating knowledge exchange and innovation adoption. According to Yoo and Yi (2022), digital transformation based on ICT development and platform technologies significantly alters economic structures and acceleration innovation activity, affecting interconnections between technological, economic, and social sectors. Digitalization also contributes to shaping digital innovation ecosystems in which participants can exchange knowledge and market data in a real time. This substantially reduces the time lag between an idea and its commercialization. Data from ITU (2023) and UNCTAD (2023) indicate that regions with developed digital infrastructure demonstrate higher rates of innovation growth, evidenced by a larger share of digital startups, intellectual property outputs, and venture investments compared to less digitalized territories.

Institutional conditions and public policy. The institutional environment is a critically important factor for regional innovation development. In particular, North (1990) formulated the concept that effective formal and informal institutions create predictable incentives for innovation activity. Within this framework, more recent studies (Rodrik, 2017; Mazzucato, 2018) emphasize the role of state innovation policy, public strategies, and regulatory mechanisms, which can either stimulate or suppress innovation processes. These studies demonstrate that national policies aimed at coordinating and sustaining innovation—such as tax incentives, grants, and startup support programs—positively correlate with regional innovation indicators. Conventional mechanisms of institutional support include public R&D funding programs, grant competitions, specialized development agencies, and innovation funds. Sociological aspects of the institutional environment, such as the level of societal trust and the quality of law enforcement, also exert a significant influence, either enhancing or diminishing the effectiveness of regulatory incentives (Acemoglu & Robinson, 2012).

Innovation financing. Access to financial resources is a fundamental element of sustainable innovation development. In the global economy, innovation financing is realized through a combination of venture capital, corporate R&D investments, public grants, and international financial instruments. Research by Gompers and Lerner (2001) and Lerner and Nanda (2020) demonstrates that access to risk capital positively correlates with the number of technology startups and their innovation productivity. Numerous studies also focus on corporate venture capital (CVC) as a means of integrating large corporations into the innovation processes of small businesses (Chemmanur et al., 2022). Moreover, regional venture ecosystems such as those in Silicon Valley, Boston, and Berlin, serve as examples of successful synergy between public, private, and scientific sources of innovation funding.

Human and social capital. Education and the professional skills of the population are a fundamental resource for innovation development. According to S. Y. Lee et al. (2010), the level of human capital, including the proportion of qualified specialists and creative workers, is positively and significantly linked to regional innovation dynamics: regions with higher human capital demonstrate greater innovation activity. Social capital, encompassing trust, network connections, and interpersonal interactions, amplifies these effects by stimulating knowledge exchange and engaging a greater number of actors in innovation processes (Putnam, 2001; Wurth et al., 2021). Regions with developed social infrastructure, professional networks and industry associations exhibit higher levels of inter-organizational cooperation, which enhances their innovation potential.

Infrastructure components. Innovation support infrastructure includes technology parks, business incubators, startup accelerators, science and technology centers, and specialized collaboration spaces. According to Leitão et al. (2022), such facilities create an environment that intensifies innovation exchange and lowers entry barriers for young technology companies. Furthermore, OECD (2024) notes that integrating accelerators and incubators into university ecosystems significantly increases startup survival rates, promotes their growth, and accelerates market entry.

International and inter-regional linkages. The globalization of innovation has made international partnership networks and transnational projects an important factor in regional development. Studies by Gereffi (2020) and Coe et al. (2008) demonstrate that participation in global value chains and international research initiatives enhances the innovation potential of regions. University and corporate research centers play a crucial role in these networks via facilitating the flow of knowledge and technologies beyond national borders. In this regard, international exchange programs, cross-border research grants, and initiatives such as Horizon Europe and EUREKA contribute to the formation of innovation ecosystems oriented toward cooperation rather than a competition, thereby strengthening overall development potential.

In summary, regional innovation development depends on a wide range of interconnected elements that extend far beyond universities, clusters, and innovative enterprises. Additional key factors include digital infrastructure and digitalization, institutional conditions and public policy, financing mechanisms, human and social capital, specialized innovation infrastructure, as well as international linkages and participation in global networks. These components enhance the innovative potential of territories, promote the diffusion of knowledge and technologies, and create conditions for the sustainable development of regional economies. Thus, although the primary actors of regional innovation ecosystems are universities, clusters, and SIEs, their successful functioning is strongly influenced by institutional conditions, financial support, the accumulation of human and social capital, digital infrastructure, and the development of international linkages. However, the nature of the influence of these factors on regional innovation development, particularly in Russia, their interconnections, and their relative significance remain insufficiently explored empirically. This necessitates a comprehensive analysis using both quantitative and qualitative methods in order to (i) assess the impact of universities, clusters, and SIEs on the innovation potential of Russian regions, and (ii) identify the key drivers of innovation growth.

4. Materials and Methods

As an integrated indicator of innovation development across Russian regions, this study employs the RRII, as it provides a comprehensive assessment the innovation performance of territories. The RRII reflects the state of the scientific-technological, educational, institutional, and socio-economic components of regional innovation systems. In this context, the RRII serves as an analytical tool for comparing regions, identifying inter-regional disparities, and determining the factors exerting the greatest influence on the innovation development. The empirical basis of the study consists of statistical data for all constituent entities of the Russian Federation for the year 2023. The sample includes 85 regions, ensuring the representativeness of the analysis and enabling the identification of stable inter-regional patterns of innovation development.

At the first stage of the research, indicators reflecting the socio-economic and innovative characteristics of Russian regions were selected. These indicators were grouped into four factor blocks in accordance with the key dimensions of regional innovation system functioning, as summarized in

Table 1. Factor groups for innovation development in Russian regions.

Factor Group	Factor	Rationale for Factor Selection
Scientific- Educational (SciEdu)	Number of anchor universities	Concentrates educational and research potential, forming the human capital necessary for innovation development.
	Organizations performing scientific research and development	Characterizes the presence and activity of research institutions conducting fundamental and applied studies that generate new knowledge.
	Internal expenditures on research and development	Indicates the priority assigned to science and innovation within the regional economy and the scale of financial support for R&D activities.
Innovation- Investment activity (InnovInvest.activity)	Number of SIEs	Serves as a key driver of technological change and a mechanism for commercializing research results and generating new high-tech ventures.
	Share of innovative goods, works, and services in total shipped output (%)	Directly measures the level of innovation adoption and commercialization within the regional economy.
	Volume of investment in fixed capital per capita	Reflects the scale of capital investment creating conditions for modernization and technological renewal.
Infrastructure- Institutional (InfrInst)	Number of innovation clusters	Facilitates cooperation among science, business, and government, promoting knowledge exchange and accelerating technology transfer.
	Number of technology parks and centers for innovation science and technology	Indicates the development level of regional innovation infrastructure and the capacity to support R&D, startups, and commercialization process.
	Accessibility of digital infrastructure (broadband internet penetration, %)	Represents the digital foundation of an innovation-based economy and enables the diffusion of knowledge and digital technologies.
Human- Social (PersonSoc)	Proportion of employed persons with higher education	Reflects workforce qualification and the region’s capacity to generate and absorb innovations.
	Number of R&D personnel (researchers)	Directly measures the human resource potential in the scientific and technological sphere.
	Per capita monetary income	Higher income levels stimulate demand for innovative goods and services, and influence the development of innovative-driven markets.

.

In the second stage, a preliminary statistical analysis was conducted to assess factor significance and test for multicollinearity among the variables. The analysis revealed that several indicators as “Number of technology parks”, “Organizations performing scientific research and development”, “Internal expenditures on scientific research and development”, and “Number of personnel engaged in scientific research and development (researchers)” exhibit a high degree of mutual correlation and exert a similar effects on the dependent variable. The presence of multicollinearity reduces the stability of regression estimates and limits the interpretability of the model. Therefore, these variables were excluded from a subsequent analysis.

At the third stage of the study, a linear regression model is applied to identify factors that have the strongest influence on regional innovative development, as well as to assess their statistical significance and the direction of their impact on the integral indicator:

$$y_i= {\beta }_0+{\displaystyle \sum }_{j=1}^k{\beta }_j{x}_{ji},$$

(1)

where $y_i$ is the dependent variable for the i-th observation (dependent variable of RRII), x_ji are the values of the explanatory variables, β_j are the estimated model parameters, and i = 1, 2, …, n are the observations (n = 85).

The model parameters were estimated using the ordinary least squares (OLS) method.

To deepen the interpretation of the results and identify typological differences among regions, cluster analysis was applied at the fourth stage of the research. Clustering was performed taking two integral indicators: RRII and the value of GRP. The simultaneous use of these indicators made it possible to account for both the level of innovation development and the overall economic development of regions. This approach ensured the formation of relatively homogeneous territorial groups and expanded the analytical potential of the study in terms of the spatial differentiation of innovation development across the country.

5. Results

Table 2. Modeling the factors which influence on the innovation development in Russian regions in 2023, n = 85.

Variables, xji	Coefficient, βj	Std. Error	p-Value	Significance 1
Constant	0.2596	0.0412	1.81 × 10−8	***
Scientific–educational (SciEdu)
Number of anchor universities (S1)	0.0272	0.0109	0.0146	**
Innovation–investment activity (InnovInvest.activity)
Number of SIEs founded on the basis of research organizations and universities (I1)	0.0009	0.0003	0.0008	***
Volume of innovative goods, works, and services as a % of total shipped goods, performed works, and services (I2)	0.0048	0.0011	4.46 × 10−5	***
Volume of investment in fixed capital per capita (I3)	2.14 × 10−8	2.55 × 10−8	0.4051
Infrastructure–institutional (InfrInst)
Number of innovation clusters (In1)	0.0379	0.0097	0.0002	***
Number of innovation science and technology centers (In3)	0.0559	0.0143	0.0002	***
Accessibility of digital infrastructure (broadband internet penetration, %) (In4)	0.0045	0.00095	8.50 × 10−6	***
Human–social (PersonSoc)
Proportion of employed persons with higher education (P1)	−0.0001	0.0011	0.8964
Per capita monetary income (P3)	−1.25 × 10−6	4.79 × 10−7	0.0112	**
p-value (F-test)	7.46 × 10−23
R2	0.8015
Adjusted R2	0.7777
Normality test of residuals, χ2	3.057	0.217
White test for heteroscedasticity, LM	55.822	0.406

¹ Note: ***—significance at p < 0.01; **—significance at p < 0.05.

presents the calculation results of the factors influencing innovation development in Russian regions in 2023. The analysis demonstrates that the third factor group, “Infrastructure–institutional”, exerts the strongest influence on regional innovation development. All three variables included in this group are statistically significant, confirming the decisive role of the institutional and infrastructural environment in shaping regional innovation potential. The number of innovation clusters reflects the degree of integration among key actors—business, research organizations, and government—necessary for effective knowledge and technology exchange aimed at generating regional competitive advantages. The presence of centers for innovation science and technology indicates the maturity of the regional research base facilitating the transfer of scientific results into practical applications and contributing to the formation of a technological groundwork for long-term development. The accessibility of digital infrastructure confirms the critical importance of advanced information and communication technologies, which underpin the digitalization of production processes and the large-scale diffusion of innovations.

Thus, the findings indicate the dominant influence of infrastructure–institutional factors on regional innovation development, thereby confirming Hypothesis H1 regarding the priority role of this factor group. These results are consistent with other studies. For instance, research by H. Zhang et al. (2025) demonstrates that developed digital infrastructure significantly improves regional innovation efficiency by strengthening agglomeration effects and stimulating innovation activity within local economic systems. This aligns with the identified importance of digital network accessibility in shaping regional innovation potential in the present study. Likewise, Jiao et al. (2025), in a systematic literature review, emphasizes that digital technologies and infrastructure constitute core components of contemporary innovation ecosystems by facilitating actor integration and knowledge diffusion. Conceptual research by Serrano et al. (2024) highlights institutional networks of DIHs as mechanisms enhancing interaction among enterprises, research institutions, and government bodies, thereby reinforcing the institutional dimension of regional innovation development. Collectively, these studies support the dominant role of the infrastructural–institutional environment identified in this analysis.

The second most influential factor group is “Innovation–investment activity”. Two of its three indicators are statistically significant. In particular, the number of SIEs established on the basis of research organizations and universities reflects the level of entrepreneurial development and the region’s capacity to transform scientific research outcomes into commercially viable products.

Such enterprises function as a structural link between the scientific–educational system and the real sector of the economy, generating new technological trajectories and strengthening regional innovation ecosystems. At the same time, the share of innovative goods, works, and services in the total volume of shipped products reflects the scale of innovation commercialization and its tangible contribution to regional economic performance. Together, these indicators confirm the substantive importance of qualitative characteristics of innovation–investment activity. Conversely, the volume of investment in fixed capital per capita does not demonstrate statistical significance. This result can be explained by the aggregated nature of the indicator, which primarily captures investments in physical infrastructure and conventional production capacities without distinguishing investments directed toward technological modernization, research & development, or innovation adoption. Therefore, an increase in capital investment does not directly translate into enhanced regional innovation performance unless it is supported by an appropriate institutional environment and targeted toward high-tech and knowledge-intensive sectors. Overall, these findings provide additional support for Hypothesis H1 confirming the greater importance of innovation–investment factors compared to scientific–educational and human–social characteristics.

Similar findings are reported in the literature on regional innovation systems. For instance, research by Audretsch et al. (2021) shows that university entrepreneurship and spin-off companies play a central role in knowledge commercialization and regional innovation growth, whereas a simple increase in overall investment volume does not guarantee improved innovation outcomes. Similarly, the study by Fritsch and Wyrwich (2021) emphasizes that the share of innovative output is a more representative indicator of regional innovation development than aggregated investment metrics. The lack of statistical significance for fixed capital investment is also consistent with findings by Guo and Zhang (2022), who show that investments primarily aimed at expanding traditional production capacities do not directly stimulate regional innovation dynamics without parallel investments in R&D and entrepreneurial innovation. Thus, the results of the present analysis reflect general economic patterns and underscore the priority of qualitative characteristics of innovation–investment activity for sustainable regional innovation development.

The next factor group, “Scientific–Educational”, demonstrates a moderate influence on regional innovation development. Within this group, only one indicator is statistically significant—the number of anchor universities. This finding suggests that the presence of universities as centers of knowledge generation and human capital development constitutes an important, though not sufficient, condition for advancing a regional innovation. Universities shape the scientific and educational base of regions and ensure the reproduction of qualified personnel; however, their impact on innovation dynamics is largely mediated by the quality of the institutional environment, the intensity of university–industry collaboration, and the efficiency of research commercialization mechanisms. This likely explains the moderate significance of this factor group relative to the infrastructure–institutional and innovation–investment ones. These results confirm Hypothesis H2 indicating that quantitative indicators of scientific–educational development do not produce a direct positive effect in the absence of supportive institutional and infrastructural conditions.

This interpretation is consistent with other findings in the literature. For example, Zenkienė and Leišytė (2024) show that universities enhance regional innovation potential through strategic cooperation the implementation a “third mission” oriented toward entrepreneurial and societal engagement. The effectiveness of this role depends on development of network interactions among universities, business actors, and government institutions. Moreover, in their systematic review, Acs et al. (2009) identify universities as core elements of regional innovation systems within integrated frameworks, influencing innovation dynamics not only through educational functions but also via technology transfer and participation in entrepreneurial programs.

Within the fourth factor group, “Human–Social Factors”, two variables were examined—the proportion of employed persons with higher education and per capita monetary income. The regression analysis results indicate that per capita income yields a moderate statistical significance while the proportion of employed persons with higher education does not exert a statistically valuable influence. Notably, both variables display negative coefficient values, indicating an inverse relationship: an increase in the share of employed individuals with higher education or a rise in per capita income does not directly translate into greater innovation activity. The effect of human capital appears to depend not only on its quantitative scale but also on its qualitative characteristics in a line with the needs of the innovation economy, and the presence of an institutional environment to enable the effective application of knowledge and skills. Moreover, the negative association with per capita income may reflect structural features of regional economies, where income growth is concentrated in sectors not directly linked to innovative activity, e.g., extractive industries or administrative sectors. In such contexts, rising income levels do not necessarily stimulate technological modernization or innovative growth. These findings fully confirm Hypothesis H2, demonstrating that human–social and scientific–educational characteristics do not generate a sustainable innovation effect outside a supportive institutional and infrastructural context. Similar trends are identified by other researchers, too. For instance, H. Lee and Lee (2025) show that higher education contributes significantly to innovation capability only when combined with complementary structural and institutional conditions. In the absence of effective knowledge transfer mechanisms and commercialization channels, an increase in the proportion of highly educated individuals may produce weak or even negative innovation outcomes. Likewise, Pinar and Karahasan (2026) demonstrate that rising income levels can exert an inverse influence on innovation indicators in regions where income growth is concentrated in traditional sectors.

Building upon the regression results, which identified the key determinants of regional innovation development, a cluster analysis was conducted to further explore spatial heterogeneity and verify the robustness of the identified patterns. In this study, regional clustering was performed using the K-means method. The variables used for clustering were the RRII and GRP, as they reflect the combination of innovation potential and economic development of the regions (Figure 1). The number of clusters was determined using the elbow method, which allowed us to identify three distinct groups of regions.

Figure 1 presents a heatmap visualizing the results of the cluster analysis for 85 Russian regions based on the RRII and GRP. The three clusters on the figure reflect different combinations of innovation potential and economic development across regions. The color scale on the right side indicates the gradient of the variables employed for clustering, serving as a visual guide to the intensity of the indicators in different regions rather than representing a separate metric. The major purpose of the figure is to visually display the spatial distribution of regions across the identified clusters and highlight territorial differences in innovation—driven development.

Cluster 0—leaders and highly developed regions (49 regions). This cluster comprises regions characterized by consistently high levels of innovation activity and substantial economic potential. The average value of RRII is approximately 0.45 (ranging from 0.402 to 0.654). Certain regions, such as the Khanty-Mansi Autonomous Okrug—Yugra (RRII = 0.409), demonstrate slightly below-average within the cluster. The average GRP value in this group amounts to approximately 1,332,000 million rubles (with a range from 1,070,000 to 12,355,000 million rubles), indicating strong economic capacity. A significant contribution to these figures comes from major metropolitan centers such as Moscow and St. Petersburg. The leading regions in this cluster further include the Republic of Tatarstan, Krasnoyarsk Krai, Moscow Oblast, Tyumen Oblast, and Sverdlovsk Oblast. Their high RRII and GRP values indicate the effective integration of scientific and technological potential into economic development supported by a dense concentration of research institutions, higher education establishments, innovation clusters, and large industrial enterprises. These regions act as drivers of the national economy and centers of innovation activity. Accordingly, key priorities of regional policy in these territories include strengthening technology transfer mechanisms, promoting the commercialization of scientific research, supporting university entrepreneurship, and expanding strategic partnerships among government, business, and academia.

Cluster 1—intermediate regions (34 regions). Intermediate regions are characterized by moderate innovation activity combined with varying levels of economic strength. The average RRII value is 0.31 (range: 0.13–0.376), indicating limited commercialization of scientific developments, weak development of SIEs, and insufficient infrastructure for technology transfer. The average GRP value in this cluster is approximately 1,000,000 million rubles (range: 182,000–3,539,000 million rubles), reflecting a wide dispersion of economic strength—from small- and medium-sized agro-industrial regions to resource-based territories with relatively high GRP. Key regions in this cluster include Amur Oblast, Astrakhan Oblast, Tver Oblast, the Republic of Sakha (Yakutia), Sakhalin Oblast, Kamchatka Krai, Chukotka Autonomous Okrug, and the Komi Republic. Within the cluster, there are subjects with relatively high RRII values, such as Sakhalin Oblast (RRII = 0.327), as well as regions with minimal indicators, such as Chukotka Autonomous Okrug (RRII = 0.133), highlighting strong heterogeneity in innovation potential. For these regions, strategically important priorities include the development of innovation infrastructure, expanded access to financing, and support for SIEs.

Cluster 2—regions with high economic strength but low innovation activity (2 regions). This cluster includes the Nenets and Yamalo-Nenets Autonomous Okrugs. Their average RRII and GRP values are 0.23 (range: 0.16–0.304) and 11,729,000 million rubles (range: 10,462,000–11,995,000 million rubles), respectively. Despite high GRP indicators driven by the concentration of extractive industries and natural resources, innovation activity in these regions remains extremely low. This pattern demonstrates a pronounced structural imbalance between economic strength and innovation development. Priority policy directions here include building basic innovation infrastructure, accelerating economic digitalization, and improving access to educational and scientific resources.

An analysis of the dispersion of RRII and GRP indicators within each cluster reveals several meaningful trends and exceptions. Leaders (cluster 0) demonstrate consistently high indicators, although major metropolitan areas force a “concentration effect” that influences average values. Intermediate regions (cluster 1) exhibit a wide spread of innovation performance at relatively comparable GRP values, indicating the need for differentiated innovation policy. In contrast, cluster 2 combines low innovative indicators with high economic capacity reflecting weak integration of innovations into resource-based regional economies.

The results of the cluster analysis reveal clearly defined spatial patterns in the distribution of innovation potential and economic capacity across Russian regions. The identified clusters demonstrate significant differences in the relationship between economic development and innovation activity.

6. Discussion

The integration of regression and cluster analysis results provides a comprehensive assessment of regional innovation development in Russia. Regression analysis identified the key determinants exerting the strongest influence on innovation dynamics, while cluster analysis revealed their spatial heterogeneity and structural disparities between economic performance and innovation potential across regions.

Detailed results of the cluster analysis, including the distribution of regions across clusters and values of the RRII and GRP, are presented in the Section 5 (see Figure 1). For descriptive purposes, two key variables were used in the analyses: (1) Gross Regional Product per capita (GRP)—an economic indicator reflecting the level of a regional economic capacity, calculated per resident. GRP characterizes the overall production and financial activity of a region and allows one comparing their economic strength. (2) Russian Regional Innovation Index (RRII)—a composite indicator assessing the level of regional innovation development, taking into account factors such as the number of patents, the activity of innovative enterprises, and the adoption of new technologies. RRII yields a possibility to compare regions in terms of innovation activity and identification of innovation leaders and lagging territories.

The observed patterns indicate that high levels of innovation potential and economic capacity are concentrated in a number of highly developed regions, which is consistent with previous studies emphasizing the concentration of innovation activity in economic centers (Vtorygin, 2024). Intermediate regions show considerable variability in innovation performance at relatively comparable levels of economic capacity, highlighting the need for differentiated innovation policies. In regions with high economic capacity but low innovation activity, a structural imbalance is observed to reflect weak integration of innovations into resource-based regional economies. Numerous studies further confirm that the spatial distribution of innovation indicators and institutional factors significantly influence innovation dynamics, while the use of composite indices and multifactor approaches enhances the quality of analysis and understanding of regional differences (Borsekova et al., 2026).

These findings underscore the importance of aligning regional innovation policies with both economic capacity and local innovation potential. They also provide a foundation for future research aimed at monitoring changes in regional innovation dynamics and testing the efficiency of targeted policy interventions.

7. Conclusions

Based on the conducted analysis of innovation activity across various Russian regions, several key trends can be identified.

The findings indicate that the infrastructure–institutional environment is the primary factor of an innovation-oriented regional economy. The strongest influence is exerted by factors related to infrastructure and institutional support. In particular, innovation clusters, science and technology centers, and the availability of digital infrastructure build a foundational platform for effective interaction among science, business, and the state. This accelerates technology adoption and enhances the overall competitiveness of regional economies. Therefore, their policy should prioritize expanding such clusters and centers, improving digital infrastructure, and fostering integration among all actors within innovation ecosystems.

To stimulate innovation–investment activity, it is essential to create favorable conditions for the commercialization of innovations, for example, by establishing SIEs based on scientific organizations and universities. The analysis also shows that the share of innovative products in the total volume of shipped output significantly affects regional innovation development. This confirms the importance of entrepreneurship and the commercialization of innovations for achieving positive economic outcomes. The experience of leading regions highlights the need to support SIE development through tax incentives, grants programs, and improved access to venture financing.

The analysis of scientific–educational and human–social factors reveals a more complex pattern of influence on innovation development. These factors demonstrate moderate significance, while indicators such as population income and the share of employed persons with higher education show negative coefficients. This suggests that higher educational attainment and/or rising incomes do not directly stimulate innovation activity in the absence of infrastructure support and an entrepreneurial environment. Accordingly, it is important to integrate educational programs with applied innovation projects, encourage the participation of qualified specialists in innovation activities, and direct social and educational resources towards supporting innovative enterprises and startups.

The results emphasize that regional policy should focus on comprehensive innovation support. As the analysis shows, regional innovation development depends not only on the availability of resources (education, income, investments) but also on how effectively these resources are embedded within institutional and infrastructural mechanisms and entrepreneurial activity. A successful strategy should combine infrastructure investment, support for innovative businesses, and stronger science–industry collaboration to foster a sustainable innovation ecosystem.

The practical significance of the findings lies in demonstrating that universal innovation policies are unlikely to be equally effective across all regions. For leading regions, the priority is enhancing the commercialization of scientific developments and improving technology transfer efficiency. For intermediate regions, the focus should be on strengthening innovation infrastructure, expanding access to funding and supporting SIE development. For lagging regions, the main priorities include removing institutional barriers, establishing a basic innovation ecosystem, and improving access to digital and educational resources.

As demonstrated, combining regression and cluster analysis enables the identification of significant territorial and structural disparities in regional innovation development. The division into three groups highlights distinctive patterns:

-

Leaders demonstrate that a high level of innovation activity promotes economic growth and serves as a benchmark for national innovation strategies;

-

Intermediate regions possess the potential for accelerated development with targeted support;

-

Economically strong but innovation-weak regions require the development of basic innovation infrastructure and the removal of institutional barriers.

A differentiated regional innovation policy allows for a more effective allocation of resources: sustaining innovation leaders, stimulating growth in intermediate regions, and reducing disparities in economically strong but innovation-weak territories. Such an approach can contribute to reducing territorial heterogeneity and enhancing the overall competitiveness of the national economy.

Moreover, the findings of this study provide a basis for future research, including: (1) extending the temporal horizon to examine the dynamics of innovation development over time; (2) incorporating additional factors influencing regional innovation activity; (3) applying alternative clustering and data analysis methods to test the robustness of the observed patterns.