INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE
ISSN: 2692-5206, Impact Factor: 12,23
American Academic publishers, volume 05, issue 04,2025
Journal:
https://www.academicpublishers.org/journals/index.php/ijai
page 685
METHODS AND MODELS FOR ASSESSING THE SOCIO-ECONOMIC
EFFICIENCY OF REGIONAL INNOVATION INFRASTRUCTURE
B.F.Azimov
Asia international university, Bukhara, associate professor
Abstract:
The development of regional innovation infrastructure is a key component in
enhancing the competitiveness of national economies and ensuring sustainable growth. This
article explores the methodological foundations and models used to assess the socio-
economic efficiency of innovation infrastructure at the regional level. Drawing upon
international practices and the context of Uzbekistan, the article proposes an integrated
approach for evaluating efficiency based on quantitative and qualitative indicators.
Key words:
Regional innovation infrastructure, socio-economic efficiency, innovation
assessment models, Global Innovation Index, European Innovation Scoreboard, innovation
policy, regional development, composite indicators.
In the context of accelerating globalization and technological transformation,
innovation has become a key driver of sustainable economic growth, regional
competitiveness, and social development. The effectiveness of a country's or region's
innovation ecosystem increasingly depends not only on the availability of advanced
technologies or research capacity but also on the functionality and efficiency of its regional
innovation infrastructure (RII). This infrastructure, comprising technology parks, innovation
centers, incubators, research institutions, and support services, plays a central role in
generating, transferring, and commercializing knowledge.
Assessing the socio-economic efficiency of RII is essential for evidence-based
policymaking and strategic development planning. It provides policymakers, investors, and
stakeholders with insights into how effectively innovation inputs—such as funding, human
capital, and institutional support—are transformed into tangible socio-economic outcomes,
including employment generation, productivity growth, regional diversification, and
improved quality of life.
Despite the recognized importance of innovation infrastructure, methods for
evaluating its efficiency—especially at the regional level—remain underdeveloped in many
countries, including Uzbekistan. Traditional assessment tools often emphasize input-output
relationships but fail to capture the complex socio-economic dynamics associated with
innovation ecosystems. In recent years, international organizations such as the Organisation
for Economic Co-operation and Development (OECD), the World Intellectual Property
Organization (WIPO), and the European Union have developed composite indices and
models—such as the Global Innovation Index (GII) and the European Innovation Scoreboard
(EIS)—to enable more comprehensive evaluations.
This article aims to explore and synthesize the main methods and models used
internationally to assess the efficiency of regional innovation infrastructure from a socio-
INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE
ISSN: 2692-5206, Impact Factor: 12,23
American Academic publishers, volume 05, issue 04,2025
Journal:
https://www.academicpublishers.org/journals/index.php/ijai
page 686
economic perspective. It also critically examines their applicability in emerging economies,
with a particular focus on Uzbekistan. The study seeks to provide recommendations for
improving assessment methodologies in line with regional development goals and innovation
policy priorities.
By drawing on best practices and comparative analysis, the article contributes to the
growing discourse on measuring innovation-driven development at the regional level and
provides a conceptual and methodological foundation for future empirical studies.
The concept of innovation infrastructure refers to the institutional, technological,
financial, and human resource systems that support the generation, diffusion, and
commercialization of innovations within a defined region or country. It is an essential
component of the broader national innovation system (NIS), providing the physical and
organizational foundation for innovative activities. Innovation infrastructure encompasses a
wide range of elements including:
Physical infrastructure (e.g., technology parks, laboratories, research facilities),
Institutional infrastructure (e.g., universities, R&D institutions, innovation agencies),
Financial infrastructure (e.g., venture capital funds, innovation grants),
Support infrastructure (e.g., incubators, accelerators, consulting services).
According to Carlsson
, innovation systems are composed of networks of institutions
and firms that interact to produce and diffuse innovation. Within this system, the regional
level has become increasingly important due to the spatial concentration of knowledge flows
and the role of local context in shaping innovation outcomes (Asheim & Gertler
).
The OECD
emphasizes that regional innovation infrastructure plays a key role in ensuring
that national innovation strategies are effectively implemented at the local level, especially
through the alignment of research and innovation capacities with regional development
objectives.
The literature identifies several core components of regional innovation infrastructure
(Cooke
, Tödtling & Trippl
):
Knowledge generation institutions: universities, public research organizations, and
private R&D centers.
1
Carlsson, B., Jacobsson, S., Holmén, M., & Rickne, A. (2002). Innovation systems: analytical and methodological issues. Research Policy,
31(2), 233–245.
2
Asheim, B. T., & Gertler, M. S. (2005). The Geography of Innovation: Regional Innovation Systems. In J. Fagerberg, D. C. Mowery, & R.
R. Nelson (Eds.), The Oxford Handbook of Innovation. Oxford University Press.
3
OECD. (2011). Regions and Innovation Policy. OECD Publishing.
4
Cooke, P., Uranga, M. G., & Etxebarria, G. (2004). Regional innovation systems: Institutional and organisational dimensions. Research
Policy, 34(8), 1173–1190.
5
Tödtling, F., & Trippl, M. (2005). One size fits all? Towards a differentiated regional innovation policy approach. Research Policy, 34(8),
1203–1219.
INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE
ISSN: 2692-5206, Impact Factor: 12,23
American Academic publishers, volume 05, issue 04,2025
Journal:
https://www.academicpublishers.org/journals/index.php/ijai
page 687
Knowledge application institutions: firms, especially SMEs, that absorb and
commercialize innovations.
Bridging institutions: technology transfer offices, innovation intermediaries,
incubators, and cluster initiatives.
Policy and governance structures: regional innovation councils, development agencies,
and funding bodies.
These components interact in complex and dynamic ways to create an environment
conducive to innovation. The strength and coherence of these linkages are critical to the
performance of innovation systems.
The Triple Helix Model (Etzkowitz & Leydesdorff
) provides a widely used theoretical
framework to understand the collaboration among universities, industry, and government in
driving innovation. This model underlines the importance of synergy among the three spheres
and the emergence of hybrid institutions (e.g., university spin-offs, public-private
partnerships) in enhancing innovation capacity.Building upon this, the Quadruple and
Quintuple Helix models (Carayannis & Campbell
) incorporate civil society and the natural
environment, emphasizing the socio-ecological context of innovation and the role of user-
driven innovation, particularly in regional settings.
The spatial dimension of innovation has gained attention in regional development theory.
Scholars such as Storper
and Malecki
have shown that innovation tends to be
geographically concentrated due to proximity advantages, localized knowledge spillovers,
and the role of place-based institutions.
The effectiveness of regional innovation infrastructure is also crucial for reducing territorial
disparities and promoting smart specialization, a concept developed by the European
Commission (Foray
) to encourage regions to focus on their unique strengths and
opportunities through innovation.
In
the
context
of
developing and transition economies, including Uzbekistan, the development of innovation
infrastructure faces several constraints:
Limited R&D investment and weak research base,
Fragmented institutional coordination,
Low levels of industry-academia collaboration,
Insufficient access to finance for innovation.
As noted by Radosevic
and more recently by the World Bank, building effective
regional innovation systems in such contexts requires not only infrastructure investment but
6
Etzkowitz, H., & Leydesdorff, L. (2000). The dynamics of innovation: from National Systems and “Mode 2” to a Triple Helix of
university–industry–government relations. Research Policy, 29(2), 109–123.
7
Carayannis, E. G., & Campbell, D. F. J. (2010). Triple Helix, Quadruple Helix and Quintuple Helix and how do knowledge, innovation
and the environment relate to each other? International Journal of Social Ecology and Sustainable Development, 1(1), 41–69.
8
Storper, M. (1997). The Regional World: Territorial Development in a Global Economy. Guilford Press.
9
Malecki, E. J. (1997). Technology and Economic Development: The Dynamics of Local, Regional, and National Competitiveness.
Longman.
10
Foray, D. (2015). Smart Specialisation: Opportunities and Challenges for Regional Innovation Policy. Routledge.
11
Radosevic, S. (1999). International Technology Transfer and Catch-up in Economic Development. Edward Elgar Publishing.
INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE
ISSN: 2692-5206, Impact Factor: 12,23
American Academic publishers, volume 05, issue 04,2025
Journal:
https://www.academicpublishers.org/journals/index.php/ijai
page 688
also capacity building, governance reforms, and better integration of local innovation actors
into global networks. Assessing the socio-economic efficiency of regional innovation
infrastructure is a complex task that requires evaluating multiple dimensions of economic and
social outcomes. The goal is to understand how well the innovation infrastructure contributes
to regional development, economic growth, social equity, and sustainability. The socio-
economic efficiency of an innovation system can be assessed using a variety of quantitative
and qualitative criteria, often based on the balance between inputs (resources, investment)
and outputs (innovation outcomes, economic impact).
Criteria for socio-economic efficiency assessment of regional innovation
infrastructure.
Table-1
The assessment of the socio-economic efficiency of regional innovation
infrastructure, as illustrated in Table 1, is conducted using a variety of criteria that take into
account both inputs (such as resources and investments) and outputs (including economic
growth, equity, and sustainability). The critical areas of focus encompass:
Economic Impact:
Innovation infrastructure drives GDP growth, employment, and productivity. Fagerberg
link innovation to productivity gains, while Rodríguez-Pose (2013) ties R&D hubs to job
creation and economic diversification. Porter (1998) emphasizes innovation’s role in
competitiveness, and Chesbrough
highlights ROI through spillover effects. Innovation
12
Fagerberg, J., Mowery, D. C., & Nelson, R. R. (2013). The Oxford handbook of innovation. Oxford University
Press.
13
Chesbrough, H. W. (2003). Open innovation: The new imperative for creating and profiting from technology.
Harvard Business School Press.
INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE
ISSN: 2692-5206, Impact Factor: 12,23
American Academic publishers, volume 05, issue 04,2025
Journal:
https://www.academicpublishers.org/journals/index.php/ijai
page 689
Output: Patents (Griliches
), start-ups (Audretsch & Keilbach
), and R&D collaborations
(Cohen & Levinthal
) reflect output. Technology transfer (Mowery & Nelson
) and
absorptive capacity determine commercialization success.
Social Impact & Inclusion: Human capital development (Autor
) and equitable
benefit distribution (Storper
) are critical. Innovation systems enhance quality of life via
sustainability-focused solutions (Fukuyama
).
Sustainability & Environmental Impact:
Green innovation (Mazzucato
) and resource efficiency (Porter & van der Linde
) position
regions as sustainability leaders. Institutional & Governance Effectiveness: Policy
coordination (Tödtling & Trippl
) and transparent governance (Harrison & Weiss
) ensure
alignment with regional goals.
International Competitiveness: Global indices (e.g., GII, EIS) and FDI inflows
(Kaufmann & Tödtling
) reflect a region’s innovation leadership.
Collectively, these criteria underscore the need for a holistic approach to assess how
innovation infrastructure fosters balanced socio-economic development.Assessing
the
socio-economic efficiency of regional innovation infrastructure requires a multidimensional
and context-sensitive approach. While global models such as the Global Innovation Index
and European Innovation Scoreboard offer valuable frameworks, they must be adapted to
the specific institutional, economic, and social realities of emerging economies like
Uzbekistan. A holistic evaluation should integrate not only input-output analysis but also
factors such as governance quality, sustainability, inclusion, and digital readiness. By
adopting an integrated and dynamic assessment model, Uzbekistan can more effectively
align innovation infrastructure with regional development goals, enhance competitiveness,
and foster inclusive, innovation-driven growth.
14
Griliches, Z. (1990). Patent statistics as economic indicators: A survey. Journal of Economic Literature, 28(4),
1661–1707.
15
Audretsch, D. B., & Keilbach, M. (2007). The theory of knowledge spillover entrepreneurship. Journal of
Management Studies, 44(7), 1242–1254. https://doi.org/10.1111/j.1467-6486.2007.00722.x
16
Cohen, W. M., & Levinthal, D. A. (1990). Absorptive capacity: A new perspective on learning and innovation.
Administrative Science Quarterly, 35(1), 128–152. https://doi.org/10.2307/2393553
17
Mowery, D. C., & Nelson, R. R. (1999). Sources of industrial leadership: Studies of seven industries.
Cambridge University Press.
18
Autor, D. H. (2014). Skills, education, and the rise of earnings inequality among the “other 99 percent.”
Science, 344(6186), 843–851. https://doi.org/10.1126/science.1251868
19
Storper, M. (2013). Keys to the city: How economics, institutions, social interaction, and politics shape
development. Princeton University Press.
20
Fukuyama, F. (2004). State-building: Governance and world order in the 21st century. Cornell University
Press.
21
Mazzucato, M. (2018). Mission-oriented innovation policies: Challenges and opportunities. Industrial and
Corporate Change, 27(5), 803–815. https://doi.org/10.1093/icc/dty034
22
Porter, M. E., & van der Linde, C. (1995). Toward a new conception of the environment-competitiveness
relationship. Journal of Economic Perspectives, 9(4), 97–118. https://doi.org/10.1257/jep.9.4.97
23
Tödtling, F., & Trippl, M. (2005). One size fits all? Towards a differentiated regional innovation policy
approach. Research Policy, 34(8), 1203–1219. https://doi.org/10.1016/j.respol.2005.01.018
24
Harrison, B., & Weiss, M. (1998). Workforce development networks: Community-based organizations and
regional alliances. Sage Publications.
25
Kaufmann, A., & Tödtling, F. (2001). Science–industry interaction in the process of innovation: The
importance of boundary-crossing between systems. Research Policy, 30(5), 791–804.
https://doi.org/10.1016/S0048-7333(00)00118-9
INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE
ISSN: 2692-5206, Impact Factor: 12,23
American Academic publishers, volume 05, issue 04,2025
Journal:
https://www.academicpublishers.org/journals/index.php/ijai
page 690
References:
1.
Qudratova, G. M. (2025). INNOVASION MARKAZLAR: RIVOJLANISHI,
TAQQOSLAMA TAHLIL VA KELAJAKDAGI TENDENSIYALAR. ANALYSIS OF
MODERN SCIENCE AND INNOVATION, 1(7), 98-104.
2.
Sodiqova, N. T., & Irgasheva, F. (2025). BANK TIZIMI MOLIYA TIZIMINING
ASOSIY TARKIBIY QISMI SIFATIDA. Modern Science and Research, 4(3), 268-278.
3.
Алимова, Ш. А., & Раджапбаев, С. (2025). ЭКОЛОГИЧЕСКИЕ ПРОБЛЕМЫ В
УЗБЕКИСТАНЕ И ИХ РЕШЕНИЯ. Modern Science and Research, 4(3), 162-167.
4.
Khalilov, B. (2025). GLOBAL ECONOMIC INFLUENCES IN THE USA. Journal of
Applied Science and Social Science, 1(2), 644-647.
5.
Toshov, M. H., & Nizomov, S. (2025). O’ZBEKISTON BANK-MOLIYA
TIZIMI. Modern Science and Research, 4(3), 194-201.
6.
Ibodulloyevich, I. E. (2024). O ‘ZBEKISTON RESPUBLIKASIDA KICHIK BIZNES
VA XUSUSIY TADBIRKORLIK SAMARADORLIGINI OSHIRISH MUAMMOLARI
VA ISHBILARMONLIK MUHITINI YAXSHILASH ISTIQBOLLARI. Gospodarka i
Innowacje., 51, 258-266.
7.
Bobur, A., & Yodgorova, Z. (2025). COMPETITION AND COMPETITIVE
STRATEGIES IN EDUCATION: NECESSITY AND IMPORTANCE. International
Journal of Artificial Intelligence, 1(1), 90-95.
8.
Raxmonqulova, N., & Muxammedov, T. (2025). IQTISODIY BILIMLARNING INSON
KAPITALINI RIVOJLANTIRISH VA BOSHQARISHDAGI AHAMIYATI VA
DOLZARBLIGI. Modern Science and Research, 4(3), 207-212.
9.
Shadiyev, A. (2025). EDUCATION MANAGEMENT IN PRIVATE UNIVERSITIES IN
UZBEKISTAN:
DEVELOPMENT
STRATEGIES,
CHALLENGES
AND
PROSPECTS. International Journal of Artificial Intelligence, 1(2), 308-313.
10.
Naimova, N. (2025). MANAGEMENT OF THE INNOVATION PROCESS IN
ENTERPRISES. International Journal of Artificial Intelligence, 1(2), 302-304.
11.
Bazarova, M. S., & Rajabboyeva, O. (2025). TIJORAT BANKLARI FAOLIYATIDAGI
KREDIT
RISKLARINI
BOSHQARISHNI
TAKOMILLASHTIRISH
YO'LLARI. Modern Science and Research, 4(3), 138-143.
12.
Jumayeva, Z. (2025). KEYNESIAN THEORY OF ECONOMIC GROWTH: STATE
INTERVENTION AND ECONOMIC STABILITY. International Journal of Artificial
Intelligence, 1(2), 744-747.
13.
Bobojonova, M. (2025). THE ROLE AND PROMISING DIRECTIONS OF GREEN
BONDS IN FINANCING THE GREEN ECONOMY IN THE GLOBAL FINANCIAL
MARKET. International Journal of Artificial Intelligence, 1(2), 1067-1071.
14.
Jumayeva, Z. Q., & Nurmatova, F. S. (2025). BANKLARARO RAQOBATNING
PAYDO BO ‘LISH TARIXI VA NAZARIY YONDASHUVLAR. Modern Science and
Research, 4(3), 361-367.
15.
Ibragimov, A. (2025). TAX SYSTEM OF THE REPUBLIC OF UZBEKISTAN:
GENERAL DESCRIPTION. International Journal of Artificial Intelligence, 1(2), 290-
293.
INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE
ISSN: 2692-5206, Impact Factor: 12,23
American Academic publishers, volume 05, issue 04,2025
Journal:
https://www.academicpublishers.org/journals/index.php/ijai
page 691
16.
Djurayeva, M. (2025). FEATURES OF THE ORGANIZATION OF PERSONNEL
MANAGEMENT
IN
MODERN
ORGANIZATIONS
AND
ENTERPRISES. International Journal of Artificial Intelligence, 1(2), 287-289.
17.
Игамова, Ш. З. (2023). ОСОБЕННОСТИ БУХАРСКИЙ ОБЛАСТИ C ПОЗИЦИЙ
ИННОВAЦИОННОГО РAЗВИТИЯ ЭКОНОМИКИ. Gospodarka i Innowacje., 42,
170-174.
