Preview

The world of new economy

Advanced search

The Scientific-Educational Corporation as the Model for the Development of Russian Universities: From Concept to Empirical Verification

https://doi.org/10.26794/2220-6469-2026-20-2-134-149

Abstract

The relevance: in the context of large-scale structural reforms and intensifying demands for educational quality in Russia’s higher education personnel, the need for objective assessment and nuanced differentiation of university development models is increasingly apparent. Significant differences in ecosystem structure, innovation potential, and the organisational dynamics of leading universities require new approaches to typology and strategies for modernisation. Traditional rankings and formal criteria prove insufficient for understanding the institutional diversity and identifying the true drivers of leadership within the university sector. The aim of this study is to propose and empirically substantiate a typology of leading Russian universities based on the concept of the scientific-educational corporation as an integrative form of the modern university. Cluster analysis is employed as the principal method, applied to a set of 52 quantitative indicators reflecting the key features of 99 major universities in the country. The scientific novelty lies in the conceptualisation of the scientific-educational corporation and multidimensional university classification. As a result, three clusters of universities are identified, differing substantially in ecosystem scales, innovation activity, and strategic and organisational characteristics. The practical significance of the study lies in the potential application of the proposed approaches and findings for enhancing strategic management, educational policy formation, and the effectiveness of the university sector as part of the national innovation system. The proposed toolkit can serve as an empirical and methodological basis for further research in university typology and higher education development.

About the Author

N. M. Abramova
Russian Presidential Academy of National Economy and Public Administration
Russian Federation

Natalia M. Abramova — Ph.D. Sci. (Econ.), Dean, the School of Public Policy and Management

Moscow



References

1. Egorov A.A. Evaluation of the effectiveness of universities based on their production functions. Universitetskoe upravlenie: praktika i analiz = University Management: Practice and Analysis. 2020;24(4):87-99. (In Russ.). DOI: 10.15826/umpa.2020.04.037

2. Grineva N.V., Yudina V.A. Multivariate analysis of indicators for assessing institutional factors of Russian universities and their ranking influence on the level of international competitiveness. Problemy ekonomiki i yuridicheskoj praktiki = Problems of Economics and Legal Practice. 2021;17(5);89-101 (In Russ.).

3. Kuznetsova O.A., Klevina M.V. Comparative analysis of the results of university clustering with different sets of parameters. Prikladnaya matematika i voprosy upravleniya = Applied Mathematics and Management Issues. 2020;(3):164-181. (In Russ.). DOI: 10.15593/2499-9873/2020.3.09

4. Melikyan A.V. Cluster analysis of Russian universities based on the dynamics of their performance indicators. Voprosy statistiki. 2021;28(5):58-68. (In Russ.). DOI: 10.34023/2313-6383-2021-28-5-58-68

5. Melikyan A.V. Typology of the results of cooperation between Russian universities and business. Vysshee obrazovanie v Rossii = Higher education in Russia. 2024;33(11):108-131. (In Russ.). DOI: 10.31992/0869-3617-2024-33-11-108-131

6. Rostova E.P., Kovelsky V.V. Cluster analysis of R&D efficiency in innovatively developing universities of Russia. Vestnik Samarskogo universiteta. Ekonomika i upravlenie = Bulletin of Samara University. Economics and Management. 2020;11(2):56-62. (In Russ.). DOI: 10.18287/2542-0461-2020-11-2-56-62

7. Talovskaya B.M., Lisyutkin M.A. On the resource stratification of Russian universities. Universitetskoe upravlenie: praktika i analiz = University management: practice and analysis. 2018;22,(6):24-35. (In Russ.). DOI: 10.15826/umpa.2018.06.055.

8. Tikhonyuk N.E., Kislyakov A.N., Kamanin I.E. EduClust: Cluster analysis and forecasting of the development of universities in the regions of Russia. Ekonomika i predprinimatel’stvo = Economy and entrepreneurship. 2025;1(174):650-653. (In Russ.). DOI: 10.34925/EIP.2024.174.1.117

9. Celik S. Classification of Foundation Universities by Cluster Analysis According to Academic, Financial and Administrative Indicators. EJOSTIMTECH. 2022;1(1):51-61.

10. Elbawab R. University Rankings and Goals: A Cluster Analysis. Economies, MDPI. 2022;10(9):1-34. DOI: 10.3390/economies10090209

11. Abderraouf A., Bentadjine M., Hamidat A., Grounga O. Using cluster analysis to classify some Arab countries into homogeneous groups according to institutional quality criteria. Intern. Journal of Profess. Bus. Review. 2024;9(5):1-23.

12. Ibáñez A., Larrañaga P., Bielza C. Cluster Methods for Assessing Research Performance: Exploring Spanish Computer Science. Scientometrics. 2013;(97):571-600. DOI: 10.1007/s11192-013-0985-9

13. Shin J.C. Classifying Higher Education Institutions in Korea: A Performance-Based Approach. Higher Education. 2009;57(2):247-266. DOI: 10.1007/s10734-008-9150-4

14. Valadkhani A., Worthington A. Ranking and Clustering Australian University Research Performance, 1998-2002. Journal of Higher Education Policy and Management. 2006;28(2):189-210. DOI: 10.1080/13600800600751101

15. Abramova N.M. From Humboldt to the 21st Century University: A Critical View of Contemporary Models. (In Russ.). Ekonomika: vchera, segodnya, zavtra = Economics: Yesterday, Today and Tomorrow. 2025;15(8A):428- 440. DOI: 10.34670/AR.2025.15.57.046

16. Abramova N. From Classical University to Scientific-Educational Corporation: Institutional Properties of a New Model. In: Actual Economy: Local Solutions to Global Challenges 2026. Proceedings of XXX International Conference. Osaka: Japan; 2026:93-97.

17. Gitis L. Kh. Statistical classification and cluster analysis. Moscow: Mining Book (MGGU); 2003:157. (In Russ.)

18. Isaev V.G., Protasov Yu.M., Yurov V.M. Clusterization of Moscow Region Higher Education Organizations According to the Results of Monitoring their Activities. Vestnik Moskovskogo gosudarstvennogo oblastnogo universiteta. Seriya Ekonomika = Bulletin of Moscow Region State University. Series: Economics. 2021;1:93-101. (In Russ.). DOI: 10.18384/2310-6646-2021-93-101

19. Mambetkaziyev A., Baikenov Zh., Konopyanova G. Cluster analysis of the effectiveness of management of higher education institutions. Eastern-European Journal of Enterprise Technologies. 2022;6(3(120)):26-31. EDN HNHCSO. DOI: 10.15587/1729-4061.2022.265860.

20. Mirkin B.G. Methods of cluster analysis for decision support: a review. Moscow: Publishing house of the National Research University “Higher School of Economics”; 2011:84. (In Russ.).

21. Kanungo T., Mount D.M., Netanyahu N.S., Piatko C.D., Silverman R., Wu A.Y. An efficient k-means clustering algorithm: Analysis and implementation. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2002;24(7):881-892. DOI: 10.1109/TPAMI.2002.1017616


Review

For citations:


Abramova N.M. The Scientific-Educational Corporation as the Model for the Development of Russian Universities: From Concept to Empirical Verification. The world of new economy. 2026;20(2):134-149. (In Russ.) https://doi.org/10.26794/2220-6469-2026-20-2-134-149

Views: 376

JATS XML


Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.


ISSN 2220-6469 (Print)
ISSN 2220-7872 (Online)