The Integral Assessment of Interorganizational Innovation Effectiveness Using Fuzzy Sets Method

In the current economic environment, the effective operation of higher education institutions is difficult to imagine without collaboration with organizations from the real sector of the economy, particularly in the context of innovation generation. Decision-makers responsible for the development of academic-industrial partnerships require modern methods to assess the potential effectiveness of such interactions. This study aims to develop a fuzzy-set-based integral aggregation model for evaluating the effectiveness of interorganizational innovations. The research contributes to scientific novelty through the following key solutions: the development of a hierarchical structure of integral indicators for assessing interorganizational innovation effectiveness; the selection of components and the evaluation of their significance using Fishburne’s weighting method; the creation of a fuzzification database to transform precise numerical values into fuzzy sets; the construction of an equation system to quantify non standardized components values to term sets membership degree and the formulation of a calculation method for intersection points of non-inversive indicators. The results of the conducted research has practical value and are possible to be used by both academic institutions and organizations in the real sector of economy for a preliminary assessment of interorganizational collaboration effectiveness in innovation generation. Future research by the author in this field will be aimed at testing the developed model refining it, and further systematizing and algorithmizing the results for efficient use in interorganizational innovation management.

1. Ivashchenko A. A. The genesis of interorganizational innovations in the scope of academic industrial partnership development. Beneficium. 2024;(1):6–13. (In Russ.). DOI: 10.34680/BENEFICIUM.2024.1(50).6–13

2. Jiao H., Yang J., Zhou J., Li J. Commercial partnerships and collaborative innovation in China: The moderating effect of technological uncertainty and dynamic capabilities. Journal of Knowledge Management. 2019;23(7):1429–1454. DOI: 10.1108/JKM-10-2017-0499

3. Schultz C., Gretsch O., Kock A. The influence of shared R&D-project innovativeness perceptions on university-industry collaboration performance. The Journal of Technology Transfer. 2021;46(3):1144–1172. DOI: 10.1007/s10961-020-09818-1

4. Grabisch M. Fuzzy integral in multicriteria decision making. Fuzzy Sets and Systems. 1995;69(3):279–298. DOI: 10.1016/0165-0114(94)00174-6

5. Myachin V., Yudina O. Fuzzy-logical approach to constructing an integral indicator in a level estimation model significant market advantage. Baltic Journal of Economic Studies. 2021;7(2):139–145. DOI: 10.30525/2256-0742/2021-7-2-139-145

6. Kozlov A., Kankovskaya A., Teslya A., Ivashchenko A. Study of labour digital potential usage by organizations of Ural Federal District. In: Rodionov D., Kudryavtseva T., Skhvediani A., Berawi M. A., eds. Innovations in digital economy (SPBPU IDE 2021). Cham: Springer-Verlag; 2022:265–276. (Communications in Computer and Information Science. Vol. 1619). DOI: 10.1007/978-3-031-14985-6_19

7. Zadeh L. A. Fuzzy sets. Information and Control. 1965;8(3):338–353. DOI: 10.1016/S0019–9958(65)90241-X

8. Kozlov A. V., Teslya A. B., Ivashchenko A. A. Creating an indicator system to survey the digitalization process of a national economy. Izvestiya vysshikh uchebnykh zavedenii. Seriya: Ekonomika, finansy i upravlenie proizvodstvom = News of Higher Educational Institutions. Series: Economy, Finance and Production Management. 2021;(1):97–107. (In Russ.). DOI: 10.6060/ivecofin.20214701.522

9. Nedosekin A. O., Maksimov O. B. Investment risk analysis using fuzzy sets. Upravlenie riskom = Risk Management. 2001;(1):51–55. (In Russ.).

10. Nedosekin A. O., Maksimov O. B. The simplest comprehensive assessment of the financial condition of an enterprise based on the fuzzy-set approach. Audit i finansovyi analiz = Audit and Financial Analysis. 2003;(3):23–28. (In Russ.).

11. Yao Y., Zhang J. Interpreting fuzzy membership functions in the theory of rough sets. In: Ziark, W., Yao Y., eds. Rough sets and current trends in computing (RSCTC 2000). Berlin, Heidelberg: Springer-Verlag; 2001:82–89. (Lecture Notes in Computer Science. Vol. 2005). DOI: 10.1007/3-540-45554-X_9

12. Khalov E. A. A systematic review of crisp one-dimensional membership functions of intelligent systems. Informatsionnye tekhnologii i vychislitel’nye sistemy = Information Technologies and Computing Systems. 2009;(3):60–74. (In Russ.).

13. Nedosekin A. O. Fuzzy-set risk analysis of stock investment. St. Petersburg: Sezam; 2002. 165 p. (In Russ.).

14. Efremova P. V. Indicators for assessing the effectiveness of the universities innovative activity development. Voprosy innovatsionnoi ekonomiki = Russian Journal of Innovation Economics. 2019;9(3):989–1010. (In Russ.). DOI: 10.18334/vinec.9.3.41001

15. Noskov A. A. Methodical directions of assessing innovative development of regions and innovative research activities of universities. Vestnik Permskogo natsional’nogo issledovatel’skogo politekhnicheskogo universiteta. Sotsial’no-ekonomicheskie nauki = PNRPU Sociology and Economics Bulletin. 2018;(4):363–372. (In Russ.). DOI: 10.15593/2224–9354/2018.4.30

16. Galimov A. M., Zakirova A. R., Makhan’ko A. V. On the assessment of the results of innovative activities of the university. Obrazovatel’nye tekhnologii i obshchestvo = Educational Technology & Society. 2013;16(4):403–411. (In Russ.).

17. Hupman A., Simon J. The legacy of Peter Fishburn: Foundational work and lasting impact. Decision Analysis. 2023;20(1):1–15. DOI: 10.1287/deca.2022.0461

18. Karpova N. A. Application of methods of fuzzy logic in valuation and forecasting of financial capability of the consolidated groups of companies. Internet-zhurnal Naukovedenie. 2015;7(5):56. (In Russ.). DOI: 10.15862/199EVN 515

19. Terenteva D. A., Kryzhko D. A., Konnikov E. A., Melnichenko A. M. Fuzzy-set approach to assessing the level of region innovation potential. Ekonomika i upravlenie: problemy, resheniya = Economics and Management: Problems, Solutions. 2023;4(9):167–192. (In Russ.). DOI: 10.36871/ek.up.p.r.2023.09.04.016

20. Nedosekin A. O. the use of fuzzy sets in economics, business and finance. Novosti iskusstvennogo intellekta. 2004;(2):27–34. (In Russ.).

21. Bondarenko P. V., Fokina E. A., Trukhlyaeva A. A. Application of the theory of fuzzy sets for assessment of the quality of life population of the region. Fundamental’nye issledovaniya = Fundamental Research. 2015;(11– 5):967–971. (In Russ.).

22. Lukashevich N.S. Assessment of creditworthiness of individuals based on the theory of fuzzy sets. Cand. econ. sci. diss. St. Petersburg: Saint Petersburg State Polytechnic University; 2009. 186 p. (In Russ.).

23. Ivashchenko A. A., Teslya A. B. Characteristics of the digital innovation environment of organizations. In: Global challenges of digital transformation of markets. St. Petersburg: Polytech-Press; 2023:746–758. (In Russ.).

24. Kozlov A., Teslya A. Digital potential of industrial enterprises: Essence, determination and calculation methods. Vestnik Zabaikal’skogo gosudarstvennogo universiteta = Transbaikal State University Journal. 2019;25(6):101–110. (In Russ.). DOI: 10.21209/2227-9245-2019-25-6-101-110

25. Burtseva T. A., Frenkel A. A., Tikhomirov B. I., Surkov A. A. Integral index as an effective tool for measuring regional labour productivity. Ekonomika truda = Russian Journal of Labor Economics. 2020;7(11):1085–1102. (In Russ.). DOI: 10.18334/et.7.11.111086

26. Velikorossov V. V., Filin S. A., Lanchakov A. B., et al. Integrated analysis model for sustainable enterprise development. Ekonomika i upravlenie: problemy, resheniya = Economics and Management: Problems, Solutions. 2022;2(3):18–28. (In Russ.). DOI: 10.36871/ek.up.p.r.2022.03.02.002