Оценка рисков недостижения целевых значений показателей интеллектуального капитала организации на основе нечеткой модели

  • Константин С. Солодухин Доктор экономических наук, профессор; профессор, кафедра математики и моделирования, заведующий лаборатории, лаборатория стратегического планирования, Владивостокский государственный университет, Владивосток, Россия https://orcid.org/0000-0003-3619-1219
  • Георгий С. Завалин Начальник отдела, отдел интеллектуального анализа данных, стажер-исследователь, лаборатория стратегического планирования, Владивостокский государственный университет, Владивосток, Россия https://orcid.org/0000-0003-4519-0242
  • Дарья В. Макарова Ведущий специалист, аналитический отдел, стажер-исследователь, лаборатория стратегического планирования, Владивостокский государственный университет, Владивосток, Россия https://orcid.org/0009-0002-8207-3010
Ключевые слова: интеллектуальный капитал, нечеткая модель, риски недостижения целей, управление рисками портфеля проектов, толерантность к неопределенности

Аннотация

Процессы формирования и развития интеллектуального капитала в цифровой экономике представляют собой слабоструктурированные процессы, протекающие в условиях значительного увеличения скорости и непредсказуемости изменений во внешней среде. Это крайне затрудняет возможность использования предыдущего опыта и вероятностных прогнозов при оценке рисков недостижения стратегических целей по развитию интеллектуального капитала организации. При этом нежелательные отклонения при достижении этих целей могут приводить к значительным негативным последствиям. В этой связи возникает необходимость разработки соответствующих нечетких методов и моделей, что обусловливает актуальность настоящей работы. Цель данного исследования состояла в разработке нечеткого метода оценки рисков недостижения стратегических целей организации в сфере развития интеллектуального капитала. В основе метода лежит разработанная авторами нечеткая модель, позволяющая учитывать толерантность к неопределенности лица, принимающего решения. Апробация метода на примере конкретной организации показала возможность его практической применимости. Приведены количественные оценки и качественные интерпретации уровней рисков недостижения целевых показателей по развитию интеллектуального капитала организации (крупного регионального университета).

Скачивания

Данные скачивания пока не доступны.

Литература

Barry D. (1987) The relationship of strategic goals and planning processes to organiza¬tional performance. Unpublished Ph. D. Dissertation, University of Maryland.

Gurkov I.B. (2008) Factors of formation and mechanisms of realization of strategic goals of Russian companies. Report at the Economics Section of the Department of Social Sciences of the Russian Academy of Sciences. March 13, 2008 (in Russian).

Gurkov I.B. (2007) Integrated metrics of strategy process – an attempt of theoretical synthesis and empirical validation. Russian Management Journal, vol. 5, no. 2, pp. 3–28 (in Russian).

Mityakov S.N., Mityakov E.S. (2023) Developing the theory of economic security risks and thresholds. The Bulletin of the Institute of Economics of the Russian Academy of Sciences, no. 5, pp. 83–113 (in Russian). https://doi.org/10.52180/2073-6487_2023_5_83_113

Vasilkov Yu.V., Gushchina L.S. (2017) Risk analysis of not achievement of the objectives in case of control of the organization. Proceedings of Voronezh State University. Series: Economics and Management, no. 1, pp. 5–12 (in Russian).

Morozov V.O. (2013) Dependence formalization between level of achievement of the strategic objective and values of its indicators on the basis of sign-variable function of usefulness. Modern Problems of Science and Education, no. 6, pp. 457 (in Russian).

Kachalov R.M. (2012) Economic risk management. Theoretical foundations and applications. Moscow; St. Petersburg: Nestor-History (in Russian).

Gushchina L.S., Vasilkov Yu.V. (2017) Technique of the account of risks at planning enterprise developments. Modern High Technologies. Regional Application, no. 2(50), pp. 105–122 (in Russian).

Lapochkina V.V., Dolgova V.N., Orshanskaya Yu.O., Shkilev I.N. (2020) Assessing the risk of benchmark and additional indicators of the Science national project. National Interests: Priorities and Security, vol. 16, no.12, pp. 2338–2362 (in Russian). https://doi.org/10.24891/ni.16.12.2338

Chereshnev V.A., Vasil'eva A.V., Korobitsyn B.A. (2017) Assessing the economic efficiency of socially oriented government programs by simulation modeling methods. Economic Analysis: Theory and Practice, vol. 16, no.1, pp. 174–187 (in Russian). https://doi.org/10.24891/ea.16.1.174

Pishchalkina I., Tereshko E., Suloeva S. (2023) Application of self-organizing maps for risk assessment of mining and metallurgical enterprises. Sustainable Development and Engineering Economics, no. 1(7), pp. 28–44. https://doi.org/10.48554/SDEE.2023.1.2

Novoselova I.Yu., Novoselov A.L. (2023) Planning and implementation of Federal projects in the Arctic regions, taking into account risk factors. Economics, Taxes & Law, vol. 16, no. 6, pp. 49–59 (in Russian). https://doi.org/10.26794/1999-849X-2023-16-6-49-59

Novoselova I.Yu., Novoselov A.L. (2023) Methods of risk assessment for the implementation of economic development projects in the Arctic regions. Economics, Taxes & Law, vol. 16, no. 3, pp. 109–119 (in Russian). https://doi.org/10.26794/1999-849X-2023-16-3-109-119

Kachalov R.M., Sleptsova Y.A. (2014) Modelling of procedure regulations of economic risk with application of fuzzy logic theory. Proceedings of the III International Youth Scientific and Practical Conference. Saratov: Saratov State University, pp. 78–83 (in Russian).

Sleptsova Y.A. (2016) Risk management in the activities of a manufacturing enterprise based on the tools of systemic economic theory and fuzzy logic. Moscow: CEMI (in Russian).

Tselykh A.N., Tselykh L.A., Prichina O.S. (2014) Fuzzy logic methods in the management of production processes. Izvestiya SFedU. Engineering Sciences, no. 1(150), pp. 111–119 (in Russian).

Strebkova L.N. (2014) Risk assessment of enterprise based on application of fuzzy neural network. Vestnik NSUEM, no. 3, pp. 147–154 (in Russian).

Mazelis L.S., Solodukhin K.S., Lavrenyuk K.I. (2017) Fuzzy model of socio-economic system development risks analysis on the stakeholder approach basis. Tyumen State University Herald. Social, Economic, and Law Research, vol. 3, no. 3, pp. 242–260 (in Russian). https://doi.org/10.21684/2411-7897-2017-3-3-242-260

Dudin M.N., Lyasnikov N.V., Protsenko O.D., Tsvetkov V.A. (2017) Quantification and risk assessment of hydrocarbon resources development projects in the Arctic region. Tyumen State University Herald. Social, Economic, and Law Research, vol. 12, no. 4, pp. 168–195 (in Russian). https://doi.org/10.18288/1994-5124-2017-4-07

Solodukhin K.S. (2019) Fuzzy strategic decision-making models based on formalized strategy maps. AEBMR-Advances in Economics, Business and Management Research, vol. 47, Proceedings of the International Scientific Conference "Far East Con" (ISCFEC 2018), pp. 543–547. https://doi.org/10.2991/iscfec-18.2019.136

Micán C., Fernandes G., Araújo M. (2021) Project portfolio risk management: a structured literature review with future directions for research. International Journal of Information Systems and Project Management, vol. 8, no. 3, pp. 67–84. https://doi.org/10.12821/ijispm080304

Hofman M., Spalek S., Grela G. (2017) Shedding new light on project portfolio risk management. Sustainability, vol. 9, no. 10, pp. 1798-1816. https://doi.org/10.3390/su9101798

Ghasemi F., Sari M., Yousefi V., Falsafi R., Tamošaitienė J. (2018) Project portfolio risk identification and analysis, considering project risk interactions and using Bayesian networks. Sustainability, vol. 10, no. 5, pp. 1609–1631. https://doi.org/10.3390/su10051609

Guan D., Guo P., Hipel K., Fang L. (2017) Risk reduction in a project portfolio. Journal of Systems Science and Systems Engineering, vol. 26, no. 1, pp. 3–22. https://doi.org/10.1007/s11518-016-5296-2

Benaija K., Kjiri L. (2015) Hybrid approach for project portfolio selection taking account of resources management and interactions between projects. Journal of Digital Information Management, vol. 13, no. 6, pp. 451–461.

Kleiner G.B., Smolyak S.A. (2000) Econometric dependencies: methods and principles of construction. Moscow: Nauka (in Russian).

Kettunen J., Salo A. (2017) Estimation of downside risks in project portfolio selection. Production and Operations Management, vol. 26, no. 10, pp. 1839–1853. https://doi.org/10.1111/poms.12727

Mohagheghi V., Mousavi S.M., Vahdani B. (2016) A new multi-objective optimization approach for sustainable project portfolio selection: A real-world application under interval-valued fuzzy environment. Iranian Journal of Fuzzy Systems, vol. 13, no. 6, pp. 41–68. https://doi.org/10.22111/ijfs.2016.2821

Shatalova O.M. (2023) Efficiency of innovative processes: fuzzy multiple modeling and evaluation in conditions of non-stochastic uncertainty. Izhevsk: Publishing House of IzhSTU (in Russian).

Zaidouni A., Idrissi M.A.J., Bellabdaoui A. (2023) A Sugeno ANFIS model based on fuzzy factor analysis for IS/IT project portfolio risk prediction. Journal of Information and Communication Technology, vol. 23, no. 2, pp. 139–176. https://doi.org/10.32890/jict2024.23.2.1

Mehlawat M.K., Gupta P., Khan A.Z. (2023) An integrated fuzzy-grey relational analysis approach to portfolio optimization. Applied Intelligence, vol. 53, pp. 3804–3835. https://doi.org/10.1007/s10489-022-03499-z

Khanjani Shiraz R., Tavana M., Fukuyama H. (2020) A random-fuzzy portfolio selection DEA model using value-at-risk and conditional value-at-risk. Soft Computing, vol. 24, pp. 17167–17186. https://doi.org/10.1007/s00500-020-05010-7

Deng X., Yuan Y. (2021) A novel fuzzy dominant goal programming for portfolio selection with systematic risk and non-systematic risk. Soft Computing, vol. 25, pp. 14809–14828. https://doi.org/10.1007/s00500-021-06226-x

Rahiminezhad Galankashi M., Mokhatab Rafiei F., Ghezelbash M. (2020) Portfolio selection: a fuzzy-ANP approach. Financial Innovation, vol. 6, no. 1, pp. 1–34. https://doi.org/10.1186/s40854-020-00175-4

Mohseny-Tonekabony N., Sadjadi S.J., Mohammadi E., Tamiz M., Jones D.F. (2024) Robust, extended goal programming with uncertainty sets: An application to a multi-objective portfolio selection problem leveraging DEA. Annals of Operations Research. https://doi.org/10.1007/s10479-023-05811-7

Wang B., Li Y., Wang S., Watada J. (2018) A multi-objective portfolio selection model with fuzzy value-at-risk ratio. IEEE Transactions on Fuzzy Systems, vol. 26, no. 6, pp. 3673–3687. https://doi.org/10.1109/TFUZZ.2018.2842752

Dehghani F. (2023) Simulation of annealing for portfolio selection in mean-pseudo-variance fuzzy model. SSRN Electronic Journal, pp. 1–12. https://doi.org/10.2139/ssrn.4660934

Nguyen V.D., Duyen N.K., Hai N.M., Duy B.K. (2023) Multicriteria portfolio selection with intuitionistic fuzzy goals as a pseudoconvex vector optimization. Lecture Notes on Data Engineering and Communications Technologies, vol. 187, pp. 68–79. https://doi.org/10.1007/978-3-031-46573-4_7

Abtahi S.H. (2023) Uncertain random portfolio optimization based on skew chance distribution. International Journal of Fuzzy Logic and Intelligent Systems, vol. 23, no. 1, pp. 44–55. https://doi.org/10.5391/ijfis.2023.23.1.44

Yang X., Liu W., Chen S., Zhang Y. (2021) A multi-period fuzzy mean-minimax risk portfolio model with investor’s risk attitude. Soft Computing, vol. 25, pp. 2949–2963. https://doi.org/10.1007/s00500-020-05351-3

Kleiner G.B. (2008) Enterprise strategy. M.: Publishing house "Delo" ANKH (in Russian).

Nazarov D.M. (2016) The evaluation model of implicit factors on the basis of fuzzy-set descriptions. Far Eastern Federal University News, no. 4(80), pp. 3–17 (in Russian). https://doi.org/10.5281/zenodo.220793

Makarova D.V., Nedoluzhko O.V., Solodukhin K.S. (2024) The role of economic digitization in the development of the organization intellectual capital theory. Proceedings of the X international Scientific and Practical Conference “Intelligent engineering economics and Industry 5.0 (IEEI_5.0_INPROM)”, St. Petersburg, April 25–28 (eds. D.G. Rodionov, A.V. Babkin), pp. 215–219 (in Russian). https://doi.org/10.18720/IEP/2024.2/50

Cosa M., Pedro E., Urban B. (2024) How to assess the intellectual capital of firms in uncertain times: a systematic literature review and a proposed model for practical adoption. Journal of Intellectual Capital, vol. 25, no. 7, pp. 1–22. https://doi.org/10.1108/JIC-05-2023-0096

Bustamante A., Liberona D., Ferro R. (2024) Approach to measuring organizational performance from the perspective of intellectual capital. Communications in computer and information science, vol. 2152, pp. 73–85. https://doi.org/10.1007/978-3-031-63269-3_6

Kozlovskyi S., Syniehub P., Kozlovskyi A., Lavrov R. (2022) Intellectual capital management of the business community based on the neuro-fuzzy hybrid system. Neuro-Fuzzy Modeling Techniques in Economics, vol. 11, no. 11, pp. 25–47. https://doi.org/10.33111/nfmte.2022.025

Çevik G., Arslan Ö. Analytic evaluation of intellectual capital for ship management companies under a fuzzy environment (2022) Journal of ETA Maritime Science, vol. 10, no. 3, pp. 185–194. https://doi.org/10.4274/jems.2022.41033

Pokrovskaia N., Margulyan Ya., Lvin Yu., Bulatetskaia A. (2020) Neuro-technologies and fuzzy logic for intellectual capital evaluation in education and business. IOP Conference Series: Materials Science and Engineering, vol. 940, article 012090. https://doi.org/10.1088/1757-899X/940/1/012090

Lucchese M., Aversano N., Di Carlo F., Polcini P.T. (2020) Assessing the intellectual capital and related performance in the teaching process using FES models: First evidence in Italian universities. WSEAS transactions on business and economics, vol. 17, pp. 325–344. https://doi.org/10.37394/23207.2020.17.34

Gross-Gołacka E., Kusterka-Jefmańska M., Jefmański B. (2020) Can elements of intellectual capital improve business sustainability? – The perspective of managers of SMEs in Poland. Sustainability, vol. 12, no. 4, article 1545. https://doi.org/10.3390/su12041545

Zavalin G.S., Nedoluzhko O.V., Solodukhin K.S. (2023) Formation of the causal field of indicators for an organization's intellectual capital development: A concept and a fuzzy economic and mathematical model. Business Informatics, vol. 17, no. 3, pp. 53–69. https://doi.org/10.17323/2587-814X.2023.3.53.69

Nedoluzhko O.V., Solodukhin K.S. (2024) Quantitative assessment of university’s intellectual capital based on fuzzy model. University Management: Practice and Analysis, vol. 28, no. 1, pp. 34–49 (in Russian). https://doi.org/10.15826/umpa.2024.01.003

Makarova D.V., Nedoluzhko O.V., Solodukhin K.S., Zavalin G.S. (2024) Fuzzy optimization models for intellectual capital enhancing project portfolio selection under risk. Journal of System and Management Sciences, vol. 14, no. 7, pp. 1–19. https://doi.org/10.33168/JSMS.2024.0701

Anshin V.M., Dyomkin I.V., Tsarkov I.V., Nikonov I.M. (2008) On application of fuzzy set theory to the problem of project portfolio selection. Issues оf Risk Analysis, vol. 5, no. 3, pp. 8–21 (in Russian).

Dubois D., Prade H. (1988) Possibility Theory. New York: PlenumPress.

Wang J., Hwang W.-L. (2007) A fuzzy set approach for R&D portfolio selection using a real option valuation model. Omega, vol. 35, no. 3, pp. 247–257.

Furnham A., Marks J. (2013) Tolerance of ambiguity: A review of the recent literature. Psychology, vol. 4, no. 9, pp. 717–728. https://doi.org/10.4236/psych.2013.49102

Minaev Yu.N., Filimonova O.Yu., Minaeva J.I. (2012) Index of fuzziness of fuzzy sets in context of concepts “Data Mining”. Problems of Informatization and Management, vol. 3, no. 39, pp. 95–101 (in Russian).

Nazarov D.M. (2016) Methodology of fuzzy set evaluation of implicit factors in organizational activities. Ekaterinburg: Ural State Economic University Press (in Russian).

De Luca A., Termini S. (1972) A definition of a nonprobabilistic entropy in the setting of fuzzy sets theory. Information and control, vol. 20, no. 4, pp. 301–312. https://doi.org/10.1016/S0019-9958(72)90199-4

Yager R.R. (1979) On the measure of fuzziness and negation. Part I: Membership in the unit interval. International Journal of General Systems, vol. 5, no. 4, pp. 221–229. https://doi.org/10.1080/03081077908547452

Опубликован
2025-03-28
Как цитировать
Солодухин К. С., Завалин Г. С., & Макарова Д. В. (2025). Оценка рисков недостижения целевых значений показателей интеллектуального капитала организации на основе нечеткой модели. БИЗНЕС-ИНФОРМАТИКА, 19(1), 72-92. извлечено от https://patria.hse.ru/index.php/bijournal/article/view/26726
Раздел
Статьи