Fuzzy Model of Socio-Economic System Development Risks Analysis on the Stakeholder Approach Basis

Tyumen State University Herald. Social, Economic, and Law Research


Release:

2017, Vol. 3. №3

Title: 
Fuzzy Model of Socio-Economic System Development Risks Analysis on the Stakeholder Approach Basis


For citation: 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. DOI: 10.21684/2411-7897-2017-3-3-242-260

About the authors:

Lev S. Mazelis, Dr. Sci. (Econ.), Head of the Department of Mathematics and Modeling, Vladivostok State University of Economics and Service; lev.mazelis@vvsu.ru

Konstantin S. Solodukhin, Dr. Sci. (Econ.), Professor, Department of Mathematics and Modeling, Vladivostok State University of Economics and Service; k.solodukhin@mail.ru

Kirill I. Lavrenyuk, Senior Lecturer, Department of Mathematics and Modeling, Vladivostok State University of Economics and Service; kirill.lavrenyuk@vvsu.ru

Abstract:

This article is devoted to the development of a method for quantitative analysis of the socio-economic system development risks that makes it possible to assess the influence of external factors of the system on the main indicators of its development, taking into account internal factors, interests of stakeholders and existing uncertainties. The proposed method is based on the fuzzy “stakeholder” model of SWOT. To simulate uncertainties, it is proposed to use a fuzzy-multiple approach that allows to take into account the blurring of expert information, as well as inaccurate information about changes in the external environment of the system and relationships with stakeholders. Approbation of the method was carried out on the example of socio-economic development of Vladivostok. For the municipal entity have been identified and ill-defined 35 factors of the internal environment, 18 external factors and 11 indicators of social and economic development. For external factors, fuzzy equalizing factors are calculated that characterize the ability of strengths and weaknesses to correct the force of impact of opportunities and threats. Fuzzy risks of failure to reach target values of indicators are calculated. The riskiness of the development of the urban district for the three main groups of stakeholders (“Population”, “Government” and “Business”) is assessed at various risk acceptance levels.

References:

  1. Lavrenyuk K. I., Mazelis L. S., Soloduhin K. S. 2016. “Kolichestvennyj analiz riskov social'no-ehkonomicheskogo razvitiya municipal'nogo obrazovaniya na osnove stejkholderskogo podhoda” [Quantitative Risk Analysis of Social-Economic Development of the Municipality Based on Stakeholder Approach]. Azimut nauchnyh issledovanij: ehkonomika i upravlenie, vol. 5, no 4 (17), pp. 262-265.
  2. Mazelis L. S., Morozov V. O. 2014. “Metodika SWOT-analiza riskov regiona v razreze makroehkonomicheskih pokazatelej social'no-ehkonomicheskogo razvitiya (na primere Kamchatskogo kraya)” [Methodology of SWOT-Analysis Risks by Region in the Context of the Main Macroeconomic Indicators of Socio-Economic Development (As an Example of the Kamchatka Territory)]. Sovremennye problemy nauki i obrazovaniya, no 6. https://science-education.ru/ru/article/view?id=16329
  3. Morozov V. O., Soloduhin K. S., Chen A. Ya. 2016. “Nechetko-mnozhestvennye metody strategicheskogo analiza stejkholder-kompanii” [Fuzzy Set Methods for Strategic Stakeholder Analysis of a Company]. Fundamental'nye issledovaniya, no 2-1, pp. 179-183.
  4. Soloduhin K. S., Rahmanova M. S. 2009. “Innovacionnaya tekhnologiya strategicheskogo analiza organizacii na osnove teorii zainteresovannyh storon” [Innovative Technology of Strategic Analysis of the Organization Based on the Stakeholders Theory]. Nauchno-tekhnicheskie vedomosti Sankt-Peterburgskogo gosudarstvennogo universiteta. Ekonomicheskie nauki, no 2-1 (75), pp. 102-111.
  5. Gao C.-Y., Peng D.-H. 2011. “Consolidating SWOT Analysis with Nonhomogeneous Uncertain Preference Information”. Knowledge-Based Systems, no 24, pp. 796-808. DOI: 10.1016/j.knosys.2011.03.001
  6. Ghazinoory S., Esmail Zadeh A., Memariani A. 2007. “Fuzzy SWOT Analysis”. Journal of Intelligent & Fuzzy Systems, no 18, pp. 99-108.
  7. Haile M., Krupka J. 2016. “Fuzzy Evaluation of SWOT Analysis”. International Journal of Supply Chain Management, vol. 5, no 3, pp. 172-179.
  8. Hassanzadeh Amin S., Razmi J., Zhang G. 2011. “Supplier Selection and Order Allocation Based on Fuzzy SWOT Analysis and Fuzzy Linear Programming”. Expert Systems with Applications, no 38, pp. 334-342. DOI: 10.1016/j.eswa.2010.06.071
  9. Hatami-Marbini A., Saati S. 2009. “An Application of Fuzzy TOPSIS in an SWOT Analysis”. Mathematical Sciences, vol. 3, no 2, pp. 173-190.
  10. Hosseini-Nasab H., Hosseini-Nasab A., Milani A.S. 2011. “Coping with Imprecision in Strategic Planning: A Case Study Using Fuzzy SWOT Analysis”. iBusiness, no 3, pp. 23-29.
  11. Kececi T., Arslan O. 2017. “SHARE technique: A Novel Approach to Root Cause Analysis of Ship Accidents”. Safety Science, no 96, pp. 1-21. DOI: 10.1016/j.ssci.2017.03.002
  12. Parthiban P., Abdul Zubar H., Katakar P. 2013. “Vendor Selection Problem: A Multi-Criteria Approach Based on Strategic Decisions”. International Journal of Production Research, vol. 51, no 5, pp. 1535-1548. DOI: 10.1080/00207543.2012.709644
  13. Saman Kheirkhah A., Esmailzadeh A., Ghazinoory S. 2009. “Development Strategies to Reduce the Risk of Hazardous Material Transportation in Iran Using the Method of Fuzzy SWOT Analysis”. Transport, no 24 (4), pp. 325-332.
  14. Yun-Huei L. 2013 “Application of a SWOT-FANP Method”. Technological and Economic Development of Economy, is. 13, no 4, pp. 570-592.