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Modeling of motivation of key executives of government agencies of regions using a multi-objective genetic algorithm

Vol. 28, Iss. 5, MAY 2022

Received: 14 March 2022

Received in revised form: 28 March 2022

Accepted: 11 April 2022

Available online: 30 May 2022

Subject Heading: INVESTING

JEL Classification: C63, E17, O21, O36

Pages: 972–999

https://doi.org/10.24891/fc.28.5.972

Sergei N. YASHIN National Research Lobachevsky State University of Nizhny Novgorod (UNN), Nizhny Novgorod, Russian Federation
jashinsn@yandex.ru

https://orcid.org/0000-0002-7182-2808

Egor V. KOSHELEV National Research Lobachevsky State University of Nizhny Novgorod (UNN), Nizhny Novgorod, Russian Federation
ekoshelev@yandex.ru

https://orcid.org/0000-0001-5290-7913

Dmitrii A. SUKHANOV Non-State Educational Private Institution for Advanced Vocational Education Biota – Plus, Nizhny Novgorod, Russian Federation
svx85@yandex.ru

https://orcid.org/0000-0002-4600-0108

Subject. This article explores the motivation of top managers of government entities to bring into line the interests of the population, the State and its key executives.
Objectives. The article aims to create a model of motivation of key executives of government institutions of the regions, which will make it possible to make the intangible motivation of top managers contingent on the achieved strategic potential of the region and their financial incentives.
Methods. For the study, we used a multi-objective genetic algorithm and the Pareto Frontier solutions set.
Results. The article proposes a procedure for reaching a conclusion about the actual bonus award (incentivization) of key executives of government agencies of the regions.
Conclusions and Relevance. The results obtained can be useful to government agencies to develop a rational system of financial and non- financial incentives of their senior leadership.

Keywords: motivation, senior leadership, multi-purpose genetic algorithm

References:

  1. Khytrova O.A., Sysoieva I.M., Dolha H.V. et al. Ensuring the Growth of Enterprises and Organizations Through the Motivation of Managerial Staff. International Journal of Economics and Business Administration, 2020, vol. 8, iss. 2, pp. 219–228. URL: Link
  2. Munna A.S. Strategic Management, Leadership and Staff Motivation: Literature Review. International Education and Culture Studies, 2021, vol. 1, iss. 1, pp. 21–29. URL: Link
  3. Dogar M.N. Breach of Psychological Contract: Impact on Workforce Motivation and Organizational Sustainability. Emerald Emerging Markets Case Studies, 2020, vol. 10, no. 1. URL: Link
  4. Kampf R., Lorincova S., Kapustina L.M., Lizbetinova L. Motivation Level and its Comparison Between Senior Managers and Blue-Collar Workers in Small and Medium-Sized Transport Enterprises. Communications – Scientific Letters of the University of Zilina, 2017, vol. 19, no. 4, pp. 43–49. URL: Link
  5. Schwarz G., Eva N., Newman A. Can Public Leadership Increase Public Service Motivation and Job Performance? Public Administration Review, 2020, vol. 80, iss. 4, pp. 543–554. URL: Link
  6. Long Q., Wu Ch., Wang X. et al. A Multiobjective Genetic Algorithm Based on a Discrete Selection Procedure. Mathematical Problems in Engineering, 2015, vol. 2015, 17 p. URL: Link
  7. Fita A. Three-Objective Programming with Continuous Variable Genetic Algorithm. Applied Mathematics, 2014, vol. 5, no. 21, pp. 3297–3310. URL: Link
  8. Khan A., Baig A.R. Multi-Objective Feature Subset Selection using Non-dominated Sorting Genetic Algorithm. Journal of Applied Research and Technology, 2015, vol. 13, no. 1, pp. 145–159. URL: Link30013-4
  9. Das S., Chaudhuri Sh., Das A.K. Optimal Set of Overlapping Clusters Using Multi-objective Genetic Algorithm. ICMLC 2017: Proceedings of the 9th International Conference on Machine Learning and Computing, 2017, pp. 232–237. URL: Link
  10. Li B., Jin B.-F. Research on Dynamic Multi-objective FJSP Based on Genetic Algorithm. 2018 IEEE 16th Int Conf on Dependable, Autonomic and Secure Computing, 16th Int Conf on Pervasive Intelligence and Computing, 4th Int Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress, 2018, pp. 347–352. URL: Link
  11. Thananant V., Auwatanamongkol S. Supervised Clustering based on a Multi-objective Genetic Algorithm. Pertanika Journal of Science & Technology, 2019, vol. 27, iss. 1, pp. 81–121. URL: Link
  12. Sardaraz M., Tahir M. A Parallel Multi-objective Genetic Algorithm for Scheduling Scientific Workflows in Cloud Computing. International Journal of Distributed Sensor Networks, 2020, vol. 16, iss. 8. URL: Link
  13. Wang J., Liu Sh., Li M. et al. Multiobjective Genetic Algorithm Strategies for Burnable Poison Design of Pressurized Water Reactor. International Journal of Energy Research, 2021, vol. 45, iss. 8, pp. 11930–11942. URL: Link
  14. Maghawry A., Hodhod R., Omar Y., Kholief M. An Approach for Optimizing Multi-objective Problems Using Hybrid Genetic Algorithms. Soft Computing, 2021, vol. 25, pp. 389–405. URL: Link
  15. Nikseresht M., Raji M. MOGATS: A Multi-objective Genetic Algorithm-based Task Scheduling for Heterogeneous Embedded Systems. International Journal of Embedded Systems, 2021, vol. 14, iss. 2, pp. 171–184. URL: Link
  16. Vasant P. A Novel Hybrid Genetic Algorithms and Pattern Search Techniques for Industrial Production Planning. International Journal of Modeling, Simulation, and Scientific Computing, 2012, vol. 03, no. 04. URL: Link
  17. Baeyens E., Herreros A., Perán J.R. A Direct Search Algorithm for Global Optimization. Algorithms, 2016, vol. 9, iss. 2, p. 40. URL: Link
  18. Guariso G., Sangiorgio M. Improving the Performance of Multiobjective Genetic Algorithms: An Elitism-Based Approach. Information, 2020, vol. 11, iss. 12, p. 587. URL: Link
  19. Yashin S., Koshelev E., Tsymbalov S. et al. Assessment of Material and Intangible Motivation of Top Management in Regions Using Multipurpose Genetic Algorithm. Proceedings of the International Conference Digital Age: Traditions, Modernity and Innovations (ICDATMI 2020), 2020, vol. 489, pp. 33–39. URL: Link

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