Home  Journals  Finance and Credit  2(830) - 2023 February

Modeling of motivation of key executives of regional management bodies using logistic regression

Received: 15 December 2022

Received in revised form: 29 December 2022

Accepted: 19 January 2023

Available online: 28 February 2023

Subject Heading: INVESTING

JEL Classification: С01, С55, E17, М12, O21

Pages: 262–289

https://doi.org/10.24891/fc.29.2.262

Subject. This article deals with modeling of the motivation of top managers of regional management bodies to bring into line the interests of the population, the State, and key executives of government agencies and innovation enterprises.
Objectives. The article aims to create a model of motivation of key executives of government institutions of the regions.
Methods. For the study, we used logistic regression.
Results. The article substantiates the importance of the ranges of planned parameters of the model, which were obtained for the leader regions. These ranges are to be compiled for each objective function, that is, intangible incentives, financial incentives, and strategic potential. This indicates greater flexibility of the model based on logistic regression.
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, as well as management structures of developing innovation-based companies in the regions.

Keywords: motivation, senior leadership, logistic regression

References:

1. Durr A.K.W. A Text Analysis of Data-Science Career Opportunities and US iSchool Curriculum. Journal of Education for Library and Information Science, 2020, vol. 61, iss. 2, pp. 270–293. URL: Link
2. Lacagnina C., Doblas-Reyes F., Larnicol G. et al. Quality Management Framework for Climate Datasets. Data Science Journal, 2022, vol. 21, iss. 1, p. 10. URL: Link
3. Peng G., Lacagnina C., Downs R.R. et al. Global Community Guidelines for Documenting, Sharing, and Reusing Quality Information of Individual Digital Datasets. Data Science Journal, 2022, vol. 21, iss. 1, p. 8. URL: Link
4. Simmonds M.B., Riley W.J., Agarwal D.A. et al. Guidelines for Publicly Archiving Terrestrial Model Data to Enhance Usability, Intercomparison, and Synthesis. Data Science Journal, 2022, vol. 21, iss. 1, p. 3. URL: Link
5. Folgado M.G., Sanz V. Exploring the Political Pulse of a Country Using Data Science Tools. Journal of Computational Social Science, 2022, vol. 5, pp. 987–1000. URL: Link
6. Peng C.-Y.J., Lee K.L., Ingersoll G.M. An Introduction to Logistic Regression Analysis and Reporting. The Journal of Educational Research, 2002, vol. 96, iss. 1, pp. 3–14. URL: Link
7. Boateng E.Y., Abaye D.A. A Review of the Logistic Regression Model with Emphasis on Medical Research. Journal of Data Analysis and Information Processing, 2019, vol. 7, no. 4, pp. 190–207. URL: Link
8. Ramosacaj M., Hasani V., Dumi A. Application of Logistic Regression in the Study of Students’ Performance Level (Case Study of Vlora University). Journal of Educational and Social Research, 2015, vol. 5, no. 3, p. 239. URL: Link
9. Abdulqader Q. Applying the Binary Logistic Regression Analysis on the Medical Data. Science Journal of University of Zakho, 2017, vol. 5, iss. 4, p. 330. URL: Link
10. Liu L. Research on Logistic Regression Algorithm of Breast Cancer Diagnose Data by Machine Learning. 2018 International Conference on Robots & Intelligent System (ICRIS), Changsha, China, 2018, pp. 157–160. URL: Link
11. Neamah M.W. et al. Utilizing the Logistic Regression Model in Analyzing the Categorical Data of Economic Effects. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 2021, vol. 12, no. 4, pp. 638–646. URL: Link
12. Oleskiv I., Mirzoieva D. Logistic Regression as Instrument for Analyzing Influence of Remittances on Economic Growth. SMEU, 2020, vol. 2, no. 2, pp. 127–132. URL: Link
13. Li X., Yan S., Lu J., Ding Y. Prediction and Analysis of Corporate Financial Risk Assessment Using Logistic Regression Algorithm in Multiple Uncertainty Environment. Journal of Environmental and Public Health, 2022, vol. 2022, article ID 2733923. URL: Link
14. Ali S.S., Mubeen M., Lal I., Hussain A. Prediction of Stock Performance by Using Logistic Regression Model: Evidence from Pakistan Stock Exchange (PSX). Asian Journal of Empirical Research, 2018, vol. 8, no. 7, pp. 247–258. URL: Link
15. Zizi Y., Oudgou M., El Moudden A. Determinants and Predictors of SMEs’ Financial Failure: A Logistic Regression Approach. Risks, 2020, vol. 8, iss. 4, pp. 1–21. URL: Link
16. Popadinets I., Andrusiv U., Galtsova O. et al. Management of Motivation of Managers’ Work at the Enterprises of Ukraine: Innovative Aspects. Management Systems in Production Engineering, 2021, vol. 29, iss. 2, pp. 120–131. URL: Link
17. Hameduddin T., Engbers T. Leadership and Public Service Motivation: A Systematic Synthesis. International Public Management Journal, 2022, vol. 25, iss. 1, pp. 86–119. URL: Link
18. Zubair S.S., Khan M.A., Mukaram A.T. Public Service Motivation and Organizational Performance: Catalyzing Effects of Altruism, Perceived Social Impact and Political Support. PLoS One, 2021, vol. 16, iss. 12. URL: Link
19. Sun S. The Relationship Between Public Service Motivation and Affective Commitment in the Public Sector Change: A Moderated Mediation Model. Frontiers in Psychology, 2021, vol. 12. URL: Link
20. Grus J. Data Science from Scratch: First Principles with Python, 2nd ed. O'Reilly Media, Inc., 2019, 406 p.

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