Neural Network Modeling of the Motivation of Top Management of Regional Management Structures as a Regression Problem
ISBN: 978-1-83797-666-9, eISBN: 978-1-83797-665-2
Publication date: 1 July 2024
Abstract
This research aims to solve the regression problem and create neural network models of motivation of top management of governing structures of regions. The authors modeled the criteria of nonmaterial and material motivation of top managers of regional state structures and the region's strategic potential using neural networks. Both types of top managers' motivation can be influenced by the development of the region's strategic potential in the future. Material motivation refers to the salary of senior civil servants. Nonmaterial motivation refers to career growth opportunities. The population growth rate of the regions is an objective function and needs to be maximized. Neural network modeling of motivation of top management of state structures in the regions is more accurate. An approximated time series is predicted in the regression problem. Therefore, the future neural network is built and trained considering the cyclical fluctuations of the objective function – the coefficient of natural population growth. A constructive system of material and nonmaterial motivation of managers is used for state structures in the regions.
Keywords
Acknowledgements
Acknowledgments
The research was funded by the Russian Science Foundation (Project No. 24-28-00464).
Citation
Yashin, S.N., Koshelev, E.V., Denisov, E.Y., Kozlova, E.P. and Polyanskaya, V.A. (2024), "Neural Network Modeling of the Motivation of Top Management of Regional Management Structures as a Regression Problem", Eshov, M.P., Abdurakhmanova, G.K., Burkhanov, A.U., Abdusalomova, N.B. and Ergasheva, S.T. (Ed.) Development of International Entrepreneurship Based on Corporate Accounting and Reporting According to IFRS (Advanced Series in Management, Vol. 33A), Emerald Publishing Limited, Leeds, pp. 147-153. https://doi.org/10.1108/S1877-63612024000033A017
Publisher
:Emerald Publishing Limited
Copyright © 2024 Sergey N. Yashin, Egor V. Koshelev, Evgeniy Yu. Denisov, Elena P. Kozlova and Viktoriya A. Polyanskaya. Published under exclusive licence by Emerald Publishing Limited