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Research on the optimal equity of duo-ou enterprise based on data analysis

TianLong Ma (School of Economics and Management, Ningxia University, Yinchuan, China)
Huiping Zhang (School of Economics and Management, Ningxia University, Yinchuan, China)

Journal of Organizational Change Management

ISSN: 0953-4814

Article publication date: 29 October 2020

Issue publication date: 2 December 2020

Abstract

Purpose

This study aims to disclose how the nature of corporate ownership, stock efficiency and wage level affect the optimal proportion of employee stock.

Design/methodology/approach

This paper studies three duopoly markets: two private enterprises, two state-owned enterprises (SOEs) and a private enterprise and an SOE. The competitions between the two parties are taken as a two-stage dynamic sequential game and studied through back-induction.

Findings

The results reveal that the enterprise ownership has a directly bearing on the optimal proportion of employee stock and determines whether to implement the employee stock ownership plan (ESOP) and the specific level of the plan. The optimal proportion of employee stock is positively correlated with its contribution to enterprise efficiency. There are many influencing factors on the effect of wage level on the optimal proportion of employee stock, namely, the ownership nature of ESOP implementer and efficiency difference of different nature stocks.

Social implications

The results of this study provide policy recommendations for companies preparing to implement ESOP.

Originality/value

The research findings provide policy implications for enterprises to prepare a suitable ESOP and the reform of national equities, especially the mixed-ownership reform in China.

Keywords

Acknowledgements

This study is supported by The Supported Project of First-class Discipline Construction (Ethnology) in Ningxia Colleges and Universities (Project Approval No. NXYLXK2017A02).

Citation

Ma, T. and Zhang, H. (2020), "Research on the optimal equity of duo-ou enterprise based on data analysis", Journal of Organizational Change Management, Vol. 33 No. 6, pp. 1201-1221. https://doi.org/10.1108/JOCM-05-2020-0129

Publisher

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Emerald Publishing Limited

Copyright © 2020, Emerald Publishing Limited