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Chinese Journal of Management Science ›› 2026, Vol. 34 ›› Issue (11): 99-107.doi: 10.16381/j.cnki.issn1003-207x.2023.1332

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Research on Incentive Allocation of Inputs in Multi-centralized Organizations Considering the Degree of Decision-making Autonomy:Reallocation of Scientific and Technological Inputs in Higher Education Institutions

Xi Xiong1, Guoliang Yang2,3, Dequn Zhou1, Zhongcheng Guan2,3()   

  1. 1.College of Economics and Management,Nanjing University of Aeronautics and Astronautics,Nanjing,211106,China
    2.School of Public Policy and Management,University of Chinese Academy of Sciences,Beijing 100049,China
    3.Institutes of Science and Development,Chinese Academy of Sciences,Beijing 100190,China
  • Received:2023-08-16 Revised:2024-04-04 Online:2026-11-25 Published:2026-11-10
  • Contact: Zhongcheng Guan E-mail:guan@casisd.cn

Abstract:

Whether or not each operating unit in a centralized organization performs in the best interests of the whole system often depends on the incentives in the allocation mechanism. However, the traditional DEA approach is a tool designed to assess past performance, with insufficient recognition of incentives in the allocation mechanism. Considering that operating units belong to different centralized organizations and face different constraints in the technical environment, it is difficult to motivate all units with a single target system. Furthermore, given their varying degrees of decision-making autonomy in resource allocation and production planning, methods for measuring performance using a uniform benchmark are no longer applicable. Therefore, focusing on the characteristics of “decision-making autonomy” and “inter-group heterogeneity”, a DEA-based allocation methodology is proposed, and the “boundary benchmark” is redifined by constructing the appropriate quantity and location of incentive maps to measure efficiency. And then inputs are reallocated based on efficiency values by expanding the scope of compensation and maximizing the incentive effect of the distribution. Finally, an empirical study is conducted using 26 undergraduate institutions in Jiangsu Province as a case example. The proposed allocation scheme exerts positive incentive effects on both efficient and inefficient units, providing a new methodological tool for optimizing the allocation of science and technology inputs in regional higher education institutions.

Key words: multi-centralized organizations, input allocation, decision-making autonomy, DEA

CLC Number: