Efficiency evaluation of parallel interdependent processes systems: an application to Chinese 985 Project universities

Abstract

Data envelopment analysis (DEA) has been widely applied in measuring the efficiency of homogeneous decision-making units. Network DEA, as an important branch of DEA, was built to examine the internal structure of a system, whereas traditional DEA models regard a system as a ‘black box’. However, only a few previous studies on parallel systems have considered the interdependent relationship between system components. In recent years, parallel interdependent processes systems commonly exist in production systems because of serious competition among organisations. Thus, an approach to measure the efficiency of such systems should be proposed. This paper builds an additive DEA model to measure a parallel interdependent processes system with two components which have an interdependent relationship. Then, the model is applied to analyse the ‘985 Project’ universities in China, and certain policy implications are explained.

Publication DOI: https://doi.org/10.1080/00207543.2018.1521531
Divisions: College of Business and Social Sciences > Aston Business School > Operations & Information Management
College of Business and Social Sciences > Aston Business School
Additional Information: © 2018 Informa UK Limited, trading as Taylor & Francis Group. This is an Accepted Manuscript of an article published by Taylor & Francis Group in International Journal of Production Research on 20 Sept 2018, available online at: http://www.tandfonline.com/10.1080/00207543.2018.1521531
Uncontrolled Keywords: Data envelopment analysis,parallel interdependent processes systems,network DEA,additive model,‘985 Project’ universities
Publication ISSN: 1366-588X
Last Modified: 12 Dec 2024 17:01
Date Deposited: 20 Sep 2018 11:46
Full Text Link:
Related URLs: https://www.tan ... 43.2018.1521531 (Publisher URL)
PURE Output Type: Article
Published Date: 2019-09-02
Published Online Date: 2018-09-20
Accepted Date: 2018-09-04
Authors: An, Qingxian
Wang, Zongrun
Emrouznejad, Ali (ORCID Profile 0000-0001-8094-4244)
Zhu, Qingyuan
Chen, Xiaohong

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