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

An, Qingxian, Wang, Zongrun, Emrouznejad, Ali, Zhu, Qingyuan and Chen, Xiaohong (2018). Efficiency evaluation of parallel interdependent processes systems: an application to Chinese 985 Project universities. International Journal of Production Research ,

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: Aston Business School > Operations & information management
Aston Business School
Aston Business School > Operations & information management research group
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
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Related URLs: https://www.tan ... 43.2018.1521531 (Publisher URL)
Published Online Date: 2018-09-20
Authors: An, Qingxian
Wang, Zongrun
Emrouznejad, Ali ( 0000-0001-8094-4244)
Zhu, Qingyuan
Chen, Xiaohong

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