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Assessing the relative efficiency of Chinese high-tech industries: a dynamic network data envelopment analysis approach

Qingxian An, Fanyong Meng, Beibei Xiong (), Zongrun Wang and Xiaohong Chen
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Qingxian An: Central South University
Fanyong Meng: Central South University
Beibei Xiong: University of Technology of Troyes
Zongrun Wang: Central South University
Xiaohong Chen: Central South University

Annals of Operations Research, 2020, vol. 290, issue 1, No 33, 707-729

Abstract: Abstract The high-tech industry in China has largely developed in recent decades. To provide a basis for the sustainable development of high-tech industry, the government should evaluate its performance to find out its strengths and weaknesses that are critical for the future improvement of business operations. Dynamic network data envelopment analysis has received considerable attention from researchers evaluating the performance of a system during long-term production. However, studies on the issue of shared outputs caused by the lagged production effect of inputs are rare. In a real high-tech industry, the outputs during a production period are derived from the inputs in that production period and also from the inputs in the previous period. These intertemporal shared outputs in a system cannot be easily divided into different periods. Thus, a new dynamic two-stage data envelopment analysis approach is proposed to measure the efficiency of such system with a two-stage structure and shared outputs. We divide a high-tech activity system into two stages: technology research and development stage and technology digestion and absorption stage, where intertemporal shared outputs occur. Empirical results from our approach indicate that Chinese high-tech industries are weak in the technology digestion and absorption stage. Finally, suggestions are provided to improve the overall efficiency of Chinese high-tech industries.

Keywords: High-tech industries; Data envelopment analysis; Two-stage system; Intertemporal shared outputs (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1007/s10479-018-2883-2

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