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
Additional contact information
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
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10)

Downloads: (external link)
http://link.springer.com/10.1007/s10479-018-2883-2 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:annopr:v:290:y:2020:i:1:d:10.1007_s10479-018-2883-2

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10479

DOI: 10.1007/s10479-018-2883-2

Access Statistics for this article

Annals of Operations Research is currently edited by Endre Boros

More articles in Annals of Operations Research from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().