Abstract
When using Data Envelopment Analysis (DEA) to evaluate the efficiency of a high-tech industrial system, it is necessary to consider the operating process in each period and the dynamic interdependence between periods of the system. Meanwhile, the shortcomings of the DEA self-evaluation mode cannot be ignored. However, few studies deal with these three problems in a unified framework. Therefore, this paper improves the DEA game cross-efficiency model to the dynamic network DEA game cross-efficiency model to evaluate the efficiencies of high-tech industries in 27 provincial-level regions of China from 2011 to 2015. The main evaluation results are as follows. Regarding overall efficiency, China’s high-tech industry still has approximately 45% room for improvement, and the development of adjacent regions is unbalanced. There are 18 regions with low Research and Development (R&D) efficiencies and 8 with low commercialization efficiencies. From a national perspective, R&D efficiency displays an inverted U-shaped trend, commercialization efficiency shows a U-shaped trend, and overall efficiency increases slightly during the study period. In addition, R&D efficiency has a greater impact on overall efficiency than commercialization efficiency does. The reasons are analyzed, and recommendations are provided based on the evaluation results to improve the efficiency of China’s high-tech industry.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
An Q, Meng F, Xiong B, Wang Z (2020) Assessing the relative efficiency of Chinese high-tech industries: a dynamic network data envelopment analysis approach. Ann Oper Res 290(1):707–729. https://doi.org/10.1007/s10479-018-2883-2
Aparicio J, Perelman S, Santín D (2022) Comparing the evolution of productivity and performance gaps in education systems through DEA: an application to Latin American countries. Oper Res 22:1443–1477. https://doi.org/10.1007/s12351-020-00578-2
Banker R, Charnes A, Cooper WW (1984) Some models for estimating technical and scale inefficiencies in data envelopment analysis. Manag Sci 30(9):1078–1092. https://doi.org/10.1287/mnsc.30.9.1078
Charnes A, Cooper WW, Rhodes E (1978) Measuring the efficiency of decision making units. Eur J Oper Res 2(6):429–444. https://doi.org/10.1016/0377-2217(78)90138-8
Chen Y, Zhu J (2004) Measuring information technology’s indirect impact on firm performance. Inf Technol Manag 5:9–22. https://doi.org/10.1023/B:ITEM.0000008075.43543.97
Chen Y, Cook W, Li N, Zhu J (2009) Additive efficiency decomposition in two-stage DEA. Eur J Oper Res 196(3):1170–1176. https://doi.org/10.1016/j.ejor.2008.05.011
Chen Y, Du J, Sherman H, Zhu J (2010) DEA model with shared resources and efficiency decomposition. Eur J Oper Res 207(1):339–349. https://doi.org/10.1016/j.ejor.2010.03.031
Chen K, Kou M, Fu X (2018a) Evaluation of multi-period regional R&D efficiency: an application of dynamic DEA to China’s regional R&D systems. Omega 74:103–114. https://doi.org/10.1016/j.omega.2017.01.010
Chen X, Liu Z, Zhu Q (2018b) Performance evaluation of China’s high-tech innovation process: analysis based on the innovation value chain. Technovation 74–75(3):42–53. https://doi.org/10.1016/j.technovation.2018.02.009
Chen Z, Kourtzidis S, Tzeremes P, Tzeremes N (2022) A robust network DEA model for sustainability assessment: an application to Chinese Provinces. Oper Res 22:235–262. https://doi.org/10.1007/s12351-020-00553-x
Chiu Y, Huang C, Chen Y (2012) The R&D value-chain efficiency measurement for high-tech industries in China. Asia Pac J Manag 29(4):989–1006. https://doi.org/10.1007/s10490-010-9219-3
Färe R, Grosskopf S (1996) Productivity and intermediate products: a frontier approach. Econ Lett 50(1):65–70. https://doi.org/10.1016/0165-1765(95)00729-6
Fukuyama H, Weber W (2009) A directional slacks-based measure of technical inefficiency. Soc Econ Plann Sci 43(4):274–287. https://doi.org/10.1016/j.seps.2008.12.001
Guan J, Chen K (2010a) Measuring the innovation production process: a cross-region empirical study of China’s high-tech innovations. Technovation 30(5):348–358. https://doi.org/10.1016/j.technovation.2010.02.001
Guan J, Chen K (2010b) Modeling macro-R&D production frontier performance: an application to Chinese province-level R&D. Scientometrics 82(1):165–173. https://doi.org/10.1007/s11192-009-0030-1
Guan J, Chen K (2012) Modeling the relative efficiency of national innovation systems. Res Pol 41(1):102–115. https://doi.org/10.1016/j.respol.2011.07.001
Guan J, Zuo K, Chen K, Yam R (2016) Does country-level R&D efficiency benefit from the collaboration network structure. Res Pol 45(4):770–784. https://doi.org/10.1016/j.respol.2016.01.003
Han U, Asmild M, Kunc M (2014) Regional R&D Efficiency in Korea from static and dynamic perspectives. Reg Stud 50(7):1170–1184. https://doi.org/10.1080/00343404.2014.984670
Han C, Thomas S, Yang M, Ieromonachou P, Zhang H (2017) Evaluating R&D investment efficiency in China’s high-tech industry. J High Technol Manag Res 28(1):93–109. https://doi.org/10.1016/j.hitech.2017.04.007
Kao C (2009) Efficiency decomposition in network data envelopment analysis: a relational model. Eur J Oper Res 192(1):949–962. https://doi.org/10.1016/j.ejor.2007.10.008
Kao C (2013) Dynamic data envelopment analysis: a relational analysis. Eur J Oper Res 227(2):325–330. https://doi.org/10.1016/j.ejor.2012.12.012
Kao C (2017) Efficiency measurement and frontier projection identification for general two-stage systems in data envelopment analysis. Eur J Oper Res 261(2):679–689. https://doi.org/10.1016/j.ejor.2017.03.016
Kao C, Liu ST (2019) Cross efficiency measurement and decomposition in two basic network systems. Omega 83:70–79. https://doi.org/10.1016/j.omega.2018.02.004
Kao C, Liu ST (2020) A slacks-based measure model for calculating cross efficiency in data envelopment analysis. Omega 95:102192. https://doi.org/10.1016/j.omega.2020.102192
Khodabakhshi M, Aryavash K (2017) The cross-efficiency in the optimistic–pessimistic framework. Oper Res 17(2):619–632. https://doi.org/10.1007/s12351-016-0243-z
Li Y, Chen Y, Liang L, Xie J (2012) DEA models for extended two-stage network structures. Omega 40(5):611–618. https://doi.org/10.1016/j.omega.2011.11.007
Li L, Liu B, Liu W, Chiu Y (2017) Efficiency evaluation of the regional high-tech industry in China: a new framework based on meta-frontier dynamic DEA analysis. Soc Econ Plann Sci 60:24–33. https://doi.org/10.1016/j.seps.2017.02.001
Liang L, Cook WD, Zhu J (2008a) DEA models for two-stage processes: game approach and efficiency decomposition. Nav Res Logist 55(7):643–653. https://doi.org/10.1002/NAV.20308
Liang L, Wu J, Cook WD, Zhu J (2008b) The DEA game cross-efficiency model and its Nash equilibrium. Oper Res 56(5):1278–1288. https://doi.org/10.1287/opre.1070.0487
Lin R, Tu C (2021) Cross-efficiency evaluation and decomposition with directional distance function in series and parallel systems. Expert Syst Appl 177:114933. https://doi.org/10.1016/j.eswa.2021.114933
Lin S, Lin R, Sun J, Wang F, Wu W (2021) Dynamically evaluating technological innovation efficiency of high-tech industry in China: provincial, regional and industrial perspective. Soc Econ Plann Sci 74:100939. https://doi.org/10.1016/j.seps.2020.100939
Liu H, Yang G, Liu X, Song Y (2020) R&D performance assessment of industrial enterprises in China: a two-stage DEA approach. Soc Econ Plann Sci 71:100753. https://doi.org/10.1016/j.seps.2019.100753
Ma C, Liu D, Zhou Z, Zhao W, Liu W (2014) Game cross efficiency for systems with two-stage structures. J Appl Math pp 1–8. https://doi.org/10.1155/2014/747596
Meng F, Xiong B (2021) Logical efficiency decomposition for general two-stage systems in view of cross efficiency. Eur J Oper Res 294(2):622–632. https://doi.org/10.1016/j.ejor.2021.01.044
Mitropoulos P (2021) Production and quality performance of healthcare services in EU countries during the economic crisis. Oper Res 21(2):857–873. https://doi.org/10.1007/s12351-019-00483-3
Sexton TR, Silkman RH, Hogan AJ (1986) Data envelopment analysis: critique and extensions. In Silkman RH (ed), Measuring efficiency: an assessment of data envelopment analysis. Jossey-Bass, San Francisco, pp 73–105. https://doi.org/10.1002/ev.1441
Tone K, Tsutsui M (2010) Dynamic DEA: a slacks-based measure approach. Omega 38(3–4):145–156. https://doi.org/10.1016/j.omega.2009.07.003
Tone K, Kweh QL, Lu WM, Ting IWK (2019) Modeling investments in the dynamic network performance of insurance companies. Omega 88:237–247. https://doi.org/10.1016/j.omega.2018.09.005
Wang Y, Chin K, Jiang P (2011) Weight determination in the cross-efficiency evaluation. Comput Ind Eng 61(3):497–502. https://doi.org/10.1016/j.cie.2011.04.004
Wang C, Lu Y, Huang C, Lee J (2013) R&D, productivity, and market value: an empirical study from high-technology firm. Omega 41(1):143–155. https://doi.org/10.1016/j.omega.2011.12.011
Wang Y, Pan J, Pei R, Yi B, Yang G (2020) Assessing the technological innovation efficiency of China’s high-tech industries with a two-stage network DEA approach. Soc Econ Plann Sci 71:100810. https://doi.org/10.1016/j.seps.2020.100810
Wang M, Huang Y, Li D (2021) Assessing the performance of industrial water resource utilization systems in China based on a two-stage DEA approach with game cross efficiency. J Clean Prod 312:127722. https://doi.org/10.1016/j.jclepro.2021.127722
Wu J, Chu J, Sun J, Zhu Q, Liang L (2016) Extended secondary goal models for weights selection in DEA cross-efficiency evaluation. Comput Ind Eng 93:143–151. https://doi.org/10.1016/j.cie.2015.12.019
Xiong X, Yang G, Guan Z (2018) Assessing R&D efficiency using a two-stage dynamic DEA model: a case study of research institutes in the chinese academy of sciences. J Infometr 12(3):784–805. https://doi.org/10.1016/j.joi.2018.07.003
Ye J, Wan Q, Li R, Yao Z, Huang D (2022) How do R&D agglomeration and economic policy uncertainty affect the innovative performance of Chinese high-tech industry. Technol Soc 69:101957. https://doi.org/10.1016/j.techsoc.2022.101957
Yu A, Shi Y, You J, Zhu J (2021) Innovation performance evaluation for high-tech companies using a dynamic network data envelopment analysis approach. Eur J Oper Res 292(1):199–212. https://doi.org/10.1016/j.ejor.2020.10.011
Zhang M, Yin X (2012) Research of the efficiency of high-tech industry based on DEA model: evidence from China. Lect Notes Electr Eng 155:333–340. https://doi.org/10.1007/978-3-642-28744-2_42
Zhang B, Luo Y, Chiu Y (2019) Efficiency evaluation of China’s high-tech industry with a multi-activity network data envelopment analysis approach. Soc Econ Plann Sci 66:2–9. https://doi.org/10.1016/j.seps.2018.07.013
Zhu Q, Li F, Wu J, Sun J (2021) Cross-efficiency evaluation in data envelopment analysis based on the perspective of fairness utility. Comput Ind Eng 151:106926. https://doi.org/10.1016/j.cie.2020.106926
Zuo K, Guan J (2017) Measuring the R&D efficiency of regions by a parallel DEA game model. Scientometrics 112(1):175–194. https://doi.org/10.1007/s11192-017-2380-4
Acknowledgements
The authors would like to thank the editors of Operational Research and all anonymous reviewers for their insightful comments and suggestions. This work was supported by the National Natural Science Foundation of China (Grant No. 71861004, 72261006).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Huang, Y., Wang, M. Efficiency evaluation of China’s high-tech industry with a dynamic network data envelopment analysis game cross-efficiency model. Oper Res Int J 24, 8 (2024). https://doi.org/10.1007/s12351-024-00815-y
Received:
Revised:
Accepted:
Published:
DOI: https://doi.org/10.1007/s12351-024-00815-y