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doi: 10.3934/jimo.2021114
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Performance evaluation of the Chinese high-tech industry: A two-stage DEA approach with feedback and shared resource

1. 

School of Management, University of Science and Technology of China, Hefei, Anhui Province 230026, China

2. 

School of Business, Nanjing Audit University, Nanjing, Jiangsu Province 211815, China

3. 

Huishang Futures, Hefei, Anhui Province 340100, China

4. 

International Institute of Finance, School of Management, University of Science and Technology of China, Hefei, Anhui Province 230026, China

* Corresponding author: Linlin Zhao

Received  September 2020 Revised  March 2021 Early access July 2021

The operational process of high-tech industry can be separated into a research and development stage (RDS) and a commercialization stage (CS). Within this, the research employees are shared by both stages, and part of the economic output of the CS becomes a feedback factor and continuously flows back to the RDS. Using this framework, this study establishes cooperative and non-cooperative two-stage data envelopment analysis (DEA) models to explore the efficiencies of regional high-tech industries in China. The proposed approach can calculate the overall efficiency and stage efficiencies simultaneously. Based on empirical data of high-tech industries in 29 regions of China from 2012 to 2016, it is concluded that (1) a harmony exists between the RDS and the CS in the cooperative case, while a disharmony happens between the RDS and CS in the non-cooperative case; (2) there exist distinct geographic characteristics regarding the stage inefficiencies of these regional high-tech industries.

Citation: Dawei Wang, Linlin Zhao, Feng Yang, Kehong Chen. Performance evaluation of the Chinese high-tech industry: A two-stage DEA approach with feedback and shared resource. Journal of Industrial & Management Optimization, doi: 10.3934/jimo.2021114
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H. LiH. HeJ. Shan and J. Cai, Innovation efficiency of semiconductor industry in China: A new framework based on generalized three-stage DEA analysis, Socio-Econ. Plan. Sci., 66 (2019), 136-148.  doi: 10.1016/j.seps.2018.07.007.  Google Scholar

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show all references

References:
[1]

A. Amirteimoori, A DEA two-stage decision processes with shared resources, Cent. Eur. J. Oper. Res., 21 (2013), 141-151.  doi: 10.1007/s10100-011-0218-3.  Google Scholar

[2]

Q. AnF. MengB. XiongZ. Wang and X. Chen, Assessing the relative efficiency of Chinese high-tech industries: A dynamic network data envelopment analysis approach, Ann. Oper. Res., 290 (2020), 707-729.  doi: 10.1007/s10479-018-2883-2.  Google Scholar

[3]

Q. AnZ. WangA. EmrouznejadQ. Zhu and X. Chen, Efficiency evaluation of parallel interdependent processes systems: An application to Chinese 985 Project universities, Int. J. Prod. Res., 57 (2019), 5387-5399.  doi: 10.1080/00207543.2018.1521531.  Google Scholar

[4]

Q. AnM. YangJ. ChuJ. Wu and Q. Zhu, Efficiency evaluation of an interactive system by data envelopment analysis approach, Comput. Ind. Eng., 103 (2017), 17-25.  doi: 10.1016/j.cie.2016.10.010.  Google Scholar

[5]

A. Charnes, W. W. Cooper and E. Rhodes, Measuring the efficiency of decision making units, Eur. J. Oper. Res., 2 (1978) 429–444. doi: 10.1016/0377-2217(78)90138-8.  Google Scholar

[6]

C.-J. ChenH.-L. Wu and B.-W. Lin, Evaluating the development of high-tech industries: Taiwan's science park, Technol. Forecast. Soc. Change., 73 (2006), 452-465.  doi: 10.1016/j.techfore.2005.04.003.  Google Scholar

[7]

K. Chen and J. Guan, Measuring the efficiency of China's regional innovation systems: Application of network data envelopment analysis (DEA), Reg. Stud., 46 (2012), 355-377.  doi: 10.1080/00343404.2010.497479.  Google Scholar

[8]

K. Chen and M. Kou, Staged efficiency and its determinants of regional innovation systems: A two-step analytical procedure, Ann. Reg. Sci., 52 (2014), 627-657.  doi: 10.1007/s00168-014-0604-6.  Google Scholar

[9]

X. ChenZ. Liu and Q. Zhu, Performance evaluation of China's high-tech innovation process: Analysis based on the innovation value chain, Technovation, 74-75 (2018), 42-53.  doi: 10.1016/j.technovation.2018.02.009.  Google Scholar

[10]

Y. ChenW. D. CookN. Li and J. Zhu, Additive efficiency decomposition in two-stage DEA, Eur. J. Oper. Res., 196 (2009), 1170-1176.  doi: 10.1016/j.ejor.2008.05.011.  Google Scholar

[11]

Y. ChenJ. DuH. D. Sherman and J. Zhu, DEA model with shared resources and efficiency decomposition, Eur. J. Oper. Res., 207 (2010), 339-349.  doi: 10.1016/j.ejor.2010.03.031.  Google Scholar

[12]

W. D. Cook and M. Hababou, Sales performance measurement in bank branches, Omega, 29 (2001), 229-307.  doi: 10.1016/S0305-0483(01)00025-1.  Google Scholar

[13]

W. D. Cook and L. M. Seiford, Towards a general non-parametric model of corporate performance, Omega, 192 (2009), 1-17.   Google Scholar

[14]

Q. Deng, S. Zhou and F. Peng, Measuring green innovation efficiency for China's high-tech manufacturing industry: A network DEA approach, Math. Probl. Eng., (2020). doi: 10.1155/2020/8902416.  Google Scholar

[15]

R. Färe and S. Grosskopf, Productivity and intermediate products: A frontier approach, Econ. Lett., 50 (1996), 65-70.  doi: 10.1016/0165-1765(95)00729-6.  Google Scholar

[16] Z. Griliches, Patent Statistics as Economic Indicators: A Survey, University of Chicago Press, 1998.  doi: 10.3386/w3301.  Google Scholar
[17]

J. Guan and K. Chen, Measuring the innovation production process: A cross-region empirical study of China's high-tech innovations, Technovation, 30 (2010), 348-358.  doi: 10.1016/j.technovation.2010.02.001.  Google Scholar

[18]

J. Guan and K. Chen, Modeling the relative efficiency of national innovation systems, Res. Poli., 41 (2012), 102-115.  doi: 10.1016/j.respol.2011.07.001.  Google Scholar

[19]

J. C. GuanR. C. M. YamC. K. Mok and N. Ma, A study of the relationship between competitiveness and technological innovation capability based on DEA models, Eur. J. Oper. Res., 170 (2006), 971-986.  doi: 10.1016/j.ejor.2004.07.054.  Google Scholar

[20]

C. Guo and J. Zhu, Non-cooperative two-stage network DEA model: Linear vs. parametric linear, Eur. J. Oper. Res., 258 (2017), 398-400.  doi: 10.1016/j.ejor.2016.11.039.  Google Scholar

[21]

G. E. HalkosN. G. Tzeremes and S. A. Kourtzidis, A unified classification of two-stage DEA models, Surveys in Operations Research and Management Science, 19 (2014), 1-16.  doi: 10.1016/j.sorms.2013.10.001.  Google Scholar

[22]

C. HanS. R. ThomasM. YangP. Ieromonachou and H. Zhang, Evaluating R & D investment efficiency in China's high-tech industry, The Journal of High Technology Management Research, 28 (2017), 93-109.  doi: 10.1016/j.hitech.2017.04.007.  Google Scholar

[23]

H. J. G. M. Hollanders and F. Celikel-Esser, Measuring Innovation Efficiency, European Commission, 2007. Google Scholar

[24]

Z. Hu, S. Yan, X. Li, L. Yao and Z. Luo, Evaluating the oil production and wastewater treatment efficiency by an extended two-stage network structure model with feedback variables, J. Environ. Manage., 251 (2019), 109578. doi: 10.1016/j.jenvman.2019.109578.  Google Scholar

[25]

C. Kao, Network data envelopment analysis: A review, Eur. J. Oper. Res., 239 (2014), 1-16.  doi: 10.1016/j.ejor.2014.02.039.  Google Scholar

[26]

C. Kao and S.-N. Hwang, Efficiency decomposition in two-stage data envelopment analysis: An application to non-life insurance companies in Taiwan, Eur. J. Oper. Res., 185 (2008), 418-429.  doi: 10.1016/j.ejor.2006.11.041.  Google Scholar

[27]

C. Kao and S. N. Hwang, Efficiency measurement for network systems: IT impact on firm performance, Decis. Support Syst., 48 (2010), 437-446.  doi: 10.1016/j.dss.2009.06.002.  Google Scholar

[28]

J. LeeC. Kim and G. Choi, Exploring data envelopment analysis for measuring collaborated innovation efficiency of small and medium-sized enterprises in Korea, Eur. J. Oper. Res., 278 (2019), 533-545.  doi: 10.1016/j.ejor.2018.08.044.  Google Scholar

[29]

C. Li, M. Li, L. Zhang, T. Li, H. Ouyang and S. Na, Has the high-tech industry along the belt and road in China achieved green growth with technological innovation efficiency and environmental sustainability?, Int. J. Environ. Res. Public Health, 16 (2019), 3117. doi: 10.3390/ijerph16173117.  Google Scholar

[30]

H. LiH. HeJ. Shan and J. Cai, Innovation efficiency of semiconductor industry in China: A new framework based on generalized three-stage DEA analysis, Socio-Econ. Plan. Sci., 66 (2019), 136-148.  doi: 10.1016/j.seps.2018.07.007.  Google Scholar

[31]

W. LiZ. LiL. Liang and W. D. Cook, Evaluation of ecological systems and the recycling of undesirable outputs: An efficiency study of regions in China, Socio-Econ. Plan. Sci., 60 (2017), 77-86.  doi: 10.1016/j.seps.2017.03.002.  Google Scholar

[32]

Y. LiY. ChenL. Liang and J. Xie, DEA models for extended two-stage network structures, Omega, 40 (2012), 611-618.  doi: 10.1016/j.omega.2011.11.007.  Google Scholar

[33]

L. LiangW. D. Cook and J. Zhu, DEA models for two-stage processes: Game approach and efficiency decomposition, Nav. Res. Log., 55 (2008), 643-653.  doi: 10.1002/nav.20308.  Google Scholar

[34]

L. LiangF. FengW. D. Cook and J. Zhu, DEA models for supply chain efficiency evaluation, Ann. Oper. Res., 145 (2006), 35-49.  doi: 10.1007/s10479-006-0026-7.  Google Scholar

[35]

L. LiangZ.-Q. LiW. D. Cook and J. Zhu, Data envelopment analysis efficiency in two-stage networks with feedback, IIE. Trans., 43 (2011), 309-322.  doi: 10.1080/0740817X.2010.509307.  Google Scholar

[36]

S. Lin, R. Lin, J. Sun, F. Wang and W. Wu, Dynamically evaluating technological innovation efficiency of high-tech industry in China: Provincial, regional and industrial perspective, Socio-Econ. Plan. Sci., (2021), 100939. doi: 10.1016/j.seps.2020.100939.  Google Scholar

[37]

Z. LiuX. ChenJ. Chu and Q. Zhu, Industrial development environment and innovation efficiency of high-tech industry: Analysis based on the framework of innovation systems, Technol. Anal. Strateg. Manage., 30 (2018), 434-446.  doi: 10.1080/09537325.2017.1337092.  Google Scholar

[38]

W. Nasierowski and F. J. Arcelus, On the efficiency of national innovation systems, Socio-Econ. Plan. Sci., 37 (2003), 215-234.  doi: 10.1016/S0038-0121(02)00046-0.  Google Scholar

[39]

L. M. Seiford and J. Zhu, Profitability and marketability of the top 55 US commercial banks, Manage. Sci., 45 (1999), 1270-1288. doi: 10.1287/mnsc.45.9.1270.  Google Scholar

[40]

Q. Shen, Measuring the R & D Performance of High-Tech Manufacturing Sectors in China: A Data Envelopment Analysis Application, J. Comput. Theor. Nanosci., 13 (2016), 7773-7778.  doi: 10.1166/jctn.2016.5777.  Google Scholar

[41]

X. Shi, Environmental efficiency analysis based on relational two-stage DEA model, RAIRO-Oper. Res., 50 (2016), 965-977.  doi: 10.1051/ro/2015059.  Google Scholar

[42]

K. Tone and M. Tsutsui, Dynamic DEA with network structure: A slacks-based measure approach, Omega, 42 (2014), 124-131.  doi: 10.1016/j.omega.2013.04.002.  Google Scholar

[43]

F.-M. TsengY.-J. Chiu and J.-S. Chen, Measuring business performance in the high-tech manufacturing industry: A case study of Taiwan's large-sized TFT-LCD panel companies, Omega, 37 (2009), 686-697.  doi: 10.1016/j.omega.2007.07.004.  Google Scholar

[44]

C. H. WangR. D. Gopal and S. Zionts, Use of data envelopment analysis in assessing information technology impact on firm performance, Ann. Oper. Res., 73 (1997), 191-213.   Google Scholar

[45]

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Figure 1.  The operational process of Chinese high-tech industry
Figure 2.  Two-stage process of Chinese high-tech industry
Figure 3.  The efficiencies in the cooperative case (2012-2016)
Figure 4.  The average stage efficiencies when the RDS as the leader (2012-2016)
Figure 5.  The average stage efficiencies when the CS as the leader (2012-2016)
Figure 6.  The annual average stage efficiencies in the non-cooperative case (2012-2016)
Table 1.  Descriptive statistics for the data set (2012-2016)
Variables Years 2012 2013 2014 2015 2016
RDS input Government funds Mean 501.39 573.54 615.54 724.66 731.88
(million RMB Yuan) Std.dev. 644.98 690.40 790.16 875.93 874.37
Shared input Full-time equivalent Mean 21487.59 23104.03 24183.14 25065.24 25186.93
(man-year) Std.dev. 43155.81 41366.88 41416.91 41394.00 41679.17
Feedback variable Self-raised fund by enterprises Mean 5359.29 6244.10 7090.63 8168.44 9171.59
(million RMB Yuan) Std.dev. 10841.96 12328.35 13644.27 15525.56 17393.22
Intermediate measure Number of patents in force Mean 3990.69 4784.72 6225.90 8321.66 10912.10
(piece) Std.dev. 11329.12 13060.45 16498.47 23087.70 30261.18
CS inputs Expenditure on new products development Mean 7338.06 8371.73 9535.57 10442.85 12266.17
(million RMB Yuan) Std.dev. 13777.38 15375.75 18288.53 20590.16 25004.15
Expenditure for technical renovation Mean 1272.00 1649.92 1291.33 1382.23 1557.09
(million RMB Yuan) Std.dev. 2279.31 2702.51 1849.80 2006.38 2568.56
CS outputs Sales revenue of new products Mean 88167.55 107676.75 122389.82 142785.98 165183.75
(million RMB Yuan) Std.dev. 185038.44 207162.49 231072.10 261637.21 321195.75
Main business income Mean 349095.52 399952.41 438946.90 482269.66 529854.14
(million RMB Yuan) Std.dev. 598955.60 656947.81 703448.92 770416.46 853237.12
Variables Years 2012 2013 2014 2015 2016
RDS input Government funds Mean 501.39 573.54 615.54 724.66 731.88
(million RMB Yuan) Std.dev. 644.98 690.40 790.16 875.93 874.37
Shared input Full-time equivalent Mean 21487.59 23104.03 24183.14 25065.24 25186.93
(man-year) Std.dev. 43155.81 41366.88 41416.91 41394.00 41679.17
Feedback variable Self-raised fund by enterprises Mean 5359.29 6244.10 7090.63 8168.44 9171.59
(million RMB Yuan) Std.dev. 10841.96 12328.35 13644.27 15525.56 17393.22
Intermediate measure Number of patents in force Mean 3990.69 4784.72 6225.90 8321.66 10912.10
(piece) Std.dev. 11329.12 13060.45 16498.47 23087.70 30261.18
CS inputs Expenditure on new products development Mean 7338.06 8371.73 9535.57 10442.85 12266.17
(million RMB Yuan) Std.dev. 13777.38 15375.75 18288.53 20590.16 25004.15
Expenditure for technical renovation Mean 1272.00 1649.92 1291.33 1382.23 1557.09
(million RMB Yuan) Std.dev. 2279.31 2702.51 1849.80 2006.38 2568.56
CS outputs Sales revenue of new products Mean 88167.55 107676.75 122389.82 142785.98 165183.75
(million RMB Yuan) Std.dev. 185038.44 207162.49 231072.10 261637.21 321195.75
Main business income Mean 349095.52 399952.41 438946.90 482269.66 529854.14
(million RMB Yuan) Std.dev. 598955.60 656947.81 703448.92 770416.46 853237.12
Table 2.  The average efficiencies of 29 regions in China (2012-2016)
Area Cooperative model Non-cooperative model Traditional model
Overall RDS CS RDS as leader CS as leader
RDS CS RDS CS
Eastern area Beijing 0.957 1.000 0.913 1.000 0.896 0.805 1.000 0.887
Tianjin 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000
Hebei 0.693 0.630 0.756 0.960 0.356 0.336 1.000 0.369
Shanghai 0.945 1.000 0.890 1.000 0.860 0.814 1.000 0.840
Jiangsu 0.830 1.000 0.661 1.000 0.681 0.546 1.000 0.848
Zhejiang 0.709 0.559 0.860 0.977 0.341 0.390 1.000 0.462
Fujian 0.714 0.613 0.815 0.977 0.417 0.383 1.000 0.913
Shandong 0.744 1.000 0.489 1.000 0.497 0.410 1.000 0.209
Guangdong 0.905 1.000 0.811 1.000 0.776 0.686 1.000 0.853
Hainan 0.751 0.999 0.502 1.000 0.502 0.289 1.000 0.850
Central area Shanxi 0.852 0.995 0.710 0.995 0.709 0.561 1.000 0.355
Anhui 0.775 1.000 0.550 1.000 0.550 0.436 1.000 0.475
Jiangxi 0.804 0.870 0.738 0.946 0.654 0.388 1.000 0.552
Henan 0.832 0.743 0.922 0.655 0.923 0.571 1.000 0.775
Hubei 0.725 0.546 0.904 1.000 0.372 0.402 1.000 0.485
Hunan 0.772 1.000 0.544 0.919 0.560 0.426 1.000 0.968
Northeastern Liaoning 0.762 1.000 0.524 1.000 0.525 0.421 1.000 0.328
area Jilin 0.906 1.000 0.812 1.000 0.812 0.700 1.000 0.556
Heilongjiang 0.637 0.346 0.929 0.873 0.166 0.237 1.000 0.682
Western area Inner Mongolia 0.880 0.709 0.849 0.995 0.709 0.561 1.000 0.957
Guangxi 0.974 1.000 0.947 1.000 0.947 0.914 1.000 0.225
Chongqing 0.978 0.991 0.964 0.930 0.977 0.874 1.000 1.000
Sichuan 0.807 1.000 0.614 1.000 0.615 0.518 1.000 0.575
Guizhou 0.658 0.700 0.617 0.995 0.259 0.196 1.000 0.250
Yunnan 0.768 1.000 0.536 1.000 0.537 0.381 1.000 0.326
Shaanxi 0.645 0.314 0.976 0.932 0.171 0.244 1.000 0.216
Qinghai 0.735 0.768 0.701 1.000 0.431 0.388 1.000 0.426
Ningxia 0.657 0.549 0.764 0.819 0.356 0.301 1.000 0.330
Xinjiang 0.778 0.999 0.558 1.000 0.558 0.455 1.000 0.497
China Mean 0.800 0.839 0.754 0.965 0.592 0.505 1.000 0.593
Area Cooperative model Non-cooperative model Traditional model
Overall RDS CS RDS as leader CS as leader
RDS CS RDS CS
Eastern area Beijing 0.957 1.000 0.913 1.000 0.896 0.805 1.000 0.887
Tianjin 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000
Hebei 0.693 0.630 0.756 0.960 0.356 0.336 1.000 0.369
Shanghai 0.945 1.000 0.890 1.000 0.860 0.814 1.000 0.840
Jiangsu 0.830 1.000 0.661 1.000 0.681 0.546 1.000 0.848
Zhejiang 0.709 0.559 0.860 0.977 0.341 0.390 1.000 0.462
Fujian 0.714 0.613 0.815 0.977 0.417 0.383 1.000 0.913
Shandong 0.744 1.000 0.489 1.000 0.497 0.410 1.000 0.209
Guangdong 0.905 1.000 0.811 1.000 0.776 0.686 1.000 0.853
Hainan 0.751 0.999 0.502 1.000 0.502 0.289 1.000 0.850
Central area Shanxi 0.852 0.995 0.710 0.995 0.709 0.561 1.000 0.355
Anhui 0.775 1.000 0.550 1.000 0.550 0.436 1.000 0.475
Jiangxi 0.804 0.870 0.738 0.946 0.654 0.388 1.000 0.552
Henan 0.832 0.743 0.922 0.655 0.923 0.571 1.000 0.775
Hubei 0.725 0.546 0.904 1.000 0.372 0.402 1.000 0.485
Hunan 0.772 1.000 0.544 0.919 0.560 0.426 1.000 0.968
Northeastern Liaoning 0.762 1.000 0.524 1.000 0.525 0.421 1.000 0.328
area Jilin 0.906 1.000 0.812 1.000 0.812 0.700 1.000 0.556
Heilongjiang 0.637 0.346 0.929 0.873 0.166 0.237 1.000 0.682
Western area Inner Mongolia 0.880 0.709 0.849 0.995 0.709 0.561 1.000 0.957
Guangxi 0.974 1.000 0.947 1.000 0.947 0.914 1.000 0.225
Chongqing 0.978 0.991 0.964 0.930 0.977 0.874 1.000 1.000
Sichuan 0.807 1.000 0.614 1.000 0.615 0.518 1.000 0.575
Guizhou 0.658 0.700 0.617 0.995 0.259 0.196 1.000 0.250
Yunnan 0.768 1.000 0.536 1.000 0.537 0.381 1.000 0.326
Shaanxi 0.645 0.314 0.976 0.932 0.171 0.244 1.000 0.216
Qinghai 0.735 0.768 0.701 1.000 0.431 0.388 1.000 0.426
Ningxia 0.657 0.549 0.764 0.819 0.356 0.301 1.000 0.330
Xinjiang 0.778 0.999 0.558 1.000 0.558 0.455 1.000 0.497
China Mean 0.800 0.839 0.754 0.965 0.592 0.505 1.000 0.593
Table 3.  Wilcoxon test results of the efficiency differences between two stages*
Efficiency comparisons Cooperative model RDS as the leader CS as the leader
Statistic z (P-value) Statistic z (P-value) Statistic z (P-value)
RDS efficiency VS. -1.389 -4.441 -4.462
CS efficiency (0.165)INSIG (0.000)SIG (0.000)SIG
* The significance level is 5%.
Efficiency comparisons Cooperative model RDS as the leader CS as the leader
Statistic z (P-value) Statistic z (P-value) Statistic z (P-value)
RDS efficiency VS. -1.389 -4.441 -4.462
CS efficiency (0.165)INSIG (0.000)SIG (0.000)SIG
* The significance level is 5%.
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