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The operational process of Chinese high-tech industry
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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 |
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.
Keywords:
Chinese high-tech industry,
two-stage process,
shared resource,
feedback factor,
data envelopment analysis.
Mathematics Subject Classification:
Primary: 58F15, 58F17; Secondary: 53C35.
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 and Management Optimization, doi: 10.3934/jimo.2021114
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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. |
| [2] |
Q. An, F. Meng, B. Xiong, Z. 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. |
| [3] |
Q. An, Z. Wang, A. Emrouznejad, Q. 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. |
| [4] |
Q. An, M. Yang, J. Chu, J. 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. |
| [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. |
| [6] |
C.-J. Chen, H.-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. |
| [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. |
| [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. |
| [9] |
X. Chen, Z. 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. |
| [10] |
Y. Chen, W. D. Cook, N. 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. |
| [11] |
Y. Chen, J. Du, H. 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. |
| [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. |
| [13] |
W. D. Cook and L. M. Seiford,
Towards a general non-parametric model of corporate performance, Omega, 192 (2009), 1-17.
|
| [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. |
| [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. |
| [16] |
Z. Griliches, Patent Statistics as Economic Indicators: A Survey, University of Chicago Press, 1998.
doi: 10.3386/w3301.
|
| [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. |
| [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. |
| [19] |
J. C. Guan, R. C. M. Yam, C. 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. |
| [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. |
| [21] |
G. E. Halkos, N. 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. |
| [22] |
C. Han, S. R. Thomas, M. Yang, P. 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. |
| [23] |
H. J. G. M. Hollanders and F. Celikel-Esser, Measuring Innovation Efficiency, European Commission, 2007. |
| [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. |
| [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. |
| [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. |
| [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. |
| [28] |
J. Lee, C. 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. |
| [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. |
| [30] |
H. Li, H. He, J. 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. |
| [31] |
W. Li, Z. Li, L. 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. |
| [32] |
Y. Li, Y. Chen, L. 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. |
| [33] |
L. Liang, W. 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. |
| [34] |
L. Liang, F. Feng, W. 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. |
| [35] |
L. Liang, Z.-Q. Li, W. 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. |
| [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. |
| [37] |
Z. Liu, X. Chen, J. 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. |
| [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. |
| [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. |
| [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. |
| [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. |
| [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. |
| [43] |
F.-M. Tseng, Y.-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. |
| [44] |
C. H. Wang, R. 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.
|
| [45] |
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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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