Performance evaluation of the Chinese high-tech industry: A two-stage DEA approach with feedback and shared resource

Dawei Wang; Linlin Zhao; Feng Yang; Kehong Chen

doi:10.3934/jimo.2021114

Article Contents

2022, Volume 18, Issue 5: 3315-3338. Doi: 10.3934/jimo.2021114

This issue Previous Article Capital asset pricing model under distribution uncertainty Next Article Filled function method to optimize supply chain transportation costs

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
^* Corresponding author: Linlin Zhao

Received: August 31, 2020

Revised: February 28, 2021

Early access: July 2021

Published: November 2022

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Abstract

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.

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Mathematics Subject Classification: Primary: 58F15, 58F17; Secondary: 53C35.

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Figure 1. The operational process of Chinese high-tech industry

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Figure 2. Two-stage process of Chinese high-tech industry

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Figure 3. The efficiencies in the cooperative case (2012-2016)

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Figure 4. The average stage efficiencies when the RDS as the leader (2012-2016)

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Figure 5. The average stage efficiencies when the CS as the leader (2012-2016)

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Figure 6. The annual average stage efficiencies in the non-cooperative case (2012-2016)

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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

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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

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Table 3. Wilcoxon test results of the efficiency differences between two stages^*

Efficiency comparisons	Cooperative model	RDS as the leader	CS as the leader
Efficiency comparisons	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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