Open access peer-reviewed chapter

Internal Relationship and Impact Path between Innovation and Entrepreneurship: Based on China’s High-Tech Industry

By Kai Zhao and Lixiang Wang

Submitted: November 24th 2019Reviewed: December 16th 2019Published: September 30th 2020

DOI: 10.5772/intechopen.90869

Downloaded: 36

Abstract

Innovation is the source of entrepreneurship, entrepreneurship is the value embodiment of innovation, and the two are inseparable. At a time when dividends such as population, reform and opening up, and resources and environment are gradually disappearing, China urgently needs to accelerate scientific and technological innovation to support economic development, incubate scientific and technological enterprises, and ease labor market pressure with technological progress and efficiency improvement. This chapter focuses on China’s high-tech industry, which is dominated by scientific and technological innovation. Starting from the overall, local, and regional perspectives, it organically integrates the traditional DEA, similar SFA, Malmquist index decomposition, chain multiple intermediary effect, and other multilevel research through cross-level analysis. Based on the research foundation of innovation efficiency after eliminating environmental and random factors, it deeply discusses the action path and impact mechanism of “double innovation” and provides targeted policy recommendations for the government and relevant local departments. The research confirms that the total effect of innovation on entrepreneurship is always positive, i.e., promoting “people-to-people innovation” is conducive to promoting “mass entrepreneurship” whether it is analyzed from the whole or from the part.

Keywords

  • Innovation
  • High-tech industry
  • DEA

1. Introduction

Innovation is the source of starting a business. In the process of starting a business, one needs to have a continuous and vigorous sense of innovation before one can produce creative ideas or schemes and explore new modes and outlets. Entrepreneurship is the embodiment of the value of innovation. The value of innovation mainly lies in transforming technology into productivity, and the fundamental way to realize this transformation is entrepreneurship. It can be seen that innovation and entrepreneurship are inseparable. Studying the internal relationship and mechanism between the two can not only provide an important theoretical basis and guidance for China to promote “double innovation” and prevent “deviation and deviation” in the middle of the process but also lead enterprises to carry out efficient innovation input and output, accelerate enterprises to adapt to the new normal, and provide power and guarantee for promoting innovation-driven strategy in depth.

With the accelerated process of economic globalization and the increasingly complicated and steep international market, China, as the largest developing country, urgently needs to play the role of a leader in scientific and technological innovation to support economic development, incubate scientific and technological enterprises, and relieve the pressure on the labor market at a time when its low-cost comparative advantages in production factors such as demographic dividend, reform and opening-up dividend, resources and environment dividend, and the resulting labor force no longer exist. Therefore, this chapter focuses on China’s high-tech industry, which is dominated by scientific and technological innovation. Starting from the overall, local, and even regional perspectives, it attempts to organically integrate the traditional DEA, similar SFA, Malmquist index decomposition, chain multiple intermediary effect, and other multilevel research. Based on the research on innovation efficiency after eliminating environmental and random factors, it comprehensively and deeply discusses the action path and impact mechanism of “double innovation” and provides targeted policy recommendations for the government and relevant local departments.

This article will summarize and review the relevant literature from three aspects: innovation, entrepreneurship, and innovation and entrepreneurship.

First, about innovation. Scholars at home and abroad have conducted in-depth research on the innovation efficiency of China’s high-tech industry from different perspectives. From the viewpoint of spatial economics, nonparametric DEA-Malmquist index method is used to explore the main factors affecting the innovation efficiency of China’s high-tech industries [1]. Starting from the perspective of industry comparison, the panel stochastic frontier method (SFA) is used to evaluate the innovation efficiency of China’s high-tech industry, and the influencing factors of innovation efficiency are analyzed [2]. The research proves that institutional innovation and the technicality of scientific researchers are the keys to improve the innovation efficiency of China’s high-tech industry. From the perspective of value chain, Xiao et al. [3] empirically tested various factors that can affect innovation efficiency by using two-stage chain-linked DEA and Tobit models. The research shows that government support, financial environment, and other factors have a significant impact on innovation efficiency. And Chiu et al. [4], based on the value chain perspective, constructed a model combining research and development investment and operation process and measured the innovation efficiency of China’s high-tech industry research and development value chain. In addition, there are many scholars from the SCP analysis perspective of industrial organization theory to study the impact of enterprise size, market power, technological opportunities, market process, and other factors on innovation efficiency [5, 6].

Second, about starting a business. Entrepreneurship is a social activity involving various phenomena, and scholars at home and abroad have conducted in-depth research on it from different angles such as economics, sociology, and management. Chen and Yang [7] based on microeconomics theory and from the perspective of industry differences analyzed the differences in the impact of various types of entrepreneurship on enterprise R&D incentives. Cao et al. [8] based on the social learning theory put forward a model of imitating entrepreneurial decision-making mechanism of high-tech industrial clusters. The study found that entrepreneurs implement entrepreneurial decision-making behavior under the stimulus of entrepreneurial motivation through observation and learning. Wang and Xue [9] set out from the theory of organizational learning to construct the framework of intellectual property entrepreneurship under the background of China’s transformation and upgrading and take high-tech industry as an example to carry out empirical analysis to perfect the theory of intellectual property entrepreneurship. Franco and Filson [10] based on the innovation theory, on the basis of the knowledge spillover effect mechanism of employee flow diffusion, allow employees of current companies to imitate the behavior of their former employers, theoretically analyze the equilibrium conditions for Pareto optimality, and find out through simulation that derivative enterprises established by R&D personnel usually have higher R&D willingness and efficiency than other types of entrepreneurs.

Third, about innovation and entrepreneurship. At present, there are few studies abroad that can consider innovation and entrepreneurship together, and there are two completely opposite views. One view is that entrepreneurship is the process of realizing innovation, and innovation is the essential core of entrepreneurship [11]. From this point of view, innovation and entrepreneurship have an internal connection mechanism. However, another view is that entrepreneurship may involve innovation process, but it may not include innovation behavior [12], and there is no inevitable connection between innovation and entrepreneurship. Domestic scholars, mainly based on high-tech industries, empirically study the interaction between innovation and entrepreneurship. Based on the questionnaire survey data of China’s high-tech industrial development zones, taking into account the dual effects of innovation activities and entrepreneurial activities within innovation clusters on the development of innovation clusters, this chapter makes an empirical analysis on the development of entrepreneurship, innovation, and innovation clusters in China’s high-tech industries [13]. Jin and Xie [14], based on the actual development of China’s high-tech industry, explored the role of government in the process of enterprise innovation and entrepreneurship.

Compared with previous literatures, the innovation of this chapter mainly includes the following two aspects: from the perspective of research content, the relationship between innovation and entrepreneurship studied in this chapter is a relatively blank research field, and the relevant research results at home and abroad are less, and the conclusions are quite different, while the research on the internal mechanism of innovation’s impact on entrepreneurship combined with intermediary effect is even less, so this chapter is novel in content. From the perspective of research methods, this chapter firstly decomposes total factor productivity (TFP) based on DEA model excluding environmental and random factors and then uses chain multiple intermediary effect model to analyze the influencing process and mechanism among various variables and further analyzes the relationship between innovation and entrepreneurship in high-tech industries. Compared with the previous literature, this chapter emphasizes the application of cross-level analysis in methodology; organically combines the traditional DEA, similar SFA, Malmquist index decomposition, chain multiple intermediary effect, and other multilevel related researches; and further discusses the action path and influencing mechanism of double innovation in a more comprehensive and in-depth way.

2. Research methods

The research can be divided into the following four stages.

2.1 The first stage: traditional DEA

Referring to the related literatures on R&D input–output efficiency of high-tech industry, this chapter selects the input-oriented variable-scale BCC model to carry out the first phase of calculation. The main reason for choosing BCC model is its variable scale. Problems such as unequal competition and resource constraints in domestic high-tech industries may cause some decision-making units (DMU) to be unable to operate at the optimal scale, and the measurement of technical efficiency will be affected by scale efficiency. Considering this situation, BCC model adds convex conditions, allowing technical efficiency (TE) to be further decomposed into pure technical efficiency (PTE) and scale efficiency (SE), which is helpful to analyze the influencing factors of R&D input–output efficiency in China’s high-tech industry in recent years.

2.2 The second stage: similar SFA

On the basis of the previous research stage, the difference between the actual input and the target input of the final accounting unit is calculated, i.e., the input slack, and a similar SFA model is constructed with the input slack as the dependent variable and the environmental factor as the independent variable, so as to separate the efficiency values affected by the external environment and random error factors and thus obtain the redundancy of the input of the decision-making unit caused only by the inefficiency of management.

2.3 The third stage: adjusted DEA and Malmquist index

According to the method proposed by Fried [15], the adjusted input data are substituted into BCC model again for efficiency analysis, thus obtaining the efficiency values of DMU after eliminating environmental factors and random factors. Compared with the traditional DEA model, the efficiency value thus obtained is closer to the real value. Since the data of DMU being evaluated is panel data including multiple time points, Malmquist TFP index can be used to analyze the effects of productivity changes, technical efficiency, and technological progress on productivity changes, respectively.

2.4 The fourth stage: chain multiple mediation effect

The total factor productivity excluding environmental and random factors is taken as the proxy variable to measure the innovation level in each region. The number of new ventures in high-tech industries, regional economic growth, and entrepreneurship in each region are included in the research framework, and an intermediary chain is formed according to the sequential characteristics of the intermediary variables. Through the intermediary effect model, the relationship between multiple variables and the process and mechanism of the influence between variables are analyzed simultaneously. This stage is mainly based on structural equation model (SEM) to carry out the above analysis. The following regression equations can be used to describe the relationship between variables:

Y=cX+b1M1+b2M2+e1M2=a2X+a3M1+e2M1=a1X+e3E1

The corresponding path of chain multiple mediation effect is shown in Figure 1.

Figure 1.

Chain multiple mediation effect path diagram.

As shown in Figure 1, the multiple mediation model includes two mediation variables M1and M2; at this time, the multiple mediation effect analysis can be carried out from three angles. The first is to analyze from the total mediation effect, namely, a1b1+a2b2+a1a3b2. Secondly, on the premise of controlling other intermediate variables (such as control M1), we can study the specific mediation effect of each intermediate variable, such as a1b1, a2b2, and a1a3b2. Third, a comparative mediating effect can be obtained so as to be able to judge which of the effects of multiple mediating variables (e.g., a1b1, a2b2) is more effective, e.g., a1a3b2a2b2, a1b1a2b2, and a1a3b2a1b1[16].

3. Variable selection and data description

3.1 Input–output indicators

Data on capital input, personnel input, and innovation output are all derived from China’s High-tech Industry Statistical Yearbook. As capital investment data are often affected by price fluctuations, the relevant data should be reduced by using fixed asset investment price index (last year = 100) of corresponding years in all provinces and cities, and the provincial fixed asset investment price index data of each year comes from China Statistical Yearbook. In addition, Tibet and Xinjiang were excluded from the sample due to the serious lack of data. In order to eliminate the time lag between input and output and the difference between innovation activity cycles, this study chooses the time lag as 1 year, i.e., the input data is 2004–2013 and the output data is 2005–2014. In addition, because the internal expenditure of R&D funds, expenditure of new product development, and expenditure of technological transformation not only have an impact on the input–output efficiency of high-tech industries in the current period but also will have an important impact in some future periods, it is first necessary to convert the flow data into stock and then measure the impact of these three capital inputs on the innovation-output efficiency. Taking the calculation of R&D fund stock as an example, the method of permanent storage is used to deal with it.

xi,t=1δxi.t1+ei,t1E2

In Eq. (2), xi,tand xi,t1are the R&D expenditures of the tand t1years of the province i, respectively; ei,t1is the R&D expenditure of the t1year after eliminating the price factor; δis the depreciation rate; according to Griliches [17], it can be set to δ=15%. Assuming that the average annual growth rate of R&D expenditures during the study period is g, the base R&D expenditure is

xi,o=ei,0/g+δE3

ei,0in Eq. (3) refers to the internal expenditure of R&D funds for the base period. On the basis of Eqs. (2) and (3), the R&D fund stock of each year can be obtained by using the perpetual inventory method. The above method can be used to process the funds for new product development and technological transformation, thus converting the flow data into stock. Input–output related variables are described in Table 1.

VariableSymbolUnitDefinition
Innovation output 1y1ItemNumber of patent applications
Innovation output 2y2Ten thousand yuanRevenue from sales of new products
Innovation (personnel) input 1x1Person/yearFull-time equivalent of R&D personnel refers to the sum of the number of R&D full-time personnel plus part-time personnel converted into the current number of personnel according to the workload during the reporting year
Innovation (capital) investment 2x2Ten thousand yuanThe stock of R&D funds is calculated by the perpetual inventory method
Innovation (capital) investment 3x3Ten thousand yuanThe stock of new product development funds shall be accounted for by the perpetual inventory method
Innovation (capital) investment 4x4Ten thousand yuanThe stock of funds for technological transformation shall be accounted for by the perpetual inventory method

Table 1.

Description of input–output variables.

3.2 Environmental variables

Environmental variables are mainly used to reflect the geographic location of high-tech industries, macroenvironment, and government innovation support policies. Based on the relevant research foundation at home and abroad, and considering the characteristics of R&D and the availability of data, this chapter mainly selects the following indicators.

3.2.1 Regional real GDP

Real GDP can accurately reflect regional differences and the real level of regional economic development, so this chapter uses real GDP excluding price factors instead of nominal GDP. Generally speaking, the higher the regional economic development level, the more capable it is to invest in R&D innovation in high-tech industries and the higher the R&D innovation level.

3.2.2 Geographical location

Whether a region is located in a superior geographical position is crucial to the development of high-tech industries in the region. This chapter quantifies this variable by setting virtual variables, in which “1” is used to represent the eastern region, including Beijing, Tianjin, Hebei, Liaoning, Shanghai, Jiangsu, Zhejiang, Fujian, Shandong, Guangdong, and Hainan. “2” means the central region, including Shanxi, Inner Mongolia, Jilin, Heilongjiang, Anhui, Jiangxi, Henan, Hubei, and Hunan; “0” means the western region, including Chongqing, Sichuan, Guizhou, Yunnan, Shaanxi, Gansu, Qinghai, Guangxi, and Ningxia. Due to the serious lack of data in Xinjiang and Tibet, this article will not consider it for the time being.

3.2.3 Quality of local workers

In view of the availability of data, this chapter chooses the number of college students in the studied area as the characterization variable of laborer’s quality. The higher the quality of local laborers, the more likely it is to provide more talents and intellectual support for the development of high-tech industries and the more conducive to the improvement of R&D efficiency.

3.2.4 Local government innovation support

Local government support has an important impact on the R&D and innovation process of high-tech industries. This chapter selects the government funds from the funds raised for scientific and technological activities to represent the innovation support of local governments. Generally speaking, local government support can provide guarantee for the infrastructure construction of high-tech industries in the region as well as financial support. The greater the support, the more conducive to R&D and innovation in high-tech industries.

3.2.5 Regional science and technology development level

Due to the spatial aggregation effect and spillover effect of high-tech industries, the degree of technological development in a region often plays an important role in the development of high-tech industries. This chapter chooses the number of high-tech industrial enterprises and the number of scientific and technological institutions to represent the degree of scientific and technological development in a region. In general, the more high-tech enterprises and scientific and technological institutions there are, the more conducive they are to R&D and innovation in high-tech industries.

Among the above environmental variables, except that the actual GDP of each region comes from China Statistical Yearbook, other data come from China High-tech Industry Statistical Yearbook. Descriptive statistics of relevant variables are shown in Table 2.

VariableSymbolObserved valueMean valueStandard deviationMinimum valueMaximum value
Regional real GDPk126113,824.8511,858572.3762,924.62
Geographical positionk226110.789402
Quality of local laborersk3261746,585437,384.535,9831,796,665
Local government innovation supportk426136,197.8751,416.8710289,006.9
Number of high-tech industrial enterprisesk5261853.291208.19145874
Number of scientific and technological institutionsk6261156.69319.0412474

Table 2.

Descriptive statistics of environmental variables.

3.3 Mediating effect variables

Taking the total factor productivity (X) obtained by Malmquist index decomposition in the third stage as the independent variable. The dependent variable is the logarithm (Y) of the number of newly built entrepreneurs in individual and private enterprises in high-tech industries. The variable is obtained by using the difference between the number of private enterprises and individual workers in urban employment at the end of the year in the China Statistical Yearbook 2005–2014.The intermediate variables are, respectively, the logarithm of the number of new ventures (M1) and the logarithm of the actual GDP of each region (M2). Among them, the number of new ventures is obtained by year-on-year difference using the number of enterprises in the China Statistical Yearbook from 2005 to 2014. The actual GDP of each region comes from the China Statistical Yearbook.

4. Empirical analysis

4.1 Empirical analysis of traditional DEA in the first stage

DEAP software is used to preliminarily measure the comprehensive technical efficiency, pure technical efficiency, and scale efficiency of 29 provinces and cities in China from 2005 to 2014. The results are shown in Tables 35. According to the calculation results before adjustment, the average value of China’s R&D comprehensive technical efficiency is 0.48–0.67 from 2005 to 2014, without considering the interference of external environment and random influence factors, which generally shows a rising trend. The average value of pure technical efficiency is 0.63–0.73, with room for improvement to varying degrees. The average value of scale efficiency is 0.753–0.926, and the overall efficiency level is relatively high. Generally speaking, the R&D efficiency of most provinces is below the efficiency frontier. As the result does not eliminate the influence of environmental factors and random interference factors, it cannot accurately measure the true level of the comprehensive technical efficiency of each province, so further adjustment and measurement are needed.

ProvincesYear
2005200620072008200920102011201220132014
BeijingBA0.270.301.001.001.001.001.001.001.001.00
AA0.450.451.001.001.001.001.001.000.970.92
TianjinBA1.001.001.001.001.001.001.001.001.001.00
AA1.001.000.900.830.820.920.640.871.000.92
HebeiBA0.120.090.100.400.280.430.280.440.430.50
AA0.060.050.070.110.130.130.160.230.310.43
ShanxiBA1.001.001.001.001.001.000.830.950.870.52
AA0.010.020.050.040.040.080.080.130.170.12
Inner MongoliaBA1.000.410.050.271.000.610.560.570.560.34
AA0.010.000.000.000.020.020.020.020.030.03
LiaoningBA0.240.140.220.480.620.470.610.450.630.59
AA0.220.160.240.360.320.300.400.430.490.49
JilinBA0.420.120.190.580.650.350.560.640.610.54
AA0.060.030.050.060.120.050.110.180.250.21
HeilongjiangBA0.500.060.080.230.190.210.240.300.290.45
AA0.130.040.050.070.090.100.170.200.250.32
ShanghaiBA0.860.530.630.880.500.550.730.570.700.87
AA1.001.000.941.000.840.810.820.700.850.83
JiangsuBA0.280.270.370.700.800.690.971.000.660.81
AA0.610.690.941.001.001.001.001.001.001.00
ZhejiangBA0.180.140.170.430.600.460.500.630.710.58
AA0.330.400.460.490.640.570.720.911.000.91
AnhuiBA0.740.500.210.340.761.001.001.001.001.00
AA0.050.060.060.070.230.330.640.831.001.00
FujianBA0.860.680.660.860.820.771.000.810.560.51
AA0.720.710.720.680.690.820.630.700.690.66
JiangxiBA0.150.110.100.180.290.370.280.420.530.69
AA0.090.070.080.080.140.180.170.260.420.58
ShandongBA0.450.240.380.500.540.630.650.600.530.58
AA0.510.540.670.710.840.840.850.800.760.75
HenanBA0.150.120.170.690.510.710.480.511.001.00
AA0.080.090.140.200.300.330.440.511.001.00
HubeiBA0.160.130.160.360.540.500.540.520.550.52
AA0.160.110.170.180.380.370.440.580.680.58
HunanBA0.190.150.120.700.940.861.001.001.000.85
AA0.040.040.050.110.270.260.560.590.790.86
GuangdongBA0.350.330.321.000.831.000.910.951.001.00
AA1.001.001.001.001.001.001.001.001.001.00
GuangxiBA0.560.150.110.550.490.410.520.530.680.57
AA0.040.020.030.030.050.040.080.110.170.16
HainanBA1.001.001.001.001.001.001.001.001.000.85
AA0.000.000.010.000.050.020.070.120.140.15
ChongqingBA0.290.250.170.440.940.711.000.810.940.97
AA0.070.080.080.130.230.220.410.340.510.53
SichuanBA0.210.230.250.290.540.351.000.820.700.98
AA0.290.330.440.380.530.410.550.800.840.90
GuizhouBA0.220.160.130.490.580.620.420.430.420.45
AA0.050.060.100.110.140.180.180.280.350.37
YunnanBA1.000.200.461.001.000.800.920.810.760.64
AA0.080.020.080.030.070.040.090.140.130.14
ShaanxiBA0.090.070.080.170.210.230.270.250.290.24
AA0.160.150.210.200.190.220.290.320.440.37
GansuBA0.530.270.170.480.420.940.460.680.590.63
AA0.020.010.010.020.030.050.050.080.080.11
QinghaiBA1.001.001.000.390.591.001.000.160.990.49
AA0.000.010.010.000.010.000.010.000.010.00
NingxiaBA0.130.240.100.280.310.410.690.691.000.35
AA0.010.010.010.020.020.030.040.040.080.03
Average valueBA0.480.340.360.580.650.660.700.670.720.67
AA0.250.250.300.310.350.360.400.450.530.53

Table 3.

Technical efficiency estimation results of technological innovation.

Note: “BA” means before adjustment; “AA” means after adjustment.

ProvincesYear
2005200620072008200920102011201220132014
BeijingBA0.500.591.001.001.001.001.001.001.001.00
AA0.960.961.001.001.001.001.001.001.001.00
TianjinBA1.001.001.001.001.001.001.001.001.001.00
AA1.001.001.001.001.001.001.001.001.001.00
HebeiBA0.130.280.150.440.340.480.280.450.440.50
AA1.001.001.001.001.001.001.001.000.991.00
ShanxiBA1.001.001.001.001.001.000.850.980.880.55
AA0.990.990.991.001.001.001.001.001.001.00
Inner MongoliaBA1.000.490.150.591.000.780.610.710.620.49
AA0.980.980.980.991.001.001.001.001.001.00
LiaoningBA0.590.290.270.480.750.530.610.460.660.60
AA1.000.991.001.001.001.001.000.991.000.95
JilinBA0.440.320.400.590.670.350.560.650.630.57
AA0.990.980.970.970.970.970.970.970.980.96
HeilongjiangBA0.640.220.100.250.210.270.270.310.320.46
AA0.980.980.991.000.970.960.940.950.950.96
ShanghaiBA1.001.000.911.000.730.700.770.570.750.88
AA1.001.000.991.000.900.890.960.890.930.99
JiangsuBA0.600.360.781.001.001.001.001.001.001.00
AA0.980.870.981.001.001.001.001.001.001.00
ZhejiangBA0.380.380.550.570.830.630.740.901.000.88
AA0.910.850.810.790.910.800.850.921.000.91
AnhuiBA0.910.510.280.641.001.001.001.001.001.00
AA0.980.990.991.001.001.001.001.001.001.00
FujianBA0.880.680.780.930.910.891.000.810.570.52
AA1.000.980.981.000.990.991.000.940.810.77
JiangxiBA0.230.220.120.200.290.380.290.420.530.70
AA0.970.990.990.990.960.950.930.900.910.98
ShandongBA0.870.580.730.650.650.780.720.690.660.66
AA1.001.001.000.950.950.990.940.870.840.78
HenanBA0.150.660.450.790.790.840.530.561.001.00
AA0.970.981.001.001.001.000.970.961.001.00
HubeiBA0.840.510.340.440.620.530.540.550.600.52
AA1.001.001.001.001.001.001.001.000.990.93
HunanBA0.290.180.170.811.000.941.001.001.000.85
AA1.001.001.001.001.001.001.001.001.000.97
GuangdongBA1.001.001.001.001.001.001.001.001.001.00
AA1.001.001.001.001.001.001.001.001.001.00
GuangxiBA0.740.230.160.560.500.420.540.550.700.61
AA0.970.970.960.970.990.980.980.991.000.98
HainanBA1.001.001.001.001.001.001.001.001.000.88
AA1.001.001.001.001.001.001.001.001.001.00
ChongqingBA0.310.490.270.610.960.771.000.810.940.98
AA0.950.940.940.950.980.981.000.970.991.00
SichuanBA0.260.340.540.440.560.521.000.820.741.00
AA0.960.950.950.900.900.921.000.980.961.00
GuizhouBA0.250.730.390.710.690.670.450.430.420.46
AA0.930.940.950.940.960.960.940.910.900.90
YunnanBA1.000.551.001.001.000.840.940.810.770.66
AA1.000.980.990.971.001.000.990.991.000.99
ShaanxiBA0.170.120.160.210.220.280.280.250.320.24
AA0.920.890.930.880.850.840.850.780.800.77
GansuBA1.000.270.180.500.460.950.460.740.640.69
AA0.950.980.970.980.990.990.991.001.001.00
QinghaiBA1.001.001.001.001.001.001.001.001.001.00
AA0.960.970.960.970.980.980.980.991.001.00
NingxiaBA0.130.240.110.320.490.470.730.831.000.49
AA0.950.950.950.950.970.970.970.980.991.00
Average valueBA0.630.530.520.680.750.730.730.730.770.73
AA0.980.970.970.970.980.970.970.960.970.96

Table 4.

Pure technical efficiency estimation results of technological innovation.

Note: “BA” means before adjustment; “AA” means after adjustment.

ProvincesYear
2005200620072008200920102011201220132014
BeijingBA0.530
(drs)
0.510
(drs)
1.000
(crs)
1.000
(crs)
1.000
(crs)
1.000
(crs)
1.000
(crs)
1.000
(crs)
1.000
(crs)
1.000
(crs)
AA0.463
(irs)
0.469
(irs)
1.000
(crs)
1.000
(crs)
1.000
(crs)
1.000
(crs)
1.000
(crs)
1.000
(crs)
0.974
(irs)
0.923
(irs)
TianjinBA1.000
(crs)
1.000
(crs)
1.000
(crs)
1.000
(crs)
1.000
(crs)
1.000
(crs)
1.000
(crs)
1.000
(crs)
1.000
(crs)
1.000
(crs)
AA1.000
(crs)
1.000
(crs)
0.900
(irs)
0.831
(irs)
0.824
(irs)
0.920
(irs)
0.637
(irs)
0.869
(irs)
1.000
(crs)
0.921
(irs)
HebeiBA0.944
(drs)
0.318
(drs)
0.672
(drs)
0.902
(drs)
0.820
(drs)
0.887
(drs)
0.988
(irs)
0.985
(irs)
0.989
(drs)
0.992
(irs)
AA0.058
(irs)
0.054
(irs)
0.071
(irs)
0.107
(irs)
0.130
(irs)
0.128
(irs)
0.160
(irs)
0.227
(irs)
0.314
(irs)
0.427
(irs)
ShanxiBA1.000
(crs)
1.000
(crs)
1.000
(crs)
1.000
(crs)
1.000
(crs)
1.000
(crs)
0.980
(irs)
0.974
(irs)
0.994
(drs)
0.944
(irs)
AA0.012
(irs)
0.022
(irs)
0.050
(irs)
0.036
(irs)
0.041
(irs)
0.081
(irs)
0.076
(irs)
0.127
(irs)
0.169
(irs)
0.118
(irs)
Inner MongoliaBA1.000
(crs)
0.838
(irs)
0.336
(irs)
0.451
(irs)
1.000
(crs)
0.781
(irs)
0.926
(irs)
0.793
(irs)
0.910
(irs)
0.691
(irs)
AA0.007
(irs)
0.003
(irs)
0.002
(irs)
0.002
(irs)
0.023
(irs)
0.015
(irs)
0.017
(irs)
0.015
(irs)
0.026
(irs)
0.025
(irs)
LiaoningBA0.414
(drs)
0.500
(drs)
0.807
(drs)
0.989
(drs)
0.831
(drs)
0.880
(drs)
0.999
(irs)
0.994
(irs)
0.951
(drs)
0.991
(irs)
AA0.218
(irs)
0.162
(irs)
0.239
(irs)
0.356
(irs)
0.319
(irs)
0.303
(irs)
0.399
(irs)
0.435
(irs)
0.490
(irs)
0.510
(irs)
JilinBA0.949
(drs)
0.388
(drs)
0.468
(drs)
0.980
(drs)
0.969
(drs)
0.989
(drs)
0.989
(irs)
0.979
(irs)
0.962
(irs)
0.959
(irs)
AA0.062
(irs)
0.029
(irs)
0.052
(irs)
0.059
(irs)
0.118
(irs)
0.054
(irs)
0.113
(irs)
0.181
(irs)
0.257
(irs)
0.219
(irs)
HeilongjiangBA0.774
(drs)
0.272
(drs)
0.800
(drs)
0.912
(drs)
0.887
(drs)
0.762
(drs)
0.874
(drs)
0.990
(irs)
0.911
(drs)
0.978
(irs)
AA0.133
(irs)
0.036
(irs)
0.054
(irs)
0.072
(irs)
0.095
(irs)
0.105
(irs)
0.176
(irs)
0.211
(irs)
0.259
(irs)
0.338
(irs)
ShanghaiBA0.862
(drs)
0.528
(drs)
0.690
(drs)
0.875
(drs)
0.690
(drs)
0.783
(drs)
0.958
(drs)
0.998
(irs)
0.928
(drs)
0.990
(drs)
AA1.000
(crs)
1.000
(crs)
0.952
(irs)
1.000
(crs)
0.934
(irs)
0.906
(irs)
0.852
(irs)
0.795
(irs)
0.913
(irs)
0.833
(irs)
JiangsuBA0.470
(drs)
0.746
(drs)
0.472
(drs)
0.696
(drs)
0.795
(drs)
0.685
(drs)
0.974
(drs)
1.000
(crs)
0.657
(drs)
0.806
(drs)
AA0.625
(irs)
0.794
(irs)
0.962
(irs)
1.000
(crs)
1.000
(crs)
1.000
(crs)
1.000
(crs)
1.000
(crs)
1.000
(crs)
1.000
(crs)
ZhejiangBA0.462
(drs)
0.373
(drs)
0.319
(drs)
0.753
(drs)
0.729
(drs)
0.738
(drs)
0.681
(drs)
0.705
(drs)
0.708
(drs)
0.661
(drs)
AA0.363
(irs)
0.470
(irs)
0.566
(irs)
0.618
(irs)
0.708
(irs)
0.708
(irs)
0.839
(irs)
0.990
(irs)
1.000
(crs)
0.997
(irs)
AnhuiBA0.820
(drs)
0.988
(drs)
0.765
(drs)
0.531
(drs)
0.758
(drs)
1.000
(crs)
1.000
(crs)
1.000
(crs)
1.000
(crs)
1.000
(crs)
AA0.050
(irs)
0.057
(irs)
0.063
(irs)
0.071
(irs)
0.226
(irs)
0.333
(irs)
0.641
(irs)
0.834
(irs)
1.000
(crs)
1.000
(crs)
FujianBA0.978
(drs)
0.996
(drs)
0.846
(drs)
0.928
(drs)
0.908
(drs)
0.869
(drs)
1.000
(crs)
1.000
(crs)
0.978
(drs)
0.990
(drs)
AA0.725
(irs)
0.720
(irs)
0.729
(irs)
0.683
(irs)
0.696
(irs)
0.834
(irs)
0.634
(irs)
0.748
(irs)
0.846
(irs)
0.851
(irs)
JiangxiBA0.621
(drs)
0.510
(drs)
0.806
(drs)
0.923
(drs)
0.984
(drs)
0.974
(drs)
0.994
(irs)
0.990
(irs)
0.997
(drs)
0.993
(irs)
AA0.090
(irs)
0.069
(irs)
0.080
(irs)
0.080
(irs)
0.148
(irs)
0.187
(irs)
0.186
(irs)
0.285
(irs)
0.467
(irs)
0.586
(irs)
ShandongBA0.522
(drs)
0.422
(drs)
0.520
(drs)
0.765
(drs)
0.837
(drs)
0.810
(drs)
0.905
(drs)
0.865
(drs)
0.807
(drs)
0.884
(drs)
AA0.505
(irs)
0.535
(irs)
0.666
(irs)
0.746
(irs)
0.881
(irs)
0.856
(irs)
0.908
(irs)
0.921
(irs)
0.904
(irs)
0.963
(irs)
HenanBA0.947
(drs)
0.186
(drs)
0.383
(drs)
0.871
(drs)
0.643
(drs)
0.839
(drs)
0.905
(drs)
0.905
(drs)
1.000
(crs)
1.000
(crs)
AA0.080
(irs)
0.090
(irs)
0.140
(irs)
0.200
(irs)
0.301
(irs)
0.326
(irs)
0.459
(irs)
0.532
(irs)
1.000
(crs)
1.000
(crs)
HubeiBA0.505
(irs)
0.259
(drs)
0.481
(drs)
0.824
(drs)
0.871
(drs)
0.935
(drs)
1.000
(crs)
0.946
(drs)
0.916
(drs)
0.999
(irs)
AA0.162
(irs)
0.109
(irs)
0.168
(irs)
0.175
(irs)
0.384
(irs)
0.370
(irs)
0.436
(irs)
0.581
(irs)
0.684
(crs)
0.620
(irs)
HunanBA0.642
(drs)
0.839
(drs)
0.702
(drs)
0.866
(drs)
0.936
(drs)
0.911
(drs)
1.000
(crs)
1.000
(crs)
1.000
(crs)
0.999
(irs)
AA0.039
(irs)
0.042
(irs)
0.049
(irs)
0.105
(irs)
0.270
(irs)
0.260
(irs)
0.555
(irs)
0.592
(irs)
0.793
(irs)
0.886
(irs)
GuangdongBA0.354
(drs)
0.327
(drs)
0.322
(drs)
1.000
(crs)
0.833
(drs)
1.000
(crs)
0.911
(drs)
0.951
(drs)
1.000
(crs)
1.000
(crs)
AA1.000
(crs)
1.000
(crs)
1.000
(crs)
1.000
(crs)
1.000
(crs)
1.000
(crs)
1.000
(crs)
1.000
(crs)
1.000
(crs)
1.000
(crs)
GuangxiBA0.752
(drs)
0.642
(drs)
0.679
(drs)
0.976
(drs)
0.980
(drs)
0.976
(drs)
0.970
(irs)
0.964
(irs)
0.975
(irs)
0.942
(irs)
AA0.043
(irs)
0.016
(irs)
0.027
(irs)
0.035
(irs)
0.052
(irs)
0.038
(irs)
0.085
(irs)
0.115
(irs)
0.170
(irs)
0.160
(irs)
HainanBA1.000
(crs)
1.000
(crs)
1.000
(crs)
1.000
(crs)
1.000
(crs)
1.000
(crs)
1.000
(crs)
1.000
(crs)
1.000
(crs)
0.966
(irs)
AA0.002
(irs)
0.001
(irs)
0.005
(irs)
0.003
(irs)
0.045
(irs)
0.022
(irs)
0.070
(irs)
0.116
(irs)
0.141
(irs)
0.150
(irs)
ChongqingBA0.934
(drs)
0.516
(drs)
0.631
(drs)
0.725
(drs)
0.973
(drs)
0.925
(drs)
1.000
(crs)
0.993
(irs)
0.999
(irs)
0.992
(irs)
AA0.076
(irs)
0.083
(irs)
0.088
(irs)
0.134
(irs)
0.235
(irs)
0.227
(irs)
0.413
(irs)
0.349
(irs)
0.519
(irs)
0.530
(irs)
SichuanBA0.815
(drs)
0.675
(drs)
0.458
(drs)
0.664
(drs)
0.973
(drs)
0.672
(drs)
1.000
(crs)
0.999
(drs)
0.947
(drs)
0.984
(drs)
AA0.302
(irs)
0.344
(irs)
0.466
(irs)
0.419
(irs)
0.584
irs)
0.447
(irs)
0.552
(irs)
0.816
(irs)
0.873
(irs)
0.899
(irs)
GuizhouBA0.865
(drs)
0.224
(drs)
0.330
(drs)
0.691
(drs)
0.835
(drs)
0.924
(drs)
0.926
(drs)
0.997
(drs)
0.999
(drs)
0.990
(irs)
AA0.050
(irs)
0.060
(irs)
0.102
(irs)
0.114
(irs)
0.143
(irs)
0.188
(irs)
0.192
(irs)
0.312
(irs)
0.393
(irs)
0.418
(irs)
YunnanBA1.000
(crs)
0.371
(drs)
0.458
(drs)
1.000
(crs)
1.000
(crs)
0.952
(drs)
0.980
(irs)
0.994
(irs)
0.987
(irs)
0.978
(irs)
AA0.082
(irs)
0.018
(irs)
0.080
(irs)
0.034
(irs)
0.072
(irs)
0.045
(irs)
0.087
(irs)
0.137
(irs)
0.132
(irs)
0.144
(irs)
ShaanxiBA0.525
(drs)
0.550
(drs)
0.493
(drs)
0.801
(drs)
0.935
(drs)
0.818
(drs)
0.957
(drs)
0.993
(irs)
0.933
(drs)
0.991
(irs)
AA0.175
(irs)
0.172
(irs)
0.221
(irs)
0.231
(irs)
0.229
(irs)
0.259
(irs)
0.340
(irs)
0.406
(irs)
0.550
(irs)
0.474
(irs)
GansuBA0.529
(drs)
0.996
(irs)
0.924
(irs)
0.966
(irs)
0.910
(irs)
0.987
(drs)
0.994
(irs)
0.928
(irs)
0.914
(irs)
0.909
(irs)
AA0.024
(irs)
0.010
(irs)
0.008
(irs)
0.016
(irs)
0.026
(irs)
0.046
(irs)
0.045
(irs)
0.084
(irs)
0.083
(irs)
0.109
(irs)
QinghaiBA1.000
(crs)
1.000
(crs)
1.000
(crs)
0.387
(irs)
0.590
(irs)
1.000
(crs)
1.000
(crs)
0.162
(irs)
0.989
(irs)
0.490
(irs)
AA0.001
(irs)
0.011
(irs)
0.011
(irs)
0.000
(irs)
0.006
(irs)
0.005
(irs)
0.005
(irs)
0.001
(irs)
0.006
(irs)
0.003
(irs)
NingxiaBA0.946
(irs)
0.999
(crs)
0.946
(irs)
0.886
(irs)
0.641
(irs)
0.870
(irs)
0.946
(drs)
0.839
(irs)
1.000
(crs)
0.730
(irs)
AA0.010
(irs)
0.010
(irs)
0.011
(irs)
0.019
(irs)
0.021
(irs)
0.030
(irs)
0.036
(irs)
0.042
(irs)
0.042
(irs)
0.029
(irs)
Average valueBA0.7530.6200.6660.8400.8730.8950.9610.9290.9470.926
AA0.2540.2550.3020.3150.3620.3690.4110.4730.5530.556

Table 5.

Estimate results of scale efficiency of technological innovation.

Note: “irs” indicates increasing scale efficiency; “crs” indicates that the scale efficiency is unchanged; “drs” indicates a decline in scale efficiency. “BA” means before adjustment; “AA” means after adjustment.

4.2 Regression analysis of SFA in the second stage

In order to separate the efficiency values influenced by external environment and random error factors, this chapter observes the influence of environmental factors and random error, respectively, by constructing similar SFA models in the second stage. SFA regression analysis is carried out by Frontier software. The relaxation values of each input variable obtained in the first stage are taken as dependent variables, and the selected six environmental variables are taken as independent variables to establish regression models to estimate the influence of environmental factors on the relaxation values of each input variable. If the regression coefficient is negative, it indicates that the positive change of environmental variables is conducive to reducing the input redundancy of high-tech industries, thus avoiding the loss and waste of input and improving the efficiency of research and development innovation. On the contrary, it will reduce the efficiency level of R&D innovation. The results of SFA regression analysis are shown in Table 6.

VariableR&D personnel FTE slack variableR&D funds slack variableNew product development funds slack variableFunds for technological transformation slack variable
Constant term−1.42E + 03***
(−2.97)
−2.94E + 05***
(−39647.79)
−4.14E + 05***
(−241,493.66)
−4.67E + 05***
(−455,599.32)
Regional real GDP−3.39E − 01***
(−8.81)
−3.22E + 00
(−0.76)
−9.96E + 00**
(−2.08)
−6.61E + 00*
(−1.49)
Geographical position1.05E + 03***
(2.23)
1.38E + 05***
(99,695)
2.27E + 05***
(215,428)
5.88E + 04***
(58,524)
Quality of local laborers7.57E − 03***
(11.39)
3.62E − 01***
(6.34)
5.16E − 01***
(7.64)
5.74E − 01***
(7.07)
Number of high-tech industrial enterprises−1.15E − 01
(−0.33)
−6.16E + 01***
(−2.8)
−5.64E + 01**
(−2.2)
−1.14E + 02***
(−2.1)
Local government innovation support2.30E − 02***
(4.03)
2.03E + 00***
(4.51)
2.20E + 00***
(3.81)
1.59E + 00***
(3.06)
Number of scientific and technological institutions1.58E + 00*
(1.45)
−1.64E + 02**
(−1.76)
−1.61E + 02*
(1.37)
−1.76E + 02**
(−2.05)
σ22.44E + 07***1.43E + 11***3.21E + 11***4.09E + 11***
γ7.52E − 01***7.76E − 01***8.46E − 01***9.04E − 01***
Logarithmic likelihood function−2.73E + 03−3.98E + 03−4.04E + 03−4.00E + 03
Unilateral likelihood function1.41E + 021.58E + 022.44E + 023.47E + 02

Table 6.

Estimated results of SFA.

Note: *** means p < 0.01; ** means p < 0.05; * means p < 0.1.

As can be seen from Table 6, the estimation results σ2and γare both significant at the level of 1%, indicating that it is necessary to eliminate the influence of environmental variables in the model. Specifically, it can be seen from the following aspects: (1) Regional actual GDP has a significant negative impact on R&D personnel’s FTE slack, new product development spending slack, and technological transformation spending slack. This shows that the higher the level of regional economic development, the more conducive it is to reduce the redundancy of input variables, and the more effective it is to promote the improvement of R&D innovation efficiency in high-tech industries. (2) Geographical location has a positive effect on all input slack variables at a highly significant level, and compared with other environmental variables, geographical location has a stronger effect on all kinds of innovation input slack. The results show that the eastern region has higher investment redundancy than the central and western regions. This is mainly due to the greater demand for innovation in the eastern region, which often spends huge sums of money on research and development innovation, thus easily causing excessive investment in innovation. (3) The influence of regional workers’ quality on four types of input slack variables also shows significant positive effects. The higher the number of college students in the region, the higher the quality of workers in the region. However, on the other hand, with the continuous improvement of China’s education level, the number of talents engaged in high-tech industries is increasing and even reaching saturation, which to a certain extent increases the redundancy of input variables and reduces the innovation efficiency level of China’s high-tech industries (4). The number of enterprises in high-tech industries has a significant negative impact on the relaxation of R&D funds, new product development funds, and technological transformation funds, which shows that the increase of competition in the industry can effectively reduce the redundancy of capital investment and is beneficial to the improvement of R&D innovation efficiency (5). Local government innovation support has significant positive effects on four types of input slack variables, which indicates that the stronger the government’s support for high-tech industries, the easier it is to increase the redundancy of each input, and the more unfavorable it is to improve innovation efficiency. This may be because some inappropriate subsidy policies adopted by the government distort the market, undermine the self-regulating function of the market, and hinder the improvement of innovation efficiency (6). The number of scientific and technological institutions has a significant positive effect on R&D personnel FTE slack variables, while it has a significant negative effect on R&D funds, new product development funds, and technological transformation funds. This shows that although the increase in the number of scientific and technological institutions will increase the redundancy of human capital investment and hinder the improvement of innovation efficiency, it can effectively reduce the waste of R&D funds, new product development funds, and technological transformation funds, thus promoting the improvement of innovation efficiency.

To sum up, environmental variables have significant effects on input redundancy, and their effects are different. Therefore, it is necessary to eliminate the effects of environmental factors and random factors when studying the true level of innovation efficiency.

4.3 DEA empirical analysis and Malmquist index

Using DEAP software again, DEA analysis is conducted based on the original output data and the adjusted input variable data. Based on the results shown in Tables 35 (after adjustment), the estimated outcomes are shown in Table 6.

Overall, the efficiency values before and after the adjustment have changed. The adjusted average technical efficiency is 0.25–0.53, the average pure technical efficiency is 0.98–0.96, and the average scale efficiency is 0.254–0.556.Compared with before adjustment, the average value of technical efficiency in different years has decreased, but the average value of pure technical efficiency has increased significantly, while the average value of pure technical efficiency is relatively stable, approaching 1.The average value of scale efficiency has not only dropped significantly, but also there are only two states after the adjustment: increasing scale returns and constant scale returns. This shows that compared with the situation before the adjustment, China’s high-tech industry does not actually have the problems of excessive scale and overcapacity. From a local point of view, the regions where the average value of technical efficiency has increased include Beijing, Shanghai, Jiangsu, Zhejiang, Shandong, Guangdong, and Shaanxi, indicating that the low efficiency of these provinces before adjustment is due to environmental factors. The average value of comprehensive technical efficiency in other regions has decreased, which indicates that the high level of innovation efficiency shown in these regions is mainly influenced by favorable environment and random factors.

In order to further investigate the changes in total factor productivity of China’s high-tech industries, this chapter constructs Malmquist index model based on the DEA model adjusted in the third stage. The results are shown in Tables 79. The columns in the table are the index changes for every two consecutive years from 2005 to 2014 and the index changes for the whole period from 2005 to 2014. The last two rows are the arithmetic average and geometric average of the index, respectively. Generally, geometric mean is used to calculate average ratio and average speed, so this chapter uses this value to represent the average growth rate of total factor productivity, technical efficiency index, and technical progress index. Table 10 summarizes Malmquist index and its decomposition index of technological innovation in order to meet the needs of total factor productivity of R&D innovation in various regions in subsequent research.

ProvincesYear
2005/20062006/20072007/20082008/20092009/20102010/20112011/20122012/20132013/20142005/2014
Beijing1.1442.6910.9990.9580.9521.0491.1810.8711.0871.141
Tianjin1.0660.8780.8690.930.9770.8431.2761.3140.9520.999
Hebei1.2991.1691.4441.480.81.4731.2071.2871.2621.251
Shanxi2.1862.2040.7541.1921.691.2041.5761.170.6931.309
Inner Mongolia0.5560.80.56519.2970.5341.560.7711.5890.9771.191
Liaoning0.8171.4511.4980.930.8011.5881.0151.0490.9331.085
Jilin0.711.4861.0782.4710.3972.6651.451.2640.8431.185
Heilongjiang0.3381.3881.3021.5360.7452.0681.141.0771.3341.100
Shanghai1.1090.9411.0570.7820.9551.1851.0950.941.2111.022
Jiangsu1.1811.3571.2110.8530.8581.540.970.9171.0771.086
Zhejiang1.2751.1151.0481.610.6891.5411.1780.9790.8511.107
Anhui1.1911.1131.1933.6391.0062.2471.2141.0541.1471.388
Fujian1.0090.9991.010.8941.0881.0781.020.9390.9370.995
Jiangxi0.9531.1111.041.8291.1361.2141.3261.4981.3091.246
Shandong1.171.2191.0791.1270.9921.0830.990.9060.9591.054
Henan1.4131.3671.3041.950.761.5971.082.4131.0731.367
Hubei0.9651.4051.072.1740.8831.4761.211.020.8531.177
Hunan1.2121.0712.0982.9050.7852.4830.9831.2191.0741.394
Guangdong1.2031.0310.9421.1351.0381.1670.9541.011.0081.051
Guangxi0.5921.4081.1911.9630.6012.8021.2531.3140.8951.191
Hainan1.0023.760.52918.5760.3543.8991.5341.0821.0511.647
Chongqing1.1791.0231.631.6740.9022.460.7721.3271.0411.256
Sichuan1.181.3370.941.2730.6041.6351.650.9041.1121.134
Guizhou1.7041.5170.9331.7070.9571.1851.4741.1021.1971.278
Yunnan0.3434.1760.3812.5150.552.3681.4710.8651.0561.102
Shaanxi1.0171.2840.9961.1050.9291.6781.1071.2390.7921.104
Gansu0.6990.7341.91.9251.3691.2161.7440.8721.3061.225
Qinghai34.9880.9430.0320.8160.5271.4520.1875.3270.5061.259
Ningxia1.2321.0871.8071.0651.2811.6081.0771.6420.3761.150
Arithmetic average2.2321.4511.1003.4590.8681.7021.1691.3170.9971.189
Geometry average1.1171.3170.9461.9220.8201.5961.1071.2020.9671.182

Table 7.

Changes in total factor productivity index of technological innovation in high-tech industries.

ProvincesYear
2005/20062006/20072007/20082008/20092009/20102010/20112011/20122012/20132013/20142005/2014
Beijing1.0072.202111110.9550.9671.083
Tianjin10.8910.9211.0031.1030.6841.3911.1580.9210.991
Hebei0.971.2861.4731.3131.0081.2661.3511.4371.3091.257
Shanxi1.8782.3140.71.1572.0150.9521.6751.3190.6831.294
Inner Mongolia0.4310.8390.67414.5040.6771.2230.8541.7470.9561.172
Liaoning0.7661.4781.4550.8760.9781.2811.0911.1610.9931.096
Jilin0.4741.7331.1372.0730.4572.1351.6061.4280.8181.151
Heilongjiang0.2621.5651.3071.3031.0641.7381.2091.2191.3021.106
Shanghai10.9341.0710.840.9410.9950.8811.1631.0240.979
Jiangsu1.131.3551.0591111111.055
Zhejiang1.1731.211.0421.3160.8651.2731.2511.0940.9071.115
Anhui1.1351.1191.1273.2391.4761.9271.291.18111.396
Fujian0.9781.020.9451.0061.1960.7751.1120.9890.9340.989
Jiangxi0.7721.1650.9881.8361.2450.9341.51.7061.3371.231
Shandong1.0841.2441.0451.1661.0090.9310.9891.0150.9691.046
Henan1.1311.5871.3971.6221.0841.3691.1471.89611.331
Hubei0.6551.5361.0472.130.9941.1831.3311.1540.8411.146
Hunan1.0821.1462.1232.6910.9652.1311.0631.3291.0741.411
Guangdong1111111111.000
Guangxi0.3781.6331.3181.5230.7112.4091.3721.4860.8981.163
Hainan0.6284.0540.63113.9620.5053.3431.631.2231.051.626
Chongqing1.0971.0651.5081.8140.9761.8050.8571.4951.0191.248
Sichuan1.1281.3610.8341.4390.7641.3281.491.0431.0681.134
Guizhou1.2011.7361.0911.2831.3751.0041.5661.2341.0551.264
Yunnan0.2154.5580.4172.1470.6491.9981.5720.9751.0571.070
Shaanxi0.8911.3350.991.0111.1061.3281.0521.4070.8271.088
Gansu0.4530.8131.8971.6141.8071.0331.8640.9831.3061.197
Qinghai21.9191.0160.03615.6460.7321.1980.1996.0030.4961.232
Ningxia1.0391.0941.651.1621.4231.2261.1481.8530.3751.131
Arithmetic average1.6161.5271.0992.8511.0391.3951.2241.4360.9721.172
Geometric average0.9031.3850.9591.7760.9861.3021.1571.3160.9441.164

Table 8.

Changes in technological efficiency index of technological innovation in high-tech industries.

ProvincesYear
2005/20062006/20072007/20082008/20092009/20102010/20112011/20122012/20132013/20142005/2014
Beijing1.1361.2220.9990.9580.9521.0491.1810.9121.1241.054
Tianjin1.0660.9860.9430.9280.8861.2310.9181.1351.0331.009
Hebei1.3390.9090.981.1270.7931.1640.8930.8960.9640.995
Shanxi1.1640.9531.0771.030.8391.2650.9410.8871.0151.011
Inner Mongolia1.2890.9530.8381.330.7881.2750.9020.911.0211.016
Liaoning1.0670.9811.0291.0610.8191.240.930.9030.940.990
Jilin1.4960.8570.9491.1920.8681.2480.9030.8851.031.029
Heilongjiang1.290.8870.9961.1790.7011.190.9430.8831.0250.995
Shanghai1.1091.0080.9870.9311.0151.1911.2430.8091.1831.044
Jiangsu1.0451.0011.1440.8530.8581.540.970.9171.0771.029
Zhejiang1.0860.9211.0061.2230.7961.210.9420.8950.9380.993
Anhui1.0490.9951.0581.1240.6821.1660.9410.8921.1470.994
Fujian1.0320.981.0690.8890.911.3910.9170.951.0031.007
Jiangxi1.2350.9531.0520.9960.9121.2990.8840.8780.9791.012
Shandong1.080.981.0330.9660.9831.1631.0010.8930.991.007
Henan1.2480.8620.9341.2030.7011.1660.9411.2731.0731.027
Hubei1.4730.9151.0231.0210.8881.2480.910.8841.0141.027
Hunan1.1210.9340.9881.080.8131.1650.9250.91710.988
Guangdong1.2031.0310.9421.1351.0381.1670.9541.011.0081.051
Guangxi1.5670.8620.9041.2890.8461.1630.9130.8840.9971.025
Hainan1.5960.9280.8381.330.7021.1660.9410.8851.0011.013
Chongqing1.0750.961.0810.9230.9241.3630.9010.8881.0211.006
Sichuan1.0460.9831.1260.8840.7911.2311.1080.8671.0411.000
Guizhou1.420.8740.8551.330.6961.180.9410.8931.1351.011
Yunnan1.5960.9160.9131.1710.8471.1850.9360.8880.9981.030
Shaanxi1.1420.9621.0061.0920.8411.2631.0510.8810.9571.014
Gansu1.5420.9031.0021.1920.7571.1760.9350.88711.023
Qinghai1.5960.9280.8381.330.721.2120.9410.8871.0191.023
Ningxia1.1860.9931.0950.9170.91.3120.9380.8871.0051.017
Arithmetic average1.2520.9530.9901.0930.8371.2280.9600.9161.0251.015
Geometry average1.2370.9510.9861.0820.8311.2250.9570.9131.0241.015

Table 9.

Changes in technological progress index of technological innovation in high-tech industries.

ProvincesTFPTPTECPTESE
Beijing1.1411.0541.0831.0031.08
Tianjin0.9991.0080.99110.991
Hebei1.2510.9951.25711.257
Shanxi1.3091.0111.29411.294
Inner Mongolia1.1911.0161.1721.0021.171
Liaoning1.0850.991.0960.9941.102
Jilin1.1841.0291.1510.9961.156
Heilongjiang1.1010.9951.1060.9991.107
Shanghai1.0221.0440.9790.9990.98
Jiangsu1.0861.0291.0551.0021.053
Zhejiang1.1070.9931.11511.114
Anhui1.3880.9941.39511.395
Fujian0.9951.0070.9890.9721.018
Jiangxi1.2461.0121.23111.231
Shandong1.0541.0071.0460.9731.075
Henan1.3671.0271.3311.0021.328
Hubei1.1771.0271.1460.9921.155
Hunan1.3940.9881.410.9971.415
Guangdong1.051.05111
Guangxi1.1911.0251.16311.163
Hainan1.6471.0131.62511.625
Chongqing1.2561.0061.2481.0041.244
Sichuan1.13411.1341.0061.128
Guizhou1.2781.0111.2640.9931.272
Yunnan1.1021.031.0711.071
Shaanxi1.1041.0141.0880.9791.112
Gansu1.2251.0231.1971.0051.191
Qinghai1.261.0231.2321.0031.229
Ningxia1.1511.0171.1311.0031.128
平均值1.1821.0151.1640.9971.168

Table 10.

Malmquist index of technological innovation in high-tech industry and its decomposition.

Note: TFP, TP, TEC, PTE, and SE in the table, respectively, represent total factor productivity, technological progress, technological efficiency, and pure technological efficiency. And TFP = TP * TEC, and TEC=PTE * SE.

Generally speaking, the average total factor productivity of R&D innovation in China’s high-tech industries increased from 2005 to 2014, with an increase of 18.2%. Except that the total factor productivity index changes in 2007/2008, 2009/2010, and 2013/2014 are less than 1, the other annual average is greater than 1, which indicates that the innovation efficiency of China’s high-tech industry is on the rise. Among them, the innovation efficiency in 2008/2009 increased the most, reaching 92.2%, while the innovation efficiency in 2009/2010 decreased the fastest, by 18%.Further according to the decomposed indexes, the pure technical efficiency decreased by 0.003%, while the scale efficiency increased by 16.8%. The increase in scale efficiency offsets the decrease in pure technical efficiency, thus increasing technical efficiency. This shows that the increase in technical innovation efficiency in China’s high-tech industries in recent years is due to the improvement in management level and technical efficiency. In addition, from a local point of view, except for Tianjin, Shanghai, and Fujian, where the total factor productivity has not increased significantly, the total factor productivity in other places has increased by different ranges, with Hainan having the largest growth range, reaching a growth rate of 64.7%. Except Hebei, Liaoning, Heilongjiang, Zhejiang, Anhui, and Hunan, the index of technological progress in other regions showed slow growth, with Beijing having the largest growth rate, with a growth rate of 5.4%.

4.4 Chain multiple mediation effect analysis

In order to clarify the internal mechanism of China’s high-tech industry innovation and “mass entrepreneurship” and find the key to accelerate the process of “double innovation,” this chapter adopts a multichain intermediary effect model to analyze the direct effect and various indirect effects between innovation and entrepreneurship from the overall, regional (east, middle, and west), and local (provinces and cities) perspectives.

4.5 Overall and regional

The overall analysis results of the multiple chain intermediary effect model are shown in Figure 2, and the effect results are shown in Table 11.

Figure 2.

Multiple chain mediating effect based on the whole.

Effect pathCalculationEffect value
IndirectX → M1 → Ya1b10.288***
X → M2 → Ya2b20.646***
X → M1 → M2 → Ya1a3b21.005***
DirectX → Yc−0.17
Total indirect effecta1b1+a2b2+a1a3b21.939
Total effectc+a1b1+a2b2+a1a3b21.769

Table 11.

Results of direct and indirect effects (overall).

Note: *** means p < 0.01; ** means p < 0.05; * means p < 0.1.

The analysis results of the east, middle, and west regions are shown in Figure 3 and Table 12.

Figure 3.

Multiple chain mediating effects in east, central, and west regions.

RegionDirect effectIndirect effectTotal indirect utilityTotal utility
East−1.5**1.95***3.942**5.5042*11.39629.8962
In0.093**0.288**0.4489**0.3430**1.079941.17294
West−0.21*0.792*0.488**0.6336**1.91361.7036

Table 12.

Direct and indirect effects of east, middle, and west regions and their effect values.

Note: *** means p < 0.01; ** means p < 0.05; * means p < 0.1.

On the whole, the total effect of innovation on entrepreneurship is 1.769, that is, the increase of total factor productivity is conducive to the increase of the number of new entrepreneurs. However, from a further perspective, the direct effect of innovation on entrepreneurship is shown as inhibition (c<0), while the mediating effect through different paths shows a significantly higher promotion effect (a1b1, a2b2, and a1a3b2are significantly positive). This shows that the promotion effect of innovation on entrepreneurship is not a simple direct effect. The government cannot rely solely on improving innovation efficiency to promote entrepreneurship so as to relieve the pressure on China’s labor market. Instead, it should promote entrepreneurship by increasing new ventures and promoting economic growth on the basis of promoting innovation drive.

From the point of view of the east, middle, and west regions, the total effect of innovation on entrepreneurship is promotion, and the promotion in the east region is obviously stronger than that in the middle and west regions, which may be caused by the superior geographical position and relatively developed economic level of the east region. The direct effects in the east and west are significantly negative, indicating that innovation will play a restraining role in the direct path of entrepreneurship. However, the indirect effects are significantly positive, especially the path in the eastern region, whose influence is much stronger than other paths, which shows that the eastern region mainly promotes entrepreneurship through the path of “innovation → enterprise increase → economic growth → entrepreneurship.” For the central region, whether it is a direct or indirect path, the impact of innovation on entrepreneurship always presents a significant positive effect.

4.6 Provinces, cities, and regions

The analysis results of direct effect, indirect effect, and total effect of innovation on entrepreneurship in various provinces and cities are shown in Table 13.

IDProvinceDirectIndirect
ca1b1a2b2a1a3b2
1Beijing5.201***
(1.757)
−6.559
(6.288)
−2.633
(4.822)
−0.831***
(0.173)
2Tianjin−4.307
(3.513)
3.616
(3.377)
11.984**
(5.399)
−0.998
(0.615)
3Hebei1.264
(2.751)
2.260
(4.549)
1.628
(3.650)
0.794***
(0.180)
4Shanxi0.862***
(0.103)
−0.406
(1.834)
1.251
(2.184)
−2.682***
(0.732)
5Inner Mongolia−0.451
(0.180)
0.303
(0.596)
0.257
(0.883)
2.882**
(1.433)
6Liaoning−0.680
(0.486)
2.317
(3.019)
−0.502
(2.617)
−0.418
(0.452)
7Jilin1.114*
(0.607)
−4.547
(3.793)
−1.757
(5.084)
1.941**
(0.939)
8Heilongjiang−0.506
(0.613)
0.007
(0.264)
5.044***
(1.948)
−0.014
(0.504)
9Shanghai1.899
(1.190)
2.565
(4.491)
1.379
(4.168)
−0.555**
(0.226)
10Jiangsu0.342
(0.337)
−2.641
(5.668)
0.969
(5.130)
0.885***
(0.189)
11Zhejiang−1.569
(1.558)
0.370
(2.733)
9.835**
(4.488)
−0.067
(0.497)
12Anhui0.355
(1.166)
3.088
(3.405)
5.097
(3.229)
0.775***
(0.085)
13Fujian−5.648***
(1.088)
2.330
(4.915)
17.260**
(7.653)
2.214***
(0.837)
14Jiangxi2.282
(1.500)
−2.912
(7.217)
0.205
(4.388)
1.012***
(0.111)
15Shandong0.184
(1.825)
−4.029
(10.165)
3.385
(7.460)
1.338***
(0.352)
16Henan−0.216
(0.557)
2.915
(1.946)
2.947
(1.832)
1.457***
(0.114)
17Hubei0.581
(0.432)
−2.694
(2.279)
−3.573
(2.799)
1.261***
(0.472)
18Hunan0.309
(0.966)
3.751
(4.610)
3.298
(3.883)
1.950***
(0.145)
19Guangdong−4.674**
(1.930)
9.218
(6.795)
9.520
(7.017)
0.982**
(0.456)
20Guangxi−0.333
(0.516)
1.835
(3.313)
3.344
(3.186)
1.176*
(0.639)
21Hainan−0.785***
(0.223)
0.596
(0.799)
1.846
(1.243)
−1.546
(1.133)
22Chongqing0.326
(0.732)
6.575
(4.528)
7.843
(5.252)
1.216***
(0.219)
23Sichuan0.092
(0.512)
−2.002
(2.406)
0.041
(1.602)
1.050***
(0.324)
24Guizhou0.299
(0.949)
−0.035
(0.357)
2.873
(3.699)
0.114
(0.767)
25Yunnan−1.977*
(1.170)
0.098
(0.624)
−2.534
(4.542)
−0.214
(0.907)
26Shaanxi2.643
(3.316)
−1.189
(5.848)
2.710
(5.332)
3.571***
(0.694)
27Gansu−0.189
(0.375)
1.510
(2.146)
−2.794
(2.800)
0.831**
(0.392)
28Qinghai0.056
(0.049)
0.206
(0.190)
0.112*
(0.067)
0.361**
(0.170)
29Ningxia−0.550
(0.710)
0.652
(0.917)
0.186
(1.056)
0.795
(0.513)

Table 13.

Direct, indirect, and total effects of provinces and cities.

Note: *** means p < 0.01; ** means p < 0.05; * means p < 0.1.

The direct effects of Beijing, Shanxi, and Jilin are significantly positive, while the direct effects of Yunnan, Hainan, Guangdong, and Fujian are significantly negative, while the direct effects of most other provinces are not significant. On the contrary, in terms of indirect effects, most provinces have significant positive effects, while only Beijing, Shanxi, and Shanghai have negative effects.

5. Conclusions and policy recommendations

This chapter decomposes total factor productivity on the basis of DEA model excluding environment and random factors and then analyzes the influencing process and mechanism among variables by using chain multiple intermediary effect model and further analyzes the relationship between innovation and entrepreneurship in high-tech industries. The research confirms: (1) Before environmental factors and random interference are eliminated, the comprehensive technical efficiency and scale efficiency of R&D in China’s high-tech industry are obviously overestimated, while the pure technical efficiency is underestimated. Therefore, it is necessary to eliminate environmental and random factors and increase the credibility of the research. (2) No matter from the overall or partial analysis, the total effect of innovation on entrepreneurship has always been positive, that is, promoting “people-to-people innovation” is conducive to promoting “mass entrepreneurship.” (3) In most cases, innovation does not directly promote entrepreneurship but indirectly promotes entrepreneurship through a micro–macro combination path.

5.1 Relevant policy recommendations

Generally speaking, the government can encourage China’s high-tech industry to explore new market areas and gain competitive advantages by formulating relevant industrial policies and promoting innovation, thus providing motive force for innovation drive. Through continuous entrepreneurship, innovative achievements will be continuously transformed into real productive forces to promote the sustainable development of high-tech industries and social economy. We should strengthen the combination of “production, study, and research” and focus on improving the “double-creation” mechanism and the “double-creation” environment.

First of all, as the direct effect of innovation on entrepreneurship is positive in Beijing, Shanxi, and Jilin, the government and relevant local departments can appropriately increase the amount of financial allocation for research and development of high-tech products and conquering high-end technologies, strengthen the government’s financial support, and give full play to the policy’s guidance and support for innovation by strengthening the government’s innovation subsidies and tax incentives, thus realizing a “double harvest” of innovation and entrepreneurship on the basis of effectively improving the innovation efficiency of high-tech industries in the region. Secondly, in Fujian, Guangdong, Hainan, and Yunnan, the government should make great efforts to promote the development of other industries while developing high-tech industries, so that the local advantageous industries and characteristic industries can develop continuously and steadily and all kinds of industries will be put together. Thirdly, in Tianjin, Heilongjiang, Zhejiang, Fujian, and Qinghai, the government should actively promote economic development. While making great efforts to improve GDP, it should also pay attention to the per capita level of GDP, scientific and technological content, and green degree and strive to provide a good macroenvironment for promoting “double innovation” by correctly grasping the “three improvements.” Finally, in Beijing, Shanghai, and Shanxi, the number of new high-tech enterprises should be controlled to ensure that a reasonable market structure can not only bring into play economies of scale but also promote effective competition, thus effectively stimulating the improvement of innovation efficiency and entrepreneurship scale.

© 2020 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution 3.0 License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

How to cite and reference

Link to this chapter Copy to clipboard

Cite this chapter Copy to clipboard

Kai Zhao and Lixiang Wang (September 30th 2020). Internal Relationship and Impact Path between Innovation and Entrepreneurship: Based on China’s High-Tech Industry, Intellectual Property Rights - Patent, Sakthivel Lakshmana Prabu, Suriyaprakash Tnk, Eduardo Jacob-Lopes and Leila Queiroz Zepka, IntechOpen, DOI: 10.5772/intechopen.90869. Available from:

chapter statistics

36total chapter downloads

More statistics for editors and authors

Login to your personal dashboard for more detailed statistics on your publications.

Access personal reporting

Related Content

This Book

Next chapter

Activities Pivotal for the Sustainability of Newly Established Technology Transfer Offices: A Case Study of Publicly Financed South African Universities

By Sebua S. Semenya

Related Book

First chapter

Introductory Chapter: Intellectual Property Rights

By Sakthivel Lakshmana Prabu, Timmadonu Narasimman Kuppusami Suriyaprakash and Rathinasabapathy Thirumurugan

We are IntechOpen, the world's leading publisher of Open Access books. Built by scientists, for scientists. Our readership spans scientists, professors, researchers, librarians, and students, as well as business professionals. We share our knowledge and peer-reveiwed research papers with libraries, scientific and engineering societies, and also work with corporate R&D departments and government entities.

More About Us