**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.

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

*Internal Relationship and Impact Path between Innovation and Entrepreneurship: Based…*

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

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

willingness and efficiency than other types of entrepreneurs.

*DOI: http://dx.doi.org/10.5772/intechopen.90869*

process of enterprise innovation and entrepreneurship.

of double innovation in a more comprehensive and in-depth way.

The research can be divided into the following four stages.

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

**2. Research methods**

**93**

**2.1 The first stage: traditional DEA**

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

#### *Internal Relationship and Impact Path between Innovation and Entrepreneurship: Based… DOI: http://dx.doi.org/10.5772/intechopen.90869*

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

out efficient innovation input and output, accelerate enterprises to adapt to the new normal, and provide power and guarantee for promoting innovation-driven strat-

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

This article will summarize and review the relevant literature from three aspects:

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 oppor-

innovation, entrepreneurship, and innovation and entrepreneurship.

tunities, 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

the government and relevant local departments.

egy in depth.

*Intellectual Property Rights - Patent*

**92**

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.

intermediate variables (such as control *M*1), we can study the specific mediation effect of each intermediate variable, such as *a*1*b*1, *a*2*b*2, and *a*1*a*3*b*2. Third, a

*Internal Relationship and Impact Path between Innovation and Entrepreneurship: Based…*

*a*1*a*3*b*2 � *a*2*b*2, *a*1*b*1 � *a*2*b*2, and *a*1*a*3*b*2 � *a*1*b*1 [16].

**3. Variable selection and data description**

method of permanent storage is used to deal with it.

**3.1 Input–output indicators**

*Chain multiple mediation effect path diagram.*

*DOI: http://dx.doi.org/10.5772/intechopen.90869*

**Figure 1.**

**95**

comparative mediating effect can be obtained so as to be able to judge which of the effects of multiple mediating variables (e.g., *a*1*b*1, *a*2*b*2) is more effective, e.g.,

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 innovationoutput efficiency. Taking the calculation of R&D fund stock as an example, the

In Eq. (2), *xi*,*t* and *xi*,*t*�1 are the R&D expenditures of the *t* and *t* � 1 years of the

province *i*, respectively; *ei*,*t*�1 is the R&D expenditure of the *t* � 1 year after eliminating the price factor; *δ* is the depreciation rate; according to Griliches [17], it can be set to *δ* ¼ 15%. Assuming that the average annual growth rate of R&D expenditures during the study period is *g*, the base R&D expenditure is

*ei*,0 in 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

*xi*,*t* ¼ ð Þ 1 � *δ xi:t*�1 þ *ei*,*t*�1 (2)

*xi*,*o* ¼ *e*i,0*=*ð Þ *g* þ *δ* (3)

#### **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:

$$\begin{aligned} Y &= c'X + b\_1M\_1 + b\_2M\_2 + e\_1\\ M\_2 &= a\_2X + a\_3M\_1 + e\_2\\ M\_1 &= a\_1X + e\_3 \end{aligned} \tag{1}$$

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

As shown in **Figure 1**, the multiple mediation model includes two mediation variables *M*1 and *M*2; 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, *a*1*b*1 þ *a*2*b*2 þ *a*1*a*3*b*2. Secondly, on the premise of controlling other

*Internal Relationship and Impact Path between Innovation and Entrepreneurship: Based… DOI: http://dx.doi.org/10.5772/intechopen.90869*

**Figure 1.** *Chain multiple mediation effect path diagram.*

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

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

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

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 regres-

*X* þ *b*1*M*1 þ *b*2*M*2 þ *e*1

The corresponding path of chain multiple mediation effect is shown in **Figure 1**. As shown in **Figure 1**, the multiple mediation model includes two mediation variables *M*1 and *M*2; 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,

(1)

sion equations can be used to describe the relationship between variables:

*M*1 ¼ *a*1*X* þ *e*3

*M*2 ¼ *a*2*X* þ *a*3*M*1 þ *e*2

namely, *a*1*b*1 þ *a*2*b*2 þ *a*1*a*3*b*2. Secondly, on the premise of controlling other

*Y* ¼ *c* 0

industry in recent years.

*Intellectual Property Rights - Patent*

**2.2 The second stage: similar SFA**

only by the inefficiency of management.

productivity changes, respectively.

**94**

**2.3 The third stage: adjusted DEA and Malmquist index**

**2.4 The fourth stage: chain multiple mediation effect**

intermediate variables (such as control *M*1), we can study the specific mediation effect of each intermediate variable, such as *a*1*b*1, *a*2*b*2, and *a*1*a*3*b*2. 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., *a*1*b*1, *a*2*b*2) is more effective, e.g., *a*1*a*3*b*2 � *a*2*b*2, *a*1*b*1 � *a*2*b*2, and *a*1*a*3*b*2 � *a*1*b*1 [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 innovationoutput efficiency. Taking the calculation of R&D fund stock as an example, the method of permanent storage is used to deal with it.

$$\mathbf{x}\_{i,t} = (\mathbf{1} - \delta)\mathbf{x}\_{i,t-1} + e\_{i,t-1} \tag{2}$$

In Eq. (2), *xi*,*t* and *xi*,*t*�1 are the R&D expenditures of the *t* and *t* � 1 years of the province *i*, respectively; *ei*,*t*�1 is the R&D expenditure of the *t* � 1 year after eliminating the price factor; *δ* is the depreciation rate; according to Griliches [17], it can be set to *δ* ¼ 15%. Assuming that the average annual growth rate of R&D expenditures during the study period is *g*, the base R&D expenditure is

$$\mathbf{x}\_{i,\boldsymbol{\rho}} = \mathbf{e}\_{i,0}/(\mathbf{g} + \boldsymbol{\delta})\tag{3}$$

*ei*,0 in 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

*3.2.3 Quality of local workers*

conducive to the improvement of R&D efficiency.

*3.2.4 Local government innovation support*

*DOI: http://dx.doi.org/10.5772/intechopen.90869*

R&D and innovation in high-tech industries.

tech industries.

shown in **Table 2**.

Local government innovation support

Number of high-tech industrial enterprises

**Table 2.**

**97**

Number of scientific and technological institutions

*Descriptive statistics of environmental variables.*

**3.3 Mediating effect variables**

**Variable Symbol Observed**

*3.2.5 Regional science and technology development level*

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

*Internal Relationship and Impact Path between Innovation and Entrepreneurship: Based…*

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

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 hightech 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-

Among the above environmental variables, except that the actual GDP of each region comes from China Statistical Yearbook, other data come from China Hightech Industry Statistical Yearbook. Descriptive statistics of relevant variables are

Taking the total factor productivity (X) obtained by Malmquist index decomposition in the third stage as the independent variable. The dependent variable is

Regional real GDP *k*1 261 13,824.85 11,858 572.37 62,924.62 Geographical position *k*2 261 1 0.7894 0 2 Quality of local laborers *k*3 261 746,585 437,384.5 35,983 1,796,665

**Mean value**

**Standard deviation**

*k*4 261 36,197.87 51,416.87 10 289,006.9

*k*5 261 853.29 1208.19 14 5874

*k*6 261 156.69 319.04 1 2474

**Minimum value**

**Maximum value**

**value**

#### **Table 1.**

*Description of input–output variables.*

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**.

#### **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.

*Internal Relationship and Impact Path between Innovation and Entrepreneurship: Based… DOI: http://dx.doi.org/10.5772/intechopen.90869*

#### *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 hightech 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 hightech industries.

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

#### **Variable Symbol Observed value Mean value Standard deviation Minimum value Maximum value** Regional real GDP *k*1 261 13,824.85 11,858 572.37 62,924.62 Geographical position *k*2 261 1 0.7894 0 2 Quality of local laborers *k*3 261 746,585 437,384.5 35,983 1,796,665 Local government innovation support *k*4 261 36,197.87 51,416.87 10 289,006.9 Number of high-tech industrial enterprises *k*5 261 853.29 1208.19 14 5874 Number of scientific and technological institutions *k*6 261 156.69 319.04 1 2474

#### **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

#### **Table 2.**

*Descriptive statistics of environmental variables.*

to process the funds for new product development and technological transformation, thus converting the flow data into stock. Input–output related variables are

method

**Variable Symbol Unit Definition** Innovation output 1 y1 Item Number of patent applications

Revenue from sales of new products

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

The stock of R&D funds is calculated by the

The stock of new product development funds shall be accounted for by the perpetual inventory method

The stock of funds for technological transformation shall be accounted for by the perpetual inventory

x1 Person/year Full-time equivalent of R&D personnel refers to the

during the reporting year

perpetual inventory method

yuan

x2 Ten thousand yuan

x3 Ten thousand yuan

x4 Ten thousand yuan

Innovation output 2 y2 Ten thousand

*Intellectual Property Rights - Patent*

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

Real GDP can accurately reflect regional differences and the real level of regional

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.

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.

described in **Table 1**.

Innovation (personnel) input 1

Innovation (capital) investment 2

Innovation (capital) investment 3

Innovation (capital) investment 4

**Table 1.**

**3.2 Environmental variables**

*Description of input–output variables.*

selects the following indicators.

*3.2.1 Regional real GDP*

*3.2.2 Geographical location*

**96**

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.

 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 3**–**5**. 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.

#### **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**.

As can be seen from **Table 6**, the estimation results *σ*2 and *γ* are 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

*Internal Relationship and Impact Path between Innovation and Entrepreneurship: Based… DOI: http://dx.doi.org/10.5772/intechopen.90869*

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.

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 3**–**5**. 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

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**.

As can be seen from **Table 6**, the estimation results *σ*2 and *γ* are 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

**4.1 Empirical analysis of traditional DEA in the first stage**

**4.2 Regression analysis of SFA in the second stage**

**4.

Tianjin BA 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00

Hebei BA 0.13 0.28 0.15 0.44 0.34 0.48 0.28 0.45 0.44 0.50

Shanxi BA 1.00 1.00 1.00 1.00 1.00 1.00 0.85 0.98 0.88 0.55

Inner Mongolia BA 1.00 0.49 0.15 0.59 1.00 0.78 0.61 0.71 0.62 0.49

Liaoning BA 0.59 0.29 0.27 0.48 0.75 0.53 0.61 0.46 0.66 0.60

Jilin BA 0.44 0.32 0.40 0.59 0.67 0.35 0.56 0.65 0.63 0.57

Heilongjiang BA 0.64 0.22 0.10 0.25 0.21 0.27 0.27 0.31 0.32 0.46

Shanghai BA 1.00 1.00 0.91 1.00 0.73 0.70 0.77 0.57 0.75 0.88

Jiangsu BA 0.60 0.36 0.78 1.00 1.00 1.00 1.00 1.00 1.00 1.00

Zhejiang BA 0.38 0.38 0.55 0.57 0.83 0.63 0.74 0.90 1.00 0.88

Anhui BA 0.91 0.51 0.28 0.64 1.00 1.00 1.00 1.00 1.00 1.00

Fujian BA 0.88 0.68 0.78 0.93 0.91 0.89 1.00 0.81 0.57 0.52

Jiangxi BA 0.23 0.22 0.12 0.20 0.29 0.38 0.29 0.42 0.53 0.70

Shandong BA 0.87 0.58 0.73 0.65 0.65 0.78 0.72 0.69 0.66 0.66

Henan BA 0.15 0.66 0.45 0.79 0.79 0.84 0.53 0.56 1.00 1.00

Hubei BA 0.84 0.51 0.34 0.44 0.62 0.53 0.54 0.55 0.60 0.52

Hunan BA 0.29 0.18 0.17 0.81 1.00 0.94 1.00 1.00 1.00 0.85

Guangdong BA 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00

**101**

#### **Table 3.**

*Technical efficiency estimation results of technological innovation.*

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

*Internal Relationship and Impact Path between Innovation and Entrepreneurship: Based… DOI: http://dx.doi.org/10.5772/intechopen.90869*

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

**Provinces Year**

*Intellectual Property Rights - Patent*

**2005 2006 2007 2008 2009 2010 2011 2012 2013 2014**

AA 0.04 0.02 0.03 0.03 0.05 0.04 0.08 0.11 0.17 0.16

AA 0.00 0.00 0.01 0.00 0.05 0.02 0.07 0.12 0.14 0.15

AA 0.07 0.08 0.08 0.13 0.23 0.22 0.41 0.34 0.51 0.53

AA 0.29 0.33 0.44 0.38 0.53 0.41 0.55 0.80 0.84 0.90

AA 0.05 0.06 0.10 0.11 0.14 0.18 0.18 0.28 0.35 0.37

AA 0.08 0.02 0.08 0.03 0.07 0.04 0.09 0.14 0.13 0.14

AA 0.16 0.15 0.21 0.20 0.19 0.22 0.29 0.32 0.44 0.37

AA 0.02 0.01 0.01 0.02 0.03 0.05 0.05 0.08 0.08 0.11

AA 0.00 0.01 0.01 0.00 0.01 0.00 0.01 0.00 0.01 0.00

AA 0.01 0.01 0.01 0.02 0.02 0.03 0.04 0.04 0.08 0.03

AA 0.25 0.25 0.30 0.31 0.35 0.36 0.40 0.45 0.53 0.53

Guangxi BA 0.56 0.15 0.11 0.55 0.49 0.41 0.52 0.53 0.68 0.57

Hainan BA 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 0.85

Chongqing BA 0.29 0.25 0.17 0.44 0.94 0.71 1.00 0.81 0.94 0.97

Sichuan BA 0.21 0.23 0.25 0.29 0.54 0.35 1.00 0.82 0.70 0.98

Guizhou BA 0.22 0.16 0.13 0.49 0.58 0.62 0.42 0.43 0.42 0.45

Yunnan BA 1.00 0.20 0.46 1.00 1.00 0.80 0.92 0.81 0.76 0.64

Shaanxi BA 0.09 0.07 0.08 0.17 0.21 0.23 0.27 0.25 0.29 0.24

Gansu BA 0.53 0.27 0.17 0.48 0.42 0.94 0.46 0.68 0.59 0.63

Qinghai BA 1.00 1.00 1.00 0.39 0.59 1.00 1.00 0.16 0.99 0.49

Ningxia BA 0.13 0.24 0.10 0.28 0.31 0.41 0.69 0.69 1.00 0.35

Average value BA 0.48 0.34 0.36 0.58 0.65 0.66 0.70 0.67 0.72 0.67

*Note: "BA" means before adjustment; "AA" means after adjustment.*

*Technical efficiency estimation results of technological innovation.*

**Table 3.**

**100**

**Provinces Year**

0.510 (drs)

0.469 (irs)

1.000 (crs)

1.000 (crs)

0.318 (drs)

0.054 (irs)

1.000 (crs)

0.022 (irs)

0.838 (irs)

0.003 (irs)

0.500 (drs)

0.162 (irs)

0.388 (drs)

0.029 (irs)

0.272 (drs)

0.036 (irs)

0.528 (drs)

1.000 (crs)

0.746 (drs)

0.794 (irs)

0.373 (drs)

0.470 (irs)

0.988 (drs)

0.057 (irs)

1.000 (crs)

1.000 (crs)

1.000 (crs)

0.900 (irs)

0.672 (drs)

0.071 (irs)

1.000 (crs)

0.050 (irs)

0.336 (irs)

0.002 (irs)

0.807 (drs)

0.239 (irs)

0.468 (drs)

0.052 (irs)

0.800 (drs)

0.054 (irs)

0.690 (drs)

0.952 (irs)

0.472 (drs)

0.962 (irs)

0.319 (drs)

0.566 (irs)

0.765 (drs)

0.063 (irs)

1.000 (crs)

*Internal Relationship and Impact Path between Innovation and Entrepreneurship: Based…*

1.000 (crs)

1.000 (crs)

0.831 (irs)

0.902 (drs)

0.107 (irs)

1.000 (crs)

0.036 (irs)

0.451 (irs)

0.002 (irs)

0.989 (drs)

0.356 (irs)

0.980 (drs)

0.059 (irs)

0.912 (drs)

0.072 (irs)

0.875 (drs)

1.000 (crs)

0.696 (drs)

1.000 (crs)

0.753 (drs)

0.618 (irs)

0.531 (drs)

0.071 (irs)

(drs)

(crs)

(drs)

(crs)

(crs)

(drs)

(drs)

(drs)

(drs)

(drs)

(drs)

(drs)

AA 0.463 (irs)

*DOI: http://dx.doi.org/10.5772/intechopen.90869*

AA 1.000 (crs)

AA 0.058 (irs)

AA 0.012 (irs)

AA 0.007 (irs)

AA 0.218 (irs)

AA 0.062 (irs)

AA 0.133 (irs)

AA 1.000 (crs)

AA 0.625 (irs)

AA 0.363 (irs)

AA 0.050 (irs)

Beijing BA 0.530

Tianjin BA 1.000

Hebei BA 0.944

Shanxi BA 1.000

Inner Mongolia BA 1.000

Liaoning BA 0.414

Jilin BA 0.949

Heilongjiang BA 0.774

Shanghai BA 0.862

Jiangsu BA 0.470

Zhejiang BA 0.462

Anhui BA 0.820

**103**

**2005 2006 2007 2008 2009 2010 2011 2012 2013 2014**

1.000 (crs)

1.000 (crs)

1.000 (crs)

0.920 (irs)

0.887 (drs)

0.128 (irs)

1.000 (crs)

0.081 (irs)

0.781 (irs)

0.015 (irs)

0.880 (drs)

0.303 (irs)

0.989 (drs)

0.054 (irs)

0.762 (drs)

0.105 (irs)

0.783 (drs)

0.906 (irs)

0.685 (drs)

1.000 (crs)

0.738 (drs)

0.708 (irs)

1.000 (crs)

0.333 (irs)

1.000 (crs)

1.000 (crs)

1.000 (crs)

0.637 (irs)

0.988 (irs)

0.160 (irs)

0.980 (irs)

0.076 (irs)

0.926 (irs)

0.017 (irs)

0.999 (irs)

0.399 (irs)

0.989 (irs)

0.113 (irs)

0.874 (drs)

0.176 (irs)

0.958 (drs)

0.852 (irs)

0.974 (drs)

1.000 (crs)

0.681 (drs)

0.839 (irs)

1.000 (crs)

0.641 (irs)

1.000 (crs)

1.000 (crs)

1.000 (crs)

0.869 (irs)

0.985 (irs)

0.227 (irs)

0.974 (irs)

0.127 (irs)

0.793 (irs)

0.015 (irs)

0.994 (irs)

0.435 (irs)

0.979 (irs)

0.181 (irs)

0.990 (irs)

0.211 (irs)

0.998 (irs)

0.795 (irs)

1.000 (crs)

1.000 (crs)

0.705 (drs)

0.990 (irs)

1.000 (crs)

0.834 (irs)

1.000 (crs)

0.974 (irs)

1.000 (crs)

1.000 (crs)

0.989 (drs)

0.314 (irs)

0.994 (drs)

0.169 (irs)

0.910 (irs)

0.026 (irs)

0.951 (drs)

0.490 (irs)

0.962 (irs)

0.257 (irs)

0.911 (drs)

0.259 (irs)

0.928 (drs)

0.913 (irs)

0.657 (drs)

1.000 (crs)

0.708 (drs)

1.000 (crs)

1.000 (crs)

1.000 (crs)

1.000 (crs)

0.923 (irs)

1.000 (crs)

0.921 (irs)

0.992 (irs)

0.427 (irs)

0.944 (irs)

0.118 (irs)

0.691 (irs)

0.025 (irs)

0.991 (irs)

0.510 (irs)

0.959 (irs)

0.219 (irs)

0.978 (irs)

0.338 (irs)

0.990 (drs)

0.833 (irs)

0.806 (drs)

1.000 (crs)

0.661 (drs)

0.997 (irs)

1.000 (crs)

1.000 (crs)

1.000 (crs)

1.000 (crs)

1.000 (crs)

0.824 (irs)

0.820 (drs)

0.130 (irs)

1.000 (crs)

0.041 (irs)

1.000 (crs)

0.023 (irs)

0.831 (drs)

0.319 (irs)

0.969 (drs)

0.118 (irs)

0.887 (drs)

0.095 (irs)

0.690 (drs)

0.934 (irs)

0.795 (drs)

1.000 (crs)

0.729 (drs)

0.708 (irs)

0.758 (drs)

0.226 (irs)

#### **Table 4.**

*Pure technical efficiency estimation results of technological innovation.*

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

*Internal Relationship and Impact Path between Innovation and Entrepreneurship: Based… DOI: http://dx.doi.org/10.5772/intechopen.90869*

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

*Note: "BA" means before adjustment; "AA" means after adjustment.*

*Pure technical efficiency estimation results of technological innovation.*

**Table 4.**

**102**

**Provinces Year**

*Intellectual Property Rights - Patent*

**2005 2006 2007 2008 2009 2010 2011 2012 2013 2014**

AA 0.97 0.97 0.96 0.97 0.99 0.98 0.98 0.99 1.00 0.98

AA 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00

AA 0.95 0.94 0.94 0.95 0.98 0.98 1.00 0.97 0.99 1.00

AA 0.96 0.95 0.95 0.90 0.90 0.92 1.00 0.98 0.96 1.00

AA 0.93 0.94 0.95 0.94 0.96 0.96 0.94 0.91 0.90 0.90

AA 1.00 0.98 0.99 0.97 1.00 1.00 0.99 0.99 1.00 0.99

AA 0.92 0.89 0.93 0.88 0.85 0.84 0.85 0.78 0.80 0.77

AA 0.95 0.98 0.97 0.98 0.99 0.99 0.99 1.00 1.00 1.00

AA 0.96 0.97 0.96 0.97 0.98 0.98 0.98 0.99 1.00 1.00

AA 0.95 0.95 0.95 0.95 0.97 0.97 0.97 0.98 0.99 1.00

AA 0.98 0.97 0.97 0.97 0.98 0.97 0.97 0.96 0.97 0.96

Guangxi BA 0.74 0.23 0.16 0.56 0.50 0.42 0.54 0.55 0.70 0.61

Hainan BA 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 0.88

Chongqing BA 0.31 0.49 0.27 0.61 0.96 0.77 1.00 0.81 0.94 0.98

Sichuan BA 0.26 0.34 0.54 0.44 0.56 0.52 1.00 0.82 0.74 1.00

Guizhou BA 0.25 0.73 0.39 0.71 0.69 0.67 0.45 0.43 0.42 0.46

Yunnan BA 1.00 0.55 1.00 1.00 1.00 0.84 0.94 0.81 0.77 0.66

Shaanxi BA 0.17 0.12 0.16 0.21 0.22 0.28 0.28 0.25 0.32 0.24

Gansu BA 1.00 0.27 0.18 0.50 0.46 0.95 0.46 0.74 0.64 0.69

Qinghai BA 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00

Ningxia BA 0.13 0.24 0.11 0.32 0.49 0.47 0.73 0.83 1.00 0.49

Average value BA 0.63 0.53 0.52 0.68 0.75 0.73 0.73 0.73 0.77 0.73

can effectively reduce the waste of R&D funds, new product development funds, and technological transformation funds, thus promoting the improvement of inno-

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

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 3**–**5** (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

vation efficiency.

**Table 5.**

**105**

innovation efficiency.

**4.3 DEA empirical analysis and Malmquist index**

**Provinces Year**

0.371 (drs)

0.018 (irs)

0.550 (drs)

0.172 (irs)

0.996 (irs)

0.010 (irs)

1.000 (crs)

0.011 (irs)

0.999 (crs)

0.010 (irs)

*decline in scale efficiency. "BA" means before adjustment; "AA" means after adjustment.*

0.458 (drs)

0.080 (irs)

0.493 (drs)

0.221 (irs)

0.924 (irs)

0.008 (irs)

1.000 (crs)

0.011 (irs)

0.946 (irs)

0.011 (irs)

1.000 (crs)

*Internal Relationship and Impact Path between Innovation and Entrepreneurship: Based…*

0.034 (irs)

0.801 (drs)

0.231 (irs)

0.966 (irs)

0.016 (irs)

0.387 (irs)

0.000 (irs)

0.886 (irs)

0.019 (irs)

Average value BA 0.753 0.620 0.666 0.840 0.873 0.895 0.961 0.929 0.947 0.926

*Note: "irs" indicates increasing scale efficiency; "crs" indicates that the scale efficiency is unchanged; "drs" indicates a*

(crs)

(drs)

(drs)

(crs)

(irs)

*Estimate results of scale efficiency of technological innovation.*

 DOI: http://dx.doi.org/10.5772/intechopen.90869*

*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.*

#### **Table 5.**

**Provinces Year**

0.996 (drs)

0.720 (irs)

0.510 (drs)

0.069 (irs)

0.422 (drs)

0.535 (irs)

0.186 (drs)

0.090 (irs)

0.259 (drs)

0.109 (irs)

0.839 (drs)

0.042 (irs)

0.327 (drs)

1.000 (crs)

0.642 (drs)

0.016 (irs)

1.000 (crs)

0.001 (irs)

0.516 (drs)

0.083 (irs)

0.675 (drs)

0.344 (irs)

0.224 (drs)

0.060 (irs)

0.846 (drs)

0.729 (irs)

0.806 (drs)

0.080 (irs)

0.520 (drs)

0.666 (irs)

0.383 (drs)

0.140 (irs)

0.481 (drs)

0.168 (irs)

0.702 (drs)

0.049 (irs)

0.322 (drs)

1.000 (crs)

0.679 (drs)

0.027 (irs)

1.000 (crs)

0.005 (irs)

0.631 (drs)

0.088 (irs)

0.458 (drs)

0.466 (irs)

0.330 (drs)

0.102 (irs)

0.928 (drs)

0.683 (irs)

0.923 (drs)

0.080 (irs)

0.765 (drs)

0.746 (irs)

0.871 (drs)

0.200 (irs)

0.824 (drs)

0.175 (irs)

0.866 (drs)

0.105 (irs)

1.000 (crs)

1.000 (crs)

0.976 (drs)

0.035 (irs)

1.000 (crs)

0.003 (irs)

0.725 (drs)

0.134 (irs)

0.664 (drs)

0.419 (irs)

0.691 (drs)

0.114 (irs)

(drs)

(drs)

(drs)

(drs)

(irs)

(drs)

(drs)

(drs)

(crs)

(drs)

(drs)

(drs)

AA 0.725 (irs)

AA 0.090 (irs)

AA 0.505 (irs)

AA 0.080 (irs)

AA 0.162 (irs)

AA 0.039 (irs)

AA 1.000 (crs)

AA 0.043 (irs)

AA 0.002 (irs)

AA 0.076 (irs)

AA 0.302 (irs)

AA 0.050 (irs)

Fujian BA 0.978

*Intellectual Property Rights - Patent*

Jiangxi BA 0.621

Shandong BA 0.522

Henan BA 0.947

Hubei BA 0.505

Hunan BA 0.642

Guangdong BA 0.354

Guangxi BA 0.752

Hainan BA 1.000

Chongqing BA 0.934

Sichuan BA 0.815

Guizhou BA 0.865

**104**

**2005 2006 2007 2008 2009 2010 2011 2012 2013 2014**

0.869 (drs)

0.834 (irs)

0.974 (drs)

0.187 (irs)

0.810 (drs)

0.856 (irs)

0.839 (drs)

0.326 (irs)

0.935 (drs)

0.370 (irs)

0.911 (drs)

0.260 (irs)

1.000 (crs)

1.000 (crs)

0.976 (drs)

0.038 (irs)

1.000 (crs)

0.022 (irs)

0.925 (drs)

0.227 (irs)

0.672 (drs)

0.447 (irs)

0.924 (drs)

0.188 (irs)

1.000 (crs)

0.634 (irs)

0.994 (irs)

0.186 (irs)

0.905 (drs)

0.908 (irs)

0.905 (drs)

0.459 (irs)

1.000 (crs)

0.436 (irs)

1.000 (crs)

0.555 (irs)

0.911 (drs)

1.000 (crs)

0.970 (irs)

0.085 (irs)

1.000 (crs)

0.070 (irs)

1.000 (crs)

0.413 (irs)

1.000 (crs)

0.552 (irs)

0.926 (drs)

0.192 (irs)

1.000 (crs)

0.748 (irs)

0.990 (irs)

0.285 (irs)

0.865 (drs)

0.921 (irs)

0.905 (drs)

0.532 (irs)

0.946 (drs)

0.581 (irs)

1.000 (crs)

0.592 (irs)

0.951 (drs)

1.000 (crs)

0.964 (irs)

0.115 (irs)

1.000 (crs)

0.116 (irs)

0.993 (irs)

0.349 (irs)

0.999 (drs)

0.816 (irs)

0.997 (drs)

0.312 (irs)

0.978 (drs)

0.846 (irs)

0.997 (drs)

0.467 (irs)

0.807 (drs)

0.904 (irs)

1.000 (crs)

1.000 (crs)

0.916 (drs)

0.684 (crs)

1.000 (crs)

0.793 (irs)

1.000 (crs)

1.000 (crs)

0.975 (irs)

0.170 (irs)

1.000 (crs)

0.141 (irs)

0.999 (irs)

0.519 (irs)

0.947 (drs)

0.873 (irs)

0.999 (drs)

0.393 (irs)

0.990 (drs)

0.851 (irs)

0.993 (irs)

0.586 (irs)

0.884 (drs)

0.963 (irs)

1.000 (crs)

1.000 (crs)

0.999 (irs)

0.620 (irs)

0.999 (irs)

0.886 (irs)

1.000 (crs)

1.000 (crs)

0.942 (irs)

0.160 (irs)

0.966 (irs)

0.150 (irs)

0.992 (irs)

0.530 (irs)

0.984 (drs)

0.899 (irs)

0.990 (irs)

0.418 (irs)

0.908 (drs)

0.696 (irs)

0.984 (drs)

0.148 (irs)

0.837 (drs)

0.881 (irs)

0.643 (drs)

0.301 (irs)

0.871 (drs)

0.384 (irs)

0.936 (drs)

0.270 (irs)

0.833 (drs)

1.000 (crs)

0.980 (drs)

0.052 (irs)

1.000 (crs)

0.045 (irs)

0.973 (drs)

0.235 (irs)

0.973 (drs)

0.584 irs)

0.835 (drs)

0.143 (irs)

*Estimate results of scale efficiency of technological innovation.*

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 3**–**5** (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

#### **Table 6.**

*Estimated results of SFA.*

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 7**–**9**. 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.

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%.  DOI: http://dx.doi.org/10.5772/intechopen.90869*

#### **Table 7.**

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 7**–**9**. 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.

**Variable R&D**

*Intellectual Property Rights - Patent*

Constant term 1.42E + 03\*\*\*

Regional real GDP 3.39E 01\*\*\*

Geographical position

Quality of local laborers

Number of hightech industrial enterprises

Local government innovation support

Number of scientific and technological institutions

Logarithmic likelihood function

function

**Table 6.**

**106**

Unilateral likelihood

*Estimated results of SFA.*

**personnel FTE slack variable**

(2.97)

(8.81)

1.05E + 03\*\*\* (2.23)

7.57E 03\*\*\* (11.39)

1.15E 01 (0.33)

2.30E 02\*\*\* (4.03)

1.58E + 00\* (1.45)

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

**R&D funds slack variable**

2.94E + 05\*\*\* (39647.79)

3.22E + 00 (0.76)

1.38E + 05\*\*\* (99,695)

3.62E 01\*\*\* (6.34)

6.16E + 01\*\*\* (2.8)

2.03E + 00\*\*\* (4.51)

1.64E + 02\*\* (1.76)

σ2 2.44E + 07\*\*\* 1.43E + 11\*\*\* 3.21E + 11\*\*\* 4.09E + 11\*\*\* γ 7.52E 01\*\*\* 7.76E 01\*\*\* 8.46E 01\*\*\* 9.04E 01\*\*\*

**New product development funds slack variable**

> 4.14E + 05\*\*\* (241,493.66)

9.96E + 00\*\* (2.08)

2.27E + 05\*\*\* (215,428)

5.16E 01\*\*\* (7.64)

5.64E + 01\*\* (2.2)

2.20E + 00\*\*\* (3.81)

1.61E + 02\* (1.37)

2.73E + 03 3.98E + 03 4.04E + 03 4.00E + 03

1.41E + 02 1.58E + 02 2.44E + 02 3.47E + 02

**Funds for technological transformation slack variable**

> 4.67E + 05\*\*\* (455,599.32)

6.61E + 00\* (1.49)

5.88E + 04\*\*\* (58,524)

5.74E 01\*\*\* (7.07)

1.14E + 02\*\*\* (2.1)

1.59E + 00\*\*\* (3.06)

1.76E + 02\*\* (2.05)

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

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

#### **Table 8.**

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

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

1.252 0.953 0.990 1.093 0.837 1.228 0.960 0.916 1.025 1.015

1.237 0.951 0.986 1.082 0.831 1.225 0.957 0.913 1.024 1.015

in China's high-tech industries in recent years is due to the improvement in

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

**Provinces Year**

**2007/ 2008**

**2008/ 2009**

*Internal Relationship and Impact Path between Innovation and Entrepreneurship: Based…*

**2009/ 2010**

1.289 0.953 0.838 1.33 0.788 1.275 0.902 0.91 1.021 1.016

Beijing 1.136 1.222 0.999 0.958 0.952 1.049 1.181 0.912 1.124 1.054 Tianjin 1.066 0.986 0.943 0.928 0.886 1.231 0.918 1.135 1.033 1.009 Hebei 1.339 0.909 0.98 1.127 0.793 1.164 0.893 0.896 0.964 0.995 Shanxi 1.164 0.953 1.077 1.03 0.839 1.265 0.941 0.887 1.015 1.011

Liaoning 1.067 0.981 1.029 1.061 0.819 1.24 0.93 0.903 0.94 0.990 Jilin 1.496 0.857 0.949 1.192 0.868 1.248 0.903 0.885 1.03 1.029 Heilongjiang 1.29 0.887 0.996 1.179 0.701 1.19 0.943 0.883 1.025 0.995 Shanghai 1.109 1.008 0.987 0.931 1.015 1.191 1.243 0.809 1.183 1.044 Jiangsu 1.045 1.001 1.144 0.853 0.858 1.54 0.97 0.917 1.077 1.029 Zhejiang 1.086 0.921 1.006 1.223 0.796 1.21 0.942 0.895 0.938 0.993 Anhui 1.049 0.995 1.058 1.124 0.682 1.166 0.941 0.892 1.147 0.994 Fujian 1.032 0.98 1.069 0.889 0.91 1.391 0.917 0.95 1.003 1.007 Jiangxi 1.235 0.953 1.052 0.996 0.912 1.299 0.884 0.878 0.979 1.012 Shandong 1.08 0.98 1.033 0.966 0.983 1.163 1.001 0.893 0.99 1.007 Henan 1.248 0.862 0.934 1.203 0.701 1.166 0.941 1.273 1.073 1.027 Hubei 1.473 0.915 1.023 1.021 0.888 1.248 0.91 0.884 1.014 1.027 Hunan 1.121 0.934 0.988 1.08 0.813 1.165 0.925 0.917 1 0.988 Guangdong 1.203 1.031 0.942 1.135 1.038 1.167 0.954 1.01 1.008 1.051 Guangxi 1.567 0.862 0.904 1.289 0.846 1.163 0.913 0.884 0.997 1.025 Hainan 1.596 0.928 0.838 1.33 0.702 1.166 0.941 0.885 1.001 1.013 Chongqing 1.075 0.96 1.081 0.923 0.924 1.363 0.901 0.888 1.021 1.006 Sichuan 1.046 0.983 1.126 0.884 0.791 1.231 1.108 0.867 1.041 1.000 Guizhou 1.42 0.874 0.855 1.33 0.696 1.18 0.941 0.893 1.135 1.011 Yunnan 1.596 0.916 0.913 1.171 0.847 1.185 0.936 0.888 0.998 1.030 Shaanxi 1.142 0.962 1.006 1.092 0.841 1.263 1.051 0.881 0.957 1.014 Gansu 1.542 0.903 1.002 1.192 0.757 1.176 0.935 0.887 1 1.023 Qinghai 1.596 0.928 0.838 1.33 0.72 1.212 0.941 0.887 1.019 1.023 Ningxia 1.186 0.993 1.095 0.917 0.9 1.312 0.938 0.887 1.005 1.017

**2010/ 2011**

**2011/ 2012**

**2012/ 2013**

**2013/ 2014**

**2005/ 2014**

**2006/ 2007**

**2005/ 2006**

*DOI: http://dx.doi.org/10.5772/intechopen.90869*

Inner Mongolia

Arithmetic average

Geometry average

**Table 9.**

**109**

*Internal Relationship and Impact Path between Innovation and Entrepreneurship: Based… DOI: http://dx.doi.org/10.5772/intechopen.90869*

#### **Table 9.**

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

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

indicates that the innovation efficiency of China's high-tech industry is on the rise.

*Multiple chain mediating effect based on the whole.*

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

*Results of direct and indirect effects (overall).*

**4.5 Overall and regional**

and **Table 12**.

**Figure 2.**

**Table 11.**

**111**

In order to clarify the internal mechanism of China's high-tech industry innova-

The overall analysis results of the multiple chain intermediary effect model are

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

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 <0), while the mediating effect through different paths shows a significantly higher promotion effect (*a*1*b*1, *a*2*b*2, and *a*1*a*3*b*2 are 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

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

Indirect X ! M1 ! Y *a*1*b*1 0.288\*\*\*

Direct X ! Y *c*0 �0.17

Total indirect effect *a*1*b*1 þ *a*2*b*2 þ *a*1*a*3*b*2 1.939 Total effect *c*0 þ *a*1*b*1 þ *a*2*b*2 þ *a*1*a*3*b*2 1.769

**Effect path Calculation Effect value**

X ! M2 ! Y *a*2*b*2 0.646\*\*\* X ! M1 ! M2 ! Y *a*1*a*3*b*2 1.005\*\*\*

shown in **Figure 2**, and the effect results are shown in **Table 11**.

tion 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 (prov-

*Internal Relationship and Impact Path between Innovation and Entrepreneurship: Based…*

*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.*

#### **Table 10.**

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

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%.

*Internal Relationship and Impact Path between Innovation and Entrepreneurship: Based… DOI: http://dx.doi.org/10.5772/intechopen.90869*

### **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**.

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

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 <0), while the mediating effect through different paths shows a significantly higher promotion effect (*a*1*b*1, *a*2*b*2, and *a*1*a*3*b*2 are 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

#### **Figure 2.**

*Multiple chain mediating effect based on the whole.*

#### **Table 11.**

*Results of direct and indirect effects (overall).*

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

*technological efficiency, and pure technological efficiency.

**ID Province Direct Indirect**

*Internal Relationship and Impact Path between Innovation and Entrepreneurship: Based…*

**(1.757)**

(3.513)

(2.751)

**(0.103)**

(0.180)

(0.486)

**(0.607)**

(0.613)

(1.190)

(0.337)

(1.558)

(1.166)

**(1.088)**

(1.500)

(1.825)

(0.557)

(0.432)

(0.966)

**(1.930)**

(0.516)

**(0.223)**

(0.732)

(0.512)

(0.949)

1 Beijing **5.201\*\*\***

*DOI: http://dx.doi.org/10.5772/intechopen.90869*

2 Tianjin �4.307

3 Hebei 1.264

4 Shanxi **0.862\*\*\***

5 Inner Mongolia �0.451

6 Liaoning �0.680

7 Jilin **1.114\***

8 Heilongjiang �0.506

9 Shanghai 1.899

10 Jiangsu 0.342

11 Zhejiang �1.569

12 Anhui 0.355

13 Fujian �**5.648\*\*\***

14 Jiangxi 2.282

15 Shandong 0.184

16 Henan �0.216

17 Hubei 0.581

18 Hunan 0.309

19 Guangdong �**4.674\*\***

20 Guangxi �0.333

21 Hainan �**0.785\*\*\***

22 Chongqing 0.326

23 Sichuan 0.092

24 Guizhou 0.299

**113**

*c*0 *a***1***b***1** *a***2***b***2** *a***1***a***3***b***2**

�2.633 (4.822)

**11.984\*\* (5.399)**

1.628 (3.650)

1.251 (2.184)

0.257 (0.883)

�0.502 (2.617)

�1.757 (5.084)

**5.044\*\*\* (1.948)**

1.379 (4.168)

0.969 (5.130)

**9.835\*\* (4.488)**

5.097 (3.229)

**17.260\*\* (7.653)**

0.205 (4.388)

3.385 (7.460)

2.947 (1.832)

�3.573 (2.799)

3.298 (3.883)

9.520 (7.017)

3.344 (3.186)

1.846 (1.243)

7.843 (5.252)

0.041 (1.602)

2.873 (3.699) �**0.831\*\*\* (0.173)**

> �0.998 (0.615)

**0.794\*\*\* (0.180)**

�**2.682\*\*\* (0.732)**

> **2.882\*\* (1.433)**

> �0.418 (0.452)

**1.941\*\* (0.939)**

�0.014 (0.504)

�**0.555\*\* (0.226)**

**0.885\*\*\* (0.189)**

�0.067 (0.497)

**0.775\*\*\* (0.085)**

**2.214\*\*\* (0.837)**

**1.012\*\*\* (0.111)**

**1.338\*\*\* (0.352)**

**1.457\*\*\* (0.114)**

**1.261\*\*\* (0.472)**

**1.950\*\*\* (0.145)**

**0.982\*\* (0.456)**

**1.176\* (0.639)**

�1.546 (1.133)

**1.216\*\*\* (0.219)**

**1.050\*\*\* (0.324)**

0.114 (0.767)

�6.559 (6.288)

3.616 (3.377)

2.260 (4.549)

�0.406 (1.834)

0.303 (0.596)

2.317 (3.019)

�4.547 (3.793)

0.007 (0.264)

2.565 (4.491)

�2.641 (5.668)

0.370 (2.733)

3.088 (3.405)

2.330 (4.915)

�2.912 (7.217)

�4.029 (10.165)

2.915 (1.946)

�2.694 (2.279)

3.751 (4.610)

9.218 (6.795)

1.835 (3.313)

0.596 (0.799)

6.575 (4.528)

�2.002 (2.406)

�0.035 (0.357)

#### **Figure 3.**

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

**Table 12.**

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

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**.

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

*Internal Relationship and Impact Path between Innovation and Entrepreneurship: Based… DOI: http://dx.doi.org/10.5772/intechopen.90869*

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 innova-

**Region Direct effect Indirect effect Total indirect utility Total utility** East �1.5\*\* 1.95\*\*\* 3.942\*\* 5.5042\* 11.3962 9.8962 In 0.093\*\* 0.288\*\* 0.4489\*\* 0.3430\*\* 1.07994 1.17294 West �0.21\* 0.792\* 0.488\*\* 0.6336\*\* 1.9136 1.7036

The analysis results of direct effect, indirect effect, and total effect of innovation

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

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

tion on entrepreneurship always presents a significant positive effect.

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

on entrepreneurship in various provinces and cities are shown in **Table 13**.

only Beijing, Shanxi, and Shanghai have negative effects.

**5. Conclusions and policy recommendations**

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

*Intellectual Property Rights - Patent*

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

**4.6 Provinces, cities, and regions**

**Figure 3.**

**Table 12.**

**112**

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

*Internal Relationship and Impact Path between Innovation and Entrepreneurship: Based…*

of innovation efficiency and entrepreneurship scale.

*DOI: http://dx.doi.org/10.5772/intechopen.90869*

**Author details**

**115**

Kai Zhao\* and Lixiang Wang

School of Statistics, Huaqiao University, China

provided the original work is properly cited.

\*Address all correspondence to<EMAIL_ADDRESS>
© 2020 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

#### **Table 13.**

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

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


**7.1 Individuals**

Individuals generally have the greatest concern for saving fossil fuel, as they may be concerned about what happens in their own lifetime, the lifetime of their children, or their children's children. There is general consensus, that consumers of today should have reduced consumption so that there is more fossil fuel left in the coming decades, and for future generations. The interest of individuals to conserve energy cannot be left entirely to companies or even governments for implementation. Efforts to save energy must include participation by conscious citizens, and academia.

Modern Transitions in Saving Energy and the Environment 205

These SUVs and large cars alongside more efficient cars today, are examples that there is great scope of improving energy conservation by implementing policies to discourage this

Another area with great scope for improvement of fuel consumption is what is popularly known as Urban Sprawl. Urban sprawl is the outwards spreading and development of a city and its suburbs (Wikipedia, 2011). It is characterized by low-density and auto-dependent development on rural land. It also has high segregation of uses (e.g. stores and residential), and various design features that encourage car dependency. Residents of sprawling

Urban sprawl is controversial, with supporters claiming that consumers prefer lower density neighbourhoods, with a suburban lifestyle with two or more cars. An opposing viewpoint is that urban sprawl forces residents to drive to conduct daily activities. Whatever the preferences of residents, it is clear that urban sprawl, and its car-dependent culture are

Employment sprawl is closely associated with urban sprawl and car-dependent communities. This is where jobs are located in areas of urban sprawl. This leaves employees little choice but to participate in the urban sprawl, by relocating to close to their suburban offices. Employment sprawl is prevalent both in cities with and without mass transit such as subways. Companies have set up employment centers well outside the subway system of Toronto, Canada. Employment sprawl causes the interesting phenomena, where residents living near city centers, must reverse-commute outside the city by cars. The mismatch of

Accompanying urban sprawl is the use of large housing that usually exceeds the physical needs of residents. Large housing costs more in heating and cooling costs. According to StatsCan, housing costs a further 30 per cent of disposable income of Canadians (Times

Compared to North America, urban and employment sprawls are less prevalent in Europe and Asia. The International Association of Public Transport policy indicates that in European and Asian counties 5-8 % of GDP is spent on transportation, compared to 13 % in

The causes of urban sprawl are acknowledged to be mainly zoning laws. Sprawl generates much revenue for real estate, automobile and oil companies. A small number of policymakers have spoken out about urban sprawl, but at this time, they are a minority (NZ

Simply reducing urban sprawl by allowing employment and residences to be located close to city centers would greatly reduce fuel consumption by reducing dependence on cars, and

"size race" by consumers in their pursuit of safety in collisions.

neighbourhoods tend to consume more fossil fuel per person.

residences with employment areas is known as spatial mismatch.

**9. Urban sprawl**

very much unsustainable.

**9.1 Employment sprawl**

**9.2 Housing**

Colonist, Dec. 18, 2011)

Herald, Dec. 17, 2011).

North America.