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Article

Effects of Technological Innovation Network Embeddedness on the Sustainable Development Capability of New Energy Enterprises

by
Yi Su
* and
Yueqi Yu
Sch. Econ. & Management, Harbin Engineering University, Harbin 150001, China
*
Author to whom correspondence should be addressed.
Sustainability 2019, 11(20), 5814; https://doi.org/10.3390/su11205814
Submission received: 3 September 2019 / Revised: 17 October 2019 / Accepted: 18 October 2019 / Published: 19 October 2019
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Abstract

:
The rapid development of China’s economy has led to increasing problems with energy security and environmental pollution. Sustainable economic and environmental development in China can be effectively ensured through the sustainable development of new energy enterprises. Moreover, network theory holds that enterprises form multiple complex and diverse social networks through their interconnection, which further boosts the sustainable development of enterprises. This study used the social network analysis method and the accelerating genetic algorithm projection pursuit model, embedded new energy enterprises in technological innovation networks, and established a conceptual model that included network embeddedness, external resource acquisition, corporate social responsibility, and the sustainable development capability of enterprises. Then, hierarchical regression analysis was used to test the conceptual model. The results of this study provide a new theoretical basis and relevant policy suggestions for the sustainable development of China’s new energy enterprises. The results are also important for China’s environmental governance and for creating a green and harmonious future ecological environment.

1. Introduction

Given China’s rapid economic development, some Chinese enterprises have entered bankruptcy as a result of the transformation in economic development mode and changes in the structure of the economy. The issue of the sustainable development of Chinese enterprises is increasingly prominent [1,2]. The data from the “China Private Enterprise Development Report” indicate that in China, the average life span of a large enterprise is 7–8 years, and the average life span of small and medium-sized enterprises is only 2.9 years. Compared with China, the average life span of large enterprises and small and måedium-sized enterprises in Japan is 58 and 12.5 years, respectively. Even the life spans of large enterprises in Europe and America have reached 40 years. The sustainable development capability of enterprises directly affects the sustainable development capability of the economy [3,4]. Hence, ensuring that Chinese enterprises have sustainable development capabilities and helping Chinese enterprises to adapt to development and change in the social and economic environment hold important strategic significance for China’s economic development [5].
China’s economy has been rapidly developing and its population has been consistently increasing over the past 40 years. These developments have led to a continuous increase in energy consumption and a deterioration in environmental quality [6]. China has exceeded the United States to become the world’s largest energy consumer. However, unlike those developed countries that use clean energy extensively, China remains highly dependent upon traditional energy sources such as coal, oil, and natural gas [7]. Excessive dependence upon fossil fuels leads to not only insufficient energy reserves but also serious air quality problems [8]. Environmental pollution not only restricts economic and social development but also seriously affects the health of residents [9]. Therefore, the issues of energy security and environmental pollution have become two of the most serious and urgent problems in China.
Currently in China, the energy structure needs to be improved, and a safe and efficient energy guarantee system also needs to be established [10]. As an alternative energy source, new energy—which includes solar energy, wind energy, tidal energy, biomass energy, and nuclear power—can effectively cope with the energy crisis and reduce environmental pollution. New energy also brings the additional advantages of low pollution and large reserves [11]. Hence, there is a high degree of consensus among countries worldwide regarding the need to rapidly develop the new energy industry to address energy, climate, and environmental change [12,13]. To relieve the stress of the international carbon emission reduction task and sustainable energy development, China has proposed a planning goal to vigorously develop new energy sources [14]. China’s achievements in tackling climate change and environmental pollution further demonstrate its position and determination to partake in and achieve the UN Sustainable Development Goals (SDGs) [15]. Currently, the Chinese government has incorporated SDGs into the 13th Five-Year Plan and the national programme plan for medium- and long-term development [16]. A series of laws and regulations, such as the environmental and resource management law, have been formulated. China has also adopted a series of international cooperation mechanisms to promote the implementation of sustainable development goals, including promoting the establishment of the global energy internet, increasing foreign aid (e.g., establishing the South–South Cooperation Assistance Fund), and promoting global sustainable development through the “One Belt, One Road” initiative [17].
Chinese enterprises are generally challenged with unsustainable development, which indirectly affects the development of China’s new energy industry. Hence, finding ways to promote the sustainable development of new energy enterprises has great significance for China. In fact, the enterprise is not an independent individual; it is embedded in, for example, social, religious, and political systems. Therefore, each enterprise cannot be analyzed separately. Hamel (2006) believes that technological innovation provides enterprises with a sustainable competitive advantage [18]. Typically, technological innovation can improve the performance and quality of products as well as the production process, thereby enhancing the core competitiveness of enterprises and enabling them to seize greater market share and obtain excess profits. Moreover, it also helps enterprises to improve the utilization efficiency of resources and produce products with low pollution, low consumption, and high added value. Hence, in this study, new energy enterprises are embedded in the technological innovation network to study the effects of network embeddedness on the sustainable development capability of new energy enterprises.
This study used social network analysis and the accelerating genetic algorithm projection pursuit model to explore the effects of technological innovation network embeddedness on new energy enterprises’ sustainable development capability and to identify its influencing factors. Based on the results, relevant suggestions are provided to help new energy enterprises achieve sustainable development. The research results can provide a theoretical basis for promoting the sustainable development capacity of new energy enterprises and the formulation of relevant policies. It also has important implications for the development of China’s new energy industry and the improvement of China’s environment.
The structure of this paper is arranged as follows. The research background of this study is presented in Section 1. Section 2 describes the relevant theoretical foundation, and Section 3 presents the research hypotheses. Section 4 includes the data sources and the selection and measurement of variables. Section 5 elaborates the process of hypothesis testing and the results of this study. This paper ends with a conclusion, managerial implications, and research limitations in Section 6.

2. Basic Theory Overview

2.1. Network Embeddedness

Embeddedness theory was proposed by Grannovetter in 1985 [19] and then expanded by Uzzi in 1996 [20]. It is widely used in the fields of business operations, innovation management, and environmental governance [21,22,23,24]. Embeddedness theory holds that the individual should not be analyzed separately due to restrictions on social relationships. Network embeddedness, which is based on embeddedness theory, is an important tool for researching the structure and relationships in networks [25]. Network embeddedness emphasizes the close relationship between economic activity and network structure and how it is influenced by the network structure characteristics and the relationship between actors in the network [26]. Network embeddedness can effectively promote information exchange between actors and the transfer of knowledge; hence, the study of economic phenomena should consider the network characteristics and the interactive relationships between actors [27].

2.2. Sustainable Development Capability of the Enterprise

The World Commission on Environment and Development was the first to formally propose the concept of sustainable development [28]. Previous studies generally emphasized sustainable development in a broad sense, that is, environmentally sustainable development, but ignored sustainable development in a narrow sense, such as social, cultural, and economic sustainable development [29,30,31]. With the gradual deepening of research on sustainable development, many scholars have gradually recognized that the sustainable development of enterprises plays an important role in economic development. Enterprise sustainable development means that enterprises respect nature as a prerequisite and then use their own efficient resources to achieve the continuous expansion of the enterprise scale and the steady growth of profit [32]. The sustainable development capability of the enterprise is the potential for it to coordinate, sustain, and stably develop and accomplish a predetermined goal within the specified time, namely, the capability of continuous operation [33]. Typically, the important approach with which an enterprise achieves sustainable development is innovation. In fact, the sustainable development of an enterprise is a dynamic process, and enterprises should constantly adjust their business model or enterprise structure to gain financial freedom [34]. Importantly, the key to the sustainable development of an enterprise is balancing the contradictions between economic growth, social development, and environmental protection [35].

3. Research Hypotheses

The theoretical framework of this study is shown in Figure 1. In Figure 1, relational embeddedness, structural embeddedness, external resource acquisition, and corporate social responsibility are considered antecedent variables, and the sustainable development capability of the enterprise is considered the outcome variable. The control variables in the model are the type of business ownership and enterprise age. Next, we will describe the relationship between the variables and propose related hypotheses.

3.1. Direct Effect of Technological Innovation Network Embeddedness on the Sustainable Development Capability of the Enterprise

According to Grannovetter, network embeddedness is typically divided into two types. One is relational embeddedness, which indicates that the actors are embedded in the relational network and affected by network relationships. The other is structural embeddedness, which indicates that the actor is embedded in a social network constituted by a relationship network and affected by network structure characteristics [36].
From the perspective of relational embeddedness, the social network relationship is generally considered an asset. High-strength and high-quality relationships between enterprises are established by long-term communication and interaction, which can effectively promote the sustainable development capacity of new energy enterprises [37]. First, stable interactive relationships can help enterprises avoid isolation from outside competitors and, furthermore, can effectively guarantee the sustainable development of the enterprise [38]. Second, the stronger the new energy enterprises’ interactive relationships are in the technological innovation network, the better their access to effective knowledge and information. Thus, new energy enterprises can obtain important market knowledge, specific information and technological secrets [39].
From the perspective of structural embeddedness, when new energy enterprises have more interactive relationships in the technological innovation network, it will lead to more redundant information coming in from the outside. However, when new energy enterprises occupy an important position within the technological innovation network, they will acquire non-redundant rare information and knowledge. Furthermore, information utilization efficiency is enhanced, information acquisition cost is reduced [20,36], and the structural advantage of the new energy enterprises can help establish enterprise alliances. Furthermore, it can help new energy enterprises to effectively acquire and transfer new energy technology and knowledge to support enterprise R&D and innovation, maintain a competitive advantage, and, finally, ensure sustainable development [40].
From the above analysis, we speculate that relational embeddedness and structural embeddedness can influence the sustainable development of new energy enterprises. Hence, the hypotheses are proposed as follows:
Hypothesis 1a (H1a).
Relational embeddedness can positively affect the sustainable development capability of new energy enterprises.
Hypothesis 1b (H1b).
Structural embeddedness can positively affect the sustainable development capability of new energy enterprises.

3.2. Mediation of External Resource Acquisition

External resource acquisition generally refers to the enterprise pre-emptively acquiring the relevant resources from an external body, such as customers, suppliers, and governments, to support the development of the enterprise [41]. To gain a competitive advantage, enterprises must obtain valuable, scarce, and inimitable key resources [42]. Commonly, the resources involved in maintaining the sustainable development of enterprises are divided into two types: own resources and resources acquired by being embedded in a network [43]. Therefore, new energy enterprises that are embedded in the technological innovation network acquire resources from the network that can help them to maintain their competitive advantage, thereby ensuring the sustainable development of new energy enterprises [44].
First, relational embeddedness establishes direct links between enterprises in the network based on a relationship of trust between two enterprises [36]. Relational embeddedness shows the enterprises’ location and status and the interactive relationships between them and other enterprises in the network. Those attributes determine the amount of resources that the enterprises can aggregate, consolidate, and configure in the network, which can further influence the behavior and performance of enterprises in the network [45,46]. When new energy enterprises have more external relationships in the technological innovation network, the opportunities for them to access external resources and communicate with the outside world will greatly increase [47]. Then, the information and resource sharing between members in the network will be easier, and the new energy enterprises will acquire more complementary key resources from the other enterprises in the technological innovation network [48]. Moreover, multiple strong external relationships can provide a positive signal to other enterprises in the network, showing that this enterprise is open to the outside world. This method can promote information sharing and exchange and cooperation between new energy enterprises and others in the network and provide more opportunities for enterprises to obtain external resources [49].
Second, from the perspective of structural embeddedness, when a new energy enterprise is embedded in the technological innovation network and located in an important structural position, this enterprise will gain a high reputation in the industry. Furthermore, it will attract key resources such as talent and funds. An excellent reputation can create more trust within the network for the new energy enterprise; thus, the probability of access to key resources is increased, and operational uncertainty is reduced [50]. A new energy enterprise located in a key position in the technological innovation network will have greater opportunities to obtain key resources from the government, such as financial support, corresponding policy adjustments, and tax incentives [51].
Based on the above analysis, this study speculates that relational embeddedness and structural embeddedness help new energy enterprises to obtain external resources, and external resources can improve the sustainable development of new energy enterprises. Therefore, hypotheses are proposed as follows:
Hypothesis 2a (H2a).
External resource acquisition mediates the relationship between relational embeddedness and the sustainable development of the new energy enterprise.
Hypothesis 2b (H2b).
External resource acquisition mediates the relationship between structural embeddedness and the sustainable development of the new energy enterprise.

3.3. Moderating Role of Corporate Social Responsibility

Corporate social responsibility refers to the commitment of an enterprise to its shareholders creditors, suppliers, customers, employees, and society [52]. However, entrepreneurs and executive teams are heterogeneous, which leads to uncertainty in decision-making; furthermore, the balance between the internal and external environments of the enterprise and relevant stakeholders is affected. Hence, there are differences in how companies enact corporate social responsibility. In fact, corporate social responsibility is an informal institution. It refers to a standard of conduct gradually formed by people during long-term social interactions that is accepted and commonly followed by society. It includes value beliefs, customs and habits, cultural traditions, moral theories, and ideologies [53]). The traditional view is that formal systems are more important to the development of an enterprise, but many studies show that sanctioning by an informal institution is often more effective than that by a formal institution. Hence, the role of informal institutions should not be ignored [54]. When the enterprise is highly engaged in corporate social responsibility, it will have a good reputation and gain more social trust. Social trust represents another important type of capital, social capital, that can promote enterprise development in addition to material capital and human capital [55]. A strong emphasis on corporate social responsibility can reduce the opportunistic behavior of the enterprise and help reduce its transaction costs [56]. High corporate responsibility also reduces information asymmetry problems between investors and enterprises. Furthermore, it enhances investment willingness and reduces external financing costs [57]. High corporate responsibility also promotes the positive effects of relationship embeddedness and structural embeddedness on the sustainable development of enterprises. However, when an enterprise violates informal institutions, it results in severe punishment by society; furthermore, the enterprise’s reputation is damaged, project cooperation is terminated, and social trust is lost. Then, the enterprise will be forced to leave the original social network, and its sustainability will be seriously affected. Following the above analysis, hypotheses are proposed as follows:
Hypothesis 3a (H3a).
The relationship between relational embeddedness and enterprises is positively moderated by corporate social responsibility: the greater the sense of corporate social responsibility is in the enterprise, the stronger the influence of relational embeddedness on its sustainable development capability.
Hypothesis 3b (H3b).
The relationship between structural embeddedness and the enterprise is positively moderated by corporate social responsibility: the greater the sense of corporate social responsibility is in the enterprise, the stronger the influence of structural embeddedness on its sustainable development capability.

4. Method

4.1. Data Sources

The research object of this study is the enterprise as selected using new energy concept stocks in Chinese Security Markets, which includes the seven blocks representing new energy: lithium battery, nuclear energy, solar energy, wind energy, shale gas, geothermal energy, and combustible ice. We used the RESSET Database to identify 413 enterprises. The full names of new energy listed companies and their subsidiary companies were then used as search keywords to search in China’s State Intellectual Property Office, and the patent count for the identified new energy enterprises was also obtained. The corporate social responsibility index for new energy enterprises was collected by means of the financial information platform Hexun (http://www.hexun.com/). Other data are taken from the annual reports of these new energy listed companies.

4.2. Variable Measurement

4.2.1. Dependent Variable

In this study, the dependent variable is the sustainable development capability of an enterprise, which is measured by the accelerating genetic algorithm projection pursuit model. This model, which can effectively analyze data with a high-dimensional non-linear and non-normal distribution, was proposed in the 1970s by the American scientist Kruskal. It has excellent robustness, anti-interference, and accuracy [58]. Hence, this study refers to the method of Su et al. to calculate the sustainable development capability of the enterprise (see the Appendix A for the detailed modelling process) [59]. More specifically, enterprise sustainable development capability is measured by five dimensions in this study: investment and income, debt solvency, profitability, operating capacity, and capital structure. The specific indicators are net assets per share, common stockholders’ equity return rate, current ratio, quick ratio, receivable turnover ratio, debt asset ratio, turnover of fixed assets, total asset turnover, net profit margin, return on total assets ratio, inventory turnover ratio, net assets ratio, and fixed assets ratio.

4.2.2. Independent Variables

In this study, the independent variables include the degree centrality and structural holes, which are used to measure relational and structural embeddedness, respectively. To obtain the value of the independent variable, we initially constructed a spatial association network that focuses on the technology innovation capability of new energy enterprises. Specifically, the new energy enterprises are the nodes in this spatial association network. If there is a relationship between two nodes, the connection is indicated with one line, and the set of these relationships creates the spatial association network. Two methods are commonly used to determine the association effect between two enterprises: The Granger causality test and the modified gravitational model [60,61]. The Granger causality test requires a longer time series, while this study uses cross-sectional data. Hence, the spatial association network was constructed using the modified gravitational model [62]. The formula of the modified gravitational model is expressed as follows:
I i j = E i E i + E j E i P i R i 3 E j P j R j 3 D i j 2   ( i = 1 , 2 , … , n ; j = 1 , 2 , … , n )
where I i j is the correlation coefficient between nodes. E i and E j are the patent counts of enterprise i and enterprise j , respectively. P i and P j are the number of R&D personnel of enterprise i and enterprise j , respectively. R i and R j are the R&D investment amount of enterprise i and enterprise j , respectively. D i j denotes the economic distance, namely, the disproportions of total assets of the enterprises. According to formula (1), the gravitational matrix was obtained by using the data on patent stock and enterprise annual reports in 2017. Thereafter, we need calculated the value of each row so that each value in the row could be compared with the average. If the value was greater than the average value, this value was changed to 1, and it indicates that a spatial association exists between enterprises. Otherwise, it was changed to 0, indicating that there is no spatial association between enterprises. Thereby, the spatial association network of this study was constructed [63].
We used the UCINET 6.0 software to calculate the degree centrality and structural holes based on the spatial association network. The theory of structural hole and degree centrality are expressed as follows:
The degree centrality can indicate the number of direct connections [64], which is used to measure the importance degree and the quantity of relationships of nodes in the network [65]. The calculation formulas are (2) and (3); more specifically, formula (2) calculates absolute degree centrality, and formula (3) calculates standardized degree centrality as follows:
C D ( n i ) = d ( n i ) = ∑ j X i j = ∑ j X j i
C D ′ ( n i ) = d ( n i ) g − 1
where X i j is 0 or 1, indicating whether there is a relationship between node i and node j . g is the number of nodes in the network, and g − 1 is the maximum number of relationships owned by a node in the network. Hence, the greater the degree centrality of a node is, the greater the number of relationships owned by that node in the social network.
The structural hole is the index used to measure the node importance in the network [66]. A node that is located in a structural hole has more advantages than other nodes in obtaining information resources. The typical method of measuring structural holes is the structural hole index, which was proposed by Burt in 1992 [67]. The structural hole index includes four fields, namely, effective size, constraint, hierarchy, and efficiency, this latter being the most important. The efficiency of the node is equal to the ratio of its effective scale to the actual scale. The effective scale equals the node individual network scale minus the redundancy of the network; that is, the effective scale equals a non-redundant factor in the network. The effective scale of node i can be expressed as follows:
∑ j ( 1 − ∑ q p i q m j q )         q ≠ i , j
where j indicates all nodes that are connected with i . q is a node that is not i or j . p i q m j q indicates that there is redundancy between i and j . p i q indicates the ratio of the relationship between i and q . m j q is the marginal strength between j and q , which equals the number of relationships between j and q divided by the maximum value of the relationship between j and other nodes. In fact, for the binary network, m j q is always 0 or 1. The sum of p i q m j q indicates the ratio of the relationship between i and j to the relationship between i and other nodes.

4.2.3. Moderator Variable

Charitable responsibility, legal responsibility, ethical responsibility, and economic responsibility all belong within the scope of corporate social responsibility. Hexun, the financial information platform, provides a corporate social responsibility score in its listed company data. Hexun selects five indicators, including responsibility to shareholders, responsibility to employees, environmental responsibility, social responsibility, and the responsibility of suppliers, customers, and consumers, to evaluate the social responsibility level of listed companies [68]. Their score for corporate social responsibility is convenient and appropriate for our research; hence, we selected this score as a moderator variable. The descriptive statistics of the moderator variable are shown in Table 1.

4.2.4. Mediator Variable

External resource acquisition in this study includes talent resources, government resources, and a financing constraint. The number of bachelor’s degrees or above was used to measure talent resources. Government grant income was used to measure government resources. The financing constraint was used to measure the financing capacity of enterprises, and this is typically measured using the SA index ((Size-Age Index) proposed by Hadlock and Pierce in 2010. The absolute value of the SA index was used to measure financing; the greater the absolute value of the SA index is, the lower the degree of the financing constraint [69]. The formula for calculating the SA index is as follows:
S A = ( − 0.737 ∗ S i z e ) + ( 0.043 ∗ S i z e 2 ) − ( 0.040 ∗ A g e )
where S i z e is the natural logarithm of the enterprise scale, which is measured by the total assets of the enterprise. A g e indicates the number of years that an enterprise has existed.

4.2.5. Control Variables

To control for other factors that influence the research results, the types of business ownership and enterprise age are selected as control variables. The types of business ownership include state-owned and non-state-owned enterprises, in which private and foreign enterprises comprise the non-state-owned enterprises. More specifically, the value of a state-owned enterprise is 1 and that of a non-state-owned enterprise is 0. The enterprise age refers to the time span from its establishment to 2017.

4.3. Methodology

The purpose of this study is to reveal the role of the specific mechanism of network embeddedness in the sustainable development of enterprises. In particular, it examines the mediating effect of external resource acquisition and the moderating effect of corporate social responsibility on the relationship between network embeddedness and sustainable development. Previous studies have shown that both hierarchical regression analysis and structural equation modelling can be used to study the specific path of action between variables in the model. However, structural equation modelling is suitable for models with many variables and complex relationships, and it also requires a larger sample size. Hence, in this study, the hierarchical regression analysis was selected for hypothesis testing.

5. Results and Discussions

5.1. Descriptive Statistics and Correlation between Variables under Study

This study used SPSS 21.0 software to calculate descriptive statistics and correlations between variables. From Table 2, the mean of Chinese new energy enterprise sustainable development capability is 1.348, and the standard deviation is 0.228. Following the results of Su et al. [59], we found that there are small differences in sustainable development capability between Chinese new energy enterprises, and most of them are at the lower middle level. The means of degree centrality and structural holes are 3.965 and 0.424, respectively, and their standard deviations are 2.577 and 0.208. The mean of corporate social responsibility is 17.702, and the standard deviation is 7.950. This result implies that there are large differences in corporate social responsibility. The mean of external resource acquisition is 0.047, and the standard deviation is 0.112. Chinese new energy enterprises have similar capabilities for acquiring external resources. Furthermore, there are significant correlations among degree centrality, structural holes, external resource acquisition, and the sustainable development capability of the enterprise. Degree centrality and structural holes also have significant correlations with external resource acquisition. The moderator variable, corporate social responsibility, can significantly affect the new energy enterprise’s sustainable development capability. Typically, the variables’ VIF value is less than 10, and the tolerance of variables is greater than 0.100, which implies that there is no multicollinearity between variables. In this study, the greatest value for VIF is 4.079, and the minimum tolerance is 0.245. Hence, there is no multicollinearity between variables in this study.

5.2. Hypotheses Testing

5.2.1. Main Effect and Mediating Effect Testing

To test the main effect and mediating effect, this study used the hierarchical regression analysis method and built eight models. The dependent variable of models 1 to 3 is external resource acquisition. The dependent variable of models 4 to 8 is the sustainable development capability of the enterprise. Model 1 and model 4 only include control variables, which are types of business ownership and enterprise age. Model 5 and model 6 are used to test the direct effect of new energy enterprise sustainable development capability degree centrality and structural holes. Furthermore, models 1 to 8 are used to test the mediating effects. Table 3 lists the results of the linear regression analysis.
  • Main effect. First, the sustainable development capability of the enterprise is used as a dependent variable. The control variables and independent variables are also added to the regression model (as shown in models 4 to 6). Model 4 only added the control variables; therefore, models 5 and 6 added the degree centrality and structural holes, respectively. Model 5 shows that degree centrality has a positive effect on the sustainable development capability of enterprises (β = 0.077, p < 0.1). When the relational embeddedness of the new energy enterprise increases by one unit, the sustainable development capability of the new energy enterprise will increase by 0.077. Model 6 shows that structural holes have a significantly positive effect on the sustainable development capability of the enterprise (β = 0.113, p < 0.05). Hence, hypotheses 1a and 1b are verified. When the structural embeddedness of the new energy enterprise increases by one unit, the sustainable development capability of the new energy enterprise will increase by 0.113.
  • Mediation effect. This study used the approach of Wen Zhongli to test the mediation effect [70]. The dependent variable in models 1 to 3 is external resource acquisition, and that in models 4 to 8 is the sustainable development capability of the enterprise. Next, the control variables and independent variables are added to the regression model. First, the relationship between the independent variables and the sustainable development capability of the enterprise is tested using models 4 to 6. Next, the relationship between the independent variables and the mediation variable is tested using models 1 to 3. Finally, models 7 and 8 are used to test the mediation effect. Models 4 to 6 are also used to test the main effect; hence, the degree centrality and structural holes have significant effects on the new energy enterprise sustainable development capability (β = 0.077, p < 0.1; β = 0.113, p < 0.05). From models 1 to 3, we find that the degree centrality and structural holes have significant effects on external resource acquisition (β = 0.210, p < 0.01; β = 0.136, p < 0.01). When the relational embeddedness of the new energy enterprise increases by one unit, the external resource capability of the new energy enterprise will increase by 0.210. Meanwhile, when the structural embeddedness of the new energy enterprise increases by one unit, the external resource capability of the new energy enterprise will increase by 0.136. Model 7 and model 8 add a mediation variable and external resource acquisition, based on models 5 and 6, respectively. In models 7 and 8, the independent variables (β = 0.110, p < 0.05; β = 0.134, p < 0.01) and mediation variable are also significant (β = −0.160, p < 0.01; β = −0.154, p < 0.01). Hence, we believe that external resource acquisition mediates the relationship between degree centrality and the new energy enterprise’s sustainable development capability. Moreover, it mediates the relationship between the structural holes and the new energy enterprise’s sustainable development capability. Therefore, hypotheses 2a and 2b are verified.

5.2.2. Moderating Effect Testing of Corporate Social Responsibility

To test the moderating effect of corporate social responsibility, this study constructed six models. Table 4 presents the results of the regression analysis. Models 1 to 3 are designed to test the moderating effect of corporate social responsibility on the relationship between degree centrality and the sustainable development capability of the enterprise. Models 4 to 6 are designed to test the moderating effect of corporate social responsibility on the relationship between structural holes and the sustainable development capability of the enterprise. Model 3 shows that the interaction between corporate social responsibility and degree centrality is significant (β = 0.001, p < 0.05), implying that corporate social responsibility has a significantly positive moderating effect on the relationship between degree centrality and the sustainable development capability of the enterprise. Furthermore, model 6 shows that the interaction between structural holes and corporate social responsibility is also significant (β = 0.015, p < 0.05), indicating that corporate social responsibility has a significantly positive moderating effect on the relationship between structural holes and the sustainable development capability of the enterprise. Consequently, hypotheses 2a and 2b are verified. In Figure 2 and Figure 3, the slope of high corporate social responsibility is positive, but that of low corporate social responsibility is negative. The figures indicate that lower corporate social responsibility can lead to the fracture of the relationship between degree centrality or structural holes and the sustainable development capability of the enterprise; furthermore, the sustainable development capability of the enterprise will also be seriously affected.

5.3. Robustness Testing

In the robustness test, investment, income, and capital structure are considered to be relatively less important dimensions. Hence, only three dimensions, debt solvency, profitability, and operating capacity, are considered to evaluate the sustainable development capability of the enterprise. This is equivalent to constructing a new outcome variable. The results of the robustness test are shown in Table 5. Models 1 to 4 test the mediating effect of external resource acquisition. Models 5 and 6 test the moderating effect of corporate social responsibility. The results of the robustness tests show that external resource acquisition plays a strong mediating role in the relationship between relational embeddedness and the sustainable development of the enterprise. Meanwhile, external resource acquisition plays a partial mediating role in the relationship between structural embeddedness and the sustainable development of the enterprise. Corporate social responsibility plays a moderating role in the relationship between network embeddedness (relational embeddedness and structural embeddedness) and the sustainable development of the enterprise. This test found that the results had not changed, indicating that the research results are robust.

6. Conclusions and Management Implications

6.1. Summary of Findings

This study used the social network analysis method and the accelerating genetic algorithm projection pursuit model to construct a conceptual model of the relationships between network embeddedness, external resource acquisition, corporate social responsibility, and sustainable development capability based on the patent and annual report data of new energy enterprises. The relationship between these variables is explored, and the results are as follows.
Previous studies on the sustainable development of enterprises have mainly focused on the qualitative aspects of enterprises. However, this study quantitatively measures the sustainable development capability of enterprises along five dimensions: investment and income, debt solvency, profitability, operating capacity, and capital structure. The results show that the sustainable development ability of new energy enterprises in China is mostly at the low–medium level, which indicates that maintaining the sustainable development of new energy enterprises is an issue in China. The results also support the views of Cai and Zhang et al. [1,2] to a certain degree.
Both relational embeddedness and structural embeddedness have positive effects on the sustainable development capability of new energy enterprises. The more network relationships that are owned by a new energy enterprise, the higher the efficiency of its new energy knowledge and technology transfer and the greater its sustainable development. Moreover, when a new energy enterprise occupies an important position in the network, its means of acquiring knowledge is greatly increased, and it becomes easier to promote its sustainable development. Wincent et al. [71] believe that network embedding has a significant positive impact on performance in terms of technological innovation. Therefore, the results once again support that enterprises that play an important role in the technological innovation network of new energy enterprises can achieve a greater sustainable development capability.
External resource acquisition mediates the relationship between network embeddedness and the sustainable development capability of new energy enterprises. This finding illustrates that new energy enterprises can enhance their competitive advantage and achieve sustainable development by becoming embedded in technology innovation networks and by acquiring key external and scarce resources [44].
Corporate social responsibility mediates the relationship between the network embeddedness and sustainable development capability of the enterprise. The research results show that when a new energy enterprise has higher corporate social responsibility, the positive effect of network embeddedness on the enterprise’s sustainable development capability will increase. However, when a new energy enterprise has low corporate social responsibility, the sustainable development capacity of the new energy enterprise will be inhibited. Zhang and Ke [72] hold that beyond the role of material and human capital, trust is the main form of social capital that determines the development of enterprises and the economy and that trust is also the most important factor in the moral basis of the market economy. Hence, the greater the reputation and popularity of the enterprise, the more serious are the consequences of damage to the corporate image. The research results once again prove Chen et al.’s [73] view that informal institutions are important influences on corporate behaviour and corporate governance.

6.2. Managerial Implications

According to the research results, policy recommendations are presented below.
First, the results show that being embedded in the technological innovation network can promote the new energy enterprise’s sustainable development capacity. Hence, on the one hand, the new energy enterprise should formulate effective strategies to join and expand its relationships in the technological innovation network. Moreover, the new energy enterprise should strengthen exchanges and cooperation with other new energy enterprises. For instance, the innovation alliance or cooperation and exchange platforms are established to promote the transfer of information and technology between enterprises. On the other hand, new energy enterprises should improve their reputation and technological innovation capability and promote their position in the technological innovation network. Then, the new energy enterprises can easily gain a competitive advantage to promote enterprise sustainable development capacity.
Second, this study examined the effect of an external resource acquisition between network embeddedness and new energy enterprise sustainable development capacity. Hence, the new energy enterprises need to strengthen their links with individuals in the technology innovation network and constantly enhance their position in the technological innovation network, as this can help them acquire key resources in the technological innovation network. Then, the new energy enterprise’s sustainable development capability is guaranteed.
Finally, this study verified the moderating effect of corporate social responsibility. Thus, improving the corporate social responsibility of new energy enterprises has a large significant effect on promoting sustainable development. The new energy enterprise should focus on improving the cognitive level of the senior executive team and the corresponding internal control strategies, fundamentally improving corporate social responsibility. The government should formulate relevant incentive policies to provide a fair-trading environment and should improve the social welfare system to promote the improvement of corporate social responsibility.

6.3. Research Limitations

This study has limitations. First, cross-sectional data were used in this study. However, further research is required to determine whether there is a cross-period effect among the various indicators in this study. Second, this study only examines new energy enterprises in technological innovation networks without considering other network scenarios. In future studies, we will embed new energy enterprises in more scenarios in different periods, and we will identify other ways to promote the sustainable development of new energy enterprises through network nesting analysis. Third, this study takes Chinese enterprises as samples to explore the impact of network embeddedness on the sustainable development capability of t enterprise. Future research should consider collecting samples from Western scenarios to verify the generalizability of the research conclusions. Finally, the network structure and the location of the network nodes are different in different networks. Hence, it is currently very difficult to conduct research across networks. This requires us to conduct further research and find solutions.

Author Contributions

Conceptualization, Y.S. and Y.Y.; methodology, Y.S. and Y.Y.; software, Y.S. and Y.Y.; writing—original draft preparation, Y.Y.; writing—review and editing, Y.S.; supervision, Y.S.

Funding

This research was funded by the National Natural Science Foundation of China (Grant No.71403066, 71774036, 71872057, 71804084); MOE (Ministry of Education in China) Project of Humanities and Social Sciences (Grant No.18YJC630245); Social Science Foundation of Heilongjiang Province (Grant No.17GLH21, 18GLB023); Natural Science Foundation of Heilongjiang Province (Grant No. QC2018088).

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

The basic modeling steps of the projection pursuit evaluation model are as follows:
(1) Data normalization. Regulating the index set of each indicator evaluation level is { x * ( i , j ) | i = 1 , 2 , ⋯ , n ; j = 1 , 2 , ⋯ , p } , and x * ( i , j ) is the j -th index value of the i -th standard. n and p are the grades and the number of evaluation indicators, respectively. Since the dimensions of the indicators are different, it is necessary to unify the range of the index values, so the original data needs to the normalized:
For the defined benefit indicator’s value, we used the following:
x ( i , j ) = x * ( i , j ) − x min ( j ) x max ( j ) − x min ( j )
For the defined cost indicator’s value, we used the following:
x ( i , j ) = x max ( j ) − x * ( i , j ) x max ( j ) − x min ( j )
where x max ( j ) and x min ( j ) are the maximum and minimum values, respectively. x ( i , j ) is the indicator value after the evaluation standard sample set normalization.
(2) Construct the projection indicator value function. Then, the optimum projection direction a = { a ( 1 ) , a ( 2 ) , a ( 3 ) , ⋯ , a ( p ) } is obtained by P-dimensional data { x ( i , j ) | j = 1 , 2 , ⋯ , p } are projected into low-dimensional sub-spaces. The formula for calculating the projection value is as follows:
z ( i ) = ∑ j = 1 p a ( j ) x ( i , j ) ,   i = 1 , 2 , ⋯ , n
where a is a unit vector.
Then, we construct the projection index function:
Q ( a ) = S z D z
where S z is the standard deviation of Z ( i ) and D z is the local kernel density of z ( i ) .
S z = ∑ i = 1 n ( z ( i ) − E ( z ) 2 n − 1
D z = ∑ i = 1 n ∑ j = 1 n ( R − r ( i , j ) ) ⋅ u ( R − r ( i , j ) )
where E z is the expectation value of z ( i ) . R is the Pei radius of the local density; r ( i , j ) represents the distance between the sample projection values, which is r ( i , j ) = | z ( i ) − z ( j ) | ; and u ( t ) is a unit step function, u ( t ) = 1 when t ≥ 0 , u ( t ) = 0 when t < 0 .
(3) Optimize the projection indicator value function. After obtaining the sample set of each indicator, Q ( a ) is only related to the projection direction. The optimal projection direction can be estimated by solving the projection index function maximization problem. Maximize the objective function as follows:
max Q ( a ) = S Z ⋅ D z
s . t . ∑ j = 1 p a 2 ( j ) = 1
(4) The projection value z * ( i ) of each sample can be obtained by putting the optimum projection direction into the formula (3). z * ( i ) is the scores of enterprise sustainable development capability.
To calculate formulas (A2) and (A3), this study used the real-coded accelerating genetic algorithm. The basic modelling steps are as follows:
(1) Optimizing the real-coded variables. Formula (A4) is used to make the linear transformation.
x ( j ) = a ( j ) + y ( j ) ( b ( j ) − a ( j ) )         j = 1 , 2 , ⋯ , p
According to formula (A2), we know that Q is the objective function to be optimized and that p is the number of optimization variables. In formula (A4), the j -th waiting optimization variable x ( j ) in the range [ a ( j ) , b ( j ) ] corresponds to the range [ 0 , 1 ] . The corresponding value in the range [ 0 , 1 ] is y ( j ) , namely, the genetic genes in the RAGA. The chromosome is the set of all the genetic genes, and this is indicated by ( y ( 1 ) , y ( 2 ) , ⋯ , y ( p ) ) . The chromosome is the code that constitutes the solution of the problem.
(2) Defining the initial parent population. The number of parent populations is n . Then, the random number in the range [ 0 , 1 ] can be obtained. The number of groups is n , and the number of each group is p . That is, { u ( j , i ) | ( j = 1 , 2 , ⋯ , p ; i = 1 , 2 , ⋯ , n } . The u ( j , i ) is defined as the number value of the initial parental population y ( j , i ) . Then, the optimal value of the variable is obtained by using formula (A4) and by ranking the value of the objective function { Q ( i ) | ( i = 1 , 2 , ⋯ , n } from small to large. According to the rank, the k excellent individuals are selected.
(3) Establishing the adaptability evaluation function. The probability of each chromosome is calculated using the adaptability evaluation function. In the function, α belongs to ( 0 , 1 ) . The evaluation function is shown in formula (A5):
e v a l ( y ( j , i ) ) = α ( 1 − α ) i − 1       i = 1 , 2 , ⋯ , N
where i = 1 illustrates that the chromosome has the best and i = N illustrates that the chromosome has the worst ranking.
(4) Choosing the next generation individuals. The number of revolving gambling wheels is N . According to the adaptability of each chromosome, a new set of chromosomes is selected in each rotation. Then, the first-generation group { y 1 ( j , i ) | j = 1 , 2 , ⋯ , p } is obtained. The calculation method is as follows:
The cumulative probability q i ( i = 0 , 1 , 2 , ⋯ , N ) of each chromosome y ( j , i ) is
{ q 0 = 0 q i = ∑ j = 1 i e v a l ( y ( j , i )         j = 1 , 2 , ⋯ , p ; i = 1 , 2 , ⋯ , N
A random number r is produced from range [ 0 , q i ] . If q i − 1 < r ≤ q i , then the i -th chromosome y ( j , i ) is selected.
Steps 2 and 3 are repeated a total of N times. Then, the N replicated chromosomes are used to form a new generation of individuals.
(5) Obtaining the second generation population from the hybrid parental population. The expression p c is the probability of a crossover operation. This means that there will be p c N chromosomes undergoing a crossover operation in the population. If the random number r < p c , then y ( j , i ) is selected as the parent generation and y 1 ′ ( j , i ) , y 2 ′ ( j , i ) , ⋯ is used to express the selected parent generation. Then, they are paired randomly as shown below: ( y 1 ′ ( j , i ) , y 2 ′ ( j , i ) ) , ( y 3 ′ ( j , i ) , y 4 ′ ( j , i ) ) , ( y 4 ′ ( j , i ) , y 5 ′ ( j , i ) ) .
Taking ( y 1 ′ ( j , i ) , y 2 ′ ( j , i ) ) as an example to explain the process, the random number c is obtained from ( 0 , 1 ) and the specific operation process is as follows:
X = c ⋅ y 1 ′ ( j , i ) + ( 1 − c ) ⋅ y 2 ′ ( j , i ) Y = ( 1 − c ) ⋅ y 1 ′ ( j , i ) + c ⋅ y 2 ′ ( j , i )
Then, the second-generation group { y 2 ( j , i ) | j = 1 , 2 , ⋯ , p ; i = 1 , 2 , ⋯ , n } is obtained.
(6) Mutating the second-generation group to obtain new populations. The mutation probability is p m . This means that there will be p m N chromosomes undergoing a mutation operation in the population. The random number r is obtained from ( 0 , 1 ) . If the random number r < p m , then y ( j , i ) is selected as a parent generation. The specific process is as follows:
The random direction d in n-dimensional space is selected as the mutation direction.
y 3 ′ ( j , i ) + M d             i = 1 , 2 , ⋯ , p
where M is the random number in the ( 0 , 1 ) .
Then, y 3 ′ ( j , i ) is replaced as X = y 3 ′ ( j , i ) + M d . A new generation population { y 3 ( j , i ) | j = 1 , 2 , ⋯ , p ; i = 1 , 2 , ⋯ , n } is obtained by repeating this process.
(7) Evolutionary iteration. According to the number value of the adaptability function, the 3 n progeny individuals, which are obtained by the process of selection, hybridization and variation, are ranged. The ( n − k ) individuals are selected as the best filial generation. Next, they are considered the new parent generation and step 3 is repeated.
(8) Accelerated processing. The range of the best individuals produced from the first and second generations is used as the iteration range in the next generation optimization. The algorithm is terminated when the optimal individual objective function value reaches the set value or set acceleration number. Now, the RAGA optimization results in the best individual. The above steps represent the complete RAGA algorithm.

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Sustainability 11 05814 g001 550
Figure 1. Framework of relationships among variables.
Figure 1. Framework of relationships among variables.
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Figure 2. The moderating effect diagram of degree centrality.
Figure 2. The moderating effect diagram of degree centrality.
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Figure 3. The moderating effect diagram of structural holes.
Figure 3. The moderating effect diagram of structural holes.
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Table
Table 1. The descriptive statistics of the moderator variable.
Table 1. The descriptive statistics of the moderator variable.
VariableNMinMaxMeanStd. Dev.
Corporate social responsibility413−3.43035.51017.7027.950
Table
Table 2. Mean, standard deviation and correlation coefficient.
Table 2. Mean, standard deviation and correlation coefficient.
VariablesMeanSDSDCDCSHCSRERA
SDC1.3480.2281.000
DC3.9652.5770.088 **1.000
SH0.4240.2080.148 ***0.862 ***1.000
CSR17.7027.9500.383 ***0.122 ***0.135 ***1.000
ERA0.0470.112−0.173 ***0.201 ***0.109 **0.088 **1.000
Note: Sustainable development capability of the enterprise (SDC); Degree centrality (DC); Structural holes (SH); Corporate social responsibility (CSR); External resource acquisition (ERA); N = 413; *** indicates significance at the 0.01 level (one sided); and ** indicates significance at the 0.05 level (one sided).
Table
Table 3. Main effect and mediating effect testing.
Table 3. Main effect and mediating effect testing.
External Resource AcquisitionSustainable Development Capability of the Enterprise
M1M2M3M4M5M6M7M8
TBO0.134 ***0.139 ***0.149 ***−0.264 ***−0.262 ***−0.252 ***−0.240 ***−0.229 ***
EA0.0410.0570.059−0.042−0.036−0.027−0.027−0.018
DC 0.210 *** 0.077 * 0.110 **
SH 0.136 *** 0.113 ** 0.134 ***
ERA −0.160 ***−0.154 ***
R20.0210.0650.0390.0740.0800.0860.1040.109
ΔR20.0210.0440.0180.0740.0060.0120.0240.023
Adj- R20.0160.0580.0320.0700.0730.0800.0950.101
F4.386 **9.415 ***5.500 ***16.416 ***11.857 ***12.892 ***11.831 ***12.517 ***
Note: Types of business ownership (TBO); Enterprise age (EA); Degree centrality (DC); Structural holes (SH); External resource acquisition (ERA); TBO and EA are control variables; DC, SH and ERA are independent variables; N = 413; the relevant variables have been treated for centralization; the coefficients listed in Table 2 are standardized regression coefficients; *** indicates significance at the 0.01 level (both sides); ** indicates significance at the 0.05 level (both sides); and * indicates significance at the 0.1 level (both sides).
Table
Table 4. Moderating effect testing.
Table 4. Moderating effect testing.
Sustainable Development Capability of the Enterprise
M1M2M3M4M5M6
Constant0.0010.001−0.0020.0010.001−0.003
Control variablesTBO−0.133 ***−0.118 ***−0.113 ***−0.133 ***−0.115 ***−0.109 ***
EA−0.002−0.002−0.002−0.002−0.002−0.002
Independent variablesDC 0.0030.002
SH 0.0700.081
CSR 0.010 ***0.011 *** 0.010 ***0.011 ***
Interaction variablesDC × CSR 0.001 **
SH × CSR 0.015 **
R20.0740.2090.2180.0740.2120.224
ΔR20.0740.1350.0090.0740.1370.013
Adj- R20.0700.2010.2080.0700.2040.215
F16.416 ***26.894 ***22.663 ***16.416 ***27.365 ***23.549 ***
Note: Types of business ownership (TBO); Enterprise age (EA); Degree centrality (DC); Structural holes (SH); Corporate social responsibility (CSR); N = 413; the relevant variables have been treated for centralization; the coefficients listed in Table 3 are non-standardized regression coefficients; *** indicates significance at the 0.01 level (both sides); and ** indicates significance at the 0.05 level (both sides).
Table
Table 5. The results of the robustness test.
Table 5. The results of the robustness test.
Sustainable Development Capability of the Enterprise
M1M2M3M4M5M6
Control variablesTBO0.142 ***0.152 ***0.115 ***0.123 **0.118 **0.018
EA0.092 *0.100 **0.080 *0.088 *0.100 **−0.012
Independent variablesDC0.087 * 0.046 0.113 **
SH 0.108 ** 0.082 * −0.048
ERA 0.198 ***0.196 ***
CSR −0.195 ***−0.132 **
Interaction variablesDC × CSR −0.113 **
SH × CSR 0.169 ***
R20.1930.2030.2720.2080.2810.265
ΔR20.0370.0410.0740.0780.0790.070
Adj- R20.0300.0340.0650.0690.0680.058
F5.291 ***5.855 ***8.142 ***8.662 ***6.972 ***5.855 ***
Note: Types of business ownership (TBO); Enterprise age (EA); Degree centrality (DC); Structural holes (SH); External resource acquisition (ERA); Corporate social responsibility (CSR); *** indicates significance at the 0.01 level (both sides); ** indicates significance at the 0.05 level (both sides); and * indicates significance at the 0.1 level (both sides).

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Su, Y.; Yu, Y. Effects of Technological Innovation Network Embeddedness on the Sustainable Development Capability of New Energy Enterprises. Sustainability 2019, 11, 5814. https://doi.org/10.3390/su11205814

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Su Y, Yu Y. Effects of Technological Innovation Network Embeddedness on the Sustainable Development Capability of New Energy Enterprises. Sustainability. 2019; 11(20):5814. https://doi.org/10.3390/su11205814

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Su, Yi, and Yueqi Yu. 2019. "Effects of Technological Innovation Network Embeddedness on the Sustainable Development Capability of New Energy Enterprises" Sustainability 11, no. 20: 5814. https://doi.org/10.3390/su11205814

APA Style

Su, Y., & Yu, Y. (2019). Effects of Technological Innovation Network Embeddedness on the Sustainable Development Capability of New Energy Enterprises. Sustainability, 11(20), 5814. https://doi.org/10.3390/su11205814

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