Do Technology Alliance Network Characteristics Promote Ambidextrous Green Innovation? A Perspective from Internal and External Pressures of Firms in China

Abstract

Corporate alliances have become an important way for firms to share the resources and costs of innovation. However, whether corporate technology alliances can effectively enhance the ambidextrous green innovation (AGI) capabilities of firms is a question that still needs to be answered. Building networks of corporate technology alliances based on joint patent application data from the China National Intellectual Property Administration (CNIPA) for the period of 2015–2019, this study investigated the impact of network centrality and structural hole characteristics on exploitative green innovation (IGI) and exploratory green innovation (RGI) from the perspective of internal and external pressures. The empirical results showed that (1) network centrality and structural holes could promote AGI and that the impact on IGI was greater than that on RGI. However, an examination based on lagged effects found a greater impact on RGI. (2) The impact of alliance networks on AGI was positively moderated by internal and external pressures. (3) There were complementary effects between the internal and external pressures. Our study emphasized that it was important to balance AGI to win short-term and long-term competition.

1. Introduction

Under the dual constraints of environment and resources, the importance of green innovation has attracted attention. As a major developing country, China proposed in Made in China 2025 to address environmental issues, with green innovation as the key breakthrough point. However, due to the environmental and technological double externality of green innovation, there is always a lack of incentive for individual firms to research and develop green technologies [1,2]. It is partly because the externalities are not fully internalized and partly because green technologies have higher costs and technical barriers [3]. Unlike technology acquisitions, corporate technology alliances allow firms to learn from each other while having access to information such as human resources and markets [4]. Innovative cooperation is an approach to accessing the resources of cooperative organizations [5,6]. Through corporate alliances, which are secured by legal contracts or shareholding constraints [7], firms are more willing to engage in green innovation when they can share risks and complementary technologies. Firms in alliance networks not only use the network to access heterogeneous information to help improve green innovation but are also subject to internal and external pressures to produce more cleanly.

The relationship between alliance networks and innovation has been extensively studied, and it revolves around four main perspectives. The first category focuses on resource dependency. A firm’s ability to win a competitive edge is often expressed as the ability to access decisive resources. In addition, a firm’s network is a critical way to access key resources [5,8]. Gulati [9] also pointed out that firms can obtain an edge in key markets by developing alliance networks. He further found that the network resources acquired by a firm when participating in an alliance impacted the firm’s entry into a new alliance [10]. The second category is based on a socially embedded perspective. Granovetter [11] further divided embeddedness into relational and structural embeddedness. Scholars have examined the impact on innovation mainly in terms of mutual relationship trust and the innovation incentives due to structural differences in alliance networks, which can create access to knowledge and information [12,13,14]. The third category is based on organizational learning and discusses the impact of experiences of alliance collaboration on innovation performance [15]. The fourth category is based on contingency theory, which explores the moderating role of changes in the surrounding environment [16].

This study was more interested in alliance networks and green innovation. As research methods, most studies have explored the relationship between alliances and cleaner production at a theoretical level. They mainly used game models or case studies. They have proved that alliances can help to improve a firm’s environmental technologies and can lead to cleaner production [17]. Some scholars also used empirical means to examine the positive relationship between alliances and a firm’s green benefits. Survey data were commonly used in these studies [18,19]. However, the questionnaire design was inevitably subjective. As a result, a growing number of scholars have recently constructed alliances based on collaborative data [1,20,21,22].

A review of the relevant studies showed that (1) corporate technology alliances were often established for the purpose of improving technological innovation. However, little attention has been paid to whether they can also improve green technological innovation. The technology spillovers when participating in technology alliances were less considered. In fact, they could also provide a good resource base for green innovation. (2) The existing research has discussed the impact of alliances on innovation. However, the studies rarely identified the influence of network characteristics on different types of green innovation. (3) Most of the studies focused on the structure and efficiency of networks. They lacked attention to the firm itself. Therefore, it was necessary to test the impact of the firm’s internal and external environment.

To enrich the existing research, this study examined the impact and mechanism of the network characteristics of technology alliances on different types of green innovation. Specifically, we built a network of corporate technology alliances based on joint patent application data; then, we analyzed the effects of network centrality and structural holes on exploitative green innovation (IGI) and exploratory green innovation (RGI). Network centrality reflects the number of connected nodes in the network, which reflects the importance of the firm in the network [23]. The structural holes refer to the role of an intermediary, which can play a role in information transmission [24]. IGI and RGI embody the two dimensions of ambidextrous green innovation (AGI). IGI is a gradual and continuous innovation caused by the improvement of the existing green innovation technology [25,26]. RGI needs a major breakthrough in green innovation technology, which may produce brand new technology [26,27]. Then, using responsibility pressure and risk pressure as internal pressures and peer pressure and regulatory pressure as external pressures, we discussed the impact of technology alliances on AGI from the perspective of internal and external pressures. Furthermore, we investigated the complementary and substitution relationships between the internal and external pressures. In this study, responsibility pressure means the pressure caused by a firm’s environmental responsibility. Risk pressure means the pressure caused by a firm’s risk-taking abilities. Regulatory pressure means the pressure caused by the government’s environmental regulations. Peer pressure means the pressure caused by the green innovation abilities of other firms in the industry. The measurement of these variables will be clarified later in the paper. Our results are expected to provide a reference for corporate green strategic transformation.

The contribution of this paper may be seen in two ways. Firstly, our research found that the network characteristics of technology alliances have a promoting effect on AGI, which provides a reference for firms to choose green strategies. At the same time, we also stressed the importance of the coordinated development of IGI and RGI to support the short-term and long-term development of firms. Secondly, how to find a balance between exploitative innovation and exploratory innovation has always been the consistent theme of organizational adaptability research. Our research finds that under the influence of the network characteristics of a technology alliance the moderating effects of internal and external pressures are factors affecting the balance of IGI and RGI. It may provide some guidance for firms to enhance their green technology innovation capabilities.

This study was structured as follows. In Section 2, the relevant literature is reviewed, and several hypotheses are proposed. Section 3 contains the data collection and variable measurement. Section 4 and Section 5 mainly depict the empirical results. The last section summarizes the inspiration and limitations of the study.

2. Hypotheses

2.1. Network Characteristics of Technology Alliance and AGI

Ambidextrous innovation is usually divided into two dimensions: exploitative and exploratory innovation [28,29]. Extending this concept to green innovation, we divided green innovation into IGI and RGI. IGI is based on the improvement of existing products and services, while RGI requires new products which may bring long-term competitive advantages [30,31]. The existing studies have found that strategic alliances can promote technological innovation by enabling firms to learn the knowledge and information of their collaborators in the process of cooperation [5,17,19,32]. The composition of the alliance network was the result of the strategic choice of firms. By using the network resources, firms can make quick responses to market changes and provide more guarantees for the success of green innovation.

A corporate technology alliance is a form of firm cooperation. A corporate technology alliance refers to the research and development activities of two or more firms jointly committed to a certain technology or product. It is a complementary organization to meet the needs of rapid technological development and market competition [33,34]. The process of innovation is becoming increasingly complex, and it is difficult for firms to obtain all the resources they need on their own; so, looking to external partnerships may be a better solution [35]. In addition, under the policy pressure of carbon peaking and carbon neutrality goals, the survival competition of firms is fiercer, which forces them to carry out high-risk green technology attempts; so, many of them choose alliances to share risks and complementary technologies. The use of an alliance network facilitates the dissemination of information and to a certain extent affects strategic decision making [16,36]. The network resource is one of the key factors affecting the formation of alliances [10]. Network centrality and structural holes are important characteristics of a firm’s mastery of resources in the network [6,37]. Therefore, we take network centrality and structural holes as the analysis objects of network characteristics.

Network centrality reflects a firm’s position in the network. With larger centrality, the firm has more heterogeneous resources and information [36]. From the perspective of knowledge dissemination, a larger network centrality is conducive to the enhancement of knowledge integration. Competition between firms is increasingly influenced by their ability to integrate knowledge [38,39]. Firms with a vantage point advantage in networks further contribute to the emergence of trust and reciprocity due to frequent interactions and closer relationships [33,40,41]. On the one hand, a high familiarity stimulates the combination of previously established technological domains [42,43]. It facilitates the integration and translation of the relevant expertise, thus contributing to an increase in IGI. On the other hand, trust increases the willingness of partners to exchange complex and diverse knowledge [44], which is necessary for RGI. From an organizational learning perspective, members that have lower centrality in the network are more willing to proactively export information to gain trust and stable collaborative relationships. Firms which have higher centrality have access to more valuable experience and information that do not exist within the firm. In these circumstances, firms can expand the base of the available knowledge reorganization, which is a core driver of exploitative innovation [45,46]. At the same time, positional advantage reduces barriers such as coordination costs for knowledge acquisition. Thus, it increases autonomy and flexibility. Additionally, this advantage provides a further suitable environment for breakthroughs in green technology.

A firm occupying a structural hole acts as a “bridge” between different partners. This intermediary role has the advantage of control over resources and information, which can lead to green technology gains for the firm. The structural hole theory suggests that structural holes reduce the redundant resources of a firm, reduce the risk of over-searching, and avoid complications in the identification and resource allocation of valuable resources, thus reducing the potential negative effects of innovative collaboration [35,47]. According to resource dependence theory, firms need to gain resources and competitive advantage by working with other partners [48]. Knowledge from external sources alleviates the scarcity of internal resources and provides ideas to promote new products and technologies [49]. On the one hand, firms can refine existing products by accumulating knowledge. On the other hand, they can integrate complementary information to provide possibilities for technological breakthroughs. In addition, social capital theory suggests that firms can increase the social capital of mutual trust by building alliance networks [18]. Firms with more structural holes have more social capital in their networks. The trust and connections with each other promote cooperation and learning, reducing the time it takes to exchange knowledge between the firms. Firms occupying structural holes will not only facilitate the elimination of uncertainties and enable small-scale continuous innovation; stakeholders will also increase effective resource investments that can serve RGI, which demands higher resource and capacity needs. Thus, we hypothesize:

H1a. 

The network centrality of corporate technology alliances can promote AGI.

H1b. 

The network structure holes of corporate technology alliances can promote AGI.

2.2. The Moderating Role of Responsibility Pressure

In this study, responsibility pressure means the pressure caused by a firm’s environmental responsibility. The environmental responsibility of firms has become a key part of social responsibility and is of common interest to policymakers, business stakeholders, and researchers [50]. Therefore, this study considered responsibility pressure as one of the internal pressures on firms to engage in AGI. Due to the requirements of legality, a firm’s responsibility pressure will affect business partnerships [51]. If a firm lacks legitimacy, the opportunities for cooperation with other firms will be limited. Based on the theory of the reputation effect, reputation comes from the recognition people have for it, and it is a special resource for the firm, representing its attractiveness [52]. Active social responsibility behavior is conducive to enhancing a firm’s recognition and brand influence, which in turn helps the firm gain a good reputation and credibility [53,54,55]. A good reputation reduces the creditors’ assessment of a firm’s debt-servicing risk and allows the firm to gain trust and recognition from external markets [56,57]. Firms with high centrality and rich structural holes will also have a high reputation [36]. So, as responsibility pressure grows, the firms acting as centers or bridges in the alliance network also need to increase their green innovation capacity to maintain legitimacy and collaborative support. Thus, we hypothesize:

H2a. 

The relationship between corporate technology alliance network centrality and AGI is moderated by responsibility pressure. That is, the relationship is more positive when the responsibility pressure is high.

H2b. 

The relationship between corporate technology alliance network structure holes and AGI is moderated by responsibility pressure. That is, the relationship is more positive when the responsibility pressure is high.

2.3. The Moderating Role of Risk Pressure

In this study, risk pressure means pressure caused by a firm’s risk-taking abilities. The high cost of green technological innovation and the high uncertainty it faces will pose a greater risk to a firm’s innovative technologies. Therefore, this study considered risk pressure as one of the internal pressures on firms to engage in AGI. Network resources can help firms make more rational and decisive decisions by integrating internal and external factors when faced with risky choices. Firstly, firms can access valuable information from the network that may lead to better opportunities for them [10]. In addition, Spithoven and Teirlinck [58] proposed that one of the key factors affecting the risky research investment of enterprises was network resources. Secondly, network resources enhance organizational learning and alliances with reliable partners [40]. In addition, partners can share risks and costs when encountering highly uncertain and complex green innovation. In some cases, the alliance partners will have a positive influence on risky outcomes [59]. This means that sources of uncertainty may be mitigated through partnerships [60]. It is also confirmed that developing green practices with other firms can reduce costs [61], which will result in better economic benefits. When firms have high risk-taking abilities, they are more able to use network resources to carry out risky AGI. Thus, we hypothesize:

H3a. 

The relationship between corporate technology alliance network centrality and AGI is moderated by risk pressure. That is, the relationship is more positive when the risk pressure is high.

H3b. 

The relationship between corporate technology alliance network structure holes and AGI is moderated by risk pressure. That is, the relationship is more positive when the risk pressure is high.

2.4. The Moderating Effect of Peer Pressure

In this study, peer pressure means the pressure caused by the green innovation abilities of other firms in the industry. Peer pressure is the most common social psychological state and is unique to social animals [62], meaning that individuals will be influenced by the surrounding environment when making choices. It also exists in firms. Peers or partners encourage firms to change their attitudes or behaviors to align the firms with influential organizations [63]. The firms that are influenced by external organizations may change their attitudes towards the environment. Because the resources of different firms are heterogeneous, such resources can influence their strategic choices [64,65,66]. Cao et al. [67] asserted that firms that focused on external behavior would outperform firms that focused only on internal behavior. In a network of technological alliances, firms will be more determined to implement green strategies when they reduce environmental penalties and will even gain more efficient production by adopting greener technologies. Correspondingly, other members of the network have a high probability of changing their old, crude production methods. Under environmental and resource constraints, peer pressure can also come from competition in the product market and stakeholder scrutiny, making it even more important for firms to use network resources for green innovation. As peer pressure increases, firms will actively make use of collaborations to cope with the uncertainty and complexity that comes with AGI. Thus, we hypothesize:

H4a. 

The relationship between corporate technology alliance network centrality and AGI is moderated by peer pressure. That is, the relationship is more positive when the peer pressure is high.

H4b. 

The relationship between corporate technology alliance network structure holes and AGI is moderated by peer pressure. That is, the relationship is more positive when the peer pressure is high.

2.5. The Moderating Role of Regulatory Pressure

In this study, regulatory pressure means the pressure caused by the government’s environmental regulations. Environmental regulation is an external motivator for firms to engage in AGI, and it is an essential way for governments to promote green technology transition [68]. Therefore, this study considered regulatory pressure as one of the external pressures on firms to engage in AGI. Achieving cleaner production will increase business costs in the short term, which leads most firms to ignore environmental influence in their production processes [69]. The Porter hypothesis believes that environmental regulation will create an innovative compensatory effect that facilitates the increase in patented technologies and the advancement of green technologies [70]. Firms are embedded in a variety of interdependent networks that can mitigate resource requirements and reduce the risk of green innovation through technological collaboration. When external regulatory pressures increase, the firms in a network of technology alliances can take advantage of the resources and information brought about by their location and structure in the network to innovate green technologies in order to meet the corresponding environmental standards. Thus, we hypothesize:

H5a. 

The relationship between corporate technology alliance network centrality and AGI is moderated by regulatory pressure. That is, the relationship is more positive when the regulatory pressure is high.

H5b. 

The relationship between corporate technology alliance network structure holes and AGI is moderated by regulatory pressure. That is, the relationship is more positive when the regulatory pressure is high.

In this study, we construct a conceptual model of the study, as shown in Figure 1.

3. Research Design

3.1. Data and Sample Selection

This study was based on a sample of A-share listed manufacturing companies in China from 2015 to 2019. To avoid the impact of the COVID-19 pandemic on corporate alliances, our observation period did not include the years beyond 2019. As listed companies in the manufacturing industry can generate a lot of pollution and carbon emissions, they receive more supervision from the government and society than the listed companies in other industries. The manufacturing industry is also characterized by strong R&D. It has a large number of patents and an accessible data collection. In addition, a large number of studies on the subject of green innovation have also tended to examine the manufacturing industry [71,72,73,74]. Manufacturing firms are technology-intensive and knowledge-intensive and are under environmental pressure, making it easier to form alliance cooperation oriented towards green innovation.

Although the observation period is from 2015 to 2019, there is a lag in the impact of the firms joining the alliance on firm innovation. The duration of a firm’s participation in an alliance is not specified in any databases. It is often assumed that the partnership will last for three years after it is established [32,36]. Therefore, it is also necessary to analyze the R&D collaborations that took place between 2013 and 2017. After obtaining the basic information on the company from the CSMAR database (http://cndata1.csmar.com/, accessed on 2 May 2022), the data were further processed as follows: first, we excluded special treatment companies without complete financial information. Next, we excluded non-manufacturing companies. Third, the final year of observation in this study was 2019; so, the sample companies must have been listed in 2017 and earlier. We excluded those listed after 2017.

We further collected the firms’ patent information by the applicants in the patent search system of China National Intellectual Property Administration (CNIPA) (https://www.cnipa.gov.cn/, accessed on 15 July 2022), based on the company’s name [75,76,77,78]. Considering that the name change of a company may cause a search omission, all aliases of companies in the study period were searched for additions. Referring to the cooperative network construction rules of Yang et al. [79], the selection of firms must satisfy two conditions. Firstly, joint patent applications should include listed manufacturing companies and their direct R&D partners from 2013 to 2019. Secondly, the researched companies must have at least one collaboration with another firm. The collaboration must be in the R&D or manufacturing category, excluding non-R&D collaborations such as investment and financing, marketing, consulting, etc.

We used Python software to process the obtained patent information and excluded duplicate patents and 1459 companies that did not apply for patents jointly with other companies. Then, the information on a total of 135,894 patents was obtained. The applicants in each patent were further disaggregated and de-duplicated. Then, a unique matching table was made based on the company’s name (including aliases). Therefore, the current year network in the study was cumulative for the current year and the previous two years of network cooperation. There were 822 listed manufacturing companies with a total sample size of 2731.

3.2. Variables

(1) Ambidextrous green innovation

We often use indicators such as R&D investment, green patents, total factor productivity, and a green innovation indicator system to examine a company’s ability to innovate in environmental protection. By comparison, patent data provide a more accurate representation of innovation capability at the output level. The International Patent Classification (IPC) information on patents helps to separate the green technological innovations from all innovation activities [80], which is more in line with this study’s theme of examining the impact of corporate alliance network characteristics on green innovation capabilities. In order to filter the number of green patents, the patent information of the sample companies derived from CNIPA was matched with the green IPC information in the international patent classification. Extending the scholars’ measurements of ambidextrous innovation to the level of green innovation, we used the granted number of utility-type patents of firms to represent IGI and the granted number of creation-type patents among the green patents to characterize RGI [81,82].

(2) Corporate technology alliance network characteristics

In social network analysis, network centrality and structural holes are the most common characteristics indicators. In this study, the centrality and structural holes of the network were calculated by Python software.

The network centrality refers to the relative degree of centrality of each node in the network, and the larger network centrality indicates the higher status of the focal firm in the network [83]. Thus, the firms with high centrality and the firms connected by them more easily form the relationship of leader and follower. In other words, the degree of centrality is related with the power of a firm in a network. If a firm has the highest centrality, it is said to be at the center of the network and has power [5]. Referring to Grigoriou and Rothaermel’s analysis of centrality [23], the network centrality in this study was calculated by the ratio of the absolute degree of centrality to the number of all possible connected points (n − 1). The calculation formula is as follows:

$${\mathrm{DC}}_i=\frac{{\sum }_j{p}_{ij}}{n-1}$$

(1)

Here, DC is the network centrality; i represents the focus firm; j represents the cooperative firm; p_ij represents the technical alliance relationship between the focus firm i and the cooperative firm j; and n is the number of nodes in the network.

Burt proposed that a structural hole was a non-redundant connection between two actors [84]. Structural holes can provide opportunities for the occupants to gain “information benefits” and “control benefits” [24]. Thus, when firms occupy the position of structural holes, they have a competitive advantage over the members in other positions in the network. According to the research of Zaheer and Bell [37], the richness of structural holes was measured by the difference between 1 and the “constraint index”. The “constraint index” was actually the concept of “limits” proposed by Burt [84]. The smaller the “limits”, the richer the structural holes occupied by the firm in the network. The calculation formula is as follows:

$${\mathrm{SHI}}_i=1-{\displaystyle \sum }_j{{(p}_{ij}+{\displaystyle \sum }_q{p}_{iq}{p}_{qj})}^2$$

(2)

Here, SHI represents the network structure holes; q represents the intermediate collaborator; p_iq represents the proportion of the relationship between the focus firm i and the intermediate collaborator q compared to the total relationship; and p_qj represents the proportion of the relationship between the intermediate collaborator q and the cooperative firm j compared to the total relationship.

(3) Internal and external pressures

Due to the high risks and costs of green transformation, there is a need to promote green innovation under a certain pressure. Therefore, this study selected four pressures as potential moderating variables of corporate technology alliances on AGI. In addition, it was divided into internal and external according to the pressure source. Responsibility pressure and risk pressure are influenced by the firm’s own perceptions and operational status. Thus, they are considered to be internal pressures. As the government and other firms in the industry are external organizations, regulatory pressure and peer pressure are considered to be external pressures.

Responsibility pressure in this study means the pressure caused by a firm’s environmental responsibility. Environmental responsibility is often measured by a firm’s score on environmental protection or an environmental disclosure index. This study selected the environmental disclosure item in the corporate social responsibility disclosure index of Bloomberg (https://www.bloombergchina.com/solution/sustainable-finance/, accessed on 10 August 2022) as a proxy for responsibility pressure; it included disclosures in seven categories: air quality, climate change, the impact on ecology and biodiversity, energy, raw materials and waste, the supply chain, and water resources.

Risk pressure in this study means the pressure caused by a firm’s risk-taking abilities. In this study, firms with higher risk-taking ability also have higher risk pressure, and these firms are more capable of high-risk innovation activities. Referring to Low [85] and Cole [86], the standard deviation indicator of the industry-adjusted return on assets (ROA) was used to measure the level of the risk-taking ability of firms. The calculation formula is as follows:

$${\mathrm{Adj}\_\mathrm{ROA}}_{i,t}{=\mathrm{ROA}}_{i,t}-\frac{1}{n}{\displaystyle \sum }_{k=1}^n{\mathrm{ROA}}_{k,t}$$

(3)

$$\mathrm{RT}=\sqrt{\frac{1}{T-1}{\displaystyle \sum }_{t=1}^T{{(\mathrm{Adj}\_\mathrm{ROA}}_{i,t}-\frac{1}{T}{\displaystyle \sum }_{t=1}^T{\mathrm{Adj}\_\mathrm{ROA}}_{i,t})}^2|T=2}$$

(4)

Here, Adj_ROA represents the industry-adjusted ROA; RT represents the level of risk-taking ability (the standard deviation of the industry-adjusted ROA); i represents the firm; t represents the year; and T represents the observation period.

Regulatory pressure in this study means the pressure caused by the government’s environmental regulations. Firstly, we standardized the treatment rates of general industrial solid waste, harmless domestic waste, and sewage treatment plants in each city [78]. After that, the comprehensive index of environmental regulation was constructed by the entropy value method; the higher the index, the more the firms were subject to local regulatory pressure.

Peer pressure in this study means the pressure caused by the green innovation abilities of other firms in the industry. We divided the same industry into peer groups. We referred to Leary’s measurement [87]; the average number of green technology patents of other firms in the same industry was used as a proxy variable for peer pressure.

(4) Control variables

The control variables were selected at the level of the firm’s basic characteristics and the firm’s R&D capabilities. The data that were still missing after manual searching from the annual reports of the firms were filled in by linear interpolation.

The variables of the firm’s basic characteristics included: (1) the age of the firm, using the number of years the firm has been in existence as a proxy variable. The age of the firm will affect the strategic choice of the firm at its current stage and will also affect the innovation vitality of the firm [88]. (2) The scale of the firm, as characterized by the natural logarithm of the firm’s total assets. The size of the firm will affect the growth of the firm and the ability of the firm to bear risks [26]. (3) The nature of the firm, which is distinguished according to whether the firm was state-owned or not. On the one hand, the nature of the firm affects the differences in financial and technical resources [22]. On the other hand, it also affects the attitude towards environmental protection. (4) The industry to which the firm belongs. (5) The year to which the firm belongs.

The variables of firm’s R&D capability characteristics include: (1) employee education, using the proportion of the firm’s personnel with a bachelor’s degree or above as a proxy variable. Employees are the main promoters of innovation; so, the education level of employees will also affect the complexity of the innovation [89]. (2) Government innovational subsidies, which are represented using the natural logarithm of the government subsidy data for the R&D innovation of listed companies. Government subsidies can not only alleviate the shortage of innovation funds but can also transmit the encouragement signal of the government and increase the possibility of obtaining external investment [90,91]. (3) The growth of the firm, which is represented using the growth rate of the firm’s revenue. The growth of the firm reflects the productivity of the firm, and profitability is also the basic guarantee of the firm’s ability to conduct research and development [92]. (4) The market competition was represented by the Herfindahl index of the firm’s revenue. The degree of competition is also an important driving force for firms to conduct new product research or service improvement [1].

4. Results

4.1. Descriptive Statistics

In Figure 2, there is an obvious technology alliance network in a large range. Some nodes had a significantly higher centrality, and the connection strength with other nodes also had significant heterogeneity. It can be found from the changes from 2015 to 2019 that the number of nodes in the larger range of alliance networks has increased, and the connection has become more complex. Although the characteristic that some nodes dominated in the network has not changed, the maximum value of centrality was obviously decreasing. It is worth noting that there were more small-scale decentralized networks around. These findings showed that the technology alliances between firms were increasing in 2015–2019. It also reflected the importance of studying technology alliances among firms. There was an information exchange in the large networks and the smaller networks, and the absolute advantage of some firms in the network was weakening.

Note: In this paper, the firms that had a joint patent application are regarded as partners in the technology alliance. The network nodes represent firms that participated in the technology alliance. The size and color of the nodes indicate the centrality of the network. As the centrality increases, the nodes get bigger and the colors get darker. If there is a partnership between the firms, then there is a line between the nodes. The thickness of the line reflects the strength of the connection, which means the number of cooperations. The nodes that did not appear to be widely connected were distributed around the circle.

The results of the descriptive statistics and the correlation analysis for each variable are shown in

Table 1. Results of descriptive statistics and correlation analysis of variables.

Variables	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15
1	1.000
2	0.645 ***	1.000
3	0.462 ***	0.337 ***	1.000
4	0.214 ***	0.190 ***	0.471 ***	1.000
5	0.018	0.011	0.032 *	0.018	1.000
6	0.237 ***	0.216 ***	0.194 ***	0.137 ***	0.001	1.000
7	0.205 ***	0.168 ***	0.214 ***	0.252 ***	−0.068 ***	−0.012	1.000
8	−0.031	0.021	−0.057 ***	−0.059 ***	−0.019	−0.075 ***	0.051 ***	1.000
9	0.097 ***	0.049 **	0.154 ***	0.153 ***	−0.087 ***	−0.034 *	0.448 ***	0.049 **	1.000
10	0.305 ***	0.274 ***	0.295 ***	0.306 ***	−0.068 ***	0.121 ***	0.671 ***	0.038 **	0.540 ***	1.000
11	−0.081 ***	−0.048 **	−0.118 ***	−0.143 ***	0.187 ***	0.009	−0.270 ***	−0.064 ***	−0.544 ***	−0.362 ***	1.000
12	0.108 ***	0.023	0.175 ***	0.156 ***	0.003	0.144 ***	0.055 ***	0.040 **	−0.025	0.042 **	−0.073 ***	1.000
13	0.174 ***	0.164 ***	0.166 ***	0.187 ***	0.031	0.115 ***	0.281 ***	−0.028	0.130 ***	0.425 ***	−0.063 ***	0.083 ***	1.000
14	−0.009	−0.003	−0.015	−0.025	0.022	−0.005	−0.030	−0.007	−0.054 ***	0.026	0.046 **	0.026	0.029	1.000
15	−0.071 ***	−0.040 **	−0.020	0.006	−0.020	−0.185 ***	−0.094 ***	0.054 ***	−0.139 ***	−0.106 ***	0.114 ***	0.165 ***	−0.027	0.023	1.000
Mean	1.936	2.776	1.247	0.318	0.912	2.078	1.148	4.651	15.423	22.514	1.663	25.670	16.908	16.722	0.937
S.D.	8.485	13.662	2.262	0.372	0.065	2.098	1.319	1.110	6.828	1.314	0.473	17.924	2.466	108.989	0.056
Max	142.000	324.000	25.854	0.980	1.081	7.805	4.118	8.272	32.000	27.468	2.000	100.000	22.275	5504.443	0.985
Min	0.000	0.000	0.273	0.000	0.500	0.000	0.000	0.000	4.000	17.806	1.000	0.000	0.000	−80.065	0.000

Note: *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively. The number 1 represents RGI, 2 represents IGI, 3 represents centrality, 4 represents structural hole, 5 represents regulatory pressure, 6 represents peer pressure, 7 represents responsibility pressure, 8 represents risk pressure, 9 represents firm age, 10 represents firm scale, 11 represents firm nature, 12 represents employee education, 13 represents government subsidies, 14 represents firm growth, and 15 represents industry competition.

. The results show that the mean value of the IGI is 2.776 with a standard deviation of 13.662. RGI had a mean value of 1.936 and a standard deviation of 8.485. It meant that the ability of IGI was higher than RGI in the manufacturing firms, and the difference between the firms was large. In terms of network characteristics, the mean value of centrality was 1.247, while the maximum value reached 25.854, indicating the presence of firms with a relative positional advantage in the network, which can also be found in Figure 2. The standard deviation of the structural holes was greater than the mean value, indicating a greater variation in the richness of structural holes in the network, making it necessary to analyze the impact of the location and the structure of the network on the AGI capability between alliance firms. In addition to this, the absolute value and significance of the correlation coefficients indicated that serious multicollinearity problems between the variables were less likely, providing a basis for further research.

4.2. Regression Results

4.2.1. Network Characteristics of Technology Alliance and AGI

The empirical results in

Table 2. Regression results of alliance network characteristics and AGI.

Variables	IGI				RGI
	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6	Model 7	Model 8
Centrality	1.902 ***		1.594 ***		1.727 ***		1.515 ***
	(16.821)		(13.558)		(26.473)		(22.161)
Structural holes		6.160 ***		3.458 ***		4.391 ***		2.310 ***
		(8.848)		(4.832)		(10.274)		(5.281)
Firm age			−0.268 ***	−0.261 ***			−0.097 ***	−0.091 ***
			(−5.447)	(−5.152)			(−3.406)	(−2.927)
Firm scale			2.814 ***	3.302 ***			1.461 ***	2.003 ***
			(10.852)	(12.383)			(9.691)	(12.287)
Firm nature			−0.460	−0.693			0.101	−0.168
			(−0.720)	(−1.053)			(0.272)	(−0.419)
Employee education			−0.057 ***	−0.035 **			0.026 ***	−0.035 **
			(−3.914)	(−2.352)			(0.260)	(2.765)
Government subsidies			0.108	0.128			0.040	0.066
			(0.964)	(1.108)			(0.608)	(0.936)
Firm growth			−0.002	−0.002			−0.001	−0.002
			(−0.690)	(−0.821)			(−0.909)	(−1.158)
Industry competition			7.710	7.249			−5.429	−6.156
			(0.462)	(0.422)			(−0.559)	(−0.586)
Year dummies	Included	Included	Included	Included	Included	Included	Included	Included
Industry dummies	Included	Included	Included	Included	Included	Included	Included	Included
Constant	−3.621 *	−1.100	−69.732 ***	−78.193 ***	−3.529 ***	−0.787	−30.808 ***	−40.087 ***
	(−1.874)	(−0.556)	(−4.287)	(−4.672)	(−3.152)	(−0.643)	(−3.257)	(−3.919)
R2	0.139	0.076	0.189	0.141	0.257	0.100	0.289	0.168

Note: *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively. The values in brackets are t values. Alliance network characteristics including centrality and structural holes. AGI including IGI and RGI. AGI—ambidextrous green innovation; IGI—exploitative green innovation; RGI—exploratory green innovation.

showed that technology alliance network centrality and structural holes significantly enhance the AGI ability of firms. Specifically, Models 1–4 illustrated a significant positive effect of network centrality and structural holes on IGI, respectively (β₁ = 1.902, p < 0.01; β₂ = 6.160, p < 0.01; β₃ = 1.594, p < 0.01; β₄ = 3.458, p < 0.01). Models 5–8 illustrated the significant contribution of network centrality and structural holes to the level of RGI, respectively (β₅ = 1.727, p < 0.01; β₆ = 4.391, p < 0.01; β₇ = 1.515, p < 0.01; β₈ = 2.310, p < 0.01). It could be found that network centrality and structural holes enhance IGI to a greater extent than RGI, where the structural holes promoted AGI better than centrality. It was worth noting that the coefficient of the regression results without control variables (Model 1, Model 2, Model 5, and Model 6) was smaller than those results with control variables (Model 3, Model 4, Model 7, and Model 8), which indicated that there were factors affecting AGI in the control variables. Therefore, after considering the control variables, we could more accurately estimate the net effect of the technology alliance network characteristics on AGI.

4.2.2. Robustness Test

(1) Model selection bias. Firstly, we excluded the influence of the time and industry control variables. As we can see in

Table 3. Results of robustness test of model selection bias.

Variables	IGI				RGI
	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6	Model 7	Model 8
Centrality	1.722 *		1.595 **		1.502 ***		1.412 ***
	(1.892)		(1.980)		(3.171)		(3.356)
Structural holes		4.250 ***		4.291 ***		2.716 ***		2.747 ***
		(2.845)		(2.913)		(3.502)		(3.723)
Firm age	−0.276 *	−0.269 *		−0.315 *	−0.113 ***	−0.108 **	−0.109 **	−0.105 *
	(−1.936)	(−1.776)	(−1.867)	(−1.748)	(−2.782)	(−2.112)	(−1.990)	(−1.669)
Firm scale	2.629 ***	3.096 **	2.953 ***	3.490 ***	1.445 ***	1.929 ***	1.617 ***	2.181 ***
	(2.896)	(2.549)	(2.848)	(2.620)	(3.583)	(3.917)	(3.840)	(3.948)
Firm nature	0.124	0.251	−0.901	−1.229	0.166	0.244	−0.127	−0.450
	(0.136)	(0.269)	(−0.613)	(−0.797)	(0.279)	(0.383)	(−0.141)	(−0.443)
Employee education	−0.030	−0.006	−0.050	−0.028	0.017	0.040**	0.013	0.034
	(−0.847)	(−0.254)	(−1.250)	(−0.789)	(1.040)	(2.295)	(0.655)	(1.607)
Government subsidies	0.171 **	0.192 *	0.053	0.081	0.070	0.098	0.023	0.052
	(1.987)	(1.880)	(0.548)	(0.748)	(1.137)	(1.258)	(0.297)	(0.561)
Firm growth	−0.002 **	−0.002 *	−0.001	−0.001 **	−0.001 **	−0.002 **	−0.001 **	−0.001 **
	(−2.210)	(−1.894)	(−1.627)	(−1.688)	(−2.272)	(−2.440)	(−2.443)	(−2.504)
Industry competition	−4.625	−6.277	6.114	6.641	−8.690*	−10.017 **	−3.759	−3.547
	(−0.832)	(−1.171)	(0.488)	(0.464)	(−1.832)	(−2.128)	(−0.425)	(−0.359)
Year dummies	Excluded	Excluded	Included	Included	Excluded	Excluded	Included	Included
Industry dummies	Excluded	Excluded	Included	Included	Excluded	Excluded	Included	Included
City × Year	Excluded	Excluded	Included	Included	Excluded	Excluded	Included	Included
Constant	−52.265 **	−61.707 **	−64.543 **	−76.979 **	−24.457 ***	−34.362 ***	−31.465 **	−43.962 **
	(−2.484)	(−2.216)	(−2.386)	(−2.303)	(−2.873)	(−3.136)	(−2.044)	(−2.491)
R2	0.163	0.103	0.337	0.299	0.256	0.126	0.380	0.295

Note: *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively. All regressions are clustered at the firm level. The values in brackets are t values. Alliance network characteristics including centrality and structural holes. AGI including IGI and RGI. AGI—ambidextrous green innovation; IGI—exploitative green innovation; RGI—exploratory green innovation.

, the results of Model 1, Model 2, Model 5, and Model 6 reflected the fact that the effects of the technology alliance network characteristics on AGI were still valid. Secondly, we included the influence of the time and industry control variables. At the same time, in order to control the annual difference between cities, we also controlled the interaction effect between the year and the city. As we can see in Model 3, Model 4, Model 7, and Model 8, the effect of the technology alliance network characteristics on is AGI still significant. The regression results in Table 3 implied that our findings were robust.

Tobit models are suitable for situations where the estimates are inconsistent due to the truncation or blocking of the explanatory variables [93]. As IGI and RGI both had a large number of zero values, they were therefore suitable for regression in the Tobit model to reduce the estimation bias introduced by the model. The regression results in

Table 4. Results of robustness tests of model selection bias and counterfactual test.

Variables	IGI				RGI
	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6	Model 7	Model 8
Centrality	1.964 ***		0.028		2.016 ***		0.056
	(9.293)		(0.256)		(14.276)		(0.844)
Structural holes		10.839 ***		−0.542		8.431 ***		−0.236
		(6.749)		(−0.817)		(7.399)		(−0.581)
Firm age	−0.634 ***	−0.638 ***	−0.261 ***	−0.260 ***	−0.208 ***	−0.205 **	−0.090 ***	−0.090 ***
	(−5.619)	(−5.511)	(−5.123)	(−5.112)	(−2.726)	(−2.465)	(−2.900)	(−2.897)
Firm scale	7.309 ***	7.763 ***	3.578 ***	3.582 ***	4.686 ***	5.459 ***	2.187 ***	2.189 ***
	(12.953)	(13.467)	(13.673)	(13.687)	(12.576)	(13.467)	(13.665)	(13.669)
Firm nature	−1.917	−1.999	−0.864	−0.847	−0.246	−0.468	−0.283	−0.275
	(−1.355)	(−1.378)	(−1.309)	(−1.283)	(−0.260)	(−0.454)	(−0.701)	(−0.681)
Employee education	−0.026	−0.007	−0.028 *	−0.028 *	0.086 ***	0.124 ***	0.030 ***	0.030 ***
	(−0.826)	(−0.209)	(−1.855)	(−1.855)	(4.057)	(5.373)	(3.302)	(3.313)
Government subsidies	0.604 **	0.610 **	0.154	0.154	0.099	0.140	0.083	0.084
	(2.268)	(2.238)	(1.331)	(1.331)	(0.640)	(0.828)	(1.170)	(1.182)
Firm growth	−0.021	−0.031	−0.002	−0.002	−0.025 *	−0.036 **	−0.002	−0.002
	(−1.056)	(−1.480)	(−0.995)	(−1.013)	(−1.776)	(−2.329)	(−1.339)	(−1.361)
Industry competition	22.986	22.523	6.230	6.700	−29.367	−32.932	−6.831	−6.634
	(0.709)	(0.676)	(0.361)	(0.388)	(−1.310)	(−1.349)	(−0.647)	(−0.628)
Year dummies	Included	Included	Included	Included	Included	Included	Included	Included
Industry dummies	Included	Included	Included	Included	Included	Included	Included	Included
Constant	−205.876 ***	−214.615 ***	−82.609 ***	−82.952 ***	−103.898 ***	−115.928 ***	−43.086 ***	−43.179 ***
	(−6.369)	(−6.461)	(−4.922)	(−4.941)	(−4.670)	(−4.794)	(−4.197)	(−4.204)
R2	-	-	0.134	0.134	-	-	0.160	0.160

Note: *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively. The values in brackets are t values. Alliance network characteristics including centrality and structural holes. AGI including IGI and RGI. AGI—ambidextrous green innovation; IGI—exploitative green innovation; RGI—exploratory green innovation. Models 1–2 and Models 5–6 used Tobit model.

implied that our findings were robust.

(2) Counterfactual test. Referring to Cornaggia et al. [94], this study randomly matched network centrality and structural holes across firms and re-performed regressions of alliance network characteristics on AGI. If it had been other unobserved factors related to network characteristics that influenced the firm’s AGI, the regression results would have remained significant. Conversely, if it had been brought by the network’s location and structure, the regression results would no longer be significant. Table 4 reflected that the effects of network centrality and structural holes on AGI were no longer significant. Therefore, the findings of this study were robust.

(3) Time lag effect test. Considering that there was a lag in the impact of the technology alliance network characteristics on corporate green innovation, IGI and RGI were postponed by three periods, respectively. The regression results in

Table 5. Results of time lag effect test for centrality and AGI.

Variables	IGI			RGI
	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6
Centrality	1.473 ***	1.431 ***	1.488 ***	1.594 ***	1.697 ***	1.815 ***
	(10.048)	(7.496)	(5.242)	(18.142)	(15.041)	(13.087)
Firm age	−0.293 ***	−0.341 ***	−0.393 ***	−0.121 ***	−0.167 ***	−0.194 ***
	(−4.444)	(−3.632)	(−2.649)	(−3.064)	(−3.012)	(−2.673)
Firm scale	3.262 ***	3.824 ***	4.863 ***	1.795 ***	2.190 ***	2.318 ***
	(9.667)	(8.164)	(4.913)	(8.873)	(7.909)	(4.795)
Firm nature	−0.628	−0.946	−1.164	−0.145	−0.753	−1.484 *
	(−0.741)	(−0.820)	(−0.653)	(−0.285)	(−1.104)	(−1.704)
Employee education	−0.065 ***	−0.083 ***	−0.108 **	−0.004	−0.015	−0.012
	(−3.364)	(−3.055)	(−2.504)	(−0.363)	(−0.959)	(−0.564)
Government subsidies	0.092	0.041	−0.321	0.065	0.099	−0.090
	(0.663)	(0.222)	(−0.393)	(0.776)	(0.904)	(−0.224)
Firm growth	−0.001	0.001	−0.010	−0.001	−0.006	−0.004
	(−0.527)	(0.070)	(−0.337)	(−0.788)	(−0.537)	(−0.266)
Industry competition	33.266	0.258	2.480	−8.533	−15.016	−29.487
	(0.772)	(0.004)	(0.011)	(−0.330)	(−0.435)	(−0.274)
Year dummies	Included	Included	Included	Included	Included	Included
Industry dummies	Included	Included	Included	Included	Included	Included
Constant	−102.983 **	−84.380	−103.573	−35.576	−38.113	−24.443
	(−2.561)	(−1.542)	(−0.508)	(−1.476)	(−1.179)	(−0.245)
R2	0.183	0.182	0.176	0.298	0.326	0.373

Note: *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively. The values in brackets are t values. Centrality is one of the alliance network characteristics. AGI including IGI and RGI. AGI—ambidextrous green innovation; IGI—exploitative green innovation; RGI—exploratory green innovation.

and

Table 6. Results of time lag effect test for structural holes and AGI.

Variables	IGI			RGI
	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6
Structural holes	3.171 ***	2.638 **	2.483	2.420 ***	2.321 ***	2.544 **
	(3.388)	(2.063)	(1.248)	(4.083)	(2.880)	(2.385)
Firm age	−0.283 ***	−0.325 ***	−0.376 **	−0.109 **	−0.148 **	−0.171 **
	(−4.192)	(−3.395)	(−2.481)	(−2.552)	(−2.451)	(−2.107)
Firm scale	3.720 ***	4.339 ***	5.313 ***	2.371 ***	2.870 ***	2.881 ***
	(10.757)	(9.027)	(5.281)	(10.827)	(9.475)	(5.338)
Firm nature	−0.866	−1.156	−1.256	−0.463	−1.060	−1.636 *
	(−0.996)	(−0.979)	(−0.688)	(−0.841)	(−1.424)	(−1.671)
Employee education	−0.042 **	−0.058 **	−0.083 *	0.022 *	0.016	0.019
	(−2.149)	(−2.091)	(−1.901)	(1.796)	(0.920)	(0.809)
Government subsidies	0.125	0.103	−0.070	0.108	0.175	0.246
	(0.879)	(0.547)	(−0.083)	(1.200)	(1.472)	(0.546)
Firm growth	−0.001	−0.000	−0.015	−0.002	−0.008	−0.010
	(−0.594)	(−0.009)	(−0.493)	(−0.957)	(−0.710)	(−0.618)
Industry competition	33.054	−3.389	−13.716	−9.588	−20.045	−50.587
	(0.749)	(−0.057)	(−0.061)	(−0.343)	(−0.533)	(−0.419)
Year dummies	Included	Included	Included	Included	Included	Included
Industry dummies	Included	Included	Included	Included	Included	Included
Constant	−111.504 ***	−91.396	−101.023	−45.564 *	−47.019	−20.670
	(−2.709)	(−1.635)	(−0.486)	(−1.748)	(−1.335)	(−0.185)
R2	0.143	0.146	0.143	0.179	0.200	0.214

Note: *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively. The values in brackets are t values. The structural hole is one of the alliance network characteristics. AGI including IGI and RGI. AGI—ambidextrous green innovation; IGI—exploitative green innovation; RGI—exploratory green innovation.

showed that centrality and structural holes made a significant contribution to IGI and RGI with a lag of three periods, respectively. They indicated that the original regression results were robust.

At the same time, it can be found that network centrality and structural holes had a greater impact on the lag period of RGI than the current period. There was an overall incremental change in this effect as the number of lag periods increased. In contrast, the impact on the lag period of IGI was smaller than the current period. Due to the short cycle time of IGI, it was possible to respond quickly to technology needs in a short period. RGI required time and capital investment, and it was difficult to achieve a large number of breakthroughs in the short term. Furthermore, as shown by the results of the third period of the lag, the positive impact of the network characteristics on RGI outweighed IGI. It indicated that the advantages of the information and resources obtained in the network had a lasting and enhanced impact on RGI.

4.3. The Moderating Effects of Internal and External Pressures

The results from the internal pressures, as shown in

Table 7. Impact of internal pressures on technology alliance network characteristics and AGI.

Variables	IGI				RGI
	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6	Model 7	Model 8
Centrality	0.850 ***		2.325 ***		1.039 ***		1.578 ***
	(4.939)		(18.464)		(10.406)		(20.854)
Structural holes		3.933 ***		3.263 ***		2.664 ***		2.261 ***
		(5.504)		(4.490)		(6.141)		(5.079)
Firm age	−0.271 ***	−0.263 ***	−0.236 ***	−0.254 ***	−0.100 ***	−0.094 ***	−0.095 ***	−0.090 ***
	(−5.505)	(−5.229)	(−4.958)	(−5.014)	(−3.500)	(−3.068)	(−3.303)	(−2.905)
Firm scale	2.749 ***	2.926 ***	2.224 ***	3.249 ***	1.394 ***	1.671 ***	1.413 ***	2.010 ***
	(9.177)	(9.506)	(8.731)	(12.177)	(8.012)	(8.942)	(9.233)	(12.301)
Firm nature	−0.545	−0.711	−0.147	−0.612	0.043	−0.189	0.122	−0.185
	(−0.858)	(−1.090)	(−0.237)	(−0.931)	(0.116)	(−0.477)	(0.327)	(−0.459)
Employee education	−0.048 ***	−0.036 **	−0.049 ***	−0.033 **	0.008	0.024 ***	0.003	0.026 ***
	(−3.272)	(−2.453)	(−3.443)	(−2.184)	(0.925)	(2.663)	(0.363)	(2.807)
Government subsidies	0.117	0.134	0.156	0.143	0.045	0.070	0.043	0.064
	(1.052)	(1.171)	(1.439)	(1.243)	(0.695)	(1.010)	(0.655)	(0.904)
Firm growth	−0.002	−0.002	−0.001	−0.002	−0.001	−0.002	−0.001	−0.002
	(−0.705)	(−0.806)	(−0.581)	(−0.803)	(−0.911)	(−1.126)	(−0.893)	(−1.164)
Industry competition	8.019	6.082	−5.622	2.309	−5.202	−7.023	−6.422	−5.911
	(0.483)	(0.358)	(−0.347)	(0.134)	(−0.540)	(−0.680)	(−0.660)	(−0.561)
Responsibility pressure	0.227	0.133			0.188	0.169
	(0.896)	(0.511)			(1.277)	(1.068)
Risk pressure			0.709 ***	0.476 **			0.011	−0.123
			(3.340)	(2.114)			(0.085)	(−0.894)
Centrality × Responsibility pressure	0.565 ***				0.360 ***
	(5.877)				(6.453)
Structural holes × Responsibility pressure		3.921 ***				3.052 ***
		(7.476)				(9.584)
Centrality × Risk pressure			1.237 ***				0.108 *
			(13.306)				(1.941)
Structural holes × Risk pressure				1.586 ***				0.081
				(2.656)				(0.222)
Year dummies	Included	Included	Included	Included	Included	Included	Included	Included
Industry dummies	Included	Included	Included	Included	Included	Included	Included	Included
Constant	−65.002 ***	−67.681 ***	−43.793 ***	−71.894 ***	−26.581 ***	−31.148 ***	−27.019 ***	−39.675 ***
	(−3.930)	(−3.988)	(−2.762)	(−4.282)	(−2.768)	(−3.023)	(−2.837)	(−3.858)
R2	0.200	0.159	0.241	0.145	0.300	0.196	0.290	0.168

Note: Decentralized treatment was carried out before the moderating effects regression. *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively. The values in brackets are t values. Alliance network characteristics including centrality and structural holes. AGI including IGI and RGI. Internal pressures including responsibility pressure and risk pressure. AGI—ambidextrous green innovation; IGI—exploitative green innovation; RGI—exploratory green innovation.

, showed that the risk and responsibility pressures could enhance the relationship between the network characteristics and AGI. According to the results in

Table 8. Impact of external pressures on technology alliance network characteristics and AGI.

Variables	IGI				RGI
	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6	Model 7	Model 8
Centrality	1.539 ***		0.692 ***		1.486 ***		0.703 ***
	(12.934)		(4.955)		(21.468)		(8.966)
Structural holes		3.602 ***		1.973 ***		2.307 ***		0.924 **
		(5.004)		(2.873)		(5.240)		(2.370)
Firm age	−0.270 ***	−0.263 ***	−0.262***	−0.259 ***	−0.098 ***	−0.091 ***	−0.093 ***	−0.089 ***
	(−5.486)	(−5.188)	(−5.461)	(−5.377)	(−3.442)	(−2.934)	(−3.433)	(−3.262)
Firm scale	2.811 ***	3.294 ***	3.076 ***	3.213 ***	1.460 ***	2.003 ***	1.698 ***	1.921 ***
	(10.852)	(12.354)	(12.091)	(12.652)	(9.687)	(12.280)	(11.892)	(13.333)
Firm nature	−0.421	−0.701	−0.433	−0.454	0.104	−0.195	0.133	0.062
	(−0.655)	(−1.056)	(−0.694)	(−0.725)	(0.277)	(−0.480)	(0.380)	(0.175)
Employee education	−0.057 ***	−0.036 **	−0.058 ***	−0.054 ***	0.002	0.025 ***	0.001	0.008
	(−3.930)	(−2.386)	(−4.071)	(−3.760)	(0.250)	(2.760)	(0.146)	(0.954)
Government subsidies	0.120	0.128	0.090	0.084	0.045	0.065	0.023	0.026
	(1.075)	(1.112)	(0.819)	(0.768)	(0.695)	(0.922)	(0.377)	(0.410)
Firm growth	−0.001	−0.002	−0.001	−0.001	−0.001	−0.002	−0.001	−0.001
	(−0.658)	(−0.784)	(−0.688)	(−0.695)	(−0.888)	(−1.165)	(−0.954)	(−1.057)
Industry competition	9.228	8.010	19.700	20.725	−4.564	−6.005	−4.273	−3.593
	(0.553)	(0.466)	(0.844)	(0.885)	(−0.470)	(−0.571)	(−0.326)	(−0.271)
Regulatory pressure	−0.773	−0.039			0.402	1.198
	(−0.201)	(−0.010)			(0.180)	(0.494)
Peer pressure			0.788	0.726			0.583 **	0.519 *
			(1.533)	(1.408)			(2.020)	(1.774)
Centrality × Regulatory pressure	5.676 ***				2.934 ***
	(3.043)				(2.704)
Structural holes × Regulatory pressure		19.404 *				0.848
		(1.876)				(0.134)
Centrality × Peer pressure			0.466 ***				0.420 ***
			(11.354)				(18.219)
Structural holes × Peer pressure				0.569 ***				0.531 ***
				(16.673)				(27.441)
Year dummies	Included	Included	Included	Included	Included	Included	Included	Included
Industry dummies	Included	Included	Included	Included	Included	Included	Included	Included
Constant	−68.993 ***	−77.479 ***	−80.944 ***	−84.178 ***	−29.606 ***	−39.435 ***	−32.341 ***	−37.227 ***
	(−4.243)	(−4.626)	(−3.690)	(−3.828)	(−3.130)	(−3.850)	(−2.626)	(−2.984)
R2	0.192	0.142	0.227	0.223	0.291	0.168	0.369	0.351

Note: Decentralized treatment was carried out before the moderating effects regression. *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively. The values in brackets are t values. Alliance network characteristics including centrality and structural holes. External pressures including regulatory pressure and peer pressure. AGI including IGI and RGI. AGI—ambidextrous green innovation; IGI—exploitative green innovation; RGI—exploratory green innovation.

, the regulatory and peer pressures had a positive moderating effect on the relationship between the network characteristics and AGI. It would appear that the centrality and structural holes of the technology alliance networks had a greater positive impact on IGI than RGI under both the internal and the external pressures. However, the moderating effect on the structural holes was significantly better than the centrality. These corresponded to the previous empirical results of the network characteristics on AGI.

Comparing the results of the moderating effects of the internal and external pressures reveals the heterogeneity. Although both promoted IGI better than RGI, the magnitude of the difference showed that the improvement of the network characteristics on IGI was significantly better than that on RGI under the regulatory pressure. This phenomenon was followed by risk pressure. On the other hand, the responsibility pressure and peer pressure did not have a substantial gap. This was because these factors were influenced in different ways and can be explained in terms of being active and passive. Although different in source, risk pressure and regulatory pressure were passive with regard to the firms’ strategic choices for green innovation because they were pressures caused by objective factors. In such cases, the firms often chose exploitative innovations that were easier to achieve. The risk-taking ability directly influenced the decision to innovate in green technologies, as too high a risk could plunge a firm into crisis. Hence, the tendency to promote IGI was less risky. The pollution cost burden and legitimacy requirements brought about by environmental regulations could promote the relatively easy-to-enhance IGI ability to meet short-term needs. In contrast, responsibility and peer pressure affected the firm’s initial green strategy choice because the influence of responsibility and peers often came from their own subjective constraints, which further influenced the behavior choice. Therefore, the short-term effect of responsibility and peer pressure without hard constraints on green innovation was not obvious. However, it was precisely because this pressure came from subjective factors that it was easier for it to evolve into a driving force for development. This kind of guidance, which may lead to innovation initiatives, was slow but lasting. Additionally, it could promote both IGI and RGI. It was conducive to the balanced development of the two innovation capabilities.

4.4. Further Tests

To further investigate the complementary and substitution relationships between the internal and external pressures, we divided the sample into high and low groups based on the external pressures. Then, we tested the moderating effect of the internal pressures. When the external pressure increased and the coefficient of the cross item in the model became smaller or even insignificant, it meant that as the external pressure increased, the moderating effects of the internal pressures became smaller or even ceased. Thus, it indicated a substitution effect of the external pressures on the internal pressures. Conversely, when the coefficient of the cross item increased significantly, it indicated that the moderating effects of the internal pressures were enhanced under the stronger external pressures. Hence, there was a complementary effect between the external and internal pressures. As shown by the regression results in

Table 9. Tests for complementary or substitution relationships between internal and external pressures.

Variables	IGI				RGI
	Low Regulatory Pressure	High Regulatory Pressure	Low Peer Pressure	High Peer Pressure	Low Regulatory Pressure	High Regulatory Pressure	Low Peer Pressure	High Peer Pressure
Centrality × Responsibility pressure	0.127	0.976 ***	0.101 **	0.734 ***	0.224 ***	0.495 ***	0.030	0.535 ***
	(1.43)	(5.78)	(2.22)	(2.73)	(2.82)	(5.81)	(1.13)	(3.50)
Structural holes × Responsibility pressure	2.071 ***	5.391 ***	1.163 ***	6.561 ***	3.111 ***	3.018 ***	0.964 ***	5.285 ***
	(4.13)	(6.51)	(4.44)	(4.72)	(6.68)	(6.87)	(6.32)	(6.24)
Centrality × Risk pressure	0.385 ***	1.655 ***	0.484 ***	1.900 ***	0.082	0.061	0.018	0.481 ***
	(4.05)	(11.68)	(5.76)	(10.52)	(0.95)	(0.81)	(0.37)	(4.42)
Structural holes × Risk pressure	−0.197	3.446 ***	1.163 ***	6.561 ***	−1.296 **	1.194 **	0.964 ***	5.285 ***
	(−0.36)	(3.40)	(4.44)	(4.72)	(−2.53)	(2.21)	(6.32)	(6.24)

Note: Decentralized treatment was carried out before the moderating effects regression. ** and *** indicate significance at the 5%, and 1% levels, respectively. The values in brackets are t values. Alliance network characteristics including centrality and structural holes. Internal pressures including responsibility pressure and risk pressure. External pressures including regulatory pressure and peer pressure. AGI including IGI and RGI. AGI—ambidextrous green innovation; IGI—exploitative green innovation; RGI—exploratory green innovation.

, the moderating effect of the responsibility and risk pressure increased and became more significant as the degree of regulatory and peer pressure increased in IGI and RGI. For example, with the increase in responsibility pressure, under higher regulatory pressure, centrality plays a better role in promoting IGI (β₂ = 0.976, p < 0.01) than under low regulatory pressure (β₁ = 0.127, p > 0.1). It suggested that there were complementary effects between the two internal and two external pressures. Comparing the previous coefficients of moderating effect in Table 7 and Table 8, we could infer that the effects of network centrality and structural holes on promoting AGI were enhanced in the scenario of complementary internal and external pressures. This suggested that the degree of regulatory and peer pressure effectively increased the positive impact of the internal environmental awareness and the resilience of the firms.

5. Discussion and Conclusions

To investigate the impact of the technology alliance networks on AGI, this study constructed an alliance network based on the data from the joint patent applications from 2015 to 2019. We examined the impact of network centrality and structural holes on IGI and RGI while verifying the reliability of our findings. We also explored the impacts of responsibility pressure, risk pressure, peer pressure, and regulatory pressure on the relationships between the alliance network characteristics and AGI. We further examined the complementary or substitution relationships between the internal and the external pressures.

The findings were reflected in the following areas:

• Network centrality and structural holes enhanced IGI to a greater extent than RGI, but the impact on IGI had poor continuity. The impact on RGI increased incrementally with the lag period and even exceeded the impact on IGI. This is because IGI is often accompanied by product and service improvements. As it is positively influenced by the ability and scale of knowledge accumulation, the growth in exploitative innovation is easily achieved in the short term through the integration of knowledge [95]. Benner and Tushman [81] also demonstrated that IGI had a shorter R&D process, which can be reflected more rapidly in a firm’s ability to innovate. Exploratory innovation requires integration and assimilation. It uses diverse knowledge to break through existing green technology trajectories [96]. Therefore, it requires a more rational allocation of the internal and external resources and a greater emphasis on the combined pressure of the external environmental factors. This means that substantial growth is difficult to achieve in the short term. However, in the long term, as the learning effect strengthens, the spillover of heterogeneous knowledge from technology alliance cooperation is learned and digested by firms, greatly increasing the possibility of achieving key technological breakthroughs.
• Structural holes promoted AGI better than centrality, suggesting that alliance firms were better at exploiting the role of network “middlemen”. It implied that the resource and information spillover from the network structure could be used effectively. Based on resource orchestration theory, having resources is as important as managing them [97]. Firms with more structural holes tend to act as information “bridges”, demonstrating an advantage in controlling and managing information and resources. On the one hand, the risk of outdated and distorted information is reduced due to more direct access to first-hand information. On the other hand, the role of the intermediary often establishes a communication mechanism of mutual trust. It can reduce the complexity and riskiness of cooperation. Thus, it improves the efficiency of information absorption and conversion.
• Both the internal and external the pressures had a positive moderating effect on the relationship between the alliance network characteristics and AGI. Environmental awareness and executive support for green innovation had a major impact on AGI. Wang et al. [98] also confirmed that a firm’s environmental responsibility could strengthen the relationship between environmental regulation and green innovation. The relationship between risk and innovation has been widely discussed, with Solo [99] being the first to offer the insight that innovative behavior was not controllable. They argued that the risk of the failure of innovation lay in the uncertain risks of R&D difficulty, technology longevity, and market prospects. Government environmental regulation has also been shown to be a key driver of corporate incentives for green innovation [100,101,102]. On the one hand, firms are incentivized to upgrade their existing green products so as to ensure their functional leadership. On the other hand, they are pushed to develop RGI to achieve market leadership. In terms of moderating effects, passive innovation pressures caused by objective factors (risk pressure and regulatory pressure) could have a significant short-term impact on IGI. In comparison, active innovation pressure (responsibility pressure and peer pressure) caused by subjective factors facilitated the balanced development of AGI capabilities. In terms of the relationship between moderating factors, there were complementary relationships between the two external pressures and the two internal pressures.

6. Implications and Limitations

6.1. Implications

This study aimed to answer the question of whether corporate technology alliances can enhance a firm’s AGI capabilities. Furthermore, we considered the impact of internal and external pressures on the relationship.

The findings of the study brought some insights.

• Both types of AGI need to exist and are important for firms to compete in the short and long term. Jansen et al. [103] and Parida et al. [27] argued that exploitative and exploratory innovations generated competition for resources. Thus, they were detrimental to firm development. In contrast to these arguments, our findings supported the view represented by the scholars He and Wong [26], Cao et al. [104], and Colombo et al. [105]. We agreed that firms needed a balanced development of AGI. IGI involved improvements to existing technologies, knowledge, and products. In addition, RGI further explored new knowledge, resources, and technologies. These built the foundation for the construction of entirely new products and services [105]. In the long run, exploratory innovation is considered to be the most conducive to achieving a sustainable competitive advantage [25,106]. However, under the pressure of objective factors such as environmental regulation, there is also a need for firms to improve their exploitative innovation capabilities to compete in the short term [45,107].
• Participating in corporate technology alliances is an important way for firms to enhance their AGI capabilities. In our research, we found that the impact degree and the impact period of IGI and RGI are different. Internal and external pressures are factors affecting the balance of IGI and RGI. Under the influence of unchangeable external pressures, firms need to work on internal pressures to improve their green competitiveness. At the same time, attention should be paid to the external environment of the firm. On the one hand, the government can guide firms in disclosing their environmental responsibility reports and in setting up cooperation platforms. On the other hand, firms should also pay attention to the supervision of the industry in order to form a mutually beneficial cooperation mechanism.

6.2. Limitations

• We only considered the most basic characteristics of technology alliance networks. Factors such as technological diversity and partnership stability in the strategic choice of green innovation should also be concerned.
• We only selected responsibility pressure, risk pressure, regulatory pressure, and peer pressure as influences on the green innovation decisions of firms in the network. However, the influences within the internal and external environment were in fact more diverse and complex.
• Our findings may change depending on the nature of the firm, industry, and other characteristics.
• There are unknown or unobserved factors that affect AGI; this made the explanation of our models less powerful.
• The relatively short time span of the study may have reduced the explanation of the model. The longer-term impact may be more convincing.

In future research, we will further discuss the above issues. Furthermore, we will continue to explore the impact of the network on the strategic decision making of the firms, as well as the factors affecting the balance between AGIs.