Network Structure and Collaborative Performance Among Environmental NGOs in China

1 Introduction

Non-governmental organizations (NGOs) are self-governing, voluntary, and nonprofit entities that are institutionally separate from the government (Anheier 2014). As third-party organizations, NGOs mainly serve the function of mobilizing social resources, providing public services, and engaging in social coordination and policy advocacy (Wang 2006). As the number of NGOs continues to rise and their influence expands, more NGOs are involved in the process of policy advocacy. For instance, in the anti-seepage construction project of Yuanmingyuan in 2005, the environmental NGOs (ENGOs) represented by Friends of Nature urged the government to prioritize ecological protection and advocated that the public should be allowed to participate in the decision-making process of environmental policies. With the concerted advocacy of ENGOs, the government invited all parties to participate in a seminar on the ecological protection of Yuanmingyuan and ultimately decided to cancel the project. The government has gradually recognized the constructive roles of NGOs, started to transition from its initial mode of control, and shifted towards establishing a collaborative relationship with them (Jing and Gong 2012).

Scholars have also recognized the importance of collaborative relations for the development of NGOs, including cooperation between NGOs and governments (Gazley and Brudney 2007; Zhan and Tang 2016), between NGOs and enterprises (Moshtari and Vanpoucke 2021), and among NGOs (Arya and Lin 2007). The government can provide NGOs with the necessary financial resources and, more importantly, legitimacy (Hsu 2010). Organizations with good political ties to the government are more likely to maintain a higher level of funding stability (Zhan and Tang 2016). NGOs with close ties with the government also find it easier to gain legitimacy, which helps them seek funding from other donors (Johnson and Ni 2015). Therefore, NGOs actively build alliances with state agencies to obtain a stable supply of resources.

Enterprises are also important partners of NGOs. They provide NGOs with financial donations and in-kind support, such as expertise and technology, management systems, infrastructure construction, and human resource support (Moshtari and Vanpoucke 2021). However, collaborative partnerships can also limit institutional autonomy (Brinkerhoff 1999; Chaves, Stephens, and Galaskiewicz 2004; Guo and Saxton 2010; Onyx et al. 2010; Schmid, Bar, and Nirel 2008). For example, in policy advocacy activities, cooperation among NGOs helps them develop into more formal and professional organizations but, at the same time, restricts their autonomy of initiating or participating in political activities (Berry 2003; Hudson 2002; Onyx et al. 2010). To quest for autonomy, NGOs call for open communication and extensive linkages with each other. They use their efforts, experience, and resources through network activities to gain government attention, raise public awareness, and engage in the policy process (Umlas 1996).

Although scholars have begun to explore collaborative partnerships among NGOs, most research is mainly concerned with NGO-government collaboration and NGO-enterprise partnerships. Only 16 % of these articles emphasize the within-sector collaboration among nonprofit organizations (Gazley and Guo 2020). Human resource capacity, technological resources, gender representation at the leadership level, organizational scale, past collaborative ties, and past friendship relationships are some of the key drivers that prompt NGOs to collaborate with each other (AbouAssi, Nadeen, and Page 2016; Gazley 2010; Kapucu and Demiroz 2015). Scholars have also found that organizational characteristics, partner attributes, and network structures can affect collaborative outcomes (Arya and Lin 2007; Chen and Graddy 2010).

However, the factors that facilitate or constrain the performance of inter-NGO cooperation are still rarely explored (Kareithi and Lund 2012). Network is one of the forms of collaborative actions NGOs engage in, and network structure is found to positively impact cooperative performance (Yi 2018; Yi, Yang, and Zhou 2021). However, few studies have analyzed the performance of NGO cooperation networks from the perspective of network structure. In addition, when it comes to analyzing collaborative relationships, social network analysis is less commonly employed, especially in exploring collaborative relationships (Gazley and Guo 2020). Therefore, to fill these gaps, this paper aims to explore the relationship between network structure and the performance of NGOs with survey questionnaires and the method of social network analysis. We intend to address the following research questions: Do NGOs engage in collaborative networks in China? Do NGOs that engage more with other NGOs achieve higher collaborative performance? In this study, collaborative performance refers to the efficiency and effectiveness of partnerships formed among organizations, particularly in contexts where multiple actors collaborate to achieve collective goals (AbouAssi, Wang, Huang 2021). This study aims to examine whether the structure of the collaboration network influences collaborative performance, specifically how bridging and bonding structures affect the efficiency and effectiveness of the environmental NGOs.

The next section is a literature review of the relationship between network structure and network performance. Informed by social capital theory, we formulate testable hypotheses to investigate the effect of different types of social capital on collaborative efficiency and substantive effects among 103 ENGOs in China. Then, we introduce our data collection process and elaborate on our research design, followed by results and discussion. The article concludes with theoretical contributions, practical implications, and directions for future research.

2 NGOs, Collaborative Network, and Network Outcomes

3 Influence of Network Structure on Network Outcomes

4 Data Collection

As the unit of analysis in this study is the individual NGOs, we select the research object and methods accordingly. We first choose environmental NGOs (ENGOs) as the research object for two primary reasons. Firstly, environmental governance is a multifaceted public issue that cannot be solved by a single entity. The only effective approach to environmental governance is to coordinate the efforts of multiple actors. Therefore, ENGOs are motivated to create collaborative networks. Second, previous research on the network of NGO collaboration focuses primarily on social services, such as the provision of HIV/AIDS care and treatment, human service, public health, arts, and cultural services (Arya and Lin 2007; Jang, Feiock, and Saitgalina 2016). In the area of environmental protection, Sullivan and Xie (2009) established a connection with online environmental activism in China. Although the NGO sector that has drawn the greatest attention from scholars is ENGO (Li, Lo, and Tang 2017; Sullivan and Xie 2009; Umlas 1996; Yang 2005; Zhan and Tang 2016), few research have focused on the likelihood of real world connections among ENGOs offline. To fill the research gap, we chose ENGOs as our research object.

A list of NGOs in China and related information are available on two websites.^[1] The NGO2.0 map website serves as a platform for creating information connections between enterprises and NGOs. According to the NGO directory of the public welfare map, there are NGOs around the country that work in 22 different project areas, including environmental protection. We can obtain basic organizational information from the website, such as organization name, mission, service area, office address, registration type, registration time, establishment date, contact person and number, email, the person in charge, the number of full-time staff and part-time volunteers, the names of partners, project influence, governance status, financial funds, and other related information. The second website is the China Development Brief (CDB), which was created in 1996. It is a platform that provides NGOs with access to expert observation, research, and online support and services, covering 14 service fields, including environmental protection. Compared with the first website, this one contains less organizational information, only listing details such as the establishment date, work area, organization size, project area, contact email, introduction, talent recruitment, classified information, and organization news.

We employ a questionnaire approach to collecting data to fully understand the information of ENGOs and build network data. The questionnaire design includes five items: personal situation, organizational characteristics, collaborative relationship, policy participation, and policy outcomes. ENGOs are located in 31 provinces and municipalities across the country. We integrate ENGOs from all 31 provinces and municipalities in the sample selection to ensure the comprehensiveness of the data source. Yet our data collection procedure encounters three main challenges. First, the questionnaire was distributed in May 2021 during the COVID-19 period, which made it difficult for us to distribute the surveys as planned. This might have impacted the representativeness of data as we were unable to contact several organizations. Secondly, the information on two websites was inaccurate. We encountered difficulty contacting the heads of some organizations due to missing or erroneous information like email addresses and phone numbers. Third, the organizations are constantly evolving. New ENGOs are launched and registered every day, while others changed their names or even ceased to exist.

Given these three limitations, we decided to distribute our questionnaires via email, with supplemental contact via phone and text messages. We conducted two rounds of surveys in response to the inconsistent information found on the two websites. The first round of questionnaires was delivered and collected from the ENGOs listed on the NGO2.0 map from May 2021 to January 2022. The second round of questionnaires was distributed to the ENGOs listed on CDB’s website and was collected between January 2022 and May 2022. It is worth noting that both questionnaires have the same contents, and the only difference is the target respondents. The ENGO directories of these two websites overlap with each other. In addition to the 677 ENGOs on the NGO2.0 map, 142 new ENGOs from the respondents are still present in the CDB, making a total of 819 ENGOs. Among them, 253 organizations lacked sufficient contact details, so we selected a total of 566 cases after removing 253 missing observations. Then, in the two rounds of the survey, we received a total of 103 completed questionnaires, resulting in a response rate of 18.2 %. It should be noted that as the number of surveys and the demands imposed on NGOs increase, these organizations may reach a saturation point, potentially diminishing their responsiveness to new data collection requests (Dupuy and Prakash 2018). Scholars have also indicated that the challenges posed by a regulated political environment, along with the pressures resulting from the COVID-19 pandemic, may also constrain the responsiveness of NGOs (Noakes and Teets 2020; Wilke, Howard, and Pop 2020). Given the prevalent “survey fatigue” during the COVID-19, a response rate of 18.2 % is the best we could obtain, and it should serve as a reasonable response rate for statistical analysis. The minimum time to complete a questionnaire was 221 s (nearly 4 min), and the maximum was 761 s (about 13 min). All questionnaires were valid in terms of response time.

Table 1 presents the information on the recovery of questionnaires. Despite collecting 103 responses in total, we encountered abnormalities in some of the questionnaires. One questionnaire had an inaccurately filled-in section that would have caused biases in the original data, so we eliminated it. In addition, two questionnaires had operational issues, such as the data from the original questionnaire being overwritten, so we removed them from the analysis. As a result, the final sample for our analysis included a total of 100 responses. As presented in Table 1, apart from Chongqing municipality and Qinghai province, where complete questionnaires could not be collected in the two rounds of the survey, data from Tianjin city were excluded due to missing data. As a result, the data for these three provinces and municipalities are zero. We received only one questionnaire each from Hainan, Sichuan, Ningxia, Heilongjiang, Jilin, and Tibet. Thus, we could not construct a network for these provinces. In addition, while two questionnaires were received for Hebei, Jiangxi, and Inner Mongolia, respectively, only one respondent in each province provided information on the partnership, and neither respondent completed the collaborator information. Burgette et al. (2021) assert that in social network analysis, researchers require a minimum of three alters to develop a preliminary social network model. As the number of alters increases, researchers can conduct a more detailed analysis of the positions as well as the interactive relationships among actors within the network. Therefore, we could not construct networks for these provinces either. Therefore, we use the remaining 19 provinces and municipalities to construct the collaboration networks.

5 Measurement

6 Methods

We employ ordinary least squares regression (OLS) to test our hypotheses. We utilize four models to estimate the relationship between independent variables and dependent variables. We specify the basic model as follows:

Where Y represents the dependent variables, including collaborative efficiency and effectiveness. X represents independent variables for the out-degree centrality and clustering coefficient. Z represents control variables, covering seven dummy variables. Lastly, a, b, and c are fitting parameters, and e is the error term.

In addition to regression analysis, we also complemented it with a detailed case study. Then, to further supplement the survey evidence, we incorporated a case of brokerage within the Beijing sub-network.

7 Results

Descriptive statistics are presented in Table 3. Our sample size is 100, but after removing missing data, the number of observations for the independent and dependent variables are 83 and 97, respectively. As shown in Table 3, the maximum value of out-degree centrality is 82, which indicates that the number of connected organizations can reach up to 82. In this study, the ENGO with the highest out-degree centrality is the Beijing Fuqun Environmental Research Institute, which has proactively established collaborative links with 82 local ENGOs in Beijing. The average values of the indicators on cooperation performance are 4.42 and 4.51, indicating that respondents’ subjective evaluation of cooperation performance is relatively positive.

Table 4 presents the regression results of four OLS models. Model 1 and Model 2 take collaborative efficiency as the dependent variable. Model 1 is a baseline model, which is used to explore the relationship between the control variables and the dependent variable. Model 1 shows that the existence of the committee is positively associated with collaborative efficiency at the level of 0.05, meaning that the ENGOs with a committee have higher cooperation efficiency. However, there is a negative correlation between whether to be supervised by the government and cooperation efficiency, which indicates that ENGOs that are not supervised by the government have higher cooperation efficiency. In Model 2, the two core network structure variables, the out-degree centrality, and the clustering coefficient, are added. The out-degree centrality of the NGO is positively associated with cooperation network efficiency at the 0.05 level, and the clustering coefficient of the NGO is positively related to cooperation network efficiency at the 0.05 level, which supports H1a and H2a.

In the Beijing sub-network, the Beijing Fuqun Environmental Research Institute serves as a pivotal broker. Established in 1989, the institute initially focused on the Mount Everest Nature Reserve, supporting ecological protection and sustainable community development. Initially, its influence was limited, hindering the effective promotion of environmental activities. Over the years, the institute expanded its influence by forging continuous cooperative relationships with peers. Notable successful initiatives include the Pendeba Project and the Green Long March Project. The Pendeba Project features a visitor and training center in the Shigatse area, facilitating training for local communities and promoting sustainable development through collaborations with other ENGOs. Through the Pendeba Project, Fuqun has extended the societal impact of ENGOs within the local region. The Green Long March Project is China’s largest youth environmental protection activity and another successful case of Fuqun. The project organizes and mobilizes student volunteers and other ENGOs to participate in environmental protection activities, facilitating effective dissemination of environmental awareness. Fuqun Environmental Research Institute has become a central actor in China’s environmental protection field by gathering the strength of other ENGOs, enhancing the efficiency and effectiveness of collective actions.

The results for Model 3 and Model 4 take collaborative effectiveness as the dependent variable. As shown in Model 3, control variables and the collaborative effectiveness were not statistically correlated. After adding network structural indicators into model 4, the results became slightly different. The out-degree centrality of the NGOs is positively correlated with the effectiveness of network cooperation at the 0.05 level, which supports H1b. However, the clustering coefficient is not statistically associated with collaborative effectiveness. In terms of control variables, whether the NGOs are supervised by the government is negatively associated with the effectiveness of network cooperation at the 0.05 level.

8 Discussions

Our empirical findings partially support the hypothesis that bridging and bonding networks can enhance the efficiency and effectiveness of the ENGO collaborative network in China. We construct ENGO networks among 19 provinces and municipalities, and we find that bridging and bonding structures in ENGO networks are helpful in achieving efficient and substantive results.

ENGOs’ out-degree centrality and collaborative efficiency are significantly correlated at the 0.05 level. Besides, the out-degree centrality of ENGOs has a significant positive correlation with the substantive effect of their cooperation. ENGOs with higher levels of out-degree centrality tend to establish collaborative relations with more organizations, suggesting that they are located in the central actor position within the network, which is conducive to information sharing and coordination. This aligns with findings from previous studies (Tan, Zhang, and Wang 2015; Yi 2018; Yi, Yang, and Zhou 2021). Out-degree centrality represents bridging social capital in collaborative networks. When organizations possess greater bridging capital and play a central role as critical actors within networks, they gain access to a broader range of information pathways. On the one hand, bridging social capital enhances the efficiency of information exchange within the network. Our study suggests that central actors integrate information and resources by engaging in active collaboration strategies. As opposed to individuals spending their time and energy searching for resources, central actors act as the hubs of the network and can gather information more easily. Thus, they can transmit information and resources more efficiently. On the other hand, bridging social capital facilitates achieving effectiveness within the network. Bridging social capital connects different types of NGOs, enhancing the collaborative network’s problem-solving capabilities. By fostering cooperation and communication among internal members, central actors facilitate the network’s ability to respond promptly to and resolve issues, thus driving the effectiveness.

Intriguingly, the effects of the clustering coefficient on collaborative efficiency and effectiveness are inconsistent. The clustering coefficient is significantly and positively associated with collaborative efficiency at the 0.05 level. However, the clustering coefficient does not exhibit a statistically significant correlation with the effectiveness of the collaborative network. Networks’ clustering coefficients indicate the degree of connectivity among their members. It appears that although close cooperation between members can enhance information acquisition efficiency, it does not necessarily result in substantive improvements. It is possible that a close collaborative relationship requires mutual trust among members to reach a consensus, which may suggest that members must discard political ties with the government to reach a consensus. On the one hand, connections between ENGOs and the government can lead to more information being shared, regulation of freedoms, and economic improvement for organizations (Chen, Chen, and Huang 2013; Luo, Huang, and Wang 2012). On the other hand, maintaining good relationships with the government tends to lose NGO representation and limit their initial actions (Guo 2007; Zhan and Tang 2016). In China, the benefits of ENGO partnerships are not always superior to those of government partnerships. As a result, a close partnership between ENGOs may undermine the support of the government, thereby affecting the effectiveness of the partnership. Hilderbrandt and Turner (2009) also argued that the government is concerned about the impact of ENGO’s close collaborative links. Instead of forming a close partnership, the government would prefer that they engage in their work, which will also influence the effectiveness of the collaborative network. Therefore, even though the collaboration between ENGOs is close, the overall strength is relatively weak and insufficient to achieve substantial effects.

In terms of the influence of ENGOs’ leadership, which refers to the internal structure of an ENGO, we find that organizations with committees are positively correlated with cooperation efficiency at the 0.05 level. This is consistent with the existing literature (Guo and Acar 2005), confirming that a well-established organizational structure is more conducive to achieving collaborative effects. Moreover, accepting government supervision and cooperative efficiency are negatively correlated at the level of 0.05. In other words, ENGOs that are not supervised by the government tend to produce more efficient cooperation. This corresponds to the previous statement of the negative consequences of government restrictions on ENGOs. Government involvement will limit the autonomy of ENGOs’ activities, which can reduce exchanges and cooperation among members. ENGOs who are supervised by the government must consider the government’s provisions, which can influence collaborative enthusiasm and efficiency.

9 Conclusions

NGOs in China are constantly growing in number, and they are playing an increasingly important role in various fields, including public service provision, policy advocacy, and the promotion of civic awareness of environmental protection and other social issues. They must gather their respective strengths to expand their overall influence. However, few studies have focused on whether Chinese NGOs form collaborative networks and what kinds of networks can produce better collaborative results. To fill these gaps, this article employs the questionnaire survey method to conduct an ENGO survey in 31 provinces and municipalities in China. Our findings confirm the existence of the ENGO network in China. Although Sullivan and Xie (2009) previously used a web crawler to find that Chinese environmentalists form connections online, real-world collaborative relationships have not been proven to exist. Additionally, our research reveals that while the bonding network structure can promote collaborative efficiency, it is not always effective. The bridging network structure, however, can produce better effectiveness and achieve higher collaborative efficiency.

Our research has several theoretical implications. First, we demonstrate that collaborative NGO networks are efficient, echoing the controversies raised by some researchers. Andreasen (1996) suggests that maintaining partnerships may consume the energy of individual NGOs, resulting in wasted resources and ineffective management. Babiak and Thibault (2009) also argue that NGO governance, roles, and responsibilities are complex and that partnerships are prone to ineffectiveness. However, we have shown that bridging and bonding structured networks can contribute to efficiency and yield substantive results. Ineffective management and resource waste may not be the result of cooperation. Cooperation should be promoted as an organizational development strategy within NGOs. The critical role of central actors is to facilitate NGOs in developing partnerships with other NGOs. NGOs can benefit from this by integrating information and filtering out duplicated information. For example, the Beijing Fuqun Social Service Center has established collaborative alliances with 82 Beijing-based NGOs, thus establishing itself as a central actor in Beijing’s NGO collaborative network. By executing its central bridging role, the Beijing Fuqun Social Service Center facilitates the dissemination of environmental conservation information to other entities. As a result, the network of Beijing’s NGOs can actively engage in environmental conservation activities, yielding substantive achievements. Also, NGOs can form a clustering relationship based on trust, and ambiguous roles and responsibilities can be alleviated through frequent exchanges. To accomplish common goals, clustering groups need to reach a consensus. However, NGOs should also consider whether sacrificing some political ties or other resources would compromise the effectiveness of their partnership.

We also contribute to the study of collaborative networks in NGO research. Previous literature examines the development of civil society in China from a state, market, and institutional perspective (Yang 2005). Instead, we analyze the evolution of civil society through a network perspective. Specifically, the development of civil society should also focus on its strengths and rely on the establishment of collaborative relationships among NGOs to increase its influence. This study introduces a novel perspective by focusing on inter-organizational cooperation among NGOs, thereby extending beyond traditional studies that typically examine collaboration with governmental and corporate entities. Existing literature on cooperation networks among environmental NGOs includes studies by Sofronova, Holley, and Nagarajan (2014) and Umlas (1996). The former examines local cooperation networks in Russia, while the latter focuses on similar networks in Mexico, both employing interview-based methods to explore cooperative relationships among environmental NGOs. In contrast, our study reveals cooperation networks among environmental NGOs in China, situated within the country’s institutional context. Through quantitative analysis, we delineate network structures and interaction dynamics, contributing to a broader understanding of collaboration patterns among NGOs in different environments and offering new empirical evidence and perspectives for cross-cultural research. Additionally, we combine resource dependence theory and social capital theory as the theoretical foundation to explain the NGO network and its logic of formation. Empirical evidence shows that the structural features of networks are closely related to the network outcomes, thus providing a new perspective for the study of the theoretical dimensions of network outcomes.

Our research also has practical implications for policymakers. To foster the development of civil society and increase the credibility of a service-oriented government, policymakers should consider the critical role of NGOs in social development. Indeed, NGOs can deliver services more easily to citizens. To improve the government’s service delivery capacities and enhance the reputation of a service-oriented government, policymakers may choose to purchase public services from NGOs or collaborate with NGOs in the provision of public services (Heurlin 2010). Moreover, citizens and NGOs are important supervisors of government activities, supervising the implementation of local government policies to ensure that implementation follows the established goals and improves the efficiency of policy implementation (Zhan and Tang 2013). Our results suggest that government-regulated NGOs are less effective in collaborative networks and fail to achieve substantive results. Government regulations also limit the actions of NGOs and fail to stimulate their enthusiasm for collaboration. A sufficiently relaxed regulatory environment would improve effective collaborative outcomes for NGOs.

This research has several limitations. Due to the impact of COVID-19, many areas were in lockdown status when we distributed the questionnaires, which led to a relatively low response rate. Second, most NGOs were preoccupied with organizing service delivery activities and did not respond to our survey timely, which resulted in unsuccessful attempts to contact all listed organizations. Future research may devote more time to collecting more data and conducting follow-up studies with a stronger research design. We adopt a subjective cognitive measure to represent collaborative performance. Although it can somewhat represent the collaborative outcomes, its robustness should be verified with more objective data and measurements, such as defining more objective criteria for efficient and effective collaboration. Future studies might consider constructing objective measures of collaborative performance through changes in the influence of the organization, such as changes in public influence indexes such as the Weibo and WeChat public accounts, as well as changes in NGOs’ sources of funding. Additionally, our research only examines the relationship between the network structure and collaborative outcomes. Yet researchers might be more concerned about the role of NGOs in policy advocacy, and future research can further connect network structure to policy advocacy. It would be interesting to examine whether the network structure may affect the capacity of NGOs to participate in the policy process, and what features of network structure may motivate NGOs to engage in policy advocacy. Lastly, given the constraints of resources and geographical scope, we have only explored the collaborative networks of ENGOs within the province. Future research can construct collaborative networks of ENGOs both within and across provinces in the field of environmental protection, allowing for a more detailed comparison and analysis of the structural characteristics and performance differences between intra-provincial and inter-provincial networks.