Introduction
Ensuring financial sustainability remains a major challenge for nonprofit organizations (Green et al., Reference Green, Ritchie, Bradley and Parry2021; Park & Matkin, Reference Park and Matkin2021). Nonetheless, there is a paucity of research investigating how funder relationships influence financial sustainability in non-Western contexts. Unlike for-profits, nonprofit organizations (NPOs) face unique challenges due to their mission-driven nature and services for high-need populations, which require ongoing external support. As such, financial sustainability and mission fulfillment are inextricably linked. Nonprofit financial sustainability can be defined as a nonprofit’s ability to secure and manage sufficient resources to fulfill its mission effectively and continuously (Bowman, Reference Bowman2011; Calabrese, Reference Calabrese2018) and to withstand short-term shocks and support long-term development goals (Shava, Reference Shava2021).
Given its importance, scholars have examined the factors that influence nonprofit financial sustainability. Organizational traits, such as professionalism and board involvement, are positively linked to sustainability (An, Reference An2021). The external environment, such as political regimes and macroeconomic conditions, is associated with organizational financial sustainability (Shen et al., Reference Shen, Yu, Li and Huang2019). Relations with funders are recognized as a critical yet understudied factor (Grønbjerg, Reference Grønbjerg1993; Hunnik et al., Reference Hunnik, de Wit and Wiepking2021). Managing good relations with individual donors and corporations is shown to enhance fundraising effectiveness and financial stability (Carroll & Kachersky, Reference Carroll and Kachersky2019).
Despite the extant literature, three key research gaps remain. First, prior studies examining NPO–funder relations have often focused on single–funder relationships, such as those between NPOs and the government or between NPOs and a philanthropic trust (Dong & Lu, Reference Dong and Lu2021). Less attention has been given to how NPOs navigate relations with multiple funders simultaneously, including government, individual donors, corporations, and foundations, each with distinct expectations, rationales, and interests (Lu et al., Reference Lu, Shon and Zhang2020).
Second, most existing studies have focused on NPO–funder relations in Western democracies, where nonprofits have more diverse funding sources and relevant data are more easily accessible (Brown, Reference Brown2018). In contrast, in nondemocratic regimes such as China’s, the government often assumes a dual role as both the regulator and the primary funder of NPOs, thereby creating unique dependencies shaped by political control, restricted funding sources, and limited open data (Li et al., Reference Li, Lo and Tang2017; Wang, Reference Wang2023). As a result, applying theoretical frameworks developed in Western contexts requires careful contextualization. Rather than assuming direct transferability, we use them as analytical lenses to examine NPO–funder relations in China, an approach that may also generate insights for refining these theories in nondemocratic settings.
Third, while prior research has examined various nonprofit subtypes, few studies have focused specifically on social service NPOs (SSNs) in relation to funder relationships and financial sustainability (Chang et al., Reference Chang, Tuckman, Chikoto-Schultz, Seaman and Young2018). Unlike other NPO subtypes (e.g., environmental or advocacy), SSNs deliver essential services to underserved populations (Maya-Jariego et al., Reference Maya-Jariego, Holgado-Ramos, González-Tinoco, Muñoz-Alvis and Ortega2020) and face unique financial challenges, often depicted by high dependence on governmental funders (MacIndoe, Reference MacIndoe2021). Their financial sustainability has a direct impact on the accessibility and quality of services for vulnerable groups (Park & Brunjes, Reference Park and Brunjes2022). Studying SSNs separately is thus crucial for strengthening their role in social service delivery.
To fill these three gaps, this study drew from the Resource Dependence Theory (RDT) (Arvidson & Linde, Reference Arvidson and Linde2021; Pfeffer & Salancik, Reference Pfeffer and Salancik2003) and the Embeddedness Theory (ET) (Granovetter, Reference Granovetter1985; Ma, Reference Ma2023), and used data from the China Social Work Longitudinal Study 2019 (N = 615) to answer the following research question:
How do relations with multiple funders impact organizational financial sustainability among social service NPOs in China?
This study’s findings advance the theoretical and practical knowledge in three important ways. First, by drawing on RDT and ET in parallel, we show how NPO–funder relations operate under political constraints. Second, we examined the nuanced impacts of funder relationships on financial sustainability in the social services sector, where dependence on government funding is significant. Third, by distinguishing among governmental, Party, and corporate embeddedness, we extend ET to capture the distinct roles of different ties. The findings also offer practical reflections for SSNs, suggesting the importance of strategically managing funder dependencies and embedded ties in nondemocratic contexts.
Theoretical framework and hypotheses development
This study draws on RDT and ET as foundational theories because they offer complementary lenses for understanding NPO–funder relations and financial sustainability: RDT emphasizes the structural power dynamics and vulnerabilities created by external resource dependence (AbouAssi & Tschirhart, Reference AbouAssi and Tschirhart2022; Cheng & Parris, Reference Cheng and Parris2020), while ET highlights how organizational behavior is shaped by relational ties and institutional embeddedness within political and social networks (Luo et al., Reference Luo, Zheng and Long2023; Zhan & Tang, Reference Zhan and Tang2016). Rather than formally testing these theories, we apply them as analytical tools to interpret the mechanisms through which funder relationships shape financial outcomes in China’s context.
Resource Dependence Theory
The RDT posits that organizations must manage external resource dependencies to survive and maintain autonomy (Liu, Reference Liu2020). A core assumption of the RDT is that the more dependent an organization is on external resources, the more vulnerable it becomes to external control (Arvidson & Linde, Reference Arvidson and Linde2021). In response, nonprofits adopt strategies such as forming alliances or pursuing commercialization to reduce reliance and enhance financial independence (Guan et al., Reference Guan, Dong and Lu2025; Suykens et al., Reference Suykens, George, De Rynck and Verschuere2021). Three important resource dependencies, dominant funder reliance, revenue diversification, and governmental support, informed the independent variables in the current study.
Dominant funder reliance (H1)
Under RDT, reliance on a dominant funder refers to a situation where a single funder provides the majority of an NPO’s revenue (Campbell & Lambright, Reference Campbell and Lambright2017), increasing financial risk despite other funding sources (Mayer, Reference Mayer2025; Qu, Reference Qu2019). High funder reliance is common in the social services sector (Kelley et al., Reference Kelley, Isom, Seeman, Silverman, Cuevas-Ferreras and Frei-Herrmann2024). Studies link such reliance to reduced financial flexibility, resource rigidity (Hung & Hager, Reference Hung and Hager2019), mission drift, and impeded long-term planning (Bennett & Savani, Reference Bennett and Savani2011).
It is important to distinguish between reliance and dependency. Reliance is a structural condition of revenue concentration, whereas dependency implies power asymmetry and reduced autonomy (Pfeffer & Salancik, Reference Pfeffer and Salancik2003). In social services, heavy reliance on government is common and does not automatically indicate dependency; indeed, government–NPO relationships are often complementary (Hsu, Reference Hsu2021; Salamon & Anheier, Reference Salamon and Anheier1998). However, in China’s context, where the government serves as both funder and regulator, the risk of reliance translating into dependency may be heightened (Lei et al., Reference Lei, Cai and Chan2023). We examine whether such reliance is associated with weaker financial outcomes as an empirical question. Hence, we hypothesized that:
High reliance on a dominant funder among social service NPOs is negatively associated with organizational financial sustainability. (Hypothesis 1)
Revenue diversification (H2)
RDT suggests that revenue diversification strengthens nonprofit financial sustainability by reducing reliance on single funders and increasing resilience to external shocks (Berrett & Holliday, Reference Berrett and Holliday2018; Lu & Shon, Reference Lu and Shon2024; Lu et al., Reference Lu, Lin and Wang2019). By spreading risk across multiple income sources, diversified NPOs are better positioned to absorb funding fluctuations and maintain operational stability (Carroll & Stater, Reference Carroll and Stater2009). Although some studies caution that managing multiple revenue streams may entail increased administrative costs (Chikoto & Neely, Reference Chikoto and Neely2014; Frumkin & Keating, Reference Frumkin and Keating2011) and may not necessarily translate into improved organizational performance (Hung et al., Reference Hung, Tsai and Wu2024), the benefits of diversification, reduced vulnerability, and greater financial flexibility are generally considered to outweigh these costs, particularly for organizations seeking long-term sustainability (Von Schnurbein & Fritz, Reference Von Schnurbein and Fritz2017). For SSNs, which often face uncertain funding environments, a diversified revenue base can enhance resilience and provide sustainable social services. Thus, this study hypothesized that:
Revenue diversification is positively associated with organizational financial sustainability among social service NPOs. (Hypothesis 2)
Governmental support (H3)
Governmental support is a key factor influencing the financial sustainability of nonprofits (Dong & Lu, Reference Dong and Lu2021). Within RDT, scholars debate between a “crowd-in” effect, in which government funding enhances legitimacy and attracts other donors (Ni & Zhan, Reference Ni and Zhan2017), and a “crowd-out” effect, in which overreliance fosters path dependency and reduces diversification incentives (Johnson & Ni, Reference Johnson and Ni2015).
In the context of China, the “crowd-in” effect is likely to be more salient. Given the government’s dominant role in resource allocation, securing governmental support can signal legitimacy and facilitate access to policy resources and institutional networks, thereby strengthening organizational capacity and resource mobilization (Lei et al., Reference Lei, Cai and Chan2023). In a relatively underdeveloped philanthropic market, such support may serve as a foundation for attracting additional resources and enhancing long-term sustainability (Lu & Cheng, Reference Lu and Cheng2023; Mok et al., Reference Mok, Chan and Wen2021). Thus, we hypothesize:
Governmental support is positively associated with organizational financial sustainability among social service NPOs. (Hypothesis 3)
Embeddedness Theory
ET posits that an organization’s strategies and outcomes are shaped by its relational, institutional, and structural contexts (Granovetter, Reference Granovetter1985; Verver et al., Reference Verver, Dahles and Soeterbroek2021). In China, SSNs rely on embedded networks to navigate funders, regulations, and social expectations. These networks enable SSNs to access resources, engage in policy, and enhance their legitimacy (Haveman et al., Reference Haveman, Jia, Shi and Wang2017; Zhang & Gan, Reference Zhang and Gan2019). Notably, Chinese SSNs often cultivate embeddedness in governmental, corporate, and Party structures to strengthen their sustainability (Wei & Tsui, Reference Wei and Tsui2018; Yee, Reference Yee2024). This study treats these three types of embeddedness as key variables.
Governmental embeddedness (H4)
Governmental embeddedness refers to the degree to which nonprofits are connected to government entities, often through staff or leaders with political ties, such as former public officials or honorary roles in state organizations (Johnson & Ni, Reference Johnson and Ni2015). Such ties play a role as a “double-edged sword” (Wei, Reference Wei2021). While this embeddedness can risk co-optation and reduced autonomy (Wigren-Kristoferson et al., Reference Wigren-Kristoferson, Brundin, Hellerstedt, Stevenson and Aggestam2022), it also offers advantages, such as privileged access to resources, operational stability, and enhanced legitimacy. In China, research shows it can lead to positive outcomes, including increased volunteerism, improved performance, and participation in voluntary governmental programs, such as the government-sponsored accreditation system (Luo et al., Reference Luo, Zheng and Long2023; Ni & Zhan, Reference Ni and Zhan2017). Therefore, this study hypothesized that:
Governmental embeddedness is positively associated with organizational financial sustainability among social service NPOs. (Hypothesis 4)
Party embeddedness (H5)
Party embeddedness, distinct from governmental embeddedness, refers to a nonprofit’s ties with the Chinese Communist Party rather than with administrative agencies (Li, Reference Li2023; Yee, Reference Yee2024). It emphasizes relational proximity to Party-led institutions, often indicated by Party-building activities, internal Party branches, or connections with political bodies like the Chinese People’s Political Consultative Conference (CPPCC) and the National People’s Congress (NPC) (Kan & Ku, Reference Kan and Ku2021; Wang & Wang, Reference Wang and Wang2023). Studies show that Party embeddedness enhances nonprofits’ access to political decision-making, strategic information, and resource channels (Enjuto Martinez et al., Reference Enjuto Martinez, Qu and Howell2022; Ye & Gong, Reference Ye and Gong2021). In the for-profit sector, Party ties signal political influence and managerial power (Fralich & Fan, Reference Fralich and Fan2018). However, no research has examined its link to financial sustainability in nonprofits. Therefore, we hypothesized that:
Party embeddedness is positively associated with organizational financial sustainability among social service NPOs. (Hypothesis 5)
Corporate embeddedness (H6)
Corporate embeddedness refers to the institutional ties NPOs form with private companies through partnerships, sponsorships, board affiliations, and joint initiatives (Al-Tabbaa et al., Reference Al-Tabbaa, Leach and March2014; Shumate et al., Reference Shumate, Hsieh and O’Connor2018). Unlike governmental or Party embeddedness, these relationships connect nonprofits with market actors, offering access to financial resources, business expertise, and public visibility. As government and traditional donations decline, corporate partnerships have become vital for nonprofit sustainability (Taylor et al., Reference Taylor, Aveling, Roberts, Bhuiya, Edmondson and Singer2023). Although ties with corporations may also risk mission drift and cause reputational vulnerability (e.g., greenwashing) (Yu & Chen, Reference Yu and Chen2018), they provide tangible support (e.g., funding, in-kind resources) and intangible benefits (e.g., reputational capital, strategic networks) that strengthen financial outcomes (Pitowsky-Nave et al., Reference Pitowsky-Nave, Almog-Bar and Schmid2023). Following this debate, this study hypothesized that:
Corporate embeddedness among social service NPOs is positively associated with enhanced financial sustainability. (Hypothesis 6)
Research context: Social service NPOs in China
This study focuses on SSNs in China, a nondemocratic regime where NPOs are officially categorized as “Social Organizations” (SOs). Since 1978, China’s nonprofit sector has experienced waves of growth and tightening control, as seen in the Overseas NGOs and Charity Laws (Ji et al., Reference Ji, Deng and Liu2024). China presents a unique context for examining SSN–funder relations and financial sustainability for three reasons.
First, SSNs play a significant role in China’s nonprofit sector, both in terms of their scale and social impact. As of 2023, there were 140,100 registered SSNs, comprising 15.89% of all SOs (Huang, Reference Huang2024). Their alignment with government priorities allows them to operate with relative political favor (Chan & Lei, Reference Chan and Lei2017). Yet, heavy reliance on state funding makes them vulnerable to policy shifts and budget cuts, often resulting in downsizing or closure (Lei et al., Reference Lei, Cai and Chan2023).
Second, NPO–funder relationships are largely framed through NPO-government dynamics (Spires, Reference Spires2020; Zhang & Guo, Reference Zhang and Guo2021). The government’s dual role as funder and regulator pushes NPOs to align with state priorities to secure support (Mok et al., Reference Mok, Chan and Wen2021). Many SSNs have engaged in what scholars describe as “embedded development,” though this raises concerns about autonomy, as heavy dependence may limit their capacity to address needs beyond state agendas (Hsu et al., Reference Hsu, Hsu and Hasmath2017).
Third, SSNs in China are navigating a complex and evolving funding environment. The government’s political agenda and the rapid digital transformation of philanthropy have catalyzed increased participation from both high-net-worth entrepreneurs and ordinary individuals in charitable giving (Huang, Reference Huang2022; Zhou & Ye, Reference Zhou and Ye2021). Simultaneously, institutional funders, including charity foundations and corporate donors, are playing an increasingly prominent role in financing the social services sector (Lu & Cheng, Reference Lu and Cheng2023).
These three contextual dimensions highlight why combining RDT and ET provides a valuable lens for understanding SSN–funder relations in China. RDT explains how SSNs manage funder dependencies, especially with dominant government funders. ET emphasizes their embeddedness in institutional and relational networks that shape access to resources. Together, these frameworks provide a nuanced understanding of how SSNs navigate financial sustainability within China.
A critical nuance lies in distinguishing governmental support from governmental embeddedness. Although both involve the government, they reflect distinct mechanisms. Governmental support, rooted in RDT, refers to resource flows (e.g., funding, subsidies, and office venues) that reflect material dependence on the government. Governmental embeddedness, rooted in ET, refers to relational ties (e.g., former government officials as leaders) that represent political connection and legitimacy. In China’s guanxi-intensive environment, for SSNs, governmental support and governmental embeddedness capture distinct organizational dimensions: resource acquisition versus political positioning. Empirically, their modest correlation in the study sample also supports treating them as separate but complementary constructs.
Materials and methods
Research design
This quantitative cross-sectional study used data from the first wave of the China Social Work Longitudinal Study (CSWLS), conducted from June to October 2019 by East China University of Science and Technology. Designed for national representativeness, the 2019 baseline included 979 valid organizations and 5,965 valid social worker questionnaires across 56 cities, with over 98% validity (Yuan et al., Reference Yuan, Liu, Sun and He2021). Although CSWLS is longitudinal, only the 2019 wave was used due to data availability. The organizational questionnaire covered eight domains, including external relations and 1-year financial data (2018–2019).
Sample
The CSWLS 2019 employed a two-stage stratified sampling design. In the first stage, 56 cities were selected to reflect variation in administrative level, the number of social service NPOs, and local socioeconomic conditions. In the second stage, social service NPOs were drawn from official registration lists compiled by the Ministry of Civil Affairs within the sampled cities. According to the CSWLS project documentation (Yuan et al., Reference Yuan, Liu, Sun and He2021), organizations were randomly selected from these city-level lists, rather than recruited through spontaneous or purely convenience-based participation. Trained regional partners, universities, social work federations, and research institutions then distributed the questionnaires and ensured quality control.
The CSWLS 2019 baseline survey collected organizational-level data from 979 valid cases. This constitutes the full organizational dataset available from the survey. For the present study, we used this organizational-level dataset (N = 979) and further retained 615 cases after listwise deletion of observations with missing values on key dependent, independent, and control variables. Thus, the analytic sample (N = 615) is a complete-case subsample drawn from the original CSWLS organizational dataset (N = 979), rather than a newly drawn sample by the authors.
Measures
Dependent variables: Financial sustainability
Nonprofit organizational financial sustainability was measured by four key indicators: (1) asset accumulation; (2) revenue generation; (3) surplus retention; and (4) asset utilization efficiency.
First, asset accumulation was measured as the total value of organizational assets at the end of 2018, reflecting an NPO’s overall resource base, including cash, investments, property, and equipment, and indicating financial resilience (De Andrés-Alonso et al., Reference De Andrés-Alonso, Garcia-Rodriguez and Romero-Merino2015; Frumkin & Keating, Reference Frumkin and Keating2011). We also acknowledge that total asset value does not capture the composition of assets (e.g., liquid vs. fixed). However, detailed asset structure data are not available in the CSWLS dataset. Additionally, to avoid sample selection bias from excluding organizations with zero assets, we apply a logarithmic transformation with a constant of one, retaining all cases in the analysis.
Second, revenue generation, calculated from seven income sources (governmental funding, Party-led mass organization funding, corporate donations, foundation grants, individual contributions, public fundraising, and other sources), represents financial viability (Mayer et al., Reference Mayer, Wang, Egginton and Flint2014; Von Schnurbein & Fritz, Reference Von Schnurbein and Fritz2017). The aggregated total revenue was also log-transformed to normalize the distribution.
Third, surplus retention was measured as net profit (revenue minus expenses), where expenses included nine categories, such as human resources, program costs, facility costs, taxes, and overheads (Chang & Tuckman, Reference Chang and Tuckman1994; Wicker & Breuer, Reference Wicker and Breuer2014). To address skewness, the net profit values were standardized via z-score transformation. Observations with zero or negative expense values were treated as missing, and analyses were conducted using complete-case observations.
Lastly, return on assets (ROA) is an indicator of asset utilization efficiency, calculated as net profit divided by total assets, multiplied by 100. ROA reflects how effectively an NPO generates financial returns from its asset base (Prentice, Reference Prentice2016). Observations with an asset value of zero were excluded to prevent division errors. Outliers beyond ±5000% were removed, and ROA was log-transformed after adjusting for negative values to improve normality.
Independent variables
The independent variables were derived from the CSWLS 2019 organizational-level survey, aligned with the study’s conceptual framework. Table 1 summarizes all variables.
Measurements of the variables
Table 1. Long description
The table consists of three columns: Variables, Description, and Scale.
* Financial sustainability: Includes four sub-variables: Total assets (Log of total assets in 2018), Total revenue (Log of total revenue in 2018), Net profit (Standardized total revenues minus total expenditures), and Return on assets (R O A) (Net profit divided by total assets). All are measured on a Ratio scale.
* Dominant funder reliance: Revenues from the dominant funder divided by total revenues. Scale: Ratio.
* Revenue diversification: One minus the Herfindahl-Hirschman index (1-H H I) based on four funding sources. Scale: Ratio.
* Governmental support: Total scores of receiving six forms of support (0-6). Scale: Ordinal.
* Governmental embeddedness: Primary managers served in government (no = 0, yes = 1). Scale: Dummy.
* Party embeddedness: Staff who are C P P C C or N P C members (no = 1, yes = 0). Scale: Dummy.
* Corporate embeddedness: Board members with business background (no = 0, yes = 1). Scale: Dummy.
* Organizational age: Difference between data collection year and founding year. Scale: Ratio.
* Registration status level: Provincial (0), Municipal (1), or County (2). Scale: Categorical.
* Party branch setup: Has the N P O set up Party branches (no = 0, yes = 1). Scale: Dummy.
* Service scope: Number of service areas provided (1-13). Scale: Ratio.
* Dominant funder type: Predominant source (governmental, corporate, foundational, or individual). Scale: Categorical.
Dominant Funder Reliance was measured as the proportion of an organization’s total revenue derived from its largest single funding source, aligning with RDT. We calculated the revenue share from four main sources: government, corporations, foundations, and individual donors, and identified the dominant funder type as the one with the highest proportion.
Revenue Diversification was operationalized as one minus the Herfindahl–Hirschman Index (1-HHI) based on four focal funding sources: government, corporations, foundations, and individual donors (Chikoto et al., Reference Chikoto, Ling and Neely2016). Other sources (e.g., public fundraising, people’s organizations) were excluded due to their relatively small scale and to avoid obscuring meaningful distinctions between corporate and foundation funding, which operate through different mechanisms.
Governmental Support was measured by aggregating six types of governmental support in the survey: governmental funding, subvention, provision of office space, personnel support, technical assistance, and policy endorsement. The variable was constructed as an ordinal score ranging from 0 to 6, indicating the number of support types received. As the CSWLS records these items as categorical indicators without information on magnitude or intensity, differential weighting was not feasible.
Governmental Embeddedness was measured using the survey item: “Your agency’s primary managers have ever served in a government department or organization.” Responses were recoded into a dummy variable, with “no” = 0 and “yes” = 1.
Party Embeddedness was measured with the survey question, “Your agency does not have staff who have ever served as People’s Congress deputies or members of the Chinese People’s Political Consultative Conference.” Responses were similarly coded into a dummy variable, where “no” = 1 and “yes” = 0.
Corporate Embeddedness was measured with the survey question, “Your agency has board members whose background is from the corporate companies.” Responses were coded into a dummy variable, where “no” = 0 and “yes” = 1.
Control variables
This study included several organizational-level control variables to account for alternative explanations of nonprofit financial sustainability.
Organizational Age was measured by the number of years from the organization’s formal establishment to the year of data acquisition, reflecting organizational maturity and resource accumulation (Zhu et al., Reference Zhu, Ye and Liu2018).
Registration Status Level was coded based on the administrative tier at which the SSN was legally registered with the Ministry of Civil Affairs (provincial, municipal, or county). In China’s administrative hierarchy, registration at a higher level typically signals greater political recognition and legitimacy, which can facilitate access to broader funding networks and government resources, factors that may influence financial sustainability (Dong & Lu, Reference Dong and Lu2021).
Party Branch Setup was measured as a binary variable indicating whether the organization had established an internal branch of the Chinese Communist Party. This form of political institutionalization has become increasingly salient in China’s nonprofit sector; Party presence can enhance organizational stability, signal political alignment, and potentially facilitate resource access (Nie & Wu, Reference Nie and Wu2022).
Service Scope counted the number of service areas (e.g., family services, eldercare, services for persons with disabilities, services for women and children, youth development) that the organizations provided, indicating organizational capacity and adaptability (Tu, Reference Tu2021).
Dominant funder type categorized the largest revenue source into four groups: government-dominant, corporate-dominant, foundation-dominant, and individual-dominant.
Data analysis
Prior to regression analysis, Pearson correlation coefficients and variance inflation factors (VIFs) were examined to assess relationships among the independent variables and to diagnose potential multicollinearity. As shown in Table 2, the correlations are generally modest, with the strongest correlation observed between dominant funder reliance and revenue diversification (r = −0.66, p < 0.01), while other correlations remain weak in magnitude. The mean VIF was 1.02, well below conventional thresholds, indicating that multicollinearity was not a concern in the analysis.
Correlation matrix among independent variables (N = 615)
Table 2. Long description
The table lists six variables: Dominant funder reliance, Revenue diversification, Governmental support, Governmental embeddedness, Party embeddedness, and Corporate embeddedness. All diagonal values are 1.00.
* Revenue diversification correlates with Dominant funder reliance at minus 0.66 (p less than 0.01).
* Governmental support correlates with Dominant funder reliance at 0.08 (p less than 0.05) and Revenue diversification at minus 0.08 (p less than 0.10).
* Governmental embeddedness correlates with Dominant funder reliance at 0.00 (p equals 0.92), Revenue diversification at minus 0.02 (p equals 0.65), and Governmental support at 0.12 (p less than 0.01).
* Party embeddedness correlates with Dominant funder reliance at minus 0.00 (p equals 0.92), Revenue diversification at minus 0.01 (p equals 0.89), Governmental support at 0.11 (p less than 0.01), and Governmental embeddedness at 0.14 (p less than 0.01).
* Corporate embeddedness correlates with Dominant funder reliance at minus 0.08 (p less than 0.05), Revenue diversification at 0.10 (p less than 0.05), Governmental support at minus 0.10 (p less than 0.05), Governmental embeddedness at 0.02 (p equals 0.60), and Party embeddedness at 0.07 (p less than 0.10).
Mean V I F is 1.02, indicating no multicollinearity concerns.
Entries are Pearson correlation coefficients.
P-values in parentheses: *** p < 0.01, ** p < 0.05, * p < 0.10.
Variance inflation factors (VIF) were computed to assess multicollinearity. All values were well below conventional thresholds (mean VIF = 1.02), suggesting that multicollinearity is not a concern.
Given the cross-sectional nature of the organizational data, the analysis did not involve temporal dependence typically associated with time-series autocorrelation. To address non-normality and skewness in the financial outcomes, dependent variables were log-transformed or standardized as described above. Four OLS models were tested to examine the impact of SSN–funder relationships on financial sustainability, using the following variables: log-transformed total assets (asset accumulation), log-transformed total revenue (revenue generation), standardized net profit (surplus retention), and log-transformed return on assets (asset utilization efficiency). Cases with missing data were excluded. Statistical significance was set at p < 0.05, while results at p < 0.10 are reported as marginally significant. All analyses were conducted using Stata MP 17.
Results
Descriptive statistics
Table 3 summarizes the descriptive statistics for the 615 sampled SSNs.
Descriptive statistics of key variables (N = 615)
Table 3. Long description
The table presents descriptive statistics (Mean, S D, Min, Max) for N = 615.
Dependent variables:
* Total assets (in R M B 10,000): Mean 112.36, S D 219.34, Min -60, Max 2674.38.
* Log assets: Mean 3.51, S D 1.61, Min 0, Max 6.40.
* Total revenues (in R M B 10,000): Mean 291.56, S D 778.90, Min 0.30, Max 7769.
* Log revenue: Mean 4.25, S D 1.66, Min 0.26, Max 8.96.
* Net profit: Mean -3.31, S D 339.52, Min -5700.55, Max 3152.
* Standardized profit: Mean 0.01, S D 1.02, Min -17.04, Max 9.45.
* Return on assets (R O A): Mean -20.88, S D 549.71, Min -4844, Max 4888.58.
* Log R O A: Mean 8.50, S D 0.17, Min 5.06, Max 9.20.
Independent variables:
* Dominant funder reliance: Mean 0.81, S D 0.26, Min 0, Max 1.
* Revenue diversification: Mean 0.12, S D 0.21, Min 0, Max 1.
* Governmental support: Mean 3.88, S D 1.60, Min 0, Max 6.
* Governmental embeddedness (yes): Mean 0.20, S D 0.40, Min 0, Max 1.
* Party embeddedness (yes): Mean 0.15, S D 0.36, Min 0, Max 1.
* Corporate embeddedness (yes): Mean 0.35, S D 0.48, Min 0, Max 1.
Control variables:
* Organizational age: Mean 5.10, S D 3.29, Min 0, Max 26.
* Registration level (Provincial): Mean 0.14, S D 0.35, Min 0, Max 1.
* Registration level (Municipal): Mean 0.30, S D 0.46, Min 0, Max 1.
* Registration level (County): Mean 0.56, S D 0.50, Min 0, Max 1.
* Party branch setup (yes): Mean 0.48, S D 0.50, Min 0, Max 1.
* Service scope: Mean 5.21, S D 2.72, Min 1, Max 13.
* Dominant funder type (Government): Mean 0.85, S D 0.36, Min 0, Max 1.
* Dominant funder type (Corporate): Mean 0.03, S D 0.16, Min 0, Max 1.
* Dominant funder type (Foundation): Mean 0.05, S D 0.22, Min 0, Max 1.
* Dominant funder type (Individual): Mean 0.07, S D 0.26, Min 0, Max 1.
a Financial variables are reported in RMB 10,000; approximate USD equivalents are calculated based on an exchange rate of 1 USD ≈ 7.2 RMB.
b ROA is reported as a percentage; extreme values were trimmed for regression analyses.
In terms of financial sustainability, the average total assets of social service nonprofits amount to 112.36 (in RMB 10,000), equivalent to approximately RMB 1.12 million (≈ USD 156,000), while the mean total revenue is 291.56 (≈ RMB 2.92 million, or USD 405,000), indicating substantial variation in organizational scale. Net profit exhibits considerable dispersion (mean = −3.31, ≈ RMB −33,100 or USD −4,600), suggesting that many organizations operate under financial pressure, with some experiencing deficits. Return on assets (ROA) also displays high variability (mean = −20.88), reflecting uneven asset utilization efficiency across organizations.
Regarding independent variables, dominant funder reliance is relatively high (mean = 0.81), indicating that most organizations depend heavily on a single primary funding source. In contrast, the average level of revenue diversification is relatively low (mean = 0.12), suggesting limited diversification of income streams. Governmental support has a mean score of 3.88 (out of 6), indicating moderate levels of support across different dimensions.
In terms of embeddedness, 20% of organizations report governmental embeddedness, 15% report Party embeddedness, and 35% report corporate embeddedness, suggesting that political embeddedness is limited and corporate ties are more prevalent among the sampled organizations.
For control variables, the average organizational age is 5.10 years, indicating a relatively young sector. In terms of registration level, 14% of organizations are registered at the provincial level, 30% at the municipal level, and 56% at the county level. Nearly half of the organizations (48%) have established Party branches. The average service scope is 5.21 (out of 13 categories), suggesting moderate diversification in service provision. Finally, most organizations are government-dominant (85%), with relatively few relying primarily on corporate (3%), foundation (5%), or individual funding (7%).
SSNs’ resource dependence and financial sustainability
Table 4 reports the results of four OLS models examining the associations between nonprofit–funder relations and financial sustainability. Regression coefficients are interpreted as the expected change in the dependent variable associated with a one-unit change in a continuous predictor, holding the other variables constant. For binary predictors, coefficients represent the expected difference between organizations coded 1 and those coded 0.
Regression results (N = 615)
Table 4. Long description
The table presents regression coefficients with standard errors in parentheses across four models.
Header Structure:
* Variables: The independent variables listed in the first column.
* Model 1: Total assets (log).
* Model 2: Total revenues (log).
* Model 3: Net profit (Std.).
* Model 4: Return on assets (log).
Data Rows (Coefficient and Standard Error):
* Dominant funder reliance: Model 1: 0.05 (0.29); Model 2: -0.62** (0.28); Model 3: -0.85*** (0.21); Model 4: -0.04 (0.04).
* Revenue diversification: Model 1: -0.03 (0.38); Model 2: -0.38 (0.37); Model 3: -0.64** (0.27); Model 4: -0.02 (0.05).
* Governmental support: Model 1: 0.10*** (0.04); Model 2: 0.07* (0.04); Model 3: -0.05* (0.03); Model 4: -0.00 (0.00).
* Governmental embeddedness: Model 1: -0.28* (0.15); Model 2: -0.32** (0.14); Model 3: 0.16 (0.10); Model 4: -0.01 (0.02).
* Party embeddedness: Model 1: 0.11 (0.17); Model 2: 0.38** (0.16); Model 3: 0.28** (0.12); Model 4: -0.00 (0.02).
* Corporate embeddedness: Model 1: 0.31** (0.12); Model 2: 0.28** (0.12); Model 3: 0.06 (0.09); Model 4: 0.01 (0.01).
* Constant: Model 1: 2.23*** (0.35); Model 2: 3.33*** (0.34); Model 3: 0.85*** (0.25); Model 4: 8.57*** (0.04).
* R-squared: Model 1: 0.26; Model 2: 0.35; Model 3: 0.06; Model 4: 0.03.
Significance levels: *** p < 0.01, ** p < 0.05, * p < 0.10.
The table shows regression coefficients with standard error in parentheses.
*** p < 0.01, ** p < 0.05, * p < 0.10.
SSNs’ reliance on a dominant funder is negatively associated with total revenue (β = −0.62, SE = 0.28, p < 0.05) and net profit (β = −0.85, SE = 0.21, p < 0.01), but showed no statistically significant association with total assets (β = 0.05, SE = 0.29, p > 0.1) or return on assets (ROA) (β = −0.04, SE = 0.04, p > 0.1). These results provide partial support for Hypothesis 1.
Revenue diversification is negatively associated with net profit (β = −0.64, SE = 0.27, p < 0.05), but is not significantly related to total assets (β = −0.03, SE = 0.38, p > 0.1), total revenue (β = −0.38, SE = 0.37, p > 0.1), or ROA (β = −0.02, SE = 0.05, p > 0.1). Therefore, Hypothesis 2 is not supported.
Governmental support is positively associated with total assets (β = 0.10, SE = 0.04, p < 0.01) and marginally positively associated with total revenue (β = 0.07, SE = 0.04, p < 0.10), while showing a marginal negative association with net profit (β = −0.05, SE = 0.03, p < 0.10), and shows no significant relationship with ROA (β = −0.00, SE = 0.00, p > 0.1). These findings provide partial support for Hypothesis 3.
SSNs’ embeddedness and financial sustainability
Governmental embeddedness is marginally negatively associated with total assets (β = −0.28, SE = 0.15, p < 0.10) and total revenue (β = −0.32, SE = 0.14, p < 0.05), while showing no significant associations with net profit (β = 0.16, SE = 0.10, p > 0.1) or ROA (β = −0.01, SE = 0.02, p > 0.1). Thus, Hypothesis 4 is not supported.
Party embeddedness is positively associated with total revenue (β = 0.38, SE = 0.16, p < 0.05) and net profit (β = 0.28, SE = 0.12, p < 0.05), but shows no statistically significant relationship with total assets (β = 0.11, SE = 0.17, p > 0.1) or ROA (β = −0.00, SE = 0.02, p > 0.1). These results provide partial support for Hypothesis 5.
Corporate embeddedness is positively associated with total assets (β = 0.31, SE = 0.12, p < 0.05) and total revenue (β = 0.28, SE = 0.12, p < 0.05), but is not significantly related to net profit (β = 0.06, SE = 0.09, p > 0.1) or ROA (β = 0.01, SE = 0.01, p > 0.1). Therefore, Hypothesis 6 is partially supported.
The R2 values for the models are 0.26 for total assets, 0.35 for total revenue, 0.06 for net profit, and 0.03 for ROA, indicating relatively stronger explanatory power for revenue- and asset-based outcomes compared to profitability- and efficiency-based measures. This suggests that funder relationships primarily shape scale-based financial outcomes (assets and revenues), rather than efficiency-based outcomes such as ROA, whose outcomes may be influenced by other organizational factors beyond funder relationships, suggesting that future research should incorporate additional organizational and environmental variables.
Robustness checks
We conducted two robustness checks to assess the stability of our findings. First, following prior recommendations (Chikoto et al., Reference Chikoto, Ling and Neely2016), we recalculated revenue diversification using a three-category revenue classification (see Supplementary Table A1). The alternative specification supported several of the main patterns, particularly those concerning dominant funder reliance and political embeddedness, while highlighting the importance of a more granular revenue classification in the Chinese context. Second, we added organizational size (log of total staff) as an additional control variable. As reported in Supplementary Table A2, controlling for organizational size increased the explanatory power of Models 1 and 2 (R 2 increased from 0.26 to 0.45 for total assets and from 0.35 to 0.65 for total revenue). The findings regarding dominant funder reliance and revenue diversification remained relatively stable, while Party embeddedness was reduced to marginal significance and several other associations became non-significant.
Discussions
To the best of our knowledge, this study is the first to utilize the China Social Work Longitudinal Study to examine the associations between nonprofit–funder relations and the financial sustainability of social service NPOs in a nondemocratic context. Building on prior research (Cortis et al., Reference Cortis, Fang and Dou2018; Searing et al., Reference Searing, Wiley and Young2021), the findings reveal that reliance on a dominant funder is negatively associated with revenue and surplus retention, while revenue diversification shows counterintuitive negative effects. Government support was positively associated with asset accumulation and showed a marginal negative association with surplus retention, reflecting both resource-enhancing and constraint-inducing effects. Critically, different forms of embeddedness present divergent outcomes: Party and corporate embeddedness are positively associated with revenue generation, whereas governmental embeddedness shows negative associations. These findings offer several avenues for further theoretical and practical reflection.
First, this study provides empirical support for key propositions of RDT in a nondemocratic context: greater reliance on a dominant funder is negatively associated with nonprofit financial sustainability (AbouAssi & Tschirhart, Reference AbouAssi and Tschirhart2022), particularly in terms of lower revenue and reduced surplus retention. This reflects how power asymmetries in resource exchanges can undermine organizational autonomy and weaken financial outcomes (Cheng & Parris, Reference Cheng and Parris2020). This finding should also be interpreted with nuance: while government-NPO relationships can be complementary in many contexts (Hsu, Reference Hsu2021; Salamon & Anheier, Reference Salamon and Anheier1998), China’s distinctive institutional arrangements, where government serves as both funder and regulator, may heighten the risk that reliance translates into dependency, a pattern that warrants future comparative investigation.
Second, contrary to RDT’s propositions, the negative association between revenue diversification and surplus retention requires careful interpretation. This finding is consistent with prior evidence suggesting that diversification does not uniformly enhance nonprofit financial capacity (Lu et al., Reference Lu, Lin and Wang2019). Theoretically, this finding may reflect the transaction cost logic embedded in revenue diversification. While Modern Portfolio Theory suggests that diversification reduces risk (Qu, Reference Qu2019), nonprofits also face external pressures around cost allocation and financial decision-making, which can further complicate the management of multiple funding streams (Schubert & Boenigk, Reference Schubert and Boenigk2021). When these transaction costs exceed the marginal benefits of additional revenue sources, diversification may become financially burdensome rather than beneficial (Berrett & Hung, Reference Berrett and Hung2024). This is not inherently a Chinese-specific phenomenon but reflects a general organizational dynamic: diversification yields net benefits only when the organization has the capacity to absorb associated costs.
Contextually, this dynamic may be amplified in China’s social service sector for two reasons. First, the sector is relatively young; most SSNs in this study sample have been established for only 5 years on average, and may lack the managerial experience, administrative infrastructure, and fundraising know-how to efficiently manage multiple revenue streams. Second, China’s philanthropic ecosystem is highly regulated, with the government playing a dual role as both primary funder and regulator (Lu & Shon, Reference Lu and Shon2024). Navigating diverse funding sources often entails additional compliance requirements and procedural complexities that may not exist to the same extent in more mature or market-oriented nonprofit environments. Consequently, for many Chinese SSNs, the costs of diversification may outweigh its benefits, especially given limited managerial capacity.
Additionally, governmental support shows a mixed picture: it was positively associated with asset accumulation but showed a marginal negative association with surplus retention, reflecting both resource-enhancing and constraint-inducing effects. While government funding enables NPOs to acquire infrastructure (Ma & DeDeoc, Reference Ma and DeDeoc2018), it may displace alternative revenues and reduce efficiency incentives, a duality that forces SSNs to navigate inherent trade-offs.
Third, this study extends ET by showing how different forms of political and corporate embeddedness affect the financial sustainability of SSNs in a nondemocratic context (Liu, Reference Liu2020). Unlike Western contexts where embeddedness centers on market and civil society linkages (Wigren-Kristoferson et al., Reference Wigren-Kristoferson, Brundin, Hellerstedt, Stevenson and Aggestam2022), embeddedness in China is structured by authoritarian politics. The findings reveal a critical divergence: governmental support (resource flows) is positively associated with financial outcomes, whereas governmental embeddedness (relational ties with administrative agencies) shows negative associations. This likely reflects the double-edged nature of the relational ties with the government, which may bring expectations of conformity, reduced autonomy, and path dependency, constraints not necessarily imposed by material support alone (Johnson & Ni, Reference Johnson and Ni2015; Wei, Reference Wei2021).
In contrast, Party embeddedness showed positive associations with revenue generation and surplus retention, suggesting that connections with the ultimate political authority may confer legitimacy and resource access without the same administrative constraints. Corporate embeddedness, board members with business backgrounds, was also positively associated with assets and revenue, illustrating a market-oriented form of diversification that complements rather than replaces political ties (Lu & Cheng, Reference Lu and Cheng2023). Together, these findings underscore that the financial implications of ties depend on whether they involve resource dependence or relational embeddedness, and on the specific institutional logic of the partner: administrative, political, or market-oriented. Hence, applying RDT and ET in China requires contextualization to reflect these nuanced distinctions and the evolving Party-market-nonprofit dynamics.
Several limitations warrant consideration. The cross-sectional design limits causal inference; longitudinal studies could better capture how these dynamics evolve. Due to data constraints, we could not incorporate important relational dimensions such as trust, commitment, and power dynamics. Additionally, the absence of liability and reserve data constrained our financial sustainability measurement. Future research should address these gaps through longitudinal designs, richer financial indicators, alternative weighting schemes, and qualitative approaches that explore the mechanisms underlying funder-NPO relationships. Emerging funding sources, such as crowdfunding, earned income, and social entrepreneurship, also merit greater attention in future studies. The secondary dataset also limits further examination of organizational heterogeneity. Specifically, it does not provide information that would allow reliable classification of domestic versus international NPOs or capture organizations’ geographic reach and branch networks. Future research using more detailed organizational data could examine whether the relationships identified in this study vary by organizational origin and geographic scope, particularly between domestic and international NPOs (Kharkevich et al., Reference Kharkevich, Pisarev, Cheresov and Novogradskaya2021) and between locally concentrated and multi-regional organizations.
Conclusion
This study examined the association between SSN–funder relationships and financial sustainability in China. Drawing on data from 615 organizations, we found that reliance on a dominant funder was associated with weaker revenue generation and surplus retention. Revenue diversification showed counterintuitive negative associations, and governmental support produced mixed effects. Party embeddedness and corporate embeddedness were positively associated with revenue generation.
A unique contribution of this study lies in disaggregating “political embeddedness” into governmental and Party dimensions. This distinction reveals that ties to the Party, the ultimate political authority, operate differently from ties to administrative state agencies in authoritarian regimes, offering a more nuanced understanding of how nonprofits navigate state–Party relations to secure financial resources. Additionally, the counterintuitive diversification result challenges the universal applicability of diversification as a financial safeguard, highlighting the importance of institutional context and organizational capacity.
Practically, SSNs should avoid overreliance on a single dominant funder and approach diversification strategically, balancing benefits against administrative costs. Governmental, Party, and corporate ties may have different financial implications for SSNs, highlighting the importance of managing these relationships in ways that reflect their distinct institutional roles. These insights, while drawn from China, may also inform research in other state-dominant or highly regulated contexts, such as Vietnam and Cambodia, where nonprofit organizations similarly navigate complex relationships with state actors, regulatory constraints, and resource dependencies (Khieng & Dahles, Reference Khieng and Dahles2015; Nguyen, Reference Nguyen2023).
In sum, this study offers new empirical evidence on how funder relationships shape nonprofit financial sustainability in nondemocratic contexts, providing a foundation for more context-sensitive research and strategic financial management among SSNs operating in complex institutional environments.
Supplementary material
The supplementary material for this article can be found at http://doi.org/10.1017/S0957876526000999.
Funding statement
This study did not receive any financial support from public, commercial, or nonprofit funding agencies.
Competing interests
The authors declare that they have no competing interests related to the research, authorship, or publication of this manuscript.
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