How Does Revenue Diversification Affect the Financial Health of Sustainable Entrepreneurship Organizations in China? A Fuzzy Set Qualitative Comparative Analysis

Abstract

The past decade has witnessed the bourgeoning development of sustainable entrepreneurship organizations (SEOs) that are engaging in advancing sustainable development in China. Revenue diversification is often considered by policymakers, scholars, and practitioners to be a desirable strategy for improving the financial health and organizational sustainability of SEOs and other types of hybrid organizations. However, previous studies on the benefits of revenue diversification for hybrid organizations have not reached a definitive conclusion, and the empirical literature has devoted little attention to the financial outcomes of revenue diversification in the SEO context. To address these knowledge gaps, this study uses fuzzy set qualitative comparative analysis to investigate how revenue diversification and organizational conditions interact to conjunctively affect the multidimensional financial health of SEOs in the Chinese context. This study identifies divergent configurations for high and low levels of financial health in four dimensions and across different types of SEOs. The results show that revenue diversification generates benefits primarily for large, established for-profit SEOs in terms of enhancing their financial flexibility but produces no observed improvements in financial flexibility, efficiency, profitability, or growth among nonprofit SEOs. These findings contribute to the sustainable entrepreneurship and sustainability literature in diverse ways, with valuable practical implications for SEO practitioners and major stakeholders.

1. Introduction

In accordance with the definition of sustainable entrepreneurship in the existing literature [1,2,3,4], sustainable entrepreneurship organizations (SEOs) are defined in this study as hybrid organizations that target multiple forms of value creation, harness entrepreneurial competencies, and leverage business mechanisms to address the environmental, economic, and social aspects of sustainable development. Typically, SEOs manifest several important features that differentiate them from conventional business ventures and traditional nonprofit organizations, with an emphasis on pursuing sustainability goals with entrepreneurial spirit and capabilities and the reconciliation of different kinds of objectives of economic, prosocial, and ecological value creation [2,5,6,7].

SEOs have emerged in China over the last two decades as an entrepreneurial and innovative engine for achieving sustainable development goals. SEOs are playing an increasingly important role in tackling the fundamental challenges faced by China in the fields of environmental sustainability, green production and consumption, and social sustainability in terms of equitable access to social services and sustainable patterns of employment [8,9]. Like social entrepreneurship organizations or social enterprises in China, which employ a variety of organizational forms [10], SEOs in China have different registration formats, including mainly for-profit companies, microfinance organizations, farmers’ specialized cooperatives, and nonprofit organizations.

As a newly emerged vehicle for sustainable development, most SEOs are small micro-organizations in their start-up stages, facing various challenges such as becoming financially self-sufficient and maintaining organizational sustainability due to the scarcity of financial and human resources; limited access to advanced technologies; insufficient organizational capacity; barriers to market entry, institutional rules, stakeholder engagement; and a lack of consumer awareness [2,5,11,12,13,14]. Similarly, hybrid organizations in China, a large proportion of which are SEOs, struggle to survive in an unfledged ecosystem that provides insufficient financial, intellectual, technical, and human resources and in which they face difficulties in gaining public recognition and consumer support [10,15,16,17].

Hybrid organizations, which include SEOs, social enterprises, social entrepreneurship organizations, and entrepreneurial nonprofits, obey three types of economic principles; namely, market, redistribution, and reciprocity principles [18]. Hybrid organizations typically create a diversified resource base that often mixes fees and dues charged to clients and customers, in addition to government grants, subsidies, contracts, donations and contributions from individuals, foundations, corporations, and interest from investments or endowments [19]. Therefore, revenue diversification has been broadly considered a desirable strategy for enhancing the financial health of SEOs [20,21] and social entrepreneurship organizations [22]. Additionally, the funding sources of SEOs are increasingly diverse, including reinvesting profits, bank loans, and government subsidies, in addition to innovative forms of financing, such as crowdfunding, impact investments, and green bonds [20,23,24,25,26,27].

Although the benefits of revenue diversification for hybrid organizations are theoretically appealing, previous empirical findings have not reached definitive conclusions. With respect to the effects of revenue diversification for entrepreneurial nonprofits, some studies have concluded that revenue diversification improves financial health by reducing revenue volatility [28,29] and financial vulnerability [22,30] while enhancing profitability [22]. In contrast, other studies have provided evidence for the negative effects of revenue diversification on financial efficiency [31] and financial growth [28,32]. With respect to the effects of revenue diversification for SEOs and social enterprises, the limited empirical studies concerning the impact of revenue diversification on financial health have also provided mixed evidence, demonstrating that revenue diversification has a positive effect on survival among Canadian social economy enterprises [33] or on reducing financial risk among SEOs in volatile markets [21] but a negative effect on profitability in China [34].

Moreover, the literature has noted that the impact of revenue diversification on financial health is not uniform among hybrid organizations with different organizational features or across different contexts. In particular, previous studies focused on explaining how the relationship between revenue diversification and the financial health of entrepreneurial nonprofits varies according to organizational size, age, the domain of activities, revenue portfolio, managerial capability, or external environment [28,29,30,31,35]. Unfortunately, earlier studies neglected the issue of how differences in the legal status of SEOs—as nonprofits or for-profits—affect the contribution of revenue diversification to their financial health. Nevertheless, robust studies have revealed differences between for-profit and nonprofit social entrepreneurship organizations in terms of their mission orientation, organizational logic, operational models, ownership structures, governance mechanisms, management practices, organizational cultures, stakeholder relationships, regulatory environments, resource ecosystems, and patterns of resource deployment [36,37,38]. Therefore, exploring how revenue diversification affects the financial health of for-profit and nonprofit SEOs differently is a theoretically intriguing issue that might also provide significant practical management benefits. Moreover, despite considerable scholarly attention to the moderating effects of organizational size and age on the association between revenue diversification and the financial health of entrepreneurial nonprofits, no study has empirically examined this issue in the SEO context.

Furthermore, prior studies on revenue strategies and financial health in hybrid organizations have been dominated by conventional quantitative statistical analyses. However, these analyses have limitations in handling multifaceted interdependencies among the three important organizational features—size, age, and legal status—as interrelated predictors of financial health; exploring whether the real-life relationships between revenue diversification and financial health are asymmetrical; or discovering new or unexpected causal relationships that are often unobtainable through the prevailing hypothesis-testing model in deductive methods.

To close these gaps in the literature, this study embraces the multiple and conjunctive effects of revenue strategies and organizational features on SEO financial health in the Chinese context by conducting a fuzzy set qualitative comparative analysis (fsQCA) of 204 SEO cases. This study makes several contributions to the sustainable entrepreneurship and sustainability literature. First, sustainable entrepreneurship studies have recently focused increasing attention on the predictors of the sustainability performance of SEOs [39,40,41,42,43]. Nevertheless, empirical studies on the determinants of SEO financial health are relatively rare [44,45], although clarifying the business case of sustainable entrepreneurship is a critical issue of theoretical and practical importance. Additionally, although there is a growing trend toward revenue diversification among SEOs and other types of hybrid organizations, no empirical study has examined the impact of revenue diversification on SEO financial health. This study addresses this gap by examining the relationship between the revenue diversification and financial health of SEOs in four dimensions, namely, financial flexibility, efficiency, profitability, and growth. Second, the previous literature examining SEO performance focused primarily on organizations addressing sustainable development issues in the environmental, ecological, and economic dimensions, devoting little attention to SEOs that seek to solve societal sustainability problems, such as equal access to education and medical care, elderly and disabled services, employment promotion for unprivileged groups, or community development and poverty alleviation, through their entrepreneurial activities. Therefore, from a holistic perspective of multidimensional sustainability, this study addresses this gap by examining the predictors of the financial health of SEOs that provide solutions to problems related to the environmental, ecological, economic, and social dimensions of sustainability. Third, going beyond a simple examination of the net effects of single variables, this study uses fsQCA to uncover the ways in which revenue diversification and organizational features interact with each other to influence financial health and simultaneously identify alternative configurations that predict the high and low levels of SEO financial health. Finally, prior studies addressing the influence of revenue diversification on the financial health of hybrid organizations have often focused on United States (U.S.)-based entrepreneurial nonprofits. Therefore, this study offers a unique contribution to the literature by examining this issue in China, where SEOs’ legal, socioeconomic, and market environments, and their organizational behavior, culture, and strategies differ from those in the U.S. and in other contexts.

The rest of this paper is structured as follows. First, we develop a configurational model for fsQCA by systemically reviewing the relevant literature. Second, we introduce the research methods, describing sample selection, measurements, and calibration. Third, we present the main fsQCA results. Fourth, we discuss the findings in light of the literature to outline their main theoretical contributions and practical implications. Finally, we draw conclusions, identify the limitations of this study, and discuss directions for future research.

2. Literature Review

2.1. Revenue Diversification and Financial Health

Although empirical studies that specifically investigate the relationship between revenue diversification and SEO financial health remain scarce, the prior literature has examined the issue generally among hybrid organizations, mainly focusing on four dimensions—namely, financial flexibility, efficiency, profitability, and growth—and has yielded competing arguments.

2.1.1. Financial Flexibility

Numerous empirical studies have shown that U.S. entrepreneurial nonprofits with diversified revenue portfolios have greater financial flexibility and are consequently less likely to be financially vulnerable [22,28]. Similar evidence on the positive contribution of revenue diversification to financial flexibility has also been reported in non-U.S. contexts, such as Israel [46], Germany [29], and Spain [30]. Moreover, Bouchard and Rousselière’s [33] study on Canadian social economy enterprises showed that organizations combining several sources of financing likewise had greater chances of survival. Venegas-Flores et al.’s [21] study on sustainable entrepreneurship companies in volatile markets highlighted the importance of diversification portfolios in reducing financial risk.

2.1.2. Financial Efficiency

Considering the increased transaction costs associated with revenue diversification, some scholars shifted their focus to revenue concentration and found that implementing a revenue concentration strategy generated greater efficiency among entrepreneurial nonprofits [31]. More recently, Berrett and Hung [47] discovered a curvilinear U-shaped relationship, indicating that entrepreneurial nonprofits are most efficient when their revenue portfolios are fully diverse or fully concentrated. In addition, Ecer et al. [35] identified revenue composition as a factor that significantly affects the financial efficiency of social entrepreneurship organizations in the U.S. context.

2.1.3. Profitability

A number of studies have examined how revenue diversification affects the profitability of entrepreneurial nonprofits. The literature on U.S. nonprofits concluded that NPOs with more revenue sources generate greater surplus margins [22]. Similarly, Wicker and Breuer [48] reported that revenue diversification had a positive effect on the financial condition of German entrepreneurial nonprofits. In contrast, Guan et al. [34] explored this issue in the Chinese context and came to the opposite conclusion, that revenue diversification had a negative effect on the profitability of social enterprises.

2.1.4. Financial Growth

Previous studies have provided growing evidence for the positive links between revenue concentration and financial growth in terms of annual revenues among U.S. [28] or Swiss [32] entrepreneurial nonprofits. In contrast, Gimmon and Spiro’s [49] study of Israeli social ventures revealed that early funding diversity had no significant effect on their long-term growth.

2.2. Impact of Organizational Characteristics

2.2.1. Effect of Organizational Size and Age

Earlier studies have shown that the effect of revenue diversification on financial health varies among hybrid organizations of different sizes and ages. In particular, scholars noted that the positive contribution of revenue diversification to the financial health of entrepreneurial nonprofits tended to be stronger for larger and older organizations because they were considered to demonstrate advantages in hiring specialized personnel to pursue revenue diversification and achieving economies of scale that reduced the disadvantages derived from revenue heterogeneity [31,35].

Although no study has empirically examined the moderating effect of organizational size and age on the association between revenue diversification and financial health among SEOs, numerous empirical studies have identified organizational size and age as important determinants of the financial health of hybrid organizations. Accordingly, it is reasonable to believe that an in-depth investigation into the relationship between the revenue diversification and financial health of SEOs should not overlook the interactive and combined effects of revenue diversification, organizational size, and age.

2.2.2. Effect of Legal Status

Additionally, no empirical study has specifically explored the relationship between legal status and SEO financial health, but the previous literature has demonstrated the impact of legal status on the financial health of social entrepreneurship organizations. Some studies have shown that nonprofits are more likely to achieve a lower level of financial health than for-profits in terms of economic productivity in France [50], profitability in South Korea [51], growth in market income in Switzerland [52], or multi-aspect economic performance in China [53]. Nevertheless, several studies have noted that the organizational registration form has no significant influence on the financial health of social entrepreneurship organizations in China [34,54], Spain [37], or the global context [55].

2.3. The Need for a Configurational Perspective

Previous quantitative statistical studies have often examined organizational characteristics, such as organizational size, age, and legal status, as important predictors of the financial health of hybrid organizations. Moreover, a few studies have explored the moderating role of organizational size and age in the relationship between revenue diversification and the financial health of entrepreneurial nonprofits. Nevertheless, these studies explored only the relationships between independent and dependent variables or the effects of single moderators. Additionally, these conventional statistical analyses were subject to limitations in terms of fully grasping the complex interactive effects between revenue diversification strategies and organizational features in multiple aspects, such as their size, age, and legal status.

To overcome these limitations in previous quantitative analyses, this study uses fsQCA, which is a set theoretical technique based on fuzzy set theory and Boolean minimization [56], and simultaneously investigates within- and cross-case logic to embrace complex causality [57]. Thus, the fsQCA approach has several important methodological advantages. First, fsQCA enables researchers to test equifinality [58], where multiple configurations of causal conditions can lead to the same outcome. Second, fsQCA investigates the potential interdependency of all antecedent conditions and thus reveals additional fine-grained information about causal complexity [59]. Third, fsQCA assumes that real-life causal relationships tend to be asymmetrical and therefore emphasizes identifying the substantially different configurations that predict both the presence and absence of an outcome [56,58]. Fourth, as an inductive, iterative method that reveals patterns in the data at the case level that tend to be obscured by statistical analysis [59], fsQCA can lead to the discovery of new and often unexpected causal relationships [60].

Given these methodological benefits, hybrid organization scholars have increasingly used fsQCA to explore the configurational conditions that influence financial health [54,57,61,62,63,64]. In the sustainable entrepreneurship literature, although several studies have used fsQCA to explore the combined conditions in entrepreneurial ecosystems that produce highly sustainable entrepreneurship [65], or innovative strategies for sustainable entrepreneurship [66], no fsQCA study has investigated the financial health of SEOs.

Despite the growing application of the fsQCA method in the recent literature on hybrid organizations, to the best of our knowledge this study is the first to use fsQCA to examine the complex relationships between revenue diversification, organizational features, and the financial health of SEOs. Figure 1 shows the configurational model for the fsQCA in this study.

3. Methods

3.1. Sample

The sample was obtained by using a theoretical sampling approach, which emphasizes that the cases are selected purposively on the basis of their potential to shed complementary insights into the analysis [67]. Specifically, an online questionnaire survey of SEOs was conducted from December 2021 to February 2022 through a purposive sampling approach to create a database of 317 SEOs that represented at least one of the major SEO typologies in China, including entrepreneurial nonprofits, social cooperatives, work integration social enterprises, fair trade organizations, microfinance institutions, and socially and environmentally responsible businesses. Additionally, the survey sample reflects a relatively high level of regional heterogeneity, including SEOs from regions where local governments have launched supportive policies for SEOs, such as Beijing (38%), Guangdong (15%) Chengdu (8%), and another 21 provinces or municipalities (39%). Next, to ensure the satisfactory variability of cases across conditions and outcomes in this study [56], we stratified all the cases into eight subsamples according to their revenue diversification practices and organizational features in terms of size, age, and legal status and then selected cases from each of these eight subsamples on the basis of their differences in financial health. Consequently, following simultaneously the principles of theoretical relevance and satisfactory variability [68], we obtained 204 cases suitable for fsQCA.

The SEOs in the sample varied in terms of their size, age, legal status, and domain of activities. The majority of cases were mid-sized SEOs hiring 10 to 100 employees (54%), followed by small-sized ones hiring less than 10 employees (36%) and large-sized ones hiring more than 100 employees (10%). The operating tenure of the cases ranged from 1 to 20 years, and the average age was 6.49 years (SD = 4.07). The sample included SEOs registered as for-profit organizations (59%) or nonprofit organizations (41%). In addition, the SEOs operated mainly in six domains of activities related to sustainable development: low-carbon, circular economy, and ecological protection (25%); education (23%); medical, elderly, and disabled services (22%); employment promotion (13%); community development and poverty alleviation (10%); and others (7%).

3.2. Measurements

3.2.1. Outcome

The outcome under investigation in this study was the SEOs’ financial health. Prior studies concerning the relationship between the revenue diversification and financial health of nonprofits and SEOs mainly used four groups of indicators to measure financial health: financial flexibility, efficiency, profitability, and growth. Drawing on prior research, we adopted a multidimensional conceptual framework and used four groups of indicators—financial flexibility, efficiency, profitability, and growth—to measure the financial health of SEOs.

Table 1. Measurements of financial health.

Indicators	Measures
flexibility	net assets divided by total revenue [30]
efficiency	total expenditures divided by total revenues [35]
profitability	net income divided by total assets [30]
growth	growth rate in total revenue [32]

presents the indicators and measures of financial health, along with the relevant literature using these measures.

3.2.2. Conditions

This study examined the complex conjunctive effect of revenue diversification and three relevant organizational features on SEOs’ financial health; hence, the configurational model included four antecedent conditions: revenue diversification, organizational age, size, and legal status.

Revenue diversification (RD) was measured using the formula developed by Chikoto et al. [69]:

$$\mathrm{R}\mathrm{D}=\left(1\right.-{\sum }_{\mathrm{i}=1}^{\mathrm{n}}\left.{\mathrm{Ri}}^2\right)/\left[\left(\mathrm{n}-\left.1\right)/\left.\mathrm{n}\right]\right.\right.$$

where Ri is the ratio of the revenue stream to the total revenue and n is the number of revenue streams. In this study, we differentiated the four revenue streams as market-based earned income (sales of goods and services), government support (grants and public purchases), charitable donations and contributions, and others (membership fees and investment income). The RD value ranges from 0 to 1, where the higher the value of RD, the greater the level of RD.

We measured Age by the number of years an SEO was in operation until 2022, Size by the natural logarithm of the number of employees, and Legal status as a dummy variable (where 1 = nonprofit and 0 = for-profit).

3.3. Calibration

The first fsQCA step requires calibration; that is, the process of assigning each case a series of membership scores ranging between 0 and 1 [56]. Following the fsQCA literature and based on the characteristics of the sample distribution, we defined the three thresholds of full membership, full non-membership, and the crossover point by adopting both percentile and manual calibration methods.

Table 2. Descriptive statistics and calibration values.

	Descriptive Statistics				Calibration Values
	Min.	Max.	Mean	S. D.	F. M.	Cr.	F. N.
flexibility	−13.25	108.70	2.63	9.98	1.21	0.44	0.10
efficiency	0.17	1.00	0.95	0.10	0.72	0.96	1.00
profitability	0.00	3.14	0.11	0.33	0.60	0.09	0.00
growth	−0.98	11.50	0.77	1.44	0.79	0.24	0.00
revenue diversification	0.00	0.90	0.33	0.29	0.61	0.31	0.00
age	1.00	20.00	6.49	4.07	10.00	5.00	3.00
size	0.00	8.31	2.91	1.30	3.66	2.71	1.95
legal status	0.00	1.00	0.40	0.49	1.00	0.50	0.00

Note: F. M. = full membership; Cr. = crossover; F. N. = full non-membership.

lists the descriptive statistics of raw data and specifies three qualitative anchors for each of the conditions under investigation in this study.

4. Results

Following the generally used fsQCA analytical procedure, necessity, sufficiency, and robustness analyses were performed by using fsQCA 3.0 software to examine the complex effects of the four antecedent conditions on the targeted outcome, namely the financial health of SEs.

4.1. Necessity Analysis

Necessity analysis examines whether any of the causal conditions are individually necessary for the outcome of interest to occur [58]. A condition is regarded as necessary if the consistency score exceeds the recommended threshold of 0.9 [56,70]. Accordingly, necessity analyses were conducted for all four antecedent conditions.

Table 3. Necessity analyses of single causal conditions.

	Flexibility				Efficiency
	High		Low		High		Low
	Cons.	Cov.	Cons.	Cov.	Cons.	Cov.	Cons.	Cov.
revenue diversification	0.50	0.47	0.61	0.63	0.42	0.17	0.53	0.83
~revenue diversification	0.61	0.59	0.48	0.52	0.58	0.24	0.47	0.76
age (old)	0.63	0.60	0.49	0.51	0.57	0.23	0.48	0.77
~age (old)	0.49	0.47	0.62	0.65	0.43	0.18	0.52	0.82
size (large)	0.68	0.65	0.42	0.44	0.63	0.26	0.46	0.74
~size (large)	0.42	0.39	0.67	0.70	0.37	0.15	0.54	0.85
legal status (nonprofit)	0.29	0.34	0.51	0.66	0.29	0.15	0.43	0.85
~legal status (nonprofit)	0.71	0.57	0.49	0.43	0.71	0.25	0.57	0.75
	Profitability				Growth
	High		Low		High		Low
	Cons.	Cov.	Cons.	Cov.	Cons.	Cov.	Cons.	Cov.
revenue diversification	0.40	0.13	0.53	0.87	0.56	0.53	0.57	0.58
~revenue diversification	0.60	0.21	0.47	0.79	0.56	0.54	0.54	0.57
age (old)	0.55	0.19	0.49	0.81	0.43	0.41	0.68	0.71
~age (old)	0.45	0.15	0.51	0.85	0.69	0.67	0.43	0.45
size (large)	0.65	0.22	0.47	0.78	0.52	0.50	0.57	0.59
~size (large)	0.35	0.12	0.53	0.88	0.58	0.56	0.52	0.54
legal status (nonprofit)	0.34	0.15	0.41	0.85	0.35	0.42	0.46	0.58
~legal status (nonprofit)	0.66	0.19	0.59	0.81	0.65	0.53	0.54	0.47

Note: ~ indicates the absence of a condition; Cons. = consistency; Cov. = coverage.

presents the consistency scores, which range between 0.12 and 0.88. None of the four conditions showed a consistency score exceeding the threshold value of 0.9, indicating that no condition is by itself necessary to produce high or low levels of financial health. As a single antecedent condition had weak explanatory power for the outcome, further analysis of the synergistic influence of multiple conditions was warranted.

4.2. Sufficiency Analysis

In sufficiency analyses, researchers must decide on three important thresholds; namely, the frequency threshold that determines the number of cases for a configuration to be included in the sufficiency analysis, the consistency threshold that indicates the extent to which cases sharing similar conditions display the same outcome, and the proportional reduction in inconsistency (PRI) threshold to avoid simultaneous subset relations of configurations in both the outcome and its absence [67]. In accordance with the recommendations of the fsQCA literature [56,58,70], we set the raw consistency threshold at 0.8, the PRI score at 0.75, and the frequency threshold at 1.

The sufficiency analyses identified distinct configurations that revealed the divergent ways revenue diversification and organizational features interacted with each other to predict differently the high and low levels of financial health in four dimensions.

Table 4. Configurations sufficient for the high and low levels of financial health.

	Flexibility			Efficiency			Profitability			Growth
	High	Low		Low			Low			High	Low
	FH1	FL1	FL2	EL1	EL2	EL3	PL1	PL2	PL3	GH1	GL1	GL2
revenue diversification	●		●				●				●
age (old)	●		Ⓧ		Ⓧ				Ⓧ		Ⓧ
size (large)	●	Ⓧ			Ⓧ	Ⓧ			Ⓧ	Ⓧ
legal status (nonprofit)	Ⓧ	●	●			●	●			●	●
consistency	0.819	0.813	0.849	0.869	0.886	0.887	0.904	0.902	0.917	0.813	0.818	0.795
raw coverage	0.168	0.350	0.227	0.347	0.252	0.295	0.346	0.246	0.155	0.277	0.292	0.190
unique coverage	0.168	0.166	0.042	0.205	0.043	0.105	0.206	0.105	0.031	0.277	0.157	0.053
overall solution consistency	0.819	0.802		0.864			0.891			0.813	0.806
overall solution coverage	0.168	0.393		0.561			0.482			0.277	0.347

Note: ● indicates the presence of a core condition; Ⓧ indicates the absence of a core condition; a blank space indicates an irrelevant condition; FH indicates high flexibility; FL indicates low flexibility; EL indicates low efficiency; PL indicates low profitability; GH indicates high growth; GL indicates low growth.

shows the consistency values for the identified configurations, which ranged from 0.795 to 0.917, all of which are higher than the recommended minimum consistency threshold (0.75).

The results suggest that the outcomes of financial health were caused by configurations with relatively high consistency rather than by the uncaptured condition(s) or random chance as alternative explanations. The overall solution coverage scores (the extent to which the minimized solutions together cover all cases with the outcome) ranged from 0.168 to 0.561, and were similar to the coverage scores reported in recent QCA studies which ranged from 0.05 to 0.54 [68].

With respect to the configurations for financial flexibility, the results yield a single pathway for high performance and two alternative pathways for low performance. FH1 indicated that revenue diversification resulted in high financial flexibility only among large-sized and established SEOs (age > 10 years) registered as for-profit entities. In contrast, FL2 indicated that among start-up SEOs (age < 3 years) registered as nonprofits, revenue diversification led to low financial flexibility. In addition, FL1 indicated that small-sized nonprofit SEOs were also linked consistently to low financial flexibility, regardless of whether their revenue sources were diversified.

With respect to the configurations for financial efficiency, the results do not support any interaction effect for revenue diversification and organizational features on high performance. Instead, the results reveal three configurations for low performance: small-sized and start-up SEOs (EL1), small-sized nonprofit SEOs (EL2), and nonprofit SEOs with highly diversified revenue sources (EL3).

With respect to the configurations for profitability, the results similarly identify no particular set of conditions for high performance but three configurations for low performance. Specifically, low profitability was associated with small-sized and start-up SEOs (PL1), small-sized nonprofit SEOs (PL2), and nonprofit start-up SEOs achieving revenue diversification (PL3).

With respect to the configurations for financial growth, three configurations emerged: one for high performance and two for low performance. GH1 implied that large-sized and start-up SEOs registered as for-profit entities achieved greater financial growth, regardless of whether their revenue sources were diversified. Alternatively, revenue diversification led to poorer financial growth among large-sized, established SEOs (GL1). Moreover, low financial growth was also linked to large-sized, established SEOs with a nonprofit legal status (GL2).

To further explore how revenue diversification influences financial health in four dimensions differently for for-profit and nonprofit SEOs, all four configurations in which revenue diversification and legal status were present as core conditions (FH1, FL2, EL3, and PL3) were compared. The results illustrate a substantial difference between for-profit and nonprofit SEOs. The mixed revenue strategy used by for-profit SEOs contributed positively to enhancing their financial flexibility when SEOs were large-sized, established entities (FH1). Conversely, for nonprofit SEOs, diversifying their revenue sources negatively affected their financial flexibility and profitability when they were start-ups (FL2 and PL3), as well as their financial efficiency regardless of whether they were start-ups or established, large- or small-sized (EL3).

4.3. Robustness Analysis

Considering that fsQCA results are usually sensitive to the parameters used to conduct the analysis, such as calibration and frequency and consistency thresholds [56], we changed these parameters to examine whether the findings were robust. As recommended by Fiss [58], we adjusted the crossover points from the 50th to the 55th percentiles, the consistency thresholds from 0.8 to 0.75, and the frequency thresholds from 1 to 2, and then replicated the analyses to assess the reliability of the findings. The resulting configurations varied slightly rather than considerably from the prior configurations. Specifically, although minor changes were observed regarding the number, consistency, and coverage of the solutions and subsolutions, the causal configurations obtained in this study were sufficiently stable.

5. Discussion

The purpose of this study is to investigate the configurational effects of revenue diversification and three other organizational conditions on the multidimensional financial health of SEOs in the Chinese context. The fsQCA yielded one pathway leading to high financial flexibility, two pathways leading to low financial flexibility, and three pathways related to low financial efficiency and low profitability, respectively, as well as one pathway producing high financial growth and two pathways producing low financial growth. The results reveal that, in most configurations (except for EL3), neither revenue diversification nor organizational features (i.e., age, size, and legal status) on their own are sufficient conditions to explain the outcomes. The results embody typical features of fsQCA; namely, conjunctional causation, equifinality, and asymmetry, justifying the use of fsQCA in this study. These findings echo or differ from those of prior studies and extend the literature by shedding light on the complexity of the causal relationships among revenue diversification, organizational conditions, and the financial health of SEOs.

Considering the configurations related to financial flexibility, the results show that high financial flexibility was present when the large-sized established for-profit SEOs adopted revenue diversification strategies. This finding partially echoes Bouchard and Rousselière’s [33] study on Canadian social economy enterprises, which suggested that mixed revenue streams led to a greater chance of survival. However, our study results extend the literature by demonstrating that revenue diversification itself is not sufficient to explain high financial flexibility, which is instead the result of configurational effects among multiple conditions, such as revenue diversification, legal status, and organizational age. Moreover, the results also reveal that low financial flexibility is present when start-up nonprofit SEOs adopt revenue diversification strategies. This finding is not consistent with prior evidence on the positive contribution of diversified revenue streams to financial flexibility among entrepreneurial nonprofits [28,29,30,46]. However, this finding is in line with the literature showing that revenue diversification is more likely to be dysfunctional for smaller and younger organizations [31]. Therefore, the findings on the interdependencies and combined effects of revenue diversification and organizational age more specifically show the causal complexity in these relationships compared with that implied by the literature.

Considering configurations related to financial efficiency and profitability, the results did not show a configuration for high performance but alternatively identified a configuration that consists of revenue diversification and organizational conditions for low financial efficiency and low profitability, respectively. The findings on the configuration for low financial efficiency partially support the previous literature highlighting that what contributes to greater financial efficiency among entrepreneurial nonprofits is not revenue diversification but its inverse, revenue concentration [31]. Moreover, the findings on the configuration for low profitability are aligned with those obtained by Guan et al. [34], who suggested a negative relationship between revenue diversification and the profitability of Chinese social enterprises. However, this study furthers our understanding of the complexity of the effects on SEOs’ profitability by shedding light on how revenue diversification interacts with legal status and organizational age to influence SEOs’ profitability.

Considering configurations related to financial growth, the results show that high performance is associated with large-sized, start-up, for-profit SEOs, whereas low performance is linked with combined conditions when large-sized and established SEOs achieve revenue diversification or among large-sized, established, nonprofit SEOs. On the one hand, this finding is consistent with the literature identifying revenue concentration as a positive predictor of financial growth [28,32]. On the other hand, this finding is not aligned with studies suggesting that larger and older organizations demonstrate more financial benefits from revenue diversification [31,35]. Thus, these findings, particularly the presence of large-sized organizations in the configurations for both high and low performance (GH1, GL1, and GL2) and the presence and absence of revenue diversification in alternative paths for low performance (GL1 and GL2), further illustrate that the causal relationships between revenue diversification, organizational features, and financial growth are largely conjunctional, equifinal, and asymmetric in nature. Therefore, these relationships are much more complex than those suggested by previous studies.

Overall, in contrast to the literature highlighting the financial benefits of revenue diversification for hybrid organizations, the fsQCA results in this study suggest that the interplay of revenue diversification with organizational conditions, such as age, size, and legal status, largely plays a negative role in influencing SEO financial health in four dimensions, despite their different configurational paths. Moreover, considerable differences were identified by more specifically comparing the ways in which revenue diversification influences financial health among for-profit and nonprofit SEOs. Among nonprofit SEOs, revenue diversification produces no benefit in enhancing financial health in four dimensions. In contrast, revenue diversification among for-profit SEOs positively affects their financial flexibility but not their financial efficiency, profitability, or growth.

The prevailing negative outcomes on financial health produced by the combined effects of revenue diversification and organizational features can probably be explained by closely investigating the Chinese-specific contextual features of the entrepreneurship ecosystem. As SEOs operate across various institutional fields and face conflicting logics, attaining legitimacy through embeddedness often presents challenges [5]. Particularly, as noted in previous studies, hybrid organizations in China operate in unfledged ecosystems that provide insufficient resources [8], lack institutionalization [71], and are dominated by the government [72,73]. Owing to the lack of a supportive institutional environment, Chinese SEOs face multiple challenges in obtaining legitimacy [15,71], such as regulatory legitimacy, which refers to legal recognition [16]; cognitive legitimacy, which covers public acceptance and recognition [54]; and normative legitimacy, which is related to sociocultural values and beliefs [72]. Arguably, the difficulties faced by SEOs in gaining legitimacy in the Chinese context can be identified as important factors that have diluted the possible positive effects of revenue diversification on financial health.

Furthermore, comparisons of the multiple ways in which revenue diversification influences financial health in four dimensions also revealed particularly interesting differences. Although revenue diversification contributed to enhancing financial flexibility for large-sized, established for-profit SEOs, this positive contribution was absent in the configurations for financial efficiency, profitability, and growth as a result of the combined effects of revenue diversification and particular organizational conditions. The contradictory impacts of revenue diversification on financial flexibility and the other three dimensions of financial health detected in this study were partially consistent with the previous literature demonstrating the risk–return trade-off effect of revenue diversification among entrepreneurial nonprofits [28,31]. These findings offer valuable evidence for the trade-off effect of revenue diversification among SEOs in the Chinese context.

5.1. Theoretical Contributions

This study makes several theoretical contributions to the sustainable entrepreneurship and sustainability literature. First, in response to the scarcity of studies on the determinants of SEO financial health, particularly the impact of revenue diversification, this study enriches the literature by demonstrating, for the first time, how revenue diversification interplays with organizational features of SEOs in predicting a high level of financial flexibility but a low level of financial efficiency, profitability, and growth. Additionally, this study extends previous discussions concerning the trade-off effect of revenue diversification on the different dimensions of financial health from the nonprofit sector to the SEO context.

Second, from a holistic perspective of multidimensional sustainability, this study expands the research scope of SEOs from organizations that address mainly sustainable development issues in environmental, ecological, and economic aspects to those that focus on social sustainability issues. Accordingly, this study contributes to the literature by providing a comprehensive understanding of the causal paths leading to the financial health of SEOs in different domains of sustainable development activities.

Third, overcoming the limitations of quantitative studies that have focused on the aggregated net effects of variables, this study makes a methodological contribution to the sustainable entrepreneurship and sustainability literature by employing the fsQCA approach, which enables new insights into the complexity of the relationships among the revenue diversification, organizational conditions, and financial health of SEOs. More specifically, by demonstrating the conjunctional causation, equifinality, and asymmetry of causal relationships, this study reveals how revenue diversification interacts with organizational age, size, and legal status and conjunctively produce alternative configurations for high and low levels of financial health in four dimensions.

Finally, this study challenges the assumption in the hybrid organization literature that revenue diversification predicts greater financial health and provides a nuanced understanding of the divergent results in the Chinese context. For example, one of the notable nuances revealed in this study is that most configurations involving the conditions of revenue diversification and organizational features yield no financial benefits to SEOs, particularly to nonprofit SEOs, largely because of the typical challenges faced by most Chinese SEOs to gain legitimacy in unfavorable institutional environments.

5.2. Practical Implications

The results of this study offer useful practical implications. For SEO managers, the findings on the multiple and asymmetric configurations for financial health suggest that revenue diversification is not a “one size fits all” strategy for all SEOs with different organizational features. Specifically, revenue diversification can be considered a suitable revenue strategy for large-sized, established for-profit SEOs aimed at fostering their financial flexibility, whereas it may be a counterproductive revenue strategy for nonprofit SEOs, given that it might reduce start-up nonprofit SEOs’ financial flexibility and profitability and reduce profitability for nonprofit SEOs of different ages and sizes. In addition, when choosing and implementing their revenue strategies, SEO managers must fully evaluate the trade-off effect of revenue diversification on the different dimensions of financial health.

Our findings also provide implications for stakeholders in sustainable entrepreneurship ecosystems, such as local governments, which act as policymakers; resource providers; incubating service providers [73]; and intermediary organizations, which act as legitimizing agents [71] for the sustainable development of SEOs. This study has generated in-depth insights into the complexity of the causal links among revenue diversification, organizational conditions, and financial health. These findings enable stakeholders to launch more tailored policies or service interventions aimed at fostering the financial health and sustainable development of SEOs through targeted revenue diversification pathways on the basis of their organizational features.

In addition, this study offers a novel understanding of the role of one important contextual factor that might weaken the contribution of revenue diversification to financial health; that is, the challenges faced by SEOs in gaining legitimacy in the Chinese context. This finding indicates that these key stakeholders should explore ways to support SEOs to strengthen their legitimacy. Specific initiatives could include promoting the legal recognition of SEOs to reinforce their regulatory legitimacy, increasing publicity about SEOs’ practices and social impact to enhance their normative and pragmatic legitimacy, and launching supportive public policies on SEOs to increase their organizational capacity and cognitive legitimacy.

6. Conclusions

Despite the emergence of SEOs as a new engine for sustainable development in China, the complex causal relationships among their revenue strategy, organizational features, and financial health are still not well studied. This study addressed this knowledge gap by performing fsQCA analyses and showed that the combined effects of revenue diversification and certain organizational conditions could improve financial flexibility, whereas alternative configurational conditions might reduce financial efficiency, profitability, and growth. More specifically, revenue diversification was primarily effective at generating greater financial flexibility for large-sized, established, for-profit SEOs but was largely ineffective for nonprofit SEOs across all four dimensions of financial health.

This study also has limitations, which suggest avenues for future research. First, with respect to the configurational model, this study mainly examined the combined effects of revenue diversification and three organizational conditions on financial health. Thus, future studies should investigate the interplay of revenue diversifications with other factors that might also affect the financial health of SEOs, such as other organizational features of SEOs, the resource pattern and institutional structures within SEO ecosystems, and the revenue composition and organizational capabilities of SEOs. Second, as this study used China-only data, the findings potentially had context specificity, suggesting uncertainty in terms of their generalizability to other contexts. Therefore, future research could examine the relevance of our findings to other countries and regions. This comparison across different contexts or settings might offer a much richer understanding of the causal complexity of the issue. Finally, this study was static and cross-sectional in nature; therefore, we have not determined the ever-changing causal links between revenue diversification and financial health over time. As Beynon et al. [74] suggested, considering how to apply fsQCA to longitudinal panel data presents a challenging but productive direction for future research.