Critical Factors for Business Sustainability in Women-Led Social Enterprises in Peru

Abstract

This study analyzes the key factors that determine the sustainability of women-led social enterprises in the Peruvian context via structural equation modeling (SEM). This research is based on a random sample of 521 social entrepreneurs and explores the influence of current employment, type of entrepreneurship, and years of entrepreneurial experience on social entrepreneurship potential. The results show that the current employment of women entrepreneurs significantly influences their social entrepreneurship potential (β = 0.383, p = 0.000), whereas the type of entrepreneurship (β = 0.653, p = 0.026) and years of experience (β = 0.269, p = 0.004) act as moderating variables in this relationship. Additionally, comparison tests revealed significant differences in social entrepreneurship potential according to age (p = 0.000), years of entrepreneurial experience (p = 0.000), and employment status (p = 0.000). Pairwise comparisons revealed that women aged 30 years or older had greater potential for social entrepreneurship than did those under 22 years (p = 0.001) and those aged 23–29 years (p = 0.006). Similarly, entrepreneurs with 6 or more years of experience presented greater potential than those with less than 3 years of experience (p = 0.000). These findings highlight the importance of considering employment status, type of entrepreneurship, prior experience, and age differences when designing policies and support programs for women’s social entrepreneurship. This study contributes to understanding the factors that influence the sustainability of women-led social enterprises and provides recommendations for future research, such as expanding the SEM, replicating it in different contexts, and complementing it with qualitative approaches.

1. Introduction

Women-led social enterprises have emerged as an essential avenue for fostering inclusion, economic development, and well-being for both women and their communities. According to [1,2], these enterprises have made significant contributions to economic and social progress. Despite preliminary evidence suggesting positive impacts, such as financial leverage, community empowerment, and ecosystem resilience, according to [3,4], these enterprises face multiple barriers that threaten their long-term sustainability compared with other business models, as indicated by [5].

According to the Global Entrepreneurship Monitor 2020/2021, the gender gap in entrepreneurship remains significant globally, especially in Europe and North America. While in some regions, such as Latin America, Central Asia, and Africa, 1 in 5 or more women are entrepreneurs, in most countries, men are more likely to start new businesses. Despite recent progress, the pandemic may have slowed progress toward gender parity in this area. In addition, women face obstacles such as perceived fewer opportunities for success, greater risk aversion, financing problems, digitization challenges, and difficulties in balancing work and family [6].

“Entrepreneurs” is a report prepared by the School of Business Administration. It collects the stories of several women who have started their own businesses and succeeded. The gender gap in entrepreneurship has also grown since 2016 due to COVID-19, according to research. In 2019, the early-stage entrepreneurial activity (TEA) rate for women was 6%, and that for men was 6.3%; however, in 2020, the TEA rate for women decreased by 1.2 points to 4.8%, and that for men decreased by 0.70 points to 5.6% [6].

Women-led social enterprises have experienced significant growth in recent years. According to [7], the global female entrepreneurial activity rate reached 10.2% in 2020, representing a 2% increase compared with the previous year. Additionally, 45% of social enterprises globally are founded and led by women. In the Peruvian context, women-led social enterprises have also undergone remarkable development. Ref. [8] reports that 45% of social enterprises in the country are led by women. Furthermore, ref. [9] highlighted that these enterprises have generated more than 10,000 direct jobs and have benefited over 100,000 vulnerable individuals since 2020.

Social enterprises face a series of challenges that can hinder their operational continuity. These challenges include difficulties in accessing financing, as noted by [1,10], as well as the lack of effective support networks, according to [2,11]. Additionally, unshared family responsibilities and gender cultural biases, identified by [12,13], can also pose significant barriers. Finally, the lack of supportive public policies, as noted by [3,14], adds complexity to this scenario.

This issue not only hinders the potential of women-led social enterprises to generate sustainable economic and social impacts but also reinforces structural inequality gaps. Despite the relevance of this topic, few studies have comprehensively analyzed how these various factors are interrelated and influence each other [2,15,16,17,18].

The present study adopts a holistic approach using a structural equation model (SEM) to capture the multifactorial nature of the phenomenon [19]. This study aims to answer the following research questions: What are the key factors that influence the sustainability of women-led social enterprises, and how are they interrelated?

From a theoretical perspective, this research makes a significant contribution to filling a gap in the specialized literature on female social entrepreneurship. To date, little systematic attention has been given to the factors that influence the success of these ventures. As noted by [20], “Although there is a substantial amount of research on women’s entrepreneurship, research on women’s social entrepreneurship remains scarce” (p. 586). While previous studies have addressed specific variables such as access to networks, entrepreneurial self-efficacy, management skills, restrictive gender norms, and the conflict between family and work obligations [16], their relative importance and interrelationships in the context of female social entrepreneurship have yet to be clarified. Furthermore, few studies have adopted a proactive approach focused on the positive predictors of success for these entrepreneurs. As [9] highlights, “more research is needed to better understand the unique challenges and opportunities faced by female social entrepreneurs” (p. 94). In response to this need, the present research seeks to generate solid empirical evidence to provide a more comprehensive understanding of the factors influencing the success of women-led social enterprises. This holistic approach will not only identify key factors but will also analyze their interrelationships and relative impact on the success of these ventures.

From a practical standpoint, the results of this research aim to fulfill the Sustainable Development Goals (SDGs) related to gender equality and female empowerment. By identifying the key factors that contribute to the success of women-led social enterprises, as well as the barriers they face, this study can guide the design of more effective public policies and support programs. As noted by [21], female social entrepreneurship has the potential to generate a positive impact on both women’s well-being and the development of their communities. However, ref. [22] cautions that specific cultural contexts must be considered when promoting female empowerment through entrepreneurship. Moreover, [20] emphasizes the importance of developing comprehensive support ecosystems that address the multiple dimensions affecting female social entrepreneurs. Understanding these dynamics can contribute to the design of more effective interventions to promote female leadership in social impact initiatives, in line with the SDGs [23].

To address this gap, the present study seeks to analyze the key factors that influence the sustainability of female social enterprises and determine their interrelationships through the application of structural equation modeling (SEM). The added value of this research is threefold: (1) it provides empirical evidence on the key drivers of competitive advantage in an emerging industry, which is crucial for understanding the dynamics of women-led social enterprises [24]; (2) it offers concrete policy guidance by identifying priority areas for intervention, thereby contributing to the creation of a more conducive environment for female social entrepreneurs [25]; and (3) it helps to bridge knowledge gaps in a relatively unexplored intersection between gender, entrepreneurship, and collective social impact, an area that requires greater attention in the academic literature [26]. Consequently, this article provides essential information for both academics and practitioners interested in promoting inclusive and sustainable entrepreneurial ecosystems, aligning with the growing importance of a gender perspective in entrepreneurship studies [27] and the crucial role of women in sustainable economic development [28].

2. Theoretical Framework

2.1. Social Entrepreneurship

Understanding the root of this concept is fundamental. To this end, the works of [29,30] have been consulted, which point out that it is an approach that applies business strategies to address pressing social or environmental challenges. Unlike traditional entrepreneurship, its primary objective is not profit maximization but the generation of significant and lasting social impact.

Many people in the business and academic world have become interested in social enterprises in recent years. It is a rapidly expanding topic because of its profound influence on both people and countries. One possible explanation for this is that it prioritizes the development and maintenance of social value over the production of monetary profits [15]. Many scholars have considered social entrepreneurship as a panacea for the unemployment crisis of recent years [24].

Initially, social entrepreneurship, led by committed individual entrepreneurs as well as social organizations, sought to generate sustainable and lasting solutions to address challenges in social, economic, and urban domains [30] (Bacq & Janssen, 2011). In this dynamic, the successful integration of social value with commercial value not only led to tangible economic benefits but also generated a positive impact on the environment. This synergy between the social and commercial becomes a powerful tool for promoting change and progress in our communities [31].

In other words, the expression “social entrepreneurship” describes a set of practices where businesses or nonprofit entities collaborate to promote social objectives. A social entrepreneur is capable of launching new projects of innovation, adaptation, and continuous learning; devising new models; using creative perspectives; and considering organizational failures as learning opportunities; all these qualities distinguish them as agents of social change [31].

Many stakeholders are involved in social entrepreneurship, including social entrepreneurs, users, citizens, governments, and communities. The goal of social entrepreneurship is to solve social problems collaboratively [9].

Social entrepreneurship is a form of business that seeks to bring about positive changes in society. Unlike traditional entrepreneurship, its primary focus is not on maximizing profits but rather on achieving social goals. Social entrepreneurs create business models that balance social impact with economic viability, seeking sustainable solutions to community problems while maintaining the financial health of their enterprises [32].

Models of practices for social entrepreneurship according to [33]:

The Charities Aid Foundation (CAF) defines social entrepreneurship via two criteria: financial risk and social impact. It classifies social enterprises according to how much financial risk they take and how likely they are to achieve positive social change. This approach helps distinguish between different types of social enterprises in a practical way. There are three ways to create social impact through developed economic activities, according to the CAF:

Profit-Generating Social Enterprise Model:

Corporations engaged in commercial activity without direct social impact derive economic benefits from it and then redirect part or all of those funds to an activity with direct social impact. This model focuses on financial returns from commercial activities, which are assumed to have no direct social consequences. Although this situation seeks the primary purpose of economic activity (i.e., maximizing monetary profit), it is conceivable that commercial activity could have desirable effects (e.g., job creation).

Compensation Model:

To achieve a balance between financial profitability and the social impact they achieve, organizations must engage in commercial activities that have direct social impacts. Before achieving a balance between profit generation and social impact creation, in this model, commercial activity itself has a direct social impact. Such a social enterprise may have an even greater effect if financial returns are lower, or vice versa. Unlike the first paradigm, this paradigm incorporates social impact into the very nature of commercial activity. Even if no financial profit is achieved, social gains can still be made. To identify this model, the key question is as follows: Is it possible to increase the social impact of the enterprise while decreasing financial returns? If the answer is yes, we are dealing with this type of social enterprise.

Direct correlation model:

Companies that not only have a direct social impact but also provide a financial return commensurate with that impact help promote social good. Can an organization’s social impact increase with decreasing financial returns? This is the key to understanding this strategy. If the answer is no, then one is dealing with this type of social enterprise.

Notably, these three models are based on practice and do not have different worldviews. Theoretically, no model is superior to another. Indeed, a company using a specific model can achieve superior results generated in another that omits that model. This could be due to factors such as the quality of the management team, the specific sectoral environment, or the strength of competition.

Among the most representative theories related to social entrepreneurship are the following:

Social Innovation Theory: Presented by Geoff Mulgan of the University of Cambridge, this theory suggests finding innovative approaches to address social problems that are not adequately addressed by the government or the economy. According to [34], this viewpoint consists of three main dimensions: addressing unmet needs, seeking creative solutions, and ensuring the long-term sustainability of these solutions. The theory of social innovation, proposed by Mulgan, focuses on how new ideas and practices can address social challenges more effectively than existing solutions can. This theory is particularly relevant for women’s social enterprises, as many of these initiatives seek to develop innovative solutions to social problems that disproportionately affect women, such as gender inequality, domestic violence, and a lack of access to economic opportunities.

Shared Value Theory: Developed by Michael Porter and Mark Kramer of Harvard University, this notion suggests that corporations can enhance their competitiveness while also promoting economic and social advancement in the communities where they do business [35]. These aspects aim to improve corporate competitiveness, promote social development, and modify corporate policies and practices. Conversely, the creation of shared value maintains that companies can generate economic value in a way that also creates value for society by addressing its needs and challenges. This perspective is especially relevant for women’s social enterprises, which often seek to combine social and economic objectives.

Social Innovation Theory and Shared Value Theory differ in their approach and scope. The former, proposed by Mulgan, focuses on developing creative and sustainable solutions to neglected social problems, especially relevant for women’s social enterprises that address gender challenges. The second, by Porter and Kramer, argues that enterprises can generate economic and social value simultaneously, enhancing their competitiveness while contributing to community development, applicable to enterprises pursuing combined social and economic objectives.

Social Entrepreneurship Theory: Developed by Johanna Mair of Stanford University, this theory defines social entrepreneurship as a process that fosters creative and lasting social transformations to address specific social problems [29]. The elements seek creative social improvements, emphasize long-term sustainability, and address specific social needs. The theory of social entrepreneurship focuses on how entrepreneurs identify and leverage opportunities to create social value. This theory is fundamental for understanding the factors that drive women to initiate and lead social enterprises, as well as the strategies they employ to overcome the barriers and challenges they face.

While these ideas share common points such as innovation, social impact, and sustainability, they vary in their specific approaches to commercial, political, and community transformation. The shared value philosophy emphasizes the role of corporations, whereas social entrepreneurship focuses more on community-driven solutions.

In the field of social entrepreneurship, compensation models and core theories form an interconnected and dynamic ecosystem whose complexity manifests itself in various forms of interaction and evolution. Social enterprises can transition between these models as they mature, often incorporating elements of multiple approaches into their operations to maximize their impact and adaptability. Applying the theories to the models reveals both synergies and tensions: social innovation theory finds resonance in all the models, especially the intermediate model; shared value theory aligns closely with the direct correlation model; and social entrepreneurship theory offers a broad framework applicable to diverse approaches. These interactions are not without challenges, as social entrepreneurs must balance multiple objectives and stakeholders but also generate opportunities for innovative solutions. The resulting evolution of the field has given rise to hybrid models that transcend traditional categories. Ultimately, the richness of social entrepreneurship lies in the diversity and complementarity of these models and theories, whose combined application offers the greatest potential for sustainable and meaningful social change.

2.2. Corporate Sustainability

Corporate sustainability is an approach that existing organizations take to integrate environmental, social, and governance (ESG) considerations into their business model and operations. This concept is mainly applied to established companies seeking to improve their long-term impact and responsibility [36,37].

The symbiotic relationship between corporate sustainability and social entrepreneurship means that these concepts are mutually reinforcing. Social enterprises adopt sustainability practices to maximize their long-term impact and viability. At the same time, the principles of corporate sustainability inspire new ideas for social entrepreneurship, creating a cycle of innovation and continuous improvement in the field of socially purposeful business [12]. The adoption of sustainable practices in conventional companies drives internal innovation and the development of social initiatives by employees, fostering social intrapreneurship within the organization [38].

Among the main challenges of linking sustainability and entrepreneurship are cultural barriers, the scarcity of skills and knowledge, and resource constraints [39]. Digital technologies such as blockchain, artificial intelligence, and big data are transforming business models and enabling new forms of environmental and social value creation [40]. Their strategic adoption can enhance sustainable entrepreneurship, but it involves ethical challenges that must be responsibly addressed [41].

2.3. Social Entrepreneurship and Sustainability

Social entrepreneurship has become a crucial approach for addressing social, economic, and environmental challenges globally [42]. This type of entrepreneurship aims to create social and environmental value, in addition to generating financial profitability [43]. Sustainability in social entrepreneurship involves balancing economic, social, and environmental dimensions [44]. According to [42], sustainability in social entrepreneurship is defined as the process of developing sustainable solutions for social, economic, or environmental problems that are not adequately addressed, with the ability to endure over time through continuous improvement in operational efficiency.

Various studies have explored the relationship between social entrepreneurship and sustainability. Ref. [45] reported that culturally supported transformational leadership theories and societal sustainability conditions positively influence the likelihood of individuals becoming social entrepreneurs. Moreover, ref. [46] suggests that there is a trade-off relationship between sustainability orientation and entrepreneurial orientation, presenting challenges for social entrepreneurs in seeking an appropriate balance between these aspects.

2.4. Women’s Social Entrepreneurship and Sustainability

In Figure 1, the research model is presented, comprising four constructs: potential entrepreneurship, current job, type of entrepreneurship, and years of entrepreneurship. Additionally, the model includes three research hypotheses, one of which is direct, whereas the other two involve moderation effects. The constructs of potential entrepreneurship, current job, type of entrepreneurship, and years of entrepreneurship were selected on the basis of a comprehensive analysis of the literature on female social entrepreneurship and business sustainability. Johanna Mair’s theory of social entrepreneurship [29] provides the principal theoretical framework for our model, as it underscores the significance of context and experience in the development of social initiatives.

The proposed multivariate model focuses on exploring the relationship between individuals’ current job and their entrepreneurial potential to explore new causalities in the model and provides an important contribution to the research. Along these lines, the independent variable, ‘Current Job’, is postulated as a significant predictor of ‘Entrepreneurship Potential’, which is the dependent variable of the study, including two moderating variables, ‘Years of Entrepreneurship’ and ‘Type of Entrepreneurship’, which are hypothesized to influence the dynamics between current job and entrepreneurial potential. ‘Years of entrepreneurship’ is presumed to exert a moderating effect, possibly reflecting the premise that previous venture experience could enhance or modify the relationship between current employment and entrepreneurial inclination. Similarly, ‘Type of entrepreneurship’ is introduced as a moderating variable to investigate whether differences based on the type of entrepreneurship (products or services) may diversify or alter the intensity or direction of the main relationship under investigation.

The construct of the current job was included because of its relevance in Geoff Mulgan’s theory of social innovation [34], which suggests that current employment can serve as a source of innovative ideas for addressing social issues. Moreover, previous studies, such as Feng et al.’s [19], have highlighted the importance of employment status in the entrepreneurial success of women.

The type of entrepreneurship was incorporated on the basis of the charities aid foundation’s social entrepreneurship practices model [33], which distinguishes between different types of social enterprises according to their focus on products or services. This variable is also supported by Porter and Kramer’s shared value creation theory [35], which posits that the creation of social value may vary depending on the type of entrepreneurial activity.

Years of entrepreneurship was included as a measure of entrepreneurial experience, supported by human capital theory as applied to social entrepreneurship. Studies such as that of Atahau et al. [47] have emphasized the importance of experience in the development of social entrepreneurship, suggesting that the accumulation of knowledge and skills over time can enhance an entrepreneur’s ability to create and sustain social enterprises.

Women play a fundamental role in social entrepreneurship, as their participation can contribute to economic empowerment and a reduction in gender inequalities [48]. However, female social entrepreneurs face additional barriers, such as limited access to financial resources, networks, and business skills [19]. In this context, women’s social entrepreneurship represents a significant opportunity to address social and environmental challenges while promoting gender equality and economic empowerment [49].

Previous research has identified various factors influencing the success and sustainability of women’s social enterprises. The authors of [47] noted that local wisdom plays an essential role in the development of social entrepreneurship to ensure the stability of microfinance in rural areas.

By integrating these factors into an SEM, we seek to understand how they interact and jointly contribute to the sustainability of women-led social enterprises. This holistic perspective helps identify key areas for intervention and policy development to support and strengthen women’s social entrepreneurship.

Women-led social entrepreneurship has emerged as an important driver of economic and social change in recent decades. However, understanding the factors that influence its success and sustainability is still a developing area of research, especially in contexts such as Peru. This study seeks to contribute to filling this knowledge gap through a comprehensive analysis of the determinants of the potential and sustainability of female social entrepreneurship.

On the basis of a thorough review of the literature and the establishment of theoretical frameworks such as social entrepreneurship theory [29], social innovation theory [34], and shared value theory [35], researchers have developed a set of hypotheses that address different aspects of the phenomenon. These hypotheses consider factors such as current employment, type of venture, prior experience, age, and various resources and capabilities that may influence the potential and sustainability of women-led social enterprises.

The use of structural equation modeling (SEM) allows us to examine these relationships holistically, capturing the multifactorial and complex nature of female social entrepreneurship [19]. This approach helps to unravel the interrelationships between various factors and their joint impact on the success of female social entrepreneurs.

The hypotheses that will guide the research, each informed by relevant theories and previous empirical studies, are presented below:

H1: 

Women entrepreneurs’ current employment significantly influences their social entrepreneurship potential.

This hypothesis is grounded in Johanna Mair’s social entrepreneurship theory [29], which emphasizes the importance of context in the development of social initiatives. Current employment can provide resources, skills, and networks that influence a woman’s ability to identify and take advantage of social entrepreneurship opportunities. In addition, ref. [19] suggested that employment status is a critical factor in women’s entrepreneurial success. Mulgan’s social innovation theory [34] also supports this hypothesis, as current employment can be a source of innovative ideas to address social problems.

H2: 

The type of entrepreneurship (product or service) moderates the relationship between current employment and women’s social entrepreneurship potential.

This hypothesis is based on the charities aid foundation (CAF) social entrepreneurship practice model [33], which distinguishes between different types of social enterprises according to their focus on products or services. The authors of [31] highlight the importance of considering different models of social entrepreneurship, as each type may require different skills and resources. Porter and Kramer’s shared value theory [35] also supports this hypothesis, suggesting that social value creation may vary according to the type of entrepreneurial activity.

H3: 

Years of entrepreneurial experience moderates the relationship between current employment and women’s social entrepreneurship potential.

This hypothesis is supported by the application of human capital theory to social entrepreneurship. Ref. [47] highlights the importance of experience in the development of social entrepreneurship, suggesting that the accumulation of knowledge and skills over time can strengthen an entrepreneur’s ability to create and sustain social enterprises. Kolb’s experiential learning theory also supports this hypothesis, as prior experience in entrepreneurship can enhance the ability to identify and take advantage of social entrepreneurship opportunities [42].

H4: 

There are significant differences in social entrepreneurship potential according to the age of women entrepreneurs.

This hypothesis is supported by studies such as [20], which suggest that demographic factors such as age may influence female social entrepreneurship. Entrepreneurial life cycle theory also supports this hypothesis, as different life stages may present different opportunities and challenges for social entrepreneurship. In addition, ref. [23] noted that age may influence the motivation and ability to address social problems through entrepreneurship.

H5: 

There are significant differences in social entrepreneurship potential according to years of entrepreneurial experience.

This hypothesis is based on [16], which highlights the importance of entrepreneurial skills in the success of social entrepreneurship. Human capital theory and organizational learning theory support this hypothesis, suggesting that the accumulation of experience can improve entrepreneurial competencies and the ability to manage social enterprises. This idea is also supported by [45], which indicates that prior experience can influence the ability of social entrepreneurs to identify opportunities and develop innovative solutions.

H6: 

There are significant differences in social entrepreneurship potential according to the current employment status of women entrepreneurs.

This hypothesis is supported by [1,2], which highlight the influence of the work context on female entrepreneurship. Dyer’s entrepreneurial career theory also supports this hypothesis, suggesting that previous work experience may influence the decision to enter entrepreneurship and subsequent success. In addition, ref. [25] highlights how policies to support female entrepreneurship should consider women’s current employment status.

H7: 

Factors such as motivation, support networks, entrepreneurial skills, and access to finance positively influence the sustainability of women-led social enterprises.

This hypothesis is supported by multiple studies and theories. The entrepreneurial motivation theory of Shane et al. supports the importance of motivation. References [1,11] highlight the crucial role of support networks in the success of female social entrepreneurship. Dynamic capabilities theory applied to social entrepreneurship [14] stresses the importance of entrepreneurial competencies. Ref. [50] emphasized the relevance of access to finance for the sustainability of social enterprises. In addition, the theory of resources and capabilities provides a theoretical framework for understanding how these factors combine to create sustainability.

3. Methodology

With respect to the research design, the study was developed with an observational, explanatory, and cross-sectional design. The primary objective was to evaluate the potential social performance of a group of individuals. To achieve this goal, a comprehensive questionnaire was employed to explore various aspects related to social entrepreneurship and the sociodemographic factors of the participants.

3.1. Population and Sample

This study focused on social entrepreneurs from various cities in Peru. The total population was estimated at approximately 537 female social entrepreneurs on the basis of data from the Ministry of Labor and Employment Promotion and records from social entrepreneurship support organizations. From this population, a stratified random sample of 521 participants was selected with a 99% confidence level and a 1% margin of error.

To ensure the representativeness and validity of the sample, a multistage selection process was implemented. First, the population was divided into strata based on five main geographical regions (northern coast, southern coast, northern highlands, southern highlands, and jungle) and three types of social enterprises (products, services, and mixed). Systematic random sampling was then conducted within each stratum via an exhaustive list of social enterprises provided by support organizations and government records.

The systematic random sampling method involves selecting a random starting point in the list of each stratum and then choosing every k-th element, where k is calculated by dividing the stratum size by the desired number of samples in that stratum. This method ensured an even distribution of the sample across each list.

The inclusion criteria were as follows: being a woman over 18 years of age, being the founder or cofounder of a social enterprise with at least one year of operation, and having a primary objective of generating a positive impact on society or the environment. Social enterprises in the ideation stage or with less than one year of operation were excluded, as were entrepreneurs unable to complete the questionnaire due to language or comprehension barriers.

3.2. Questionnaire Design

The questionnaire used in the research became a central tool. It consists of several sections and initially collects sociodemographic data such as age, gender, employment status, geographic location, educational level, type of enterprise, and duration of entrepreneurial activity. The entrepreneurship construct uses formative indicators specific to the PLS-SEM analysis.

Subsequently, 30 questions on a Likert scale ranging from 1 to 5 were incorporated to assess the social entrepreneurship potential of each participant thoroughly. The questionnaire was administered under specifically controlled and supervised conditions to ensure the accuracy and reliability of the responses obtained. The participants completed the surveys voluntarily and autonomously, with clear instructions to ensure accurate and consistent responses. Data collection was carried out by administering surveys in which the participants responded in detail to the questions posed, thus generating a database for subsequent analysis and processing. Importantly, informed consent was obtained from all participants prior to data collection to ensure that the research was conducted ethically and respected the rights of the participants.

3.3. Data Analysis

The data analysis phase began with the development of a statistical summary of the sociodemographic characteristics of the participants, followed by quality tests, such as Cronbach’s alpha, composite reliability, and average variance extracted (AVE). These procedures provided a solid basis for proceeding with the descriptive and inferential analysis of the data. Data processing began with a detailed analysis of the relationship between sociodemographic variables and social entrepreneurship potential, which provided an understanding of how these factors interrelate with and influence the variable of interest. This study was enriched with binary logistic regression analysis, which focused on identifying which sociodemographic variables can predict social entrepreneurship potential. Finally, structural equation modeling (SEM) analysis was applied, which provided important evidence for the study by incorporating moderating variables (gender and years of entrepreneurial activity).

These analyses provided an overall view of the results and their relevance in the context of scientific research. Importantly, the limitations of the study should be mentioned. These include possible selection biases due to the random nature of the sample and the fact that the results are based on the self-reported responses of the participants. These limitations should be taken into account when the results are interpreted. Finally, this study ensured compliance with all applicable ethical rules and regulations.

The combination of binary logistic regression and SEM analysis offers a comprehensive perspective on the potential for social entrepreneurship. Logistic regression identifies specific sociodemographic predictors, while SEM examines more complex relationships and incorporates moderating variables such as gender and entrepreneurial experience. This dual approach allows for a deeper understanding of the factors influencing social entrepreneurship, providing more robust results and a more comprehensive view of the phenomenon under study.

4. Results

The sociodemographic analysis of the sample reveals a diverse picture in terms of age, employment, residence, educational level, type of venture, and years of venture experience (See

Table 1. Summary of sociodemographic data.

		Fi	%
Age	Less than 22	176	33.8%
	23 to 29	178	34.2%
	30 or more	167	32.1%
Current job	No	113	21.7%
	Yes	408	78.3%
Place of residence	Cajamarca	58	11.1%
	Lambayeque	406	77.9%
	Piura	12	2.3%
	Trujillo	44	8.4%
Study	Bachelor	45	8.6%
	Master’s Degree	37	7.1%
	Superior	263	50.5%
	University	176	33.8%
Type of enterprise	Groceries	53	10.2%
	Various consultancies	64	12.3%
	Beauty and health	69	13.2%
	Warehouses	78	15.0%
	Catering	87	16.7%
	Food-Bars-Residential	24	4.6%
	Consulting	21	4.0%
	Decoration	37	7.1%
	Sports-Court rental	40	7.7%
	Teaching	24	4.6%
	Clothing	24	4.6%
Years of entrepreneurship	Less than 3	178	34.2%
	3 to 6 years	138	26.5%
	6 or more years	205	39.3%

). The sample is evenly distributed among the different age groups, with 33.8% of respondents under 22 years of age, 34.2% between 23 and 29 years of age, and 32.1% aged 30 years or older. In terms of employment, a large majority (78.3%) were currently employed, whereas 21.7% did not have a job at the time of the survey. The majority of participants reside in Lambayeque (77.9%), followed by Cajamarca (11.1%), Trujillo (8.4%), and Piura (2.3%), although a duplication error in the data for Piura (1.2%) should be noted. In terms of educational level, the data indicate a predominance of higher education, with 50.5%, followed by 33.8% with a university education, 8.6% with a bachelor’s degree, and 7.1% with a master’s degree. The respondents are engaged in a variety of businesses, including catering (16.7%), wineries (15.0%), beauty and health (13.2%), and various consultancies (12.3%). In terms of experience in entrepreneurship, there is a balanced distribution, with 34.2% with less than 3 years, 26.5% with between 3 and 6 years, and 39.3% with more than 6 years. This analysis provides a comprehensive view of the surveyed population, highlighting the diversity in terms of age, employment, education, and type of venture, showing that the majority of respondents are well established in their respective fields, with a significant trend toward higher education and considerable venture experience.

In the process of evaluating the reliability and validity of the measurement instrument, the analyses yielded robust and precise results (See

Table 2. Summary of reliability and validity.

Items		Mean	D.E.	Factor Loadings	α	CR	AVE
I have a strong motivation to create a positive social impact through my venture.	EmpSoc1	3.66	0.994	0.812	0.796	0.788	0.825
I identify opportunities to address social problems in my community.	EmpSoc2	3	0.54	0.851
I am willing to take calculated risks to achieve my social goals.	EmpSoc3	3.01	0.729	0.865
I have skills to develop business models that combine social and financial objectives.	EmpSoc4	4.39	0.905	0.801
I am able to innovate to find creative solutions to social problems.	EmpSoc5	4.43	0.794	0.833
I can effectively balance the demands of my venture and my family responsibilities.	EmpSoc6	2.81	1.116	0.836
I have access to support networks that help me in my social enterprise.	EmpSoc7	2.85	0.611	0.809
I am able to access and manage financial resources for my social venture.	EmpSoc8	4.01	0.926	0.838
I have knowledge of digital technologies that can benefit my social enterprise.	EmpSoc9	4.11	1.149	0.843
I can measure and communicate the social impact of my venture.	EmpSoc10	3.7	0.63	0.791
I have the ability to adapt my venture to changes in the environment.	EmpSoc11	3.54	0.624	0.792
I am persistent in the face of challenges and obstacles in my entrepreneurial journey.	EmpSoc12	3.67	1.027	0.86
I can lead and motivate a team toward shared social goals.	EmpSoc13	3.67	1.103	0.789
I understand the policies and regulations relevant to my social enterprise.	EmpSoc14	3.02	0.522	0.838
I am able to collaborate with different stakeholders to amplify my social impact.	EmpSoc15	3.73	1.116	0.871
I have a clear vision of how my venture can grow and be sustainable in the long term.	EmpSoc16	3.83	0.82	0.898
I can identify and leverage funding opportunities specific to social enterprises.	EmpSoc17	3.27	0.912	0.856
I am able to integrate sustainable practices into my business model.	EmpSoc18	3.98	0.827	0.907
I have skills to effectively manage the finances of my venture.	EmpSoc19	4.07	0.945	0.893
I can effectively communicate the mission and value of my social enterprise.	EmpSoc20	4.04	0.532	0.91
I am able to build strategic alliances to strengthen my venture.	EmpSoc21	4.11	0.839	0.819
I have the ability to balance social and economic objectives in decision-making.	EmpSoc22	3.74	0.78	0.875
I can design and implement appropriate marketing strategies for my social enterprise.	EmpSoc23	3.82	0.509	0.884
I am able to use technology to improve the efficiency and reach of my venture.	EmpSoc24	2.99	1.19	0.855
I have skills to manage and resolve conflicts in my enterprise.	EmpSoc25	4.05	0.791	0.871
I can identify and develop new market opportunities for products/services with social impact.	EmpSoc26	3.83	0.968	0.892
I am able to handle the stress and uncertainty associated with social entrepreneurship.	EmpSoc27	4.17	0.594	0.834
I have the ability to negotiate effectively for the benefit of my social enterprise.	EmpSoc28	3.85	1.085	0.873
I can develop and maintain an organizational culture aligned with social values.	EmpSoc29	3.82	0.896	0.85
I am able to continuously learn and apply new knowledge to my social enterprise.	EmpSoc30	2.84	0.812	0.895

). The Cronbach’s alpha, positioned at 0.796, indicates considerably high internal reliability. This value, surpassing the generally accepted threshold of 0.7, suggests that the instrument is consistent in its ability to reliably assess the construct of interest over time. Additionally, the composite reliability, with a value close to 0.788, corroborates the previous measure of internal consistency, providing a complementary perspective on the stability of the construct measured by the instrument. This finding reinforces the idea that variations in participant responses are more due to real differences in the variable of interest rather than inconsistencies in the instrument itself.

On the other hand, the validity of the instrument, another fundamental pillar in the evaluation, is equally supported by the data. The factor loadings of the items, all exceeding the threshold of 0.7, indicate a significant contribution of each item to the overall construct that the instrument aims to measure. This finding is crucial, as it indicates that the items are well aligned with the underlying theoretical construct, thereby reinforcing the construct validity of the instrument. Additionally, the average variance extracted (AVE), which stands at 0.825, goes beyond confirming the convergent validity of the instrument, exceeding the desired threshold of 0.5. This value indicates that a large proportion of the variance observed in the item responses can be reliably attributed to the construct being measured rather than random errors. This suggests that the instrument not only accurately captures the construct of interest but also minimizes the influence of measurement error, a vital aspect of the validity of any research tool.

Overall, the internal consistency, along with the solid construct and convergent validity, highlights the instrument’s ability to provide precise and meaningful measurements of the theoretical construct in question. This level of rigor in the construction and evaluation of instruments is essential for advancing the understanding of the complex social phenomena being explored.

Table 3. Goodness-of-fit indices.

Criteria	Estimated Model	Threshold	Author	Decision
SRMR	0.049	<0.85	[51]	Accepted
d_ULS	6.872	p > 0.05	[52]	Accepted
d_S	3.982	p > 0.05	[52]	Accepted
X2/df	1.982	Between 1 and 3	[53]	Accepted
NFI	0.975	>0.90	[53]	Accepted

shows the goodness-of-fit indices that yield acceptable results.

4.1. Group Comparison Analysis

In

Table 4. Entrepreneurship potential compared by age.

Summary of hypothesis testing
	Null Hypothesis	Test	Sig. a,b	Decision
1	The distribution of Social Entrepreneurship Potential is the same across Age categories (Grouped).	Kruskal–Wallis test for independent samples	0.000	Reject the null hypothesis
a. The significance level is 0.050.
b. The asymptotic significance is shown.
Age-pair comparisons (Grouped)
Sample 1-Sample 2	Test statistic	Deviation Error	Deviation Test statistic	Sig. a	Sig. adjusted
Less than 22–23 to 29	−10.950	15.999	−0.684	0.494	1.000
Less than 22–30 or more	−60.979	16.259	−3.750	0.000	0.001
23 to 29–30 or more	−50.029	16.214	−3.085	0.002	0.006
a. Significance values have been adjusted by Bonferroni correction for several tests.

, hypothesis analysis and statistical tests were performed to determine whether the distribution of social entrepreneurship potential varies across age groups. The initial hypothesis posited that these distributions were equal across all grouped age categories. However, the results of the Kruskal–Wallis test showed a significance value (Sig.) of 0.000, leading to the rejection of the null hypothesis. This indicates strong statistical evidence supporting the idea that the distribution of social entrepreneurship potential significantly differs among age groups. Specific comparisons between various pairs of age groups were made to determine where significant differences were observed. Some of these comparisons revealed substantial discrepancies, whereas others did not show statistically significant differences. This analysis provides a valuable perspective on how age relates to variations in “Social Entrepreneurship Potential” within the study sample, thus contributing to the understanding of how age influences this aspect of social entrepreneurship.

4.2. Entrepreneurship Potential Compared by Years of Entrepreneurship

The objective is now to investigate whether the ability to lead social entrepreneurship initiatives changes according to the length of time people have been entrepreneurs. Initially, it was assumed that this ability was consistent regardless of years of entrepreneurial experience. However, the use of the Kruskal–Wallis statistical test challenged this assumption by showing a significance value of 0.000, which meant that the uniformity hypothesis was discarded. This statistical result strongly suggests that there are significant variations in entrepreneurial ability among different levels of entrepreneurial experience (see

Table 5. Summary of hypothesis testing.

	Null Hypothesis	Test	Sig. a,b	Decision
1	The distribution of Social Entrepreneurship Potential is the same across categories of Years in entrepreneurship.	Kruskal–Wallis test for independent samples	0.000	Reject the null hypothesis.
a. The significance level is 0.050.
b. The asymptotic significance is shown.
Pairwise comparisons of years in business
Sample 1-Sample 2	Test statistic	Deviation Error	Deviation Test statistic	Sig. a	Sig. adjusted
Less than 3 and 3 to 6 years	−41.133	17.071	−2.410	0.016	0.048
Less than 3 and 6 or more years	−67.566	15.420	−4.382	0.000	0.000
3 to 6 years and 6 or more years	−26.433	16.573	−1.595	0.111	0.332
a. Significance values were adjusted by Bonferroni correction for several tests.

). To go further, comparative analyses were carried out between the different levels of entrepreneurial experience, with the goal of identifying where the most marked differences in entrepreneurial potential were to be found. This process revealed that certain comparisons between experience groups revealed clear and statistically significant differences in their potential for social entrepreneurship, whereas other comparisons revealed no statistically significant differences.

4.3. Entrepreneurship Potential Compared by Employment Status

This study also examined whether the interest and capacity to engage in social entrepreneurship vary according to individuals’ current employment type. The initial question was whether this tendency was consistent across all employment sectors. Using the Mann–Whitney U test for data analysis, a significance value of 0.000 was found, clearly below the 0.050 threshold set for determining statistical relevance (See

Table 6. Summary of hypothesis testing.

	Null Hypothesis	Test	Sig. a,b	Decision
1	The distribution of Social Entrepreneurship Potential is the same across current job categories.	Mann–Whitney U test for independent samples	0.000	Reject the null hypothesis.

^a. The significance level is 0.050. ^b. The asymptotic significance is shown.

). This result convincingly demonstrates that the type of work individuals currently perform significantly influences their potential to undertake social initiatives.

This finding implies that, depending on their employment status (employed vs. unemployed), individuals may have different inclinations or capacities to engage in social entrepreneurship, with those not in stable or dependent work showing greater potential. This insight is vital for understanding how the nature of a person’s employment can affect their contribution to social entrepreneurship, underscoring the importance of considering the work environment in future studies on social entrepreneurship.

4.4. Binary Regression

The

Table 7. Binary regression results.

Classification a,b			Step 0		Step 1
Observed		Forecast			Forecast
		Potential of social entrepreneurship (Grouped)			Percent correct	Potential of social entrepreneurship (Grouped)		Percent correct
		YES		NO		YES	NO
Step 0	Social entrepreneurship potential (Grouped)	SI	264	0	100.0	161	103	61.0
		NO	257	0	0.0	104	153	59.5
	Overall percentage				50.7			60.3

^a. The significance level is 0.050. ^b. The asymptotic significance is shown.

shows an attempt to predict whether a venture has social potential. Initially, the model makes all its predictions solely on a general rule, without considering differences between cases, resulting in perfect accuracy in one category and complete failure in the other, with an overall effectiveness of 50.7%, similar to random guessing. By introducing some specific variables for making predictions, the accuracy notably improves, reaching 61.0% for ventures considered to have social potential and 59.5% for those without it, increasing the overall accuracy to 60.3%.

The results in

Table 8. Summary of the model.

Step	Logarithm of the Likelihood 2	Cox and Snell R-Square	R Square of Nagelkerke
1	688.248 a	0.063	0.084

^a. The estimation was terminated at iteration number 3 because the parameter estimates changed by less than 0.001.

show that the model fits the data well. A fit was achieved with a specific value (−2 log likelihood of 688.248). The model was able to explain 6.3% of the variability of the data according to one method (Cox and Snell’s R-squared) and 8.4% according to another method (Nagelkerke’s R-squared). Model optimization was stopped after three attempts because the additional fits were very small, less than 0.001, indicating that the model was stable and effective at this stage of the analysis.

The finding is that factors such as age, current employment, educational level, and years of entrepreneurship significantly affect the chances of a venture being socially relevant. Specifically, as people age, the likelihood of leading a socially impactful venture tends to decrease, as evidenced by a negative coefficient in the age category, shown by an odds ratio of 0.410 for age (See

Table 9. Variables in the equation.

		B	Standard Error	Wald	gl	Sig.	Exp(B)
Step 1 a	Age			13.101	2	0.001
	Age (1)	−0.890	0.246	13.084	1	0.000	0.410
	Age (2)	−0.507	0.237	4.591	1	0.032	0.602
	Current job	−0.590	0.229	6.628	1	0.010	0.554
	Study			5.123	3	0.163
	Study (1)	−0.620	0.353	3.087	1	0.079	0.538
	Study (2)	−0.616	0.390	2.491	1	0.114	0.540
	Study (3)	−0.349	0.215	2.643	1	0.104	0.705
	Years in business			5.149	2	0.076
	Years in Business (1)	−0.471	0.214	4.829	1	0.028	0.624
	Years in Business (2)	−0.336	0.228	2.173	1	0.140	0.715
	Constant	0.813	0.390	4.342	1	0.037	2.254

^a. Variables specified in step 1: Age, Current Job, Education, Years in Business (Grouped).

). Additionally, having a current job reduces the chances of having a social enterprise, as indicated by another negative coefficient. These results are important because they highlight which factors can influence the social success of ventures, which can guide the development of policies and support aimed at entrepreneurs.

4.5. SEM Analysis

The analysis of hypothesis testing in the multivariate model shows statistically significant results in all the examined relationships (See Figure 2 and

Table 10. Hypothesis testing.

Hypothesis	Path	DE	Statistics t	Values p
Current job → Entrepreneurship potential	0.383	0.103	3.728	0.000
Entrepreneurship type x Current job → Entrepreneurship potential	0.653	0.293	2.227	0.026
Years of entrepreneurship x Current job → Entrepreneurship potential	0.269	0.093	2.910	0.004

). First, the direct relationship between current employment and entrepreneurship potential is positive and moderate, with a coefficient of 0.383. The statistical significance of this relationship is confirmed by a p value close to zero and a robust t statistic of 3.728, clearly rejecting the null hypothesis. Second, the interaction effect between the type of entrepreneurship and current employment on entrepreneurship potential yields an even higher coefficient of 0.653, indicating a potentially stronger relationship. Although the p value increases slightly to 0.026, it remains within the threshold for statistical significance, with a t statistic of 2.227, supporting the presence of a moderating effect of the type of entrepreneurship.

The interaction of years of entrepreneurship with current employment also has a coefficient of 0.269. Although this relationship is the weakest of the three, with a t value of 2.910 and a p value of 0.004, it remains statistically significant, indicating that prior entrepreneurial experience modifies the influence of current employment on entrepreneurship potential. Taken together, these findings suggest that both the type of entrepreneurship and the years of entrepreneurship play significant moderating roles in how an individual’s current employment can impact their entrepreneurial potential, with the type of entrepreneurship showing the most pronounced moderation effect.

4.6. Discussion of Results

The present study aimed to analyze the key factors that determine the sustainability of women-led social enterprises via structural equation modeling (SEM). The hypothesis posited that motivation, support networks, entrepreneurial competencies, and access to financing positively influence the sustainability of these enterprises.

The SEM analysis results partially support this hypothesis. The current employment of women entrepreneurs significantly influences their social entrepreneurship potential, with a coefficient of 0.383 and a p value of 0.000. This suggests that women’s employment status can be a determining factor in their capacity to initiate and maintain sustainable social enterprises. Additionally, the type of entrepreneurship and years of entrepreneurship act as moderating variables in the relationship between current employment and social entrepreneurship potential. The interaction between the type of entrepreneurship and current employment had a coefficient of 0.653 and a p value of 0.026, indicating that the type of entrepreneurship can intensify or modify this influence. On the other hand, the interaction between years of entrepreneurship and current employment presented a coefficient of 0.269 and a p value of 0.004, suggesting that prior experience also moderates this relationship.

These findings are consistent with previous research highlighting the importance of access to support networks, entrepreneurial competencies, and financing for the success of women’s social enterprises [1,11,14,50]. However, this study expands knowledge by identifying the moderating role of the type of entrepreneurship and entrepreneurial experience in the relationship between current employment and social entrepreneurship potential. Theoretically, these results contribute to understanding the factors influencing the sustainability of women-led social enterprises. The proposed SEM integrates socioeconomic and entrepreneurial experience variables, providing a holistic view of the dynamics shaping female entrepreneurial potential. This aligns with theoretical approaches such as social innovation theory [37], shared value creation [35], and social entrepreneurship theory [30], which emphasize the importance of considering contextual and personal factors.

Practically, the findings have relevant implications for designing public policies and support programs for women’s social entrepreneurship. This highlights the need to consider women’s employment status when fostering their participation in social entrepreneurship initiatives. Furthermore, interventions should account for differences in the type of entrepreneurship and prior experience, adapting support strategies to these moderating factors.

However, it is important to acknowledge this study’s limitations. The sample was random, and the results are based on self-reported responses, which may introduce biases. Additionally, the proposed SEM, although significant, explains a relatively low percentage of the variability in social entrepreneurship potential (Nagelkerke R squared = 0.084). This suggests that other relevant factors not considered in this study could contribute to a more comprehensive understanding of the phenomenon.

For future research, expanding the SEM by including other variables that may influence the sustainability of women’s social enterprises, such as access to education and training, family support, public policies, and the cultural context, is recommended. It would also be valuable to replicate the study in different geographical and cultural contexts to assess the generalizability of the results. Additionally, complementing quantitative analyses with qualitative approaches, such as in-depth interviews and case studies, could provide a more detailed understanding of the experiences and challenges of women social entrepreneurs. This could offer valuable insights into the underlying mechanisms linking the identified factors to enterprise sustainability. Finally, investigating the long-term impact of women-led social enterprises, both in terms of economic outcomes and social and environmental benefits, would allow a more comprehensive evaluation of the effectiveness and sustainability of these initiatives, as well as the identification of best practices and lessons learned.

5. Conclusions

This study contributes significantly to the understanding of the factors that influence the sustainability of women-led social enterprises in the Peruvian context. Through the application of structural equation modeling (SEM), it was determined that the current employment of women entrepreneurs exerts a direct and significant influence on their potential to develop successful social enterprises. Variables such as type of entrepreneurship and years of entrepreneurial experience act as moderators in this relationship, intensifying or modifying the effect of current employment on entrepreneurial potential. The SEM proposed in this study holistically integrates socioeconomic variables and entrepreneurial experience, which allows for a more complete understanding of the dynamics that shape female entrepreneurial potential, in line with relevant theoretical approaches such as social innovation theory, shared value creation, and social entrepreneurship theory.

The findings of this study have practical implications for the design and implementation of policies and programs to support women’s social entrepreneurship in Peru. First, it highlights the importance of developing specialized training and mentoring programs that consider the employment situation and entrepreneurial experience of women. These programs should be tailored to the specific needs of women entrepreneurs, providing them with tools and knowledge that will enable them to strengthen their business skills and ensure the long-term sustainability of their initiatives. In addition, it is important to establish and strengthen support and collaboration networks among women social entrepreneurs, encouraging the exchange of experiences, knowledge, and best practices. These networks can be facilitated by governmental organizations, NGOs, or business associations and should promote the creation of synergies and opportunities for cooperation among women entrepreneurs, thus contributing to the consolidation of a more solid and collaborative entrepreneurial ecosystem.

Another fundamental aspect that emerges from the results of this study is the need to implement public policies that favor access to financing and resources for social enterprises led by women. This may include the creation of specific funds, preferential credit lines, or grant and seed capital programs that take into account the particularities and challenges faced by women entrepreneurs in the Peruvian context. These measures would help reduce barriers to accessing financial resources and strengthen the capacity of women entrepreneurs to develop and scale their social initiatives.

The importance of promoting the visibility and recognition of women social entrepreneurs through awareness campaigns, awards, and events that highlight their achievements and impact on society is also emphasized. These initiatives can contribute to changing perceptions and stereotypes, inspiring other women to venture into social entrepreneurship and generating greater support and appreciation from society as a whole.

Finally, this study underscores the need to foster collaboration between the public and private sectors and civil society to create an ecosystem favorable to female social entrepreneurship. This implies the articulation of efforts and resources, as well as the generation of spaces for dialog and participation, that involve all relevant actors in the design and implementation of policies and programs to provide comprehensive support to women social entrepreneurs in Peru. Only through a collaborative and multisectoral approach will it be possible to create the necessary conditions for women to develop their full entrepreneurial potential and contribute significantly to the sustainable development of the country.

Recommendations for Future Research

To gain a more comprehensive understanding of the sustainability of women-led social enterprises, expanding the structural equation modeling (SEM) model to include additional variables such as access to education and training, family support, public policies, and the cultural context is recommended. Including these factors would allow for a more complete evaluation and identification of other influential elements.

Replicating this study in various geographical and cultural contexts is essential for evaluating the generalizability of the results. Since women’s social entrepreneurship may be influenced by specific contextual factors, determining whether the findings hold in different settings is crucial.

Complementing quantitative analyses with qualitative approaches, such as in-depth interviews and case studies, would provide a more detailed understanding of the experiences and challenges faced by women social entrepreneurs. This approach could offer valuable insights into the underlying mechanisms linking the identified factors with the sustainability of enterprises.

Future research should focus on the long-term impact of women-led social enterprises, considering both economic outcomes and social and environmental benefits. This would enable a more comprehensive evaluation of the effectiveness and sustainability of these initiatives, as well as the identification of best practices and lessons learned.

Exploring the role of support networks and mentorships in the success and sustainability of women-led social enterprises is crucial. Since the literature suggests that access to networks is a significant factor, investigating how different types of networks (family, professional, community) influence women’s entrepreneurial potential would be valuable.

Additionally, examining the intersection between women’s social entrepreneurship and other aspects of identity, such as ethnicity, age, and sexual orientation, would allow for a more nuanced understanding of how various identity factors shape the experiences and challenges of women social entrepreneurs.

Finally, the impact of public policies and existing support programs on promoting women’s social entrepreneurship should be investigated. This includes evaluating the effectiveness of different approaches, such as business training, access to financing, and mentorship initiatives, to identify the most promising strategies.

This study provides a solid foundation for future research to delve deeper into the factors influencing the sustainability of women-led social enterprises. Addressing current limitations and expanding the scope of research will enable a more complete and nuanced understanding of this phenomenon, informing the development of more effective policies and programs to support women social entrepreneurs.