The mediating role of female migration on social entrepreneurship activity

Abstract

Social entrepreneurial firms exist within environments that are often severely resource constrained. The purpose of this study is to investigate several macro-level factors that can stimulate or impede the emergence of social entrepreneurship. Although little prior research on how these determinants impact social entrepreneurship has been conducted, this study reveals that several crucial macro-level variables appear to be related to social entrepreneurship. Unlike previous studies, this study employs enhanced variables designed to capture local perceptions as well as secondary data inputs. Ordinary Least Squares (OLS) regression techniques were used to understand their effects on social entrepreneurial activity. The results reveal that a country’s governance and female migration are related to the level of social entrepreneurial activity. In addition, positive female migration serves as an important mediating role between governance and increased levels of social entrepreneurial activity. Moreover, implications for understanding the role of macro-level factors on social entrepreneurship as well as the study’s limitations are discussed.

Keywords

context female migration governance social entrepreneurship

Introduction

Social entrepreneurship has the potential to confront and address some of society’s most challenging and complex problems arising from market and government inadequacies or failures. Instead of focusing solely on financial value creation, social entrepreneurship often centers on environmental sustainability and/or the creation of social value for disenfranchised members of society (Kickul and Gundry, 2015). Both social and commercial entrepreneurship address similar questions about the discovery, evaluation, and exploitation of opportunities and the set of individuals who engage in these actions (Shane and Venkataraman, 2000); offer products and services to gain financial sustainability; and are instrumental in supporting economic activities historically viewed by the private sector as unprofitable (Di Domenico et al., 2010; Jelen, 2009). However, to date, little attention has been focused on understanding the macro-level issues and impediments that influence the creation and development of social entrepreneurship firms.

The purpose of this study is to investigate several macro-contextual factors that can stimulate the emergence of social entrepreneurship. There has been limited research that has explained the causes of differences in the nations and conditions under which social entrepreneurship flourishes. In the authors’ opinion, understanding the impact of these variables, including governance and migration patterns, is crucial since there are significant challenges in producing and marketing products and services at the base of the economic pyramid. Such challenges include imperfect and incomplete markets, fluctuating prices and costs, unreliable or absent infrastructure, and weak or absent formal governance (Thompson and McMillan, 2010). Unlike previous studies, this study employs enhanced (compound) variables from various sources designed to capture local perceptions as well as secondary data inputs. Hence, the results should be understood within the limitations of the data employed and the data sources from which they were obtained.

Literature review

The emergence of social entrepreneurship

Research on social entrepreneurship has grown rapidly by applying theories and practices from the general study of commercial entrepreneurship, and it addresses a similar set of conceptual questions about the discovery, evaluation, and exploitation of opportunities and the individuals who engage in these actions (Kickul and Gundry, 2015; Shane and Venkataraman, 2000). The process of social venturing is within the domain of entrepreneurship scholarship and is substantiated by an established and growing body of work (Steyaert and Hjorth, 2006; Yang et al., 2015).

Social entrepreneurship often emerges and thrives within resource-constrained environments (Bacq et al., 2015; Desa, 2007), leading social entrepreneurs to create innovative solutions to society’s most challenging problems. By introducing innovative processes and practices, financially sustainable organizations are able to weather and/or adapt to difficult times and can successfully continue to operate under these conditions. A broader infrastructure that supports the establishment and growth of these firms is required if they are to be effective and carry out their missions and goals. Specifically, research has shown that favorable government and regulatory policies along with a combination of financial and human resources are needed to sustain the growth of social innovation (Griffiths et al., 2013; Sullivan, 2007).

The macro determinants of social entrepreneurship

An infrastructure that supports the establishment and growth of these firms is required if the firms are to be effective in carrying out their missions and goals (Yang et al., 2015). The present study is intended to contribute to the emerging body of research with regard to the contextual drivers of social entrepreneurship. These structural supports include both the governmental and human capital that enables the firms to implement social innovation and change. These supports form an infrastructure for innovation known as the ecology (Griffiths et al., 2009), and entrepreneurs’ perceptions of this ecology directly affect the idea and opportunity generation that ultimately sustains the innovative capacity of social entrepreneurs. The study’s objective is to identify which general macro-level aspects of a country’s ecology contribute most to a conducive environment for social entrepreneurship.

To operationalize the human capital element of a country’s ecology, this study draws from Hoogendoorn’s supply-side theory that “the necessary conditions for any type of entrepreneurial activity to emerge include the availability of individuals who are willing to and capable of exploiting opportunities and who, as a result, choose the entrepreneurial option” (Hoogendoorn, 2016: 282). Hoogendoorn (2016) explored the relation between cultural values and social entrepreneurship activity (SEA) and found that countries with high self-expression values (emphasis on quality of life, environmental protection, and self-expression) have higher rates of social entrepreneurship than countries with higher rates of survival values. This study builds upon Hoogendoorn’s (2016) findings and hypothesizes that countries that embrace those groups of individuals who are typically marginalized will see higher rates of SEA.

Immigrants and ethnic minorities are one such group, and women are another. Research has shown that immigration is predictive of entrepreneurship (Neupert and Baughn, 2013) and that “gross migration flows, or migration churn, has a higher correlation with new business activity than other commonly-used regional demographic or economic development measures” (Levie, 2007:143). In addition, women are more likely to engage in social entrepreneurship than commercial entrepreneurship (Estrin et al., 2013). Therefore, this study posits that countries with higher female immigration rates will see more “supply” for social entrepreneurs and therefore more SEA.

Conceptual framework and hypotheses development

The systems and processes that oversee the direction, effectiveness, supervision, and accountability of organizations, including private and public enterprises, are known as governance (Cornforth, 2003; Monks and Minnow, 2011). Among the important characteristics of governance are transparency and accountability (Mason et al., 2007). Through methods including social auditing, owners and managers of enterprises build trust in their competence and demonstrate compliance with institutional norms and shared goals, thus acquiring legitimacy (Yeoh, 2012). Researchers have uncovered the influences of trust, legitimacy, and sound governance on improving performance and growth of social entrepreneurial enterprises (Han and McKelvey, 2016; Jelen, 2009) and the need for international governance mechanisms to promote economic and social efficiency and equity (Zahra et al., 2014). Consistent with literature on the role of governance in the development of SEA, this article proposes the following:

H1: Governance positively relates to greater social entrepreneurship activity.

In general, it has been found that migrant workers aspire to more power, position, and status than simply acquiring permanent residence and citizenship (Khondaker, 2009). These workers undergo many challenges, establish and manage new businesses, and eventually become naturalized (Rahman and Rahman, 2011). In a report from the Global Entrepreneurship Monitor (GEM), higher entrepreneurial activity was noted among first-generation immigrants compared to natives in the majority of the 69 countries surveyed (Xavier et al., 2013). Additional research has suggested that the ability to identify entrepreneurial opportunities contributes to increased entrepreneurial behavior among immigrant entrepreneurs and return migrants (Vandor and Frank, 2016). In addition, according to the World Bank, female entrepreneurs make significant contributions to economic growth and to poverty reduction.¹ In the United States, for example, women-owned firms are growing at more than double the rate of all other firms, contribute nearly US$3 trillion to the US economy, and are directly responsible for 23 million jobs. In developing countries, female entrepreneurship is also increasing—there are about 8–10 million formal Small-to-Medium Enterprise (SMEs) with at least one female owner.²

While the number of women operating their own business is increasing globally, research shows that different factors are driving this trend. In developed countries, opportunity is the driving factor. In developing countries, as discussed above, entrepreneurship comes about largely due to necessity.³ In the absence of other viable alternatives to provide for or supplement household incomes, entrepreneurship or self-employment is the only viable option. Further, female-owned businesses are characterized by low capital requirements, low barriers to entry, and low income, with a high concentration of firms in the service sector.

Governance systems and processes influence the development and growth of enterprises, and in any economy, immigrant entrepreneurship is affected by rules and regulations, demand, and capital investment required (Kloosterman and Rath, 2001). Female migrants in particular can encounter social attitudes and behaviors that impede the launch of their businesses, and governmental policies can reduce or even remove obstacles identified by these migrants to shape a more favorable environment for entrepreneurial activities (Billore, 2011; Schneider and Ingram, 1993). Previous research has shown that female entrepreneurship is hampered by less support relative to male entrepreneurship in emerging countries (Ozgen, 2012), and female immigrant business owners are thus affected in their entrepreneurial endeavors by these constraints. Given previous research on the effect of supportive conditions at the governmental level on female migration, and the subsequent growth of social entrepreneurial activities, the following is proposed:

H2: Supportive governance positively relates to female migration.

H3: Female migration mediates the relationship between supportive governance and social entrepreneurship activity.

Methodology

The 2016 GEM Special Issue on Social Entrepreneurship was used as a guide to determining the countries of interest and the availability of data. The variables were (1) social entrepreneurial activity, (2) governance, and (3) net female migration. To fully examine these variables, the study also controls for other variables that may already be associated with social entrepreneurial activity including (1) the percentage of the population living below the poverty line, (2) labor participation, and (3) the size of the gender gap.

The study initially used the 2016 Legatum Prosperity Index that assesses 142 countries, accounting for over 90% of the world’s population and is based on 104 different variables, each of which has a demonstrated effect on economic growth or on personal well-being. The index consists of eight subindexes, each of which represents a fundamental aspect of prosperity and entrepreneurial ecology.⁴ However, this study limited its analysis to the 55 countries for which there are data on the level of SEA. These data, which form the dependent variable, were drawn from the 2016 GEM Special Report on Social Entrepreneurship by Bosma, Schott, Terjesen, and Kew. This report is based on interviews with 167,793 adults in 58 economies in 2015 and is thus the largest comparative study of social entrepreneurship in the world. Due to data availability restrictions, complete data were only obtained for 55 of the countries. The study uses the broad measure of SEA, which GEM defines as being “involved in social entrepreneurship activity as a nascent or operational leader.” The 55 countries for which the 2016 GEM Special Report on Social Entrepreneurship provided data are listed in Table 1.

Table 1.

SEA data for the 55 countries included in the study.

Nation	SEA	Nation	SEA	Nation	SEA
Argentina	4.6	Indonesia	3.0	Romania	4.8
Australia	11.1	Iran	1.7	Senegal	18.1
Belgium	6.2	Ireland	11.1	Slovakia	6.4
Botswana	6.2	Israel	12.8	Slovenia	4.6
Brazil	2.5	Italy	5.5	South Africa	2.9
Bulgaria	1.0	Kazakhstan	3.1	South Korea	1.5
Cameroon	8.5	Latvia	2.8	Spain	1.3
Chile	11.5	Lebanon	4.1	Sweden	6.9
China	6.6	Luxembourg	13.8	Switzerland	6.6
Colombia	10.8	Macedonia	3.1	Thailand	2.9
Croatia	7.0	Malaysia	1.7	Tunisia	6.3
Ecuador	2.6	Mexico	2.7	The United Kingdom	5.4
Egypt	3.4	Morocco	1.1	The United States	11.0
Estonia	7.4	Netherlands	3.6	Uruguay	6.5
Finland	5.9	Norway	7.1	Vietnam	1.4
Germany	2.2	Panama	1.8
Greece	1.6	Peru	13.1
Guatemala	5.0	Philippines	10.1
Hungary	11.3	Poland	7.5
India	6.6	Portugal	4.5

Note: SEA: social entrepreneurship activity; GEM: Global Entrepreneurship Monitor.

Source: 2016 GEM Special Report on Social Entrepreneurship by Bosma, Schott, Terjesen, and Kew.

The annual GEM assessment monitors each economy’s proportion of working-age (18–64) individuals who are either in the process of starting (within the last 12 months) a social entrepreneurial business (nascent entrepreneurs) or are owner-managers of such businesses. The average rate for this variable across all 58 countries of the GEM sample economies is 3.2%, but ranges from 0.3% (South Korea) to 13.8% (Luxembourg). These rates appear to suggest that while social entrepreneurship is a fairly rare phenomenon, certain economies seem to report healthier levels of social entrepreneurial activity.

It should be noted that the GEM results are based on self-reporting (primary data) rather than on an official count or any similar efforts that count firm activity, such as new firm registration or tax filings. This methodology is advantageous in that it captures informal entrepreneurial activity, but there is no guarantee of the veracity of interviewee accounts.

There are strong grounds for augmenting national accounts. Economist Richard Easterlin⁵ has shown that while average income increased sevenfold in Japan over the 1950s and 1960s, Japanese citizens did not seem happier. Indexes based on subjective well-being, such as the Prosperity Index, use an empirical approach that examines which social, demographic, or institutional factors correlate with higher reported individual well-being. Entrepreneurs at the microlevel need effective economic policies at the macro level. Innovation and entrepreneurship are more strongly related to economic fundamentals than any other factor in a society (Baumol et al., 2007).

Governance: Well-governed societies enjoy national economic growth and citizen well-being. Since citizens—not governments—are ultimately responsible for the creation of wealth, it is important to have a measure of how well governed a country is, such as the extent of political participation it grants to its citizens, their trust in the electoral process, and perceived levels of corruption (Kaufmann et al., 2009).⁶ Countries with high levels of public expenditure, on average, show a higher share of social entrepreneurial start-ups; indeed, in a recent study, this contextual factor had the greatest effect on a country’s share of social entrepreneurship (Hoogendoorn, 2016).

The governance subindex measures how a country’s governance directly affects the quality of life of its citizens. This subindex combines three objective governance variables with a variety of subjective responses to survey questions. The result reflects the level of confidence people have in the fairness and predictability of government actions.

The Legatum governance subindex comprises statistical scales for the rule of law, regulatory quality, political participation, population confidence in the country’s judiciary system, belief in honest and fair elections, belief in widespread corruption in business, confidence in the country’s military, and belief in government corruption. In a study using the GEM data from 49 nations collected in 2009, a relationship was found between the rule of law and the share of social entrepreneurial start-ups, which the authors link to the favorable influence that stable and predictable formal institutions exert on social start-ups. Their research supported the results obtained by Estrin et al. (2013) that social start-ups tend to receive benefit from constitutional level institutions more than do traditional start-ups.

The study calculates the average annual change (8yr_governance) in this index value over the 8-year period from 2007 to 2015. The underlying argument from Legatum is that stable and democratic governing institutions safeguard political and economic freedom and create an environment of civic participation, leading to higher levels of income and well-being.

Participants and measures

To capture the influence of these issues, the study utilizes the following control variables that may influence the role of governance and female migration on social entrepreneurial activity.

Population living in poverty

The study utilized the data for the percentage of the population living below the poverty line from the CIA Factbook database. This variable (poor_pop) represents the percentage of individuals living below the poverty line as estimated by the government or private sources. The data were downloaded on February 18, 2017, and are available for the latest year for which the then in-country estimate was made. Individual country data are available upon request.

Labor participation

The study used the female labor participation rate from the database maintained by the World Bank. This variable (lab_part) represents the percentage of women in the total labor force as determined by the International Labour Organization, using World Bank population estimates. The data recorded are for the latest year available—usually 2014.

Female migration

The study extracts the data on net female migration from the International Migration Report 2015 published by The Department of Economic and Social Affairs of the United Nations Secretariat. In particular, the study calculates the net female migration (10yr_fem) into the 55 countries in the study’s sample over the 10-year period, from 2005 to 2015.

Gender gap

The study’s measure for the gender gap is obtained from The Global Gender Gap Index 2015 published by the World Economic Forum. The index benchmarks national gender gaps on economic, political, education, and health criteria and provides country rankings that allow for effective comparisons across regions and income groups.

Results

Nonparametric correlation analyses where Spearman’s rank correlations were calculated revealed significant correlations between social entrepreneurial activity (dependent variable), governance (independent variable), and female migration (mediator). Thus, hypothesis 1 (governance is positively related to social entrepreneurial activity) and hypothesis 2 (governance is positively related to female migration) were both supportive. Moreover, as indicated in Table 2, the study found significant relationships between the control variables of gender gap and labor participation as well as labor participation and female migration.

Table 2.

Correlation results (Spearman’s rho).

	Variables	1	2	3	4	5	6
1	Gender gap	1.0
2	Labor participation	0.68^*	1.0
3	Population living in poverty	−0.16	−0.16	1.0
4	Governance	0.12	0.14	0.08	1.0
5	Female migration	0.23	0.38*	−0.06	0.26*	1.0
6	SEA	0.19	0.24	0.13	0.28*	0.28*	1.0

Note: SEA: social entrepreneurship activity.

*Correlation is significant at the 0.05 level (two-tailed); N = 55.

To evaluate the effect of any remaining multicollinearity, the study calculated the variance inflation factors (VIFs) for each coefficient in each model. The maximum estimated VIF value found was 5.048, which is well below the recommended ceiling value of 10 (see Cohen et al., 2003).

We adopted a mediational analysis to test our third hypothesis since it contributes to the understanding of the relationship between supportive governance and SEA in an indirect way (i.e. considering the role that female migration may serve as an important link between the two variables). As such, our mediator variable (female migration) allows us to clarify the relationship between our independent variable of supportive governance and our dependent variable of SEA (Cohen et al., 2003; MacKinnon, 2008).

The mediated regression approach recommended by Baron and Kenny (1986) was used to test the third hypothesis. This is a common approach to test a mediational relationship (Baron and Kenny, 1986). In the mediational approach, three equations are estimated. First, the mediator is regressed on the independent variable. Second, the dependent variable is regressed on the independent variable. In the last equation, the dependent variable is regressed simultaneously on both the independent and mediational variables. Mediation is indicated when the following conditions are met: The independent variable must affect the mediator in the first equation; the independent variable must affect the dependent variable in the second equation; the mediator must affect the dependent variable in the third equation; and lastly, assuming that all of these conditions are in the proper direction, the effect of the independent variable on the dependent variable must be less in the third equation than in the second equation. Full or perfect mediation is supported when the independent variable has no significant effect when the mediator is controlled, while partial mediation is indicated if the effect of the independent variable is reduced in magnitude but still significant when the mediator is controlled (Baron and Kenny, 1986).

In the mediational analyses, as shown in Table 3, the authors found initial support for the first part of the study’s mediational model. That is, the relationship between governance and the study’s mediator of female migration was significant. While this was supported at a one-to-one relationship (zero-order correlations), the authors wanted to utilize the mediation approach to better understand and evaluate the proposed research model. With the three-equation approach suggested by Baron and Kenny (1986) and as discussed above, the study found that governance is fully mediated by female migration. That is, the relationship between governance and SEA was no longer significant after accounting for female migration. Thus, female migration is a crucial link between governance and SEA.

Table 3.

Ordinary Least Squares-mediated regression results.

Control variables	β
Gender gap	0.02
Labor participation	0.11
Population living in poverty	0.16
Independent and mediator variables
(1) Governance → Female migration	0.26*
(2) and (3) Female migration → SEA^†	0.30*
Governance → SEA^†	0.22
R ²	0.24
Adjusted R ²	0.15
F	2.67*

Note: SEA: social entrepreneurship activity.

*Significant at the 0.05 level.

^†The beta weights represent the values in the last step of the meditational analysis.

Discussion

The overall purpose of this study was to examine the macro-contextual factors that can stimulate the emergence of social entrepreneurship. To date, most of the research has not explained the causes of differences in countries and none addresses the conditions under which social entrepreneurship develops and contributes to solving some of society’s most intractable problems (Yang et al., 2015).

The relation of governance to SEA

The authors found support for the hypothesis that governance was an important variable associated with the study’s SEA measure. Governance assesses how a nation’s government influences the quality of life of its citizens, including the level of confidence people have in the fairness and predictability of government actions. It encompasses the regulatory quality, political participation, population confidence in the country’s judiciary system, belief in honest and fair elections, and the overall rule of law. Community-based social entrepreneurship has the capability to transform society, and therefore governments around the world encourage this activity through the formation and promotion of not-for-profit and for-profit programs (Rattan and Welpe, 2011). For social entrepreneurship ventures to be created and developed, it is important that individuals feel secure in how government makes decisions and its role in helping foster and stimulate participation in the private sector (via public–private partnerships) to solve society’s problems and have a positive social and/or environmental impact.

Additionally, governance assesses the level of internal security of a country, from domestic crime, oppressive government, homicide, and assaults, as well as measures of human flight and the “brain drain” among middle-class professionals. In having stability in overall safety and security, a country may be able to maintain its intellectual and social capital that is essential to stimulating a social entrepreneurship ecosystem and generating new ideas and innovations that can tackle society’s most pressing challenges.

The influence of female migration on SEA

The study discovered that increased female immigration and overall social and political gender equity are linked with increased SEA within a country. This supports the hypothesis and suggests that gender is even more predictive of SEA than the authors originally anticipated. Generally, immigration is “significantly predictive of entrepreneurship even when controlling for key aspects of the country context” (Neupert and Baughn, 2013: 303). The GEM’s 2015 Special Report on Women’s Entrepreneurship stated that there is “little difference between the genders in the rate of perceived opportunities” (Kelley et al., 2015: 43), but there is much greater gender disparity in perceptions of personal capability. In countries where women have a lower social status than men and thus face more constraints, they are less likely to become entrepreneurs (Kelley et al., 2015). While there is a global gender gap in both traditional and social entrepreneurship, women are more likely to engage in social entrepreneurship than traditional entrepreneurship (Estrin et al., 2013). Thus, in countries where women enjoy a greater sense of equality, they are better able to act on entrepreneurial opportunities. Since women are more likely to engage in social than commercial entrepreneurship, social entrepreneurial activity is greater in countries with higher levels of parity between men and women. Given that immigrants are more likely to be entrepreneurial than native residents, countries with higher proportions of female immigrants tend to see higher levels of SEA.

Implications for policy and practice

The findings of this study offer several implications for key stakeholders. For leaders of countries, it is important to understand macro-level factors that allow SEA to flourish as those countries seek to address intractable social and environmental challenges in ways that will grow local economies. The study’s findings suggest that countries that wish to increase SEA should seek to improve their governance practices. In addition, immigrants should be viewed as potential social entrepreneurs when determining immigration policy. Furthermore, countries can promote the resources that are often lacking for potential social entrepreneurs. This may be especially important for women and immigrants, given that the two groups are often at a disadvantage and that female immigrants may be the most likely individuals to create social enterprises.

For instance, a South African study found that women face difficulties in accessing seed capital and creating ventures in nontraditional industries (Derera et al., 2014). This represents a significant hurdle to entrepreneurship (including social entrepreneurship) as women may be less likely to have access to financial capital when they start a firm. Immigrants face an additional barrier in that they often have a social capital deficit (Behtoui and Neergaard, 2010). This deficit limits their ability to build trust, reduce transaction costs, and contribute to economic development (Karlsson, 2012). These factors reduce the supply of social entrepreneurs and weaken the infrastructure of human and financial capital for a country’s innovation ecology.

There is an opportunity to empower women and immigrants to contribute to SEA, and thus economic development, to an even larger extent. Interested parties should aim to decrease both the intentional discrimination and subtle biases that contribute to individuals’ self-perception and access to resources for entrepreneurs and social entrepreneurs.

Study limitations

Although this research makes several important contributions, the study’s results and conclusions must be evaluated within the context of the limitations of this study. As stated previously, the GEM data set used in this research comes with a number of limitations such as treatment, and underrepresentation of disputed parts of the world, and when combined with the Legatum Institute’s data set, data lags and differential effects and weights of subindices in various parts of the world. Further, there will be different methodological approaches in obtaining and presenting the data between the various data sources given the different objectives and philosophies of the researchers involved in the data collection.

That said, it would be interesting to develop new research addressing many of the relationships using an improved measure of SEA to investigate whether many of variables are associated with such activity and entrepreneurship in general. Moreover, and equally important, would be an assessment of the culture or climate of the country’s ecosystem for social enterprise development, including a more in-depth examination of the intellectual and social and financial resources available to nascent and growing social ventures. Finally, the limitations also include common method bias. As such, some of the measures used to tap each of these constructs were taken from one source, and these associations could, therefore, be attributed to a bias related to how the index was implemented. An additional limitation was that the study was cross-sectional. Causal inferences created from cross-sectional designs are only inferences. Future research should examine many of the same relationships in the study with longitudinal data to establish causality. This type of data collection along with a case study approach would provide an additional perspective of how the macro-level variables influence SEA throughout the life cycle and strategy of the social enterprise.

Recommendations for further research

It is suggested that further research focus on the other types of support and resources women, immigrants, and other marginalized groups need to launch and sustain their ventures. As noted above in the discussion of study limitations, future research should explore these relationships with longitudinal data to establish causality, and employing the case study approach can offer different perspectives on the influence of these macro-level variables on SEA. In addition, the role of more widely available social entrepreneurship education can be explored with regard to its relation to increased formation rates of social enterprises and higher levels of the confidence, skills, and mindsets required for social entrepreneurship to flourish.

Conclusion

Although little research on the ways in which macro-level determinants impact social entrepreneurship has been conducted, this study investigates several enhanced variables crucial in traditional entrepreneurial studies but that have yet to be examined within the extant social entrepreneurship literature. As previous research has indicated, social entrepreneurs enact opportunities utilizing strategies and methods similar to commercial entrepreneurs. However, there are distinctions/characteristics of the specific entrepreneurial contexts in which social entrepreneurs operate that are important to examine. Continued work and research in this area can deepen the understanding of the role the factors examined can have on SEA that may be crucial to assisting social entrepreneurs in driving long-term systematic change for individuals and the broader communities they serve.

Footnotes

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Notes

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