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Closing the gender divide: how social status, connections, media, and culture relate to public attitudes towards female social entrepreneurs
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Closing the Gender Divide: How Social Status, Connections, Media, and Culture Relate to
Public Attitudes towards Female Social Entrepreneurs
By
Misha Kouzeh
Rossier School of Education
University of Southern California
A dissertation submitted to the faculty
in partial fulfillment of the requirements for the degree of
Doctor of Education
August 2024
© Copyright by Misha Kouzeh 2024
All Rights Reserved
The Committee for Misha Kouzeh certifies the approval of this Dissertation.
Dr. Lawrence Picus
Dr. Patrick Cates
Dr. Dennis Hocevar, Committee Chair
Rossier School of Education
University of Southern California
2024
Abstract
Any nation to grow today in the world of intense competition requires entrepreneurs at
the core of economic development. Social entrepreneurship has emerged as a key phenomenon,
concerning individuals who start new organizations with the goal of addressing pressing social
and environmental challenges. Women’s social entrepreneurship plays a crucial role in boosting
economic growth and overcoming a host of environmental and social issues. However, despite
its increasing importance, women remain underrepresented in social entrepreneurship in virtually
all industrialized countries, and the gender gap exhibits a remarkable persistence. Thus, closing
the gender gap is essential to foster sustainable economic growth. This study aimed to investigate
the factors influencing public attitudes toward female social entrepreneurship (FSE) in the
United States, guided by Bronfenbrenner’s socio-ecological systems theory. This study presents
the development of a novel scale instrument designed to measure attitudes towards FSE. Using a
quantitative survey methodology, data from 497 participants were analyzed to assess how
socioeconomic, cultural, and environmental factors shape perceptions of FSE. Based on the
statistical analyses, the variables socioeconomic status, network and connections, mass and
social media, and societal roles and expectations significantly influence attitudes towards FSE.
The research identified significant demographic influences, including age, educational level,
income, occupational status and geographic location, and their impact on attitudes toward FSE.
Therefore, the research provides a robust tool to measure attitudes towards FSE, offers a deeper
understanding of gender disparities in social entrepreneurship and proposes actionable insights
for policy and practice aimed at enhancing female social entrepreneurial engagement.
Keywords: Female Social Entrepreneurship, Public Attitudes, Socio-Ecological Systems,
Gender Disparities, Entrepreneurial Engagement.
v
Dedication
To my spouse, Samir Shahdoost, who has been a constant source of support, love, and
inspiration through every step of this journey. Thank you for your endless support and for always
believing in me! To my loving parents and sibling, who have always stood by me and instilled
the values of hard work and commitment. I wish to acknowledge and thank my powerful mom,
Mahshid Moshkeri, my academic role model father, Matt Kouzeh, and my cheerleader sister,
Shanna Kouzeh, for always being by my side throughout my education journey; you three have
been the guiding light and my foundation through the journey of this project. To my professors,
mentors, and close friends near and far, you know who you are. I could write a novel about the
community I left behind, my personal journey from Europe to the United States, and how much I
learned and gained along the way to become resilient. To my OCL Cohort 22 friends, you built a
community for us! To the people who did not believe in me, you fueled the fire. To my students
who bring me joy. To supporters and allies of female social entrepreneurs all over the world. To
female social entrepreneurs who are doing well by doing good. To all individuals doing their part
to make this world a better place. You all have a special place in my heart. And last but certainly
not least, thank you to the study participants who graciously took time out of their day to
complete this survey. Let us change the world together, one step at a time!
vi
Acknowledgments
To my family, friends, colleagues, and professors, I deeply appreciate you. This
dissertation is a product of teamwork. First, I thank my Dissertation Chair, Dr. Dennis Hocevar,
whose direction, motivation, high expectations, and consistent support provided a constant
reminder of what a visionary leader and academic can bring. I am also grateful for the support,
guidance, and professional learning that my committee members Dr. Lawrence Picus, and Dr.
Patrick Cates provided me during the process of this research project. Finally, I would like to
acknowledge Dr. Ayesha Madni for inspiring my academic journey, and Dr. Marc Pritchard for
his lighthouse guidance in times of need, and my friends and professional colleagues in our
cohort, and my fellow Rossier colleague and new friend Donna Ghalambor, who have all
supported me throughout this endeavor.
vii
Table of Contents
Abstract.......................................................................................................................................... iv
Dedication ....................................................................................................................................... v
Acknowledgments.......................................................................................................................... vi
List of Tables .................................................................................................................................. x
List of Figures................................................................................................................................ xi
List of Abbreviations .................................................................................................................... xii
Chapter One: Introduction to the Problem of Practice.................................................................... 1
Background of the Problem ................................................................................................ 2
Purpose of the Study and Research Questions and Hypotheses ......................................... 3
Importance of the Study...................................................................................................... 6
Overview of Theoretical Framework and Methodology .................................................... 7
Organization of the Study ................................................................................................... 9
Chapter Two: Review of the Literature ........................................................................................ 10
Conceptual Framework..................................................................................................... 10
Evolution of Social Entrepreneurship............................................................................... 12
Overview of Gender Gap in Social Entrepreneurship ...................................................... 14
Public Attitude toward Female Social Entrepreneurship.................................................. 17
Microsystem...................................................................................................................... 20
Mesosystem....................................................................................................................... 24
Exosystem......................................................................................................................... 26
Macrosystem..................................................................................................................... 28
Conclusion ........................................................................................................................ 32
viii
Chapter Three: Methodology........................................................................................................ 33
Research Questions........................................................................................................... 33
Overview of Design .......................................................................................................... 34
Research Setting................................................................................................................ 35
The Researcher.................................................................................................................. 36
Data Sources ..................................................................................................................... 36
Participants........................................................................................................................ 37
Instrumentation ................................................................................................................. 37
Data Collection Procedures............................................................................................... 39
Validity and Reliability..................................................................................................... 39
Ethics……......................................................................................................................... 40
Chapter Four: Results and Findings.............................................................................................. 42
Preliminary Analysis......................................................................................................... 43
Participants........................................................................................................................ 45
Descriptive Statistics..........................................................................................................51
Results Research Question One: Socioeconomic Status (Microsystem).......................... 57
Results Research Question Two: Connections and Network (Mesosystem).................... 61
Results Research Question Three: Mass and Social Media Exposure (Exosystem) ........ 63
Results Research Question Four: Expected Role of Women (Macrosystem) .................. 65
Results Research Question Five: Demographic Variables and Attitudes......................... 67
Chapter Five: Recommendations for Practice .............................................................................. 80
Limitations and Delimitations........................................................................................... 88
Conclusion ........................................................................................................................ 92
ix
Appendix A: Recruitment Email and Social Media Post................................................ 110
Recruitment Email .......................................................................................................... 110
Social Media Post ........................................................................................................... 112
Appendix B: Survey Questions....................................................................................... 113
Appendix C: Descriptive Statistics of Demographic Variables...................................... 120
List of Tables
Table 1 Data Sources.................................................................................................................... 35
Table 2 Scales Reliabilities of Five Constructs: ATT, SES, CN, MS and RE …..…........…….....44
Table 3 Demographic Variables of the Sample (n) and Total Population (N=497)……………...46
Table 4 Descriptive Statistics of Attitudes by Demographic Breakdown ..………..….…...……....51
Table 5 Descriptive Statistics of Attributes by Demographic Breakdown…..…..….…..…..……..52
Table 6 Descriptive Statistics of Independent Variables by Demographic Breakdown...............55
Table 7 Descriptive Statistics of Four Scales and Three Independent Variables………….…....58
Table 8 Correlations between Four Systems and Three Dependent Variables…...…..…….….....60
Table 9 Independent Variables t-test Results with Equal Variances Not Assumed…..….…….....69
xi
xi
List of Figures
Figure 1 Bronfenbrenner’s socio-ecological systems theory .......................................................... 8
Figure 2 Framework for attitudes towards female social entrepreneurship................................. 12
Figure 3 Socioeconomic Status frequency chart……………………….………………………...59
Figure 4 Connections and Network frequency chart……………….....……….………………….......62
Figure 5 Mass and Social Media frequency chart…………………..…………………...……....64
Figure 6 Roles and Expectations frequency chart………………...…………………………......66
Figure 7 Knowledge frequency chart……………...…………………………..............................67
Figure 8 Belief in Economic Driver frequency chart…………...……………..............................68
Figure 9 Unique Challenges frequency chart……………...……...…………..............................68
xii
xii
List of Abbreviations
ATT Attitudes
CN Connections and Network
SE Social Entrepreneurship
FSE Female Social Entrepreneurship
MS Mass and Social Media
MTurk Amazon Mechanical Turk
SES Socioeconomic Status
RE Roles and Expectations
USC University of Southern California
1
Chapter One: Introduction to the Problem of Practice
To grow today in the world of intense competition, any nation requires entrepreneurs at
the core of the economic development (Garg & Agarwal, 2017). In recent decades, academia and
governments have shown interest studying social entrepreneurship (Pulido et al., 2014). The
phenomenon of social entrepreneurship concerns individuals who start new organizations with
the goal of solving social and environmental needs (Terjesen, 2017). Social entrepreneurship
emphasizes the social mission, or the need to contribute positively to society over the need to
make profits, as with traditional businesses (Dees, 1998). Thus, social entrepreneurship serves as
a key driver for social and economic development due to its role in the process of tackling social
challenges innovately, as well as seeking financial sustainability with a market orientation
(Nicholls & Cho, 2008; Nicholls, 2010; Haugh, 2005).
However, in virtually all industrialized countries, women remain underrepresented in
entrepreneurship and the gender gap exhibits a remarkable persistence (Markussen & Røed,
2017). Total entrepreneurial activity in the United States continues to rise with rates of 18% for
women and 20% for men (Hill et al., 2023). Globally, 55% of those involved in social enterprises
are men and 45% are women (Bosma et al., 2016). Additionally, research shows that there are
more men than women trying to start a social enterprise at 9.9% (males) and 7.3% (females)
respectively (Bosma et al., 2016). These statistics indicate a gap in the participation of women
versus men in social entrepreneurship ventures.
Despite the increasing involvement of women in social entrepreneurship and business
creation in recent decades (Brush et al., 2009; Langowitz & Minniti, 2007), an understanding of
the factors that can close the gender gap in social entrepreneurship remains limited. Previous
research has primarily focused on investigating factors that affect self-efficacy (Kickul et al.,
2
2
2004), as well as women’s interest, motivation, and intentions related to involvement in social
entrepreneurship on both the individual level (Marín et al., 2019), and at the communal level
where the social entrepreneur lives (Garcia-Lomas & Gabaldon, 2020). Additionally, it has
explored the influence of socio-cultural factors (Pulido et al., 2014) and gender stereotypes
(Hechavarría, 2017) on women’s participation. Little attention has focused on understanding the
factors that influence public attitudes around female social entrepreneurship. Closing the
research gap can offer further understanding of how to lessen the gender disparity in social
entrepreneurship.
Background of the Problem
The link between gender and social enterprises remains relatively under researched
(Garcia-Lomas & Gabaldon, 2020). Social enterprises came into force during the recession of the
1970s, motivated partially by heavy cuts in government funding for non-profit organizations
(Poon, 2011), a considerable share of the estimated cuts of over $38 billion during the 1970s1980s (Salamon, 1997). During this time, social entrepreneurship became accepted as a tool to
address social problems due to the limited role of the state (Crimmins & Keil, 1983; Eikenberry
& Kluver, 2004). Indeed, more than 70% of social enterprises in the United States in 2002
started less than 30 years earlier (Davis, 2002). Additional research shows that entrepreneurship
is a compelling solution to wealth inequality, but wealth inequality can impede success in
entrepreneurship (Pantin & Lynnise, 2017). These authors find that the historic creation of the
wealth gap affects entrepreneurship, highlighting how the wealth gap adversely particularly
affects entrepreneurs of color. This finding adds another layer to the gender gap that the research
must explore further through an intersectional lens to ensure that the research includes women of
color.
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3
Hechavarría’s (2017) study identifies one statistically significant variable that predicts a
greater likelihood of pursuing environmental entrepreneurship: gender stereotypes around
employment, income, political power, and education. In addition, Marín et al. (2019) found that
an entrepreneur’s social orientation i.e., desire to generate social value, is stronger for women,
the more educated, and older people. However, the gender gap in social entrepreneurship is still
prevalent today.
This study aims to explore the factors influencing public attitudes, as these attitudes may
significantly impact women’s decisions to pursue or refrain from female entrepreneurship.
Research by Achtenhagen and Welter (2003) shows that in cultures where female
entrepreneurship has lower legitimacy in comparison with male entrepreneurship, women’s selfperceptions and attitudes can affect their likelihood of pursuing this career choice, and thus
constrain women-led new ventures. In contrast, countries that provide normative support for
women entrepreneurs, exhibiting admiration and respect along with gender equality, are likely to
observe a higher level of female entrepreneurship activity (Baughn et al., 2006).
Purpose of the Study and Research Questions and Hypotheses
The purpose of this study is to examine factors that shape and influence the general
public’s attitudes towards female social entrepreneurship by leveraging Bronfenbrenner’s socioecological systems theory. There is limited knowledge of the driving forces behind the public’s
view on female social entrepreneurship. This study seeks to examine the extent to which various
systems, including socioeconomic, environmental, and cultural factors influence the public’s
attitude towards female social entrepreneurship. Findings from this study will inform
recommendations for policymakers in the public and private sectors. The following are the
research questions the study seeks to explore.
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4
1. According to public perceptions, to what extent does socioeconomic status
(education, income, and occupational/social status) influence attitudes about female
social entrepreneurship (microsystem)?
2. According to public perceptions, to what extent does the presence of mentors and
participation in professional organizations influence attitudes about female social
entrepreneurship (mesosystem)?
3. According to public perceptions, to what extent does mass and social media exposure
influence attitudes about female social entrepreneurship (exosystem)?
4. According to public perceptions, to what extent does the expected role of women in
society influence attitudes about female social entrepreneurship (macrosystem)?
5. To what extent do demographic variables (gender, age, ethnicity, race, education,
family’s average annual income, current annual income, marital/relationship status,
family’s annual income, employment status and geographical location relate to
attitudes around female social entrepreneurship?
Research has yet to explore these factors empirically in terms of public attitude toward
Female Social Entrepreneurship. Accordingly, the following hypotheses emerge accordingly.
The first hypothesis suggests that individuals with higher levels of education, income,
and occupational/social status hold more favorable attitudes towards female social
entrepreneurship compared to those with lower socioeconomic status.
H10: There is no significant positive relationship between socioeconomic status
(including education, income, and occupational/social status) and attitudes about female social
entrepreneurship.
5
5
H11: There is a significant positive relationship between socioeconomic status (including
education, income, and occupational/social status) and attitudes about female social
entrepreneurship.
The second hypothesis suggests that individuals with mentors and professional
organization alliances hold more favorable attitudes toward female social entrepreneurship
compared to those without mentors and professional organization alliances.
H20: There is no significant positive relationship between the presence of mentors and
professional organization alliances and attitudes about female social entrepreneurship.
H21: There is a significant positive relationship between the presence of mentors and
professional organization alliances and attitudes about female social entrepreneurship.
The third hypothesis proposes that increased exposure to mass and social media content
related to successful female social entrepreneurs will lead to more positive attitudes towards
female social entrepreneurship.
H30: Increased exposure to mass and social media content related to successful female
social entrepreneurs does not lead to more positive attitudes towards female social
entrepreneurship.
H31: Increased exposure to mass and social media content related to successful female
social entrepreneurs leads to more positive attitudes towards female social entrepreneurship.
The fourth hypothesis posits that societal expectations regarding the role of women will
significantly impact attitudes, with more progressive societies likely to exhibit more positive
attitudes while more traditional societies may show less favorable attitudes towards female social
entrepreneurship. These hypotheses form the basis for exploring the influences on attitudes about
female social entrepreneurship in different socio-cultural contexts.
6
6
H40: The expected role of women in society does not significantly impact attitudes about
female social entrepreneurship.
H41: Societal expectations regarding the role of women have a significant impact on
attitudes, with more progressive societies likely to exhibit more positive attitudes, while more
traditional societies may show less favorable attitudes toward female social entrepreneurship.
Lastly, the fifth hypothesis proposes that demographic factors influence how individuals
perceive and support female social entrepreneurship.
H50: There is no relationship between demographic variables (gender, age, ethnicity,
race, education, family’s average annual income, current annual income, marital/relationship
status, employment status, and geographical location) and attitudes towards female social
entrepreneurship.
H51: There is a relationship between demographic variables (gender, age, ethnicity, race,
education, family’s average annual income, current annual income, marital/relationship status,
employment status, and geographical location) and attitudes towards female social
entrepreneurship.
Importance of the Study
In reviewing the current literature on the topic of women as social entrepreneurs in the
United States, researchers give little attention to the public’s attitude toward women in social
entrepreneurship and how this may impact the gender gap in the field. This problem is critical to
address because greater gender equality in participating in the market can play a role in boosting
economic growth and overcoming many social issues (Lyon & Humbert, 2012). For example,
“Women have had a positive impact on society through their involvement in the third sector, by
putting some topics such as children, family, women’s health, violence and discrimination
7
7
towards certain groups of population on the social agenda” (Humbert, 2012, p. 8). Indeed,
closing this gender gap is essential to foster sustainable economic growth (Rietveld & Patel,
2022).
This study has both theoretical and practical contributions. First, this work could be
useful for the progress of studies on social entrepreneurial activity carried out by women,
especially using the institutional approach, where gender variables can be crucial, and attitudes
held by the public have substantial impact on economic growth and innovation for the nation.
Second, this study contributes theoretically to the literature on social entrepreneurship, with the
creation of knowledge related to how various ecological factors affect attitudes regarding female
social entrepreneurship in the United States. Closing this research gap is crucial for gaining
insights into the factors contributing to gender disparities in social entrepreneurship and
facilitating more equitable opportunities. Finally, public attitudes significantly influence the
support and opportunities available to female social entrepreneurs. These results help in the
process of designing government and industry policies to foster female social entrepreneurship
across the nation and beyond.
Overview of Theoretical Framework and Methodology
Bronfenbrenner’s socio-ecological systems theory provides a lens to understand gender
inequity in social entrepreneurship ventures. Bronfenbrenner (1979) explores the ecological
environment as “a nested arrangement of structures, each contained within the next.” It divides a
person’s environment into four different systems: the microsystem, the mesosystem, the
exosystem, and macrosystem (Bronfenbrenner, 1979). Later work included the fifth element, the
chronosystem. This system consists of all the environmental changes that occur over the lifetime
that influence development, including major life transitions and historical events
8
8
(Bronfenbrenner, 1979). By employing this framework, the study hones in on four systems to
explore how they impact female social entrepreneurship namely teachers, peers, and parents
(microsystem) who sit within a wider community, the educational and industry network
comprising of mentors and ties to professional associations (mesosystem), access to relevant
human and physical infrastructure (and the policies that shape it) in school and social
environments (exosystem), and by the broader cultural values, beliefs and societal norms around
gender and social entrepreneurship in the wider American community (macrosystem). Figure 1
demonstrates the five different systems as a set of nested structures, highlighting the
microsystem, mesosystem, exosystem, macrosystem, and chronosystem.
Figure 1
Bronfenbrenner’s socio-ecological systems theory
Note. This figure reflects the five systems of Bronfenbrenner’s (1995) socio-ecological systems
theory.
Chronosystem: Changes
over Time
Macrosystem: Social and
Cultural Values
Exosystem: Indirect
Environment
Mesosystem:
Connections
Microsystem:
Immediate
Environment
Individual
9
9
Organization of the Study
The dissertation follows a traditional five-chapter model. Chapter One provides an
introduction and outline of the study and the research questions that informed it. Chapter Two
highlights the relevant literature on gender balance and social entrepreneurship and the
conceptual framework. Chapter Three details the research methodology used in the quantitative
surveys. Chapter Four examines and analyzes the results of the survey protocol and the findings
of the study. Chapter Five offers proposed recommendations, limitations, as well as a conclusion
that incorporates broader future impacts of this research.
10
10
Chapter Two: Review of the Literature
This literature review examines the current research on gender balance within the domain
of social entrepreneurship. The review begins with a review of Urie Bronfenbrenner’s conceptual
framework and related theories to view the problem of practice (Bronfenbrenner, 1995).
Following the general research literature, the review culminates in the evolution of social
entrepreneurship and the subsequent theories of the gender gap in social entrepreneurship. These
theories reveal several factors that influence attitudes, including the immediate environment,
educational and industry connections, indirect environment as well as social and cultural values.
The review provides an in-depth exploration of each of these influences.
Conceptual Framework
Urie Bronfenbrenner defines the ecology of human development as the mutual
relationship between an active, growing individual and the changing elements of the immediate
settings in which the person lives (Bronfenbrenner, 1979). The interaction is reciprocal and twodirectional in nature. Bronfenbrenner (1979) argues that human development occurs within a
specific environmental context, not in a vacuum. He also describes the ecological environment as
a series of concentric structures, nested within one another, beginning with the individual
surrounded by the microsystem, mesosystem, exosystem, and macrosystem. The microsystem
represents the activities, roles, and interpersonal relations experienced by the individual in a
specific setting. Bronfenbrenner underscores the importance of the word experienced, as the
individual’s perceptions are paramount. The mesosystem involves the interrelations of two or
more individual settings such as work and family. Bronfenbrenner describes the mesosystem as a
system of microsystems, created when an individual enters a new setting. The exosystem
comprises the connections between two or more settings, where at least one of those settings
11
11
does not directly include the individual as an active participant, yet the activities in the setting
affect the individual. Enveloping these layers is the macrosystem, which encompasses the
consistencies in culture, values, and overarching structures that affect the individual in each
society (Bronfenbrenner, 1979). Bronfenbrenner describes the element of time permeating these
structures to illustrate how people change in relation to the environment. Bronfenbrenner later
refers to the element of time as the chronosystem (Bronfenbrenner, 1979; Gardiner &
Kosmitzski, 2007).
Bronfenbrenner’s model provides a helpful lens through which to view the problem of
the gender gap in social entrepreneurship. Viewing the microsystem level at the center of
Bronfenbrenner’s model, demographic variables play an important role in determining attitudes
around female social entrepreneurship. Secondly, extending the view to the mesosystem, this
system includes the relationships between individuals and other connections in their educational
and industry network. Thirdly, the exosystem sheds light on how mass media and social media
help or hinder attitudes around female social entrepreneurship. Lastly, the U.S. gender and
cultural norms for female social entrepreneurs envelop the entrepreneurship industry as the
macrosystem. Research shows in cultures where female entrepreneurship is perceived to have
lower legitimacy in comparison with male entrepreneurship, women’s self-perceptions and
attitudes can affect their likelihood of pursuing this career choice, and thus constrain women-led
new ventures (Achtenhagen & Welter, 2003). In contrast, countries that provide normative
support for women entrepreneurs, exhibiting admiration and respect along with gender equality,
are likely to observe a higher level of female entrepreneurship activity (Baughn et al., 2006).
12
12
Figure 2 illustrates how Bronfenbrenner’s model relates to the framework for attitudes
towards female social entrepreneurship. A closer examination of additional theories affecting the
individual as well as the related microsystems will provide further insights into the problem of
practice.
Figure 2
Framework for attitudes towards female social entrepreneurship
Evolution of Social Entrepreneurship
It is the age of entrepreneurship (Davis, 2002). For example, when Bill Gates, the founder
and CEO of Microsoft is better known around the world than most heads of state, one might
conclude that the age of the entrepreneur has arrived (Davis, 2002). They exercise influence
beyond economics, helping shape political, social, environmental, and cultural arenas (Davis,
2002). Indeed, Davis (2002) shares that entrepreneurs of large multinational corporations have
had a distinctly important role in shaping today’s process of globalization.
Macrosystem: Social and
Cultural Values
Exosystem: Indirect
Environment
Mesosystem:
Connections
Microsystem:
Immediate
Environment
Individual
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William Drayton has coined the term ‘social entrepreneur’ (Davis, 2002). As a prominent
figure in the United States social enterprise sphere (Stankord, 2012), he is widely credited with
creating the world’s first organization to promote the profession of social entrepreneurship,
Ashoka: Innovators for the Public. Drayton (2002) recognized that “social entrepreneurs have the
same core temperament as their industry-creating, business entrepreneur peers but instead use
their talents to solve social problems on a society-wide scale -- why children are not learning,
why people cannot access technology equally, why pollution is increasing, etc. The essence,
however, is the same. Both types of entrepreneurs recognize when a part of society is stuck and
provide new ways to get it unstuck. Each type of entrepreneur envisages a systemic change,
identifies the jiu jitsu points that will allow them to tip the whole society onto this new path, and
persists until the job is done (Drayton, 2002).
Muhammad Yunus (Bangladesh) is another prominent figure in the social enterprise
sphere (Stankorb, 2012). Lauded as a 2006 Nobel Peace Prize laureate, Yunus created Grameen
Bank, also known as “the bank for the poor” (Stankorb, 2012, p. 161). In 1983, he established a
system of microcredit for poor Bangladeshi villagers. His system helped lift millions of people,
especially women, out of poverty in Bangladesh (Vicente, 2022).
By embracing sustainability, social entrepreneurs are determined to drive social change
by serving the needs of greater numbers of people (s) including the bottom of the pyramid
market which may not be feasible for commercial entrepreneurs and governments (Hart, 2005).
This begs the question: is social enterprises a new form of business enterprise or is it merely a
measure to address unavoidable problems like climate change, economic inequality, social
injustice, and employee max exodus in the “great resignation” following the COVID-19
pandemic (Kaplan, 2021)?
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In the last decade, social enterprise grew from the idea that businesses can do well by
doing good (Vicente, 2022). In a statement issued by the Business Roundtable in 2019, 181
CEOs of major corporations committed to “lead their companies for the benefit of all
stakeholders—customers, employees, suppliers, communities and shareholders.” (Business
Roundtable, 2019, para. 1). It took a decade marked by the Great Recession, unthinkable levels
of poverty and inequality, migratory fluxes, and social unrest, to prompt this statement (Business
Roundtable, 2019).
To assess a company’s commitment to social causes and the environment, the non-profit
B Lab uses the company’s ownership structure, size, and profitability to determine the broader
impact (Vicente, 2022). The author states that companies like Patagonia, TOMS, and Dr.
Bronner’s, have more than 250 employees and landed on B Lab’s Best for the World list for their
positive impact and best business practices regarding community, environment, customers,
workers, and governance (B-Lab, 2021). However, despite these advancements in recognizing
socially responsible companies, there remains a significant gender gap in social
entrepreneurship.
Overview of Gender Gap in Social Entrepreneurship
The literature on gender and entrepreneurship is quite extensive, finding a broad
consensus that men start businesses more often (Eagly, 1987; Langowitz & Minniti, 2007;
McKay et al., 2010; Themudo, 2009). Research shows that this gender gap becomes even more
pronounced as the level of a country’s development rises (Coduras & Autio, 2013).
The social role theory or its extended version, the gender role theory developed by Eagly
(1987), explains that the male group’s greater propensity is not due to biological predisposition
but rather to culturally defined, socially acceptable behaviors for each gender. While the male
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role is associated with control or achievement, making them responsible for financial family
support, traditionally women are often associated with work in the home, performing household
chores and taking care of children and other dependent people. These roles and stereotypes lead
to the conclusion that the ideal gender to start and run businesses is the male one (Carter & Rosa,
1998).
Meanwhile, Connell (1990) also supports this argument with his theory of hegemonic
masculinity, stating that in the business world there is a hierarchical order between men and
women, in which society sees men as the standard and sees women as the exception to the rule
(Godwin et al., 2006). However, the fact that the principles of hegemonic masculinity condition
women have these roles preset can cause differences in the way in which they run their
businesses, since the objectives that guide their decisions are different from those of men
(Langowitz & Minniti, 2007). Specifically, some studies show that women decide to become
entrepreneurs guided primarily by social rather than economic (main motivation of men)
objectives (Fernández-Serrano & Liñán, 2014, Urbano et al., 2014).
This fact might clarify why women are the goal of many social actions and key players in
social entrepreneurship (Hechevarría et al., 2012). The missions of these types of enterprises are
directly related to altruism, care, and protection of others. In contrast, commercial enterprises
pursue to create an economic benefit for the person who started it. Women who establish social
enterprises often find environments that are better suited to their roles and emotional goals
compared to those who start commercial businesses (Dietz et al., 2002, Mckay et al., 2010,
Urbano et al., 2014).
Additionally, while creating economic benefits is the principal aim of commercial
enterprises, social enterprises shift their focus from this objective, explicitly aiming to forge
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sustainable solutions that yield social value through economic actions (Mair & Martí, 2006).
Precisely, social enterprises realign more with the female role due to social objectives, as
demonstrated by the work of Themudo (2009) and Hechevarría et al. (2012). Research continues
to highlight the care-giving nature of women as one of the main drivers of their social
entrepreneurial involvement. Therefore, Hechevarría et al. (2012) propose that women are more
inclined towards assistance-related activities. They are the ones who are most likely to get
involved in more volunteer activities, and even the participation of women in the third sector is
higher than that of men in countries such as the United States. Besides, a strong family influence
lays the ground for pro-social values (Hechavarría et al., 2017). Indeed, social enterprises are a
vehicle for “helping others and helping nature” (Hechavarría et al., 2017, p. 5). As such, there is
a consensus that social enterprises proliferate in sectors such as social care, education, and
health, with a strong traditional presence of women (Addicott, 2017). Indeed, scholars such as
Hechavarría et al. have focused on the influence of ethics theory (Flanagan & Jackson, 1987),
both ethics of care (in terms of nurturing) and justice (as fairness) drive the involvement of
women in social enterprises explaining how cultural pro-social values related to ethics of justice
drive a transformational desire to create new opportunities within society. Despite these
discoveries, the gender gap in social entrepreneurship persists.
From a theoretical perspective, both entrepreneurship and social entrepreneurship have
gained significant prominence as vital sources of employment for women in numerous countries
(Langowitz & Minniti, 2007). However, the literature on commercial entrepreneurship is more
developed than that on social entrepreneurship (Alexová, 2014). Despite the rising interest
among practitioners, research to date to empirically assess women’s involvement in social
enterprises has been anecdotal (Bruni et al., 2014; Hechavarría et al., 2012). Prior reviews have
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been useful in mapping this emerging research on women in social enterprises (Fotheringham &
Saunders, 2014; Granados et al., 2011; Lehner & Kansikas, 2013; Smith, 2014). Their narrow
scope resulted in a weak effort to define the participation of women in social enterprises and
move the field forward. Interestingly, research on social entrepreneurs does not adequately
consider gender (Humbert, 2012). Indeed, Alexová (2014) also states that there is almost no
research linking perceptual variables and their influence on a person to become a social
entrepreneur. Moreover, the same study states that there is a gap in female social
entrepreneurship literature.
To fill that research gap, Garcia-Lomas and Gabaldon (2020) conducted a literature
review to examine women’s involvement in social enterprises. The authors found that social
entrepreneurship is a growing arena that underpins women’s increasing influence. As a result,
the gender gap is smaller in social enterprises, with an estimated “55 percent [are] male and 45
percent are female” (Bosma et al., 2016, p. 5). Although the gender gap is narrower in social
enterprises compared to traditional enterprises, it still signifies an unequal level of participation
between women and men in social entrepreneurship ventures.
Public Attitude toward Female Social Entrepreneurship
Positive societal attitudes toward entrepreneurship improved in the United States in 2021,
with 76% of respondents believing that starting a new business is a desirable career choice, 77%
stating that entrepreneurs achieve a high level of status, and 80% perceiving positive media
and/or internet attention about successful entrepreneurs (GEM, 2022). Improving the public’s
attitude toward female social entrepreneurship is vital for closing the gender gap in social
entrepreneurship. Recent research, conducted by Yang et al. (2023), has examined the masses’
social entrepreneurial intention (SEI) to grasp the public perceptions of social entrepreneurship.
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However, this study did not specifically investigate the crucial factors that drive these public
perceptions. Thus, little is known about the determinants of the public’s attitude toward this topic
and to what extent those determinants impact public attitude.
In the literature, the term public attitude usually suggests the feelings or evaluations of
the public toward certain issues, such as pleasant or unpleasant, good or bad, and harmful or
beneficial (Ajzen, 2001). Eagly and Chaiken (1993, p. 1) offer a widely applicable definition of
attitude as “a psychological tendency that is expressed by evaluating a particular entity with
some degree of favor or disfavor;” in this definition, they emphasize the features of evaluation,
attitude object, and tendency. Similarly, Breckler (1984) defines attitude as an individual’s
response to external stimuli or specific subjects. In this study, public attitude stands for “the
evaluation judgments that pertain to support among the public for particular issues” (Guan et al.,
2019, p. 3). In addition, attitude reflects one’s expression in support of or opposition to a
particular view to varying degrees. Thus, the authors define public support as “public attitude
that reflects the preferences and favorability among the public on certain issue” (Guan et al.,
2019, p. 3).
Many research efforts aim to decode how attitudes are formed. McGuire et al. (1985)
pinpoint fundamental elements that shape the early formation of attitudes, such as genetic
factors, temporary physiological conditions, firsthand interaction with the objects of attitude, and
societal mechanisms such as socialization, indoctrination, and influence from peers. Schwartz
(1977) examines how altruistic standards affect prosocial actions, assuming that individual
norms stem from the value systems of the person. According to Schwartz’s norm-activation
model, personal values, information about attitude objects, and social interactions generate
attitudes (Schwartz, 1977, Schwartz & Howard, 1981). Similarly, Stern et al. (1995) developed
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the value-belief-norm (VBN) theory, suggesting that referencing value orientations and beliefs
about the consequences of the objects for their values shape individual attitudes. In VBN theory,
personal values are pivotal in shaping individuals’ lives and perspectives, affected by personal
beliefs regarding awareness of consequences and the attribution of responsibility, as well as by
personal norms (Stern et al., 1995). An understanding of how factors, particularly public attitude,
drive women’s educational and occupational choices become important in studying the gender
gap in social entrepreneurship. Eccles (1994) demonstrated early on that despite recent efforts to
increase the participation of women in advanced educational training and high-status
professional fields, women and men still concentrate on different occupations and educational
programs, and women remain underrepresented in many high-status occupational fields. Many
factors, ranging from outright discrimination to the processes associated with gender role
socialization, contribute to these gendered patterns of educational and occupational choices.
Drawing upon the theoretical and empirical work associated with decision-making, achievement
theory, and attribution theory (Crandall, 1969; Weiner, 1974), Eccles (1994) links educational,
vocational, and other achievement-related choices most directly to two sets of beliefs: the
individual’s expectations for success and the importance or value the individual attaches to the
various options perceived by the individual as available. The model also specifies the relation of
these beliefs to cultural norms, experiences, aptitudes, and how these beliefs relate to cultural
norms, experiences, abilities, and the personal beliefs and attitudes that researchers typically link
to achievement-related activities in this domain (Adler et al., 1983; Eccles, 1987; Meece et al.,
1982). In particular, the model links achievement-related beliefs, outcomes, and goals to
interpretative systems like causal attributions, to the input of socializers to gender role beliefs, to
self-perceptions and self-concept, and to one’s perceptions of the task itself. Each of these factors
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influence both the expectations one holds for future success at the various achievement-related
options and the subjective value one attaches to these options. These expectations and the value
attached to the various options, in turn, assumed to influence choice among these options. Recent
research shows that value orientations and norms of the individual shape attitudes (Guan &
Zhang, 2023). Researchers have not yet examined how socioeconomic, media, and cultural
factors influence public attitudes toward female social entrepreneurship.
Microsystem
A different set of variables determines entrepreneurship. The following section explores
pertinent demographic variables and microsystem components, examining how immediate
settings such as education, income, and occupational/social status can influence public attitudes
toward female social entrepreneurship. The aim is to analyze the nuanced impact of these
interrelated environments on shaping public perception.
Individual traits and characteristics
At the individual level, several studies (Handy et al., 2002; Hechavarría & Ingram, 2016),
identify that individual traits and characteristics (such as gender, personal experiences,
educational level, number of children, work training, age, religion, self-beliefs, ideology, and
feminist ideology) may influence women’s involvement in social enterprises. For instance,
studies have shown that older individuals are more likely to pursue entrepreneurship than their
younger counterparts (Arenius & Minniti, 2005; Beugelsdijk & Noorderhaven, 2005; Walker &
Webster, 2007; Weber & Schaper, 2004). Bandura (1997) defines self-efficacy as the notion that
an individual can successfully accomplish a behavior expected to produce the needed outcome.
Scholars have generally found that males have higher conventional entrepreneurial self-efficacy
than females (Wilson et al., 2007; Zhao et al., 2005). Kickul et al. (2008) found that “self-
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efficacy seemed to have a stronger effect on entrepreneurial interest for girls than for boys, and
that having an entrepreneurial mother or father had a significant and positive effect on girls’ (but
not boys’) levels of the entrepreneurial interest” (p. 321). Additionally, literature on
entrepreneurial self-efficacy suggests that younger individuals may feel more confident in their
potential success in entrepreneurship and thus might more strongly perceive the influence of
socioeconomic status on this success (Boyd & Vozikis, 1994). Research shows that self-efficacy
is pivotal to social entrepreneurship (Venugopal et al., 2015). However, for youth, selfefficacy—a belief in their own ability to perform behaviors needed to achieve an outcome
(Bandura, 1986)—is part of a broader set of developing social-cognitive skills. Youth develop
abilities such as self-efficacy and explore their self-identities through curricular and
extracurricular activities (Pechmann et al., 2020). Engaging youth in bringing new ideas and
tactics to address an ongoing societal problem provides “developmentally constructive” activities
that advance well-being (Pechmann et al., 2020, p. 208) and channel youth toward activities with
the potential to create social change.
Education
Scholars have found contradictory results for education’s role in increasing
entrepreneurial self-efficacy. Shinnar et al. (2014) found that a course in entrepreneurship
increased entrepreneurial self-efficacy for males, but not females. One study found that
individuals with higher levels of education (e.g., college and graduate school) tend to be more
likely to start social ventures (Terjesen et al., 2016). In contrast, other studies reveal that the
percentage of women who decide to pursue an entrepreneurial career is still lower than that of
men and that the higher the level of education, the greater the gender gap in entrepreneurial
undertakings (Elam et al., 2019; Guzman & Kacperczyk, 2019; Neumeyer et al., 2019). Building
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on this, a study by Nieuwenhuizen and Tselepis (2022) adds nuance by showing that education
not only influences the gender gap but also shapes the way female social entrepreneurs construct
knowledge, potentially fostering a growth mindset that enables them to meet community needs.
Markussen and Røed (2017) posit that existing entrepreneurship among neighbors, family
members, and recent schoolmates affects early career entrepreneurship. Based on an instrumental
variables’ strategy, they identified strong and heavily gendered peer effects. While men are more
influenced by other men, women are more influenced by other women (Markussen & Røed,
2017). The authors estimate that differences between male and female peer groups explain
approximately half of the gender gap in early career entrepreneurship.
Additionally, there is a strong family influence that lays the ground for pro-social values
(Hechavarría et al. 2017). In particular, family’s previous entrepreneurial experience and family
volunteering experience in the social sector have a positive influence on the development of
social entrepreneurial intentions (Handy et al., 2002; Sastre-Castillo et al., 2015).
Another relevant dimension comes from a study conducted by Dawson and Henley
(2015), in which the authors found that the gap between men and women in starting an
entrepreneurial career is due to women’s lower-risk attitude. Dawson and Henley (2015) suggest
that the modest number of female entrepreneurs correlates with a heightened fear of failure,
limited confidence in their abilities, and the view of inadequate support from social networks.
Income
Limited research focuses solely on the relationship between income and attitudes toward
female social entrepreneurship. In the literature, income is often associated with access to social
capital and resources crucial for entrepreneurial success (Bourdieu, 1986). In addition, Evans and
Jovanovic (1989), and Kihlstrom and Laffont (1979) have shown that entrepreneurial decisions
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are positively related to individuals’ incomes and wealth. Higher income can translate into a
greater ability to invest in and support entrepreneurial ventures (Brüderl & Preisendörfer, 1998).
According to recent reports, women-led startups receive less than 3% of all venture capitalist
(VC) investments and women also account for less than 15% of check-writers (Harvard Business
Review, 2023). Furthermore, research by Lévesque and Minniti (2006) indicates that higherincome individuals may have a greater tendency to perceive opportunities and believe in the
efficacy of entrepreneurial action.
Blanchflower and Oswald (1998), and Taylor (1996) have studied the importance of
work status and labor markets and have shown that employed individuals are more likely to start
new businesses. Sara and Peter (1998), and Verheul and Thurik (2001) have discussed the role of
financial constraints for women entrepreneurs and their influence on women’s strategic
decisions. Devine (1994a, 1994b) and Ajayi-Obe and Parker (2005), among others, have studied
the relationship between female entrepreneurship and labor market constraints and found them to
be more pervasive for women than men in almost all countries.
Occupational/Social Status
Empirical evidence shows that a woman’s decision to start a business depends on her
socio-cultural background (Ahl, 2006). Baughn et al. (2006), and Langowitz and Minniti (2007)
observe that in societies where the role of the woman is closely tied to family responsibilities,
entrepreneurial activity is perceived as being less desirable. The research, however, is not
conclusive on this. In contrast, work–family balance can be an important factor which motivates
women to start a business. Venture creation can offer women more flexibility in work schedules,
avoiding work–family conflicts (Kirkwood & Tootell, 2008; Rembulan et al., 2016; Thébaud,
2015).
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In earlier years, Mannheim (1993) noted that, although the aggregate women are less
work-centered than men, further specification indicates that this is true mainly in the
intermediate socioeconomic status (SES) categories, but not in others. The author proposes that
this is related to the greater status inconsistency that women experience in these SES categories,
and to their dual role as wives and mothers and employed workers. For women only, the country
of origin is relevant to work role centrality (WRC), suggesting the importance of socialization
(Mannheim, 1993). Thus, this study shows that the combined model of status, work values, and
job satisfaction explains WRC best for men, whereas status, socialization, and job satisfaction
explain it best for women. Another angle to consider is racial and ethnic identity.
Mesosystem
The following section delves into the mesosystem elements that play a role in this study,
particularly focusing on mentors and professional associations. This section scrutinizes how
relationships between immediate settings, like the presence of mentors and participation in
professional associations, contribute to public views on female social entrepreneurship. The
section aims to dissect how these secondary environments indirectly influence public attitudes.
Mentors
Eesley and Wang (2017) investigated the impact of mentors (entrepreneurs and
nonentrepreneurs) on entrepreneurial behavior. Their results show that although entrepreneurial
mentors had greater social influence compared to non-entrepreneurs, these mentors had an even
greater impact on students with no family-related entrepreneurial history. They argue that having
an entrepreneurial parent or peers with entrepreneurial experience is not possible for everyone,
but that educational programs can foster entrepreneurial development by creating connections
between individuals and entrepreneurial mentors.
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Research by Muldoon et al. (2019) considers the role of the mentor in female
entrepreneurship. They posit that discrimination and false narratives often remain in society due
to the dominance of various influential actors and state that a mentor can make the difference in
that a non-biased mentor could encourage female entrepreneurship through vicarious experience,
social persuasion, and mastery opportunities.
As seen in other studies, the benefits of an entrepreneurial mentor should be obvious (StJean et al., 2018). Vicarious experience is an extremely important consideration in building selfefficacy through the shattering of incorrect perceptions about ability (Bandura, 1997).
However, a word of caution is that although role modeling can have beneficial aspects, it
can also lead to narratives that may encourage gender stereotypes through the use of successful
female entrepreneurs as role models (i.e., vicarious experience; Byrne et al., 2019). Thus the
research suggests that mentorship should go beyond vicarious experience and mentors should
consider other ways to build entrepreneurial self-efficacy.
Participation in Professional Associations
There are many professional and organizations in existence. The professional school
approach advocated by Schibrowsky et al. (2002) would suggest that one of the most important
activities driving students to join a campus-based professional organization is to develop
practical experiences needed in the business environment, a finding corroborated by the
importance of contacts with business in the regression model. Although many may understand
the concept of networking, this particular type of organization provides a relatively safe platform
for skill development through active interaction with skilled businesspeople.
Peltier et al. (2008) find that students join professional organizations to gain
entrepreneurial experience. This dimension incorporates researching and developing new
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products as well as starting and running one’s own business (Peltier et al., 2008). As with most
learning experiences, there is clear evidence that more active participation through leadership
roles leads to greater satisfaction.
Exosystem
Following that, the literature review turns its attention to the exosystem, specifically
exploring the role of mass media and social media. This section considers how these external but
influential platforms shape public sentiments toward female social entrepreneurship. The
objective is to evaluate the external factors that exert an unseen yet significant impact on public
opinion.
Mass Media
Due to the sustained scholarly interest in the gender stereotyping of entrepreneurship,
much knowledge has now accumulated on the presence and consequences of this phenomenon.
Individuals in general (Baron et al., 2001; Gupta et al., 2008; Gupta et al., 2009) as well as
entrepreneurs themselves (Swail & Marlow, 2018; Verheul et al., 2005) tend to associate
entrepreneurship with stereotypically masculine traits (Bruni et al., 2004).
In addition, male-typing of entrepreneurial activity is prevalent across a wide range of
outlets. Indeed, the masculinization of entrepreneurship is predominant within newspapers
(Achtenhagen & Welter, 2011; Nicholson & Anderson, 2005), books/movies/television shows
(Smith 2010, 2013), educational/training materials (Ahl, 2007), policy publications (Ahl &
Marlow, 2021; Ahl & Nelson, 2015; Arshed et al., 2019), and even academic research (Ahl 2004,
2006; Hamilton 2014). Not only do women see their interactions with entrepreneurship
differently than men, media and research construct and portray their businesses differently as
well, if they do at all n(Zisser et al., 2019). Media and research often render women’s
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entrepreneurship invisible (Baker et al., 1997). Theorists and researchers have critiqued this
disparity and proposed new ways of examining women’s entrepreneurship. Because primarily
male samples dominated the early literature and much of traditional entrepreneurship genders as
masculine, women’s entrepreneurship is frequently viewed as secondary (Ahl, 2006; Bruni et al.,
2004; De Bruin et al., 2006; Gupta et al., 2009).
Indeed, the reality of female entrepreneurship links intricately to media representations of
women entrepreneurs (Eikhof et al., 2013). Media representations shape what people believe
women business owners typically do and how they experience it in two important ways. They
influence whether women perceive entrepreneurship as desirable and attainable and if so, what
type of entrepreneurship they pursue (Eikhof et al., 2013).
Recognizing the importance of media representations of women entrepreneurs, a growing
number of studies have analyzed these representations and their impact on perceptions of female
entrepreneurship (Achtenhagen & Welter, 2007; Ahl, 2004; Ahl, 2007; Bruni et al., 2004a,
2004b). These studies have shown that business media, newspapers and research publications
portray female entrepreneurship as less purposeful, professional, and successful than male
entrepreneurship (Achtenhagen & Welter, 2007; Ahl, 2007; Ahl, 2004; Bruni et al., 2004a,
2004b).
Social Media
Social media has been found to assist entrepreneurs in acquiring and building
entrepreneurial intention (Azhar & Akthar, 2020). Along similar lines, role models are crucial for
raising awareness about the potential of entrepreneurship, specifically enhancing women’s
motivation in this domain (Chaker & Zouaoui, 2023). This concept manifests in two critical
areas in the study by Chaker and Zouaoui: the influence of existing women digital entrepreneurs
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and the subsequent influence these women have on inspiring others to launch their own digital
ventures. Firstly, almost all the women interviewed cited the influence of a female entrepreneur
who had successfully launched her own digital business. These role models provided inspiration,
courage, and guidance for succeeding in the digital landscape. Secondly, the women interviewed
were passionate about their ventures and, in turn, served as role models for other aspiring female
entrepreneurs.
Macrosystem
Lastly, the discussion expands to include the macrosystem, with an emphasis on societal
expectations about the role of women. This part reviews how overarching cultural beliefs and
social norms affect attitudes toward female social entrepreneurship. The intent is to examine the
underlying cultural values that mold public perceptions.
Historical Perspectives
Scholarly interest in the existence, nature, and effects of gender-based stereotyping
emerged very early in the development of the women’s entrepreneurship literature. Indeed, the
first entrepreneurship article to raise the notion of gender stereotyping (Schwartz, 1976) was also
the first academic paper published on women entrepreneurs (as previously identified by Jennings
and Brush, 2013).
Growing support for gender equality and a shift toward less restrictive views of gender
roles suggest a significant transformation of U.S. public opinion during the second half of the
twentieth century. Following the initial discovery of these trends in the 1970s, attitudes toward
gender roles received less scholarly attention in the 1980s, due largely to questions about the
relevance of this type of opinion change for understanding patterns of stability and change in
behaviors and institutions related to gender. Since the early 1990s, a new generation of research
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has reported evidence linking changing gender role attitudes to subsequent change in individual’s
behavior and also to the level of gender inequality within specific institutions.
The Expected Role of Women
Social dominance theory (SDT) explains why gender stereotypes still exist today.
Scholars have found that individuals possess stable beliefs about traditional gender and race
roles, which Sidanius and Pratto (1999) call social dominance orientation (SDO) and are the
result of myths that society believes. Society often encourages males to be aggressive, risktaking, and competitive, whereas females receive encouragement to be more community
orientated, passive, and less aggressive (Eagly et al., 2000; Murray, 2001). Society generally
encourages prejudice against women who have the qualities necessary to succeed as
conventional entrepreneurs (aggression, competitiveness, ambition, control, etc.). In contrast, it
encourages some women to have communal traits associated with social entrepreneurship
(gentleness, sensitivity, affection, sympathy, etc.) through the use of myths (Eagly and Karau,
2002). Although these factors do not include all antecedents impacting the creation of new
ventures, extant literature suggests that these constructs will be highly influential (Langowitz and
Minniti, 2007).
Generally, gender stereotyping of entrepreneurial activity contributes to women’s lower
intentions (Gupta et al., 2009; Gupta et al., 2008; Gupta et al., 2014) and lower likelihood of
starting their own business (Langowitz & Minniti 2007). There is also information about
strategies that some women enact to help overcome their lower perceived legitimacy as
entrepreneurs (Alsos & Ljunggren 2017; Swail & Marlow, 2018), as well as whether and how
gender stereotypes shape founding teams (Ruef et al., 2003; Yang & Aldrich, 2014; Jung et al.,
2017). Finally, the implications of gender-based stereotyping for resource acquisition are well-
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documented. Multiple studies illustrated the primarily negative consequences of such biases for
the financing of women-founded ventures (for theory see Tonoyan & Strohmeyer, 2021; for
empirical evidence see Alsos & Ljunggren, 2017; Eddleston et al., 2016; Kanze et al., 2018;
Kanze et al., 2020; Malmström et al., 2017; Thébaud, 2015). Emergent work has also revealed
similar differentials with respect to network contact formation (Abraham, 2020), human resource
attraction (Tonoyan & Strohmeyer, 2021), and treatment by other stakeholders such as
employees, customers, and suppliers (Gupta et al., 2014; Jones & Clifton, 2018; Tak et al., 2019).
A vast majority of studies have also focused on gender disparities in early investment
(e.g., Brush et al., 2003; Canning et al., 2012; Coleman & Robb, 2009; Gatewood et al., 2003;
Greene et al., 2003; Sørensen & Sharkey, 2014), suggesting that women are much less likely
than men to obtain external capital from investors (Brush et al., 2003; Canning et al., 2012;
Greene et al., 2003; Gatewood et al., 2003). According to a study by Guzman and Kacperczyk
(2019), females are 63% less likely than males to obtain external financing in terms of risk
capital, and the most significant part of the gap derives from differences in gender.
Literature shows that entrepreneurs are described as aggressive and with high-risk
proclivities (Bird & Brush, 2002), as well seem more socially inclined to achieve and obtain
economic benefits, an image which does not fit in women (Ahl, 2004; Dileo & Pereiro, 2019),
who seem closer to care and the emotional sphere, therefore, in pursuit of social value
(Hechevarría et al., 2012; Urbano Pulido et al., 2014). Additionally, in an analysis aimed to
investigate how academics contribute to perpetuating stereotypes about female entrepreneurship,
Ahl (2004) noted that in all the texts reviewed, women entrepreneurs were considered secondary
to men. The reason for this negative representation remains the subject of international debate,
for which there are no common results. This stereotyped and male-centered vision discourages
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some women from participating in business activities, which could also have a consequence on
people who interact with women at the community level, creating an additional barrier
(Langowitz & Morgan, 2003). The results of the systematic analysis conducted by Sullivan and
Meek (2012) suggested that the attributions of society and the different socialization processes
relating to men and women may create obstacles for women due to the unequal distribution of
assets and services, educational objectives and daily life activity expectations. In many countries,
society usually expects women to remain at home to take care of their families; therefore, they
bear a disproportional part of childcare (Bruin et al., 2006)
Social and Cultural Norms
Culture greatly influences the way in which entrepreneurs develop their business
initiatives, referring to prejudices, social roles and a stereotyped vision of the gender (for
example, women are seen as incompatible with the business because it is too emotional and less
rational in making decisions) that contribute to a men-centered vision of entrepreneurship
(Rubio-Bañón & Esteban-Lloret, 2016; Shinnar et al., 2012). Similarly, Hoyt and Murphy (2016)
conclude that the prejudices women face in business are the result of gender stereotypes. These
factors related to a country’s different perceptions of the role of women in society, explain that
the differences concern attitudes toward entrepreneurship, but also some psychological traits that
influence entrepreneurial intention: higher levels of self-efficacy, self-confidence, independence,
risk appetite, and autonomy in men compared to women (Langowitz & Minniti, 2007; Robb &
Watson, 2012).
In addition, women compared to the male counterpart, to a greater extent reject the choice
of an entrepreneurial career because they consider themselves as lacking in entrepreneurial skills
and knowledge (Wilson et al., 2007; Kirkwood, 2009) and unable to respond to the challenges of
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a company as it is not very socialized in corporate roles (Yordanova & Tarrazon, 2010).
Ultimately, these studies show how gender roles could influence the types of careers deemed
acceptable for women, further increasing gender differences (Griffiths et al., 2013; Kalafatoglu
& Mendoza, 2017).
The cluster analysis reveals that entrepreneurial education is closely linked to culture and
gender differences and serves as a potential tool for increasing entrepreneurial intentions and
narrowing the gap between men and women. This occurs in the company’s consolidation and
start-up phases (Mazzarol et al., 1999; Rotefoss & Kolvereid, 2005). A country that promotes
entrepreneurial educational initiatives, encourages women’s participation in entrepreneurship and
reduces the woman-man gap (Petridou et al., 2009).
Conclusion
The literature review commences by utilizing Urie Bronfenbrenner’s theoretical model
(1995) to interpret the challenges The literature review scrutinizes the gender disparities in the
field of social entrepreneurship, from its historical development to current theories. The literature
review ends by investigating multiple elements that shape attitudes, including surrounding
environments, educational networks, industry ties, and societal norms.
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Chapter Three: Methodology
This study aims to examine factors that shape and influence attitudes towards female
social entrepreneurship. Chapter Two documented previous research on these influences. The
conceptual framework illustrated in Figure 2 encompasses these influences, which stems from
Bronfenbrenner’s socio-ecological model (Bronfenbrenner, 1979). This section begins by
reasserting the study’s main research questions, outlines the specific demographics of the
participants and describes the methods used in collecting and analyzing data.
Research Questions
The following questions guide the study:
1. According to public perceptions, to what extent does socioeconomic status
(education, income, and occupational/social status) influence attitudes about female
social entrepreneurship (microsystem)?
2. According to public perceptions, to what extent does the presence of mentors and
participation in professional organizations influence attitudes about female social
entrepreneurship (mesosystem)?
3. According to public perceptions, to what extent does mass and social media exposure
influence attitudes about female social entrepreneurship (exosystem)?
4. According to public perceptions, to what extent does the expected role of women in
society influence attitudes about female social entrepreneurship (macrosystem)?
5. To what extent do demographic variables (gender, age, ethnicity, race, education,
family’s average annual income, current annual income, marital/relationship status,
family’s annual income, employment status and geographical location) relate to
attitudes around female social entrepreneurship?
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Overview of Design
The research design incorporates quantitative research methods. Quantitative research is
an approach that tests objective theories by measuring and analyzing numerical data to identify
patterns and relationships among variables (Creswell & Creswell, 2018). This approach aligns
with the purpose of the study as it allows for the collection of numerical data and statistical
analysis to investigate the extent to which various systems (microsystem, mesosystem,
exosystem, and the macrosystem) influence attitudes towards female social entrepreneurship.
The goal is to administer a survey to a sample of people to describe the population’s attitudes,
opinions, behaviors, or characteristics of the population (Creswell, 2014). The study recruited
participants with diversity in age, gender, income, social/occupational status, geographical
location, and racial/ethnic identity to examine their attitudes regarding female social
entrepreneurship.
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Table 1
Data Sources
Research questions Quantitative method
RQ1: According to public perceptions, to what extent
does socioeconomic status (education, income, and
occupational/social status) influence attitude about
female social entrepreneurship (microsystem)
X
RQ2: According to public perceptions, to what extent
does the presence of mentors and participation in
professional associations influence attitude about
female social entrepreneurship (mesosystem)?
X
RQ3: According to public perceptions, to what extent
does exposure to mass and social media influence
attitude about female social entrepreneurship
(exosystem)?
X
RQ4: According to public perceptions, to what extent
does the expected role of women in society influence
attitude about female social entrepreneurship
(macrosystem)?
X
RQ5: To what extent do demographic variables
(gender, age, race/ethnicity, highest level of education,
parent’s highest level of education, current annual
income, family’s annual income, relationship status,
children, employment status and geographic location)
relate to attitudes around female social
entrepreneurship (attitudes)?
X
Research Setting
To understand public attitudes toward female social entrepreneurship, the study benefited
from using a survey design approach to gather insightful data. A survey design provides a
quantitative description of trends and attitudes of a population, or tests for associations between
variables (Creswell & Creswell, 2018). A survey method was a preferred approach for this study
given the geographic distribution of residents throughout the United States and the time
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constraints of participants, as many residents have competing priorities. Given these constraints,
the participants will receive the survey electronically. The survey is cross-sectional, meaning that
the data collected at one point in time (Creswell & Creswell, 2018). The survey is distributed via
email because eligible participants are located throughout the United States, and most of the
population uses the Internet. To facilitate data collection, the researcher used the survey program
Qualtrics. The researcher will house the response data in the University of Southern California’s
Google Drive database to ensure secure data storage.
The Researcher
As a female social entrepreneur conducting a study on the underrepresentation of women
in social entrepreneurship, my identity as a practitioner in this field introduced potential biases.
Having navigated the challenges and opportunities in social entrepreneurship, my experiences
may have led to an inclination towards inflating the gender gap and systemic barriers faced by
other women. To mitigate this bias, I adopted a reflexive stance, critically reflecting on my
assumptions, and actively seeking diverse perspectives during data collection and analysis.
Additionally, as a non-Western immigrant woman of Middle Eastern descent in the United
States, my positionality could have influenced the data collection process. To address this, I
employed reflexive sociology approach, scrutinizing my biases and acknowledging my
privileges to ensure an objective and comprehensive exploration of factors contributing to gender
inequity in social entrepreneurship (Bourdieu, 1989; Secules et al., 2021).
Data Sources
This study utilized one main data source, namely the census-level survey posted online
via Amazon Mechanical Turk (MTurk), LinkedIn and Facebook group networks. In addition to
those three channels, personal networks and word of mouth proved beneficial for participant
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recruitment. This study aims to achieve a diverse and representative sample by employing a
multi-platform approach for participant outreach.
Recruitment and collection of data began as a census-level online survey posted and
advertised through LinkedIn, Facebook and MTurk sites in fall of 2023. The research project
utilized convenience sampling, inviting all United States residents 18 years of age and over (N =
497) to participate in the study based on availability (Creswell & Creswell, 2018; Pazzaglia et
al., 2016). According to Pazzaglia et al. (2016), a large participant pool enables a greater number
of respondents and increases the likelihood of generalizing results to the overall population.
Because the survey is open to all United States residents in 50 states, the participants are in
various geographic regions. As a result, the researcher communicated with the participants
electronically.
Participants
Defining the characteristics of participants is critical to meeting research demands
(Creswell & Creswell, 2018). As the research study aimed to evaluate factors that affect the
general public’s attitudes towards female entrepreneurship, the research project includes 497
residents residing in the United States in 2024. Participants represent a wide variety of race,
class, ethnicities, socio-economic status, culture, and other social factors for generalizability
purposes. These participants are best situated to shed light on how their socio-ecological systems
impact their attitudes around female social entrepreneurship. Appendix A highlights the
recruitment email and social media postings.
Instrumentation
In this study, the researcher developed a new scale due to the absence of an existing scale
for measuring attitudes towards social entrepreneurship and limited literature. The first part of
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the questionnaire obtains information on socio-economic and demographic factors of participants
which contains 11 items namely, gender, age, ethnicity, race, education, family’s average annual
income, current annual income, marital/relationship status, family’s annual income, employment
status and geographical location. The second part of the instrument poses 24 inquiries addressing
five independent variables: (a) three attitude items (ATT), (b) three microsystem items (SES), (c)
six mesosystem items (CN), (d) six exosystem items (MS) and (e) six macrosystem items (RN)
to examine its impact on attitudes towards female social entrepreneurship. In this study, the
researcher developed a new scale due to the absence of an existing scale for measuring attitudes
towards social entrepreneurship and limited literature, with the notable exception of an industry
report (Cherie Blair Foundation for Women, 2023). Appendix B presents the survey questions
used in this particular study of participants. To validate the reliability of the newly created scale,
the researcher employed Cronbach’s alpha, a widely recognized measure of internal consistency.
This approach ensured that the scale is both reliable and tailored to the specific constructs of
interest within the domain of social entrepreneurship. The survey includes nominal, ordinal and
open-ended questions, providing various types of measurement to assess the research questions
(Robinson & Leonard, 2019). The ordinal questions reflect verbal labels on all scale points to
mitigate participants with extreme response styles (Robinson & Leonard, 2019). The survey
measured attitude items using 4-point Likert scale categories (Likert, R., 1977): strongly agree
(SA) = 4, agree (A) = 3, disagree (D) = 2 and strongly disagree (SD) = 1. Likert scale has been
the most popular technique to measure attitude (Fabrigar et al., 2005). The even-numbered, 4-
point scale was used to remove the middle choice and to impose a forced-choice response,
essentially removing the “middle-of-the-road cop-outs” (Kurpius & Stafford, 2006, p. 8;
DeVellis, 1991, p. 199). Thus, the absence of a midpoint or “neutral” option ensured that
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participants provide an opinion regarding the influences that affected their viewpoints on female
social entrepreneurship. The researcher created composite scales by overarching items. Chapter
Four provides the coefficient alphas for the composite items. The researcher scaled items so that
high numbers indicate a high influence of socio-economic status, connection and network, mass
media and social media, and gender roles and norms.
Data Collection Procedures
Participants received a potential incentive to participate in the study. Participants who
completed the entire survey were entered in a drawing to receive one of ten $25 Amazon gift
cards. The incentive aligns with other surveys on MTurk and helps encourage response rates
(Robinson & Leonard, 2019). Participants receive the opportunity to complete the survey over
eight weeks to enable time amidst competing priorities. The researcher administered the survey
during a time of year with the least potential conflicts for participants, avoiding summer season.
The months of November and early December provide optimal timing for data collection. The
survey took approximately 10-15 minutes to complete. The brevity of the protocol counteracted
the potential for participants to develop survey fatigue (Robinson & Leonard, 2019) and peer
review and pilot testing ensured that questions are concise, understandable, and manageable on a
mobile device platform.
Validity and Reliability
To maximize the validity and reliability of a quantitative study, the researcher should pay
careful attention to the research procedures, the data collection, and the data analysis (Merriam &
Tisdell, 2016). To safeguard the validity of the research, the study encompasses a large
population (N = 260,836,730), providing an increased likelihood of respondents to generate a
subsequent confidence level of 95% or greater with a margin of error less than 5% (Johnson &
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Christensen, 2014; Pazzaglia et al., 2016). To meet this objective, 385 participants needed to
respond to the survey for a total response rate of 0.000147%. However, the study garnered more
responses, totaling 497 respondents. Additionally, the participants are individuals between 18
and 65 years with a basic understanding of the term social entrepreneurship, which ensured that
the participants’ experiences align to the research questions (Pazzaglia et al., 2016). The
researcher facilitated content validity checks by providing the potential survey questions to peers
and professors and soliciting feedback in advance of the study (Salkind, 2014). The survey was
also pretested with individuals outside of the potential participant pool.
To strengthen reliability, the researcher standardized the methods of communication with
participants by corresponding through email and sending the survey electronically consistently
across the population. To optimize response rates, the initial email emphasizes the value of the
survey and enable confidential responses to increase participants’ willingness to participate
(Pazzaglia et al., 2016). The documentation provides transparency of the research steps, and
illustrates the study limitations (Creswell & Creswell, 2018). The researcher will use a statistical
analysis computer program such as IBM® SPSS® Statistics 28 to analyze the data (Creswell &
Creswell, 2018). The analysis includes evaluation of individual questions for internal consistency
reliability whenever possible. Researchers use this process to gauge whether specific items are
consistent with one another (Salkind, 2014). These steps increased the likelihood of generalizing
results to other settings (Creswell & Creswell, 2018).
Ethics
The researcher has underlying ethical considerations involving multiple constituents.
Foremost, the research serves the interest of social entrepreneurship, as the research may reveal
new learnings regarding the public’s attitudes. The interests of this research serve to address
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gender inequality, economic growth by inclusion as well as advancement of social justice. The
beneficiaries of the research would be women who aspire to become social entrepreneurs, as well
as policymakers, organizations, and communities interested in promoting gender equality,
economic growth and innovation, and sustainable development. The research may also benefit
participants as they reflect upon their experiences and gain personal insight. Researchers must
ensure several ethical practices (Glesne, 2011). Prior to conducting research, the researcher
submitted the proposal to the University of Southern California (USC) Institutional Review
Board (IRB). The IRB review process ensured that researchers utilize appropriate measures to
minimize potential risks to participants (Creswell & Creswell, 2018).
When conducting a study, researchers should demonstrate beneficence, protecting
participants from harm and safeguarding participant privacy whenever possible (Merriam &
Tisdell, 2016). Study participants should have informed consent, including voluntary
participation, and the flexibility to withdraw from the study without penalty at any time
(Creswell & Creswell, 2018; Glesne, 2011). For this study, participants did have access to these
important voluntary options.
Given the researcher’s role as a part-time faculty member at USC, it was important to
provide participants transparency of the researcher’s role and assure participants their
confidentiality of responses. The selection of non-USC students, or alumni, rather than current
students, helped mitigate perceived power dynamics. Consequently, participants received
assurance of the anonymity of individual responses in the form of aggregate data in its secure
database. Finally, participants received the option to view the aggregate findings (Creswell &
Creswell, 2018).
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Chapter Four: Results and Findings
This section reviews the preliminary analysis, general results analysis and findings
associated with this study. The findings presented in this chapter are organized by research
question, followed by a summary of findings, which are also organized by research question.
This quantitative research aimed to examine factors that shape and influence the general public’s
attitudes towards female social entrepreneurship by leveraging Bronfenbrenner’s socioecological systems theory. According to Bronfenbrenner (1979), there are five systems within a
person’s ecological environment. The five systems identified by Bronfenbrenner (1979) include
the microsystem, mesosystem, exosystem, macrosystem and chronosystem. However, this study
excluded one of the five systems - "chronosystem". The researcher deemed the chronosystem,
which emphasizes the role of time in development, less relevant for examining the public’s
attitudes towards female social entrepreneurs. For this study, social entrepreneurs are defined as
individuals who start new organizations with the goal of solving social and environmental needs
(Terjesen, 2017). The following five research questions guided this study:
1. According to public perceptions, to what extent does socioeconomic status
(education, income, and occupational/social status) influence attitudes about female
social entrepreneurship (microsystem)?
2. According to public perceptions, to what extent does the presence of mentors and
participation in professional organizations influence attitudes about female social
entrepreneurship (mesosystem)?
3. According to public perceptions, to what extent does mass and social media exposure
influence attitudes about female social entrepreneurship (exosystem)?
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4. According to public perceptions, to what extent does the expected role of women in
society influence attitudes about female social entrepreneurship (macrosystem)?
5. To what extent do demographic variables (gender, age, ethnicity, race, education,
family’s average annual income, current annual income, marital/relationship status,
family’s annual income, employment status and geographical location) relate to
attitudes around female social entrepreneurship?
Preliminary Analysis
As shown in Table 2 below, all four scales have an acceptable level of reliability as
measured by their Cronbach’s alpha coefficient (α) of .65 or greater. The questionnaire
(Appendix B) used in this study comprised three attitude (ATT) items for Female Social
Entrepreneurship (FSE) namely knowledge of FSE (ATT1), belief in FSE being an economic
driver (ATT2), and unique challenges faced in FSE (ATT3) and four scales: socioeconomic
status (SES), connections and network (CN), media and social media (MS), and roles and
expectations (RE). These scales correspond to Research Questions 1 through 4. For each of the
scales, an item analysis was performed, including reliability analysis, and descriptive statistics
were generated that included the scale mean and standard deviation using IBM® SPSS®
Statistics 28 statistical software. Cronbach’s alpha coefficient (α) was computed for each scale to
verify the internal consistency of the survey instrument. As shown in Table 2, all scales have an
acceptable level of reliability as measured by their Cronbach’s Alpha at or above .70, except one.
The composite of the SES had a α <0.70; however, this was judged not to be low enough to
exclude from the findings. Specifically, the SES subscale, which includes four items, recorded
reliability of α = .65. The CN subscale, with six items, achieved reliability of α =.73; the MS
subscale, also with six items, reported a reliability of α =.70; and the RE subscale, comprising six
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items, showed the highest internal consistency with a reliability of α =.77. Removing any of the
25 items would have reduced reliability. Thus, all 22 items measuring SES, CN, MS and RE were
included in the findings.
Table 2
Scale Reliabilities of Five Constructs: ATT, SES, CN, MS and RE
Factor and Item M SD Corrected Item
Total Correlation
Cronbach’s Alpha if Item
Deleted
Cronbach’s Alpha
SES1 3.22 0.76 0.48 0.53
SES2 3.17 0.77 0.49 0.56 0.65
SES3 3.31 0.75 0.48 0.57
SES4 3.20 0.73 0.26 0.65
CN1 3.34 0.69 0.41 0.70
CN2 3.38 0.67 0.42 0.70
CN3 3.35 0.66 0.40 0.71 0.73
CN4 3.33 0.66 0.57 0.66
CN5 3.38 0.71 0.53 0.67
CN6 3.35 0.66 0.43 0.70
MS1 3.32 0.73 0.27 0.70
MS2 2.99 0.66 0.42 0.66
MS3 3.23 0.72 0.53 0.62 0.70
MS4 3.10 0.74 0.40 0.67
MS5 3.27 0.78 0.51 0.63
MS6 3.35 0.67 0.44 0.65
RE1 3.08 0.64 0.57 0.72
RE2 3.25 0.76 0.50 0.73
RE3 3.20 0.74 0.53 0.73 0.77
RE4 3.35 0.73 0.52 0.73
RE5 3.16 0.80 0.57 0.71
RE6 3.35 0.66 0.36 0.77
SES = Socioeconomic Status; CN = Connections & Network; MS = Media & Social Media; RE = Roles & Expectations
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Participants
The population of this study was the public over the age of 18 years in the United States.
The researcher collected a total of 497 responses, far exceeding the minimum participant
threshold (N = 385) recommended by Creswell and Creswell (2018) and Pazzaglia et al. (2016)
for enhancing the potential of generalizing descriptive findings to the larger population. The
study’s recruitment strategy and data collection started on November 6, 2023 and ran for eight
weeks. The researcher designed it to be inclusive, extending an open invitation to all residents of
the United States aged 18 and above who had access to email. The survey’s reach spanned all 50
states, tapping into a diverse demographic and ensuring a comprehensive geographical
representation. The researcher exported data from Qualtrics to SPSS for analysis. The researcher
cleaned the data by removing 6 blank surveys in preparation for analysis. The table below
summarizes the demographic characteristics of the study’s participants, presenting descriptive
statistics based on their responses to eleven demographic questions. The researcher categorized
all demographic variables into two subgroups to simplify the analysis, as displayed in Table 3.
Appendix C contains the full table.
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Table 3
Demographic Variables of the Sample (n) and Total Population (N=497)
Variable N %
Gender 465 100%
Female 316 68%
Male 149 32%
Age 445 100%
28 or Younger 230 52%
29 or Older 215 48%
Race/Ethnicity 457 100%
Non-White 169 37%
White 288 63%
Highest Level of Education 459 100%
Bachelor-Doctorate 181 39%
Less than Bachelor 278 61%
Parents Highest Level of Education 460 100%
Bachelor-Doctorate 130 28%
Less than Bachelor 330 72%
Current Annual Income 364 100%
$41K or more 207 57%
Less than $41K 157 43%
Family’s Annual Income 363 100%
$50K or more 316 87%
Less than $50K 47 13%
Relationship Status 456 100%
Committed Relationship 309 68%
Not in a Relationship 147 32%
Children 454 100%
No 293 65%
Yes 161 35%
Employment Status 459 100%
Exempt (Exec-Sup) 217 47%
Unemployed-Hourly-Mid 242 53%
Geographic Location 465 100%
Northeast and Midwest 161 35%
Southeast-Southwest-West 304 65%
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In terms of gender, the participants include more females (n = 316, 64.4%) than males (n
= 149, 30.3%) in the participants. A smaller portion of the sample, consisting of two participants
(0.40%), indicated they were non-binary. In contrast, a further one participant (0.20%) declined
to answer the gender question and one participant (0.20%) responded otherwise.
In terms of age, the distribution among participants is diverse. Appendix C shows the
entire table. The largest segment falls within the age range of 25 to 29, with 154 individuals,
accounting for approximately 31.3% of the total sample. Participants aged 30 to 34 follow, with
130 individuals making up 26.2% of the sample. Participants aged 22 to 24 also represent a
significant portion, with 63 individuals (12.8%). Beyond the early 30s, participants' numbers
gradually decline, with fewer individuals in their mid to late 30s and beyond. Notably,
participants aged 18 to 21 represent a substantial portion (n = 33, 6.6%), indicating a strong
presence of younger individuals. The sample includes participants aged 45 and older, though
their representation is smaller, comprising only 2.4% of the total sample
In terms of ethnicity, the largest portion of the participants identified as White (not
Hispanic) (n = 288, 58.66%), followed by individuals of Asian Origin (n = 66, 13.44%), and then
Hispanic n = 41, 8.35%) as displayed in Appendix C. African American or Black participants
were also represented (n = 29, 5.91%), along with American Indian or Alaskan Native (n = 27,
5.50%). The least represented were Native Hawaiian or Other Pacific Islander (n = 6, 1.22%) and
those who selected ‘Other’ or preferred not to answer, each comprising a smaller fraction of the
sample (n = 4 and 6, respectively, both at approximately 0.81% and 1.22%) (Appendix C).
Regarding the highest level of education among participants (Appendix C), the data
reveals that the majority held a bachelor’s degree (n = 102, 20.8%), followed by those with some
college experience (n = 96, 19.6%), and individuals with a high school diploma or equivalent (n
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= 81, 16.5%). Seventy participants (14.3%) held a master’s degree, while forty-four participants
(9.0%) reported holding associate degrees and fifty-six participants (11.4%) reported having
trade/technical school certificates. Fewer participants held a Doctoral or professional degree (n =
9, 1.8%), and only 1 participant (0.2%) had not graduated from high school. A small number
preferred not to answer (n = 2, 0.4%).
For the participants' parents' highest level of education, 209 respondents (42.6%) most
frequently reported 'some college.' Participants also reported their parents having associate
degrees (n = 54, 11.0%), bachelor’s degrees (n = 73, 14.9%), and master’s degrees (n = 45,
9.2%). Twelve respondents (2.4%) reported their parents holding doctoral or professional
degrees. Participants also indicated that their parents had a high school diploma or equivalent (n
= 25, 5.1%) or a trade/technical school certificate (n = 27, 5.5%). Furthermore, fifteen parents
(3.1%) did not graduate from high school, and a small number (n = 6, 1.2%) preferred not to
answer (Appendix C).
Regarding current annual income, the distribution among participants shows a wide range
(Appendix C). The largest segment falls within the $30,000 to $49,999 range, comprising 127
individuals, which accounts for approximately 31.0% of the total sample. This category is
followed by participants earning between $50,000 and $69,999, with 74 individuals making up
18.1% of the sample. Participants with incomes from $0 to $9,999 represent 13.7%, while those
earning between $10,000 and $29,999 account for 13.0%. Participants earning between $70,000
and $99,999 make up 10.0% of the sample. Finally, participants with incomes of $100,000 and
above comprise 14.2% of the total sample, indicating a diverse range of income levels among the
participants.
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In terms of family annual income, the distribution among participants shows a wide range
as well (Appendix C). The largest segment falls within the $60,000 to $79,999 range, comprising
131 individuals, which accounts for approximately 33.7% of the total sample. This category is
followed by participants with incomes of $100,000 and above, with 97 individuals making up
25.0% of the sample. Participants earning between $80,000 and $99,999 represent 13.6%, while
those earning between $40,000 and $59,999 account for 13.4%. Participants with incomes from
$0 to $19,999 make up 10.8%, and those earning between $20,000 and $39,999 account for
3.6%. This distribution indicates a diverse range of family income levels among the participants.
Regarding relationship status (Appendix C), the majority of participants reported being
married (n = 272, 55.4%), followed by those who reported being single (n = 130, 26.5%), in a
committed relationship (n = 37, 7.5%), divorced (n = 17, 3.5%), and a smaller percentage who
reported being widowed (n = 4, 0.8%). A minority of participants chose not to disclose their
relationship status (n = 4, 0.8%).
Concerning children, a significant proportion of participants reported not having children
(n = 293, 59.7%), while 161 participants (32.8%) reported having children. There were also two
participants (0.4%) who provided different responses under ‘Other’, and notably, 6 participants
(1.2%) preferred not to answer.
Regarding employment status, the largest group consisted of those at the
Managerial/Supervisor Level (n = 199, 40.5%), followed by Mid-Level professionals (n = 84,
17.1%). The sample also included Hourly/Full-Time Employees (n = 54, 11.0%), Hourly/PartTime Employees (n = 46, 9.4%), and those who were unemployed (n = 58, 11.8%). A smaller
number of participants reported being at the Executive Level (n = 18, 3.7%) or identified as
Homemakers (n = 4, 0.8%).
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Geographically, most participants reported being located in the West (n = 145, 29.5%),
followed by those in the Mid-West (n = 96, 19.6%), Southeast (n = 78, 15.9%), Southwest (n =
81, 16.5%), and Northeast (n = 65, 13.2%).
Descriptive Statistics
This section presents the descriptive statistics for data on participants' attitudes towards
female social entrepreneurship. Descriptive statistics summarize the main features of the data,
capturing participants' responses effectively. The analysis covers the three items i.e. dependent
variables in this study: familiarity with the concept of female social entrepreneurship (ATT1),
perception of female social entrepreneurs' role in economic growth and innovation (ATT2), and
belief in unique challenges faced by female social entrepreneurs compared to males (ATT3).
Examining the response distribution across these items highlights overall trends and central
tendencies within the sample. Participants recorded a total of 465 responses for each item out of
497 possible responses, providing a robust dataset for analysis.
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Table 4
Descriptive Statistics of Attitude Variables of the Sample (n)
Variable N %
ATT1 (Knowledge) 465 100%
Strongly disagree 7 1.5%
Disagree 22 4.8%
Agree 207 44.7%
Strongly agree 227 49.0%
ATT2 (Belief in Economic Driver) 465 100%
Strongly disagree 7 1.5%
Disagree 47 10.1%
Agree 159 34.2%
Strongly agree 252 54.2%
ATT3 (Unique Challenges) 465 100%
Strongly disagree 9 2.0%
Disagree 63 13.9%
Agree 200 44.1%
Strongly agree 182 40.1%
Note: N=497
As Table 4 demonstrates, the first item (ATT1) measured participants’familiarity with the
concept of female social entrepreneurship. The total number of responses was 465 out of 497.
The distribution of responses showed a strong inclination towards familiarity, with a near
majority of 49.0% (227 responses) strongly agreeing and 44.7% (207 responses) agreeing that
they are familiar with female social entrepreneurship. Only a small fraction disagreed (4.8%, 22
responses) or strongly disagreed (1.5%, 7 responses).
For the second item (ATT2), which assessed whether participants view female social
entrepreneurs as essential for driving economic growth and innovation, participants again
recorded 465 responses. A majority viewed them as essential, with 54.2% (252 responses)
strongly agreeing and 34.2% (159 responses) agreeing. Fewer participants were less supportive,
with 10.1% (47 responses) disagreeing and 1.5% (7 responses) strongly disagreeing.
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The third item (ATT3) focused on whether participants believe female social
entrepreneurs face unique challenges compared to their male counterparts. Here, participants
provided slightly more balanced responses but still leaned towards agreement: 40.1% (182
responses) strongly agreed, and 44.1% (200 responses) agreed, making up a substantial majority.
A smaller percentage disagreed (13.9%, 63 responses) or strongly disagreed (2.0%, 9 responses).
Table 5 presents the descriptive statistics of the three independent variables ATT1, ATT2,
and ATT3 by demographic categories: gender, age, race/ethnicity, education level, parental
education, income, family income, relationship status, children, employment, and geographical
location. For each group, the analysis provides the mean (M) and standard deviation (SD),
summarizing the data across various demographic groups.
Table 5
Descriptive Statistics of Attributes by Demographic Breakdown
ATT1 ATT2 ATT3
Demographic
Variable
Group
Comparison
n M SD n M SD n M SD
Gender Female
Male
316
147
3.44
3.36
.65
.67
316
147
3.44
3.36
.70
.78
305
149
3.25
3.17
.70
.78
Age 28 or Younger
29 or Older
227
214
3.48
3.33
.65
.67
228
214
3.47
3.34
.70
.78
227
204
3.30
3.12
.70
.78
Race/Ethnicity White
Non-White
285
166
3.42
3.40
.63
.70
287
166
3.43
3.39
.66
.83
276
166
3.30
3.09
.72
.81
Highest Level
of Education
Less than Bachelor
Bachelor-Doctorate
276
178
3.42
3.41
.60
.72
277
179
3.45
3.35
.68
.80
277
168
3.34
3.04
.64
.86
Parental Level
of Education
Less than Bachelor
Bachelor-Doctorate
329
127
3.43
3.38
.64
.70
330
128
3.43
3.36
.68
.84
321
126
3.33
2.95
.71
.79
Income Less than $41K
$41K or more
207
154
3.46
3.26
.61
.76
207
155
3.40
3.30
.70
.78
198
153
3.31
3.12
.71
.78
Family Income Less than $50K
$50K or more
47
314
3.68
3.40
.59
.61
47
316
3.43
3.37
.71
.71
46
306
3.46
3.20
.89
.71
Relationship No relationship
Relationship
47
314
3.68
3.40
.59
.61
47
314
3.68
3.40
.59
.61
47
314
3.68
3.40
.59
.61
Children No
Yes
287
161
3.46
3.36
.66
.65
287
161
3.46
3.36
.66
.65
287
161
3.46
3.36
.66
.65
Employment Unemployed-Exempt
Exempt (Exec-Sup)
239
216
3.34
3.49
.81
.62
239
216
3.34
3.49
.81
.62
239
216
3.34
3.49
.81
.62
Geographical
Location
Northeast & Midwest
Southeast/West & West
160
299
3.32
3.47
.73
.61
160
299
3.32
3.47
.73
.61
160
299
3.32
3.47
.73
.61
Note: N=497.
ATT1 = knowledge of female social entrepreneurship; ATT2 = belief in their impact as economic drivers; ATT3 = recognition of female social
entrepreneur’s unique challenges
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Table 5 shows small differences between females and males across all female social
entrepreneurship attributes, with females scoring slightly higher on average. The standard
deviations are similar, suggesting comparable variability within both groups. Second,
participants 28 or younger have higher mean scores for knowledge and belief in FSE as an
economic driver but show a decrease in the third attribute related to challenges faced. Older
participants (29 or older) score lower on all attributes. Third, white participants have marginally
higher mean scores on knowledge and belief in FSE as an economic driver and awareness of
unique challenges when compared to non-White participants (Table 5).
Next, those with less than a bachelor’s degree and those with a bachelor’s to doctorate
level have almost identical scores on knowledge of FSE. However, there is a noticeable
difference in the belief of FSE as an economic driver and challenges faced, with more educated
participants scoring lower on challenges faced. Similar to personal education level, parental
education shows a slight difference in knowledge of FSE and belief in its economic impact, with
higher scores for those whose parents have less than a bachelor’s degree. However, those whose
parents have a higher level of education perceive fewer challenges in FSE. Additionally,
participants with an income less than $41K score higher in knowledge and belief in FSE as an
economic driver but report more challenges compared to those earning $41K or more. Moreover,
the family income follows a similar pattern to personal income, with those from families earning
less than $50K scoring higher in knowledge and belief in FSE as an economic driver but
reporting more challenges than those from higher-earning families (Table 5).
The scores are the same across all attributes, indicating no observed effect of relationship
status on perceptions of FSE. Having children does not seem to affect the scores on any of the
attributes; both groups have identical means and standard deviations. Employed individuals score
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higher on all attributes compared to the unemployed or those exempt from employment. This
may indicate that employment status is positively associated with knowledge and perceptions of
FSE. Finally, participants from the Northeast and Midwest have lower mean scores across all
attributes compared to those from the Southeast, Southwest and West.
Table 6 shows the descriptive statistics for four independent variables by demographic
categories: gender, age, race/ethnicity, education level, parental education, income, family
income, relationship status, children, employment, and geographical location. The variables
analyzed include socioeconomic status (SES), connections and network (CN), media and social
media (MS), and roles and expectations (RE). The mean (M) and standard deviation (SD) are
provided for each group, summarizing the data for a total sample size of 497 participants.
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Table 6
Descriptive Statistics of Independent Variables by Demographic Breakdown
SES CN MS RE
Demographic
Variable
Group
Comparison n M SD n M SD n M SD n M SD
Gender Female
Male
315
149
3.25
3.17
.53
.56
316
149
3.35
3.39
0.44
0.41
316
148
3.23
3.18
0.46
0.41
316
149
3.27 0.46
3.15 0.53
Age 28 or Younger
29 or Older
228
213
3.27
3.17
.56
.51
228
214
3.43
3.27
0.41
0.45
227
214
3.26
3.16
0.41
0.48
228
214
3.29 0.48
3.18 0.49
Race/Ethnicity White
Non-White
286
166
3.32
3.09
.48
.58
287
166
3.37
3.34
0.40
0.49
286
166
3.25
3.14
0.39
0.51
287
166
3.23 0.40
3.23 0.59
Highest Level
of Education
Less than Bachelor
Bachelor-Doctorate
277
178
3.27
3.18
.47
.60
277
179
3.42
3.29
0.35
0.52
277
178
3.27
3.13
0.40
0.50
277
179
3.25 0.44
3.21 0.54
Parental Level
of Education
Less than Bachelor
Bachelor-Doctor
329
128
3.25
3.15
.54
.52
330
128
3.38
3.33
0.42
0.47
329
128
3.25
3.13
0.43
0.47
330
128
3.26 0.44
3.20 0.56
Income
Less than $41K
$41K or more
207
154
3.27
3.17
.50
.57
207
155
3.37
3.26
0.41
0.48
207
154
3.22
3.15
0.44
0.46
207
155
3.23 0.45
3.17 0.45
Family Income
Less than $50K
$50K or more
47
315
3.32
3.25
.68
.50
47
316
3.51
3.34
0.50
0.40
47
316
3.34
3.19
0.45
0.44
47
316
3.36 0.42
3.20 0.45
Relationship No Relationship
Relationship
144
308
3.11
3.29
.62
.48
144
309
3.37
3.36
0.47
0.40
144
308
3.11
3.26
0.49
0.41
144
309
3.16 0.61
3.26 0.40
Children No
Yes
289
160
3.19
3.32
.54
.51
289
161
3.35
3.40
0.42
0.44
288
161
3.20
3.25
0.45
0.43
289
161
3.23 0.51
3.27 0.44
Employment
Unemployed-Exempt
Exempt (Exec-Sup))
238
216
3.14
3.34
.60
.43
239
216
3.34
3.38
0.48
0.38
238
216
3.16
3.28
0.50
0.37
239
216
3.20 0.55
3.27 0.38
Geographical
Location
Northeast & Midwest
Southeast/West & West
161
299
3.17
3.27
.59
.50
161
300
3.26
3.42
0.47
0.40
161
299
3.11
3.28
0.49
0.41
161
300
3.12 0.46
3.30 0.49
Note: N=497
SES = Socioeconomic Status; CN = Connections & Network; MS = Media & Social Media; RE = Roles & Expectations
Building on the analysis of the three independent variables (ATT1, ATT2, ATT3) in
Table 5, Table 6 examines the four dependent variables—socioeconomic status (SES),
connections and network (CN), media influence (MS), and roles and expectations (RE). This
table provides the descriptive statistics for these variables, detailing their distribution and
assessing their effects across the same demographic groups.
The data in Table 6 shows that in terms of gender, females scored slightly higher than
males in the perceived influence of socioeconomic status and roles and expectations on attitudes
around female social entrepreneurship, with males showing higher scores on connection and
network. However, for mass and social media, both genders reported similar scores. Moving to
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age demographics, the younger cohort, those 28 or younger, generally scored higher in perceived
influence of socioeconomic status, connection and network, and roles and expectations, while the
older group, those 29 or older, had lower scores across the board. Racial and ethnic differences
revealed that white participants had higher scores in socioeconomic status and mass and social
media whereas non-white participants scored higher in connection and network. Interestingly,
both groups perceived roles and expectations equally. Educational background also influenced
the outcomes, with individuals holding less than a bachelor’s degree scoring slightly higher in
perceived influence of socioeconomic status. In contrast, those with a bachelor’s to doctorate
level had marginally higher scores in connection and network. Nonetheless, both groups shared
similar perceptions in mass and social media and roles and expectations. Parental education
levels mirrored this pattern to some extent, with children of parents holding less than a
bachelor’s degree scoring slightly higher in socioeconomic status. However, children of parents
with higher education levels noted higher scores in connection and network. Income levels also
played a role, with individuals earning less than $41K annually reporting higher socioeconomic
status and those earning more having higher scores in connection and network. This trend
continued with family income, where those from families earning less than $41K outscored their
counterparts in socioeconomic status, yet the latter group led in connection and network.
Relationship status introduced a unique dynamic, with individuals not in a relationship scoring
lower in both socioeconomic status and roles and expectations, while those in a relationship
scored comparably higher in connection and network.
Interestingly, having children seemed to elevate scores in socioeconomic status and
connection and network, with parents scoring higher than those without children. Employment
status also affected the scores; the unemployed or exempt group reported lower socioeconomic
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status, whereas the employed group, particularly those in exempt roles such as executive or
supervisory positions, scored higher in connection and network. Finally, geographical location
was a factor, with individuals from the Northeast and Midwest scoring lower in socioeconomic
status compared to those from the Southeast and Southwest and West, who reported higher
scores in connection and network.
Results Research Question One: Socioeconomic Status (Microsystem)
The first hypothesis predicts that individuals with higher levels of education, income and
occupational/social status hold more favorable perceptions towards female social
entrepreneurship compared to those with lower socioeconomic status (SES). Both descriptive
and correlational findings support hypothesis 1. First, the public strongly believes that SES
influences attitudes about female social entrepreneurship (mean = 3.22 on a 1-4 scale). Second,
attitudes about the influence of SES on female social entrepreneurship correlate positively with
knowledge about female social entrepreneurship, beliefs that female social entrepreneurship is
essential for driving economic growth and innovation, and beliefs that female social
entrepreneurs face unique challenges compared to their male counterparts. These findings for
Research Question 1 will be discussed in the section below.
As shown in Table 7, the public strongly believes that SES influences attitudes about
female social entrepreneurship (mean = 3.22 on a 1-4 scale). Mean scores for the composite
items of each independent variable—socioeconomic status, connection and network, mass and
social media, and roles and expectations, and each independent variable knowledge of female
social entrepreneurship, belief in FSE’s impact as an economic driver and recognition of female
social entrepreneur’s unique challenges—ranged from 1.00, suggesting strong disagreement, to
4.00, indicating strong agreement. A score of 1.00 implies respondents strongly disagree that the
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constructs influence attitudes toward female social entrepreneurship, while a score of 4.00
indicates strong agreement, highlighting the perceived impact of these constructs on fostering
attitudes toward female social entrepreneurship. Table 6 below presents the descriptive statistics
computed for the seven constructs as generated by the respondents to the survey for the
participants in this study (N = 497). Salkind and Frey (2020) articulated that descriptive statistics
describe a sample. Thus, these statistics are a way to describe and analyze self-reported results.
On a scale from one to four, the distribution was near normal for a sample of 497. Figures 3-9 are
a graphic reflection of the independent variables for the four constructs (SES, CN, MS and RE)
and three independent variables (ATT 1, ATT2 and ATT3) in Table 7.
Table 7
Descriptive Statistics of Four Scales and Three Independent Variables
SES CN MS RE ATT1 ATT2 ATT3
Mean
Std. Error of Mean
Std. Deviation
Variance
Skewness
St. Error of Skewness
Kurtosis
Std. Error of Kurtosis
Range
Minimum
Maximum
Sum
3.22
0.02
0.53
0.20
-0.74
0.11
0.20
0.20
3.00
1.00
4.00
483
3.35
0.02
0.44
0.20
-0.70
0.11
0.30
0.20
2.17
1.83
4.00
481
3.20
0.02
0.45
0.20
-0.60
0.11
0.00
0.20
2.33
1.67
4.00
477
3.23
0.02
0.49
0.20
-0.60
0.11
0.10
0.20
2.50
1.50
4.00
477
3.41
0.03
0.66
0.43
-1.00
0.11
1.24
0.23
3.00
1.00
4.00
452
3.41
0.03
0.73
0.53
-1.05
0.11
0.46
0.23
3.00
1.00
4.00
452
3.22
0.04
0.76
0.57
-0.67
0.11
-0.85
0.23
3.00
1.00
4.00
452
Note: ATT1 = Knowledge; ATT2 = Belief in Economic Driver; ATT3 = Unique Challenges; SES = Socioeconomic Status; CN =
Connections & Network; MS = Media & Social Media; RE = Roles & Expectations
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The data presented in Table 7 above and the histogram in Figure 3 below reveal that the
mean score for SES is 3.22 (SD = 0.47) on a scale from 1 to 4 (1=Strongly Disagree; 4=Strongly
Agree) which indicates a consensus among participants regarding its influence on attitudes
toward female social entrepreneurship.
Figure 3
Socioeconomic Status Frequency Table
Note: Distribution for socioeconomic status (n = 483, M = 3.22, SD = 0.53)
Second, attitudes about the influence of SES on female social correlate positively with
knowledge about female social entrepreneurship, beliefs that female social entrepreneurship is
essential for driving economic growth and innovation, and beliefs that female social
entrepreneurs face unique challenges compared to their male counterparts.
The researcher performed a Spearman rho nonparametric correlation analysis on the four
scales to establish the relationship between SES, CN, MS, RE and attitude toward female social
entrepreneurship. Table 8 below presents the correlations between four systems—socioeconomic
status (SES), connections and network support (CN), media and social exposure (MS), and role
expectations (RE)—and three dependent variables reflecting attitudes toward female social
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entrepreneurship. Knowledge of female social entrepreneurship (ATT1), belief in their impact as
economic drivers (ATT2), and recognition of their unique challenges (ATT3) show significant
correlations. This data suggests that higher socioeconomic status, supportive connections and
networks, exposure to mass and social media that portray female social entrepreneurs, and
progressive role expectations are all positively associated with more knowledge, belief in female
social entrepreneurship’s role as economic drivers and acknowledgment of the unique challenges
of female social entrepreneurship.
Table 8
Correlations between four systems and three dependent variables
SES CN MS RE
ATT1 Spearman rho .34* .41* .38* .40*
N 462 463 462 463
ATT2 Spearman rho .28* .36* .32* .47*
N 464 465 464 465
ATT3 Spearman rho .45* .40* .41* .36*
N 453 454 453 454
Note:*. Correlation is significant at the 0.05 level
ATT1 = Knowledge; ATT2 = Belief in Economic Driver; ATT3 = Unique Challenges; SES = Socioeconomic Status; CN =
Connections & Network; MS = Media & Social Media; RE = Roles & Expectations
The data analysis in Table 8 reveals a positive relationship between socioeconomic status
(SES) and attitudes towards female social entrepreneurship, as captured by the three attitudinal
measures—ATT1, ATT2, and ATT3. The Spearman rho values of .34, .28, and .45, respectively,
reach statistical significance at 0.05. Thus, higher SES is associated with more favorable
attitudes towards female social entrepreneurship. Based on these results, the null hypothesis
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(H10) that no significant positive relationship exists between socioeconomic status (SES)—
including education, income, and occupational/social status—and attitudes about female social
entrepreneurship is rejected. Therefore, the alternative hypothesis (H11) proposing a significant
positive relationship between socioeconomic status and attitudes about female social
entrepreneurship is not rejected.
Results Research Question Two: Connections and Network (Mesosystem)
The second hypothesis suggests that individuals with mentors and professional
organization alliances hold more favorable attitudes toward female social entrepreneurship
compared to those without mentors and professional organization alliances. Both descriptive and
correlational findings support hypothesis 2. First, the public strongly believes that connections
and network (CN) influences attitudes about female social entrepreneurship (mean = 3.35 on a 1-
4 scale). Second, attitudes about the influence of CN on female social entrepreneurship correlate
positively with knowledge about female social entrepreneurship, beliefs that female social
entrepreneurship is essential for driving economic growth and innovation, and beliefs that female
social entrepreneurs face unique challenges compared to their male counterparts. These findings
for Research Question 2 will be discussed in the section below.
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Figure 4
Connections and Network Frequency Chart
Note: Distribution for connection and network (n = 481, M = 3.35, SD = 0.44)
First, the analysis presented previously in Table 7 and Figure 4 below indicates that the
connections and network variable has an average of 3.35 (SD = 0.44) from 1 to 4 (1=Strongly
Disagree; 4=Strongly Agree), suggesting that participants mostly agreed that having connections
and a network influences attitudes towards female social entrepreneurship. Among the four
scales, ‘connection and network’ emerges as the most influential factor in shaping attitudes
toward female social entrepreneurship.
Second, Table 8 above presents the correlations between connections and network support
(CN) and three dependent variables reflecting attitudes toward female social entrepreneurship.
Significant correlations are observed with knowledge of female social entrepreneurship (ATT1),
belief in their impact as economic drivers (ATT2), and recognition of their unique challenges
(ATT3). The data in Table 8 demonstrates that connection and network (CN) have a significantly
positive effect on attitudes towards female social entrepreneurship. This is evidenced by
Spearman correlation (rho) coefficients of .41 for ATT1, .36 for ATT2, and .40 for ATT3, each
statistically significant at the 0.05 level. Individuals with more extensive and stronger networks
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tend to have more positive attitudes toward female social entrepreneurship. This data suggests
that supportive connections and networks is positively associated with more knowledge, belief in
their role as economic drivers and acknowledgment of the unique challenges of female social
entrepreneurship.
Based on the results in this section, the null hypothesis (H20) that there is no significant
positive relationship between connections and networks and attitudes about female social
entrepreneurship would be rejected. Therefore, the alternative hypothesis (H21) proposing a
significant positive relationship between connections and networks and attitudes about female
social entrepreneurship is not rejected.
Results Research Question Three: Mass and Social Media Exposure (Exosystem)
The third hypothesis proposes that increased exposure to mass and social media content
related to successful female social entrepreneurs will lead to more positive attitudes toward
female social entrepreneurship. Both descriptive and correlational findings support hypothesis 3.
First, the public strongly believes that mass and social media (MS) influences attitudes about
female social entrepreneurship (mean = 3.20 on a 1-4 scale). Second, attitudes about the
influence of MS on female social entrepreneurship are positively correlated with knowledge
about female social entrepreneurship, beliefs that female social entrepreneurship is essential for
driving economic growth and innovation, and beliefs that female social entrepreneurs face
unique challenges compared to their male counterparts. These findings for Research Question 3
will be discussed in the section below.
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Figure 5
Mass and Social Media Frequency Chart
Note: Distribution for media and social media (n = 477, M = 3.20, SD = 0.45)
First, based on Table 7 and Figure 5, the data analysis for mass and social media (MS)
reflects a mean score of 3.20 (SD = 0.45) on a scale from 1 to 4 (1=Strongly Disagree;
4=Strongly Agree). This mean score represents a consensus on mass and social media’s
influence on attitudes towards female social entrepreneurship, albeit slightly lower than the
influence perceived for connections and networks (CN), and socioeconomic status (SES).
Despite being slightly less influential than CN and SES, mass and social media still play a
considerable role in shaping perceptions and attitudes towards female social entrepreneurship.
Second, based on data from Table 8, the correlation between exposure to mass and social
media (MS) and the attitudinal measures ATT1 (knowledge of female social entrepreneurship,
ATT2 (belief in their impact as economic drivers, and ATT3 (recognition of their unique
challenges) shows a significant positive relationship. Spearman correlation coefficients of .38 for
ATT1, .32 for ATT2, and .41 for ATT3—all significant at the 0.05 level—indicate that the way
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female social entrepreneurs are depicted in mass and social media influences attitudes towards
female social entrepreneurship.
Based on these results, the null hypothesis (H30) that there is no significant positive
relationship between exposure to mass and social media and attitudes about female social
entrepreneurship would be rejected. Therefore, the alternative hypothesis (H31) proposing a
significant positive relationship between exposure to mass and social media and attitudes about
female social entrepreneurship is not rejected.
Results Research Question Four: Expected Role of Women (Macrosystem)
The fourth hypothesis posits that societal expectations regarding the role of women will
have a significant impact on attitudes, with more progressive societies likely to exhibit more
positive attitudes while more traditional societies may show less favorable attitudes towards
female social entrepreneurship. Both descriptive and correlational findings support hypothesis 4.
First, the public strongly believes that the expected role of women (RE) influences attitudes
about female social entrepreneurship (mean = 3.23 on a 1-4 scale). Second, attitudes about the
influence of RE on female social entrepreneurship are positively correlated with knowledge
about female social entrepreneurship, beliefs that female social entrepreneurship essential for
driving economic growth and innovation, and beliefs that female social entrepreneurs face
unique challenges compared to their male counterparts. These findings for Research Question 4
will be discussed in the section below.
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Figure 6
Roles and Expectations Frequency Chart
Note: Distribution for roles and expectations (n = 477, M = 3.23, SD = 0.49)
First, Table 7 and Figure 6, which present the findings on the expected role of women
(roles and expectations), show a mean score of 3.23 (SD = 0.49) on a scale from 1 to 4
(1=Strongly Disagree; 4=Strongly Agree). Second, the study reveals significant positive
correlations between the roles and expectations of women in society (RE) and attitudes toward
female social entrepreneurship, as assessed by three attitudinal measures: ATT1 (knowledge of
female social entrepreneurship), ATT2 (belief in their impact as economic drivers, and ATT3
(recognition of their unique challenges). Table 8 highlights Spearman correlation coefficients for
these variables measured at .40 for ATT1, .47 for ATT2, and .36 for ATT3, with each correlation
significant at the p < .05 level.
Based on these findings, the null hypothesis (H40) stating that the expected role of
women in society does not significantly influence attitudes about female social entrepreneurship
is rejected. Consequently, the alternative hypothesis (H41) suggesting that societal expectations
have a significant impact—with more progressive views correlating with more positive attitudes
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towards female social entrepreneurship, and more traditional views associating with less
favorable attitudes—is not rejected.
Results Research Question Five: Demographic Variables and Attitudes
Lastly, the fifth hypothesis examines how demographic factors impact individuals’
perceptions and support for female social entrepreneurship. Figures 7, 8, and 9 present the
findings regarding attitudes, including knowledge, belief in female social entrepreneurship as an
economic driver, and acknowledgment of unique challenges faced by females in this field. The
mean scores for these factors are 3.41 (SD = 0.66) for knowledge, 3.41 (SD = 0.73) for belief in
female social entrepreneurship as an economic driver, and 3.22 (SD = 0.76) for acknowledgment
of unique challenges females face on a scale from 1 to 4 (1=Strongly Disagree; 4=Strongly
Agree). These mean scores are high and suggest a consensus among respondents regarding the
knowledge of, belief in, and acknowledgment of the unique challenges female social
entrepreneurs face. Table 9 presents the correlation between demographic variables and the three
attitudes indicating a consensus among survey participants of the influence societal roles and
expectations on attitudes toward female social entrepreneurship.
Figure 7
Knowledge Frequency Chart (ATT1)
Note: Distribution for knowledge (n = 463, M = 3.41, SD = 0.66)
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Figure 8
Belief in Economic Driver Frequency Chart (ATT2)
Note: Distribution for belief in FSE as economic driver (n = 465, M = 3.41, SD = 0.73)
Figure 9
Unique Challenges Frequency Chart (ATT3)
Note: Distribution for unique challenges for females (n = 454, M = 3.22, SD = 0.76)
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Table 9
Independent Variables t-test Results with Equal Variances Not Assumed
Dependent
Variable
Demographic
Variable
Group
Comparison n M SD t df p d1
ATT1
Gender
Female
Male
316
147
3.44
3.36
.65
.67 1.17 461 .25 .12
ATT2 Female
Male
316
147
3.44
3.36
.70
.78 1.11 463 .27 .11
ATT3 Female
Male
305
149
3.25
3.17
.70
.78 1.08 452 .28 .11
ATT1
Age
28 or Younger
29 or Older
227
214
3.48
3.33
.65
.67 2.37 439 .02* .23
ATT2
28 or Younger
29 or Older
228
214
3.47
3.34
.70
.78 1.83 440 .07 .17
ATT3
28 or Younger
29 or Older
227
204
3.30
3.12
.70
.78 2.60 429 .01* .25
ATT1
Race/Ethnicity
White
Non-White
285
166
3.42
3.40
.63
.70 0.37 449 .71 .04
ATT2 White
Non-White
287
166
3.43
3.39
.66
.83 0.56 451 .58 .05
ATT3 White
Non-White
276
166
3.30
3.09
.72
.81 2.80 440 .01* .28
ATT1
Highest Level of
Education
Less than Bachelor
Bachelor-Doctorate
276
178
3.42
3.41
.60
.72 0.22 452 .82 .21
ATT2 Less than Bachelor
Bachelor-Doctorate
277
179
3.45
3.35
.68
.80 1.44 454 .15 .33
ATT3
Less than Bachelor
Bachelor-Doctorate
277
168
3.34
3.04
.64
.86 4.29 443 .00* .42
ATT1
Parental Highest
Level of Education
Less than Bachelor
Bachelor-Doctorate
329
127
3.43
3.38
.64
.70 0.69 454 .49 .07
ATT2 Less than Bachelor
Bachelor-Doctorate
330
128
3.43
3.36
.68
.84 0.93 456 .35 .10
ATT3
Less than Bachelor
Bachelor-Doctorate
321
126
3.33
2.95
.71
.79 4.92 445 .00* .52
ATT1
Income
Less than $41K
$41K or more
154
207
3.26
3.46
.76
.61 2.85 359 .01* -.30
ATT2 Less than $41K
$41K or more
155
207
3.30
3.40
.78
.70
1.25 360 .21 -.13
ATT3
Less than $41K
$41K or more
153
198
3.12
3.31
.78
.71
2.39 349 .02* -.26
ATT1
Family Income
Less than $50K
$50K or more
47
314
3.68
3.40
.59
.61 2.92 359 .00* .46
ATT2 Less than $50K
$50K or more
47
316
3.43
3.37
.71
.71 0.50 361 .62 .08
ATT3
Less than $50K
$50K or more
46
306
3.46
3.20
.89
.71 2.24 350 .03* .35
ATT1
Relationship Status
Not in a relationship
Relationship
47
314
3.68
3.40
.59
.61 -.47 449 .64 -.05
ATT2 Not in a relationship
Relationship
47
316
3.43
3.37
.71
.71 1.43 250 .17 .14
ATT3
Not in a relationship
Relationship
46
306
3.46
3.20
.89
.71 -1.06 243 .26 -.11
1
In this table, "d" represents Cohen’s d.
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70
ATT1
Children
No
Yes
287
161
3.46
3.36
.66
.65 1.50 446 .14 .15
ATT2 No
Yes
289
161
3.45
3.37
.70
.75 1.14 313 .25 .11
ATT3 No
Yes
279
160
3.24
3.24
.75
.74 0.13 335 .89 .01
ATT1
Employment
Status
Unemployed- Mid
Exempt (Exec-Sup)
238
215
3.35
3.47
.71
.59 -1.82 451 .07 -0.17
ATT2 Unemployed- Mid
Exempt (Exec-Sup)
239
216
3.34
3.49
.81
.62 -2.16 453 .03* -0.20
ATT3 Unemployed- Mid
Exempt (Exec-Sup)
228
216
3.14
3.31
.82
.67 -2.53 442 .01* -0.24
ATT1
Geographical
Location
Northeast & Midwest
Southeast/West-West
160
299
3.32
3.47
.73
.61 -2.34 457 .02* -0.23
ATT2 Northeast & Midwest
Southeast/West-West
161
300
3.37
3.44
.75
.72 -0.99 459 .32 -0.10
ATT3 Northeast & Midwest
Southeast/West-West
160
290
3.16
3.27
.78
.74 -1.48 448 .14 -0.15
Note: ATT1 = Knowledge; ATT2= Belief in Economic Driver; ATT3 = Unique Challenges
Attitudes toward females were generally more positive than toward males, yet
independent groups t-tests showed no statistically significant differences between genders in
familiarity with female social entrepreneurship (ATT1), belief in female social entrepreneurship
as an economic driver (ATT2), and beliefs about unique challenges faced by female social
entrepreneurs (ATT3) (p > .05).
Participants aged 28 or younger reported higher familiarity with the concept of female
social entrepreneurship (ATT1), with a small but significant difference compared to older
participants (p = .02). Age did not significantly affect the view that female social entrepreneurs
are crucial for economic innovation (ATT2), although younger participants rated slightly higher.
Younger participants believed more strongly that female social entrepreneurs face unique
challenges (ATT3), with this result being statistically significant (p = .01).
White participants and non-white participants did not significantly differ in their
familiarity with female social entrepreneurship (ATT1) or in viewing female social entrepreneurs
as essential for economic innovation (ATT2). However, white participants were significantly
more aware of the unique challenges female social entrepreneurs face (ATT3, p = .01).
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Education level did not significantly influence familiarity with female social
entrepreneurship (ATT1) or the perception of the essential role of female social entrepreneurs in
economic growth (ATT2). Participants with a lower education level (bachelor’s degree or lower)
significantly recognized the unique challenges faced by female social entrepreneurs (ATT3, p <
.001).
Participants whose parents had less than a bachelor’s degree did not significantly differ in
their attitudes toward female social entrepreneurship (ATT1 and ATT2); however, there was a
significant difference in the belief that female social entrepreneurs face unique challenges
(ATT3), with those having parents with lower education levels recognizing these challenges
more (p < .001).
Individuals with an income of $41K or more reported higher familiarity with female
social entrepreneurship (ATT1) and believed more strongly that female social entrepreneurs face
unique challenges (ATT3). Both findings show statistical significance (p = .01 for ATT1 and p =
.02 for ATT3), while participants do not show a significant difference in their perception of the
importance of female social entrepreneurs to economic growth (ATT2).
Participants with a family income of $50K or more exhibited significantly more
familiarity with female social entrepreneurship (ATT1) and believed more strongly that they face
unique challenges (ATT3) than those with higher incomes, with p-values indicating significance
(p < .01 for ATT1 and p < .03 for ATT3); no significant difference was found for attitudes
toward the importance of female social entrepreneurs to economic growth (ATT2).
Relationship status and presence of children did not significantly affect attitudes towards
female social entrepreneurship, with no statistical differences in familiarity (ATT1), perceived
importance for economic growth (ATT2), or belief in unique challenges faced (ATT3) between
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those in relationships and those not in relationships and those who had children versus those who
did not have children.
Executives and supervisors significantly appreciate the economic role of female social
entrepreneurs (ATT2, p = .03) and are more aware of the unique challenges they encounter
(ATT3, p = .01) compared to their counterparts who are unemployed or in mid-level exempt
positions. However, they do not significantly differ in their familiarity with female social
entrepreneurship (ATT1, p = .07).
Finally, individuals from the Southeast and West regions were more familiar with female
social entrepreneurship (ATT1) than those from the Northeast and Midwest, and this finding was
statistically significant (p = .02). There were no significant differences in views on the economic
role of female entrepreneurs (ATT2) or the unique challenges they face (ATT3).
Table 9 includes the Cohen’s d data for all attitude variables by demographic breakdown.
Cohen’s d is the ratio of the difference to the standard deviation. Typically, .20 is considered a
small effect size, .50 is considered an average effect, and .80 is considered a large effect size in
behavioral and social science research studies. Such effect sizes indicate to what extent there is
reason to believe that in practical, real-world terms these effects are small or large. In this study,
the effect size of Cohen’s d ranged from -0.30 to 0.52, suggesting substantial consistency across
various demographic variables, implying that these effects will most likely not diminish. Based
on the effect size, the results in this study show a significant, small-to-average effect size and a
real-world application or utility.
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Discussion of Findings
Chapter Four presented the survey data and the analysis of the five research questions.
The researcher analyzed the data using descriptive statistics, Spearman correlation rho with a
two-tailed test of significance at the .05 level, and an independent variables t-test to determine
the strength and direction of the relationships between the variables, as well as to assess any
significant differences between the 11 demographic groups. Based on the findings, the data
overwhelmingly supports all five hypotheses. The following sections will provide a deeper
discussion of the findings from each research question.
Microsystem: Public perceives socioeconomic status to influence attitudes
RQ1 identified the significant influence of socioeconomic status (SES) on attitudes
toward female social entrepreneurship (FSE), supporting a body of literature emphasizing the
role of individual characteristics and demographic factors in entrepreneurial engagement. The
correlation between socioeconomic status and attitudes toward female social entrepreneurship
aligns with seminal and contemporary academic perspectives. Bourdieu’s (1986) concept of
social capital as critical for entrepreneurial success is underscored by this correlation, as higher
SES typically offers more access to valuable networks and resources. This is consistent with the
works of Evans and Jovanovic (1989), and Kihlstrom and Laffont (1979), which demonstrate a
positive relationship between personal wealth and entrepreneurial decisions, emphasizing the
role of financial resources in the pursuit of entrepreneurial ventures. Brüderl and Preisendörfer
(1998) further expand on this by indicating that higher income enables investment in
entrepreneurship and fosters a supportive environment for such activities. The study’s findings
demonstrate that individuals with higher incomes reported greater familiarity with female social
entrepreneurship and a stronger belief in the unique challenges female social entrepreneurs face.
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This suggests that socioeconomic status affects access to resources and networks and influences
perceptions and attitudes toward female social entrepreneurs. Higher-income individuals, likely
benefiting from greater social and financial capital, may be more exposed to or engaged with
entrepreneurial activities, thereby shaping their attitudes more significantly. Conversely, those
with lower incomes may have less exposure and thus less familiarity or awareness of the specific
challenges that female social entrepreneurs encounter. This consensus among respondents
indicates that socioeconomic status carries a considerable influence on attitudes toward female
social entrepreneurship.
Such findings highlight the necessity for targeted initiatives that consider the influence of
SES. Given that educational attainment, income, and occupational status are interrelated with
entrepreneurial attitudes and opportunities, it is imperative that support systems—whether
educational, financial, or career-focused—address these intersecting factors. Furthermore, the
discussion of income by Blanchflower and Oswald (1998), and the work of Sara and Peter
(1998) on financial constraints for women entrepreneurs, coupled with the study’s results
showing that women perceive a greater influence of SES on attitudes compared to men,
reinforces the need for strategies that mitigate specific barriers faced by female entrepreneurs.
Mesosystem: Connections and networks are perceived to be the most influential factor
The second research question in this study focused on the mesosystemic elements—
particularly mentors and professional organizations—and their influence on attitudes toward
female social entrepreneurship. The study’s results highlight that the presence of mentors and
participation in professional associations significantly influence attitudes toward female social
entrepreneurship. The study’s results suggest that supportive connections and networks are
positively associated with more knowledge, belief in their role as economic drivers and
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acknowledgment of the unique challenges of female social entrepreneurship. This aligns with the
work of Eesley and Wang (2017), who emphasize the profound impact of entrepreneurial
mentors on those without a family history of entrepreneurship, thereby illustrating the potential
of educational programs to bridge this experiential gap. Among all four scales, connection and
network stands out as the most influential factor in shaping attitudes toward female social
entrepreneurship.
Moreover, Muldoon et al. (2019) supports the significance of mentors in combating
discrimination and fostering female entrepreneurship through various forms of social persuasion
and skill mastery. This study’s findings corroborate the literature, such as St-Jean et al. (2018),
on the importance of vicarious learning in bolstering entrepreneurial self-efficacy, a concept
grounded in Bandura’s (1997) seminal work. Nonetheless, the caution advised by Byrne et al.
(2019) regarding perpetuating gender stereotypes through role modeling echoes the complex
dynamics within the mesosystem that influence public attitudes.
Finally, participation in professional organizations emerges as a significant factor. As
Schibrowsky et al. (2002) point out, these entities play a critical role in providing practical
business experiences. Peltier et al. (2008) also note that organizations seek involvement to gain
entrepreneurial experiences, a sentiment that the study’s respondents positively reflect.
Exosystem: Public believes that mass media and social media shape attitudes
For the third research question focusing on the influence of mass and social media
exposure on attitudes toward female social entrepreneurship, the data shows that this aspect of
the exosystem plays a critical role in shaping public perception. The study’s results indicate a
significant relationship between media exposure and attitudes toward female social
entrepreneurship. These findings align with the scholarly discourse which suggests that media
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not only reflects but also shapes societal norms and expectations regarding gender and
entrepreneurship.
Firstly, the mean score for mass and social media influence, although slightly lower than
that of the socioeconomic status and connections and network, signifies an agreement among
participants on the considerable role of media. These findings resonate with the observations by
Eikhof et al. (2013), who emphasize the profound impact of media representations on the
perceived attainability and desirability of entrepreneurship for women. Women scored higher on
this scale, indicating that they find the role of mass and social media more influential than men.
Baron et al. (2001) and Gupta et al. (2009) highlighted the association of entrepreneurship with
stereotypically masculine traits within media. This reinforces the notion that mass and social
media exposure could either challenge or reinforce gender stereotypes in entrepreneurship,
depending on the nature of the content presented. Thus, this study supports the concept that
strategic media representations can play a role in elevating the status of female entrepreneurs in
the public eye. This is consistent with the critiques and new perspectives proposed by Ahl (2006)
and Bruni et al. (2004), advocating for a more equitable and inclusive portrayal of female
entrepreneurs in media.
Furthermore, the role of social media as highlighted by Azhar and Akthar (2020) and
Chaker and Zouaoui (2023) underpins the potential of digital platforms to inspire and mobilize
aspiring female entrepreneurs by showcasing successful role models. The finding that women in
this study rated the influence of mass and social media on attitudes toward entrepreneurship
higher than men suggests that positive and empowering portrayals of female entrepreneurs on
social media can serve as a catalyst for change and motivation.
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Macrosystem: Gender roles and expectations influence attitudes
As evidenced by the findings of the fourth research question, a correlation exists between
societal roles and expectations placed on women and the attitudes surrounding female social
entrepreneurship. The data indicates that progressive roles and societal expectations of women
are positively linked to supportive attitudes towards female social entrepreneurship. The results
echo historical scholarly concerns regarding gender stereotypes within the entrepreneurship
literature, highlighting a persistent association of entrepreneurship with masculine traits, as noted
by Schwartz (1976) and later discussed by researchers such as Gupta et al. (2009) and Langowitz
and Minniti (2007).
As outlined by Social Dominance Theory (Sidanius & Pratto, 1999), cultural beliefs and
social norms suggest that these stereotypes are deeply rooted in society’s fabric, often
discouraging women from adopting traditionally viewed masculine entrepreneurial roles.
Studies have consistently shown that gender stereotyping of entrepreneurial activities results in
lower intentions and a reduced likelihood of women starting their own businesses. The work of
Alsos and Ljunggren (2017) and Swail and Marlow (2018) illustrates that gender-based
stereotypes affect the perceptions of female entrepreneurship and have tangible negative
consequences for funding and resource acquisition for women-led ventures.
These empirical findings resonate deeply with the literature. For instance, the influence of
societal expectations noted in the study parallels Schwartz’s (1976) early recognition of gender
stereotyping within entrepreneurship. It further confirms the theories proposed by Sidanius and
Pratto (1999), where social norms dictate traditionally masculine roles as the benchmark for
entrepreneurial success. The persistent stereotype that entrepreneurship is a male domain, as
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discussed by Gupta et al. (2009), is therefore not just a reflection of individual biases but a
societal norm hindering female entrepreneurship's advancement.
Furthermore, the study’s findings underscore the repercussions of such societal
expectations, mirroring Alsos and Ljunggren’s (2017) and Swail and Marlow’s (2018) insights
into how gender-based stereotypes tangibly affect both the perception of female entrepreneurs
and their access to crucial resources. The gender disparity in early investment opportunities,
highlighted by Guzman and Kacperczyk (2019), further illustrates the real-world implications of
these societal norms.
Demographic Variables Influence Public’s Attitudes towards Female Social Entrepreneurship
The final research question examined how demographic factors influence perceptions of
and support for female social entrepreneurship. The results indicate a significant relationship
between several demographic variables and attitudes toward female social entrepreneurship.
This research echoes studies such as Handy et al. (2002) and Hechavarría and Ingram (2016),
which found that attributes like gender, educational level, and family background shape one’s
inclination toward social entrepreneurship. The study results demonstrate that younger
participants believed more strongly that female social entrepreneurs face unique challenges. This
could explain why studies have shown that older individuals are more likely to pursue
entrepreneurship than their younger counterparts (Arenius & Minniti, 2005; Beugelsdijk &
Noorderhaven, 2005; Walker & Webster, 2007; Weber & Schaper, 2004).
Additionally, educational background had a significant bearing, with those holding a
bachelor’s degree or lower, and individuals whose parents had less education, more likely to
recognize the challenges faced by female social entrepreneurs. These demographic variations
mirror findings by Nieuwenhuizen and Tselepis (2022), who suggested that education not only
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contributes to the gender gap but also influences the entrepreneurial mindset among women,
fostering a growth orientation that can be crucial in meeting community needs.
Furthermore, income levels correlated with attitudes, where those with an income of less
than $41K reported a higher familiarity with and perceived more challenges for female social
entrepreneurs. This pattern was also evident when comparing participants based on family
income. The observation that individuals from less educated backgrounds display a significant
recognition of SES’s influence aligns with Bourdieu’s (1986) theories on social capital and
entrepreneurial success. Additionally, occupational status was another variable affecting
attitudes; executives and supervisors acknowledged the economic contributions and challenges
of female social entrepreneurship more than those unemployed or in mid-level exempt positions.
Finally, this pattern is consistent with the insights from Brüderl and Preisendörfer (1998) and
Lévesque and Minniti (2006), which link income levels to the ability to invest in entrepreneurial
ventures and to perceive opportunities. Finally, individuals from the Southeast and West regions
had more familiarity with female social entrepreneurship (ATT1) than those from the Northeast
and Midwest, highlighting the potential for targeted educational initiatives on this concept in
these regions.
Summary
The results of this study offer insights into the impact of four key constructs—
socioeconomic status, connections and networks, mass and social media, and roles and
expectations—on perceptions of female social entrepreneurship and the influence of
demographic variables on attitudes around female social entrepreneurship. The study shows a
strong link between socioeconomic status and support for female social entrepreneurship,
echoing Bourdieu’s social capital theory. Additionally, mentors and professional networks play a
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crucial role in shaping positive attitudes toward female entrepreneurship, aligning with existing
literature on supportive connections. Mass and social media also influence public perception,
challenging gender stereotypes and boosting the status of female entrepreneurs. Moreover,
progressive societal roles correlate with supportive attitudes, reflecting the influence of gender
norms on entrepreneurial engagement. Lastly, demographic factors like age, education level,
race, parental level of education, income, family income, occupation and geographical location
shape perceptions and support for female entrepreneurship, highlighting the need for targeted
initiatives to foster inclusivity in entrepreneurial ecosystems.
Chapter Five: Recommendations for Practice
This study aimed to examine factors that shape and influence the general public’s
attitudes toward female social entrepreneurship by leveraging Bronfenbrenner’s socio-ecological
systems theory. The findings have real implications for the practice of social entrepreneurship
including educational institutions, corporate entities including financial and media organizations,
and policymakers in the entrepreneurial ecosystem. The following section provides a summary
of the discussion and then proceeds to focus on the recommendations for practice based on the
findings of this study. The overarching recommendations have been designed based on the
significant influence of socioeconomic status, connections and network, mass and social media,
roles and expectations of women and demographic variables on attitudes towards female social
entrepreneurship.
Drawing on the study’s findings, the researcher recommends that stakeholders in the
ecosystem of social entrepreneurship take targeted actions to foster a more supportive
environment for female social entrepreneurs. This approach involves a two-pronged strategy that
not only addresses the immediate needs of these entrepreneurs within their microsystem and
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mesosystem, but also seeks to transform the role socioeconomic status, connections and network,
and mass and social media play, as well as societal perceptions and norms that have historically
impeded their success within the exosystem and macrosystem, respectively. The following
recommendations will be organized by microsystem, mesosystem, exoystem, and macrosystem,
concluding with the influence of demographic variables on attitudes.
Microsystem: Access to Support Systems for Entrepreneur’s Socioeconomic Advancement
Firstly, this study’s results on the role of SES in shaping attitudes toward FSE require a
multi-faceted approach in policy and program design within the microsystem. Nieuwenhuizen
and Tselepis (2022) suggested that education not only contributes to the gender gap but also
influences the entrepreneurial mindset among women, fostering a growth orientation that can be
crucial in meeting community needs. Recognizing that SES is intertwined with education,
income, and occupational status, it is critical to develop tailored interventions that address these
dimensions, thereby fostering a supportive environment for female social entrepreneurs across
different demographic groups. Thus, developing comprehensive support systems for
socioeconomic advancement can serve as a solid foundation for their entrepreneurial ventures.
To this end, stakeholders should craft and implement comprehensive initiatives that
address the socioeconomic barriers faced by female entrepreneurs and facilitate access to capital
and financial resources, critical for start-up and scale-up phases. According to recent reports,
women-led startups receive less than 3% of all venture capitalist (VC) investments and women
also account for less than 15% of check-writers (Harvard Business Review, 2023). This
underscores the importance of initiatives to improve access for female founders to financial
resources, capital, training, and mentorship programs. This can include providing VC funding,
grants, low-interest loans, and subsidies to women-led startups, coupled with policies that
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encourage equal opportunities in entrepreneurship by targeting female entrepreneurs from lowerincome backgrounds or underrepresented groups.
Additionally, tailored financial literacy programs and entrepreneurship training can equip
women with the necessary skills and knowledge to navigate the business world effectively,
thereby enhancing their socioeconomic status and potential for success. Moreover, it is crucial to
develop training programs tailored to the unique challenges faced by female entrepreneurs,
covering areas such as business planning, digital marketing, and financial literacy.
Considering the significant role that age plays in impacting attitudes around female social
entrepreneurship, it is important to start “planting the seeds” at a young age. Since youth develop
abilities such as self-efficacy and explore their self-identities through curricular and
extracurricular activities (Pechmann et al., 2020), more educational opportunities should be
offered to the youth in higher education to foster ‘youth female social entrepreneurs’ who can
drive change.
Mesosystem: Expand and Strengthen Entrepreneurs’Access to Networks and Connections
Building robust networks is crucial for the success of female entrepreneurs. Eesley and
Wang (2017) have demonstrated that mentors significantly influence entrepreneurial behavior,
particularly for those without a family business background, while Schibrowsky et al. (2002)
underscore the importance of professional organizations in developing entrepreneurial skills. The
study’s findings show that the mesosystem’s influence on societal views towards female social
entrepreneurship are significant.
Thus, it is imperative to offer access to tailored programs that facilitate networking
opportunities. Mentorship schemes pair aspiring female entrepreneurs with seasoned business
leaders, and networking events that connect women with potential investors, partners, and
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customers can significantly impact their ventures’ growth. These programs can be enriched by
integrating cross-sector partnerships, aligning female entrepreneurs with leaders from diverse
industries, leading to innovative collaborations, and broadening their commercial horizons.
Additionally, creating online platforms that support community-building among women
entrepreneurs can foster a sense of belonging and mutual support. These virtual spaces can serve
as fertile grounds for exchanging ideas, securing funding, and scaling businesses globally.
Establishing mentorship programs is equally important, creating avenues for aspiring female
entrepreneurs to gain insights from seasoned business leaders. Such mentorship can provide
invaluable guidance, support, and network access, enhancing the potential for success. It is
important to note that these programs should be structured, long-term programs that focus on the
comprehensive development of female entrepreneurs. This includes regular performance
reviews, personalized growth plans, and establishing accountability partnerships that ensure the
mentee’s goals are continuously being met. Such detailed programs can help inculcate resilience,
adaptability, and strategic foresight—attributes that are crucial for long-term entrepreneurial
success.
To further these endeavors, there must be a concerted effort to research the outcomes of
networking initiatives to continuously refine their effectiveness. Capturing data on mentorship
impact, network expansion, and tangible business outcomes for participants can inform the
strategic direction of these programs. This data-driven approach ensures networking
opportunities are available, impactful, and aligned with female entrepreneurs' needs and
challenges in the contemporary business landscape.
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Exosystem: Organizations Need to Utilize Mass and Social Media Strategically
Mass and social media play a pivotal role in shaping societal norms and perceptions. The
study establishes a positive relationship between mass and social media exposure and attitudes
toward female social entrepreneurship. Azhar and Akthar (2020) and Chaker and Zouaoui (2023)
highlight the role of social media, underscoring the potential of digital platforms to inspire and
mobilize aspiring female entrepreneurs by showcasing successful role models. This calls for
strategically using media channels to promote positive and empowering content about female
entrepreneurs, particularly targeting the identified demographic groups that show a greater belief
in media influence.
This research advocates for a conscientious approach to media content creation and
dissemination, where the success stories of female entrepreneurs are highlighted to inspire and
encourage a diverse range of individuals. Campaigns designed to highlight the success stories of
female entrepreneurs, workshops on effectively using social media for business growth, and the
promotion of positive narratives around female leadership in business can challenge and alter
traditional roles and expectations. Such efforts can demystify stereotypes, inspire prospective
female entrepreneurs, and cultivate a societal mindset that champions female entrepreneurship.
This recommendation entails using mass and social media platforms to challenge and
redefine traditional roles and expectations associated with female entrepreneurship. It will be
beneficial to showcase successful female entrepreneurs more in the media, thereby providing
role models and counteracting stereotypes. Campaigns aimed at changing societal attitudes
should highlight the diversity of female entrepreneurship and its potential to contribute
significantly to economic development and social change. By creating content that celebrates the
achievements of female entrepreneurs and advocates for gender equality in the business sector,
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these platforms can play a transformative role in altering public perception and encouraging
more women to embark on entrepreneurial ventures. Through these efforts, mass and social
media can become a powerful driver in the exosystem, fostering an environment that supports
and champions female entrepreneurs.
Macrosystem: Organizations Need to Reframe Roles and Expectations through Education
and Policy Reform
Given the considerable impact of societal expectations on attitudes towards female
entrepreneurship as demonstrated in this study, it is important to foster an environment that
promotes gender equality and provides equitable opportunities for women. This study contributes
to the growing body of evidence that calls for a shift in societal norms and underscores the
importance of fostering an environment that challenges gender stereotypes. By acknowledging
and addressing these ingrained societal norms, stakeholders can create strategies that challenge
the status quo and pave the way for more inclusive and supportive attitudes towards female
entrepreneurs. Encouraging participation in entrepreneurship through educational initiatives
could narrow the gender gap and pave the way for a more inclusive entrepreneurial landscape.
Such efforts would not only challenge deep-seated norms and stereotypes stemming from
“traditional views” regarding gender roles, but also support the emergence of a diverse and
robust entrepreneurial ecosystem.
Education systems and policy frameworks should be revisited to encourage gender
equality in entrepreneurship from a young age. Incorporating entrepreneurship education into
school curriculums, focusing on successful female role models, can help reshape perceptions
about gender roles. Policy reforms aimed at eliminating gender discrimination in all business
sectors, ensuring equal rights for women, and enforcing equal pay for equal work are critical.
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These measures can collectively dismantle the structural barriers that perpetuating traditional
roles and expectations, paving the way for a more inclusive and supportive environment for
female social entrepreneurs. Additionally, leveraging mass and social media to challenge and
change traditional roles and expectations can significantly influence societal attitudes towards
female entrepreneurship, creating a more conducive environment for their success.
Implementing these recommendations requires a coordinated and collaborative effort
among government bodies, private sector entities, non-profit organizations, the media, and
entrepreneurs targeting a wide range of age and geographical reach in the United States. By
addressing these critical areas, stakeholders can significantly contribute to creating a more
equitable and thriving ecosystem for female social entrepreneurship. Such a holistic approach
will address the specific constructs identified in the survey and foster a broader cultural shift
towards valuing and supporting female social entrepreneurship. Through targeted actions and
collective effort, it is possible to create an ecosystem that nurtures the growth of female
entrepreneurs and contributes to a more inclusive and equitable entrepreneurial landscape for
women from diverse socioeconomic and demographic backgrounds. This approach aligns with
the current scholarly movement that advocates for a more equitable and diverse entrepreneurial
ecosystem where outdated societal expectations do not confine women’s roles.
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Demographics and Attitudes: Fostering Diverse and Inclusive Programs to Level the
Playing Field
The study’s findings highlight significant relationships between demographical variables
and attitudes. This emphasizes the need for tailored initiatives to promote inclusivity within
entrepreneurial ecosystems, addressing various demographic characteristics such as age,
education level, race, parental education, income, family income, occupation, and geographical
location. Considering that younger participants are more aware of the unique challenges of
female social entrepreneurship, it is important to empower them. Youth develop abilities such as
self-efficacy and explore their self-identities through curricular and extracurricular activities
(Pechmann et al., 2020). Implementing youth-centric educational initiatives, like integrating
entrepreneurship programs into school curricula or after-school programs, can be crucial in
shaping the attitudes and aspirations of young people towards entrepreneurship, including female
entrepreneurship. By providing mentorship and practical experiences, these initiatives can break
down barriers and foster the much-needed self-efficacy and confidence among future generations
of entrepreneurs.
Finally, geographically speaking, individuals do not have the same level of familiarity
with the concept of female social entrepreneurship, nor do they have the same levels of
education, income or family income. Fostering partnerships with regional and local communities,
businesses, and organizations is crucial to create a supportive ecosystem for female
entrepreneurs. By leveraging existing networks and resources within each region, policymakers
can maximize the impact of their initiatives and embed support for female entrepreneurship
within the local community. Such collaborative efforts can contribute to a more inclusive
entrepreneurial landscape, driving economic growth and innovation across diverse backgrounds.
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Limitations and Delimitations
This study has several potential limitations. Quantitative research has inherent
limitations, including unreliability due to measurement error, statistical power limitations,
challenges with internal validity due to correlation not proving causation, and challenges to
external validity due to the selection of participants, settings and instruments. To mitigate these
limitations, the researcher aimed to increase the number of participants to offset statistical power
limitations. Furthermore, the researcher sampled the entire US, thereby increasing external
validity in this study.
The research assumes that participants are truthful in their responses. With any
convenience sampling, it is important to note that those who choose to participate may lend to
bias in sharing their personal preferences (Locke et al., 2010). For example, in this study,
females were more likely to respond. If non-respondents had chosen to participate, the responses
may have altered the survey results, resulting in response bias (Creswell & Creswell, 2018). To
mitigate response bias, the researcher conducted a wave analysis, reviewing select survey
responses each week during the eight-week survey period to determine any changes in responses
(Creswell & Creswell, 2018). Typically, individuals who complete surveys in the final days of a
survey timeframe may otherwise be non-respondents, so substantial changes in responses during
various weeks may show increased response bias (Creswell & Creswell, 2018).
A key limitation of this study is the use of a newly developed survey instrument, which
has not yet been tested for reliability and validity beyond the initial assessment with Cronbach’s
alpha. This lack of prior testing means the results should be interpreted cautiously, as the
instrument’s ability to accurately measure the intended constructs may not be fully established.
Further research is necessary to validate the instrument across different populations and settings
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to ensure its reliability and generalizability. This limitation highlights the need for cautious
interpretation of the findings and underscores the importance of continued evaluation and
refinement of the instrument in future studies.
Moreover, the researchers measured the three dependent attitude variables using one-item
scales. Single-item measures can lead to measurement error and bias, lacking the reliability of
multiple-item scales. Multiple-item scales for ATT1, ATT2 and ATT3 would have provided a
more comprehensive and reliable assessment.
In addition, this study did not include a unidimensional measure of attitudes toward FSE.
This omission could further limit the depth and accuracy of the findings. Thus, including such a
measure would have provided a clearer understanding of the participants' attitudes.
Furthermore, the sample reveals potential biases, notably an overrepresentation of young,
female, and White individuals, alongside a notable concentration in the Managerial/Supervisor
Level, across gender, age, ethnicity, and employment status. Such disparities challenge the
generalizability of the findings to the broader population. Additionally, the methodology and
potential self-selection of participants underscore a limitation in the study’s representativeness.
The research project also has several delimitations. Firstly, the study exclusively invites
residents of the United States. This geographical scope limits responses and data collection to
this specific demographic.
Additionally, the researcher administered the survey over a defined period of eight
weeks. This temporal delimitation allows for a structured data collection process within the
specified timeframe. The focus of the survey centers exclusively on the knowledge, attitudes, and
perceptions of respondents regarding female social entrepreneurship. The researcher does not
address other unrelated topics or issues.
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Furthermore, the research adopts a survey methodology with a structured questionnaire
format, omitting qualitative data collection methods like interviews or open-ended inquiries.
These methodological parameters will influence every aspect of the data gathering process.
Lastly, it is imperative to note that the survey language is English, with cultural
considerations specific to the United States considered in the design of the survey instrument.
These delimitations collectively serve to define the boundaries and parameters of this survey
study, ensuring a clear and focused investigation into the awareness and perceptions of social
entrepreneurship among the targeted demographic.
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Recommendations for Future Research
Building on the findings of the current study, future research could focus on several
avenues to deepen the understanding of factors influencing attitudes toward female social
entrepreneurship (FSE). First, given the significance of age and location, further qualitative
studies could explore how regional cultural norms and generational differences shape
perceptions of FSE. This would provide richer, contextual insights beyond quantitative data.
Second, since socioeconomic status has been identified as influential, future research might
examine the nuanced impacts of education, income, and occupational status across diverse
populations. Additionally, the role of mentors and professional organizations in supporting FSE
could be investigated through longitudinal studies to assess their impact over time.
The effect of mass and social media exposure on FSE attitudes also warrants deeper
examination. Studies could analyze the content and messaging in media and its influence on
societal attitudes, potentially informing strategies for advocacy and awareness campaigns.
Finally, understanding the expected role of women in society and its effect on FSE could be
explored through cross-cultural research. This could reveal how societal norms and gender roles
differently impact entrepreneurship in varying contexts.
Acknowledging the limitations and delimitations, new research should aim to validate the
survey instruments used, involve a more representative sample that reflects the broader
population, and incorporate both qualitative and quantitative methods to capture a multifaceted
view of the phenomenon. Future research could also incorporate multiple-item scales to enhance
measurement precision and overall study robustness and consider including a unidimensional
measure to ensure a more thorough analysis of attitudes toward FSE. Addressing the
delimitations, future studies might expand beyond the United States to include global
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perspectives on FSE, incorporate multilingual surveys to encompass non-English speaking
participants, and explore related topics to capture broader societal and economic factors
influencing FSE.
Conclusion
This study aimed to examine factors that shape and influence the general public’s
attitudes towards female social entrepreneurship by leveraging Bronfenbrenner’s socioecological systems theory. Rietveld and Patel (2022) point out that closing the gender gap in
social entrepreneurship is essential to fostering sustainable economic growth.
Therefore, it is important to understand that overcoming barriers to female social
entrepreneurship contributes to efforts toward entrepreneurship and, ultimately, economic
growth that benefits the nation. The findings here demonstrate that the microsystem,
mesosystem, exosystem, and macrosystem are important to all measures of attitudes in this study
and, therefore, Bronfenbrenner’s four systems could be maintaining the structures that perpetuate
the gender gap within social entrepreneurship. This is a reminder of the crucial role played by
stakeholders at every level. There is a compelling need for collective action—efforts from
policymakers, educational institutions, media outlets, and the business community to champion
the cause of female entrepreneurs.
In the present study, connection and network were the most influential factors in shaping
attitudes around female social entrepreneurship. Additionally, socioeconomic status, mass and
social media, and gender roles and expectations play a significant role in shaping these attitudes.
Furthermore, the results demonstrate that demographic variables influence attitudes as well.
Perceptions of female social entrepreneurship are crucial for the advancing the female
founder community as we strive to challenge gender biases and norms within our society.
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Addressing the impact of structural barriers around female social entrepreneurship (FSE) is
important because it is a critical factor in fostering positive attitudes towards FSE and in
influencing a woman’s decision to pursue this career path, thus narrowing the gender gap.
Consequently, there is a need for further efforts to enhance understanding and recognition of
gender discrimination and its impact on social entrepreneurship. This study sets the stage for a
systemic transformation by advocating for strengthened support systems that promote
socioeconomic advancement for female entrepreneurs and for the reshaping of societal norms
through education and policy reform. The need for change is urgent because less than 3% of
venture capital (VC) investments are directed towards women-led startups.
Organizations and policymakers must improve their efforts to inform and engage female
founders from diverse backgrounds in ways that increase access to entrepreneurship
opportunities and pave the way for policies that promote a more inclusive entrepreneurial
landscape. In such a landscape, female social entrepreneurship is not merely supported but is a
central driver of innovation, economic growth, and social progress.
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Appendix A: Recruitment Email and Social Media Post
Recruitment Email
Subject: Earn a $25 Amazon Gift Card - Invitation to Participate in Study on Female Social
Entrepreneurship
Dear [Recipient’s Name],
I am conducting a research study aimed at understanding the factors that influence public
attitudes towards female social entrepreneurship. Your participation will provide valuable
insights and contribute to a deeper understanding of this important subject.
Study Details:
• Duration: 10-15 minutes
• Reward: Drawing for $25 Amazon Gift Card (10 winners)
• Eligibility: Open to ages 18 years and over in the United States
To participate in this study, please click on the following link: [Survey Link].
As a token of my appreciation for your valuable time and input, 10 respondents will randomly
receive a $25 Amazon Gift Card. If you have any questions or concerns about this study, please
do not hesitate to reach out to me at kouzeh@usc.edu.
111
111
Thank you for considering participation in this vital research. I look forward to your
contribution.
Sincerely,
Misha Kouzeh
USC Rossier School of Education Doctoral Student
kouzeh@usc.edu
112
112
Social Media Post
113
113
Appendix B: Survey Questions
Category Question Measures
Demographic 1. Gender
Male
Female
Non-binary/third gender
Other
Prefer Not To Answer
Demographic 2. Age Open-ended (numeric)
Demographic 3. Race/Ethnicity
American Indian or Alaska Native
Asian
Black or African American
Native Hawaiian or Other Pacific Islander
White
Other
Prefer Not To Answer
Demographic 4. Highest Level of Education
Did Not Graduate from High School
High School Diploma or Equivalent
Some College
Trade/Technical School Certificate
Associates Degree
Bachelors Degree
Masters Degree
Doctorate or Professional Degree
114
114
Category Question Measures
Other
Prefer Not To Answer
Demographic 5. Parental Highest Level of Education
Did Not Graduate from High School
High School Diploma or Equivalent
Some College
Trade/Technical School Certificate
Associates Degree
Bachelors Degree
Masters Degree
Doctorate or Professional Degree
Other
Prefer Not To Answer
Demographic 6. What is your current annual income? Open-ended (numeric)
Demographic 7. What is your marital/relationship
status?
Single
Committed relationship (unmarried)
Married
Divorced
Widowed
Other
Prefer Not To Answer
Demographic 8. Do you have children?
Yes
No
115
115
Category Question Measures
Other
Demographic
9. What is your family’s (you, spouse,
partner, significant other, etc.) annual
income?
Open-ended (numeric)
Demographic 10. What is your current employment
status?
Unemployed
Homemaker
Hourly/part-time
Hourly/full-time (entry)
Exempt/full-time employee (mid)
Exempt/full-time (manager)
Exempt/full-time (executive)
Demographic 11. Select your geographical location.
West (1)
Southwest (2)
Mid-West (3)
Southeast (4)
Northeast (5)
ATT1
12. I am familiar with the concept of
female social entrepreneurship.
4-point Likert scale*
116
116
Category Question Measures
ATT2
13. Female social entrepreneurs are
essential for driving economic growth
and innovation.
4-point Likert scale*
ATT3
14. Female social entrepreneurs face
unique challenges compared to their
male counterparts.
4-point Likert scale*
SES1
15. A woman’s education level affects
her ability to pursue social
entrepreneurship.
4-point Likert scale*
SES2
16. A woman’s income level affects her
ability to pursue social
entrepreneurship.
4-point Likert scale*
SES3
17. Access to resources and funding is
easier for female entrepreneurs from
higher socioeconomic backgrounds.
4-point Likert scale*
SES4
18. People from higher socioeconomic
backgrounds are more likely to
support female-led social enterprises.
4-point Likert scale*
CN1
19. A woman’s network affects her
ability to pursue social
entrepreneurship
4-point Likert scale*
117
117
Category Question Measures
CN2
20. Having a mentor positively
influences the female social
entrepreneurship journey.
4-point Likert scale*
CN3
21. Professional organization alliances
are effective in providing resources
and support for female
entrepreneurship initiatives.
4-point Likert scale*
CN4
22. Mentorship can help women
overcome challenges and obstacles in
their social entrepreneurship
endeavors.
4-point Likert scale*
CN5
23. Involvement with professional
organizations improves women’s
networking opportunities and
collaboration prospects within the
social entrepreneurship sector.
4-point Likert scale*
CN6
24. Mentors and professional
organization alliances are essential
for the success and sustainability of
female social entrepreneurship
ventures.
4-point Likert scale*
118
118
Category Question Measures
MS1
25. Media shows more examples of
successful male social entrepreneurs
than successful female social
entrepreneurs.
4-point Likert scale*
MS2
26. Media portrayal of successful female
social entrepreneurs inspires me.
4-point Likert scale*
MS3
27. Social media exposure has made me
more aware of female-led social
enterprises.
4-point Likert scale*
MS4
28. Mass media promotes gender equality
by showcasing female social
entrepreneurs.
4-point Likert scale*
MS5
29. Social media platforms play a
significant role in advocating for
female social entrepreneurship.
4-point Likert scale*
MS6
30. Positive media portrayals of female
social entrepreneurs encourage their
success.
4-point Likert scale*
RE1
31. Society expects women to prioritize
family over social entrepreneurship.
4-point Likert scale*
119
119
Category Question Measures
RE2
32. Society values male social
entrepreneurs more than female
social entrepreneurs.
4-point Likert scale*
RE3
33. Gender stereotypes limit the success
of female social entrepreneurs.
4-point Likert scale*
RE4
34. Women face more criticism and
scrutiny in leadership positions.
4-point Likert scale*
RE5
35. Cultural norms discourage women
from taking on social entrepreneurial
roles.
4-point Likert scale*
RE6
36. Supportive cultural attitudes towards
women in business foster female
social entrepreneurship.
4-point Likert scale*
Note. *Measures include strongly disagree, disagree, agree, and strongly agree.
120
120
Appendix C: Descriptive Statistics of Demographic Variables
Demographic Variables of the Sample (n) and Total Population (N=497).
Variable n %
Gender 465
Female 316 64.4
Male 149 30.3
Non-Binary 2 0.40
Prefer Not to Say 1 0.20
Age 445
18-21 33 3.6
22-24 63 12.8
25-29 154 31.3
30-34 130 26.2
35-44 53 10.6
45+ 12 2.4
Education Level 491
Did Not Graduate from High School
High School Diploma or Equivalent
Some College
Trade/Technical School Certificate
Associates Degree
Bachelors Degree
Masters Degree
Doctorate or Professional Degree
Other
Prefer not to answer
1
81
96
56
44
102
70
9
2
2
5.1
16.5
19.6
11.4
9.0
20.8
14.3
1.8
.4
.4
Parental Highest Education Level 491
Did Not Graduate from High School
High School Diploma or Equivalent
Some College
15
25
209
3.1
5.1
42.6
121
121
Trade/Technical School Certificate
Associates Degree
Bachelor’s Degree
Master’s Degree
Doctorate or Professional Degree
Prefer not to answer
27
54
73
45
12
6
5.5
11.0
14.9
45
2.4
1.2
Current Annual Income 409 7.9
$0 - $9,999 56 13.7
$10,000 - $29,999 53 13.0
$30,000 - $49,999 127 31.0
$50,000 - $69,999 74 18.1
$70,000 - $99,999 41 10.0
$100,000 and above 58 14.2
Family’s Annual Income 389
$0-$19,999
$20,000-$39,999
$40,000-$59,999
$60,000-$79,999
$80,000-$99,999
$100,000 and above
42
14
52
131
53
97
10.8
3.6
13.4
33.7
13.6
25.0
Relationship Status 456
Committed Relationship 1 7.5
Divorced 4 3.5
Married 1 55.4
Single 5 26.5
Widowed 4 0.8
Prefer not to answer 4 0.8
Children 462
No 293 59.7
Yes 161 32.8
Other 2 0.4
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122
Prefer not to answer 6 1.2
Employment Status 463
Executive Level 18 3.7
Managerial/Supervisor Level 199 40.5
Mid-Level 84 17.1
Homemaker 4 0.8
Hourly/Full-Time Employee 54 11.0
Hourly/Part-Time Employee 46 9.4
Unemployed 58 11.8
Geographical Location 465
Mid-West 96 19.6
Northeast 65 13.2
Southeast 78 15.9
Southwest 81 16.5
West 145 29.5
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Asset Metadata
Creator
Kouzeh, Misha
(author)
Core Title
Closing the gender divide: how social status, connections, media, and culture relate to public attitudes towards female social entrepreneurs
School
Rossier School of Education
Degree
Doctor of Education
Degree Program
Organizational Change and Leadership (On Line)
Degree Conferral Date
2024-08
Publication Date
08/13/2024
Defense Date
04/26/2024
Publisher
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(original),
University of Southern California
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Tag
entrepreneurial engagement,female social entrepreneurship,gender disparities,public attitudes,socio-ecological systems
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), Picus, Lawrence (
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Tags
entrepreneurial engagement
female social entrepreneurship
gender disparities
public attitudes
socio-ecological systems