Universiteit van Amsterdam and Vrije Universiteit Amsterdam MSc in Entrepreneurship
Master Thesis
Sensemaking of female entrepreneurial role models in
technology field
Author: João Dutra da Silva Oliveira UvA Student Number: 11373717
VU Student Number: 2607899 Supervisor: Prof. Dr. Karen Verduijn
Application date: July 1st 2017
The copyright rests with the author. The author is solely responsible for the content of the thesis, including mistakes. The university cannot be held liable for the content of the author’s thesis.
ABSTRACT
Despite advances within a wide range of professional roles, women remain a minority in science, technology, engineering, and mathematics (STEM) degrees and occupations. In the entrepreneurship field, this gender gap limits the number of technologic-based startups founded by women. An often-cited cause for the perpetuation of this gender gap is the absence of female role models, that can inspire new generations of women. To understand the stories told by female entrepreneurial role models, it was adopted a narrative analysis of blog posts, to explore the sensemaking process of those successful female entrepreneurs in a male-dominated field. It was found that female entrepreneurial role models make use of fours dimensions of the organizing process to make sense of their journeys: embracing multiple identities empower entrepreneuring processes of female entrepreneurial role models; communal motivation is a source of value through multiple inclusions of female entrepreneurial role models; female entrepreneurial role models embed themselves in supportive networks to intensify the reduction of equivocality in entrepreneuring process; and female entrepreneurial role models overcompensate to state the need of environments with diversity of actors. Those four insights were transformed into propositions to be deeply explored in future research. This study brings theoretical contributions in methodological terms and the adoption of the entrepreneuring approach to role modelling and practical implications to organizations which goal is to reduce the gender gap in the tech-startup scene.
TABLE OF CONTENTS
1. INTRODUCTION 5
2. LITERATURE REVIEW 7
2.1 Gender gap in STEM: a psychological, an economical and an educational approach 7 2.2 Women & tech startups: an old and persistent problem in the entrepreneurial environment 8 2.3 Role models & entrepreneurial intentions: solving the gender gap in the tech industry 9 2.4 Female role models: reducing gender gaps through examples 11
3. METHODS 13
3.1 Research design 13
3.2 Data collection 13
3.3 Data analysis 14
3.4 Organizing, entrepreneuring and sensemaking 15
3.5 A note on gender bias of the researcher 16
4. RESULTS 18
4.1 Characters 18
4.2 Context 18
4.3 Emerging themes 19
4.3.1 Sensemaking in causes of the gender gap in STEM fields 19
4.3.1.1 Communal motivation and empowerment 19
4.3.1.2 Work-life balance as a propelling to success 20
4.3.1.3 Networks of mutual support 21
4.3.2 Sensemaking in effects on the gender gap in tech field 21
4.3.2.1 Paving the way for innovative women 21
4.3.2.2 Human-oriented skills to enter tech scene 22
4.3.2.3 Well-informed decision makers 23
4.3.3 Sensemaking in learnings from role modelling in entrepreneurship 24
4.3.3.1 Learning observing tech-saving parents 24
4.3.3.2 Creating communities to spread positive narratives 25
4.3.3.3 Coaching to make self-efficacy flourish 25
4.3.4 Sensemaking in learnings from role modelling in other fields 26
4.3.4.1 Overcompensate to disconfirming stereotypes 26
4.3.4.2 Supporting networks to give voice to women 27
4.3.4.3 Family embeddedness as a path to success 27
4.3.5.1 Multiple identities empowering entrepreneurs 28
4.3.5.2 Valuable multiple inclusions communally motivated 29
4.3.5.3 Reducing equivocality nurturing a supportive network 29
4.3.5.4 Overcompensating to stimulate diversity of actors 30
5. CONCLUSION 31
5.1 Sensemaking of female entrepreneurial role models in a technological field 31
5.2 Findings and prior literature 31
5.3 Theoretical and practical implications 32
5.4 Limitations and future research 33
REFERENCES 34
APPENDIX I 38
APPENDIX II 40
1. INTRODUCTION
Despite advances within a wide range of professional roles, women remain a minority in science, technology, engineering, and mathematics (STEM) degrees and occupations. Diekman, Weisgram & Belanger (2015) adopted a psychological perspective to explain the phenomenon. The goal
congruity perspective contends that a fundamental cause of gender gaps in STEM pursuits is the
gender difference in communal motivation (i.e. an orientation toward others) rather than gender differences in ability or achievement motivation. From an economic perspective, Beede et al. (2011) mention that, although women fill close to half of all jobs in the U.S. economy, they hold less than 25% of STEM jobs. Griffith (2010), based on an educational perspective, points out that during college many students switch from their planned major to another, particularly so when that planned major was in a STEM field.
In the entrepreneurship field, this gender gap limits the number of technologic-based startups founded by women. When it comes to the Silicon Valley, the birth-place of some of the most innovative companies of our era, the problem is evident. Wadhwa & Chideya (2014) found that executive teams of the Valley's top tech firms have very few, if any, women technology heads. Additionally, virtually all of Silicon Valley's investment firms were male-dominated.
An often-cited cause for the perpetuation of this gender gaps is the absence of female role models, that can inspire new generations of women to come into STEM fields. In the entrepreneurship field, role models are viewed as influential people by a significant proportion of the entrepreneurs who use them in the startup phase of their venture. Role modelling has been considered a key driver to solving gender gaps in areas such as politics and education (Marx & Roman, 2002; Wolbrecht & Campbell, 2007; Singh, Vinnicombe & James, 2006). However, the study of female role models is scarce in entrepreneurship literature.
The absence of female entrepreneurial studies in the literature can be justified partly by the complexity of the subject. Role modelling demand multiple perspectives to be understood and it has a process nature, what makes it hard for positivist cross-sectional research designs to get valuable insights. Then, it comes another constraint from the entrepreneurship research point of view, which is the lack of works that assume the ongoing process nature of entrepreneurship.
To address that challenge, the present study adopts an organizing perspective, focused in the sensemaking process, to find the answer of the following research question: how female role models make sense of their entrepreneurial journey in technology field? Specifically, it is wanted to
know 1) how female role models face the challenges of being a woman in technology field?; 2) what messages are being sent to women about entrepreneuring in technology field?; and 3) how these messages can increase entrepreneurial intentions of women in technology field?.
Adopting the organizing perspective, allied to a methodology that captures the process nature of entrepreneurship, the present research brings theoretical contributions to the field of role modelling in entrepreneurship, applied to a relevant and growing subset of the entrepreneurial field: the creation of high-growth tech-startups firms.
The present work is structured in 5 chapters. Chapter 1 introduces the subject and clarifies the research questions and its relevance to the literature. Chapter 2 describes the theoretical background of the problem, including causes of the gender gap in STEM fields, effects on the gender gap in tech field, learnings from role modelling in entrepreneurship and from role modelling in other fields. Chapter 3 clarifies the methodology, with special attention to the narrative approach and use of blogs in social science research. Chapter 4 highlights the results and offers four propositions that emerged from the data analysis. Chapter 5 summarizes the findings, with a reflection on the research question, followed by the limitations and suggestions for future research.
2. LITERATURE REVIEW
2.1 Gender gap in STEM: a psychological, an economical and an educational approach
Despite advances within a wide range of professional roles, women remain a minority in science, technology, engineering, and mathematics (STEM) degrees and occupations. Diekman, Weisgram & Belanger (2015) adopted a psychological perspective to explain the phenomenon. The goal
congruity perspective contends that a fundamental cause of gender gaps in STEM pursuits is the
gender difference in communal motivation (i.e. an orientation toward others) rather than gender differences in ability or achievement motivation. STEM fields may be exceptionally likely to deter communally oriented individuals because these fields are thought to impede goals of directly benefitting others, altruism, or collaboration. In the communal goal congruity framework, the orientation toward others can include two distinct aspects of collaboration (i.e. working with others) and helping (i.e. working to benefit others).
From an economic perspective, Beede et al. (2011) mention that, although women fill close to half of all jobs in the U.S. economy, they hold less than 25% of STEM jobs. This has been the case throughout the past decade, even as college-educated women have increased their share of the overall workforce. Women with STEM jobs earned 33% more than women in non-STEM jobs – considerably higher than the STEM premium for men. As a result, the gender wage gap is smaller in STEM jobs than in non-STEM jobs. Women hold a disproportionately low share of STEM undergraduate degrees, particularly in engineering. Unexpectedly, women with a STEM degree are less likely than their male counterparts to work in a STEM occupation; they are more likely to work in education or healthcare. There are many possible factors contributing to the discrepancy of women and men in STEM jobs, including: a lack of female role models, gender stereotyping, and less family-friendly flexibility in the STEM fields.
Griffith (2010), based on an educational perspective, points out that during college many students switch from their planned major to another, particularly so when that planned major was in a STEM field. The author mentions that persistence in one of these majors is much lower for women and minorities, suggesting that this may be a leaky joint in the STEM pipeline for these two groups of students. Although descriptive statistics show that a smaller percentage of women and minorities persist in a STEM field major as compared to male and non-minority students, regression analysis shows that differences in preparation and the educational experiences of these students explains much of the differences in persistence rates. A higher percentage of female STEM field graduate students positively impacts on the persistence of female students. However, there is little evidence
that having a larger percentage of STEM field faculty members that are female increases the likelihood of persistence for women in STEM majors.
The problem is even more intense among minority women. Malcom & Malcom (2011) clarify that, in the sciences and engineering, as in all fields, minority women have made considerable progress relative to their minority male counterparts. In 1975, women earned just below 20% of all doctorates awarded to African Americans, Latinos, and American Indians. By 2008, this figure had risen dramatically, to more than 57%. White women, however, continue to earn less than half of STEM doctorates awarded to the white population. Though the fact that minority women earn most doctorates awarded to underrepresented minorities appears to be positive on its face, much of this progress is an artifact of the minority male crisis in higher education; there is a significant decline in participation levels and degree attainment, especially among African American males.
From the gender gap in STEM field explicated above, from multiple perspectives, derives a gap from an entrepreneurship perspective. As less women enter and persist in STEM fields, it becomes harder for them to start businesses and be responsible for innovative initiatives in companies, as they demand knowledge related to those fields.
2.2 Women & tech startups: an old and persistent problem in the entrepreneurial environment
Wadhwa & Chideya (2014) illustrate the gender gap in tech-entrepreneurship describing the scenario in the birthplace of the most innovative companies of our century: Silicon Valley. The authors highlight that executive teams of the Valley's top tech firms had very few, if any, women technology heads. In 2014, the entire management team of Apple didn't have a single woman. Virtually all of Silicon Valley's investment firms were male-dominated. The few women found on their websites are either in marketing or human resources. Venture capital firms were the worst offenders - of the eight-nine people on the 2009 TheFunded.com list of top venture capitalists, only one was a woman.
The tech-gender gap is not recent. Hisrich & Brush (1984), surveyed hundreds of female entrepreneurs and identified that none of their businesses was based on a product innovation or product modification. Most women founded their enterprises using an established or slightly modified product for an existing market. Few women entrepreneurs formed companies that entered new markets with distinctly new inventions. Female entrepreneurs in nontraditional business areas (finance, insurance, manufacturing, and construction) also differed from their counterparts in more
traditionally business areas (retail and wholesale trade). The latter group had difficulty in gaining access to external financial sources.
Researchers have been suggesting different diagnoses and solutions for solving the problem. Rosser (2005) cite that most engineers and others involved with information technology take what is called a liberal feminist stance and assume that the focus should be on employment, access, and discrimination issues. Social scientists studying the gender distribution of the technology workforce point out that historically and today, the technology workforce represents a vertically and horizontally gender-stratified labor market, with women concentrated in the lowest-paid positions, closest to the most tedious, hands-on making of the products and furthest from the creative design of technology. Most women working in the IT industry engage in the tedious, eye-straining work of electronic assembly. Men predominate in the decision-making, creative design sectors as venture capitalists, computer scientists, and engineers creating startups, and hardware design.
Public policies also have been trying to reduce the gap with specific recommendations. Mitchell (2011) suggests that more women’s startups need to be aimed at growth targets far above the benchmark of $1 million in revenues. There is a need for innovative, transformative new firms that can grow to serve global markets. Many (though not all) high-growth firms are built around new science and technology. With more women than ever entering these fields, the upside potential for women’s tech startups is huge. Mitchell (2011) reinforce that the solution must be built by multiple sources: what women themselves might need to do, what men might do, and what might be done collectively in the way of public policies or private initiatives.
A recurrent theme, popular both in academia and public policies has been the increasing of female role models to create a friendlier environment to female founders in the tech industry. In fact, the presence of role models is considered one of the drivers of entrepreneurial intentions.
2.3 Role models & entrepreneurial intentions: solving the gender gap in the tech industry
A role model is "a common reference to individuals who set examples to be emulated by others and who may stimulate or inspire other individuals to make certain (career) decisions and achieve certain goals" (Bosma et al., 2012). They are viewed as influential persons by a significant proportion of the entrepreneurs who use them in the startup phase of their venture. Entrepreneurs and their role models tend to resemble each other in terms of the characteristics that facilitate role identification, such as gender, sector and nationality.
There are multiple sources and profiles of role models. When it comes to entrepreneurial role models, Van Auken, Fry & Stephens (2006) found that business owner role models influence respondents to a much larger extent than non-business role models. Individual activities are also more influential for respondents with entrepreneurial role models. They also found that role model activities that actively involve the respondent are more influential than activities in which the respondent is passive. Previous research stated several times that the individual learned or gained experience “vicariously.” This suggests that just being around the role model was sufficient to develop the desire and self-efficacy to become an entrepreneur. In the end, it is not the role model
per se that leads to entrepreneurship, but rather it is the opportunities for gaining hands on
experience and knowledge that the role model provides that is important.
Krueger, Reilly & Carsrud (2000) clarify the relationship between role models and entrepreneurial intentions and conclude that the effect is indirect. According to them, personal and situational variables typically have an indirect influence on entrepreneurship by influencing key attitudes and general motivation to act. Evidence from entrepreneurial role models supports the potential of
intentions models for predicting new venture creation. Intentions explain conflicts in research
findings such as the effects of role models and mentors on eliciting subsequent entrepreneurial behaviors. Entrepreneurial role models only weakly predict future entrepreneurial activity. Instead, the subjective impact of role models is a stronger predictor. That is, role models affect entrepreneurial intentions only if they affect attitudes such as self-efficacy.
BarNir, Watson & Hutchins (2011) also argue that exposure to role models has a positive effect on intention. Such exposure may lead to an increase in motivation to start new ventures by facilitating information regarding possible opportunities, by providing specific guidance and support, or by providing a supporting environment that encourages entrepreneurial behavior. It was found that exposure to role models also has a direct effect on entrepreneurial self-efficacy. Because entrepreneurial self-efficacy positively predicted career intention, this finding suggests that exposure to role models has both direct and indirect effects on career choice. In other words, the results suggest that exposure to role models positively affects one's belief in the ability to be successful in an entrepreneurial career, most likely through increasing one's knowledge, mastery, or general set of abilities about engaging in tasks required for this vocation.
The study of female role models is scarce in entrepreneurship literature; however, it has been a focus of other fields. Role modelling has been considered a key driver to solving gender gaps in areas such as politics and education.
2.4 Female role models: reducing gender gaps through examples
Marx & Roman (2002) exposes the relevance of role models in math field. They highlight that female role models in math-related domains might be particularly helpful for math-talented women because they represent stereotype-disconfirming evidence about women’s inferior math ability, so that women’s math test performance is protected after encountering or learning about a female role model. In addition, if a low score on a standardized math test is one of the reasons why women are seriously underrepresented in math and engineering, then the benefits of having female role models for female students in those academic domains may be considerable.
The political perspective adopted by Wolbrecht & Campbell (2007) is essential to connect the gender problem to a sociological approach. They point out that several normative theorists have proposed—and hoped—that greater numbers of women in political office will have many positive effects: compensate for past and present injustice, provide a voice for overlooked interests, and contribute to the overall legitimacy of a democratic system. Wolbrecht & Campbell (2007), then, found that female politicians in democratic nations do function as true role models, inspiring women and girls to be politically active themselves. Women of all ages are more likely to discuss politics, and younger women become more politically active, when there are more women in parliament. The findings regarding young women also contribute to the revival of research into political socialization, particularly in the comparative context where it has been long neglected.
More specifically to the career intentions, Singh, Vinnicombe & James (2006) findings provide more evidence that ambitious young women tend to use a selection of role models to guide them through their careers and build appropriate identities. Role modelling is assumed to include social
learning involving observing and emulating behaviours, styles and attributes of role models. Whilst
the choice of celebrity role models might occur because they are frequently in the public gaze, offering glimpses of their behaviour.
The findings of this Literature Review are summarized in Table 1. As a framework to the following analysis, it will be adopted four dimensions of the problem: 1) causes of the gender gap in STEM fields; 2) effects on the gender gap in the field; 3) learnings from role modelling in entrepreneurship; and 4) learning from role modelling in other fields.
Causes of the gender gap in STEM fields
Psychologic perspective: communal motivation (Diekman, Weisgram & Belanger, 2015) Economic perspective: less family-friendly flexibility (Beede et al., 2011) Education perspective: lack of persistence and preparation (Griffith, 2010)
Effects on the gender gap in tech field
Lack of innovation brought by female founded businesses (Hisrich & Brush, 1984)
Women get the lower-paid, not creative positions in tech industry (Rosser, 2005)
Lack of women in the top management decision-making teams (Wadhwa & Chideya, 2014) Learnings from role
modelling in entrepreneurship
Gaining experience vicariously (Van Auken, Fry & Stephens, 2006)
Identification with gender, sector and nationality (Bosma et al., 2012)
Increase the belief in self-efficacy (BarNir, Watson & Hutchins, 2011)
Learnings from role modelling in other fields
Provides stereotype disconfirming evidence (Marx & Roman, 2002)
Provides a voice for overlooked interests (Wolbrecht & Campbell, 2007)
Helps to build
appropriate identities (Singh, Vinnicombe & James (2006)
3. METHODS
3.1 Research design
The present work adopts a qualitative research design. Creswell (2013) explain that qualitative research is useful for exploring and understanding the meaning individuals or groups ascribe to a social or human problem. It fits the research question of this study which is to explore how female role models make sense of their entrepreneurial journey in technology field. As sub-questions of, it is expected to understand 1) how female role models face the challenges of being a woman in a technology field?; 2) what messages are being sent to women about entrepreneuring in a technology field? And; 3) how these messages can increase entrepreneurial intentions of women in a technology field?
Female role models and their sensemaking process is a nascent field of study. Edmondson & McManus (2007) mention that nascent theory proposes tentative answers to novel questions of how and why, suggesting new connections among phenomena. Following authors orientation, based on an open-ended inquiry about a phenomenon of interest, it was collected qualitative data that need to be interpreted for meaning. The aim is to generate new constructs guided by pattern identification of the data. The ultimate result of this design is to suggest a theory, inviting further work on the issue opened by the study.
3.2 Data collection
The qualitative material adopted in this research is a set of blog posts. Hookway (2008) mentions that blogs offer a low-cost, global and instantaneous tool of data collection. Klastrup (2010) points out that little has been written about the potential for blogs to provide an online location for the collection and storage of qualitative data. While blogs are a recognized, valid way of communicating in business, art and research contexts, few examples of research use blogs as a data collection tool. Murthy (2008) highlights that, as social interactions increasingly move online, social scientists should include digital content in their toolkits for research. In line with that recommendation, this study adopts blog posts as qualitative material for analysis.
The data is derived from the blog Women of Silicon Valley (https://medium.com/women-of-silicon-valley/), an initiative that showcases excellent women and gender nonconforming people in tech. Each blog post is formatted as an interview with a founder or executive of a tech company, in which she shares personal stories related to life in general and specifically the challenges of being a woman in a male-dominated field. The initiative was created in 2015 by Lea Coligado, a young computer science major at Stanford who has interned at Facebook and Apple, two of the biggest
tech companies in the world. The blog was selected as a data collection source given the alignment between the present research question and the purpose of the initiative: the sensemaking process of female entrepreneurial role models in a technological field. "I feel like these women deserve more recognition. I was like hyperventilating when it happened because they're huge role models for me" (Lea Coligado, ABC, 2015).
60 blog posts of Women of Silicon Valley, short interviews with female tech entrepreneurs were selected, coded and analysed. The full list of interviews and its online links is in Appendix I. The coding process identified excerpts of the narratives that fitted each of the 12 quadrants of Table 1, that summarized the theoretical background. It was defined keywords that guided the analysis process, related to each of the literature findings. For example, to fit the quadrant about vicarious learning in role modelling, the keywords were observing, learning and watching. The full list of keywords can be found in Appendix II.
The context of Silicon Valley, the place where the stories emerge from, is relevant for this study as it is the birthplace of the biggest tech-based startups in the world. This way, it is assumed that Silicon Valley itself plays a role in defining declared and undeclared rules to drive tech-startups (and its founders) into success. To understand the sensemaking process of female role models in that environment brings insights for the same situation in other contexts that mirror the Valley's entrepreneurial context.
3.3 Data analysis
The content of the blog posts is interpreted as life stories of female role models. Ollerenshaw & Creswell (2002) explain that telling stories helps people to think about, and understand, their personal or another individual’s, thinking, actions, and reactions. Thus, it is not surprising that collecting stories has emerged as a popular form of interpretive or qualitative research. Larty & Hamilton (2011) point out that narrative is recognized as a credible source of knowledge for scholars engaged in theory building in entrepreneurship. A wide range of methods for the analysis of narrative empirical material have been adopted in research to date. For the present work, a framework proposed by Larty & Hamilton (2011) is adopted. It serves as an entry point for researchers new to narrative analysis to identify fruitful avenues for research. The framework contemplates three stages, as following.
The first stage explores the role that entrepreneurs play in their own stories. It was previously studied that the ability of entrepreneurs to position themselves as characters in their stories,
enhances credibility in relationships with customers, investors and employees (O’Connor, 2002). In the present study, it will be analyzed how the role models position themselves towards the challenges of being a woman in a male-dominated field.
The second stage considers the context of narrative production and use this contextualization as a starting point to identify areas worthy of further, more critical analysis. This determination involves the careful consideration of the co-constructed nature of stories and their embedded nature within cultures. In the present study, the fact that Silicon Valley is a male-dominated field and imposes challenges to women to start businesses or be responsible for intrapreneurial initiatives will help us to identify how its entrepreneurial culture empower or prevents the role models in their journey.
In the third and last state, themes emerges from the data analysis. Edmondson & McManus (2007) point out that working within the nascent theory arena requires an intense learning orientation and adaptability to follow the data in inductively figuring out what is important. To address this challenge, the stage 3 of the narrative analysis will be based on the key learnings of the Literature Review above described. This way, it will be possible to identify the intersection between the sensemaking process of the female role models and the challenges theory has previously pointed out regarding the gender gap in STEM fields.
3.4 Organizing, entrepreneuring and sensemaking
The present research assumes the organizing perspective toward entrepreneurship. Peverelli & Verduyn (2012) define organizing as a continuous process of ongoing interaction between people, referred to as ‘actors’, in their quest to make sense of the world. Derived from that, the concept of
entrepreneuring refers to the sensemaking process by entrepreneurs and their stakeholders through
ongoing social interaction, to couple their behaviour in ways that suit the realization of an opportunity.
Weick, Sutcliffe & Obstfeld (2005), thinking of entrepreneurship as ongoing creative organizing, consider the arrays of activities to include bundles of (personal) relations. Using this approach when inquiring into entrepreneurship means on one hand that the idea of entrepreneurship as anything but genuinely social and collective is denounced, on the other hand that all human faculties, not just the cognitive but also the emotional ones, are recognized as instrumental in the enactment of new ventures, whether on the market or in other settings.
An integral part of this perspective is the process of sensemaking. Peverelli & Verduyn (2012) explain that during the ongoing sensemaking process, groups of actors gradually emerge, sharing certain beliefs on reality, use of language, common symbols etc. In this respect, sensemaking is not simply about ‘making sense,’ it is also the basic process of actors gradually forming increasingly complex social structures.
When analysing the narratives of the female entrepreneurs, it was identified the set of elements that were an integral part of their entrepreneuring processes and the social structures they influenced and were influenced by in their journeys. It ultimately made possible to point out how female role models face the challenges of being a woman in a technological field, what messages are being sent to women about entrepreneuring in a technological field and how these messages can increase entrepreneurial intentions of women in a technological field.
3.5 A note on gender bias of the researcher
Qualitative sociology has often emphasized the distance that should separate the situation observed and the observer, as well as insisting on a certain neutrality in the latter's behaviour (Hammersley and Atkinson, 1995). However, it is known that the gender of the researcher may play a role in terms of performing power, obscuring hetero-normativity professional identities and neglecting the emotional engagement that characterizes research activities (Bruni, 2016). The researcher of the present work, being male, must consider those warnings.
In this research, the risk of bias occurs in two stages. First, the data collection, as the interaction between the male researcher and a female interviewee could interfere both on the questions and the answers that would be used as qualitative material. This risk was reduced by the adoption of blog posts as the source of narratives, as the content is already online, without the need of that interaction. Secondly, the data analysis, as the mindset of the researcher is molded by his gender. This risk was reduced by following Bruni (2016) advice, that it is essential for those who study organizing processes to interrogate constantly the intellectual assumptions of their research and their reality lest they fall into the trap of ‘cultural blindness’ that is, an inability to focus on certain concepts and organizational practices because they are common to different organizational realities, including that of the researcher. The research design is summarized in Figure 1.
Figure 1 - Research design
Entrepreneuring & Sensemaking
Peverelli & Verduijn (2012)
STAGE 1
Characters
Female role models
STAGE 2
Context
Silicon Valley
STAGE 3
Emerging themes
Based on prior theory
QUALITATIVE RESEARCH
Blog Women of Silicon Valley
Stories of female role models
Narrative analysis
4. RESULTS
4.1 Characters
There are people who assume I am where I am in my career because of my last name and gender, and therefore, that I am not qualified or don’t deserve to be here. That used to really get to me. I’d waste a lot of time and energy thinking about how I could prove those people wrong. But after a while you realize your time is much better spent listening to the people who believe in you and striving to reach your full potential, vs. trying to prove yourself to the people who are quick to make assumptions (Arielle Zuckerberg, Partner at Kleiner Perkins Caufield & Byers).
The above quote represents a pattern identified in the female role model stories: the presence of antagonists, people whose attitudes towards women in technology field work as barriers for adaptation and growth. In other words, women often mention groups of people who make them feel pressured by their decision of being into tech and make emerge a need of proving themselves. Indeed, narratives have a protagonist and, frequently, an antagonist as well. The characters may not be developed or even identified by name, but, along with sequence, they provide a thread that ties the events in a narrative together (Pentland, 1999).
Besides the antagonists, female role models mention the challenges of dealing with other’s beliefs that they achieved success because reasons other than their own merit, talent or skills. In above quote’s case, last name and gender are mentioned. In the case of the creator of the blog, “lucky” was the justification of the fact she was interning at Facebook. Shading light on these antagonists and their arguments helps women to make sense of their stories and works as a common ground on which young prospective entrepreneurs can identify with their role models.
4.2 Context
"Silicon Valley" refers to the microelectronics-based high-tech industrial region located just south of San Francisco in Santa Clara County, California. The area has been heralded as an economic panacea and as a regional prototype for localities around the globe that seek rapid economic growth and incorporation into the international market (Hossfeld, 1990).
One of the biggest business opportunities in Silicon Valley will result from increasing diversity among engineers. Sometimes people mistakenly view the space as a small, niche market but in actuality, it’s huge. Diversity is an investment, and I’m happy my success and the success of other technical minorities and women showcase its returns (Shola Oyedele, Software Engineer at Intuit).
The above quote shows diversity as a keyword for empowering women in the tech field. Also present in other stories in the blog is the need for a feminine diverse element in a male-dominated Valley. The literature has shown robust results positively linking diversity to innovation (Nathan & Lee, 2013). It fits perfectly to the overall thinking about what makes Silicon Valley the birth-place of most innovations in the last couple of years. In Silicon Valley, ideas, capital, and talent circulate freely, gathering into whatever combinations are most likely to generate innovation and wealth. Unlike most traditional companies, which spend their energy in resource allocation--a system designed to avoid failure--the Valley operates through resource attraction--a system that nurtures innovation (Hamel, 1998).
This way, female founders reinforce the argument that to generate the wealth expected by the stakeholders – investors, above all – with their tech startups and intrapreneurial initiatives. Contextually, they make sense of their presence arguing in favor of a more diverse environment.
4.3 Emerging themes
4.3.1 Sensemaking in causes of the gender gap in STEM fields 4.3.1.1 Communal motivation and empowerment
In sociological studies, communal motivation by women is usually associated with weakness. For instance, people assume that men are more competent and knowledgeable than women are, that women are warmer and more communal than men are, that men have more right to act as authorities than women do, and that women must communicate communal motivation more than men (Carli, 2001). However, when it comes to the female role models studied here, they make sense of their communal motivations adopting it as fuel to face the challenges of building an innovative business.
I get out of bed every morning with the dream of utilizing the power of people to change the world. I genuinely believe people are our greatest assets and the world will do itself a great
disservice by not finding ways to fully leverage the inner gifts of people from all walks of life (Fatima Dicko, Founder & CEO of Jetpack).
As mentioned in the above quote, instead of a constraint that prevents them to enter the tech-startup scene, a view towards others needs to empower them to do it more effectively. In fact, according to Blatt (2009), entrepreneurial teams often operate under conditions of novelty—the lack of familiarity. Novelty can undermine team members' ability to develop the relational capital (trust, identification, and mutual obligation) needed for a venture to succeed. Then, entrepreneurial teams can counteract the challenges of novelty by adopting communal relational schemas (caring about one another' s needs). Entrepreneurial communal leaders build that emotional structure needed to innovation flourish.
4.3.1.2 Work-life balance as a propelling to success
Previous research has shown that women pursue more flexible work conditions to balance professional and family responsibilities. DeMartino & Barbato (2003), for example, found that women are motivated to a higher degree than equally qualified men to become entrepreneurs for family-related lifestyle reasons. Women are less motivated by wealth creation and advancement reasons. Female role models, surprisingly, rely on work-life balance not as trade-off to wealth, but as a propelling to success.
For people who believe that women juggling a career and kids are a liability, I would posit that there are few people in the world who know how to multitask and ruthlessly prioritize like a mother of young kids. It’s been the key to my success (Ann Miura-Ko,
Cofounding Partner at Floodgate).
The quote from the co-founder of Floodgate goes against the traditional view, that women choose to work less hours and change priorities when start a family - especially when having children – replacing it by a view in which the balance bring new skills – multitasking – to them as entrepreneurs, making them more prepared to build their ventures.
Her sensemaking process suggest she realized what previous research on motherhood and entrepreneurs has shown. Schindehutte, Morris & Brennan (2003) found that mothers who are entrepreneurs clearly impact the childhood experiences of their offspring and that the overall experience is positive. While children indicate the business does not affect closeness with their
mother, they are generally close, see their mother as a little different from other mothers, and view her as a role model.
4.3.1.3 Networks of mutual support
There is a high dropout rate among women in engineering in science degrees. According to Brainard & Carlin (1997), the reasons for leaving are also the most frequently reported concerns, or "barriers to progress" reported by women students who persist: fear of losing interest, intimidation, lack of self-confidence, poor advising, and not being accepted in their department.
I noticed many of my female peers were facing similar challenges — they were armed with impressive academic backgrounds, but felt lost and insecure navigating work environments not particularly inviting to women. That’s when I decided to launch Miss CEO. I wanted to provide leadership training, mentorship, and career exploration opportunities to young women looking to excel in all phases of their lives (Nita Singh Kaushal, Founder of Miss CEO).
In line with that, as illustrated by the quote from the founder of Miss CEO, female role models’ stories are full of mentions to challenges during their academic careers. They make sense of that issue both providing and joining network of female students that, aware of those barriers, reinforce each other's capabilities and grow together, as a community. That mutual support is a key driver to achieving their goals.
That support may contribute to increase the entrance of high-performance women in tech, as previous research found that female students in science and engineer programs do better than male students in degree completion and program switch (Huang, Taddese & Walter, 2000). Although women are less likely than men to enter science and engineer, those women who do enter are likely to do well.
4.3.2 Sensemaking in effects on the gender gap in tech field 4.3.2.1 Paving the way for innovative women
Researchers have tried to argue in favor of a gender perspective on innovation. Blake & Hanson (2005) say that understandings of innovation are predominantly technological and product driven and defined in universal terms such that the nature of innovation is stripped of its contextual
influence and is overly masculinist. Female role models in this study demonstrate having overcome the view that women usually don't innovate when starting businesses.
I decided to pursue basic/translational science, not a common choice for women. After striving and learning for several years in the lab I realized that my true passion would be to apply this knowledge to the innovation of therapies for the patients I saw in my HIV clinic and the millions of others worldwide (Devi SenGupta, Associate
Director at Gilead Sciences).
The above quote highlights the pro-innovation environment of Silicon Valley, which contributes to attract women that adopt technology to disrupt industries and become examples for newer generations of female entrepreneurs. They make sense of their journey pointing out the important of their pioneer work, to pave the way for new innovative women to bring their ideas into life.
Blake & Hanson (2005) confirm this idea, mentioning that through the act of owning a business in a male-dominated industry, such as trucking, construction, or auto repair, women entrepreneurs can become innovators. Owning a gender-atypical business is innovative because the owner does not conform to an assigned gendered position. Although our discussion centers on examples of women, the point holds true both for women and for men.
4.3.2.2 Human-oriented skills to enter tech scene
A survey among people working in Silicon Valley startups shows there is a gender pay gap and the disparities are based on the nature of the position assumed by men and women. Within the wide variety of roles in tech-companies, software engineers’ positions pay more and are male-dominated (Hired, 2016), reinforcing the previous studies about lower-paid jobs for women. To make sense of that barrier, the female role models in this study found an alternative to access the industry, through their human capital: many of them choose to specialize in sub-disciplines in the tech field that demanded an intersection between technological and design skills.
I ultimately fell into user experience because it allowed me to leverage technology and empathy to make people’s lives easier. It was both fun and rewarding, and it continues to motivate me
As mentioned in the above quote, the founders build their startups upon pillars of user experience and people centered approaches, which allow them to bring value to the market without fighting the battle for the hard-technological skills. Hassenzahl & Tractinsky (2006) highlight a strong interest in user experience, which encompasses both practice and research. Many interactive products found their way into our daily lives. State-of-the-art machinery allows for more than mere functionality. At the same time, the growing and changing base of users shifts the parameters of demand for interactive products.
The user experience perspective takes this shift seriously. Its focus on aspects beyond the functional, on the positive, the experiential and emotional is no coincidence. It is driven by commercial vendors, who are sensitive to the changes in business climate, by designers who appreciate new design opportunities, and by a scientific community that shows renewed interest in the affective system and its interplay with cognition.
4.3.2.3 Well-informed decision makers
Schubert et al. (2009) point out a widespread view concerning financial decision-making in which women are more risk-averse than men. The authors add that the perception that female managers are less risk-prone than men has been put forward as a major cause of "glass ceilings" in corporate promotion ladders and it would also undermine the willingness to start a business, an intrinsically risk initiative. Surprisingly, female role models argue in favor of a more informed, slower decision-making process, that can prevent them to assume top management positions in tech-companies, including their own.
I think the biggest difference is men don’t feel the need to be fully informed with facts to speak from a position of confidence. Women don’t do this. What I’ve observed over the years, time and time again, is men take a conclusive position and fill that in with evidence, then look to others to find solutions. Women like to gather evidence and facts and then take a position, then offer solutions, which include how to collaborate to be successful (Holly Rollo, Chief Marketing Officer at RSA).
The above quote is a representation of how female entrepreneurs deconstruct the idea that women can't make hard decisions and, instead, make sense of their mental process considering multiple sources of information and interest of stakeholders, generating, then, better decisions than their
male counterparts. That argument is in line with the previous study of Schubert et a. (2009). They showed that the comparative risk propensity of male and female subjects in financial choices strongly depends on the decision frame. Gender-specific risk propensities arise in abstract gambles, with men being more risk-prone toward gains but women more risk-prone toward losses. Moreover, when identical decisions are presented as investment and insurance choices, no gender differences in risk attitudes are found. This way, it is suggested that gender-specific risk behavior found in previous survey data may be due to differences in male and female opportunity sets rather than stereotypic risk attitudes.
4.3.3 Sensemaking in learnings from role modelling in entrepreneurship 4.3.3.1 Learning observing tech-saving parents
Negative stereotypes about girls’ and women’s abilities in mathematics and science persist despite girls’ and women’s considerable gains in participation and performance in these areas during the last few decades. Two stereotypes are prevalent: girls are not as good as boys in math, and scientific work is better suited to boys and men (Hill, Corbett & St Rose, 2010). Those stereotypes were frequently mentioned in the female role models’ narratives. Phrases like technology "is not for girls" were often present in their lives, especially in childhood.
My dad — he’s an anthropologist of technology. He’s been conducting ethnographic research on technology and media my entire life. I have vivid memories of him teaching me how to read via prodigy computer games. As kids we often participated in his focus groups and user-experience tests on emerging technologies, and I loved when I got to miss school to spend the day with him at work (Eileen Carey,
Cofounder of Glassbreakers)
As the case of Eileen Carey, above cited, many of the female entrepreneurs make sense of their tech careers inspiring themselves in their parents, merely through the observation of their behavior. Observing their mothers or fathers working in tech-related tasks gave the meaning needed for them to pursue, later in their life, tech-related degrees. In fact, Schmitz & Steinfield (1990) point out that social influence may take the form of vicarious learning from observing the experiences of others. When the choices have led to positive consequences, behavior modeling may occur. Thus, effective behavior by one person may well be repeated by others through a process of observational learning. Similarly, choices that lead to undesirable consequences may be avoided by other.
4.3.3.2 Creating communities to spread positive narratives
Bettinger & Long (2005) found that women are underrepresented on university faculties, particularly in the sciences and quantitative fields, and many worry about the lack of potential role models for female undergraduate. A similar issue occurs when it comes to female role models in tech-startup scene. To address that issues, many of the female role models make sense of their journey creating themselves communities to attract women looking for role models in their roles in tech.
Stories are powerful, and people want to hear the stories of other people who are like them. It’s been so awesome being able to see these stories not only 1) help the women we feature in their careers, but also 2) make the world aware of their presence. It’s so empowering meeting other women engineers and hearing their stories (Erin
Summers, Software engineer at Facebook, Cofounder of Wogrammer).
As cited by the cofounder of Wogrammer, female entrepreneurs build networks which goal is collect stories and share them among the network members, spreading positive narratives that can inspire aspiring entrepreneurs and benefiting them with all the already mentioned effects of role modelling.
4.3.3.3 Coaching to make self-efficacy flourish
Previous research found females show significantly lower entrepreneurial self-efficacy than males in a management education environment (Wilson, Kickul & Marlino, 2007). Notably, teen girls— with far fewer life experiences—demonstrate the same pattern in this regard as do adult female. The authors add that differing expectations imposed by society may well shape self-efficacy at an early age, long before actual experiences take place that may further shape or solidify one's self-confidence in different domains. Being aware of that, female founders share stories of coaching younger professionals and potential entrepreneurs, to increase their confidence and reinforce their responsibility as role models.
I have been mentoring and coaching a soft-spoken and introverted engineer for years, encouraging her to speak up, and to be courageous and confident. A couple of years ago, she overcame her shyness, and gave a terrific speech about women and leadership in front of a large audience. Just watching and listening to her
teared me up, and I felt like a proud parent watching their kid grow up
(Vijaya Kaza, Senior Vice President of Cloud Business at FireEye).
The above quote shades light to the positive feeling of solving the lack of self-efficacy problem with direct mentorship. That kind of support occurs both through independent initiatives (such as NGOs and communities) and direct mentoring to employees. In fact, Day & Allen (2004) mention that self-efficacy beliefs are usually determined and modified by four informational sources: performance attainment (personal accomplishments), vicarious experience (modeling), verbal persuasion, and physiological states and reactions. Two of these sources are especially important to the mentoring relationship. Vicarious experience, or observing similar others succeed or fail at a activity affects self-efficacy. Role modeling provided by a mentor should provide this vicarious experience for the protégé. Secondly, verbal persuasion (telling the protégé that he/she possessed capabilities) should also contribute to self-efficacy.
4.3.4 Sensemaking in learnings from role modelling in other fields 4.3.4.1 Overcompensate to disconfirming stereotypes
Gupta et al. (2009) show that entrepreneurs are perceived to have predominantly masculine characteristics. Also, although both men and women perceive entrepreneurs to have characteristics like those of males (masculine gender-role stereotype), only women also perceived entrepreneurs and females as having similar characteristics (feminine gender-role stereotype). That issue is even more complex in the case of non-straight women and transgender.
As a non-straight woman in tech, belonging to a minority group is challenging because I simply don’t fit the norm, and that causes discomfort. As a result, I have often found myself having to overcompensate, a common pattern in these situations. I think this has been a constant challenge in my career and as a result, I have taken on fewer risks and assignments than I could have. (Katja Lotz, Agile coach at Spotify)
The above quote represents how female entrepreneurs make sense of the challenge, assuming a posture of overcompensation, if they must make even more effort than it would be needed if they were part of the dominant gender. In the recent past, research found a lack of support of a marriage partner, which is often cited by men as important to entrepreneurial success (Stevenson, 1986). According to the author, at that time, women had also to assume the role of mother and household
caregiver, which limits the time and energy they must give to their businesses. As mentioned by the above quoted entrepreneur, not fitting the norms, can bring barriers to their personal development, demanding an extra-effort to deal with others normalized judgments and assumptions.
4.3.4.2 Supporting networks to give voice to women
There are several challenges faced by minorities in the work life. For example, performance information is ignored. Heilman & Chen (2003) found there is a good deal of evidence that indicates that even when she produces the identical product as a man, a woman's work is regarded as inferior. To make sense of that situation, female role models shade light over the problem and highlight that is essential for women to be surrounded by a supportive network of people that see them beyond the stereotypes of gender and race.
For people who aren’t the minority in their field, imagine if you walked into a room where every other person was a different race, or a different age, or a different gender from you. Even if no one said anything mean or rude to you, or even if people didn’t really notice you, you would likely still feel a sense of unease or discomfort. That’s even when you might be surrounded by friendly supportive people, and that’s not always the case in my field (Omosola Odetunde, Software
Engineer at Clue, Formerly at Shopify).
The word “discomfort” was used again in the above quote, reinforcing the feeling of not being part of the Valey’s community being a woman. Entrepreneurship itself could be a way of escaping from the prejudices already identified in the corporate world. However, even in their independent venture journey, women face that challenge. Although entrepreneurship can provide individuals with a way to gain career autonomy and control, it does not necessarily provide an escape from all the problems that women and minorities face in the business world (Heilman & Chen 2003).
4.3.4.3 Family embeddedness as a path to success
Motherhood is a frequent theme when it comes to studies about female founder. Brush, De Bruin & Welter (2009) say that for women entrepreneurs, motherhood or family embeddedness will directly influence how the entrepreneurial process unfolds. Family role will influence information networks used to identify the market opportunity. Hence, women with high commitment to family will be less likely to interact in market, financial and industry networks, possibly affecting the growth prospects or even novelty of the venture. In a similar vein, family embeddedness can influence entrepreneurial
self-efficacy and the aspirations for the exploitation and/or value of the opportunity. In a different direction, female role models make sense of their journey mentioning family embeddedness as a way of empowering their role as entrepreneurs.
Having 3 kids presents its own unique challenges. Pair that with co-founding a startup, and there are sure to be difficulties. I’m (still) figuring out the work-family balance. My work is very important to me, as is my family. They each require my time, my love and my attention. Ultimately, I feel that being a mom strengthens my passion for our mission and my resolve to make KiraKira a great success (Malena
Southworth, Cofounder at KiraKira).
As it is possible to see in the above quote, according to many of the female founders being a mother increased both the ability to deal with routine challenges of managing a business and flourished the purpose of creating a new venture, that will make positive impact in the world. Nel & Thongprovati (2010) point out that moms have critical entrepreneurial skills such as patience, stamina and persistence. They know how to prioritize and are master schedulers. It doesn't mean they must deny the struggles of balancing family and entrepreneurial roles. Women entrepreneurs often feel a huge level of conflict between work and family roles, leading to a negative impact on their well-being (Carrigan & Duberley, 2013).
4.3.5 Four propositions from an entrepreneuring and sensemaking point of view
To propose theoretical propositions emerged from the results, the categories were grouped into 4 complementary dimensions, that explain the sensemaking process of women in the tech-entrepreneurial environment. The grouping process can be found in detail in Appendix III.
4.3.5.1 Multiple identities empowering entrepreneurs
Peverelli & Verduyn (2012) mention that sensemaking requires a minimal social situation of at least two actors. Sensemaker is singular and no individual ever acts like a single sensemaker, as sensemaking is the exchange of perceptions through social interaction. During sensemaking, actors gradually develop an idea about their role in that situation vis a vis the others); who they are is what they are to other people. This perceived role is decisive for the actions an actor will undertake. The ensemble of perceived role and linked activities is called identity. Again, in this view, identity is not understood as a static trait, but as a process; the process of identity construction. A social identity is constantly reconstructed in ongoing interaction.
The narratives showed female role models not only embrace multiple identities (e.g. mother, daughter, mentee, student) they also assume throughout the entrepreneuring process, but also make sense of them as an empowering force to face the challenges of starting a new tech venture. In that sense, it is proposed:
Proposition 1: Embracing multiple identities empower the entrepreneuring processes of female entrepreneurial role models.
4.3.5.2 Valuable multiple inclusions communally motivated
Peverelli & Verduyn (2012) say that multiple inclusions refer to the fact that each actor will be part of several groups of actors with interlocked behavior. The formation of such groups is a continuous process; groups form and disband all the time. Actors enter groups, while others leave them. During an effort to stabilize his inclusion in a certain group, an individual actor may be forced to integrate more of himself into that group.
Women of Silicon Valley make sense of their communal motivation, often seen as weakness, as a way of finding purpose in their ventures, a reason to wake up in the morning, and a mission to create a positive impact in people's lives. They pertain to groups that ended up being the customers or other stakeholders of their ventures. They create value in their businesses driven by those inclusions. Thus, it is proposed:
Proposition 2: Communal motivation is a source of value through multiple inclusions of female entrepreneurial role models.
4.3.5.3 Reducing equivocality nurturing a supportive network
Peverelli & Verduyn (2012) point out that human actors are constantly exposed to such a large amount of information, that they are unable to cope with it all. Actors who must cooperate in performing a certain task will initially hold different interpretations of various aspects related to that task (a situation we refer to as equivocality). During their initial interaction, the actors will exchange these interpretations through social interaction and mutually adapt their perceptions of the task until a common interpretation has been aimed. This is not a deliberate process, but simply happens. The human mind, however, is subconsciously aware of this process.
The narratives of female role models are permeated by people incentivizing and demotivating them. From that, emerges a need for being surrounded by people that understand and support entrepreneurs, helping them to overcome the challenges of being a woman in a male-dominated field. In that sense, it is proposed:
Proposition 3: Female entrepreneurial role models embed themselves in supportive networks to intensify the reduction of equivocality in entrepreneuring process.
4.3.5.4 Overcompensating to stimulate diversity of actors
According to Peverelli & Verduyn (2012), in organization theory, we speak of ‘actors’ instead of ‘humans’ or ‘people’. We use the term ‘actor’ about ‘act’ which is the basis constituent of ‘interaction’. Furthermore, ‘actor’ refers to the fact that, within this model, we regard the things people do as determined by the social context; (almost) none of it happens because of ‘free will’. Moreover, the actions of people are social in nature. Even when we seem to do something ‘on our own’, most of our actions can be directly or indirectly related to other people.
Female entrepreneurs tell stories of disadvantage in their journeys, what demands an extra effort to achieve same results as actors pertained to dominant groups. That extra effort is, then, transformed in a way of proving the value of their similar actors and increasing the chance of creating diverse environments. They make sense of the experience seem themselves as the explorers to pave the path for a more diverse entrepreneurial scene in tech. From that, it is derived the proposition:
Proposition 4: Female entrepreneurial role models overcompensate to state the need of environments with diversity of actors.
5. CONCLUSION
5.1 Sensemaking of female entrepreneurial role models in a technological field
Overall, the female entrepreneurial role models were found to make use of fours dimensions of the organizing process to make sense of their journeys. Firstly, they deal with the multiple identities they assume, particularly when it comes to business and family, generating synergies and extracting meaning of the multiple responsibilities they handle. Secondly, they express to be motivated by the caring of others as a way of offering value to the multiple inclusions they are part of in their entrepreneuring processes. Thirdly, the embeddedness in pro-female entrepreneurial network was an often-cited element of their sensemaking effort. Finally, as normative environments make them feel uncomfortable to be themselves, the overcompensation emerges to overcome social barriers of not being part of the dominant group in technology field.
Most of the stories are characterized by an effort to transform constraints into opportunities. An emblematic example comes from the balance between being a mother and lead a company. Instead of sharing narratives of difficulties or trade-offs among familiar or business decisions, they focus on the skills and knowledge acquired by the multiple roles and how they complement each other, making them better entrepreneurs, more able to overcome the traditional barriers of starting a venture.
In that sense, young potential entrepreneurs that follow the stories of the female role models in the blog, are receiving optimistic messages that go against the limits imposed by society – e.g. economic, psychologic and educational perspectives – and invite them to join the challenging environment of the Silicon Valley. These messages can play a key role in the entrepreneurial intentions of prospective new entrepreneurs. By addressing elements such as self-efficacy constraints, gender stereotypes and social norms, the narratives help potential entrepreneurs to finding meaning in the journey and make sense of their own stories, as facing similar challenges as their entrepreneurial examples.
5.2 Findings and prior literature
Rindova et al. (2009) mention that it is important for entrepreneurship research both to acknowledge that entrepreneuring is often an act toward emancipation and to draw attention to challenges entrepreneurs face. Addressing these opportunities and challenges will require approaches that cross levels of analysis and extend beyond prevalent theoretical views, integrating theory that enables inquiries into the micro and macro aspects of entrepreneuring. The present study
