Chapter 2: Literature Review

The first appearance of a research article on women in entrepreneurship was written by Eleanor Brantley Schwartz in 1976, with little published after that until the 1980s. While some research has been conducted on women’s participation in technology entrepreneurship, most of the research to date has been focused either on women in technology or women in entrepreneurship. Some studies have tried to explain the low participation of women by looking at factors such as women’s quantitative skills, aversion to risk, or reluctance to sacrifice time with family. However, this research has been unsuccessful in showing any clear differences between men and women (Henry et al., 2016, Ahl and Marlow, 2012, Santos et al., 2016). While women represent fifty-seven percent of the professional occupations in the U.S. workforce, their rates of participation in entrepreneurship and technology occupations have remained disproportionately low (Womenable, 2014). The issue of low participation of women and minorities in technology and entrepreneurship has attracted much attention. Organizations such as the National Center for Women and Information Technology (NCWIT), the Anita Borg Institute, Lean In, Girls Who Code, The Diana Project, to name a few, are actively working to increase the number of women in computing fields. Despite this attention, little progress has been made to successfully address current inequities and lack of participation in this field.

Using the lenses of gender bias and ambient belonging, this study aims to understand how an experiential learning based educational program contributes to empowering women in technology and entrepreneurship. This literature review will explore the status of women’s participation in technology and entrepreneurship and address why diversity in these intersecting fields is important to society. Several related theories are explored, including gender theories, entrepreneurship theories, and educational theories. This chapter begins with a discussion of why diversity is important in technology entrepreneurship. Next, it presents relevant research on gender theories including gender bias and ambient belonging, entrepreneurship motivational theories, and relevant research which focuses on increasing entrepreneurial attitudes and intentions. The chapter concludes with an overview of the gaps in the literature.

Women’s Low Participation in Technology and Entrepreneurship

It is difficult to find specific data about women’s participation in technology entrepreneurship. While it is known that the involvement of women in technology and entrepreneurship careers is low, the actual numbers of women working in technology occupations are often difficult to track due in part to tech firms self-reporting their own data. Often, technology firms will report diversity data based on the total number of employees, but when the data are further analyzed, it is revealed that many of their female employees occupy non-tech roles such as human resources or sales (Fussell, 2016).  A broader understanding of the trends involved in women’s participation in technology and entrepreneurship can be developed by considering (1) occupational employment data, (2) the degrees females earn in higher education, and (3) data related to women’s representation in venture capital funding and patenting.

Using the data from the 2014 noninstitutionalized resident population of the United States aged 18 to 64 (NSF, 2017) in Figure 1, we can see that white men currently represent 49% of the individuals working in science and engineering occupations (S&E), including computer science.  Asian men represent 14% and Asian women make up 7% of the S&E workforce. When these populations are combined, white men, Asian men, and Asian women make up 69% of the S&E workforce, yet they only represent 36.7% of the total U.S. population (Figure 2). From this, we can see that the rest of the populations (White women 18%, Black women and men 5%, Hispanic women and men 6%, and other 2%) are underrepresented in that their proportions in the S&E workforce equals 31% yet their percentage of the U.S. population is 64%. Women overall represent only 30% of the S&E workforce. Unfortunately, these numbers are not getting any better, in fact, in many cases, they are getting worse. The number of women in computing-related jobs has dropped from a high of 35% in 1990 to a low of 18% in 2014, while the number of men in computing increased by 11% during this same period (Hill and Corbett, 2015).

Figure 1-1. Scientists and engineers working in science and engineering occupations: 2015
Figure 1-2. Noninstitutionalized resident population of the United States ages 18-24, by race, ethnicity, and sex: 2014. Source: National Science

Another source for examining women participation in technology and entrepreneurship is students’ degree choices by gender. From 1993 to 2013, the percentage of women earning college degrees in the U.S. rose from 46% to 53% (Finamore and Khan, 2015) and by 2014 women earned 57% of all bachelor’s degrees (NSF, 2017). A variety of means have been identified that have led to these gains including a) decreasing discrimination against women; b) better supports for balancing work with family life; c) girls receiving better academic preparation for higher education; d) the feminism of the teaching profession; and e) a learning environment more conducive to girls’ social and cognitive dispositions (Ashcraft and Blithe, 2009). Despite this parity in some areas of education and overrepresentation in others (e.g., 70% in psychology) women’s participation in technology-related disciplines is still disproportionally low. Women receive more than 40% of the bachelor’s and master’s degrees in math, but these numbers tend to drop off to 30% at the doctoral level (NSF, 2017) and Computer Science education programs have seen the number of female students drop from a high of 37% in 1986-87, down to 18% in 2014 at the bachelor degree level (Barker et al., 2014, NSF, 2017).

Third, studies have have shown a drop-in participation rates in venture capital firms and continued low participation of women in top-level management positions. As of 2014, nearly 9.1 million women-owned firms accounted for 30% of all enterprises (Womenable, 2014). Moreover, firms owned by women of color represent only one-third of the women-owned companies or only 10% of total enterprises. Teare and Desmond (2015) conducted a review of the more than 14,000 U.S.-based startups listed in the CrunchBase database. They found that only 18% of startups listed had at least one female founder. Brush et al. (2014) found that the number of women partners in venture capital firms was only 6% in 2012, down from a high of 10% in 1999, and only 2.7% of venture-backed companies had female CEOs. Many women who do make it up the ranks into technology leadership positions tend to leave the industry at mid-career. Some estimate as many as 45% will leave before moving beyond mid-level positions (Harkinson, 2014, Labor, 2014)

A similar pattern of low participation by women exists in obtaining technology-related patents. The percentage of U.S. patents in information technology (IT) for the 30-year period from 1980 to 2010, that had at least one female inventor was only 13% (Ashcraft and Breitzman, 2012). When controlling for the percentage of those patents that included male participation, the women’s participation was only 6.1% of U.S. invented IT patents.

Why is Diversity Important?

Research has shown that more diverse teams are more creative, more successful, and create a higher net profit and return on investment (Barker et al., 2014). In addition, there is the simple fairness argument of ensuring women and underrepresented minorities have access to high paying and rewarding jobs (Hill, 2016).

Economic Success

Having diverse development teams helps to ensure the development of technologies that are more robust and efficient in meeting the needs of a diverse society, and in turn earn more profit for their developers. Problems cited in machine learning and artificial intelligence illustrates that software development often does not include the needs or perspectives of minority individuals (Eveleth, 2016). Some examples include the inability of facial recognition software to see dark-skinned faces or voice recognition software to hear and interpret higher pitched voices. “Leaving out half of the population is leaving out half of the ideas, and they’re ideas that the world needs. Diverse founding teams, employees, and board members are good for the bottom line (Gilpin, 2015).” A diverse society benefits from technological and educational tools that are designed to meet its needs. Diversifying the workforce that creates and produces these products will help ensure that we design technology that works for the broad spectrum of our diverse population. Brush et al., (2014) report that venture-backed companies with leadership teams which include women earn 12% more revenue than male-led companies, and successful technology startups have twice as many women in senior positions as do unsuccessful companies.

Equitable Opportunities

In 2014, women working full-time in the United States earned only 79% of what men working full-time earned, representing a gender pay gap of 21% (DeNavas-Walt and Proctor, 2015). While the gap has narrowed since the 1970s, progress towards pay equality continues to be slow. The pay gap is even larger for minority women. Hispanic and Latina women earn only 54% of what white men earn and 89% of what Hispanic and Latino men are paid (Hill, 2016). While educational achievement, career choice, years of experience, and other factors can explain a part of this gap, as much as 12% still is unexplained (Dey and Hill, 2007). Corbett and Hill (2012) compared earnings of approximately 15,000 women and men one year after college graduation and showed that in 2014 women earned only 82% of what their male counterparts were earning, even when accounting for differences in degree fields. That pay gap had widened ten years after graduation when women earned only 69% of what men earned.

Choices of college major and career field do affect overall pay and lifetime earnings. Again, there is a pay gap, where more traditionally female-dominated career fields tend to have lower overall salaries than do male-dominated career fields, even when those fields have similar educational and skill preparation requirements (Dey and Hill, 2007). For example, women are more likely to choose fields such as education and social science, both of which have proportionately lower average pay, than areas such as engineering and computer science which are more often chosen by men (Hill, 2016, Corbett and Hill, 2012, Dey and Hill, 2007). This variance in salaries has far-reaching implications, including lower overall lifetime earnings, lower ability to repay college loans and fewer resources for providing support for families.

 “Because field of study is viewed as a free choice, many people do not consider the segregation of men and women into different college majors to be an issue of equal opportunity. Yet subtle and overt pressures can drive women and men away from college majors that are nontraditional for their gender. The segregation of men and women into different college majors is a long-standing phenomenon that persists today (Corbett and Hill, 2012, p. 12).”

Women can be encouraged to enter fields such as science, technology, engineering, and math (STEM) which offer higher pay scales (Dey and Hill, 2007). Educators can help to teach women about the opportunities available in STEM fields as well as how to succeed in these areas, including how to negotiate higher salaries that fairly compensate them for their knowledge and skills.

Gender Theories

While research has explored the question of why women are not participating in technology and entrepreneurship at the same rates as men, little progress has been made in increasing diversity in these fields. Some research has explored the question of whether females on average are less competent than males in the area of quantitative skills, others reveal an unwillingness to sacrifice time with family, while others question whether there may be a biological difference between the genders (Ahl and Marlow, 2012, Ahl, 2006). Research has been unsuccessful in showing a clear difference between males and females in regard to technology leadership roles, and in many cases, has been more successful in showing a lack of difference (Ahl, 2006, Doyle and Paludi, 1991, Fausto-Sterling, 1992). Feminist researchers suggest the need to broaden approaches, to explore cultural and societal issues that influence gender and racial diversity, including the use of a poststructuralist definition of gender as a societal construct versus gender as a biological binary representation of sex (Calás and Smircich, 2009). A broadened approach includes consideration of physical and virtual environments; the role of media and the projection of female professional role models; and how families and cultural experiences influence diversity (Barker et al., 2014).

More current theories have highlighted the role of stereotypes about culturally constructed gender identities and how those stereotypes may affect diverse populations participation in technology and entrepreneurship careers. An individual’s perception of stereotypes about their gender identity may impact how they perceive their fit or belonging to the culture in these fields (Cheryan et al., 2009). This is a concept described by Sapna Cheryan as ambient belonging. While commonly identified stereotypes do not accurately reflect all the individuals and environments in a given area, it can be argued that some of them do reflect the effects of gender bias inherent in a gendered field (Cheryan et al., 2015). To affect change, both external and internal influences need to be addressed. The following section begin by defining gender and then will discuss external influences of gender bias along with societal perceptions of gender and gender related social roles. Next, the internal influences of ambient belonging in a gendered field and stereotype threat are considered within the context of how stereotype threat affects ambient belonging.

Defining Gender

For the purposes of this research, a social constructionist or poststructuralist feminist perspective on gender is used. Biological sex is the binary representation of male versus female assigned at birth based on biological and anatomical features. Feminist scholars introduced the term gender to represent the socially constructed social practices and representations associated with masculinity or femininity (Acker, 1992). Gender, as expressed by masculine or feminine characteristics, is thus independent of an individual’s biological sex. Society constructs its understandings of gender and gender expression to conform to norms of masculinity and femininity. As Spence (1993) showed, a person’s gender identity does not always conform to all the attributes that are typically thought to be appropriate for their sex. Many individuals exhibit gendered characteristics that deviate from these culturally constructed norms. Some people are more masculine or more feminine than their socially constructed stereotypes would indicate. Gender expression is fluid in the respect that everyone has varying degrees of masculine and feminine characteristics. Gender expression is fluid in that at different times, and in different situations, a person may present more feminine or masculine qualities (Browne, 2007, Calás and Smircich, 2009).  

Gender Bias in a Gendered Field

According to the Macmillan Open Dictionary (2017), gender bias is an “unfair difference in the treatment of men or women because of their sex.” Research has shown that gender bias very often occurs unconsciously. Gender bias may include small decisions or subliminal messages based on the cultural understandings we have of how gender affects an individual’s ability to succeed in a circumstance. When a characteristic or skill (e.g., decisiveness) is culturally aligned with the performance of one gender (e.g., masculine), then that value may take on a gendered identity. The assumption that individuals of the opposite gender (feminine) would not be as proficient or successful with this characteristic or skill would constitute gender bias. This response can be conscious or entirely unconscious, which complicates the process of trying to shift and change cultural norms around gender bias (Ahl and Marlow, 2012, Bruni et al., 2004b, García and Welter, 2013, Yang and Aldrich, 2014). To understand more about how gender bias effects women in entrepreneurship we will first look at the biases inherent in culturally constructed gendered roles and then we will consider how bias manifests in a gendered field.

Gendered roles

Social Role Theory originated as an attempt to describe the underlying causes of gender differences in social behavior (Eagly, 2013). Social role theory attempts to explain the interplay between cultural expectations and the expression of gender roles (Eagly et al., 2000).  Gender roles differ based on situational cultural norms. They represent the cognitive and evaluative beliefs that members of our society hold about how individuals of a binary sex should look and act, and they facilitate the activities and behaviors of adults of each sex. Gender roles create constraints on behavior which influence the social structure of a given society. They are reinforced and replicated through the process of socialization and maintained through replicating patterns of behavior{Eagly, 2000, Social role theory of sex differences and similarities: A current appraisal} (Eagly et al., 2000).

Feminine gendered roles are often described as having communal qualities (e.g., pleasing physical appearance, kindness, and nurturance). Masculine gendered roles are often described as having agentic qualities (e.g., physical strength, assertiveness, and leadership). Consistent with these examples, occupational success is perceived to derive from agentic personal qualities to the extent that occupations are male-dominated, and from communal personal qualities to the extent that they are female-dominated (Eagly et al., 2000, Cejka and Eagly, 1999).

Research shows that there are greater differences in gender expression and behaviors within rather than between the two sexes (Santos et al., 2016, Eagly et al., 2000, Hoffman and Hurst, 1990). If a Venn diagram were drawn from the range of gender expression and behaviors, one would see that there is more area in the overlapping section of the chart than there is in the non-intersecting areas. One would also see that the size of the variation within the sexes is larger than the variation between the sexes (Eagly, 1987, Swim, 1994). However, this evidence does not hold consistent with cultural norms and opinion about gender differences. In fact, gender stereotypes may serve to perpetuate gendered role distributions. Individuals assume the differences outsiders observe in attributes of the role occupants can be attributed to gender, when they may, in fact, correlate to learned behaviors (Hoffman and Hurst, 1990, Berndt and Heller, 1986, Broverman et al., 1972, Cejka and Eagly, 1999, Eagly, 2013, Jost and Banaji, 1994).

The long-held belief that women are more communal and men are more agentic is based on the traditional division of labor between men and women as providers and as homemakers (Cejka & Eagly, 1999). While some believe these role assignments are based on innate gendered characteristics, research has shown that Western societal structure effectively supports the development of the skills necessary to perform the roles individuals of each sex are expected to occupy (Williams and Best, 1990). The traditional categorization of women and men in sex-typical social roles and the incorporation of those roles into the history of societal culture and consensual gendered roles create a paradigm that encourages behavioral tendencies that differ in women and men. This influence creates both gendered stereotypical expectations and the self-regulation of behavior based on those expectations. Females and males thereby learn different skills and acquire different attitudes, as far as they occupy sex-typical roles (Eagly, 2013).

Gender role conforming behaviors are difficult to change due to the fact that this behavior may bring on a variety of negative reactions, or may not be rewarded in the same way that gender conforming behaviors are (Eagly et al., 2000). Cialdini and Trost (1998) suggests that whether people step outside stereotypical gender roles is influenced by whether they break idealistic or behavioral norms. They make a distinction between injunctive norms and descriptive norms. Injunctive norms are expectations about what people ought to do or ideally should do. Observations of deviations from an injunctive norm might be met with strong emotions or moral disapproval. Descriptive norms are expectations about what people do. Deviations from descriptive norms might be met with surprise but would not be considered inappropriate or met with moral disapproval.

Gendered field

 Current feminist theory proposes that technology and entrepreneurship have become gendered fields (Ahl and Marlow, 2012, Bruni et al., 2004b). That is, the culture of entrepreneurship has taken on predominant characteristics that align with a culturally accepted perception of masculinity. This gendering process can be reflected in a) how certain characteristics are valued and define success in a field, b) the use of gendered language, or c) in the way research is conducted (Ahl, 2006, Bruni et al., 2004a, Calas et al., 2009, Bruni et al., 2004b). The masculine culture of these areas is brought into line with societal norms in such a way that those norms have become the standards of the individuals and organizations in these fields (technology and entrepreneurship). This gendering has the effect of causing masculinity to disappear from critical reflection, as it is the norm, and causes femininity to stand out, as it is the “other” from the norm. Anyone who behaves in a manner other than the masculine norm is not conforming to the culture of the field. This “othering” will include not only biological females but also anyone who operates in a manner that is not consistent with the norms of the field. Since many of these norms align with both masculinity and white culture, this has a strong adverse effect on racial diversity as well as gender diversity.

Examining the common characteristics of technology and entrepreneurship, and of the individuals who work in these fields, provides a greater understanding of the features that create a gendered field. In Gender and Entrepreneurship: An Ethnographical Approach (2004b), Bruni, Gherardi, and Poggio look at entrepreneurship through the lens of a gendered field. Through ethnographic analysis, they examine five entrepreneurial companies to observe how gender influences their ways of doing business within the gendered entrepreneurial social culture. They found that while entrepreneurship pretends to be gender neutral, it has taken on the norms and values based on hegemonic masculinity, thus aligning the neutral position with masculinity and in effect creating a gender bias or “gender blindness” with relationship to male participation vs. female participation.

Helene Ahl’s article Why Research on Women Entrepreneurs Needs New Directions (2006) compared the words and phrases that are often used to describe successful entrepreneurs and entrepreneurship with words from Bem’s widely used masculinity and femininity index. Ahl found a high level of correlation to words associated with masculinity. For example, masculine identified words associated with entrepreneurship included “self-reliant,” “strong,” and “assertive.” She then used an antonym dictionary to construct a table using the antonyms of each of the words related to entrepreneurship. She found a high level of correlation between the antonyms and words associated with femininity. For example, feminine words that did not appear in the texts analyzed included “affectionate,” “sympathetic,” and “understanding.” This analysis shows that how language is used to describe successful entrepreneurship is not gender neutral and that it favors descriptors that society has aligned with cultural perceptions of masculinity.

The tendency towards a masculine description of technology and entrepreneurship carries over into the research literature. Henry, Foss, and Ahl (2016) conducted a review of gender and entrepreneurship literature published in 18 journals over a 30-year period. They found the majority of research focused on large-scale, quantitative, male-female comparative research that attempts to show that there are core biological differences between men and women. These differences supposedly cause women to be less than suited for these fields. This type of research is fundamentally flawed because it evaluates binary differences against a gendered norm and has been unsuccessful in finding statistically significant differences between the sexes. Unfortunately, researchers have often attempted to explain away this lack of statistically significant differences between the sexes by making theoretical connections to differences between genders (Ramazanoglu and Holland, 2002, Henry et al., 2016, Ahl and Marlow, 2012, Ahl, 2006).

The theory of gender bias is used to help understand how external factors such as culturally gendered roles and expectations affect an individual’s entrepreneurial attitudes and intentions. In addition, the theory of entrepreneurship as a gendered field is used to examine how gender bias in the form of language and the way we do business can affect our perception of individual success rates. Gender bias can include ways of being, including “doing gender” or “doing entrepreneurship.” For example, if decisiveness is considered a desirable trait exhibited by someone who is successful in entrepreneurship and decisiveness is considered by society to be a masculine trait, then individuals that align with society’s perception of masculinity are regarded as more effective in this field. An issue occurs when that trait (i.e., decisiveness) may not be a trait required for success, but rather a historical norm. There may be other traits that lead individuals to be successful that are filtered out of the workforce because of the gender bias inherent in these unconscious cultural alignments.

Ambient Belonging

Ambient belonging is a theory developed by Sapna Cheryan (Cheryan et al., 2009) to explain an individual’s ability to imagine themselves as belonging in a particular environment. Cheryan identifies stereotype threat as playing a key role in women’s ambient belonging. Stereotype threat is the concept that commonly held stereotypes about individuals identity can influence how we act and the choices we make (Steele, 2011). It has a long tradition of research linking identity to perceived performance according to specific stereotypes, whether they be based on race, gender, or other characteristics (Steele, 2011).

Cheryan et al. (2011) looked at how cultural stereotypes form and how gender stereotypes impact women’s choices and behaviors and their sense of ambient belonging. She and her research team conducted several studies using stereotypical vs. non-stereotypical environments, including physical classrooms, virtual classrooms, and written descriptions of corporate environments. In some of the scenarios, they varied the gender of the individuals represented in the classrooms and work environments using both stereotypical male/female representatives as well as non-stereotypical male/female representatives. One hundred and twenty-one students in two different experiments were asked to complete a questionnaire about their interest in entering a degree or career in computer science or engineering. Female students who completed the questionnaire in the non-stereotypical environments were more likely to respond positively about technical careers than did the female students who were in the stereotypical settings. Female students also showed a higher rating of self-efficacy when they completed the questionnaire in non-stereotypical settings. In most cases, the male students were equally interested in computer science and engineering, regardless of the environment where the questionnaire was completed (Cheryan et al., 2012, Cheryan et al., 2015, Cheryan et al., 2009).

Stereotype threat can affect performance in school, our choices of careers, or even how we imagine ourselves in society (Steele, 2011). Stereotypes about culture may prevent diverse populations from entering technology and entrepreneurial careers because they do not see themselves as belonging to these areas. The perpetuation of stereotypes has an impact on how individuals make decisions about choosing a college major or career track. For example, popular TV shows or movies that perpetuate a stereotype about technology geeks may in effect deter individuals from choosing to enter a technology-related field. In essence, stereotypes of the field act as educational gatekeepers which constrain those who enter educational programs and ultimately the workforce in each of these areas (Cheryan et al., 2015). 

Contextual influences have an impact on how likely individuals are to think of themselves outside of stereotypical gender assumptions. Cheryan (2012, 2015) (2012, 2015, 2011, 2009) shows that just being exposed to stereotypical items influences female students’ decisions to pursue a career in technology as well as their personal feelings of self-efficacy. Steele (1997, 2011) shows that just being reminded of one’s identity can trigger negative responses and impact performance. Turner (1987) showed that individuals might be more likely to think of themselves with respect to stereotypical gender roles in a mixed gendered group. When in a single sex group, a wider range of non-traditional expressions was observed. This difference could be because the presence of the other sex triggers the perception of gender roles, and thus the stereotype threat is evoked.

Entrepreneurship Theories

When considering gender and the field of technology and entrepreneurship, it is also important to consider the influence of entrepreneurial attitudes and intention as a precursor to entrepreneurial behaviors (Ajzen, 1991). This section will first discuss two rational models of entrepreneurial intent: Shapero’s (1982) Entrepreneurial Event Model and Ajzen’s (1991) Theory of Planned Behavior as models for entrepreneurial intention.These models were not conceptualized to capture the role of gender in entrepreneurial intention. Therefore, I propose that theory of entrepreneurship as social change as compared to entrepreneurship as a positive economic activity as more appropriate for considering gender and entrepreneurial activity.

Rational Models of Entrepreneurial Intent

In Shapero’s Entrepreneurial Event Model (SEE), entrepreneurial intention reflects the perceived desirability and feasibility of becoming an entrepreneur. In Ajzen’s Theory of Planned Behavior (TPB), the entrepreneurial intention is described as one’s personal attitude, perceived behavioral control, and perceived social norms. Personal attitude describes an individual’s attraction towards becoming an entrepreneur (desirability). Perceived behavioral control describes the ability to develop entrepreneurial behavior (feasibility). Subjective norm refers to the support of an individual’s social environment or the culture in which they live and work (Hindle et al., 2009, Santos et al., 2016). These two models are comparable with a direct correspondence between perceived feasibility (SEE) and perceived behavioral control (TPB). Personal attitude (TPB) and perceived social norms (SN) are social and cultural influences of perceived desirability (SEE). Armitage and Conner (2001) in a meta-analysis of 185 independent studies provided efficacy for the use of TPB as a predictor of entrepreneurial intentions, especially for personal attitude and perceived behavioral control.  They however, found a weaker link with perceived social norms and recommend “work on normative variables (e.g. moral or descriptive norms) may increase the predictive power of the normative component of the model” (Armitage and Conner, 2001, p. 489)

Santos, Roomie, and Liñán (2016) used the Theory of Planned Behavior model to evaluate “the interplay between gender differences and the social environment in the development of entrepreneurial intentions.” In their study, they used the “Entrepreneurial Intention Questionnaire” (EIQ) (Liñán and Chen, 2009) as a measure of entrepreneurial intention and the factors of personal attitude, perceived behavioral control, and subjective norm. They administered the EIQ to 516 final-year business undergraduate students in two different European regions. Their results show that women do not naturally have lower entrepreneurial intentions than men. However, women are less likely to see themselves as entrepreneurs, which results in a lower personal attitude (perceived desirability) and perceived behavioral control (perceived feasibility). In addition, they discovered that an increase in social valuation of entrepreneurship leads to an increase in entrepreneurial intention for men, but not for women. Santos argue that “females do not see entrepreneurship as a career choice for them” and “as a consequence, women’s personal perceptions and intentions are not affected by the value society puts on this activity” (pg. 59). These results are consistent with the theory of gender bias in a gendered field and ambient belonging. If women do not see themselves as entrepreneurs (perceived social norms), they are less likely to see entrepreneurship as a desirable career path.

Figure 2-1 proposes a new model to illustrate how the existing models of entrepreneurial intention relate with the concepts of gender bias and ambient belonging. In this model, the theory of ambient belonging, or an individual’s perception of fit in the field, affects their PA. For a female, the masculine gendered norms in the field would lower their perception of fit, and thus their PA. The effects of gender bias, and the related issues of social cultural roles and stereotype threat, effectively create a filter that prevents women from experiencing the advantages of increased cultural value or perceived subjective norms of entrepreneurship. Thus, when society, or the individual’s close community, more highly values entrepreneurship, they do not see that increased value as applying to them as females.

Figure 2-1. Models of entrepreneurial intention as related to sociocultural theories.

Extended Theories of Entrepreneurship

To address the issue of low participation of women in entrepreneurship, Calas and Bourne (2009) proposed extending the boundaries of entrepreneurship theory and research by reframing entrepreneurship as positive economic activity to entrepreneurship as social change. This moves the focus of entrepreneurship research from an outcome metric of positive economic activity or economic growth, to a process outcome of the ‘doing’ of entrepreneurship or creating social change. By reframing the research agenda to entrepreneurship as social change, the culture (or perceived culture) of entrepreneurship becomes the foci. This reframing of entrepreneurship as social change by Calas and Bourne is an attempt to bring the critical theorist approach of feminist theory into the examination process. By applying the variety of feminist methodological viewpoints gender and racial diversity is viewed from varying perspectives. Each of these perspectives lends new insights to the equation and gives us a better viewpoint from which to understand the issues.

Helen Ahl (Ahl, 2006) also recommended reframing the research agenda in this manner. She points out that much of prior research has focused on entrepreneurship as an instrument for economic growth. By changing the discourse around entrepreneurial research from a focus on outcome, to a focus on process, we can begin to look more deeply at issues of gender equality and gender/power relations. We move away from research that looks at gender as a variable, to research that considers the context and social constructs of entrepreneurship. This could include studies of institutionalization of support systems for female entrepreneurs, cultural norms surrounding entrepreneurship, societal and familial support structures, and gendered divisions of labor.

 In “Shattering Stereotypes: Women in Entrepreneurship” (2015), Fink and Haisley surveyed 483 C-suite executives and entrepreneurs from UK business.  What they found shatters earlier stereotypes about women’s interest and propensity to succeed in an entrepreneurial environment. Fink and Haisley found women self-identify as just as interested in growing a business (92% vs. 90%) and are more interested in starting a new business (69% vs. 29%) or starting another business (47% vs. 18%) than men. However, women characterized their process of growing a business differently than did their male counterparts. Female entrepreneurs described a process of striving for steady, profitable growth trajectories where they often prefer to reinvest business profits to scale sustainably, whereas male entrepreneurs tend to be more concerned with growth and a quick exit. Women also tend to see more barriers to growth and spend more time mitigating risk. They tend to rate their business skills similarly to men but are more likely to identify areas where they need to increase knowledge and skills. They also cite limitations to their support networks or describe barriers to growth (Fink and Haisley, 2015).  Women describe the following factors related to their interests and success in entrepreneurship: (a) steady, profitable growth trajectories, (b) desire to reinvest profits and scale sustainably, (c) less likely to overestimate their businesses profitability, (d) more sensitive to risk, (e) prefer education or to be “coached,” and (f) show interest in networking events.

Educational Theories

Educational interventions can contribute to changing the perception of cultural stereotypes and personal attitudes that lead to a lack of ambient belonging. By changing the perpetuation of cultural stereotypes, perceptions can be shifted to a more realistic representation of the diversity of individuals and opportunities available to women and underrepresented minorities in technology-related fields and entrepreneurial enterprises. By challenging the prevalent cultural stereotypes about these areas, the diversity of students choosing technology entrepreneurship as a field of study may be increased. Recommendations from the current literature include (a) using effective pedagogy that employs constructivist and experiential learning opportunities (Piperopoulos and Dimov, 2015, Rideout and Gray, 2013) and depicting a wide diversity of individuals who are successful in these fields (Santos et al., 2016).

Experiential learning is based on a social constructivist model and is the “process through which a learner constructs knowledge, value, and skill from direct experience” (AEE, 1994). It builds on the belief that education is not merely the transmission of facts, but the education of the entire person where the educational experience involves both the teacher and the learner engaged in a purposive experience (Dewey, 1917, Dewey, 1938). By exposing learners to real world problems and engaging them in solving these problems, they will be empowered to construct their understanding of these systems.

Specifically in the area of entrepreneurial education, Rideout and Gray (2013) found that “gender (being a woman) has a negative effect on intentions” and indicated a “need to develop a different causal model and perhaps different entrepreneurial education interventions for women” (Rideout and Gray, 2013, p. 354). Their recommendations include two approaches to entrepreneurial education: 1) the small business management model, and 2) the entrepreneurial venture focus model. Teaching technology entrepreneurship should use a more experiential learning model that includes “entrepreneurial self-efficacy, cognitive skills and knowledge, values and attitudes, social networks, and other contextual variables on policy-relevant entrepreneurial outcomes” (Rideout and Gray, 2013, p. 348). Entrepreneurship education should include the process of discovery, evaluation, and exploitation of opportunities, including the individuals who discover, evaluate and exploit these possibilities (Shane and Venkataraman, 2000).

Piperopoulos and Dimov (2015) found that participation in an entrepreneurship course can be effective in increasing or decreasing an individual’s entrepreneurial self-efficacy and ultimately their entrepreneurial intentions. They found results differed between theoretically or practically-oriented entrepreneurial education. Using Regulatory Focus Theory, they evaluated 114 students enrolled in entrepreneurial educational classes that were either theoretically oriented (i.e., designed to teach the theories of entrepreneurship) or practically oriented (i.e., designed to teach how to run their own real-life business using a hands-on, team-based approach). They found the students in the theoretically-based courses saw a decrease in their self-efficacy and resulting entrepreneurial intentions, while the students in the practically-oriented courses saw an increase in their entrepreneurial self-efficacy and resulting entrepreneurial intentions.

Santos et. al. (2016), recommend focusing on helping women to increase their perceived attraction to and feasibility of entrepreneurship as a potential career choice. They recommend focusing on educational interventions that would help women gain both practical knowledge and access to resources as well as change their perceptions of the cultural environment of the field of entrepreneurship. These educational interventions could incorporate the inclusion of successful female entrepreneur role models and guest speakers who do not reflect the norms of a masculine-dominated field. They also recommended the creation of clubs and associations for female entrepreneurs that would help increase the visibility of entrepreneurship as a career choice for women.

Gaps in the Literature

While there is some research on entrepreneurial education, research studies which focus on entrepreneurship education models designed to help increase women’s participation in entrepreneurship are lacking. The empirical research studies point to a need for more research into women’s participation in both technology and entrepreneurship, entrepreneurship education, and the university technology transfer pipeline (Ahl, 2006, Ahl and Marlow, 2012, Bliemel, 2014, Cabrera and Mauricio, 2017). Recent literature reviews including “Women Start-ups in Technology” (Kuschel and Lepeley, 2016), “Women Entrepreneurship: Research Review and Future Directions” (Yadav and Unni, 2016), “Gender and Entrepreneurship Research: A Review of Methodological Approaches” (Henry et al., 2016), “Does Entrepreneurship Education Really Work?” (Rideout and Gray, 2013), and “Why Research on Women Entrepreneurs Needs New Directions” (Ahl, 2006) all point to the need for additional research. In particular, these reviews indicate a need for research that a) considers gender as an influence in entrepreneurship and not as an independent variable or a comparison between male and female entrepreneurs (Marlow, 2002, Ahl and Marlow, 2012), b) focuses on the experiences of women as founders of technology start-ups (Kuschel and Lepeley, 2016), c) takes a more “critical view of how methodology in gender research needs to expand in the future” (Henry et al., 2016, p. 19), d) is grounded in post-structuralist feminist epistemology, including a balance of quantitative and qualitative data collection and analysis methods such as case-study, narrative and discourse analysis, and d) help us understand how to more effectively increase women’s participation in technology entrepreneurship (Kuschel and Lepeley, 2016).


This literature review began with an overview of women’s low participation in technology and entrepreneurship including a discussion of why diversity is important in technology entrepreneurship. Next, the use of gender in this study was defined, along with a discussion of gender theories including gender bias, social role theory, ambient belonging, and stereotype threat. Third, relevant theories of entrepreneurial intention were presented including Shapero’s Entrepreneurial Event Model, Ajzen’s Theory of Planned Behavior, and Entrepreneurship as Social Change. Fourth, educational theories as they relate to entrepreneurial education were explored. And finally, the chapter concluded with a discussion of gaps in the literature.