1 WE ARE EWITS HEAR US ROAR : EMPOWERING WOMEN IN TECHNOLOGY STARTUPS (EWITS) AS AN EXPERIENTIAL LEARNIN G MODEL TO CHALLENGE GENDERED SOCIAL NORM S IN THE FIELD By CHERYL D. CALHOUN A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 2017
2 2017 Cheryl D. Calhoun
3 To my wife and family
4 ACKNOWLEDGMENTS First, I would like to thank my wife, Ester, for her support and encouragement throughout this process She provided many hours of reflection, including some challenging debate, about my ideas and understanding of my work. She was also patient, and incredibly supportive, when I just needed space to work things out for myself. I would also like to thank my ch ildren, Sean, and Jessica, who supported me along the way and never doubted that I would ac complish this goal. I would like to thank my Santa Fe College family The incredible professionals at the educational centers and throughout the college who provide d support and understanding while I juggled a full time academic career along with a full time pursuit of a PhD I thank the faculty at the University of Florida, who challenged me in the classroom, and worked with me as I integrated my classroom learning into my years of experience in academia I thank my committee members, Dr. Kara Dawson, Dr. Rosana Resende, and Dr. Andy Naranjo who provided challenging feedback and pushed me to refine my thinking and align my research goals I would like to thank my c hair, Dr. Carole Beal, who continually reminded me the dissertation is just one step in the Without her constant encouragement to limit my scope creep, I am sure this volume would be twice as long. Her clarity of thoug ht and focus on doing what I needed to do and leaving the rest for later is what allowed me to complete in a timely fashion
5 TABLE OF CONTENTS page ACKNOWLEDGMENTS ................................ ................................ ................................ .. 4 LIST OF TABLES ................................ ................................ ................................ ............ 8 LIST OF FIGURES ................................ ................................ ................................ .......... 9 LIST OF ABBREVIATIONS ................................ ................................ ........................... 10 ABSTRACT ................................ ................................ ................................ ................... 11 CHAPTER 1 INTRODUCTION ................................ ................................ ................................ .... 13 Background and Rationale for the Study ................................ ................................ 13 Theoretical Perspective ................................ ................................ .......................... 15 Gaps in the Literature ................................ ................................ ............................. 16 Research Design ................................ ................................ ................................ .... 17 Context ................................ ................................ ................................ ............. 17 Methodology ................................ ................................ ................................ ..... 18 Research Questio ns ................................ ................................ ......................... 19 Significance of the Study ................................ ................................ ........................ 19 Organization of the Dissertation ................................ ................................ .............. 20 2 LITERATURE REVIEW ................................ ................................ .......................... 21 .......................... 22 Why is Diversity Important? ................................ ................................ .................... 26 Economic Success ................................ ................................ ........................... 26 Equitable Opportunities ................................ ................................ .................... 27 Gender Theories ................................ ................................ ................................ ..... 28 Defining Gen der ................................ ................................ ............................... 29 Gender Bias in a Gendered Field ................................ ................................ ..... 30 Gendered roles ................................ ................................ .......................... 31 Gendered field ................................ ................................ ........................... 33 Ambient Belonging ................................ ................................ ........................... 36 Entreprene urship Theories ................................ ................................ ..................... 38 Rational Models of Entrepreneurial Intent ................................ ........................ 38 Extended Theories of Entrepreneurship ................................ ........................... 41 Educational Theories ................................ ................................ .............................. 43 Gaps in the Literature ................................ ................................ ............................. 45 Summary ................................ ................................ ................................ ................ 46
6 3 METHODOLOGY ................................ ................................ ................................ ... 48 Research Questions ................................ ................................ ............................... 48 Research Design ................................ ................................ ................................ .... 48 Case Study Approach ................................ ................................ ...................... 49 Mixed Me thods Design ................................ ................................ ..................... 50 Feminist Epistemology ................................ ................................ ..................... 51 Context ................................ ................................ ................................ ................... 52 Data Collection ................................ ................................ ................................ ....... 53 Phase One (Review of Existing Artifacts) ................................ ......................... 54 Phase Two (Semi Structured Interviews) ................................ ......................... 55 Data Analysis ................................ ................................ ................................ .......... 57 Procedures ................................ ................................ ................................ ....... 57 Phase One Analysis ................................ ................................ ......................... 58 Learner ap plication ................................ ................................ .................... 58 Learner end of course surveys ................................ ................................ .. 59 Phase Two Analysis ................................ ................................ ......................... 63 Participant interviews ................................ ................................ ................. 63 Qualitative analysis ................................ ................................ .................... 64 Limitati ons and Assumptions ................................ ................................ .................. 65 Perspectives of the Researcher ................................ ................................ ....... 65 Intersectionality ................................ ................................ ................................ 66 Population Bias ................................ ................................ ................................ 67 Summary ................................ ................................ ................................ ................ 67 4 RESEARCH FINDINGS ................................ ................................ .......................... 68 EWITS ................................ ................................ ................................ .................... 68 Program Description ................................ ................................ ......................... 69 Ewits Participants ................................ ................................ ............................. 73 Organizers and subject matter experts ................................ ...................... 73 Mentors ................................ ................................ ................................ ...... 74 Learners ................................ ................................ ................................ ..... 77 Entrepreneurial Identities And Role Models ................................ ............................ 81 Entrepreneurial Identities ................................ ................................ .................. 82 Role Models ................................ ................................ ................................ ..... 83 Participan ................................ ................................ .......... 85 The Reported Impact of Ewits ................................ ................................ .......... 85 Competence building and validation ................................ .......................... 86 Increased entrepreneurial intention ................................ ............................ 89 Awareness of gender issues ................................ ................................ ...... 90 A Unique Learning Environment ................................ ................................ ....... 93 Challenging yet rewarding ................................ ................................ .......... 94 Safe space ................................ ................................ ................................ 95 Mentorship and collaboration ................................ ................................ ..... 97 ................................ ................................ ................................ ......... 101 Chapter Summar y ................................ ................................ ................................ 103
7 5 DISCUSSION, LIMITATIONS AND RECOMMENDATIONS ................................ 105 Discussion ................................ ................................ ................................ ............ 105 Research Question One ................................ ................................ ................. 105 Experi ential learning model ................................ ................................ ...... 106 Learning environment ................................ ................................ .............. 107 Research Question Two ................................ ................................ ................. 108 Confidence in entrepreneurial abilities ................................ ..................... 109 Combating gendered sociocultural norms ................................ ................ 110 Research Question Three ................................ ................................ .............. 111 Envisioning Entry in Technology Entrepreneurship ................................ .............. 112 Limitations ................................ ................................ ................................ ............. 113 Recommendations ................................ ................................ ................................ 116 Recommendations for the Educational Model ................................ ................ 116 Recommendations for Future Study ................................ ............................... 117 APPENDIX A LEARNER APPLICATION ................................ ................................ .................... 118 B END OF COURSE SURVEY ................................ ................................ ................ 119 C INTERVIEW PROTOCOL DOCUMENTS ................................ ............................. 121 D TIDYING THE DATA ................................ ................................ ............................. 127 REFERENCES ................................ ................................ ................................ ............ 142 BIOGRAPHICAL SKETCH ................................ ................................ .......................... 149
8 LIST OF TABLES Table page 3 1 Alignment of research questions to data sources and analysis. ......................... 54 3 2 Interview Participants, Their Career Field, Highest Degree, and Entrepreneurial Experience ................................ ................................ ................ 57 3 3 Themes and number of references for each theme ................................ ............ 65 4 1 Applicant Results by Cohort ................................ ................................ ............... 79 4 2 Percent of Applicants Who Indicated Source of Motivation ................................ 79 4 3 Learner background information for Cohorts 2015 and 2016 ............................. 80 4 4 Mean responses on Entrepreneurial Intention Questionnaire (EIQ) ................... 90
9 LIST OF FIGURES Figure page 1 1 Scientists and engineers working in science and engineering occupations: 2015 ................................ ................................ ................................ ................... 23 1 2 Noninstitutionalized resident population of the United States ages 18 24, by race, ethnicity, and sex: 2014. Source: National Science Foundation. ............... 24 2 1 Models of entrepreneurial intention as related to sociocultural theories. ............ 41 4 1 Mentor Feedback ................................ ................................ ................................ 77 4 2 Distribution of Applicants by Degree ................................ ................................ ... 79 4 3 Self reported impact on perceived competencies before and after Ewits ........... 88 4 4 Self Reported impact on perceived challenges before and after Ewits ............... 89 5 1 A model for understanding the impact of gender bias and ambient belonging on entrepreneurial intentions. ................................ ................................ ........... 113
10 LIST OF ABBREVIATIONS AAUW American Association of University Women Ewits Empowering Women in Technology Startups NCWIT National Council of Women and Information Technology PA Personal Attitude PBC Planned Behavioral Control SEE Model SME Subject Matter Expert SN Social Norm TPB Theory of Planned Behavior
11 Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy HEAR US ROAR : EMPOWERING WOMEN IN TECHNOLOGY STARTUPS (EWITS) AS AN EXPERIENTIAL LEARNIN G MODEL TO CHALLENGE GENDERED SOCIAL NORM S IN THE FIELD By Cheryl D. Calhoun D e c e m b e r 2017 Chair: Carole Beal Major: Curriculum and Instruction This research seeks to understand whether educational intervention can help women succeed in technology entrepreneurship, a career field where women are underrepresented but where economic opportunity is high. This study examines female experiential entrepreneurial education program, Empowering Women in Technology Startups (Ewits). Ewits use s an experiential learning model of entrepreneurship education, specifically designed to expose women to the technology commercialization process, develop the skills to form a technology startup and inspire and empower them to pursue leadership roles in te chnology based companies. Using a feminist epistemology and a mixed methods case study design, the data were analyzed through a theoretical lens of ambient belonging and gender bias. The theories of ambient belonging and gender bias were used to guide inqu iry, as well as to examine and intentions. The findings of this study indicate that program is effective in both helping women to develop both the competencies and t he confidence to become
12 entrepreneurs. More importantly, the program created a learning environment that was effective in shifting participants perspectives of the gende red social norms of the field. This research has implications for the field in understa nding how women experience entrepreneurship and how their perceptions of gendered social norms affect their desire to enter technology entrepreneurship. An updated model of planned behavior is presented which considers the effect of biased social norms on intention.
13 CHAPTER 1 INTRODUCTION This research seeks to understand whether educational intervention can help women succeed in technology entrepreneurship, a career field where women are underrepresented but where economic opportunity is high. The study focuses on an experiential learning model of entrepreneurial education EWITS, specifically designed to expose women to the technology commercialization process, develop the skills to form a technology startup, and inspire and empower them to pursue leader ship roles in technology based companies E xperiences of program participants were documented and analyzed to understand what e ffect this model of education has on entrepreneurial attit udes and intentions as well as to learn how women attempt to overcome barriers to entry into technology entrepreneurship. Using a mixed methods case study design, t he data w ere analyzed through a theoretical lens of ambient belonging and gender bias The resulting case study describe s the experiences of the program participants, highlights the factors that led them to the field of technology entrepreneurship, and examines how they have responded to barriers they experienced along the way. Background and Rationale for the Study The participation of women in the fields of technology entrepreneurship, technology licensing and patenting is historically low. At the time of this writing, Harvard Business School Working Paper 17 046, Diversity in Innovation (Gompers and Wang, 2017) is reporting that fewer than 10% of tech startups are owned by women, and less than 6% of venture backed technology startups are owned by women. While women represent 39% of all small business owners, they are more highly represented in small lifestyle businesses and are underrepresented in both technology start ups and
14 venture capital backed firms (Gilpin, 2015, Brush et al., 2014, Hill, 2016) Women are underrepresented in many STEM related fields, both in academia and in the workforce. While there have been some gains in female participation in science and math, the number of women entering technology and engineering fields remains extremely low. Not including more women in the technology and entrepreneurial workforce affects many societal issues including gender pay equality, t he effectiveness of technological innovation, the overall profitability of startup ventures and the ability to continue to grow the economy. According to the American Association of University Women (AAUW), in 2014, women working full time in the United S tates earned only 79% of salaries compared to men for the same position (AAUW, 2016). Women are often more highly concentrated in lower paying career fields and underrepresented in higher paying careers such as technology and entrepreneurship. J ust as we s ee in the general workforce, women are more highly represented in low paying entrepreneurial ventures and under represented in high er paying entrepreneurial business enterprises Having more diverse leadership and development teams is good for our society Research shows that companies with diverse leadership teams are more successful, develop better products, and earn higher revenues (Dawson et.al 2014). V enture backed companies with leadership teams that include women on average earn 12% more revenue t han male led companies. Successful technology startups have twice as many women in senior positions as do unsuccessful companies (Brush et al., 2014, Canning et al., 2012) By increasing the number of women entering technology entrepreneurship we can therefore potentially increase the wage earning potential of
15 females as well as increase the economic success of technology startups in our economy. Theoretical Perspective T wo sociological theories guide the theoretical perspective of this research. The first is the theory of gender bias which applies to the external influences and inherent biases in the gendered fields of technology and entrepreneurship that act as barriers to an individual entry and later success The second is the theory of ambient belonging which addresses internal m otivation and intentions about entering a career or field of study Gender bias may also affect ambient belonging in that knowled ge of gende r biases may be the catalyst for the development of cultural stereotypes or serve as a deterrent to anyone who feels these biases will create an unfair disadvantage for their success. Gender bias : This is a bias that affects the way we perceive certain thi ngs, persons, or groups of people based on their gender (Rhode, 2017) Feminist t heory proposes that the fields of technology and entrepreneurship have become gendered fields (Cals and Smircich, 2009) The culture has aligned with masculine societal norms in a way that creates a gender bias. This bias may cause women to appear less successful than men because their way of undertaking technology entrepreneurship may be different from the norm (Ahl, 2006; Bruni, 2004; Hughes, 2012) Some studies show th at women value a return on social investment and thus focus on creating more sustainable long term growth businesses (Cals and Smircich, 2 009, Fink and Haisley, 2015) This approach to conducting entrepreneurship differ s from the traditional high economic growth models of entrepreneurship. Because t raditional standards of success value strong economic growth model s a bias may exist again st entrepreneurs who
16 more highly value return s on social investment. Since women, on average value return s on social investment more highly they are more adversely affected by this bias. Ambient belonging : This is a term developed by Sapna Cheryan (Cheryan et al., 2009) to describe the ability of individuals to imagine themselves as belonging in a particular envir onment. The social cultural stereotypes affect the sense of ambient belonging an individual has of person s in that area If one cannot imagine themsel ves as belonging to the culture of a field or connecting to the individuals in th e field they are less likely to choose this as their career path. For example, if we think of an entrepreneur as someone who is confident, driven, and works 80+ hours a week ruthlessly pursuing business and economic goals, and an individual cannot imagine themselves as this person, then they may have difficulty imagining themselves as an entrepreneur. Gaps in the Literature To address the gender gap in technology entrepreneurship, a better understanding of the barriers women face in these fields is needed. The research on entrepreneurship and educational intervention is contradictory and not conclusive (Ahl, 2006, Piperopoulos and Dimov, 2015) Some studies have tried to explain the low participation of women in entrepreneurship and technology aversion to risk, or reluctance to sacrifice time with fami ly (Henry et al., 2016) This research has been unsuccessful in showing any clear differences between men and women (Henry et al., 2016, Ahl and Marlow, 2012, Henry et al., 2015, Santos et al., 2016) The empirical research studies point to a need for more research into the participation of women in both technology and entrepreneurship, entrepreneurship
17 education and the university technology transfer p ipeline (Ahl, 2006, Cabrera and Mauricio, 2017, Cals and Smircich, 2009, Calas et al., 2009) During this literature review, n o research studies were identified which focus on experiential learning models of entrepreneurship education specifically designed to address the issues women face when preparing to enter technology entrepr eneurship. Research Design This research is situated within the context of Empowering Women in Technology Startups (Ewits) an experiential entrepreneurial education program offered through the University of Florida Innovation Hub. The results are reported using a case study design which allow s the researcher to tell a complete story about the participants and their experiences. The data collection and analysis uses a mixed methods design with both quantitative and qualitative data collected from program art ifacts and semi structured interviews. The data were analyzed to answer the research questions and to evaluate the theoretical perspectives of gender bias and ambient belonging using feminist epistemology as a guiding principle. Context The case study utilizes the place and time bound system of Empowering Women in Technology Startups (Ewits ) educational program. Ewits education program designed to help women learn about the technology licensing process and gai n the self efficacy to succeed in the technology start up sector. By selecting Ewits for the case study this researcher could look more deeply at how and why program participants approach ed technology entrepreneurship and how their participation in Ewits affected their entrepreneurial attitudes and intentions.
18 The women associated with this program, regardless of their roles, describe d a dynamic process of learning and growth for their participation. The participants can be grouped into four different sta keholder groups. T he following descriptor terms are used to describe each of the different stakeholder groups. Organizers: the women who conceptualized, designed, and facilitated the program; Mentors: the women who led the teams through the learning proc ess; SMEs (Subject Matter Experts): the authors and presenters of program curriculu m including the venture capitalists and angel investors who served as judges and evaluated the investor pitch competitions and business plans; Learners: The women who appl ied to and participated in the program. The Ewits program has been offered annually since 2012. To date, there have been five cohorts, with 283 applicants, 239 women participating as learners in the program and 222 women completing the program. There are eight program organizers, 35 mentors, and 2 9 nine subject matter experts (SMEs), for a total of 311 program participants In addition there were several women who were invited to participate as presenters during the weekly informational sessions. These women talked about their experiences as female leaders and entrepreneurs including how they experienced gender disparities, gender bias, and other gender related issues in their careers and leadership roles Methodology The mixed methods design was selected because it allows the researcher to address some of the concerns of earlier research in the ability to tell a deeper story about study participants. The mixed method design incorporates the most compelling features of both quantitative and qualitative data. The feminist epistemology uses a
19 post structural feminist approach which looks at gender as socially and culturally constructed. Data collection occurred in two phases. During Phase One, existing program artifacts, including learner applications, summative course evaluations, follow up surveys, course curriculum, team business plans, investor pitch presentations and One inf ormed the development of Phase Two, which consisted of semi structured qualitative interviews with individuals from three groups of program participants (organizers, mentors, and learners). The interviews were thematically coded and analyzed along with dat a from Phase One to formulate answers to the research questions. Research Question s This research study seeks to answer three main questions : 1. How does Ewits s trive to help women overcome barriers to entry into technology entrepreneurship? 2. What impact doe s Ewits have on particip attitudes and i ntentions ? 3. How do participants describe their experience with entreprene urship? Significance of the Study The study contribute s to the knowledge of how an educational intervention can help women gain efficacy towards technology entrepreneurship These understandings will guide development of future educational programming as well help programs such as Ewits improve their impact on the women that participate in the program The experiences of thes e participants can inform our understanding of how women actively engage in practices that lead to positive entrepreneurial attitudes and intentions and that may negate the negative influences of gender bias in technology entrepreneurship.
20 In addition, ins ights are ambient belonging in a gendered field. The results from this research can be used to impact the gender inequality in technology entrepreneurship and help to diversify the technology startup workforce. Organiz ation of the Dissertation This introduction presented an overview of the research including background and rationale, theoretical perspectives, and research design In Chapter 2, the Literature R eview will discuss the relevant literature including the stat us in technology entrepreneurship, the case for diversity, including relevant gender theories, entrepreneurial theories, and educational theories Chapter 3 covers Methodology which will include data collection, data analysis, and the limitations and assumptions of the research design Chapter 4 includes the findings from t he data collection and analysis Chapter 5 will d iscuss the main findings from this research, including the limitations of the study and conclude with recommendations for future studies
21 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. Wh 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 aversion to risk, or reluctance to sacrifice time with family. However, t his 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 rep resent fifty seven percent of the professional occupations in the U.S. workforce, their rates of participation in entrepreneurship and technology occupations ha ve remained disproportionately low (Womenable, 2014) The issue of low participation of wom en 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 inequitie s and lack of participation in this field. Using the lenses of gender bias and ambient belonging, this study aims to understand how a n experiential learning based educational program contributes to empowering women in technology and entrepreneurship. This literature review will
22 address why diversity in these intersecting fields is important to society. Several related theories are explored including gender theories, entrepr eneurship 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, entreprene urship 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. It is difficult to find 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, tech nology 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 femal e employees occupy non tech roles such as human resources or sales (Fussell, 2016) technology and entrepreneurship can be developed by considering (1) occupational employment data, (2 ) the degrees females earn in higher education, and (3) data Using the data from the 2014 noninstitutionalized resident populati on of the United States aged 18 to 64 (NSF, 2017) in Figure 1, we can see that w hite men currently represen t 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,
23 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 t he rest of the populations (White women 18%, B lack women and men 5%, Hispanic women and men 6%, and other 2%) are underrepr esented in that their proportions in the S&E workforce equals 31 % yet their p ercentage 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 i n 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
24 Figure 1 2 Noninstitutionalized resident population of the United States ages 18 24, by race, ethnicity, and sex: 2014. Source: National Science Found ation. Another source for examining women participation in technology and percentage of women earning college degrees in the U.S. rose from 46% to 53% (Finamore and Khan, 2015) and b y (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 (Ashcraft and Blithe, 2009) Despite this parity in some areas of education and overrepresentation in others (e.g., 70% in participation in technolo gy related disciplines is still disproportionally low
25 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 hi gh of 37% in 1986 87, down to 18% in 2014 at the bachelor degr ee 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, f irms owned by women of color represent only one third of the women owned companie s or only 10% o f total enterprises. Teare and Desmond (2015) conducted a review of th e more than 14,000 U S based startups listed in the CrunchB ase 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 part ners in venture capital firms was only 6% in 2012, down from a high of 10% in 1999, and o nly 2.7% of venture backed companies had female CEOs Many w omen 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 mov ing 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) W hen controlling for the percentage of those patents that included male participation, the women's participation was only 6.1% of U.S. invented IT pat ents.
26 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 eff icient 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 per spectives 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 ideas that the world needs. D iverse 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 pro ducts 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 companie s with leadership teams which include women earn 12% more re venue than male led companies, and s uccessful technology startups have twice as many women in senior positions as do unsuccessful companies
27 Equitable Opportunities In 2014, women working full tim e 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 Hispan ic 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 o f approximately 15,000 women and m en 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 on ly 69% of what men earned. Choices of college major and career field do a ffect 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 area s such as engineer ing 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 c ollege loans and fewer resources for providing support for families.
28 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 su btle 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 th ese area s including how to negotiate higher salaries that fairly comp ensate 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 o thers reveal an unwilling ness to sacrifice time with family, while others que stion whether there may be a biological difference between the genders (Ahl and Marlow, 2012, Ahl, 2006) R esearch has been unsuccessful in showing a clear differen ce between m ales 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 sugges t 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
29 construct vers u s gender as a biological binary representation of sex (Cals 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 constr ucted gender identities and how those stereotypes may affect diverse populations participation in technology and entrepreneurship careers stereotypes about their gender identity may impact how they perceive their fit or belong ing to the culture in these fields (Cheryan et al., 2009) This is a concept described by Sapna Cheryan as ambi ent 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 exte rnal 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 affect s 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 re presentation of male versus female assigned at birth b ased 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,
30 1992) Gender as expressed by masculine or feminine characteristics, is th us 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 th e 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, Cals 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. shown that gend er bias very often occurs unconsciously. Gender bias may include small decisions or sub liminal messag es based on the cultural understandings we have y to succeed in a circumstance When a characteristic o r 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 wit h 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, Garca and Welter, 2013, Yang and Aldrich, 2014) To understand more about
31 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 th eory 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 a dults 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 et al., 2000) Feminine gend ered 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 qualitie s (e.g., physical strength, assertiveness, and leadership ) Consistent with the se 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 ra ther than between the two sexes (Santos et al., 2016, Eagly et al., 2000, Hoffman and H urst, 1990) If a Venn diagram were drawn from the range of
32 gender expression and behaviors, one would see that there is m ore 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, gend er 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 (Hoff man 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 enco urages 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 differe nt attitudes, as far as they occupy sex typical roles (Eagly, 2013)
33 Gender role conforming behaviors are difficult to change due to the f act 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 behavior al norms. They make a distinction between injunctive norms and descriptive norms. Injunctive norms are expectations about what people ought to do or id eally should do. O bservations 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 f ield C urrent feminist theory proposes that technology and entre preneurship have become gendered fields (Ahl and Marlow, 2012, Bruni et al., 2004b) T hat is, t he culture of entrepreneurship ha s t aken 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 area s is b rought into line with societal norms in such a way that those norms have become the standard s of the indivi duals and organizations in these fields (technology and entrepreneurship) This gendering has the effect of causing masculinity to disappear from cri tical reflection, as it is the norm, and causes femininity to stand out as Anyone who behaves in a manner other than the masculine norm is not conforming to
34 iological 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 th ese fields, provides a greater understanding of the feature s th at create a gendered field. In Gender and Entrepreneurship: An Ethnographical Approach (2004b) Bruni, Gherardi and Poggio look at entrepreneurship through the lens of a ge ndered 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 particip ation. Helene Ahl Why Research on Women Entrepreneurs Needs New Directions (2006) compared the words and phrases that are often used to describe successful entr epreneurs and entrepreneurship masculinity and femininity index Ah l found a high lev el of correlation to words associated with masculinity. For example, masculine identified words associated with entrepreneurship 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 co rrelation between the antonyms and
35 words associated with femininity. For example, feminine words that did not appear in This analysis shows that how language is used to describ e 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 res earch literature. Henry, Foss, and Ahl (2016) conducted a review of gender and entrepreneurship literature published in 18 journal s over a 30 year period. They found the majority of research focuse d on large scale, quantitative, ma le female comparative research that attempts to show that there are core biological differences between men and women These differences supposedly cause w omen to be less than suited for these fields. This type of research is fundamentally flawed because it evaluates binary differ ences against a gendered norm and h as been unsuccessful in finding statistical ly significant differences between the sexes Unfort unately, 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 ntrepreneurial 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 bia s can include
36 ways of being decisiveness is considered a desirable trait exhibited by someone who is successful in entrepreneurship and decisiveness is considered by society to be a mas culine trait, then individuals that align with societ 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. The re 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 b elonging is a theory developed by Sapna Cheryan (Cheryan et al., 2009) to explain an ability to imagine themselves as belonging in a part icular environment Cheryan identifies stereotype threat as playing a key role 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 lon g tradition of research linking identity to perceived performance according to specifi c stereotypes, whet her they be based on race, gender o r other characteristics (Steele, 2011) Cheryan et al. (2011) looked at how cultural stereotypes form and how gender stereotypes impact choices and behaviors and their sense o f ambient belonging. She and her research team conducte d 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
37 male/female representatives. One hundred and twenty one s tudents 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 setting s Female students also showed a higher rating of self efficacy when they completed the questionnaire in non stereotypical setting s 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 scho ol, our choices of careers, or even how we imagine ourselves in society (Steele, 2011) Stereotypes about c ulture may prevent diverse populations f rom entering technology and entrepreneurial careers because they do not see themselves as belonging to these area s T he 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 educat ional gatekeepers which constrain those who enter educational programs and ultimately the workforce in each of these area s (Cheryan et al., 2015) Conte xtual influences have an i mpact 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
38 female students decisions to pursue a career in technology as well as their personal feelings of self efficacy Steele (1997, 2011) identity can trigger negative responses and impact performance. Turner et.al. (1987) showed that individuals m ight 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 becau se 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 in fluence of entrepreneurial attitudes and intention as a precursor to entrepreneurial behaviors (Ajzen, 1991) This section will first discuss two rational models of entrepreneurial intent: (1982) Entrepreneurial Event Model (1991) Theory of Planned Behavior as models for ent repreneurial intention. These models were not conceptualized to cap t u re 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 Theory of Planned Behavior (TPB), the entrepreneurial pers onal attitude, perceived behavioral control, and perceived social norms. Personal
39 (desirability). Perceived behavioral control describes the ability to develop entrepreneurial b ehavior (feasibility). Subjective norm refers to the support of an (Hindle et al., 2009, Sant os 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 perc eived 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 (Armitage and Conner, 2001, p. 489) Santos, Roomie and Lin (2016) used the Theory of Planned Behavior model to evaluate study, the y used the (Lin and Chen, 2009) as a measure of entrepreneurial intention a nd 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
40 feasibility). In addition, they discove red that an increase in social valuation of entrepreneurship leads to an increase in entrepreneurial intention for men, but not for These results are consistent with the theory of gender bias in a gendered field and ambient belonging. If women do not see themselves as entrepr eneurs (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 ambien t belonging. 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 close community, more highly values entrepreneurship, they do not see that increased value as applying to them as females.
41 Figure 2 1 Models of entrepreneurial intention as related to sociocultural theories. Extended Theorie s 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 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 an d Bourne is an attempt to bring the critical theorist approach
42 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 per spectives 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 foc us 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 e ntrepreneurship. 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. (2015) Fink and Haisley surveyed 483 C suite executives and entrepreneurs from UK business. What succeed in an ent repreneurial environmen t. 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
43 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 wh ere 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 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, perception s can be shifted to a more realistic representation of the diversity of individuals and opportunities available to women and underrepresented minorities in technology re lated fields and entrepreneurial enterprises. By challenging the prevalent cultural stereotypes about these area s the diversity of students choosing technology entrepreneurship as a field of study may be increased R ecommendations from the current literat ure include (a) using effective pedagogy that employ s 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
44 (AEE, 1994) It builds on the belief that education is not mere ly 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 exposin g 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) need to develop a different causal model and perhaps different entrepreneurial ed ucation interventions for women (Rideout and Gray, 2013, p. 354) Their recommendations include two approach es to entr epreneurial education: 1) the small business management model and 2) the entrepreneurial venture focus model T eaching technology entrepreneurship should use a more experiential learning model tha t 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) E ntrepreneurship education should include the process of discovery, evaluation and exploitation of opportunities including the individuals who discover, evaluate and exploit these possibil ities (Shane and Venkatar aman, 2000) Piperopoulos and Dimov (2015) found that participation in an entrepreneurship co urse can be effective in increasing or decreasing an individual s entrepreneurial self efficacy and ultimately the ir entrepreneurial intentions. They found results differed between theoretically or practically oriented entrepreneurial education. Using
45 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 resul ting entrepreneurial intentions, w hile the students in the practically orient ed 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 norm s of a masculine dominated field. They also recommended the creati on 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 participation in entrepreneurship are lacking The empirical research studies point to a entrepreneurship, entre preneurship education and the universit y technology transfer
46 pipeline (Ahl, 2006, Ahl and Marlow, 2012, Bliemel, 2014, Cabrera and Mauricio, 2017) rt (Kuschel and Lepeley, 2016) (Yadav and Unni, 2016) of (Henry et al., 2016) (Rideout and Gray, 2013) (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) (Henry et al., 2016, p. 19) d) is grounded in post structu ralist feminist epistemology, including a balance of quantitative and qualitative data collection and analysis methods such as case study, narrative and discourse analysis, and d ) participation in technology entrepreneurship (Kuschel and Lepeley, 2016) Summary techno logy 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 Theory of
47 Planned Behavior, and Entrepreneurship as Social Change. Fourth, educational theories as they relate to entrepreneurial education were explored. And finally, t he chapter concluded with a discussion of gaps in the literature.
48 CHAPTER 3 METHODOLOGY This study was designed to evaluate whether an all female experiential entrepreneurship education program Ewits, had a positive impact on participating attitudes and intentions including what factors contributed to this impact The study also considered how involvement in the program helped participating women prepare to deal with barriers to entry and retention. Through a mixed methods design the study analyzed five cohorts of data including participant applications, end of course survey s, and follow up surveys and conducted interviews with 15 participants from three stakeholder groups. The quantitative data were analyzed using R and the qualitative data were thematically coded using NVivo. The resulting analysis were reported in a case study format. Research Questions The questions this research addressed are: 1. How do es Ewits s trive to help women overcome barriers to entry into technology entrepreneurship? 2. What impact does Ewits have on particip attitudes and intentions ? 3. How do participants describe their experience with entrepreneurship? Research Design The case study research approach for this study includes both quantitative (descriptive statistical analysis of demographic data as well as statistical analysis of program end of course survey s) and qualitative approaches (program document review and informal se mi structured interviews). T his is a case study within the bounded system of an educational program and its participants, mentors, and program
49 organizers. By using the case study approach, we are better able to tell the story of the e ducational program and its participants By looking more deeply at both the program and the participants we can learn more about why and how they approach entrepreneurship and how program participation has affected their entrepreneurial competencies, att itudes and intentions This research was conducted using the guiding principles of feminist epistemology. Case Study Approach C ase study is a research approach where the investigator explores a real life, contemporary bounded system over time. The research uses multiple sources of information, which may include observations, interviews, audiovisual material s documents, and reports. The resulting research is reported as a case description with case themes. The research could include a single case (a within site design) or a multiple case approach. The purpose of the case study is to understand a specific issue, problem, or concern. The results present an in depth understanding of the case. Collecting many sources of data is required because relying o n one source is not enough to develop this in depth level of understanding (Creswell, 2013, Merriam, 1988, Merriam and Tisdell, 2015) This research report uses a case study within a bounded system The case study tell s the story of Empowering Women in Technology Startups (Ewits) and its participants (organizers, mentors, and learners) By looking more deeply at both the program and the participants we can learn more abo ut how they approach entrepreneurship and how their participation in Ewits has affected their entrepreneurial attitudes and intentions. The case study approach was selected because the study review ed multiple sources of information in a bounded system. A w ithin site design is
50 used to look at the program over time. The research examine d data from five cohorts of Ewits participants. The results are reported as a case description along with themes that emerge from the study. Mixed Methods Design A mixed metho ds design allows the researcher to use a variety of data collection methods which tak e advantage of the strengths of both quantitative and qualitative methods (Creswell and Clark, 2011) Both together can be used either linearly or iteratively to understand the problem more th oroughly faced by the community and work together with them to develop solutions or make a change (Creswell, 2013, Creswell and Clark, 2011, Mertens, 2012, Teddlie and Tashakkori, 2009) The mixed methods design was selected because it allows the researcher to address some of the concerns of earlier research in the ability to tell a deeper story about study participants. This is consistent with recommendations from Ahl, Henry, and Marlow (2012, 2016) recommending more innovative methodological approaches which utilize qualitative data and avoid male/female com parisons. Data collection occurred in two phases. During Phase One, existing program artifacts, including learner applications, summative course evaluations, follow up surveys, course curriculum, team business plans, investor pitch presentations and judges and analyzed. This data included both qualitative data from program artifacts and open ended survey questions. It also included quantitative data in the form of ordinal response survey questions. Phase Two consisted of semi structured qualitative interviews with individuals from three groups of program participants (organizers, mentors, and learners). The interviews were thematically coded and analyzed along with data from Phase One to formulate answers to the research questi ons.
51 Feminist Epistemology Feminist methodology is a means of conducting scientific investigations and generating theory from an explicitly feminist standpoint. It is a response to concerns about the limits of traditional methodology and an effort to capt ure the experiences of women and others who have been marginalized by society and by previous academic research (Henry et al., 2016, Naples, 2007, Ramazanoglu and Holland, 2002) This study utilizes a post structuralist approac h to feminist epistemology. As such, the research a) use s a definition of gender as socially and culturally constructed, b) approach es gender as an influence in te chnology entrepreneurship, c) is mindful of the intersectionality of race, class, gender, and sexuality d) closely collaborate s with Ewits organizers on data collection and analysis, and e ) share s and implement future changes in the Ewits program. The research acknowledges and continues to be aware of issues of pri vilege and the sociocultural paradigms and constructs that affect biases. In addition, the researcher as a formal and informal mentor, has been part of the Ewits community, and, as a female community college computer networking professor and entrepreneur i s part of the broader tech and entrepreneurial community. The data collection and analysis methodologies were selected to conform with current recommendations on feminist approaches to research on gender in entrepreneurship. Feminist research techniques a nd practices (choice of approach) include discourse analysis, ethnography, case study, interviews and surveys Feminist t heories about how research is conducted (the approach in action) include qualitative, quantitative, or mixed methods (Cals and Smircich, 2009, Creswell, 2013) Henry and
52 Foss (2016) recommend the use of more in depth qualitative methodologies such as life histories, case studies or discourse analysis. Context The context of this study is an all female 10 week educational program designed to help women learn how to license a technology through the university technology transfer process and prepa re to launch a start up based on the technology. The program is situated within an entrepreneurial incubator located at a large research one university. Program participants were recruited within the academic setting and the local community. The program is designed for women who already have college degrees and who have expressed interes t in technology entrepreneurship. The experiential educational model uses a constructivist learning model where participants are matched with teams to s erve as founders for mock start up companies. Each team is matched with a mentor and a technology is sele cted from the technology licensing office. The mentor act s as the mock CEO and helps to guide the team through the business plan creation process. The teams take part in eight weekly educational sessions which introduce them to the technology licensing pr ocess, funding options, and business plan creation. T opics include value propositions, forming the management team, market analysis and strategy, commercialization strategies, intellectual property, financials, corporate structures, sources of funding, bus iness plan development and company presentations. The teams work between the weekly session s to develop their business concept, write a business p lan, and prepare an investor pitch presentation The teams present their start up plans at an investor pitch c ompetition during the final week of the program.
53 Four distinct categories of stakeholders were identified within the program participants. They are : 1) Organizers, 2) Mentors, 3) Learners, and 4) Subject Matter Experts (SMEs) The Organizers are women working in the technology licensing office and in various aspects of technological entrepreneurship who originally envisioned the need for the program and took part in the design and implementation of t he program and curriculum. Mentors are succ essful female entrepreneurs and business women from the local community with a wide variety of expertise in startup companies, venture capital financing, and financial management. The organizers recruited them to serve as for th e mock startup compani es. The Learners are women who applied and were selected to take part in the program. Most of them have already obtained college degrees. Many have graduate degrees, while some are current graduate students. They make up the team members and founders of th e mock startup companies. The SMEs are successful entrepreneurs, venture capitalists, and angel investors. They were recruited by the organizers from both inside and outside of the local area to serve as presenters and judges. They helped with the developm ent of curriculum, recorded video presentations, presented during the course, reviewed business plans, rated investor pitches, and gave feedback to the learner teams. Data Collection Phase One of the data collection consisted of collecting, compiling, and analyzing existing program artifacts f rom five annual cohorts of program participants starting in 2012 through 2016 Phase Two of the data collection consisted of fifteen semi structured interviews, including participants from each of the three stakeholde r groups of Organizers, M entors, and Learners. The SMEs were not interviewed as part of this research.
54 Phase One (Review of Existing Artifacts) During Phase One a variety of program artifacts were collected and analyzed. These include d learner applications and res umes, end of course learner surveys, two annual learner follow up surveys, mentor, and SME bios; team business plans and including presenter vi deos, participant guides, session outlines, and mentor training materials. For some years, there were minutes available from program organization, including both pre planning and follow up meetings as well as other correspond ence documenting participant feedback and recommendations. While all artifacts were reviewed to develop a deeper understanding of the program, the primary analysis was conducted on the learner application and the end of course summative surveys Table 3 1 describes the available artifacts and the alignment of each to research questions and analysis methods. Table 3 1 Alignment of research questions to data sources and analysis. Research Question Data Sources Data Analysis 1 How does Ewits s trive to help women overcome barriers to entry into technology entrepreneurship? Program artifacts Organizer Interviews Descriptive statistics Case description Content analysis Thematic coding 2 What impact does Ewits have on particip attitudes and intentions? Applications Follow up Surveys Interviews Inferential statistics Thematic coding 3 How do participants describe their experience with entrepreneurship ? Interviews Thematic coding To answer RQ1 the following artifacts were analyzed: Program description and curriculum materials were analyzed to develop an overall description of the program and to understand how the organizers attempted to affect entrepreneurial attitudes and intentions and to address the issues of g ender bias and ambient belonging
55 Participant applications (n=283 ) were analyzed to develop a demographic description of program participants including degree level, prior entrepreneurial experience and an understanding of why they want ed to participate in the program. To answer RQ2 the following artifacts were analyzed: Learner end of course survey s (n=151 ) were analyzed to establish a baseline understanding of program effectiveness. Participant surveys were collected by program organizers for all fi ve cohorts. They are anonymous a nd required by all team members before they can enter the investor pitch competition. They include questions about program effectiveness and participant self efficacy on entrepreneurial competencies. Both quantitative and op en ended qualitative question formats are used. Phase Two (Semi Structured I nterviews) During Phase Two interviews were conducted with program participants representing three different stakeholder perspectives ( Organizers, M entors and Learners) The interviews are used to develop more in depth understanding of salient themes that emerge d during Phase One The interviews provide a deeper look at the impact participation in this experiential entrepreneurial educational model had on repreneurial attitudes and intentions (RQ2). They also provided insights into how p articipants construct ed their un derstanding of entrepreneurship (RQ3) including the experience of ambient belonging, socially learned stereotypes, and gender bias A dditiona l factors and barriers were explored that affect ed entrepreneurial activities (RQ2 ) By including the perspectives of different stakeholders, the research questions were examined from multiple p oints of view P rogram organizers pr ovided e mail and phone contact information for all stakeholders so that potential interviewees could be contacted. All Organizers (n=8) were contacted for participation. The first five to respond were interviewed. For Mentors (n =35) and L earners (n=283), participants were assigned a r andom number ranking Mentors and L earners were then contacted in order of random selection until adequate
56 responses produced the desired number of interviews. Interviews were conducted in person or via video conferencing. Video conferencing was used to conduct interviews with participants who are not able to meet for an in person interview. Interviews were conducted using a semi structured, open ended protocol (Appendix C ) All interviews were re corded and transcribed. Interviewees were allowed the opportunity to review and respond to their written transcript Some participants used that opportunity to clarify or add to contents of the transcript. l interview participants were assigned a pseudonym generated from a random name generator. Table 3 2 provides the interviewee pseudonyms, their career field and/or discipline, their highest degree attained and a brief description of their previous entrepre neurial experience. The stakeholder groups are not noted in this table as it could be used to identify the interviewee, and thus compromise confidentiality. The entrepreneurial experiences noted are those in which the interviewees had actual experience. Th ey include working in entrepreneurial support services, participating as a founder in one or more startup companies and working as an employee for a startup company. In one case, the interviewee was a long time owner of a single company (entrepreneur), in another the interviewee had attempted several startup ventures, but none had yet experienced a level of success that allowed her to leave her primary employment. In total, fifteen women were interviewed. Five from each of the three stakeholder groups of organizers, mentors, and learners. They represent all five cohorts of Ewits participants with four from 2012, two from 2013, five from 2014, two from 2015 and two from 2016. They hold a variety of degrees with four holding doctoral degrees, seven
57 with mast experience includes seasoned entrepreneurs, early stage startups, entrepreneurial support services, and innovators in larger companies. Their fields of expertise cover a wide variety of science and technology disciplines including engineering, health sciences, business administration and communications. Table 3 2 Interview Participants, Their Career Field, Highest Degree, and Entrepreneurial Experience Interviewee Career Field / Discipli ne Highest Degree Entrepreneurial Experience Brandi Communications PhD Support Services Deborah Environmental Engineering MS Multiple startups Gretchen Computer Science MBA Multiple startups Harriet Business Administration BS Startup employee Jan Journalism MBA Support services Julia Biology BS Multiple startup attempts Kim Health Science MS Startup employee Monique Communications MS Startup Nadine Political Science PhD Technology Startup Natalie Molecular Biology PhD Candidate Startup employee Rebecca Builder / Developer BS Multiple startups Roberta Marketing BA Support services Rosa Accounting MS Support services Shari Biochemical Engineering MS Multiple startups Tasha Medical MD Technology startup Data Analysis Procedures For this article, the program applications, end of course survey s, and interview transcripts were analyzed. The program applications and end of course survey s from Phase One included both closed ended quantitative questions and open ended qualitative quest ions. All of the data collected in Phase Two was qualitative data. There are several tools used in this analysis The initial combining of data sets was conducted
58 using Microsoft Excel Qualtrics was used to capture the data from the paper surveys. Quantitative data analysis was conducted in R Studio v 1.0.126 using R x63 3.3.3 and RMarkdown v 1.3. Qualitative data analysis was conducted in NVivo 184.108.40.2062 using thematic coding and text analysis. Phase One Analysis Before beginning statistical ana lysis, the datasets from cohorts 2013 2015 were combined into a single file and the data tidied. The initial combining of data sets was completed using Microsoft Excel. The data tidying and quantitative statistical analysis was conducted using R. The fin al data sets have been cleansed to remove all identifying information so it is an anonymous data set for analysis. All participant identifying information and references to program name and universi ty are anonymized. Leaners and t eams were given random ide ntifiers so that the Learner relationship to Team assignment is retained, but the identifying information is removed. The resulting data frames were stored for use in data analysis. Learner a pplication The program applications include both close ended qua ntitative and open ended qualitative questions. The responses provided by Learners on the program applications were combined with information tracked by the program organizers about Learner acceptance, attendance, and com pletion. Data from cohorts 2013 2 016 were combined in a single data set for analysis. This provided the ability to compare results for the entire program by cohort and within cohorts. The data were analyzed using summary statistics providing an overall description of both the program app licants and the demographics of the Learners. The open ended questions were exported into a text file and uploaded into NVivo for analysis.
59 The application was conducted as a paper and pencil application in 2012 and then as an online survey application in 2013 2016. The online application contained the same question set as was included on the paper application. A copy of the original printed application is included in Appendix A. Unfortunately, the program organizers were unable to locate the paper appli cations for the 2012 cohort. Some 2012 participant data was collected from other program documentation to provide as complete of a data set as possible. The data analysis was completed using the combined tools of Microsoft Excel and R Studio. Before begin ning the analysis, the data was combined into a single data set containing the responses from the 2013 through 2016 cohorts. Each applicant was assigned a random identifier and all personally identifiable information was removed from the dataset. The anony mized application data set was stored in the Applicants.csv file. To begin the analysis, the anonymized application data set is read from Applicants.csv and assigned to the Applicants data frame. The Applicants data frame was then filtered to extract th e r ows corresponding to only the learner data and create the data frame Learners. Learners are defined as the applicants who accepted the invitation to attend the program, attended at least one session, and were assigned to a project team. The Learners data f rame is used for all data analysis after the initial Application Decision table is created. This focus es the application analysis on the responses from learners who participated in at least one session of the program. Learner e nd of course s urveys End of c ourse surveys were conducted for all five cohorts. The surveys are anonymous, so cannot be connected to individual learners. However, they do include a question to indicate which team the Learner was assigned to. The end of course
60 survey s for cohort 2012 a re missing from the collected data set, so this analysis includes responses from 2013 2016 cohorts. The program organizers designed the questions which were updated each year before administration of the survey Learners were required to complete the surve y prior to entering the investor pitch competition. Theoretically, all Learners completing the course should have completed the survey. But the number of surveys available (n=151) were smaller than the number of participants who were designated as completi ng the course for cohorts 2013 2016 (n=170). The End of course Surveys for cohorts 2013 2015 were provided in the form of scanned copies of the paper surveys. A copy of the End of course Survey from the 2015 cohort is included in Appendix B. To prepare th e data for analysis Qualtrics surveys were developed that matched the items in the paper surveys and all surveys were manually en tered. The paper surveys were n u mbered 1 n and this number was recorded as one of the variables in the data set. This allowed validation of data against the paper survey if needed. The end of course survey for cohort 2016 was conducted using Survey Monkey and results were provided in electronic format. The data from all four cohorts was merged into a single dataset so that analysis can be run within each cohort and across cohorts. Since there were small adjustments to the survey each year, "Tidying the Data" in Appendix D for m ore information on how this dataset was merged and prepared for analysis. The end of course survey s included both closed ended quantitative questions and open ended qualitative questions. The quantitative questions on the end of course
61 Survey are ordinal L ikert scal e and multiple choice questions. The qualitative questions are open ended response. The qualitative questions are often attached to an ordinal response question allowing a more detailed response T here are a few standalone open ended response que stions at the end of the survey. The survey questions evolved slightly over the years with additional questions being added in 2015 and 2016. Most of the quantitative questions used ordinal or Likert scale ordinal responses. All quantitative data were anal yzed using descriptive statistics, analysis of variance and inferential statistics. The analysis was completed in R Studio using RMarkdown with a variety of R packages which provided both numeric and ordinal analysis techniques. The qualitative open ended questions were exported and analyzed thematically in Nvivo. The results are presented as an overview of emerging themes with a sample of representational answers. The analysis procedures for the Like rt and Ordinal scale questions is based on "Descriptive S tatistics for Likert Data," Summary and Analysis of Extension Program Evaluation in R by Salvatore S. Mangiafico (Mangiafico, 2016) The Likert scale questions in this data set all use five point scales. Most of them use the scale (1=poor, 3=average, 5=excellent ) The scale responses are symmetrical in that there are corresponding options for both positive and negative responses, surrounding a neutral response. Some of the questions include an opt out (N/A) response. The analysis of Likert scale questions included individual question by question analysis and grouping of related items to dev elop an overall measure For example, questions about the effectiveness of program elements were individually evaluated and then combined to
62 obtain an overall rating of effectiveness. This overall score can then be used to highlight elements that were eith er above the overall mean or below the overall mean. One consideration when evaluating Likert scale data is whether to treat the data as ordinal, interval/ratio, or numeric data. While there is some controversy about which is more accurate (Boone Jr and Boone, 2012, No rman, 2010) evaluating the data as numeric gives the option to use a variety of statistical analysis techniques and evaluating as ordinal all ows for the creation of useful data visualizations. One argument for treating the data as ordinal data is that it is often unclear whether the space between interval ratios in that Likert data are not necessarily continuous and do not allow for midpoint or off scale responses. Ordinal data can be evaluated using nonparametric tests including bar graphs, medians, and o rdinal regression Treating Likert data as numeric data allows the use of parametric tests such as t tests and ANOVA, and allows for reporting of means. While some may attempt to challenge the use of parametric tests on Likert scale data, Geoff Norman (2010, p. 631) showed that parametric tests non normal distributions, with no fear of c The following R packages were used in this analysis. The likert package provides tools for analyzing and visualizing Likert scale results. The ordinal package allows for analysis of Likert data as the dependent variable in ordinal regression.
63 The FSA package provides a Summarize function which provides summary statistics for a factor variable. The psych package provides a general purpose toolbox for working with both ordinal and numeric data. The lattice package provides additional graphs and visualization options for multivariate relationships. The rcompanio n package provides a groupwise m edian function to calculate medians and confidence intervals for one way or multi way data. Phase Two Analysis The Phase Two data col lection include s 15 semi structured interviews as well as the qualitative feedback from the open ended questions on the applications and end of course Surveys. This section summarize s the analysis procedures, provide s a description of the interview partici pants, and present s the coding methods and codes used in the qualitative analysis. To preserve the anonymity of all respondents, any identifying information in the transcribed data is blacked out when reporting the findings from this study. This blacked ou t data can include names of participants, names of companies, organizations, or university affiliations. Participant i nterviews Interviews were conducted either in the office, a local coffee shop, or via video conferencing. Three of the fifteen interviews were conducted using Zoom teleconferencing, the remainder were conducted in person. Each of the interviews were recorded using duplicate recording methods to ensure th ere was no loss of data due to technical difficulties. One recording was captured using a LiveScribe pen which allowed correlation to notes taken during the interview. A second recording was captured using a cell phone app. For the videoconference intervie ws, zoom was used along with the built in recording function The resulting recordings were uploaded into secure cloud
64 based storage. The duplication of recording proved a beneficial safeguard as two interview recordings were lost due to technical failures In both cases, the backup recording prevented the loss of audio data. Qualitative a nalysis All qualitative analysis was conducted in NVivo using thematic analysis. Both open ended qualitative questions from the applications and end of course survey s wer e analyzed along with transcripts from the semi structured interviews. Text from open ended questions were exported into text files and uploaded into NVivo. Interviews were recorded using an audio recording device. They were then transcribed and shared wit h the interviewees for additional input. Two interviewees provided updates and edits to their transcripts. The updated interview transcripts and audio files were imported into NVivo for analysis. All qualitative data were analyzed and coded thematically u sing NVivo (Seidman, 2013) The coding process included several rounds of review. An initial code book was developed from themes arising from the literature review. The first step of coding involved aligning the written transcription to the audio recording so that the audio could b e listened to while coding the written transcription. The written transcripts were then coded while listening to the audio. This step was very helpful in allowing the researcher to listen to the interview in the voice of the interviewee, as well as to ensu re the written transcription was accurate. During the first pass through the interviews they were coded using four broad categories 1) Background, 2) Entrepreneurial Experience, 3) Ewits Experience, and 4) Gender Related Experience. The coding reports wer e reviewed and seven emerging themes were identified. They are 1) Role Models, 2) Entrepreneurial Identity, 3) Mentor
65 and Team Dynamics, 4) Empowerment, 5) Do the Work, 6) Safe Environment, 7) On going Support. During subsequent passes, the coding was upda ted to ensure these emerging themes were captured. The coding reports were reviewed again, refining the coding alignment and narrowing the focus on the text of the interviews, eliminating unneeded information (Seidman, 2013) Table 5 lists the themes that emerged, the total number of references identified from all interviewees, and the total number of interviewees who discussed each item. After coding the interviews, the qualitative answers were revisited from Phase One. These answers were then coded using the code book developed for the interview analysis. Table 3 3 Themes and number of references for each theme Theme Total Number of Participants Who Discussed each Theme Total Number of References from all Interviewees Significant Role Models 15 Entrepreneurial: 12 (80%) Female: 9 (60%) They are empowered 10 14 Entrepreneurial Identity 15 16 Mentorships and team relationships 12 25 They have to do the work 8 13 Ewits as safe space 7 8 They want more ongoing support 5 15 Limitations and Assumptions Perspectives of the Researcher One of the basic tenants of a feminist epistemology is that the researcher is integrally involved and co constructs knowledge along with the research participants and the cultural environment. As the researcher, my current knowledge about these issues has evolved ov er time. They are influenced by my experiences as a female working in the information technology (IT) field and as a co owner of a feminist
66 bookstore. My experiences as an IT professional and as a feminist bookstore owner provided me with firsthand knowled ge of many of the issues that are discussed and analyze d in this research. These perspectives evolved as I enhanced my knowledge through review of research on these topics. In addition, I participated as a mentor in the 2014 cohort of Ewits of Ewits This direct participation in the program allowed me to situate myself wit hin the context of the research and gain efficacy with the program participants. In addition, I was able to see and experience the process both working on a team and in supporting other mentors as they experienced the process. I recognize that my perspectives are my own and may be different from others who have had similar experiences. The researcher acknowledges and continues to be aware of issues of privilege an d the sociocultural paradigms and constructs that affect biases. Intersectionality Women differ in many ways including race, gender expression, age, background, in entr epreneurship and technology it is important to acknowledge that there are many intersecting influences on how women approach their lives and their careers It is not the intent of this research to ignore the other diversity issues that so desperately need attention. While this study looks at the rates of female participation in technology and entrepreneurship, the rates of minority participation in these fields are also of concern Throughout this report both gender and racial diversity data are presented to gain a full understanding of the issue of diversity in these fields. Wh at this research discovers about how gendered field may also apply to other underrepresented minorities. The experiences and barriers fac ed by women
67 may be different, but the paths through th ese obstacle s may be similar. The support structures needed to overcome gender bias and stereotype threat are the human issues of respect, mentoring and access which come s realatively easily to individuals who have been privilege d as the majority representation of success. Population Bias The population studied in this research is a highly privileged population. They are highly educated and have many resources available to them. All the women wh o participated in this program self selected and indicated prior interest in entrepreneurship. What was learned from this population may not be generalizable to the general population. Additional r esearch is being conducted on a parallel program which targ ets a more broadly diverse, less affluential, and heterogeneous gendered group The results from this study can be compared to the results from the parallel program to determine how the female only program compares in its effectiveness. Summary This study uses a mixed methods methodology with a case study approach to examine how this all entrepreneurial competencies, attitudes, and intentions. The research design, context, participants, data co limitations were explained. Chapter 4 will include findings from both the quantitative and qualitative portions of the study.
68 CHAPTER 4 RESEARCH FINDINGS This chapter presents Empowering Wome n in Technology Startups (Ewits) as the focus of this case study. The experiences of organizers, mentors and learners who participated in the program were reviewed, based on the analysis of documents, survey, and interview data. After presenting the progra learners and mentors. The chapter will conclude by highlighting the ongoing support needs as expressed by the learners and other partic ipants. EWITS Empowering Women in Technology Startups (Ewits) is an entrepreneurial education progr am designed to help women learn about the technology licensing process and gain the self efficacy to succeed in technology entrepreneurship. The program focu confidence, lack of training, lack of mentor network, waiting to be asked) and strategies (Ewits, 2017a, p. 3) They do this by providing educated women with hands on entrepreneurial training and skills that will empower them for the rest of their lives (Ewits, 2017a) (Ewits, 2017b) approach immerses the lear ners and mentors in a realistic process of creating
69 k with successful female entrepreneurs, highly The mission and vis ion of the program consist of the following: Mission : To e ducate, inspire, and empower women to pursue leadership roles in technology based companies worldwide. Vision : A world in which gender issues are neutralized. The program was conceptualized by Jane Muir and her team in the Office of Technology Licensing (OTL) at the University of Florida Innovation Hub as an effort to increase the number of women who were participating in the university technology transfer proce ss. Jane Muir, the founding organizer, tells the story of the beginnings of Ewits: I started doing brown bag lunches with a few of my team members and a number of those where the at some point, I said, none of us have the bandwidth to take all this on, so with the m ission of the HUB and so that we can justify allocating resources and where we can really have the biggest impact using the limited resources that we have. And believe it or not, we created that program and had it running in either three or four mont we had over 50 people in that room and we were just rocking it. (2016 11 18) Since 2012, Ewits has been offered annually to cohorts of women interested in learning more about technology commercialization. Program Description Ewits is organized as a 10 week educational program with a content component (two hour educational sessions) and a culminating investor pitch competition where
70 teams present startup plans to an all female panel of angel and venture capital investors. The participant handbook describes the program as: Ewits is an experiential learning program designed to provide educated women with an introduction to the processes required to form a startup venture and develop a commercialization strategy for cutting edge, innovative technolo gies developed at research universities. Each week participants hear presentations on various aspects of technology commercialization and business planning from experienced, successful women entrepreneurs. Participants work in teams, and each team is assig ned a specific technology. Each team is also led by an experienced woman entrepreneur or business leader who serves as the virtual CEO/mentor and guides the creation of a business plan for developing and commercializing the technology and an investor prese ntation. There are no exams or grades. Business plans and investor presentations are judged by a panel of women investors with recognition and awards presented to the winning teams during the last session of the program (Ewits, 2017a) Prior to each session, the program organizers select a variety of technologies from the OTL, and recruit experienced entrepreneurial mentors and female leaders who up teams. As specified in the Training Manual for Program Coordinators (Ewits, 2015) entrepreneurs that an OTL would trust with commercialization of one of its information about the prog ram, guidance on how to effectively mentor their teams, and a review of technologies available for the program. The mentors then review the available technologies and make recommendations for which technologies to choose for the program. During this time, the organizers are hosting a series of informational sessions designed to recruit learners for the program. Learners then complete a program application which is reviewed by the organizers to ensure the learners have the background and desire to be success ful in the program.
71 The first session of the program is a mentor/technology matching session where the learners and mentors participate in a speed dating style process designed to introduce learners to the mentors and their selected technologies. The learn ers then rank their preferences both for technology and mentor matching. Over the next week, the organizers work to assemble the program teams, taking into consideration the learner's preferences but also trying to create teams that have a balance of scien tific, technical, marketing, financial, and other expertise. During session two, the mentors and learners receive their team assignments. They work with this same team, mentor, and technology throughout the entire program. Each team is assigned a color and the teams are program. They are provided with t shirts matching their assigned color to wear during the investor pitch competition at the end of the program. The weekly sessions start with an educational component, followed by a team work session. The sessions begin with a presentation from a successful female entrepreneur or innovator. These presentations usually consist of an overview of their entrepreneurial experiences includi ng a discussion of barriers, challenges, or gender biases they have experienced in their careers and leadership roles. These presentations often serve as catalysts for in class discussions. The sessions then present an informational topic related to the st artup process. The informational topics were video recorded in advance by a variety of entrepreneurial experts including expe rienced entrepreneurs, entrepreneurial support professionals and consultants. The final week of the session is an investor pitch competition. During this competition, one member of each team presents their startup plans to an all female
72 panel of angel and venture capital investors. One week prior to the competition, the teams submit their business plans for judges to review and scor e. Immediately after the investor pitch, the judges give the teams direct feedback on both their presentation and their business plans. The investor pitch and business plans scores are tallied at the end of the competition, and First, Second, and Third pla ce winners are announced. There are prizes for the winning teams, often including iPads for the first place winners. During the program learners work together to research the technology, write a business plan, and prepare an investor pitch presentation. Th e mentors and learners are advised that this program is an instructional simulation and that business plans they are conceptualizing are not intended to be real business startups. As such, they are given the leeway to be creative and make educated assumpti ons about their technology and startup that are based on market research, but cautioned not to get too bogged through the technology exploration process, business plan, and i nvestor pitch development. The teams and mentors sit together during the weekly instructional plans. To support these activities, the program offers eight weekly ins tructional sessions that cover topics related to technology licensing and the business startup process. These activities include value propositions, forming the management team, market analysis and strategy, commercialization strategies, intellectual prope rty, financials, corporate structures, sources of funding, business plan development and company presentations. The program organizers originally designed the curriculum in conjunction
73 pics. The informational sessions and be made available online via a learning management system (LMS). Additional educational curriculum is available online in an LMS, includ ing a program handbook, articles and content related to technology licensing and the startup process, business plan examples and templates, and financial spreadsheet models. Mentors and learners have access to the University of Florida library and research resources throughout the duration of the program. During one of the weekly sessions, licensing officers from the Office of Technology Licensing come in to talk with the teams about their technologies and answer questions about the status of the technology and the licensing process. Ewits Participants E arly in the analysis, it became apparent that the women associated with this program, regardless of their roles, describe d a dynamic process of learning and growth associated with their participation. To honor this shared experience, all the women involved in the program are referred to as participants. Four distinct categories of stakeholders were identified within the program participants. They were: 1) Organizers, 2) Subject Mat ter Experts, 3) Mentors, and 4) Learners. Organizers and subject matter experts The Organizers included six women who worked in the technology licensing office, the UF Innovation Hub, and in various aspects of technolo gy entrepreneurship who originally en visioned the need for the program and took part in the design and implementation of t he program and curriculum. In addition, two additional women who
74 worked with the program as facilitators, managing the daily program, and coordinating the informational se ssions were also considered Organizers. The Subject Matter Experts (SMEs ) were successful entrepreneurs, venture capitalists, and angel investors. They came from both inside and outside of the local area to serve as presenters and judges. They helped with the development of curriculum, recorded video presentations, presented during the course, reviewed business plans, rated investor pitches, and gav e feedback to the learner teams. It was a bit more difficult to determine the exact number of SMEs who partic ipated in the program over the five cohorts; however, there were at least forty documented in the program records. There may have been others who participated as presenters, or who reviewed curriculum and provided support to teams that were not recorded in the program records. Mentors The Mentors consisted of successful female entrepreneurs and businesswomen from the local community with a wide variety of expertise in startup companies, venture capital financing, and financial management. The organizers rec ruited them to serve as for th e mock startup companies. There were a total of thirty five mentors who participated in at least one cohort. To date, only three of the mentors have participated as a mentor in more than one cohort. After feedback from t he first two cohorts, the support to the mentors and to fill in when a mentor was absent. The roving mentor sits in with teams whenever their mentor is absent during one of th e informational sessions. The roving mentors were selected from among the most successful mentors in previous cohorts and were available to meet with mentors as they prepared to enter the program,
75 giving them guidance, and helping to pass along many of the lessons learned and best practices from previous cohorts. The end of course Surveys include several items intended to give program organizers feedback on the mentor relationship as well as on the overall effectiveness of the mentor team collaborations. T hree Likert Scale questions address the mentor relationship:1) Knowledge: How would you rate your mentor's knowledge regarding entrepreneurship? 2) Accessibility: How would you rate your mentor's accessibility outside of the scheduled program meeting hours ? 3) Overall: How would you rate your mentor overall? The response options are scaled as 1=poor and 5=Excellent. In addition, there is an open ended response question which asks, Overall the mentor team relationship feedback is positive. Figure 4 1 shows that with knowledge at 89% above average, and accessibility at 80% above average. When the feedback data are analyzed by team, we can see variations among teams. Using a combined mean score for all responses, we see a range of 3.6 5.0 among all teams. Analys is of variance shows that both team and cohort have a significant relationship to mentor feedback scores, with an F value for cohort of 3.28, and an F value for team of 1.74 at a 95% level of significance. This would imply that learner experience may be af fected by the mentor learner relationship, team dynamics, or other effects attributable to the cohort that they participated in. There were 78 responses to the open ended feedback question, with 78% of them being positive and supportive of the mentor ex perience. The negative responses
76 knowledge, time availability, and or guidance to the team. One example of a positive comment from the 2015 end of course Survey is: Our mentor was great; she kept us focused and many times used her experience to keep us on track. The process was made easier with her feedback. She often steered us away from getting bogged down in details. We all enjoyed working with her. In this respon se, the learner is validating that her mentor was effective in guiding the collaborative team process. She helped to guide them and keep them from getting bogged down in the details. An example of a negative comment from the 2016 end of course survey is: Our mentor was extremely knowledgeable and helpful regarding the technology licensing process and our chosen technology but was not strong as a leader or facilitator for our team's activities. She was often not present during weekly Ewits meetings, and ( with one exception) did not join us for meetings outside of regular session hours. There was very little monitoring or guidance of our progress, and our group's work was very chaotic. In this response, the learner acknowledges that their mentor is knowledg eable and helpful, but that her absences made it difficult to benefit from her guidance. This caused the learner to perceive the process as chaotic. In later cohorts, Ewits implemented an adjustment to the program where they utilized roving mentors. These mentors were selected from mentors who participated successfully in an earlier round of Ewits. They provided guidance to current mentors, and were available to serve as substitute mentors when a mentor was absent from one of the weekly sessions. This ensur ed no team would be without a mentor during a weekly session.
77 Figure 4 1 Mentor Feedback Learners The Learners consisted of women who applied and were selected to participate in the program. Most had already obtained college degrees. Many of them had g raduate degrees, while some were current graduate students. They made up the team members and founders of the mock startup companies. The learners were recruited through a variety of methods from the university and general community, including a series of information sessions that provided potential applicants with information about the program and an overview of the application process. The program accepted an average of 89% of applicants. Of those who attended the first mentor matching session, a total of 93% completed the program through to the investor pitch competition (Table 4 1). Those who did not complete the program cited a variety of reasons for leaving, including obtaining a new job, moving out of the area, or unexpected family obligations.
78 Accor ding to the learner applications for cohorts 2013 2015 (n=227), 82% of 1) and 50% of applicants had a graduate degree or higher. Some were current graduate students; others were business owners, faculty or women who were interested in the university technology transfer process. The two reasons cited most as motivation for participating in the program were 2). All the learners indicated having access to computers and the internet. They were adept at using a variety of software and technology tools, including statistical analysis software. Less than 8% of applicants reported having any prior exposure to patenting or technology licensing (Table 4 2). T he end of course survey s further illuminated traits of the program learners. Table 4 3 provides background information for respondents in the 2015 and 2016 cohorts (the only years this data was collected). The survey shows that the learners come from a var iety of disciplines, with 62% of them coming from science and technology related disciplines and 38% of them coming from business or marketing related disciplines. They represented a wide range of ages with about 50% of them being under 35 years of age. Th e participants were pretty well distributed by racial or ethnic heritage, with East Asian or Asian American being over represented and African American women being under represented The median household income fell between the $35,000 to $49,999 and $50,0 00 to $74,999 groupings.
7 9 Table 4 1 Applicant Results by Cohort n Accepted %Accepted Attended %Attended Completed %Completed 2012 56 56 1.00 54 0.96 52 0.96 2013 54 50 0.93 47 0.94 41 0.87 2014 52 50 0.96 45 0.90 42 0.93 2015 66 57 0.86 47 0.82 47 1.00 2016 55 55 1.00 46 0.84 40 0.87 Totals 283 268 0.89 239 0.89 222 0.93 Figure 4 2 Distribution of Applicants by Degree Table 4 2 Percent of Applicants Who Indicated Source of Motivation What is your primary goal for participation in the program? (Check All That Apply) 2013 2014 2015 2016 All cohorts Gain Self confidence 72% 81% 77% 69% 60% Start a Company 74% 69% 65% 64% 54% Networking 80% 83% 85% 82% 66% Entrepreneurship Training 94% 92% 95% 85% 74% Attending Seminars/Workshops 61% 60% 61% 58% 48% Job/Career Opportunities 0% 0% 0% 25% 5% Valuable knowledge and Skills 0% 0% 0% 51% 10% Interest in Technology Commercialization 0% 0% 0% 45% 9% All of the above 44% 52% 42% 38% 35% Other 9% 6% 5% 0% 4%
80 Table 4 3 Learner background information for Cohorts 2015 and 2016 What is your area of expertise? (Circle One) Total 2015 2016 Business 13% 12% 14% Computer/IT 9% 8% 11% Engineering 17% 10% 24% Finance 5% 8% 3% Marketing/Communications/Design 19% 25% 14% Other 3% 5% 0% Science 34% 32% 35% What is your age? (Circle one) Total 2015 2016 18 24 16% 15% 17% 25 34 34% 32% 36% 35 44 23% 22% 24% 45 54 18% 20% 17% 55 64 9% 10% 7% Circle the answer that best describes your current situation. Total 2015 2016 Married or in a committed relationship with no children 29% 21% 36% Married or in a committed relationship with grown children (18+) 15% 18% 12% Married or in a committed relationship with school aged children (5 18) 12% 13% 12% Married or in a committed relationship with younger child/ Ren (under 5) 5% 8% 2% Single parent with grown children (18+) 1% 0% 2% Single with no children 38% 39% 36% Which of the following best represents your racial or ethnic heritage? Total 2015 2016 Black, Afro Caribbean, or African American 5% 6% 5% East Asian or Asian American 11% 6% 15% Latino or Hispanic American 14% 9% 20% Middle Eastern or Arab American 3% 3% 2% Non Hispanic White or Euro American 61% 69% 54% Other 4% 9% 0% South Asian or Indian American 3% 0% 5%
81 Table 4 3 Continued What was your total household income before taxes during the past 12 months? Total 2015 2016 Less than $25,000 22% 23% 21% $25,000 to $34,999 4% 3% 5% $35,000 to $49,999 11% 13% 10% $50,000 to $74,999 17% 17% 17% $75,000 to $99,999 17% 17% 17% $100,000 to $149,999 19% 20% 19% $150,000 or more 10% 7% 12% Please circle the option(s) that best describe(s) your current situation. OK to choose more than one if applicable. Total 2015 2016 Employed at a non technology non startup company 7% 0% 12% Employed at a non technology startup 11% 6% 14% Employed at a technology non startup company 12% 16% 10% Employed at a technology startup 7% 6% 7% Master's student 11% 9% 12% MBA student 3% 3% 2% Owned my own non technology business 3% 0% 5% Owned my own technology business 12% 19% 7% PhD student 11% 3% 17% Postdoc 12% 19% 7% Unemployed (not a student) 5% 6% 5% Work part time (not a student) 1% 0% 2% Work part time (not a student) 5% 12% 0% Entrepreneurial Identities And Role Models The interviewees were representative of the broader participant population as well. As noted in chapter 3, those who were learners in Ewits were well educated (50% graduate degree) and had some entrepreneurial exp eriences ranging from support services to multiple start up experiences. The interviews revealed two common experiences among the fifteen interviewees, regardless of their role. First, th ey have strong entrepreneurial and innovator identities. Second, they can describe at least one significant role model which gave them a first exposure to entrepreneurship or to a strong female leader.
82 E ntrepreneurial I dentities Each of the interviewees had a story about a person or an experience that they can point to whi ch introduced them to entrepreneurship. Twelve of the participants described a previous experience with entrepreneurship, either through a close family member or friend who was an entrepreneur or an experience in providing support services to startup compa nies. Regardless of their current role or position, the interviewees described themselves as innovators who were involved in the process of making something new happen. Natalie stated: So the comp any I work in currently, we are a small innovative arm. So, a lot of what we do I play a pretty big role in launching new products, thinking about new products. So even though I'm working for a larger company, under our parent company, we still have pretty innovative roles identity wa s inte gral to her work as she plays a leading role in creating products and bringing new products to market. She sees herself as an innovator within a larger company. The participant, Deborah, talked about her entrepreneurial identity from the perspective of wanting to make something happen. And it's not that I really thought about, "Hey, I want to be an entrepreneur because that's a cool sexy thing." It's just like, "I have this thing and I want to see this thing happen." And my two things that I've been trying to do as an entrepreneur, they're so completely different. They're two different sides of one's more desig ned and the other one is more engineering. O ne's definitely a lot more scientific than the other For Deborah, being an entrepreneur is conn ected to changing the world and is not limited to only one dimension but brings together her interest in design and engineering. By contrast, Julia struggled a bit more with her entrepreneurial endeavors, yet she still maintained a strong entrepreneurial identity. She claimed, This is for me and
83 I'm still pursuing some technology ventures and some small business venture s as well, that I want to incorporate some technology in Role M odels All fifteen of the interviewees describe significant female or entrepreneurial role models who inspired them to succeed in their careers and as entrepreneurs. Nine of them described a strong female role model a mother, grandmother, aunt, or other close f amily member who influenced how they saw themselves as a scientist, a technologist, or an entrepreneur. Twelve of them described someone in their close circle of family or friends who was an entrepreneur. Four of them describe working in a family business that gave them their first hands on experience with entrepreneurship. Nadine talked about her father and godparents who were all entrepreneurs: M y dad owns a number of rental properties, and then my godparents, they retired when they were 40. They were do uble income, no kids, making really nice salaries. I saw that, and I also saw the flexibility that owning your own business afforded them. They could travel wherever. They had a few months where they would just hunker down, and do a lot of their invent ory, and all that kind of stuff. And he could work anywhere, really. So, I saw that, and that was also interesting. I thought about, "What could I possibly do?" Like the other interviewees, Nadine grew up seeing adults engage in entrepreneurial activity a nd saw first Harriet described her mother as a role model and the experiences she had a My mother owned a very large marketing co mmunications business. She did all of the franchising in the early days of the cable business for Warner Brothers and American Express which today is Time Warner I t was not unusual for our family to have very hig h level executives at our dinner table on a regular basis because we operated the business 24/7. I entered that business in college and I worked for her, and I did all the
84 billing for the company and a variety of things. I did market research for companies like Pioneer and GE So I entered the e ntrepreneurial environment at a very young age because that was our life Even though Harriet is not currently working for an entrepreneurial venture, she saw these experiences as shaping who she is as a female professional as well as her personal entrepr I think that's a result of growing up with a person like I did, my mother who really set the bar at a very high level It is interesting to note that several of the interviewees did not immediately recognize these role mod els. For example, when Natalie was asked if anyone in her family, or close network of friends was an entrepreneur, she initially replied in the negative but then observe d that her mother operated her own business from their home, No. No. But, my mom, back in Columbia where she would make, I don't know, binding of books and printing, printing f liers, more like graphic design so doing commercials it was like a spare room in my grandfather's house, and it was facing the street So, she just opened that space and did it Natalie went on to talk about how that experience affected her perception of But it was really tough Securing the income, the customers, havin g hav ing a constant source of income rewarding. She spoke fondly about the relationships her mother developed with her clients That's one of the things that I like seeing, and she was very happy about it. When asked about role models, Gretchen also rep But I didn't have any strong I don't recall having besides my family just saying, "Yeah, go be an astronaut." But it wasn't strongly. L ater in the interview she tells the story of her founded the first Women's Flyi ng Club at Fort Bragg, North Carolin I was three, and so she put me in the back seat with a coloring book and crayons is in the front seat practicing touch and goes. Gretchen realized at that
85 moment that her mother was a stron g female role model. She emailed several weeks later to share the following statement: childhood was and my parents' contribution to my interests in science a telescope when I was 9 was my prized Christmas present and I never realized how unique that was! I want you to know that I wrote a thank you note to my mom right after that! The analysis of Learner survey data and the interviews with learners, m entors, and organizers led to three motifs. First, the analysis demonstrated that participants in Ewits shared certain experiences that facilitated their engagement with the program. Second, participants described the impact of the program in terms of comp etence, empowerment, and awareness of gender bias. Third, two key conditions that made a difference per the participants were that Ewits created a safe space to explore gender inequities and that the program provided mentorship to help them navigate the pr ocess. Each of these motifs is described in detail below. The Reported Impact of Ewits All participants who were interviewed described Ewits as an experience that empowered them. It was not just the learners who described this empowerment, but also the me ntors, the organizers, the presenters, the judges, and everyone else who had in some way been involved with the program. Empowerment emerged in three ways throughout the data: (a) the development or validation of the competencies necessary to patent, licen se, and launch a startup and an understanding of challenges for entering leadership positions in technology startups as women; (b) increased entrepreneurial intention and (c ) a greater awareness of the effects of the gender related issues in the field.
86 Co mpetence building and validation technology startups and the perceived challenges to entering technology entrepreneurship. It accomplishes this through the educational content pr esented at the informational sessions as well as the constructive and collaborative learning that occurs when the teams research their technologies and prepare their business plans and investor pitches for their technology startups. Learners describe gain s in their personal understanding of entrepreneurial competencies. The end of course survey of changes after participating in Ewits. L earners were first asked to rate their skill levels before attending Ewits ; then they were asked to rate their skill levels after participating in Ewits. The difference between these two levels was quite significant as can be seen in Figure 4 3. For example, Learners rated their knowledge of commercialization before attending as 70% below the midpoint, 21% at the midpoint, and 9% above the midpoint. After attending Ewits, their perceived understanding of commercialization was 9% below midpoint, 29% at the midpoint, and 62% above the midpoint. These data show that learners reporte d an increase in their personal understanding for each of these five areas because of their participation in Ewits. A second question on the End of Course surveys addresses perceived challenges to assuming leadership positions in technology startups (Figur e 4 4). L earners were first asked to rate their perceived challenges before attending Ewits; then they were asked to rate perceived challenges after participating in Ewits. Again, we can see that the Learners reported positive impacts on their personal per ceived challenges. For example, Learners rated Role Models as 36% above the midpoint before attending
87 Ewits, and 79% above the midpoint after attending Ewits. This data shows that learners feel Ewits helped mediate perceived personal challenges to taking o n leadership roles. The interviews and short answer items on the End of Course survey confirmed and further expanded on these patterns. The following comments were taken from the End of Course survey. Ewits was a great opportunity for me to validate that I have the necessary technical skills to take a patent and create a product. It also helped me to visualize my strengths and what things I need to work on in order start my own business S aid another learner : It was very validating for me. I realized I have much more skill and business knowledge than I thought I had. I am now contemplating starting a conference to tackle this issue. I would not have done that prior to These comments show how the program provides an explicit validation of technical skills (competencies) that these two women had not necessarily experienced previously. For the first learner, it was the ability to patent and start a business. For the second learner, it was a validation of her business knowledge. Both women als o note how the program helped them take initiative and take ownership (challenges) of their own professional development. In addition, they both described an intent to launch a startup or a new venture (entrepreneurial intention). Similarly, Nadine talks about her own increased confidence, as well as the validation of feedback from her mentor. She says: I think that my work or my participation in Ewits gave me a good amount of confidence that the work that I contributed to our project was sound becaus e we had tons of feedback from our coach. And so, that was just another big confidence builder that I could do work that would help this product potentially go to market.
88 Her comments showed that she both valued the feedback from her mentor, and felt she had confidence in her ability to prepare to launch a technology startup. All three of these interviewees validated the data on the End of Course surveys by confirming that they either developed or validated the competencies associated with technology start ups, as well as developed the confidence that they could launch a startup. Figure 4 3 Self reported impact on perceived competencies before and after Ewits
89 Figure 4 4 Self Reported impact on perceived challenges before and after Ewits Increased e ntrepreneuri al i ntention The 20 items from the Entrepreneurial Intention Questionnaire (EIQ) by Lin Urbano, and Guerrero, were added to the end of course survey in 2016. These questions were added to provide additional insight into the effectiveness of the program. The questionnaire uses a Likert scale with values of one to seven with one being low, se ven being high four representing the mid point. The questions are designed to evaluate entrepreneurial PA, PBC, SN and Intention. The questionnaire has been empirically validated for its effectiveness (SANTOS & LINAN). In an attempt to understand the impa ct of Ewits on participants entrepreneurial intentions the questions were included on the survey twice In the first presentation,
90 participants were asked to evaluate the questions based on their perceptions after Ewits On the second presentation, partic ipants were asked to evaluate the questions based on their perceptions before Ewits. The results from those res ponses are included in Table 4 4 below. The Ewits after participation scores were higher than all results from Santos et. al. with the exception of the Entrepreneurial intentions mean score for Bedfordshire men of 5.29 as compared to Ewits women mean score of 5.04. The before Ewits scores were lower than all of the results from Santos et. al. Seville women. Entrepreneurial SN was only evaluated on ce on the questionnaire and was grouped with the after questions. Table 4 4 Mean responses on Entrepreneurial Intention Questionnaire (EIQ) Before Ewits After Ewits Difference % Increase Entrepreneurial Intention 4.11 0 5.04 0 0.93 0 23% Entrepreneurial PA 4.718 5.748 1.03 0 22% Entrepreneurial PBC 3.605 4.9 00 1.295 36% Entrepreneurial SN 5.62 0 Awareness of g ender i ssues The Ewits program addresses gender issues in two ways. First it is an all female program in which the organizers, mentors and learners are all female. Second, the weekly information sessions include presentations and discussions designed to create awarenes s around both gender issues as well as barriers to entry that are more prominently experienced by women. Increased awareness of gender bias in technology and entrepreneurship was reported on the open ended questions in the End of Course survey and emerged in the interview data. It also helped me gain greater awareness of the
91 gender bias that I face and has spurred me to take a more active role in dealing with gender bias in the workplace ( end of course survey s) Another learner addres sed the low participation of women in the field, and noted that participation in Ewits as an all female program helped to build the confidence to break through barriers to entry. She wrote: In my estimation women themselves are hesitant about getting into this field. I think programs like Ewits are the answer. The program offers guidance and builds confidence in the abilities of the participants that can then be used to break the self imposed barrier of entry Not all the participants were as adept at desc ribing gender bias. Some of them talked about the opportunity to work with other women, which they had not experienced before I was just wanting to see how well I worked with other women because I didn't rea lly h Monique goes a bit farther and talks about the importance of building relationships with other professional women, learning to work both for and with other women. She describes a common gender bias about working for other women She also expresses her wish that she could have had an opportunity to work for a woman and to have more female role models. She said: I think it's really important for women to bond and to understand the importance of supporting one another and cheerlead ing each other. We all hear that thing about I don't want to work for a woman and I think that's B S. I think I would love to have worked for more women. I would love to have had role models that were women. Shari talked about finding her own understand ing in relationship to gender bias issues. She said: And I think sometimes the behaviors that I was attributing to gender issues were maybe relationship issues. And the relationship issues that I was attributing you know, and vice versa. So, that was rea lly interesting to see. There were enough women talking about their issues that it felt safe.
92 And famous women, on video for example. And there were also local women who came in you know, not super famous, but very successful at what they do. Each one wo uld kind of talk about how they overcame a challenge and what it meant to them. work place. She evaluated her own understanding of gender issues as they related to rela tionship issues. She felt that having enough women talk about these issues created a safe space for exploration of her own understanding. Shari also made reference to the women who presented in class about gender challenges and how they overcame them. Not everyone was comfortable with the emphasis on gender issues. Yet, they often acknowledged that they too had experienced some sort of gender bias or at least noticed the low participation of women in entrepreneurial spaces or in their own fields. For exampl e, one learner indicated: Some women felt that the discussion around gender differ ence was slightly too much than what they realistically have experienced. However, refl ecting on my attendance to the Celebration of I nnovation event, I was really touched when I saw very few women there! I think the discussion of gender might be improved a little too, by adding more substantial materials such as scientific evidence and what gender researchers think about it The Celebration of Innovation event is an event that occurred at the Gainesville Hilton during the time of this cohort of Ewits. It is held in association with the UF Innovation Hub and the OTL, but was not part of the Ewits program. This learner would have attended because of a larger interest in techn ology entrepreneurship. As she described, she was surprised that with her new awareness of gender issues she was aware of how few women were participating. She also provided a critical recommendation to the program to include more scientific based evidence on what research shows about gender bias. This feedback has been discussed with program organizers and they are
93 implementing plans to include more research based information about gender bias in the upcoming 2017 session. From the interview and learner r esponses on the End of Course survey, it is difficult to determine whether the awareness of gender issues by participants came from the nature of the all female program or from the gender related discussions in the information sessions. There was however, evidence that these women were more aware of gender bias and were attempting to process and understand how that new awareness affects their confidence and perception of technology entrepreneurship. A Unique Learning Environment Study participants described three elements in the Ewits program that made it a unique and empowering experience for them. First, the participants were both challenged and empowered by the experiential learning project. Second, the all female taking and a gender related educational and career challenges. Furthermore, the program created a collaborative learning experience where participants learned from the mentoring, mentorship, and team relationship. In the data, it is often difficult to differentiate between responses to the pure rigor of the project, the experience of working with other women, and the collaborative lear ning experience of working with a mentor and a team. It is apparent that these experiences combined to create a shared identity related to their experience as Ewits participants and as technology entrepreneurs (see Entrepreneurial Identities above). The fo llowing sections will attempt to highlight each of these elements along with how they relate to their shared Ewits identity.
94 Challenging yet rewarding Participants described the business plan and investor pitch creation process as extremely challenging, ye t rewarding. The challenge pushed their limits while confirming new skills and competencies. The learner below discussed both the rigor and reward of the program, as well as the experience of working together with other women, and her resulting Ewits ident ity. From the 2015 End of Course survey she said: Ewits was at once an experience so challenging I sought to embrace it fully and so challenging I wanted to shed it. I would not have wanted to face the challenges alone therein lies strength: wome n from a variety of background s and experiences joining together to learn, to share, to succeed, to get an experience, to empower whether we're starting a business or changing the world. We are Ewits hear us roar This learner described her experience as so challenging she wanted to embrace it fully and to shed it all at the same time. She referred to the strength that she experienced in this shared space with the support of other women, who she saw as contributing a variety of backgrounds and experienc es to the success of the project. She experienced this as empowering. This quote was also interesting as it referred to a shared entrepreneurial identity including both the options of starting a business or changing the world (see Entrepreneurial Identitie s above). She also referred to herself the identity of Ewits. In a similar manner, Harriet also described the experience: I will tell you, it was an amazing prog ram as a mentor and I think when you talk to the participants, for the most part, you're going to hear the same thing. Every group takes on a life of its own, every technology takes on a life of its own B ut ultimately, it was an awesome experience. It's a little overwhelming to think about doing it again now, to be honest with you.
95 experi on the experiences she observed among the other participants. Her language, and her enthusiasm illustrated a confidence, and an excitement, surrounding what the participants could accomplish in this environment of Ewits. This was a reoccurring theme that participants described as a borderline overwhelming experience, that at the same time was an awesome experience they valued highly. This was an experience that pushed them to their limits, but allowed them to find their strengths, step up, and meet the challenge. Harriet showed that limit when she expressed that it would be overwhelming to think about doing it again. It seems that this combination of hard work, pushing them to new limits of their own understanding, combined with the elation of success after the investor pitch created a common shared experience which seemed to bond them together. Their participation in Ewits has become a part of their entrepreneurial identity. Sa fe space Ewits not only created a shared experience among participants, but it did so in a environment where they learned how to work with other highly qualified, competent women. For some, this experience of being a gender majority, not a gender minority, was a unique experience. Several of the interviewees specifically referred to Ewits as a safe space. They talked about how they felt that they had the efficacy and ability to be s uccessful prior to participation, but that Ewits helped them test their abilities in a safe
96 environment. As Natalie explained Harr iet also described the safe environment. She said: I do think that it was it made it a fun and interesting environment and I can't think of the right words to put this in maybe a safe environment. I don't think that people felt if there was anythi ng I don't think people hesitated as much as they might have if men were in the room. I hate to say that, but I really think that's probably true. Harriet alluded to the possibility of the safe environment being somehow linked to the nature of the all fe male program. She felt this allowed the participants to reject as The learners and interviewees also talked about the experience of working with other talented, highly qualified women. From the 2015 end of course survey : It was wonderful to be part of a group of such smart and diverse women! member adds to a proje ct. Also how to communicate and come to conclusions and solutions together. With all women! In this comment, the learner expresses (with a bit of surprise) both the empowerment of and diverse women and she explained that it helped her to work on her own barriers to entry and to gain efficacy through networking, collaboration, and communication. Some of the women described the opportunity to validate their technical skills, while developing an understanding of their strengths and additional learning challenges. From the 2016 end of course survey : Ewits was a great opportunity for me to validate that I have the necessary technical skills to take a patent and create a product. It also helped me to visualize my strength s and what things I need to work on in order start my own business. As an engineer, I suffered understanding financials and
97 marketing. However, after Ewits I am not afraid of those areas especially because I learned that it is OK to ask for help. to be vulnerable enough to admit she did not know everything, and she could ask for hel p when needed. Mentorship and collaboration A core ten e t of the Ewits program was the mentor ship and team collaboration process. The Ewits Participant Workbook (Ewits, 2017a) described one of its learning collaboratio n not only provided space for team members to work together on developing their startup plan, but it also created space for them to have discussions related to the challenges and barriers to being women in a masculine gendered field. Evidence of these conv ersations showed up in the open ended questions and in the interviews. Evidence of the rigor of the program again showed up in the conversations about the mentor team relationship. The i nterview d ata provided further descriptions of mentor team collaborat ion. The mentors described a process of watching their teams develop, learning how to work with each other, struggling through learning how to work out differences, and developing trust relationships along the way. They related the struggles in the teams t o the struggles they had experienced in their own careers. They described Ewits as a very lifelike and authentic experience For instance, Kim talked about how she navigated that relationship as a mentor, maintaining her distance, but providing guidance al ong the
98 way. She saw her role as helping the team to stay focused and on task as they worked together to accomplish their goals. She also discussed her own learning process as she learned to work with women who did things differently than she did. She say s: The teamwork aspect of it was very intriguing, as it always is to watch how people interact with each other and with me. And you know you almost always have dysfunctions on the team and experiencing not necessarily how I deal with it always about me as a mentor, but you back away from it and watch how these women deal with dysfunction down enough to pay attention, because that was my job. To figure out how to hel p them win. And when I say win, I [sic] necessarily mean winning that prize at the end. accomplish? And learning to work with women that are different than I am. Not necessarily in personality and character, but looking at how they solve problems. ow she as a mentor saw herself as part of this process, and how she learned along the way. She portrayed blems with other women who may have a different approach. Harriet found the mentoring process harder than expected. She talked about keeping her distance as a mentor, not stepping in to take over, but to provide guidance. She described the process of chall enging her team to step up and make purposeful choices about how they wanted to see this challenge through. She said: I t's an amazing experience. It's like childbirth, literally. It was harder than I expected it to be. I'll be honest with you. I did no t expect it to be so hard. B ut I also was very disciplined with my role, and I maintained to them, "I can't do this for you. I am your mentor. I can help you, I can guide you, I can direct you, but I can't do this for you." That was hard not to do it. Beca use rarely are you working by yourself. Some th ings, of course you are. But
99 we needed to be able to complete our investment pitch because one of the members this always happens right? Somebody be? And what are you made of? Agai n, we see the theme of challenging and rewarding as Harriet referenced the challenge of the process in her description. She talked about rallying at the end, stepping up, even in the face of adversity, to meet the challenge. Learning how to fill in the gap s, and how to make up for the team member who did not come through. decision on them, and recognized that her role as mentor was to help them make decisions and then guide t hem through the process. of learning to work in a team, navigating the challenges and frust rations of team dynamics and figuring out how to get the job done. Rosa talked about the process of dealing with the internal frustrations and pulling together to complete the project. I definitely learned a lot about all that it takes, and of just working together as a team, a nd how much that takes to put something together at a really quality level. And I think you can run into some challenges there. And on my team, we did have some internal frustrations by some of the team members and just trying to deal with that. It was a challenge, and that happens though. Towards the end, it was kind of, "Let's just get through and present." And maybe, I don't know i t's hard to figure out how to better manage that, so I think that was something just kind of lear ned. find her internal strength to step up to the challenge even when there were internal dynamics and frustrations among the team. She discussed the moment when they
100 d ecided to just push through and finish the project and the ultimate recognition of Deborah talked about finding her own confidence in a team full of leaders. She struggled with how to speak up when she did not think the right choice was being made. She felt like she should have spoken up sooner, instead of waiting. As she explained: I like to take charge but I thought in this case I shouldn't do that because it was a whole table full of lead ers. I admire every single person that I worked with. But on the other hand, there were a couple of us that were like, "Is this really the right choice?" And we really should have spoken up more and not worried about it so much. That's one thing I learned. In her statement, her struggle to find her own confidence and her own voice was clear as she attempted to step up and express her opinion when she felt like the team was making choices she did not agree with Some of the teams experienced normal everyda y outside influences that negatively affected the team, including sick family members, a team member dropping out because they got a new job, or team members who had outside demands which took up too much of their time. As this comment from the 2016 end of course survey explains: Overall, I thought it was a great experience. It went a little too fast, but then again, we had problems slowing us down... sickness, family emergencies, one person dropped out. But the learning environment was great and building a business plan together was a very positive experience This learner expressed the frustration of normal team dynamics, and unexpected issues of family, illness, etc. Even with the frustrations, she felt that the learning environment was great and describe d an overall positive experience with the process. Overall, the mentor team relationships seemed to have created an environment where both learners
101 and mentors were able to both test their efficacy in the competencies of entrepreneurship as well as their c ollaborative work skills. In both the end of course survey s and in the Interviews, participants expressed a desire for on going support and networking after Ewits is over. Many of the teams have maintained ongoing relationships and have attemp ted to stay in contact with each other and with their mentors. One learner stated: I learned so much from this program and from my team. I will miss attending Ewits but I know I will remain close with my team. They were very special women, and it was such a remarkable experience in getting to know them Overall, most of the participants indicated a positive experience and a strong sense of bonding with those who shared the experience with them. There was a group of participants after cohort 2013 who attemp ted to establish an ongoing professional networking group for Ewits participants. However, due to the sometimes transient nature of a University environment, many of those participants moved on to other locations and the group ultimately fizzled. Several of the learners interviewed have either launched or are attempting to launch startups. They describe d a need for ongoing support with their new ventures From the 2016 end of course survey : My recommendation is to start a transitioning program for those o f us that have already identified a patent and are ready to start. A follow up program will use our momentum to actually start our business They express ed a need for mentors, networks, funding, and access to resources. They wanted a place where they coul d ask questions and learn from others who ha d already experienced the struggles they are working through. One respondent stated :
102 I really think that we need to create a real network, where women can feel supported, receive the experienced advice at the rig ht time and feel that we are not alone in this journey T ogether we are stronger and can achieve more than every single one of us on their own. Tasha indicated university projects or into the hub, or getting things really rolling in Gainesville, and I She expressed that she felt like after Ewits, she was on her own and there was not a lot of perspective in terms of other resources in the community Later in the intervi ew she expresse d: I think if you'd have interviewed me right after I did the class, I would have thought, oh my God, this is the best thing since sliced bread. But then getting out there and actually trying to apply these things, there's so much more to k now. Monique also talked about a desire to maintain contact, and to continue the I think the only missing piece is that connecting, counseling, keeping in touch after the program is over and how you do that with people scattered all over the place The organizers talked about wanting to create that next step. In fact, at the time will be a sp ace where women can continue both with their entrepreneurial education and a space where they can connect, continue to network, and have on going access to mentors. Jane talks about the vision for the Collaboratory: E wits is an amazing program but the shor tcoming is that after ten weeks we empower them with the knowledge, the confidence, and we provide them the role models and the mentoring and the network gone. And when gone is when they need it the most. Because now somebody to take them through that next level, right? Where they can take their idea, and their concept instead of the university technology and really move forward with it.
103 C the Collaboratory is because we want it to be not just a UF and not just a Collaboratory, I whoever has expertise in things that they can bring to bear on helping those women get from that have to have gone through the EWITS program to really cont inue to push the ball forward. In her comments, Jane Collaboratory that will provide ongoing support to take them to the next level. The organizers recognize that Ewits is a short term contribution to a larger long term problem. If they are going to effect lasting change in the number of women participating in technology startups, they need to provide more resources, education, and networking to support them through the licensing, launching and capit alization processes. Chapter Summary Chapter 4 presented the research findings from this study. The findings include both quantitative and qualitative results from the curriculum and artifact review, analysis of learner applications, end of course survey s, and the fifteen semi structured interviews. The research found that the learners described increased confidence in their entrepreneurial competencies, teamwork and collaborative skills, and their understanding of gender bias and barriers to entry for tech nology entrepreneurship. They also portrayed the experiential learning component as rigorous, challenging, and empowering. Many described the all female environment as a safe space where they could ask questions and learn from other highly qualified women in the program. The mentor team relationships contributed to the dynamic nature of the learning environment. The interviewees reported previous experience with either entrepreneurial
104 or strong female role models that inspired them. They described entrepren eurial identities as fluid, including innovation as well as entrepreneurship.
105 CHAPTER 5 DISCUSSION LIMITATIONS AND RECOMMENDATIONS This chapter will discuss the research findings as they relate to the research questions and the theoretical frameworks o f ambient belonging and gender bias. Limitation of the research will be presented. It will conclude with recommendations for future research and for developing and implementing educational programs like Ewits. Discussion This study aimed to understand whe ther educational interventions can help women succeed in technology entrepreneurship, a career field where women are underrepresented but where economic opportunity is high. Using a mixed method case study design, it examined the following three research q uestions: 1) How does Ewits strive to help women overcome barriers to entry into technology entrepreneurship? 2) attitudes and intentions? 3) How do participants describe their experience with entrepreneurship ? The following sections synthesize the findings related to each question and considers these findings in the context of the research literature. Research Question One How does Ewits strive to help women overcome barriers to entry into tec hnology entrepreneurship? The barriers to entry into technology entrepreneurship that Ewits sought to address include work/life balance choices, self confidence, lack of training, lack of a mentor network or role models and self initiative (waiting to be asked) (Ewits, 2017a) Ewits aimed to help program participants overcome barriers to entry into entrepreneurship first, by providing an experiential lea rning program designed to
106 startup, and second, by creating a learning environment where the participants can explore their own understandings of gender bias and other barri ers that impact their desire to enter entrepreneurship. Experiential l earning m odel Piperopoulos and Dimov (2015) showed that individuals completing a theoretical only entrepreneurship education program often experience a decrease in entrepreneurial self efficacy and resulting entrepreneurial intentions as compared to those who completed a practically based entrepren eurial education program. Those in the practically oriented programs saw their self efficacy and resulting entrepreneurial intentions increase. Ewits provided professional development to participants through an experiential learning project focused on incr easing the entrepreneurial competencies and skills needed to successfully launch a technology venture Shane and Venkataraman (2000) similarly contend that entrepreneurship education should include the process of discovery, evaluation, and ex ploitation of opportunities including the individuals who discover, evaluate, and exploit these possibilities Ewits was successful in providing most if not all of these elements through an experiential learning simulation where participants worked togeth er to select a technology from the Office of Technology Licensing (discover), research the opportunities for marketing this technology (evaluate), and prepare to launch a technology startup (exploit). The simulation provided a realistic understanding of th e challenges, dedication and effort that are required to gain success in a technology startup. Participants met with licensing officers, worked with entrepreneurial mentors and other team members just as if they were planning to launch a real technology st artup During the research and discovery process and the investor pitch, learners
107 received feedback and evaluation from experienced entrepreneurial mentors, angel investors and venture capitalists, and from each other. This feedback and evaluation served as further validation of achie ved competencies. During the investor pitch competition, the participants observed the investor pitches from the other teams and heard the feedback provided to all the teams. This allowed them to learn more about how a real in vestor pitch would be evaluated for potential funding as well as compare their own competencies and understandings with other team members and experienced entrepreneurs. Learning e nvironment Ewits created a learning environment where the participants are able to explore their own understandings of gender bias and other barriers that impact their desire to enter entrepreneurship. Santos et. al (2016) recommend implementing educational interventions that focus on the cultural environment in the field of entrepreneurship and that include successful female role models and guest speakers that do not reflec t the norms of a masculine dominated field. Ewits attempted to do this first, by creating an authentic entrepreneurial environment where participants experienced entrepreneurship, second, by providing experienced entrepreneurial female role models as ment ors, presenters, and judges, and third, by creating space where participants were able to freely discuss, as well as examine their own ex periences and understandings of gender bias. The program was hosted at the Universit This situated the learning environment within the context of entrepreneurship, both in the authentic nature of the simulation, as well as with the people who do entrepreneurship and in a place where entrepreneurship is conducted. The organizers and facilitator s
108 either worked in or were highly familiar with the role of the Innovation Hub in both licensing and housing startup ventures. As participants walked through the building, they could see pictures, awards and other evidence of startup companies and their su ccesses. Ewits provided participants with opportunities to engage with female role models through the experiential technology transfer project where learners and mentors closely collaborated and also through guest speakers and the investor pitch judges. Or ganizers specifically attempted to recruit successful entrepreneurial mentors, presenters and judges who could serve as role models throughout the program. T he mentor team structure of the program, allowed participants to engage with many highly educated f emale leaders in science, technology, and entrepreneurship The Ewits program attempted to address issues of gender bias through guest speakers who talked about their experiences as female technology and entrepreneurial leaders including the biases they h ad experienced in their careers. The weekly presentations serv ed as a catalyst to engage the l earners in discussions about how women experience gender bias and stereotype threat as leaders in technology and entrepreneurship careers. These discussions occur red during the weekly informational sessions often carrying over into the weekly group work sessions. Research Question Two What impact does Ewits have on particip attitudes and intentions ? Entrepreneurial intentions (EI) Attitude (PA), or desirability of entrepreneurship (Shapero and Sokol, 1982, Ajzen,
109 1991) Ajzen includes the influence of the sociocultural context, or Subjective Norm (SN) as a variable which reflects the value culture (CV) places on ent repreneurship. Ewits had a positive impact on participants entrepreneurial attitudes and intentions. Through the experiential learning model, they were successful in helping learners develop an increased understanding of and confidence in their abilities t o be successful at entrepreneurship (PBC & PA). Through the learning environment they were able to help learners shift their perception of the gendered sociocultural norms (SN) of entrepreneurship. Confidence in entrepreneurial abilities Ewits had a posit competencies (PBC) and on their personal attitudes (PA). Learners report they feel more confident about their entrepreneurial abilities as a result of their participation in Ewits. End of course survey s show that learn ers reported gains in both the mastery of entrepreneurial competencies and their understanding of barriers to entrepreneurship entrepreneurial abilities as meas ured by the increase in entrepreneurial PBC and PA on the 2016 end of course survey s I n the responses to the open ended qualitative questions and interview learners noted increased confidence in learning new skills and experiencing validati on of the skill s they already ha d prior to the program B oth the learners and mentors describe the rigor and frustrations of the challenge of the experiential learning exercise as well as the resulting confidence that was gained when they succeeded in accomplishing the task. The amount of knowledge and learning required was at first overwhelming, but as they applied that knowledge and worked out what it takes to accomplish the task, they saw their efficacy and confidence increase.
110 This feedback loop allowed for confirmat ion and development of self confidence in their entrepreneurial abilities. Because they had the opportunity to then test their knowledge and abilities in a realistic simulation with feedback from experienced entrepreneurs they were able then build confid ence in their new knowledge. Combating gendered sociocultural norms Santos et al (2016) found that when the cultural value (CV) of entrepreneurship argue tha t females do not see an increase in PA because they do not see entrepreneurship as a career option for them and thus the value society places on entrepreneurship does not affect them in the same way as it does males This difference in perception of cultu ral value could be a result of gendered social norms (SN) gendered environment of entrepreneurship. The premise that women do not see entrepreneurship as a career option, aligns wi th the theory of ambient belonging. If women do not see themselves as fitting in technology entrepreneurship, then they will not perceive increased SN, and will continue to have low PA or desirability for technology entrepreneurship. What makes Ewits unique is the learning environment which placed a strong cultural value not just on entrepreneurship, but specifically entrepreneurship. Thus, they increased the CV and subjective norm (SN) of entrepreneurship in a way that the female participants felt their contribution was valued. Ewits created this environment by (1) recruiting highly educated women, from science, technology, and engineering fields, for the all female educational program, (2) including experi enced female entrepreneurial role models a s team mentors, presenters, and
111 judges, and (3) creating space to talk about gender bias. In those efforts, they were successful in creating an environment where the participants felt they belonged. The lack of mas culine gendered norms may have also created an environment where individuals did not feel the pressures of stereotype threat and where they were free to act according to their own identities (Steele, 2011) Zanna and Pack (1975) showed that individuals in a si ngle gendered group are less likely to act according to stereotypical social roles. They draw a connection between being in the presence of the other gender as invoking behaviors that confirm to societal gender roles. In a single gendered space, individual s must take on all roles, even those that might normally be reserved for individuals of a different gender. This gives individuals an opportunity to try on and gain efficacy in non traditional roles. Research Question Three How do participants describe th eir experience with entrepreneurship? Yardav and Unni (2016) suggest that women perceive entrepreneurship within the context of their social networks including family, society and personal relations hips. This is different from the traditional view of entrepreneurship as creating economic value and puts more focus on the experience of doing entrepreneurship and the value it brings to their lives and their families. The participants in this study descr ibe their experience with entrepreneurship in terms of experiences with role models, opportunities for innovation, or a desire to create something new. All the interviewees could identify at least one entrepreneurial or strong female role model that inspi red them and influenced their career choices including their own entrepreneurial identity. They talked about how these role models exhibited lifestyle choices and benefits they wanted to have in their lives. Even when they struggled to
112 succeed financially, the participants could see how the experience was rewarding and provided many advantages in building networks, or providing quality of life benefits. The participants see themselves as entrepreneurs, as innovators, and as supporters o f entrepreneurship. T hey talk about their own entrepreneurial identity as being intertwined with an innovator identity. Some describe efforts to launch new startups after their Ewits pa rticipation, others describe an interest in participating in a startup if the conditions are conducive to their needs. Even the women who do not expect that they will l aunch their own startup venture, see themselves as innovators and as leaders that contribute to the development of innovative ideas and technologies within their current careers. When the participants talk about their own entrepreneurial ideas they often focus on the intrinsic benefits of entrepreneurship. They focus more on the process of doing entrepreneurship rather than on the economic or financial benefits of entrepreneurship. They talk about having an idea they want to see happen or a problem they want to solve. They also talk about the barriers to success, the lack of capital, lack of networks and support structures. These experiences are consistent with those that were ident ified in literature (Fink and Haisley, 2015, Calas et al., 2 009) Envisioning Entry in Technology Entrepreneurship Scholars have argued that PBC ( e ntrepre neurial a bilit y) and PA (e ntrepreneurial i nterest ) are both necessary c onditions for entrepreneurial intention (EI) and that intention is a precursor for behavior and actual participation in the field Using feminist theory, the framework proposed two addi tional components to this model. First, it included stereotype threat and gender bias as potential filters or barriers that might prevent women from turning feasibility and desirability into intention and actual
113 engagement in the field. Second, it reframed the notion of entrepreneurial interest as embedded in a broader sociocultural context of ambient belonging. Whereas stereotype belonging prevents women from choosing a career i n this field. Figure 5.1 presents a simplified version of this original model. The findings of this study suggest that this model can and needs to be further refined to account for decreased PA even in the presence of increased cultural value of entrepreneurship. Figure 5 1: A model for understanding the impact of gender bias and ambient belonging on entrepreneurial intentions. Limitations While this research focused primarily on personal attitude and ambient belonging, we must not lose si gh t of the overall societal responsibility to end gender bias in a gendered field. It is not the minority participants burden to shift societal norms
114 but the burden of those who have the privile ge of naturally representing the m ajority norms that need to reflect on their own personal biases and begin to change the way we evaluate those who operate in a manner that is different from the gendered norm of society The researcher was integrally invo lved with the Ewits program throughout the course of the study, including participation as a mentor in the 2014 cohort and as a representation of the data that was gather ed from the program, there is a limitation in that the researcher might be more likely to portray a more positive representation of the program and miss negative representations. There are limitations with the generalizability of the learner populat ion. T his is a highly educated mostly affluent group of women who self selected into the program. These participants have resources and a certain amount of privilege which will help them succeed in spaces where others with less privilege may struggle. The resu lts from this study will need to be compared with other learner groups who may not have the same level of privilege to support their self efficacy and subjective norms. At this time, t here are additional concurrent studies being conducted with StartUp Ques t, a mixed gendered implementation of the same experiential learning model The Startup Quest studies can provide a comparison to Ewits results which will help to better understand the long term effectiveness of the educational model and the generalizabili ty of the research results. Another limitation is the nature of the data collection. Phase One of the study relied on applications and surveys that were collected prior to the research period
115 While the survey results give us insight into the perception of the women completing the program, they do not validate their competencies. In addition, there was missing data from 2012, and some anomalies in the data set which made it difficult to full y in terpret the responses. It would be useful in future iterations of the program if the questions on the End of course survey s could be vetted and validated to ensure they will produce the results that are intended. The organizers indicated that all of the le arners were required to turn in their End of course survey s prior to entering th e investment pitch competition. However, the number of surveys provided in the data set was lower than the number of program complete rs. T his could be due to an im precise metho d of ensuring survey collection, or could represent a potential missing set of data Due to the number of surveys would adversely affect the results of this study. As a mixed methods study, some of the data for this research is gathered from eptions and anecdotal evidence. While the researcher reviewed the transcripts and coding reports several times, there are undoubtedly other themes that could emer ge from a different reading of the data. Further study of the existing data may be needed to better understand the themes that emerged and to consider other perspectives in the analysis. One limitation with the data collected in the End of course survey is that it only does not reflect an external evaluation of their competencies Learners are provided with feedback on their demonstrated competencies during the inv estor pitch competition on both their business plan and their presentation. Experienced venture capital and
116 angel investors provide this feedback. While this data was collected during this research, it was not analyzed. It will be included in the recommend ations for future research. There is little data available to answer the question of whether this educational program While some of the participants do describe efforts to launch new startups after t heir Ewits participation, there will need to be further study to understand the long term effects of Ewits on entrepreneurial behavior This study focused primarily on participant perceptions as such there is not independent review of those perception s n or is there an effective measure of external influences. Some of the limitations of this approach include: (a) there is no measure of actual gender bias, (b) there is no measure of actual stereotype threat, and (c) there is no measure of actual competency proficiency Recommendations As a result of this study there are both recommendations for future study, and recommendation s for the educational model Recommendations for the Educational Model The following recommendations are based on feedback on the end of course survey s and interviews. They are not part of the findings, but are included here to give feedback on the overall effectiveness of the program. Consider implementing a flipped classroom model where participants watch videos before coming to t he weekly information sessions. This will allow more time in class for guest speakers, discussion, and team work Improve the educational component addressing stereotype threat and gender bias ensuring that it is based on research and effective intervention methods.
117 Continue with plans to develop a follow as well as other resources to assist early stage entrepreneurs in achieving their startup goals. Recommendations for Future Study During this study, a wide variety o f data was collected, and not all of it was analyzed for this study. This includes follow up survey s business plans and investor pitches, including judges scoring sheet and recommendations. The follow up surveys have been administered annually since 2014 and captures data from learners over a period of time. Analysis of t his data could provide insights into the long rang impacts of the program. Additional research needs to do a deeper dive into the effect s of gendered norms in a gendered field and shiftin g individual perceptions of these field s While Ewits was successful in shifting participants perception of the culture of entrepreneurship during course the program, an understanding of the long term effects of this program on program participants c ould r eveal insights into whether or not this experience was intentions, and behaviors. Future research should include research into effective engagement practices in educational settings that not on ly impact gender bias, but help other underrepresented students engage in a way that they develop efficacy and a sense of belonging that will allow them to succeed.
118 APPENDIX A LEARNER APPLICATION
119 APPENDIX B END OF COURSE SURVEY
121 APPENDIX C INTERVIEW PROTOCOL D OCUMENTS Interview Protocol Prior to the Interview Review and complete Informed Consent if not already complete. Begin recording. The Interview: Thank participants for agreeing to participate in this research project. Confirm with interviewee that the consent to the interview and the recording. Note: Some questions are only applicable to a particular stakeholder group. They are indicated as follows: T: Team member, M: Mentor, O: Organizers & Facilitators. Research Question Interview Question Background, demographics & warm up 1. For our first question, can you tell me a bit about yourself and your background? (Education (level of degree & discipline area), job experience, family/friends, age, interests). 2a, 3b 2. Tell me about your background with entrepreneurship and how you came to be interested in learning more about entrepreneurship? 2a, 3b 3. Have you ever worked for or started an entrepreneurial venture / startup company? 2a, 3b 4. Has anyone in your family or close network of friends worked for or started an entrepreneurial venture / startup company? 2a, 3b 5. Did you have any previous entrepreneurial education prior to participating in Ewits? Background 6. T&M: How did you learn about Ewits? 2a 7. T&M: What did you hope to learn from your participation in Ewits? 2a 8. T&M: What do you feel you gained from your participation in Ewits? 2a, 2c 9. T: Do you feel confident that you have the knowledge, skills and or inclination to launch or work for a startup ve nture? 2a, 2c 10. T: How did participation in Ewits affect your confidence the you could be a successful entrepreneur or employee of a startup? 2a, 2c 11. T: How did participation in Ewits contribute to your personal desire to become an entrepreneur or work for a startup? 2a, 3c 12. M: How well do you believe the Ewits program prepared your team to understand the skills and challenges of becoming an entreprene ur? 1 13. O: Tell me about how you first got involved with Ewits? 3a 14. Do you feel like gender will impact your ability to be successful in entrepreneurship or a science/technology focused career?
122 1, 3a 15. M&O: How do you envision Ewits will help participants t o address the gender issues in technology and entrepreneurship? 3a 16. T: Do you feel like participating in an all female entrepreneurship education program had any impact on your perception of your ability to succeed in entrepreneurship or technology careers? 2a, 2b 17. T: Do you have any future entrepreneurial interests or plans? 3b 18. T: Are there any other factors or considerations that impact your entrepreneurial interests or plans? 1,2,3 19. Is there anything else you would like to share with us about your participation in Ewits that you think would benefit our research? Information after the interview: Thank participants for their participation and remind them that I will follow up with a transcript for their review and further input. Interview protocol page 1
123 Informed Consent Working Title Please read this consent document carefully before you decide to participate in this study. Purpose of the research study The overarching goal of this research is to develop an understanding of whether participation in Empowering Women in Technology Startups ( Ewits ) entrepreneurial education program has a positive ility of entrepreneurship) and intentions (feasibility of entrepreneurship). What you will be asked to do in the study During this study we will interview program organizers, mentors and team members who participated in one of the five offerings of Ewits held between 2012 and 2016. Interviews will be conducted in p erson or via video conferencing at a mutually agreed upon time, date and place scheduled between the interviewee and interviewer. Interviews are expected to last approximately 45 minutes to one hour. All interviews will be recorded using an audio recording device and transcribed for analysis. The written transcriptions will be shared with you to giv e you an opportunity to review your responses to ensure we fully understood your perspectives. Time required 1 hour Risks and Benefits There are no known risks to participants. There are no expected benefits for individual participants. Although the results from this study may help Ewits assess an d improve the program, neither the principal researc her or faculty advisor have any direct involvement in the establishment or oversight of Ewits. The principal researcher has participated in the Ewits program as a team mentor. Confidentiality Your identity will be kept confidential to the extent provided by law. Your information will be assigned a code number. The list connecting your name to this number will be kept in a secure file, in a secure server. When the study is completed and the data have been analyzed, the list will be destroyed. Your
124 name will not be used in any report. All recordings and transcripts will be stored in a secure server and will not be connected to your name or any personally identifying information. At the end of this study, all audio recordings will be destroyed. Voluntary participation Your participation in this study is completely voluntary. There is no penalty for not participating. In addition, during the interview, you will have the right to waive any question yo u do not wish to answer. Right to withdraw from the study You have the right to withdraw from the study at any time without consequence. Who to contact if you have questions about the study Principal Researcher : Cheryl Calhoun, PhD Candidate, School of Te aching & Learning, College of Education, University of Florida, email@example.com 352.575.0261 Faculty Advisor: Dr Carole Beal, Professor, College of Education, University of Florida, firstname.lastname@example.org 352.273.4178 Who to contact about your rights as a research participant in the study IRB02 Office Box 112250 University of Florida Gainesville, FL 32611 2250 phone 392 0433. Agreement I have read the procedure described above. I voluntarily agree to participate in the procedure and I have received a copy of this description. Participant: ___________________________________________ Date: _________________ Principal Investigator: ___________________________________ Date: _________________
125 Interview Questions Note: Some questions are only applicable to a particular stakeholder group. They are indicated as follows: T: Team member, M: Mentor, O: Organizers & Facilitators. 1. For o ur first question, can you tell me a bit about yourself and your background? (Education (level of degree & discipline area), job experience, family/friends, age, interests). 2. Tell me about your background with entrepreneurship and how you came to be intere sted in learning more about entrepreneurship? 3. Have you ever worked for or started an entrepreneurial venture / startup company? 4. Has anyone in your family or close network of friends worked for or started an entrepreneurial venture / startup company? 5. Did you have any previous entrepreneurial education prior to participating in Ewits? 6. T&M : How did you learn about Ewits? 7. T&M : What did you hope to learn from your participation in Ewits? 8. T&M : What do you feel you gained from your participation in Ewits? 9. T : Do you feel confident that you have the knowledge, skills and or inclination to launch or work for a startup venture? 10. T : How did participation in Ewits affect your confidence the you could be a successful entrepreneur or employee of a startup? 11. T : How did participation in Ewits contribute to your personal desire to become an entrepreneur or work for a startup? 12. M: How well do you believe the Ewits program prepared your team to understand the skills and challenges of becoming an entrepreneur? 13. O : Tell m e about how you first got involved with Ewits? 14. Do you feel like gender will impact your ability to be successful in entrepreneurship or a science/technology focused career? 15. M&O : How do you envision Ewits will help participants to address the gender issues in technology and entrepreneurship? 16. T: Do yo u feel like participating in an all female entrepreneurship education program had any impact on your perception of your ability to succeed in entrepreneurship or technology careers? 17. T : Do you have any future en trepreneurial interests or plans? 18. T: Are there any other factors or considerations that impact your entrepreneurial interests or plans? 19. Is there anything else you would like to share with us about your participation in Ewits that you think would benefit ou r research?
126 Note: This letter will be sent via e mail to all potential interviewee candidates from my official UF e mail at email@example.com Dear Potential Interviewee Name : I am an PhD Candidate at the University of Florida. As part of my dissertation research I am conducting interviews with individuals who have previously participated in Empowering Women in Technology Startups ( Ewits ) entrepreneurial education program as either a program organizer, a mentor, or a team member. T he purpose of these interviews is to develop an understanding of whether participation in Ewits has a n I am asking you to participate in this interview because you have been identified as a past p articipant in the Ewits program Your interview will be conducted in person or via video conferencing at a time, date and place mutually agreed upon. Interviews will last approximately 45 minutes to one hour W ith your permission I will record the interview using an audio recording device The audio recording will be used to create a transcript of the interview which you will have an opportunity to review and provide additional input to ensure I fully understand your perspectives. For your review, schedule of questions to this e mail. also attached a copy of the Informed Consent agreement which will need to be signed and returned to me prior to conducting an interview. Feel free to sign and scan a copy to return via e mail, or if we are conducting an in person interview, we can sign the agreement prior to beginning our interview. If you have any questions about this research protocol, please contact me at firstname.lastname@example.org or 352.575. 0261 or my faculty supervisor, Dr. Carole Beal at 352.273.4178 Questions or concerns about your rights as a research participant rights may be directed to the IRB02 office, University of Florida, Box 112250, Gainesville, FL 32611; (352) 392 0433. Thank y ou in advance for considering participation in this research project. I look forward to speaking with you and learning more about your experiences with the Ewits program. Cheryl Calhoun Attachments: Informed Consent Interview Questions
127 APPENDIX D TIDYING THE DATA Cheryl Calhoun 26 March, 2017 The following is a list of artifacts currently available from five annual offerings of the program. The artifacts from the first cohort (2012) are incomplete. There is some summary data a vailable that can help in developing an understanding of the 2012 cohort. The 2012 ummary sdata will be incorporated into the narrative where appropriate. The artififacts from the most current cohort (2016) are still being assembled. Data Available by Coho rt as of July 24, 2016 Cohort 2012 2013 2014 2015 2016 Mentor Bios NO YES YES YES NO Speaker Bios NO YES YES YES NO Judges Bios NO YES NO YES NO Mentor Meeting Agendas NO YES NO NO NO Session Agendas NO YES YES YES NO Technology Descriptions NO YES YES YES NO Applications NO YES YES YES YES Application Results YES YES YES YES YES Attendance Results YES YES YES YES YES Resumes NO YES YES YES YES Business Plans YES YES YES YES YES Team Assignments YES YES YES YES YES Business Plan Scoring Sheets NO YES YES YES NO Investor Presentations YES YES YES YES YES Investor Plan Scoring Sheets NO YES YES YES NO Participant Surveys Partial YES YES YES YES The applications were collected using an online application. The composition of the application evolved over time and questions were added or updated before each new cohort. This required a bit of data collection and data wrangling to get the data into a form for analysis.
128 Application Data Code Book Applications Table Field Data Type Description Identifier character Unique identifier concatenated from (Cohort + P + Entry ID) TeamID 1 character Program Team Assignment concatenated from (Cohort + T + Team ID) Path 1 character Path to case node in Envivo. Cohort 1 character Year of cohort (YYYY). Accepted 1 logical Was participant accepted to program? Decision 1 logical Did participant attend program? Finished 1 logical Did particpant finish program? Entry.Id character Unique identifier assigned by application system. Team 12 charac ter Program Team Assignment. Assigned each team an ID (A:H). First 2 character First Name Middle 2 character Middle Name Last 2 character Last Name Phone 2 character Participant's Phone Number Phone2 2 character Participant's Alternate Phone Number Email 2 character Participant's Email Address Email2 2 character Participant's Alternate Email Address Address1 character First line of address. (2014+) Address1 character Second line of address. (2014+) city character Name of City (2014+) State character Two digit State code (2014+) Zip character Mailing address zip code (2014+) Country character Mailing address country (2014+) Degree factor Participants Highest Degree Earned (PhD, Master, Bachelor, Associate, HS) Experience character A description of the field of work/educational experience. Q4 factor How did you hear about the program? (Check All That Apply) Q4a Checked: Program website Q4b Checked: Past participant Q4c Checked: Facebook/Twitter/LinkedIn Q4d Checked: Email
129 Q4e Checked: OTL newsletter Q4f Checked: Newspaper article Q4g Checked: Word of mouth Q4h Checked: Other Q4Other character Fill in the blank for Other. (2014+) Q5 factor What is your primary goal for participation in the program? (Check All That Apply) Q5a Checked: Gain self confidence Q5b Checked: Start a company Q5c Checked: Networking Q5d Checked: Entrepreneurship training Q5e Checked: Attending seminars/workshops Q5f Checked: All of the above. Q5g Checked: Other Q5h Checked: Job/career opportunities (2016) Q5i Checked: Valuable knowledge and skills (2016) Q5j Checked: Interest in technology commercialization (2016) Q5Other character Fill in the blank for "Other". (2014+) Q7 factor Do you have regular access to a computer? (Yes/No) Q7a f factor Do you have access to the following software? (2014) Q7a Checked: Internet Q7b Checked: Microsoft Word Q7c Checked: Excel Q7d Checked: PowerPoint Q7e Checked: Email Q7f Checked: Other Q7g character Fill in the blank for "Other". (2014) Q8 factor Have you been involved with any new discoveries that have been patented by the University of Florida? (Yes/No) Q8a character If you answered "Yes" to the previous question, please briefly describe the technology and your affiliation. (Ex : inventor, graduate student, post doc, other) Q9 character Attach a copy of your Resume in PDF format. (Limit 3 pages.) 1 These fields were added to aid in data analysis. 2 These fields will be redacted in final data set.
130 ** N otes:** Some of the records from the original data set contain duplicate data. These records were created when participants completed the application twice. The last application (Highest Entry ID #) submitted was retained for data analysis. The earlier sub missions are marked as "Duplicate" in the full data set for reference. These will be removed prior to data analysis. It is also noted that for the for some years the "Entry ID" numbers do not start at #1. This could be data that was removed by the program organizers, or it could be the earlier entries were test data from testing the application from prior to publication. Compiling the Data The application data were provided in the form of an Excel spreadsheet. For some cohorts (2014 & 2015) there is a raw data spreadsheet as exported from the online application and a separate spreadsheet with additional information that was used in the admissions decision making pro cess. For other cohorts (2013, 2016), it appears as if the application export spreadsheet was edited to add information and document the decision making process. Notes: For 2012, the applications were collected on paper forms and e mailed. Unfortunately, the 2012 forms have been lost and are not available for analysis. In some cases, information had to be compiled from other data sources to complete the data set. Team assignments and completion data were gathered and verified through the attendance worksh eets. Teams were assigned an ID (A:H) so that they can be anonymized in the final data set. Team letter assignments were recorded on the original attendance spreadsheets. This portion of the data collection process was completed by hand using an Excel spre adsheet. Each cohort data was stored in an individual spreadsheet and then exported into a .csv file for import into R. Preparing the Data for analysis In this first R block, the individual cohort .csv data files are combined into a dataframe. Empty values are properly coded with "NA" and then duplicate records (rows) are removed from the dataset. Once all the dataframes were complete, they are combined together into a single dataframe. This dataframe is stored as 'Applicants.csv' for use in the quantitativ e analysis. ## Reading data and combining into one dataframe. ## This block does not need to execute after initial data tidying. # Read Applicants data from cohort application files and remove observations marked duplicate. Applicants12 < read.csv ( "2012Applicants.csv" header= TRUE sep= "," na.strin gs = c ( "" "NA" ))
131 Applicants12 < filter (Applicants12, Accepted != "Duplicate" ) Applicants13 < read.csv ( "2013Applicants.csv" header= TRUE sep= "," na.strin gs = c ( "" "NA" )) Applicants13 < filter (Appli cants13, Accepted != "Duplicate" ) Applicants14 < read.csv ( "2014Applicants.csv" header= TRUE sep= "," na.strin gs = c ( "" "NA" )) Applicants14 < filter (Applicants14, Accepted != "Duplicate" ) Applicants15 < read.csv ( "2015Applicants.csv" header= TRUE sep= "," na.strin gs = c ( "" "NA" )) Applicants15 < filter (Applicants15, Accepted != "Duplicate" ) Applicants16 < read.csv ( "2016Applicants.csv" header= TRUE sep= "," na.strin gs = c ( "" "NA" )) Applicants16 < filter (Applicants16, Accepted != "Duplicate" ) # Merge Applicants from individual cohort Applicants tables into one 'Applica nts' table. Applicants < rbind (Applicants16, Applicants15) Applicants < rbind (Applicants, Applicants14) Applicants < rbind (Applicants, Applicants13) Applicants < rbind (Applicants, Applicants12) str (Applicants) Tidying the Data After reviewing the data with 'str(Applicants)' it is apparant the factors are not consistent for all variables. This may be due to the fact that as the application form was edited, different spe llings for various factors were used. This block cleans up the inconsistencies for each of the factors and shortens some of the factor labels so that they are easier to manipulate and display in tables. ## Cleaning up factor labels so that analysis will be consistent. ## This block does not need to execute after initial data tidying. # Create degree indicators that are one word in length. Applicants$Degree < str_replace_all (Applicants$Degree, "GED or High school d iploma" "HS" ) Applicants$Degree < str_r eplace_all (Applicants$Degree, "undergrad" "HS" ) Applicants$Degree < str_replace_all (Applicants$Degree, "High school graduate "HS" ) Applicants$Degree < str_replace_all (Applicants$Degree, "Associate's degree" "Associate" ) Applicants$Degree < str_repla ce_all (Applicants$Degree, "A.A." "Associate" ) Applicants$Degree < str_replace_all (Applicants$Degree, "Bachelor's degree" "Bachelor" ) Applicants$Degree < str_replace_all (Applicants$Degree, "B.A." "Bachelor" )
132 Applicants$Degree < str_replace_all (Applicants$Degree, "B.S." "Bachelor" ) Applicants$Degree < str_replace_all (Applicants$Degree, "B.F.A." "Bachelor" ) Applicants$Degree < str_replace_all (Applicants$Degree, "Master's degree" "M aster" ) Applicants$Degree < str_replace_all (Applicants$Degre e, "M.S." "Master" ) Applicants$Degree < str_replace_all (Applicants$Degree, "M.A." "Master" ) Applicants$Degree < str_replace_all (Applicants$Degree, "MBA" "Master" ) Applicants$Degree < str_replace_all (Applicants$Degree, "M.B.A." "Master" ) Applicants$D egree < str_replace_all (Applicants$Degree, "J.D." "Master" ) Applicants$Degree < str_replace_all (Applicants$Degree, "Ph.D." "Doctorate" ) Applicants$Degree < str_replace_all (Applicants$Degree, "PhD" "Doctorate" ) Applicants$Degree < factor (Applicants$Degree, levels= c ( "HS" "Associate" "B achelor" "Master" "Doctorate" ), ordered= TRUE ) #Update Enties for questions which are parsing into multiple factors Applicants$Q5a < str_replace_all (Applicants$Q5a, "Gain self confidence" "Ga in self con fidence" ) Applicants$Q5a < factor (Applicants$Q5a) Applicants$Q5f < str_replace_all (Applicants$Q5f, "All of the above." "All o f the above" ) Applicants$Q5f < factor (Applicants$Q5f) Applicants$Q8 < str_replace_all (Applicants$Q8, "Yes (Please answer Question 11.)" "Yes" ) Applicants$Q8 < str_replace_all (Applicants$Q8, "Yes (Please answer Question 9.)" "Yes" ) # Remove Duplicate factors and ensure variables are factors. Applicants$Accepted < factor (Applicants$Accepted, levels= c ( "Yes" "No" ), ord ered = TRUE ) Applicants$Decision < factor (Applicants$Decision, levels= c ( "Yes" "No" ), ord ered= TRUE ) Applicants$Finished < factor (Applicants$Finished, levels= c ( "Yes" "No" ), ord ered= TRUE ) Applicants$Q8 < factor (Applicants$Q8) Annoynomizing the Data Set The final data set has been cleansed to remove all identifying information so it is a truly anonymous data set for analysis. Only columns with respondant data will be included, all participant identifying information and all references to program name and univ ersity will be changed. The dataframe is saved as 'Applicants.csv'. For all future iterations of analysis, the data will be loaded from 'Applicants.csv', and previous blocks will not execute ('eval=FALSE'). This annonymized data set is the only version of the data that will be made available for analysis via GitHub. ** Notes:** All references to the program name, the university, and other identifiable indicators have been replaced with generic terms surrounded by asteriks. Ex. pro gram
133 university company etc. This process was completed manually in Excel by reading through applicant responses to find all identifying data and replacing it with generic placeholders. # Choosing the colums to be included in the annoymized data set. Ap plicants < select (Applicants, Identifier, TeamID, Cohort, Accepted, Decisi on, Finished, Team, Degree, Experience, contains ( "Q" )) # Save the file for later analysis. write.csv (Applicants, file= "Applicants.csv" ) save (Summative, file = "data/Summative.Rda" ) This code can be used to Tidy individual years, or the aggregated data. To analyze an individual year, comment out the lines that combine the data sets. CodeBook.xlsx contains the variable names (question numbers), the text of the co rresponding question, and an inventory for which years the question appeared on the survey. Summative.csv contains the combined survey data. Individual files 2013Summative.csv through 2016Summative.csv were created to hold individual year data. The finishe d dataframe Summative.Rda has been exported for use in the analysis. Compiling the Data For cohorts 2013 2015 the surveys were completed in paper and pencil format. A matching survey was created in Qualtrics and survey responses were hand entered and exp orted as a .csv file. For 2016, the survey results were collected in Survey Monkey, results were exported as a .csv file. Survey responses for 2012 are missing from the available data. While many of the questions in the 2016 survey are identical to 2013 2015, the export from SurveyMonkey was substantially different. To prepare the data for use in this analysis, variable names had to be hand entered to match those from the Qualtrics import. Likert Scale responses all used the same 5 point scale, but were c onfigured to use different indicators. These differences are noted in the code book. Summative Data Code Book # Read the data file. # Cohort 2016 has 40 completers, and 42 surveys. S2016 < read.csv ( "./data/2016_Summative.csv" sep = "," header = TRUE ) nrow (S2016) ##  42
134 # Cohort 2015 has 47 completers, 5 missing surveys. S2015 < read.csv ( "./data/2015_Summative.csv" sep = "," header = TRUE ) nrow (S2015) ##  42 # Cohort 2014 has 42 completers, 8 missing surveys. S2014 < read.csv ( "./data/2014_Summ ative.csv" sep = "," header = TRUE ) nrow (S2014) ##  34 # Cohort 2013 has 41 completers, two did not attend 11/12 session, 6 missing surveys. S2013 < read.csv ( "./data/2013_Summative.csv" sep = "," header = TRUE ) nrow (S2013) ##  33 # Convert rename column one and convert to character colnames (S2013)[ colnames (S2013) == "..V1" ] < "V1" colnames (S2014)[ colnames (S2014) == "..V1" ] < "V1" colnames (S2015)[ colnames (S2015) == "..V1" ] < "V1" colnames (S2016)[ colnames (S2016) == "..V1" ] < "V1" S2013 [, 1 ] < as.character (S2013[, 1 ]) S2014[, 1 ] < as.character (S2014[, 1 ]) S2015[, 1 ] < as.character (S2015[, 1 ]) S2016[, 1 ] < as.character (S2016[, 1 ]) # Review the data file variables. # str(S2015) # Read the code book. CodeBook < read.xlsx ( "./data/Summative_CodeBook.xlsx" sheetIndex= 1 header = TRUE ) # CodeBook = data.table(CodeBook) # View the code book. # head(CodeBook) The following code block coverts the numerical response for Question 6: "Who is your mentor?" to the team codes. These codes are assigned to maintain anonymity of teams and mentors. The 2016 Mentors were updated by hand due to the data being encoded using full text descriptions. # Question 6 Factor: "Who was your mentor?" # Note this question uses the TeamID which matches the TeamID in the applican t.csv file. # 2016 Mentors # These were updated using Excel. The SurveyMonkey version of the datafile i
135 ncluded all names and technologies. To maintain anonymity, they were updated before importing the data into this analyisis. # 2015 Mentors S2015$Q6[S2015$Q6 == "1" ] < "2015TF" S2015$Q6[S2015$Q6 == "2" ] < "2015TG" S2015$Q6[S2015$Q6 == "3" ] < "2015TD" S2015$Q6[S2015$Q6 == "4" ] < "2015TA" S2015$Q6[S2015$Q6 == "5" ] < "2015TC" S2015$Q6[S2015$Q6 == "6" ] < "2015TB" S2015$Q6[S20 15$Q6 == "7" ] < "2015TU" # This is unknown in the survey. n=0 S2015$Q6[S2015$Q6 == "8" ] < "2015TE" S2015$Q6 = factor (S2015$Q6, levels= c ( "2015TA" "2015TB" "2015TC" "2015TD" "20 15TE" "2015TF" "2015TG" "2015TU" ), ordered= TRUE ) # 2014 Mentors S2014$Q6[S2014$Q6 == "1" ] < "2014TA" S2014$Q6[S2014$Q6 == "2" ] < "2014TG" S2014$Q6[S2014$Q6 == "3" ] < "2014TF" S2014$Q6[S2014$Q6 == "4" ] < "2014TB" S2014$Q6[S2014$Q6 == "5" ] < "2014TC" S2014$Q6[S2014$Q6 == "6" ] < "2014TD" S2014$Q6[S2014$Q6 == "7" ] < "2014TU" # This is unknown in the survey. n=0 S2014$Q6[S2014$Q6 == "8" ] < "2014TE" S2014$Q6 = factor (S2014$Q6, levels= c ( "2014TA" "2014TB" "2014TC" "2014TD" "20 14TE" "2014TF" "2014TG" "2014TU" ), ordered= TRUE ) # 2013 Mentors S2013$Q6[S2013$Q6 == "1" ] < "2013TD" S2013$Q6[S2013$Q6 == "2" ] < "2013TC" S2013$Q6[S2013$Q6 == "3" ] < "2013TA" S2013$Q6[S2013$Q6 == "4" ] < "2013TG" S2013$Q6[S2013$Q6 == "5" ] < "2013TB" S2013$Q6[S2013$Q6 == "6" ] < "2013TF" S2013$Q6[S2013$Q6 == "7" ] < "2013TE" # This is unkn own in the survey. n=0 S2013$Q6[S2013$Q6 == "8" ] < "2013TU" S2013$Q6 = factor (S2013$Q6, levels= c ( "2013TA" "2013TB" "2013TC" "2013TD" "20 13TE" "2013TF" "2013TG" "2013TU" ), ordered= TRUE ) Combine the individual survey response files into one complete Summative dataframe. This will allow cumulative data analysis as well as comparisons across cohorts. # Combine files into one Summative dataframe Summative < bind_rows (S2015, S2014) ## Warning in bind_rows_(x, .id): Unequal factor levels: coercing to char acte r ## Warning in bind_rows_(x, .id): Unequal factor levels: coercing to characte r
136 ## Warning in bind_rows_(x, .id): Unequal factor levels: coercing to characte r ## Warning in bind_rows_(x, .id): Unequal factor levels: coercing to characte r ## Warnin g in bind_rows_(x, .id): Unequal factor levels: coercing to characte r ## Warning in bind_rows_(x, .id): Unequal factor levels: coercing to characte r ## Warning in bind_rows_(x, .id): Unequal factor levels: coercing to characte r ## Warning in bind_rows_( x, .id): Unequal factor levels: coercing to characte r ## Warning in bind_rows_(x, .id): Unequal factor levels: coercing to characte r ## Warning in bind_rows_(x, .id): Unequal factor levels: coercing to characte r Summative < bind_rows (Summative, S2013) T he following code block coverts the numerical response items into factor response items that will be easier to analyze. This block only affects 2013 2015 data as 2016 already has the factored response items for these questions. # Question 20 Factor: "Tim e alloted each week for speakers was:" Summative$Q13[Summative$Q13 == "1" ] < "Too Short" Summative$Q13[Summative$Q13 == "2" ] < "About Right" Summative$Q13[Summative$Q13 == "3" ] < "Too Long" Summative$Q13 = factor (Summative$Q13, levels= c ( "Too Short" "About Right" "Too Long" ), ordered= TRUE ) # Question 14 Factor: "Time alloted each week for teamwork was:" Summative$Q14[Summative$Q14 == "1" ] < "Too Short" Summative$Q14[Summative$Q14 == "2" ] < "About Right" Summative$Q14[Summative$Q14 == "3" ] < "Too Long" Summative$Q14 = factor (Summative$Q14, levels= c ( "Too Short" "About Right" "Too Long" ), ordered= TRUE ) # Question 19 Factor: "How many hours a week on average did your team meet ou rside of the program?" Summative$Q19[Summative$Q19 == "1" ] < "1 2 hrs" Summative$Q19[Summative$Q19 == "2" ] < "1 2 hrs" Summative$Q19[Summative$Q19 == "3" ] < "3 5 hrs"
137 Summative$Q19[Summative$Q19 == "4" ] < "3 5 hrs" Summative$Q19[Summative$Q19 == "5" ] < "More than 5 hrs" Summative$Q19 = factor (Summative$Q19, levels= c ( "1 2 hrs" "2 3 hrs" "3 4 hrs" "4 5 hrs" "More than 5 hrs" ), ordered= TRUE ) # Question 20 Factor: "The programs duration was:" Summative$Q20[Summative$Q20 == "1" ] < "Too Short" Summative$Q20[Summative$Q20 == "2" ] < "About Right" Summative$Q20[Summative$Q20 = = "3" ] < "Too Long" Summative$Q20 = factor (Summative$Q20, levels= c ( "Too Short" "About Right" "Too Long" ), ordered= TRUE ) # Question 21 integer: Convert to range of values Summative$Q21[Summative$Q21 <= "2" ] < "1 2 hrs" Summative$Q21[Summative$Q21 <= "5" ] < "3 5 hrs" Summative$Q21[Summative$Q21 >= "6" ] < "More than 5 hrs" Summative$Q21 = factor (Summative$Q21, levels= c ( "1 2 hrs" "3 5 hrs" "More tha n 5 hrs" ), ordered= TRUE ) # Question 22 Factor: "Would you recommend this program to other women?" Summative$Q22[Summative$Q22 == "1" ] < "Yes" Summative$Q22[Summative$Q22 == "2" ] < "No" Summative$Q22[Summative$Q22 == "3" ] < "Unsure" Summative$Q22 = factor (Summative$Q22, levels= c ( "Yes" "No" "Unsure" ), ordered= TRUE ) # Question 24 Factor: "What is your highest level of education completed?" Summative$Q24[Summative$Q24 == "1" ] < "HS" Summative$Q24[Summative$Q24 == "2" ] < "Associate" Summative$Q24[Summative$Q24 == "3" ] < "Bachelor" Summative$Q24[Summative$Q24 == "4" ] < "Master" Summative$Q24[Summative$Q24 == "5" ] < "PhD" Summative$Q24[Summative$Q24 == "6" ] < "Other" Summative$Q24 = factor (Summative$Q24, levels= c ( "HS" "Associate" "Bachelor" "M aster" "PhD" "Other" ), ordered= TRUE ) # Question 29 Factor: "What is your area of ex pertise?" Summative$Q29[Summative$Q29 == "1" ] < "Finance" Summative$Q29[Summative$Q29 == "2" ] < "Business" Summative$Q29[Summative$Q29 == "3" ] < "Science" Summative$Q29[Summative$Q29 == "4" ] < "Engineering" Summative$Q29[Summative$Q29 == "5" ] < "Compu ter/IT" Summative$Q29[Summative$Q29 == "6" ] < "Marketing/Communications/Design" Summative$Q29[Summative$Q29 == "7" ] < "Other" Summative$Q29 = factor (Summative$Q29, levels= c ( "Finance" "Business" "Science" "Engineering" "Computer/IT" "Marketing/Communications/Design" "Other" ), ordered= TRUE ) # Question 30 Factor: "What is your age?" Summative$Q30[Summative$Q30 == "1" ] < "18 24"
138 Summative$Q30[Summative$Q30 == "2" ] < "25 34" Summative$Q30[Summative$Q30 == "3" ] < "35 44" Summative$Q3 0[Summative$Q30 == "4" ] < "45 54" Summative$Q30[Summative$Q30 == "5" ] < "55 64" Summative$Q30[Summative$Q30 == "7" ] < "65 74" Summative$Q30[Summative$Q30 == "6" ] < "75 or older" Summative$Q30 = factor (Summative$Q30, levels= c ( "18 24" "25 34" "35 44" "45 54 "55 64" "75 or older" ), ordered= TRUE ) # Question 31 Factor: "Choose the answer that best describes your current sit uation:" Summative$Q31[Summative$Q31 == "1" ] < "married or in a committed relationshi p with no children" Summative$Q31[Summative$ Q31 == "2" ] < "married or in a committed relationshi p with grown children (18+)" Summative$Q31[Summative$Q31 == "3" ] < "married or in a committed relationshi p with school aged children (5 18)" Summative$Q31[Summative$Q31 == "4" ] < "married or in a commi tted relationshi p with younger child/ren (under 5)" Summative$Q31[Summative$Q31 == "5" ] < "single with no children" Summative$Q31[Summative$Q31 == "6" ] < "single parent with grown children (18 +)" Summative$Q31[Summative$Q31 == "7" ] < "single parent with school aged childr en (5 18)" Summative$Q31[Summative$Q31 == "7" ] < "single parent with younger child/ren (under 5)" Summative$Q31 = factor (Summative$Q31, ordered= TRUE ) # Question 32 Factor: "Which of the following best represents your racial or ethnic heritage?" Summative$Q32[Summative$Q32 == "1" ] < "Non Hispanic White or Euro American" Summative$Q32[Summative$Q32 == "2" ] < "Black, Afro Caribbean, or African Ame rican" Summative$Q32[Summative$Q32 == "3" ] < "Latino or Hispanic American" Summative$Q32[S ummative$Q32 == "4" ] < "East Asian or Asian American" Summative$Q32[Summative$Q32 == "5" ] < "South Asian or Indian American" Summative$Q32[Summative$Q32 == "6" ] < "Middle Eastern or Arab American" Summative$Q32[Summative$Q32 == "7" ] < "Native American or Alaskan Native" Summative$Q32[Summative$Q32 == "8" ] < "Other" Summative$Q32 = as.factor (Summative$Q32) # Question 33 Factor: "What was your total household income before taxes duri ng the past 12 months?" Summative$Q33[Summative$Q33 == "1" ] < "Less th an $25,000" Summative$Q33[Summative$Q33 == "2" ] < "$25,000 to $34,999" Summative$Q33[Summative$Q33 == "3" ] < "$35,000 to $49,999" Summative$Q33[Summative$Q33 == "4" ] < "$50,000 to $74,999" Summative$Q33[Summative$Q33 == "5" ] < "$75,000 to $99,999" Summ ative$Q33[Summative$Q33 == "6" ] < "$100,000 to $149,999" Summative$Q33[Summative$Q33 == "7" ] < "$150,000 or more"
139 Summative$Q33 = factor (Summative$Q33, levels= c ( "Less than $25,000" "$25,000 to $34,999" "$35,000 to $49,999" "$50,000 to $74,999" "$75,000 to $99,999" "$100,000 to $149,999" "$150,000 or more" ), ordered= TRUE ) # Question 34 Factor: "Please circle the option(s) that best describe(s) your current situation. Ok to choose more than one if applicable." Summative$Q34[Summative$Q34 == "1" ] < "Master's student" Summative$Q34[Summative$Q34 == "2" ] < "MBA student" Summative$Q34[Summative$Q34 == "3" ] < "MD student" Summative$Q34[Summative$Q34 == "4" ] < "PhD student" Summative$Q34[Summative$Q34 == "5" ] < "Postdoc" Summative$Q34[Summative$Q 34 == "6" ] < "Unemployed (not a student)" Summative$Q34[Summative$Q34 == "7" ] < "Work part time (not a student)" Summative$Q34[Summative$Q34 == "8" ] < "Employed at a technology startup" Summative$Q34[Summative$Q34 == "9" ] < "Employed at a non technology startup" Summative$Q34[Summative$Q34 == "10" ] < "Employed at a technology non startup company" Summative$Q34[Summative$Q34 == "11" ] < "Employed at a non technology non sta rtup company" Summative$Q34[Summative$Q34 == "12" ] < "Owned my own technology business" Summative$Q34[Summative$Q34 == "13" ] < "Owned my own non technology business Summative$Q33 = as.factor (Summative$Q33) # Update Likert scale questions to represent NA as NA (Qualtrics exported it as #6) Summative$Q1_ 1[Summative$Q1_1 == "6" ] < NA Summative$Q4_1[Summative$Q4_1 == "6" ] < NA Summative$Q7_1[Summative$Q7_1 == "6" ] < NA Summative$Q7_2[Summative$Q7_2 == "6" ] < NA Summative$Q7_3[Summative$Q7_3 == "6" ] < NA Summative$Q9_1[Summative$Q9_1 == "6" ] < NA Summa tive$Q9_2[Summative$Q9_2 == "6" ] < NA Summative$Q9_3[Summative$Q9_3 == "6" ] < NA Summative$Q9_4[Summative$Q9_4 == "6" ] < NA Summative$Q9_5[Summative$Q9_5 == "6" ] < NA # Add 2016 data to Summarize Summative < bind_rows (Summative, S2016) ## Warning in bind_rows_(x, .id): Unequal factor levels: coercing to characte r ## Warning in bind_rows_(x, .id): Unequal factor levels: coercing to characte r ## Warning in bind_rows_(x, .id): Unequal factor levels: coercing to characte r
140 ## Warning in bind_rows_(x, .i d): Unequal factor levels: coercing to characte r ## Warning in bind_rows_(x, .id): Unequal factor levels: coercing to characte r ## Warning in bind_rows_(x, .id): Unequal factor levels: coercing to characte r ## Warning in bind_rows_(x, .id): Unequal fact or levels: coercing to characte r ## Warning in bind_rows_(x, .id): Unequal factor levels: coercing to characte r # Rename ID Column Summative < rename (Summative, ID = V1) # Rename Cohort column and factor Summative < rename (Summative, Cohort = Q1) Summa tive$Cohort < factor (Summative$Cohort, levels= c ( "2013" "2014" "2015" "2016" ), ordered= TRUE ) # Rename Degree column and clean up factor levels Summative < rename (Summative, Degree = Q24) Summative$Degree[Summative$Degree == "High School Diploma" ] < "HS" Summative$Degree[Summative$Degree == "Master's Degree" ] < "Master" Summative$Degree[Summative$Degree == "Bachelor's Degree" ] < "Bachelor" Summative$Degree[Summative$Degree == "Ph.D." ] < "PhD" Summative$Degree = factor (Summative$Degree, levels= c ( "HS "Associate" "Bachelo r" "Master" "PhD" "Other" ), ordered= TRUE ) # Rename Team Column, Survey ID, Age, Race, Income, Relationship Summative < rename (Summative, Team = Q6) Summative < rename (Summative, PaperID = Q25) Summative < rename (Summative, Discipline = Q29) Summative < rename (Summative, Age = Q30) Summative < rename (Summative, Relationship = Q31) Summative < rename (Summative, Race = Q32) Summative < rename (Summative, Income = Q33) Summative < rename (Summative, Employment = Q34) # Renam e Questions. Use TEXT to identify open ended response items. Summative < rename (Summative, Q1 = Q1_1) Summative < rename (Summative, Q4 = Q4_1) Summative < rename (Summative, TEXT2 = Q2) Summative < rename (Summative, TEXT5 = Q5) Summative < rename (Summative, TEXT8 = Q8) Summative < rename (Summative, TEXT10 = Q10) Summative < rename (Summative, TEXT12 = Q12)
141 Summative < rename (Summative, TEXT35 = Q35) Summative < rename (Summative, TEXT17 = Q17) Summative < rename (Summative, TEXT23 = Q23) Summati ve < rename (Summative, TEXT24 = Q24_TEXT) Summative < rename (Summative, TEXT26 = Q26) Summative < rename (Summative, TEXT29 = Q29_TEXT) # Saving the data for later analysis. write.csv (Summative, file= "data/Summative.csv" ) save (Summative, file = "data/Summative.Rda" )
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149 BIOGRAPHICAL SKETCH Cheryl Calhoun currently serves as the Dean of Educational Centers and Director of the Blount Center for Santa Fe College. Her career includes over 25 years as a professor, systems analyst, and a community organizer. Her community involvement includes co owning Wild Iris Books, serving as the founding director of Protect Gainesville Citizens, as well as serving on numerous advisory boards. She is Co Chair of the Academic Alliance for the National Council of Women and Information Technology (NCWIT) and serves on the Academic Advisory Counc il for the Anita Borg Institute Her academic preparation includes a B.S. in Food and Resource Economics (Computer Science minor), a MBA (Decision and Information Science), both from the University of Florida, and a graduate certificate in Information A ss urance from the University of Illinois Springfield She is a lifelong learner as can be evidenced by her return to UF after a 20 year hiatus to complete a PhD in Curriculum & Instruction (Educational Technologies) She graduated in December 2017. Her rese arch interests online and web enhanced collaborative learning environments.