The influence of gender, and race/ethnicity on advancement in information technology (IT)

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Information and Organization
  Contents lists available at ScienceDirect Information and Organization  journal homepage: www.elsevier.com/locate/infoandorg The in 󿬂 uence of gender, and race/ethnicity on advancement ininformation technology (IT) Kimberly McGee, PhD  Fielding Graduate University, School of Leadership Studies, Santa Barbara, CA 93105-3814, USA A R T I C L E I N F O  Keywords: Advancement barriersBiasCIODiversityEthnicityGenderInclusionIndividual di ff  erences theory of gender and ITInformal networksIntersectionalityRaceStereotypesWomen executives in ITWomen of color A B S T R A C T With increased attention paid to the lack of diversity in U.S. technology  󿬁 rms, little attention ispaid to women already in the technology workforce who aspire to senior leadership roles. Thisresearch study focused on understanding the experiences of African American/black, AsianAmerican, European American/white, and Hispanic American/Latina women who advancedfrom technical/operational IT roles to senior IT executive (SITE) roles in corporate America.Previous research solely focused on gender, race, or ethnicity provided an insu ffi cient and limiteddescription of women's advancement journey. Rather than treat gender and race/ethnicity asmutually exclusive categories of experience and analysis, this study approached the topic from agender intersectionality perspective and used the individual di ff  erences theory of gender and ITas an analytical lens. Speci 󿬁 cally, the study examined women's career progression in a morenuanced manner — observing gender  within  race or ethnic group — which provided greater insightinto the participants' advancement journey.The intent of this study was to understand how individual and organizational factors in 󿬂 u-enced each participant's career progression and what role, if any, gender and race/ethnicityplayed in the journey.As a result of the analysis, six themes emerged: Pathways to the SITE Role; Informal Networks;Bias; Credibility and Legitimacy; Support; and Technical Skills. Issues of gender and race/eth-nicity permeated most of the factors and in 󿬂 uenced whether the factors helped or hindered.Although some  󿬁 ndings support existing research on women's career barriers, the intent wasnot to generalize the 󿬁 ndings to all women. Rather, the study's results demonstrate that racial andethnic variations among the women in addition to a variety of other factors contribute to dif-ferent career progression experiences. 1. Introduction Silicon Valley high-tech  󿬁 rms made front-page news in the summer of 2014 when several technology companies includingGoogle, Facebook, and Microsoft publicly disclosed the demographic makeup of their entire workforce. The disclosures were mo-mentous because, with a few exceptions, technology companies do not disclose their workforce demographics publicly. For some, thedisclosures were predictable because they con 󿬁 rmed a widely-known phenomenon — the U.S. technology workforce is predominantlymale and white and increasingly Asian.The disclosures sparked debates about the lack of diversity in the tech sector, generated public pressure for data transparency, andincreased analysis and reporting of IT workforce diversity statistics. Why all the fuss? Perhaps it is because the United States is facinga skills shortage. The U.S. Bureau of Labor Statistics anticipates a 13% increase (about 9 million jobs) in science, technology, https://doi.org/10.1016/j.infoandorg.2017.12.001Received 2 March 2017; Received in revised form 22 October 2017; Accepted 4 December 2017  E-mail address:  kchapman@email. 󿬁 elding.edu. Information and Organization 28 (2018) 1–361471-7727/ © 2017 Elsevier Ltd. All rights reserved.    engineering, and mathematics (STEM) occupations between 2012 and 2022, with technology occupations among the fastest growing(Vilorio, 2014). The implication is that there are not enough information technology workers to  󿬁 ll this demand (Casey, 2012;Mans 󿬁 eld, Welton, & Grogan, 2014). Perhaps it is because industry research shows that a diverse and inclusive workforce, especiallyin IT, correlates to increased innovation, greater creativity, and higher revenue and pro 󿬁 ts (Catalyst, 2013; Hunt, Layton, & Prince,2014; Phillips, 2014; Scott, Kapor Klien, & Onovakpuri, 2017; Thomas, Dougherty, Strand, Nayar, & Janani, 2016). Nonetheless, ITworkforce statistics suggest that large segments of the U.S. population, namely women and people of color, remain underutilized.In the aftermath of the disclosures, companies committed millions of dollars per year to initiatives meant to increase workforcediversity, mostly focused on recruitment. Despite the increased focus, diversity numbers remain stagnant (e.g., Donnelly, 2017; Gee &Peck, 2017; Guynn, 2017b; Kendall, 2017), allegations of gender discrimination and sexual harassment have increased (e.g., Dickey,2017; Fiegerman, 2017; Simon-Lewis, 2017), and retention and advancement receive little attention.Workforce demographics and labor statistics show that rather than an unusual occurrence, women's underrepresentation, par-ticularly in IT, is widespread, complex, and persistent. According to the National Center for Women and Information Technology(2015), women represented 25% of the U.S. IT workforce in 2015; of which 3%, 5%, and 1% are African American, Asian American,and Hispanic American/Latina, respectively. There is also considerable underrepresentation of women in senior leadership roles inIT. The Nash (2016) reports that women represent 9% of global senior IT leadership. Ashcraft and Blithe (2010)  󿬁 nd that womenaccount for 9% of U.S. executive and senior management IT positions. Furthermore, women of color (WOC) 1 are a rarity at theexecutive IT level.Although academic research continues to investigate the dearth of women in SITE roles, researchers tend to depict women as ahomogeneous group and portray the experiences of white women as prototypical of all women. Besides ignoring di ff  erences inexperience among women, this tendency makes it di ffi cult to uncover nuances that could provide a richer understanding of women'sunderrepresentation in SITE roles. Additionally, research in this area tends to lump people of color into one group, identi 󿬁 ed as “ minority ” . Using these assumptions as the basis for studies causes study results to aggregate or universalize the experiences of peopleof color (POC) and overlook the collective and individual experiences of WOC.To complicate the issue, advancement in IT is not straightforward. Some researchers suggest the representation of women andPOC at executive levels will increase as their numbers increase in the broader IT workforce. The assumption is that employees atlower levels naturally advance up the corporate ladder (Kanter, 1977; Kilian, Hukai, & McCarty, 2005). However, few studies takeinto account that technical/operational IT positions typically do not provide a career path to senior executive-level IT roles. Researchindicates that a strong technical background, for example, may not align with skills perceived necessary for executive-level positionsin IT (Carter, Grover, & Thatcher, 2011). Consequently, individuals in technical/operational IT roles may not be invited to participatein the pool from which executive-level IT people are chosen, mentored, or developed. Assuming that advancement in IT is a naturalresult of time in grade also ignores how IT culture and gender stereotypes a ff  ect advancement.The purpose of this qualitative study was to investigate the experiences of African American/black, Asian American, EuropeanAmerican/white, and Hispanic American/Latina women who advanced from technical/operational positions to executive-level rolesin IT. Though women's underrepresentation in the IT workforce is a global issue, this study focused on women in the United States ITworkforce. The study's intent was to understand how individual and organizational factors helped and hindered the women's ad-vancement. Though relevant, the study did not address issues within the educational system that contribute to the lack of partici-pation and representation of females and students of color in STEM disciplines. Rather, the research focused on women in SITE rolesin corporate America and addressed the impact of perceived individual and organizational factors on advancement. As such, the corequestions that guided this study were: ã  What perceived individual and organizational factors help and hinder advancement? ã  What role, if any, did gender and race/ethnicity play in the advancement journey? 2. Literature review Because little is known about women in SITE roles in corporate America, understanding them as a particular category of executiveinvolves connecting several distinct scholarly texts. The IT profession and  󿬁 eld cannot be understood without consideration of itshistorical gender and race-segregated workforce. Therefore, a historical overview of women in computing and the development of SITE roles underpin the literature review. Because the IT profession and SITE roles emerged within corporate environments, thereview also looks at underlying cultural assumptions found in corporate and corporate IT cultures. The intent is to highlight howbeliefs about women found in the broader society and the IT profession continues to shape cultures and norms and a ff  ect women'scareer development and advancement opportunities. This historical overview and organizational culture literature lay the foundationfor understanding women's advancement barriers. The advancement literature reviewed examines how race/ethnicity and gendercreate qualitatively di ff  erent perceptions and experiences of factors commonly believed to impede the advancement of women intosenior executive roles. These pieces of literature construct the framework for studying women who advanced from technical/op-erational roles in IT to SITE roles. The bulk of research focused on the underrepresentation of women in senior executive and SITEroles emphasizes gender-related issues. However, this review departs from such conventions because it makes a concerted e ff  ort toilluminate how gender and race or ethnicity impact advancement. 1 For the purpose of this research, women of color included women who identify as African American/black, Asian American, or Hispanic American/Latina  K. McGee  Information and Organization 28 (2018) 1–36 2   2.1. Female pioneers in computing  IT is a relatively young profession. Its roots stem from the  󿬁 eld of computer science, and women played a signi 󿬁 cant role in the 󿬁 eld's development. Nevertheless, their contributions are not well documented in the  󿬁 eld's history, which dates back to the early19th century. Women helped develop early programming languages and generated new ideas which shaped the  󿬁 eld. Their con-tributions in 󿬂 uenced the design and functionality of the  󿬁 rst electronic computers, and women developed the  󿬁 rst programminglanguages. It is important to recognize that the omission and minimization of the role of women perpetuate misconceptions aboutwomen as uninterested in computers or incapable of leadership within the  󿬁 eld. A closer examination of computing history reveals a 󿬁 eld open to women, which runs contrary to contemporary notions of   opening   the  󿬁 eld to women. Although the early history of computing highlights the contributions of white women, their experiences illuminate gender barriers as well as gendered and racedconcepts that continue to in 󿬂 uence the career advancement of IT WOC. For these reasons, a brief review of a few female pioneers andan overview of the  󿬁 eld's  󿬂 uctuating gender dynamics seem helpful.  2.1.1. Ada Lovelace (1815  –  1852) Born Augusta Ada King, Countess of Lovelace, Ada Lovelace was a British mathematician and writer. She is best known for herwork on Charles Babbage's Analytical Engine. Some consider Ada to be the founder of scienti 󿬁 c computing and the  󿬁 rst computerprogrammer. Others argue that this recognition in 󿬂 ates and misrepresents her role, which they liken to a trusted assistant.Most texts that detail the history of computing omit or downplay Ada's contributions. Instead, they highlight the accomplishmentsof English mathematician Charles Babbage, referred to as the father of computing. Babbage's Analytical Engine laid the groundworkfor the  󿬁 rst electronic computer, ENIAC (discussed later). Intended to perform any arithmetic operation, the Analytical Engine wasprogrammable using punch cards; stored numbers and intermediate results; processed calculations, and provided printed results.Although the Analytical Engine was never fully built in Babbage's lifetime, the design principles served as a model for moderncomputers (Campbell-Kelly, Aspray, Ensmenger, & Yost, 2014).But to those who view history with a modern eye, Ada must be considered a partner in Babbage's work and instrumental in fullyarticulating his ideas about the Analytical Engine. At Babbage's request, Ada translated from French to English a lecture Babbage gaveat the Italian Scienti 󿬁 c Academy in Turin (Campbell-Kelly et al., 2014). In the process of translating, Ada added considerable contentof her own that built upon Babbage's ideas. While Babbage envisioned the machine solely as a calculator, Ada imagined programmingthe machine to perform a variety of tasks, such as generating computer-composed music (Smith & Webb, 2012); She theorizedprogramming constructs widely used in modern computer programs but unheard of at the time of her writing. Lovelace also outlinedseveral programs to show how the machine could solve mathematical problems, which led to her recognition as the  󿬁 rst person towrite and publish a computer program (Charman-Anderson, 2015; Gürer, 1995; Smith & Webb, 2012).Ada's translated article,  “ Sketch of the Analytical Machine, ”  which included her extensive notes, was published in 1843 in anEnglish science journal. At the time, it was improper for women of the titled class to sign public documents and highly unusual forwomen, in general, to author scienti 󿬁 c and mathematical articles (Morrow & Perl, 1998; Yount, 1999). Consequently, Ada signed thepiece with her initials, A.A.L. Thirty years passed before anyone knew she was the author (Yount, 1999). For nearly a century thearticle and her accomplishments received little attention, until its  󿬁 rst reprinting in 1953. In 1980, the U.S. Department of Defensenamed their standard computer language  Ada , in honor of Lovelace's contributions to the  󿬁 eld of computer science.  Ada  remains inuse around the world in aviation, health care, transportation, and  󿬁 nancial systems. In 2009 Suw Charman-Anderson founded AdaLovelace Day to illuminate Ada's contributions to the  󿬁 eld of computing.Critics dismiss Ada's importance, suggest her intellectual contributions are exaggerated, and object to any reference of Ada as the 󿬁 rst computer programmer. For example, critics maintain that because Ada struggled with calculus, she did not have the necessaryaptitude to develop a sophisticated computer algorithm. Critics also argue that the computer programs were not Ada's srcinal work.They contend that most of the technical content and all of the programs were Babbage's ideas, and Ada's contribution was merelyinterpretative (Campbell-Kelly et al., 2014). Other historians  󿬁 nd these points of criticism less than credible. They maintain that Adawas studying, at the time, what many considered the forefront of calculus, and although Ada struggled with complex mathematicsbefore working on the translation, she likely understood the concepts by the time she began writing her notes (Campbell-Kelly et al.,2014; Kim & Toole, 1999). Various criticisms exist about Charles Babbage. However, unlike Lovelace, critics rarely dispute theveracity or validity of his ideas. Criticisms about Lovelace provide an early example of challenges aimed at women's competency andnormative assumptions about men's capabilities.  2.1.2. Grace Hopper (1906  –  1992) Rear Admiral Grace Hopper, referred to as  Amazing Grace  and the  Queen of Code , is among the most notable women in computerscience. She is best known for developing the  󿬁 rst computer compiler. Hopper was the  󿬁 rst woman to earn a Ph.D. in mathematicsfrom Yale (Linik, n.d.; Graham, 2011). Over the course of her career, she worked as a college professor, naval o ffi cer, softwaredeveloper, and business executive. She joined the Navy during World War II, and her  󿬁 rst assignment was at Harvard Universityworking on a small team tasked with programming the Mark I, the  󿬁 rst digital computer. Hopper used numerous subroutines toprogram the Mark I, concepts srcinally theorized by Lovelace. She also wrote an instruction book that included the Mark I's historyand a programming guide. According to Isaacson (2014), the book represents the  󿬁 rst computer programming manual.After the war, Hopper joined the Eckert-Mauchly Computer Corporation where she supervised the development of the  󿬁 rstcompiler (Yale University, n.d.; Gürer, 1995). While compilers are indispensable and commonplace in present-day software devel-opment, they were revolutionary in Hopper's time. Hopper's compiler served as the model for the  󿬁 rst computer programming  K. McGee  Information and Organization 28 (2018) 1–36 3  language, COBOL, an acronym for COmmon Business-Oriented Language (Campbell-Kelly et al., 2014; Gürer, 1995). Though modernprogrammers consider COBOL a relic, its in 󿬂 uence on the computing industry and modern organizations remains evident. Major 󿬁 nancial institutions, such as the IRS and the Social Security Administration, still use COBOL in their daily operations (Sahadi, 2015;Social Security Administration, O ffi ce of the Inspector General, 2013). The U.S. Navy recalled Hopper to active duty in the late 1960swhere she served as the director of the Navy Programming Languages Group, a senior-level role.Widely distributed accounts of Hopper's accomplishments and contributions to the  󿬁 eld of computing overlook the challenges sheencountered. Initially, Hopper served as an o ffi cer in the all-female branch of the United States Naval Reserve, better known asWAVES (Women Accepted for Volunteer Emergency Service). Unlike her male counterparts, congressional restrictions limited thenumber of positions open to WAVES and the number of women in each rank (Williams, 2001). Additionally, concerns about womenasserting authority over men restricted female o ffi cer's supervisory authority to female reservists (Williams, 2001). These gender-based restrictions limited Hopper's and other Navy women's career opportunities and mobility. According to Beyer (2012), Hoppertraded marriage and family for a career and experienced gender bias while working in the private sector. Beyer (2012) maintains thatdemanding job responsibilities, the unexpected death of a friend, and her employer's near bankruptcy contributed to Hopper'sstruggles with alcoholism and depression which nearly ended her career. It would be easy to conclude that despite personal strugglesand societal barriers, Hopper's self-determination, perseverance, and intellect helped her forge, single-handedly, an illustrious career.Such assertions, however, minimize the breadth of gender-related obstacles and emotional stress she endured.  2.1.3. ENIAC programmers Well-known names in the history of computing are Presper Eckert and John Mauchly. They built the  󿬁 rst electronic computer, theElectronic Numerical Integrator and Computer or ENIAC. Eckert and Mauchly srcinally designed ENIAC to automate ballisticscomputations, but its  󿬁 rst computations facilitated the design of the hydrogen bomb. Less well-known are the six women whoprogrammed ENIAC: Fran Bilas, Betty Jennings, Ruth Lichterm, Kay McNulty, Betty Snyder, and Marilyn Wesco ff  (Gürer, 1995; Light,1999). Most held a mathematics degree. In addition, ballistics computation and programming required the programmers to haveadvanced mathematical training (Light, 1999).The women's o ffi cial job title was computer. At the time, it was not uncommon for the term  computer   to denote an occupationmuch like modern uses of the term  programmer  . Despite the quali 󿬁 cations of the ENIAC programmers, the ENIAC project emphasizedhardware development while programming the machine (software development) was viewed as a secondary and less important task.Programming was seen as a low-skill clerical function suitable for women whereas hardware development was regarded as in-tellectually challenging and better suited for men (Light, 1999; Rossiter, 1995). Also, programming did not carry the same profes-sional status as hardware development. Speci 󿬁 cally, the women's subordinated status as  computers  disquali 󿬁 ed them from authoringpapers, giving lectures, or joining professional societies. As such, job segregation and the devaluation of programming resulted inlong-term invisibility for the female ENIAC programmers (Light, 1999).  2.1.4. From feminization to masculinization According to Ensmenger (2010), by the late 1960s women made up at least 30 and at most 50% of the computer programmingworkforce. Ensmenger (2010) believes women gravitated to the profession in the 1950s and 1960s because of low barriers to entry,minimal strati 󿬁 cation along gender lines, and opportunities for advancement. Also, the rapid expansion of the computer industry inthe 1970s created a demand for computer specialists, especially programmers. However, public perception about programming skillsremained aligned with beliefs about women's capabilities. Take for example a 1967 article in  Cosmopolitan Magazine  that featured aninterview with Grace Hooper who famously compared programming to planning a meal  – “ It's just like planning a dinner. You have toplan ahead and schedule everything, so it's ready when you need it. Programming requires patience and the ability to handle detail.Women are  ‘ naturals ’  at computer programming ”  (Mandel, 1967, p. 52). Despite the article's optimistic outlook, the programmingprofession was already undergoing a transformation. Historians a ffi rm that a process of professionalization was underway with theexpressed purpose of elevating the  󿬁 eld's status (Campbell-Kelly et al., 2014; Ensmenger, 2001, 2010). Professionalism meant formalacademic programs in computer science, professional journals and societies, and professional certi 󿬁 cation programs. For computerspecialists, professionalism o ff  ered increased social status, greater autonomy, more opportunities for advancement, and better wages(Ensmenger, 2001). For corporations, professionalism provided a way to determine who was quali 󿬁 ed for employment, promotion,and salary increases.Nevertheless, professionalism had detrimental consequences for women. A widespread use of aptitude tests and personalitypro 󿬁 les during the hiring process helped to construct an  ‘ ideal ’  image of computer programmers as highly intelligent, socially inept,and male (Ensmenger, 2001, 2010). On the other hand, some historians argue that professionalism increased perceptions of computerprogrammers as potential managers (Ensmenger, 2001; Hearn, 1982; Oldenziel, 1999). Neither of these constructs included women,who were deemed unquali 󿬁 ed for leadership roles and unable to supervise male colleagues. Ensmenger (2010) and Fitzsimons (2002) argued that professionalization implanted and magni 󿬁 ed perceptions of masculinity because it necessitated a division and hier-archical structure of labor that tended to con 󿬁 ne women to low-level professional jobs. Although professionalization raised the statusof the occupation and the individuals within it, it also created entry barriers that, according to Ensmenger (2012) led to programming(and by association IT) being one of the  “ most stereotypically male professions, inhospitable to women ”  (p. 237).The history of women in computing shows how women pioneered the occupation of computer programming. It provides clearexamples of women's interest and leadership in the  󿬁 eld. It is important, however, to go beyond acknowledging individual con-tributions. The history of women in computing shows how gender and class in 󿬂 uence visibility and perceived credibility. It exposesstructural inequalities that reduced career advancement opportunities and disquali 󿬁 ed women in technical positions from advancing  K. McGee  Information and Organization 28 (2018) 1–36 4
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