Making the Leap: Recent College Graduates’ Early Career Experiences in Computing Fields ^†

Abstract

Prompted by participation gaps in the tech industry, this study explores the relationship between recent college graduates’ college experiences and their perceptions of their tech work environments. Using survey data from 15 research universities across the U.S., the findings suggest that gender and racial/ethnic identity influence the likelihood of viewing the field of computing as inclusive. Participants who were computing majors or felt a strong sense of belonging within the computing community were more likely to view the computing career environment positively. Notably, positive predictors of perceptions of an inclusive tech work environment, including majoring in computing as an undergraduate and feeling connected within computing, directly related to early career professionals’ undergraduate experiences. This study’s implications are relevant to various higher education stakeholders, including STEM department leaders, career development staff, and student affairs staff focused on fostering a strong pipeline from computing undergraduate programs to computing careers.

1. Introduction

One of the functions of higher education is to prepare graduates to enter the workforce and contribute their expertise to their chosen field (Chan, 2016). This is especially true in fields such as computing, where there are critical shortages of individuals who hold degrees in computer science and related fields. Indeed, the Department of Labor Statistics’ employment projections suggest there will be approximately 4.7 million computing-related jobs in the United States by the year 2030 but only about 20% of these jobs could be filled by U.S. computing graduates (NCWIT, 2022). There is a particular need to retain women and people of color1 who earn computing degrees in technology fields. Women make up only 22.6% of the high-tech workforce, even as they make up nearly half (47.3%) of the total American workforce (U.S. Equal Employment Opportunity Commission, 2024). Even more concerning, women’s representation in the tech workforce has not increased in the past two decades (U.S. Equal Employment Opportunity Commission, 2024). When we consider women of color in the high-tech workforce, disparities persist:

Black and Hispanic/Latina women each make up only 2% of the high-tech workforce, despite Black women making up 6% and Hispanic/Latina women making up 8% of the U.S. workforce (U.S. Equal Employment Opportunity Commission, 2024).

While similar gender disparities exist at the undergraduate level—women earned only 21% of computing bachelor’s degrees awarded in 2019 (National Center for Education Statistics, 2020)—retaining recent graduates in the field is a major contributor to participation gaps in the technology workforce. Some research indicates that approximately 15% of women who earn degrees in engineering and computing never hold a job in their field (Fouad & Singh, 2012). Further, a staggering 56% of women employed in the technology sector leave their organizations by the mid-career point (i.e., approximately 10 years after college graduation; Hewlett et al., 2014). This “quit rate” for women is more than double that of men in technology roles (Hewlett et al., 2014).

The existing literature suggests that the transition period from college to career is essential for job satisfaction and retention for recent college graduates in science and technology fields as they navigate social and cultural aspects of the workplace (Lutz & Paretti, 2021). Yet, little research focuses on recent college graduates’ transition to the workforce in computing fields, with some emphasis on women’s experiences but little work focusing on the experiences of people of color or the intersectional experiences of women of color. To retain more college graduates in the tech workforce, particularly those who have been historically minoritized in the field, there is a need to increase our knowledge about the factors that shape recent graduates’ perceptions of the field of computing. To this end, this study draws on a national survey of recent college graduates who hold full-time jobs in the field of computing to explore their identity and belonging within computing, their experiences in their computing jobs, and how these factors relate to their perception of the field of computing’s degree of inclusivity.

2. Literature Review

Most college students seek employment after graduation; indeed, 86% of 25- to 34-year-olds with a bachelor’s degree or higher were employed in 2021 (National Center for Education Statistics, 2022). Yet the experience of searching for and transitioning to a post-college job is not universal. There are key differences in students’ job-seeking behaviors (i.e., method of search, search quality, and search intensity) as well as gender and racial/ethnic differences that shape their employment success and quality (Mau & Kopischke, 2001; van Hooft et al., 2021). Once employed, recent graduates are likely to encounter a markedly different environment. That is, in most college and university settings, the institution provides myriad resources for students’ personal, professional, and academic development (Lutz & Paretti, 2021). This may be limited in the work environment, since most organizations are profit- or mission-driven (Lutz & Paretti, 2021). Further, employees are often expected to complete tasks that are not necessarily related to their interests nor are they intended to advance their intellectual or professional growth (Hu & Wolniak, 2013; Lutz & Paretti, 2021). In short, recent college graduates face steep learning curves and a difficult transition to the workforce that may shape their early career experiences and their sense that their field is welcoming. This may be particularly true for those entering STEM workplaces (Lutz & Paretti, 2021; Xu, 2017). Further, the challenges inherent in moving through the college to career transition may be exacerbated for women, women of color, and people of color in STEM fields, where there are known concerns relating to structural supports for those holding marginalized identities (Barker, 2009; Margolis & Fisher, 2002; Margolis et al., 2008; Strayhorn, 2012; Williams et al., 2022).

2.1. Early Career Experiences in STEM and Computing

There is a growing body of literature that focuses on explanations for the gender gaps in the tech workforce. Sassler et al. (2017) examined gender gaps in STEM degree recipients’ first jobs. They reported that most of the gender gap in STEM occupations overall was explained by women’s underrepresentation in computer science and engineering majors, but that a large share of gender gaps in the STEM workforce remained unexplained by their data. Further, Main and Schimpf (2017) conducted a literature synthesis on explanations for gendered participation gaps in computing fields and identified 14 studies that focused on post-baccalaureate employment. Their review identified several key themes in the explanations for gender gaps in the tech workforce, including conflicts between work and family responsibilities (e.g., Armstrong et al., 2007; Riemenschneider et al., 2006; Wardell et al., 2006), the professional culture within computing domains (e.g., Guzman & Stanton, 2008; Wardell et al., 2006), gendered value orientations (e.g., Trauth, 2002, 2006; Trauth et al., 2004), and women’s restricted access to mentoring and networking resources (e.g., Trauth et al., 2008; Wardell et al., 2006; Windeler & Riemenschneider, 2013).

Relatively few empirical studies have investigated the broader early career experiences of recent college graduates working in computing fields. Existing work demonstrates that students whose major is congruent with their career (e.g., a STEM major in a STEM career) tend to have better earnings and job satisfaction than those whose major and career are incongruent (Xu, 2017). Further, women, women of color, and people of color face gender and racial pay gaps in STEM fields (Hu & Wolniak, 2013; Sterling et al., 2020; Zhang, 2008) and these inequities are present from the point of transition from college to career within STEM disciplines (Xu, 2017).

The best source of data on recent graduates’ experiences would come from tech companies themselves. While many conduct climate surveys of their own employees, they do not publish or share this proprietary information. In fact, it is only recently that some tech companies have begun to share data on the demographics of their technical workforce (AnitaB.org, 2022). However, two organizations have surveyed individuals working in tech fields with a goal of understanding differences in workplace experience for women and women of color. AnitaB.org surveys Grace Hopper Celebration attendees, their social media followers, and subscribers of their newsletter each year as part of their Technical Equity Experience Survey (AnitaB.org, 2021), which provides some information on technologists’ perceptions of their field. Over 1400 individuals working in the field of tech responded to their survey; 30% of these respondents were in an entry-level tech role. Due to the nature of their recruitment pool, nearly all (96%) of their respondents were women. Their survey reports that practically all women respondents experienced discrimination and/or harassment while working in tech, yet approximately 64% reported feeling a sense of belonging in the workplace and, on a scale of 1 (very dissatisfied) to 10 (very satisfied), respondents’ average job satisfaction rating was 7.3. Although this report is not representative of the broader computing workforce, it demonstrates a need to better understand how workplace experiences shape employees’ perceptions of working in computing and how these experiences and perceptions may vary by gender and race/ethnicity. The Center for WorkLife Law at the University of California College of the Law, San Francisco also conducted a smaller survey (n = 214) of individuals working in tech workplaces, though these individuals did not necessarily fall into entry-level roles. Still, their findings highlight the intersectional nature of workplace experiences, as women of color who responded to their survey reported higher levels of bias in the workplace than did White women (Williams et al., 2022).

Additionally, research has explored how college students develop and sustain interest in a computing career that may be relevant to their early career experiences in computing. As noted above, many students who earn technical degrees do not go on to hold jobs in the field (Fouad & Singh, 2012). This may be due to students’ declining interest in a computing career throughout college; a recent study of students who took introductory computer science courses showed there were more students planning to leave the computing career pipeline than there were students planning to join it two years after taking the course (George et al., 2022). Further, women across racial/ethnic groups showed greater declines in interest than men, while Asian students were more likely than their White and Black peers to sustain their interest in a computing career (George et al., 2022). College students’ interests in a computing career are shaped by several factors, including their family backgrounds and career values (Barker, 2009; George et al., 2022; Lehman et al., 2016), experiences with computing in college (George et al., 2022), social relationships in computing (Ross et al., 2020), and sense of belonging and identity (Blaney & Stout, 2017; Cheryan et al., 2009; George et al., 2022; Stormes, 2023). More information is needed about whether these same factors continue to be important in how those newly employed in computing perceive the field and their place in it.

2.2. Sense of Belonging and Computing Identity

Sense of belonging is intrinsically linked with students’ computing identity, or the extent that one sees themselves as a computing person (Rodriguez & Lehman, 2017), as sense of belonging is a core component of computing identity (Lunn et al., 2021; Taheri et al., 2018). Computing identity and sense of belonging seem particularly important to sustaining interest in computing, as they not only predict interest in a computing career but also are key to persistence and retention (Aschbacher et al., 2010; Blaney & Stout, 2017; Cheryan et al., 2009; George et al., 2022; Hausmann et al., 2007; Perez et al., 2014; Stormes, 2023; Strayhorn, 2018; Taheri et al., 2018). Further, sense of belonging has been correlated with women technologists’ comfort in asking for a promotion and their intention to stay at their current company for at least a year (AnitaB.org, 2021). Hence, both computing identity and sense of belonging are key points of interest in the present study, as we seek to understand the early career experiences of individuals working in the field of computing and how these experiences shape their perceptions of it. However, the research on sense of belonging in computing has been largely limited to educational spaces; there is a need to understand how sense of belonging evolves as individuals transition to the workforce.

2.3. Perceptions of the Computing Field

Though there is some research on the career experiences of women working for tech companies and how they relate to women’s intentions to stay at their current workplace (AnitaB.org, 2021), we identified no work that considered how employees in the tech work force perceived the field more broadly. This is essential because women and people of color are leaving the field altogether, and not just their particular employer. Hence, empirical work is needed to understand how newly graduated employees are experiencing their computing workplaces and how this shapes their perceptions of the field.

3. Study Objectives

Despite knowledge that the college to career transition is a time of considerable stress for many recent college graduates, there is limited research on their career transitions and early career experiences, and there is almost no work focused on the unique experiences of individuals transitioning to careers in the field of computing. While scholars have investigated the career aspirations, as well as the sense of belonging and identity, of computing students in higher education, there is a need to extend our knowledge of how these factors may change as individuals enter the computing workforce, particularly for those who have been historically minoritized in computing spaces. Our study investigates how recent college graduates navigate the transition from college to a career in computing and how their perceptions of workplace inclusivity may evolve. Given the persistent participation gaps in computing fields, we emphasize differences in perceptions by gender and race/ethnicity. To this end, we ask the following research questions:

• How do recent college graduates working full-time in computing fields perceive their belonging and identity in computing? How does this vary by gender and/or race/ethnicity?
• How do recent college graduates working in computing fields perceive the inclusivity of their work environments? How do their perceptions vary by gender and/or race/ethnicity?
• How do the background characteristics, college experiences, and work environments of recent college graduates working in computing fields predict their perception that the general environment in computing careers is inclusive of women, people of color, and women of color?

4. Conceptual Framework

Given this study’s focus on recent college graduates’ transitions into computing careers and their perceptions of their work environments, we draw on Carlone and Johnson’s (2007) Science Identity Model alongside an extension of Lent et al.’s (1994) Social Cognitive Career Theory (SCCT), referred to as a Social Cognitive Model of Career Self-Management (CSM) (Lent & Brown, 2013), to provide relevant theoretical grounding for this work. It is important to note the intention in using these two seemingly disparate works in tandem. Carlone and Johnson’s work centers science identity and is typically cited in work that focuses on the development of this identity within higher education spaces (e.g., Espinosa, 2011; Ong et al., 2011). Using this work to frame the current study underscores the extent to which students’ self-perceptions (related to the study’s independent variable of belonging and identity) have the potential to shape their perceptions of inclusivity in their tech workplaces, the study’s key dependent variable. Moreover, pairing this model with Lent and Brown’s work, which focuses on the transition from a collegiate setting to a corporate setting, allows the overall conceptual framework guiding this study to highlight the continued importance of collegiate experiences (i.e., science identity formation) even as students become full-time professionals. The following sections provide an overview of each theory along with a discussion of its application to the study.

4.1. Science Identity Model

Carlone and Johnson (2007) point to three key arguments that support a focus on identity in the context of STEM education research. These arguments include using identity as an analytic tool to understand participation in STEM and centering identity as a means of viewing STEM as a community into which individuals are socialized. Their model extends standard notions of identity such as race or gender to consider what it might mean for someone to consider themself a “science person.” The Science Identity Model depicts three overlapping dimensions of competence, performance, and recognition, all of which are predicated on the assumption that these dimensions are necessarily influenced by racial, ethnic, and gender identities.

The three dimensions of the model balance the understanding of identity as a concept that is simultaneously internally held and externally confirmed. The model’s competence dimension refers to the cultivation of a deep knowledge base within a chosen scientific field (i.e., computing, for the purposes of the present study), whereas the performance dimension centers on the ability to communicate and demonstrate relevant scientific knowledge. The recognition dimension refers to a student’s self-perception of being a “science person” and having this perception affirmed by meaningful others in the field (e.g., professors, internship supervisors, etc.). Taken together someone with a strong science identity would rate highly (and receive high ratings from meaningful others) on all three of these dimensions.

This model informed the variable selection process for this study’s descriptive research questions. In addition to considering early career professionals’ racial, ethnic, and gender identities, this model underscored the importance of items such as “I see myself as a computing person” (i.e., the recognition dimension) and “I see myself as a leader in computing” (i.e., the competence dimension). Further, the overarching framing of the field of computing as a community into which early career professionals must gain both professional and social entry guided the development of this study’s research questions to focus on sense of belonging and perceptions of inclusivity.

4.2. Social Cognitive Model of Career Self-Management (CSM)

Lent et al.’s (1994) SCCT provides a comprehensive view of career trajectories by framing a person’s background characteristics as influencing their learning environment, which in turn influences expectations for self-efficacy and outcomes. The model identifies personal characteristics and experiences (referred to as person inputs and background contextual affordances, respectively) as initial drivers of career development. These are then combined with learning experiences that inform goals, actions, and outcomes.

As an extension of this model, CSM maintains this framing with key nuances. Most importantly, whereas SCCT was designed to address questions of career content, CSM is instead conceived as a mechanism through which to understand career processes. For the purposes of this study, the process is understood to be that of transitioning to a new job upon graduating from college. The CSM also emphasizes the influence of self-efficacy in shaping outcome expectations, thereby serving as a key point of convergence between this model and the Science Identity Model. While several studies have previously applied SCCT to understanding computing students (George et al., 2022; Lent et al., 2008, 2011; Sax et al., 2017), the focus of this study presents a valuable opportunity to use the CSM extension of this theory. Figure 1 shows this study’s adapted CSM model.

5. Methods

5.1. Data and Sample

This study uses data from a longitudinal survey of undergraduate students in introductory computing courses at 15 universities across the U.S. that participated in the Building, Recruiting, and Inclusion for Diversity (BRAID) initiative. These institutions committed to working to diversify their undergraduate computing majors. Alongside this program, the BRAID research team studied two cohorts of undergraduate students: those enrolled in introductory computing courses during the 2015–2016 and 2016–2017 academic years, respectively. Then, the BRAID research team administered annual follow-up surveys to track baseline respondents through college and into graduate school and early careers. This study relies on data from the most recent BRAID follow-up survey in 2020. While prior BRAID follow-up surveys examined students’ persistence in computing majors and career and graduate school aspirations, the 2020 survey—four to five years after the introductory computing courses—captured the early career experiences of thousands of young adults across the U.S.

We restricted our study sample (n = 845) to those in both cohorts who responded to the 2020 survey, had graduated from college, and were working full-time in a computing field. Approximately 65% of respondents identified as men and 35% identified as women. The racial/ethnic breakdown of our sample is as follows: 40% White, Caucasian, or European American; 34% Asian or Asian American; 5% Black or African American, 9% Hispanic or Latina/o/x; 12% Indigenous, Middle Eastern, Multiracial, or Other. See

Table 1. Profile of Recent Graduates in a Computing Career (n = 845).

	Percentage of Respondents	Respondent Ns
Gender (Dichotomous)
Man	65.4	553
Woman	34.6	292
Race/Ethnicity (Aggregated)
White, Caucasian or European American	39.5	334
Asian or Asian American	34.0	287
Black, African American	5.1	43
Hispanic or Latina/o/x	9.0	76
Indigenous, Multiracial, Middle Eastern or Other	12.4	105

for detailed sample demographics.

5.2. Measures

The dependent variable (DV) for the inferential model is a composite measure of the perception that the environment in computing careers is inclusive of women, people of color, and women of color. Although the dependent variable was based on Likert-type items, it was treated as approximately continuous in the regression analysis due to its multi-item composition and high internal consistency (See Table 3; Cronbach’s alpha = 0.939).

Independent variables for the descriptive analyses were selected with guidance of Carlone and Johnson’s (2007) Science Identity Model. Appendix A includes the full list of variables and their coding schemes. In the inferential model, 24 independent variables (IVs) were selected and organized in accordance with CSM (Lent & Brown, 2013). As guided by CSM, independent variables included person inputs and background contextual affordances, including gender, race/ethnicity, computer major or minor, and number of computing-related internships. To disaggregate participants’ racial/ethnic identities as much as possible, we created dichotomous variables to include racial/ethnic groups as separate predictors in the regression model to the extent possible given the sample sizes of each racial/ethnic group (i.e., binary variables were included in the regression model for the following races/ethnicities: Black/African American, Hispanic/Latinx, and Asian/Asian American, with White serving as the reference group).

The remaining independent variables are composite measures our team developed by conducting exploratory and confirmatory factor analysis to reduce the overall number of predictors and increase the parsimoniousness of the final model. The threshold for reliability was set at a Cronbach’s alpha of 0.65, and variables were only considered valid for inclusion in a factor if they loaded at 0.40 or higher, consistent with recommendations for exploratory research involving diverse constructs (Taber, 2018). While some scales approached this threshold, we prioritized conceptual breadth over strict internal consistency to capture the multifaceted nature of early career experiences. See

Table 2. Descriptive statistics on key characteristics.

	Mean	SD	Min, Max	% Missing
Dependent Variable (three-item factor scale)
What are your perceptions of the general environment in computing careers? Rate how much you disagree or agree with the following statements.
There is a supportive environment for women in computing careers.	3.56	1.03	1, 5	0.44
There is a supportive environment for people of color in computing careers.	3.53	1.07	1, 5	0.55
There is a supportive environment for women of color in computing careers.	3.33	1.19	1, 5	0.44
Independent Variables
Gender (Dichotomous)	0.35	0.48	0, 1	7.54
Asian or Asian American Race/Ethnicity	0.34	0.47	0, 1	6.88
Black or African American Race/Ethnicity	0.05	0.22	0, 1	6.88
Hispanic or Latinx Race/Ethnicity	0.09	0.29	0, 1	6.88
Computing major	0.82	0.39	0, 1	0.00
Computing minor	0.09	0.29	0, 1	0.00
Num. of Computing-Related Internships or Co-ops	2.32	1.15	1, 5	0.00
How much do you agree or disagree with the following statements? Please answer to the best of your ability regardless of your current career or your academic major/minor.
I feel like I belong in computing	3.93	0.98	1, 5	0.55
I see myself as a computing person	4.08	0.95	1, 5	0.22
Computing is a big part of who I am	3.48	1.13	1, 5	0.40
I feel welcomed in the computing community	3.75	0.91	1, 5	0.40
I feel like an outsider in the computing community (reverse-coded)	3.40	1.15	1, 5	0.33
I see myself as a leader among my colleagues in computing	3.03	1.12	1, 5	0.33
I aspire to be a leader in the field of computing	3.33	1.19	1, 5	0.44
I care about doing well in computing	4.29	0.77	1, 5	0.33
How would you rate yourself in the following areas as compared to the average person your age?
Computing programming ability	3.60	0.84	1, 5	0.44
Ability to use multiple programming languages	3.55	0.89	1, 5	0.33
Computer skills	4.00	0.71	1, 5	0.33
Leadership ability	3.46	0.92	1, 5	0.66
Self-confidence (social)	3.11	0.99	1, 5	0.55
Drive to achieve	3.81	0.93	1, 5	0.44
Competitiveness	3.55	0.93	1, 5	0.33
I am confident that I can…
Quickly learn a new programming language on my own	4.07	0.92	1, 5	0.22
Become a leader in the field of computing	3.27	1.07	1, 5	0.33
Please indicate the extent to which each of the following considerations is/was important in choosing which jobs to apply for:
Racial/ethnic diversity of your team	2.62	1.26	1, 5	0.55
Gender diversity of your team	2.60	1.27	1, 5	0.44
Organization values	3.45	1.08	1, 5	0.87
Reputation of the organization	3.59	0.99	1, 5	0.87
To what extent is each of the following kinds of support available in your workplace?
Coworkers to help me with projects at work	4.00	0.94	1, 5	0.98
Coworkers to help me navigate difficult programming or engineering tasks	4.00	0.99	1, 5	1.00
Coworkers to hang out with (within or outside of work)	3.20	1.17	1, 5	0.87
Coworkers to confide in or talk to about my problems	3.00	1.16	1, 5	1.00
In your current job, to what extent are you doing the following:
Mentoring others	2.32	1.04	1, 5	0.87
Supervising others	2.02	1.01	1, 5	0.55
Teaching	1.80	0.96	1, 5	0.66
Coding/programming	3.73	1.15	1, 5	0.66
Solving computing-related problems	4.09	0.88	1, 5	0.66
How do you feel about the environment of your organization? There is a welcoming atmosphere for…
Women	4.24	0.81	1, 5	0.98
People of Color	4.19	0.81	1, 5	0.77
How do you feel about the environment of your organization?
There is a sense of community within the workplace	4.01	0.85	1, 5	0.66
I feel valued, heard, and respected within the workplace	3.90	0.87	1, 5	0.87
The workplace environment inspires me to do the best job that I can	3.78	0.94	1, 5	0.66
There is respect for the expression of diverse beliefs within the workplace	3.92	0.86	1, 5	0.77
To what extent do you agree with the following:
I am proud to tell others that I work for my current organization	4.08	0.91	1, 5	0.55
The mission and purpose of my organization aligns with my values	3.82	0.97	1, 5	1.09
I feel like I belong at my organization	3.91	0.87	1, 5	0.55
I feel satisfied with my chances for salary increases	3.76	1.03	1, 5	0.55
I feel I am being paid a fair salary for the work I do	3.75	1.06	1, 5	0.66
I am satisfied with the fringe benefits I receive (e.g., company-provided meals, product giveaways, bonuses)	3.58	1.07	1, 5	0.87
The benefits package we have (e.g., health, dental insurance, vacation policies) is comparable to what is offered by other similar organizations in my field	3.93	0.98	1, 5	0.66
Those who do well on the job stand a fair chance of promoted	3.88	0.88	1, 5	0.55
I am satisfied with my chances for promotion	3.73	0.94	1, 5	0.77
I feel that the work I do is appreciated at my place of employment	3.99	0.88	1, 5	0.80
I am generally satisfied with my supervisor/manager	4.16	0.91	1, 5	0.66
My supervisor/manager supports my professional development	4.18	0.89	1, 5	0.98
My supervisor/manager supports my work-life balance	4.19	0.90	1, 5	1.20
My supervisor/manager is unfair to me (reverse-coded)	4.27	1.03	1, 5	0.77

for descriptives of all independent variables included in the regression model. Descriptions of all composite variables are included in

Table 3. Factor loadings for all composite variables.

Variable		Factor Loadings
DV: Perception of Supportive Environment in Computing Careers (3 items; Cronbach’s alpha = 0.939)
What are your perceptions of the general environment in computing careers? Rate how much you disagree or agree with the following statements.
	There is a supportive environment for women of color in computing careers. 1	0.982
	There is a supportive environment for people of color in computing careers. 1	0.895
	There is a supportive environment for women in computing careers. 1	0.874
Computing Identity and Belonging (5 items; Cronbach’s alpha = 0.814)
How much do you agree or disagree with the following statements?
	I feel like I belong in computing 1	0.880
	I see myself as a computing person 1	0.806
	Computing is a big part of who I am 1	0.699
	I feel welcomed in the computing community 1	0.597
	I feel like an outsider in the computing community 2	0.491
Success in Computing (3 items; Cronbach’s alpha = 0.701)
How much do you agree or disagree with the following statements?
	I see myself as a leader among my colleagues in computing 1	0.935
	I aspire to be a leader in the field of computing 1	0.621
	I care about doing well in computing 1	0.472
Computing Self-Concept (3 items; Cronbach’s alpha = 0.825)
How would you rate yourself in the following areas as compared to the average person your age?
	Computer programming ability 3	0.921
	Ability to use multiple programming languages 3	0.906
	Computer skills 3	0.582
I am confident that I can…
	Quickly learn a new programming language on my own 1	0.562
Leadership and Confidence (2 items; Cronbach’s alpha = 0.662)
How would you rate yourself in the following areas as compared to the average person your age?
	Leadership ability 3	0.827
	Self-confidence (social) 3	0.568
I am confident that I can…
	Become a leader in the field of computing 1	0.527
Striver Personality (2 items; Cronbach’s alpha = 0.625)
How would you rate yourself in the following areas as compared to the average person your age?
	Drive to achieve 3	0.733
	Competitiveness 3	0.653
Prioritizing Inclusive Work Environment (4 items; Cronbach’s alpha = 0.809)
Please indicate the extent to which each of the following considerations is/was important in choosing which jobs to apply for…
	Racial/ethnic diversity of your team 4	0.908
	Gender diversity of your team 4	0.879
	Organization values 4	0.583
	Reputation of the organization 4	0.492
Colleague Support (4 items; Cronbach’s alpha = 0.810)
To what extent is each of the following kinds of support available in your workplace?
	Coworkers to help me with projects at work 5	0.813
	Coworkers to help me navigate difficult programming or engineering tasks 5	0.738
	Coworkers to hang out with (within or outside of work) 5	0.708
	Coworkers to confide in or talk about my problems 5	0.641
Collaborative Job Functions (3 items; Cronbach’s alpha = 0.766)
In your current job, to what extent are you doing the following…
	Mentoring others 6	0.832
	Supervising others 6	0.728
	Teaching 6	0.612
Computing Job Functions (2 items; Cronbach’s alpha = 0.617)
In your current job, to what extent are you doing the following…
	Coding/programming 6	0.679
	Solving computing-related problems 6	0.679
Welcoming Job Environment (3 items; Cronbach’s alpha = 0.809)
How do you feel about the environment of your organization?
	There is a welcoming atmosphere for women 1	0.921
	There is a welcoming atmosphere for People of Color 1	0.880
	There is a sense of community within the workplace 1	0.537
Personal Value in Job Environment (3 items; Cronbach’s alpha = 0.876)
How do you feel about the environment of your organization?
	I feel valued, heard, and respected within the workplace 1	0.892
	The workplace environment inspires me to do the best job that I can 1	0.821
	There is respect for the expression of diverse beliefs within the workplace 1	0.806
Job Satisfaction with Organizational Values (3 items; Cronbach’s alpha = 0.842)
To what extent do you agree with the following…
	I am proud to tell others that I work for my current organization 1	0.852
	The mission and purpose of my organization aligns with my values 1	0.798
	I feel like I belong at my organization 1	0.754
Job Satisfaction with Salary and Benefits (4 items; Cronbach’s alpha = 0.793)
To what extent do you agree with the following…
	I feel satisfied with my chances for salary increases 1	0.770
	I feel I am being paid a fair salary for the work I do 1	0.702
	I am satisfied with the fringe benefits I receive (e.g., company-provided meals, product giveaways, bonuses, etc.) 1	0.692
	The benefits package we have (e.g., health, dental insurance, vacation policies) is comparable to what is offered by other similar organizations in my field 1	0.636
Job Satisfaction with Appreciation and Promotions (3 items; Cronbach’s alpha = 0.795)
To what extent do you agree with the following…
	Those who do well on the job stand a fair chance of being promoted 1	0.881
	I am satisfied with my chances for promotion 1	0.851
	I feel that the work I do is appreciated at my place of employment 1	0.540
Job Satisfaction with Supervisor or Manager (4 items; Cronbach’s alpha = 0.846)
To what extent do you agree with the following…
	I am generally satisfied with my supervisor/manager 1	0.890
	My supervisor/manager supports my professional development 1	0.905
	My supervisor/manager supports my work-life balance 1	0.805
	My supervisor/manager is unfair to me 2	0.500

¹ Five-point scale: 1 = “strongly disagree” to 5 = “strongly agree”; ² five-point scale: 1 = “strongly agree” to 5 = “strongly disagree”; ³ five-point scale: 1 = “lowest 10%” to 5 = “highest 10%”; ⁴ five-point scale: 1 = “not at all” to 5 = “extremely”; ⁵ five-point scale: 1 = “not at all” to 5 = “very much”; ⁶ five-point scale: 1 = “never” to 5 = “always”.

.

5.3. Analyses

We addressed the first two (descriptive) research questions using three-way crosstabulations with z-tests with the Bonferroni correction. This allowed us to assess significant differences in early computing career experiences and perceptions by gender and race/ethnicity in how the sample perceived their belonging and identity in computing (research question one) and in their perception of inclusivity in computing work environments (research question two). In the tables reporting these crosstabs, note that bold values indicate significant differences between men and women within each racial/ethnic group (the higher value is bolded). Superscripts indicate significant differences between graduates of the same gender but different racial/ethnic identities (White, Caucasian, or European American = W, Asian or Asian American = A, Black or African American = B, Hispanic or Latina/o/x = L, and Indigenous, Middle Eastern, Multiracial, or Other = O).

To address research question three, which examined the factors that predict participants’ perception that the general environment in computing careers is inclusive of women, people of color, and women of color, we ran an Ordinary Least Squares (OLS) linear regression model. Before running the regression, we examined a correlation matrix using Pearson correlation coefficients and determined that none of the independent variables were highly correlated (greater than r = 0.5), which might present issues relating to multicollinearity. To further assess multicollinearity among predictors, we calculated Variance Inflation Factors (VIFs). All VIFs were below the commonly accepted threshold of 5, ranging from 1.01 to 2.97, indicating no significant multicollinearity.

6. Findings

6.1. Research Question One

This study first investigated how recent college graduates working full-time in computing fields perceive their belonging and identity in computing (see

Table 4. Recent graduates’ perceptions of their belonging and identity in computing by gender and racial/ethnic identity, percent rating “Agree” or “Strongly Agree” (n = 845).

	All Students		White		Asian/Asian American		Black/African American		Hispanic or Latina/o/x		Indigenous, Multiracial, Middle Eastern or Other
	Men	Women	Men	Women	Men	Women	Men	Women	Men	Women	Men	Women
	(n = 553)	(n = 292)	(n = 233)	(n = 101)	(n = 175)	(n = 112)	(n = 25)	(n = 18)	(n = 55)	(n = 21)	(n = 65)	(n = 40)
	(%)	(%)	(%)	(%)	(%)	(%)	(%)	(%)	(%)	(%)	(%)	(%)
I see myself as a computing person.	87.3	70.2	90.6	67.3	84.0	67.0	75.0	77.8	89.1	76.2	87.7	80.0
I feel like I belong in computing.	80.6	60.3	87.1 A	55.0	74.7 W	62.5	70.8	55.6	76.4	61.9	80.0	69.2
I see myself as a leader among my colleagues in computing.	41.0	25.7	46.4	23.8	34.3	25.0	45.8	16.7	38.2	38.1	40.6	30.0
I aspire to be a leader in the field of computing.	50.7	46.6	53.0	44.6	46.6	41.1	58.3	61.1	58.2	61.9	44.6	52.5
I feel like an outsider in the computing community.	19.1	36.3	18.5	36.6	17.8	32.1	12.5	55.6	20.0	33.3	26.2	40.0
Computing is a big part of who I am.	63.3	34.6	65.9	27.7	58.0	37.5	62.5	44.4	61.8	33.3	69.2	40.0
I feel welcomed in the computing community.	72.2	48.1	73.7	50.5	70.3	47.7	50.0	33.3	78.2	57.1	75.4	45.0
I feel like I belong at my organization.	77.0	73.7	80.3	81.0	71.3	66.4	64.0	55.6	80.0	85.7	83.1	77.5
My supervisor/manager supports my professional development.	82.3	83.0	83.5	88.0	79.3	78.2	75.0	83.3	83.6	81.0	87.5	85.0
My supervisor/manager is unfair to me.	8.7	5.5	8.6	3.0 B	9.8	5.5	4.2	22.2 W	9.3	4.8	7.7	5.0

Note. Bold values indicate significant differences between men and women within each racial/ethnic group; the higher value is bolded. The superscripts indicate a significant difference between graduates of the same gender but of different racial/ethnic identities, where White, Caucasian, or European American = “W”, Asian or Asian American = “A”, Black or African American = “B”. Statistical significance was set at the p < 0.05 level.

). We then further examined how these perceptions vary by gender and/or race/ethnicity. Most participants responded positively on these measures, indicating they generally felt a sense of belonging and identity in computing. More than half of participants agreed with the statements “I see myself as a computing person” and “I feel like I belong in computing.” However, there were notable gender differences with regard to these perceptions. Overall, men consistently reported higher belonging and identity in computing than women. For example, 87% and 81% of men agreed with “I see myself as a computing person” and “I feel like I belong in computing”, respectively, compared to only 70% and 60% of women. Further, 36% of women agreed that they “feel like an outsider in the computing community,” compared to only 19% of men. Finally, 48% of women reported feeling “welcomed in the computing community,” compared to 72% of men. These gender-based differences also emerged within racial/ethnic groups. For instance, 17% of Black women reported seeing themselves as leaders in computing compared to 46% of Black men. Similarly, only 33% of Latina women agreed with “Computing is a big part of who I am,” compared to 62% of Latino men. Finally, 48% of Asian or Asian American women reported feeling welcomed in the computing community compared to 70% of Asian or Asian American men.

When examining responses from respondents of the same gender (i.e., among men or among women) but across racial/ethnic groups, we found significant differences in perceptions of belonging and identity in computing. For example, over 22% of Black women agreed they had an unfair supervisor/manager compared to only 3% of White women. Asian or Asian American men reported significantly lower belonging in computing (75%) than White men (87%).

6.2. Research Question Two

The second research question examined how recent college graduates working in computing fields perceive the inclusivity of their working environments as well as how these perceptions vary by gender and/or race/ethnicity (see

Table 5. Recent graduates’ perceptions of a supportive work environment in computing by gender and racial/ethnic identity, percent rating “Agree” or “Strongly Agree” (n = 845).

	All Students		White		Asian/Asian American		Black/African American		Hispanic or Latina/o/x		Indigenous, Middle Eastern, Multiracial or Other
	Men	Women	Men	Women	Men	Women	Men	Women	Men	Women	Men	Women
	(n = 553)	(n = 292)	(n = 233)	(n = 101)	(n = 175)	(n = 112)	(n = 25)	(n = 18)	(n = 55)	(n = 21)	(n = 65)	(n = 40)
	(%)	(%)	(%)	(%)	(%)	(%)	(%)	(%)	(%)	(%)	(%)	(%)
There is a supportive environment for women in computing careers.	64.7	49.1	63.9	48.0	64.4	52.7	48.0	61.1	69.1	47.6	70.8	37.5
There is a supportive environment for people of color in computing careers.	64.1	41.2	63.9	37.0	63.0	47.3	40.0	27.8	70.9	47.6	70.8	37.5
There is a supportive environment for women of color in computing careers.	58.2	34.0	60.1	31.0	56.9	39.3	32.0	22.2	63.6	38.1	60.0	30.0
There is a competitive environment in computing careers.	91.5	90.1	91.0	91.1	90.9	89.3	96.0	77.8	89.1	95.2	95.3	92.5
People who succeed in computing careers tend to fit a certain stereotype (e.g., “hacker,” “geek,” “nerd”).	40.3	47.9	40.8	48.5	44.0	43.8	32.0	50.0	40.0	66.7	32.3	47.5

Note. Bold values indicate significant differences between men and women within each racial/ethnic group; the higher value is bolded. Statistical significance was set at the p < 0.05 level.

). Generally, participants reported inclusive work environments in computing fields. For example, nearly 60% of participants agreed there is a supportive environment for women in computing careers and 56% of participants agreed there is a supportive environment for people of color in computing careers. However, despite these supportive environments, over 90% of participants strongly agreed there is a competitive environment in computing careers.

While we did not observe any racial/ethnic differences in participants’ perceptions of the inclusivity of their working environments, there were many significant differences by gender. Overall, the results suggest that women working in computing fields hold a more critical view of the environment in computing careers compared to their male counterparts. For example, 65% of men reported a supportive environment for women in computing careers compared to only 49% of women. Similarly, 58% of men compared to only 34% of women reported a supportive environment for women of color in computing careers. Given that women generally reported less inclusive work environments, it follows that a higher proportion of women than men (48% vs. 40%) reported that people who succeed in computing tend to fit a certain stereotype.

These findings held when examining differences by gender within racial/ethnic groups (e.g., White, Asian/Asian American, Black/African American, and Hispanic or Latinx), such that men generally reported more inclusive work environments in computing careers than women. For example, 64% of Latino men reported a supportive environment for women of color in computing careers compared to only 38% of Latina women. Similarly, 63% of Asian or Asian American men reported a supportive environment for people of color in computing careers compared to only 47% of Asian or Asian American women.

6.3. Research Question Three

The third research question examined how the background characteristics, college experiences, and work environments of recent college graduates in computing fields predict their perception that the general environment in computing careers is inclusive of women, people of color, and women of color. After conducting OLS linear regression, the resulting model indicated six statistically significant predictors. Overall, the model predicted 28.7% of the variance in recent graduates’ perception of computing as inclusive. See

Table 6. Summary of OLS regression predicting perception of inclusivity in computing careers.

Variable	Standardized Coefficient
Gender	−0.124
Asian or Asian American Race/Ethnicity	0.057
Black or African American Race/Ethnicity	−0.006
Hispanic or Latinx Race/Ethnicity	0.034
Computing Identity and Belonging	0.136
Computing Major	0.067
Computing Minor	0.049
Num. of Computing-Related Internships or Co-ops	−0.019
Leadership and Confidence	−0.022
Striver Personality	0.042
Prioritizing Inclusive Work Environment	−0.149
Colleague Support	0.028
Collaborative Job Functions	0.044
Computing Job Functions	−0.028
Welcoming Job Environment	0.337
Personal Value in Job Environment	0.049
Job Satisfaction with Organizational Values	−0.092
Job Satisfaction with Salary and Benefits	0.064
Job Satisfaction with Appreciation and Promotions	0.001
Job Satisfaction with Supervisor or Manager	−0.116
Computing Self-Concept	0.034
Success in Computing	0.040
Model R2	0.287

Note: Bold indicates significance at the p < 0.05 level.

for a summary of the final OLS regression model.

The following were positive predictors, meaning they were associated with perceiving a more inclusive environment in computing careers for the three groups specified above: computing identity and belonging, computing major, and a perception of the work environment as welcoming. Across all significant predictors, perceiving a welcoming work environment was the strongest predictor of perceiving general inclusivity in computing careers. Moreover, it is noteworthy that two of the three positive predictors (i.e., feeling connected to the computing community and majoring in computing) related to identities and experiences largely cultivated during students’ undergraduate experiences, further underscoring the utility of the decision to undergird the Social Cognitive Model of Career Self-Management with the Science Identity Model. As discussed in more detail below, these findings suggest the crucial role that undergraduate academic environments can play in shaping students’ later perceptions of their chosen career field.

The three negative predictors associated with perceiving the general environment in computing careers as less inclusive were identifying as a woman, prioritizing an inclusive work environment in the job search process, and reporting satisfaction with supervisor or manager (i.e., those who reported higher satisfaction with their manager were less likely to view the field of computing as inclusive). The relationship between identifying as a woman and holding a view of the field of computing as less inclusive reflects the previously discussed findings from the descriptive analyses: women are more likely than men to report experiencing exclusion or isolation in computing spaces, which may signal a lack of inclusivity in the computing work environment. Though they may seem somewhat counterintuitive, the other negative predictors point to the challenges of navigating the college-to-career transition, as students’ job search process may lead to a mismatch between the expectations and realities of their role as well as their experiences with their manager and their company overall. These findings will be discussed fully below.

7. Limitations

This study addresses questions related to recent college graduates’ perceptions of inclusivity in their tech workplaces. The study’s results make valuable contributions to the literature that examines how higher education institutions can broaden participation in computing, from the classroom to careers, particularly among women and people of color. Alongside these contributions, there are some limitations that nuance the ways in which these findings should be understood. First, the sample sizes necessitated aggregating across some racial/ethnic groups. Given the notable findings among racial groups, particularly among Asian and Asian American college graduates, future research should aim to further disaggregate these groups so as to gain a more focused understanding of their unique experiences. Additionally, although we have longitudinal data on many of the study participants, using multiple timepoints for this study would have significantly reduced our analytical sample, which would have further limited our ability to study individuals from minoritized groups. Therefore, this study uses a single timepoint (the fall 2020 follow-up survey). This leads to some “chicken or the egg” uncertainties, especially about the role of measures like computing identity or belonging. Given our conceptual framework, we have largely focused on the role of colleges and universities in strengthening computing identity and belonging, but we recognize that students’ experiences prior to college also shape this and other psychosocial traits (e.g., striver personality). Related to the timing of the survey, fall of 2020 was a tumultuous time globally, as the world was still immersed in the COVID-19 pandemic and there was heightened attention on racial justice. Both of these and other factors may have shaped how respondents perceived inclusivity and belonging. Further, recent college graduates in this study all attended institutions that had made an expressed commitment to broadening participation in computing. Hence, majoring in computing at a BRAID institution may have especially primed them to view computing careers as inclusive. Future work should draw from broader institutional samples. Finally, this study broadly defined a career in computing. Future studies should consider narrowly focusing on a specific functional area (e.g., coders) or a specific type of work environment (e.g., small start-ups) to better understand how perceptions of inclusivity can vary within more narrowly defined environments.

8. Discussion and Implications

This study investigated the early career experiences of recent college graduates working full-time in computing fields and the relationship between these experiences and their perception of the field of computing as inclusive, emphasizing their sense of belonging and computing identity. In the following sections, we discuss these findings in conversation with the extant literature and our conceptual frameworks, drawing conclusions about the study’s implications for research and practices.

8.1. Computing Identity and Belongingness Among New Computing Professionals

Our findings underscore the need to continue developing an understanding of how identity shapes participation in computing. Carlone and Johnson’s (2007) work is often used to point to ways that undergraduate students can be socialized into STEM. The findings from this study suggest that early career professionals similarly need to have tangible markers of their belongingness in their chosen field. Indeed, we found that one’s computing identity and belonging was a significant positive predictor of participants’ perception of the field as inclusive. Previous research has routinely found that sense of belonging is a key predictor of various outcomes related to an individual’s success in computing, particularly retention in the field (Aschbacher et al., 2010; Blaney & Stout, 2017; Cheryan et al., 2009; George et al., 2022; Hausmann et al., 2007; Perez et al., 2014; Stormes, 2023; Strayhorn, 2018; Taheri et al., 2018). This finding builds from that body of work and makes clear the connection between the individual’s own sense of fit in the field and their larger view of the field as a whole. In other words, this finding suggests that, when a person sees themselves as a computing person and feels a sense of fit in the field, they are more likely to believe the field is inclusive, but the opposite is also true, such that negative experiences not only impact an individual’s own experience in computing but also their overall view of the field.

Gender and Racial/Ethnic Differences

Our analyses revealed widespread gender and racial/ethnic differences across all dimensions of our study. We note that women rate themselves significantly lower than men on their sense of belonging and computing identity, and they are more likely than men to report negative experiences in their workplace. Further, we found that identifying as a woman was a negative predictor of viewing the overall field of computing as inclusive. This study extends what is known about sense of belonging among undergraduate computing students—women tend to come to computing courses with lower computing belonging than men and this gap widens over time (Sax et al., 2018)—and highlights how gender gaps in computing identity and belonging persist from college into career and have longer-term implications for individuals’ view of the field of computing.

Further, this study underscores the need to examine individuals’ computing experiences intersectionally. For instance, White and Asian/Asian American men are often aggregated into a single “majority” group, even as Asian and Asian American technologists have varying experiences in computing and face systemic and exclusionary barriers in the field (Tari et al., 2021). This study highlights the importance of disaggregating groups as much as possible in analyses, as Asian and Asian American men reported significantly lower sense of belonging than their White male peers. This finding reminds us that while academic identities (e.g., Carlone and Johnson’s Science Identity) play a key role in shaping perceptions, social identities such as gender and race also powerfully influence how early college graduates make sense of their place within the tech field.

Indeed, it is important that we disentangle our understanding of “representation” from the experiences of people in computing. This is particularly true for women of color who hold multiple marginalized identities and navigate systemic barriers founded on racist and sexist practices in computing (Ong et al., 2011; Rankin & Thomas, 2020). The Black women in our study reported having an unfair manager at significantly higher rates than did White women. These findings echo the results of AnitaB.org’s (2021) Technical Equity Experience Survey, which unveiled a number of ways in which Black women working in computing have negative experiences in the workforce, ranging from reporting the lowest sense of belonging of women in any racial/ethnic group to being the most likely of women from any racial/ethnic group to report feeling unsafe on their work teams. Indeed, gendered racism influences Black women’s ability to persist in computing and shapes their early career experiences (Rankin & Thomas, 2020); more intersectional research is needed that highlights the lived experiences of Black women in computing, as well as the experiences of those from other oft-overlooked marginalized groups.

8.2. The Role of Colleges in Recent Graduates’ Career Transitions

The respondents in our sample were all college graduates within one or two years of graduation who held full-time jobs in the field of computing. As suggested by CSM (Lent & Brown, 2013), our study demonstrates the importance of college experiences in shaping their transitions to the workforce and their early career experiences. In alignment with previous work suggesting that congruence between one’s major and career promotes job satisfaction (Xu, 2017), we found that majoring in computing in college was a positive predictor of viewing the field as inclusive. Drawing on SCCT/CSM (Lent & Brown, 2013) and Science Identity Theory (Carlone & Johnson, 2007), we hypothesize that this finding relates to the socialization process graduates who majored in computing might have experienced in college. Indeed, previous work found that participating in computing organizations in college predicted college students’ computing career aspirations (George et al., 2022); relatedly, a prior study indicated that having social relationships in college positively predicted CS career intentions for Black women (Ross et al., 2020). Computing departments can leverage these opportunities to prepare students for the college-to-career transition by embedding identity-and-belonging work into the curriculum, infusing career readiness initiatives into all levels of the program, and formalizing alumni mentoring programs to create near–peer support for students’ transition to their first professional role.

At the same time, this finding raises the question as to how those who enter a computing career without having majored in computing fare; this is especially important because historically marginalized individuals tend to develop interests in computing later such that they may take their first computing course at the end of their college career (Lehman et al., 2020) or come to the field after completing a computing internship, rather than a computing degree (Lehman et al., 2023). Hence, colleges and universities play a key role in preparing their graduates for the transition to their career in computing, regardless of major. Career offices can serve as important intermediaries between students and future employers by facilitating internships and supporting students in their job search.

Related to the job search process, we found that those who prioritized an inclusive work environment in their job search were less likely to view the field as inclusive, thereby supporting the prior literature which ties job search behaviors to an individual’s job satisfaction (Mau & Kopischke, 2001; van Hooft et al., 2021). Indeed, college students are likely to meet barriers and unmet expectations in their career transition, particularly for women, women of color, and people of color (AnitaB.org, 2021; Lutz & Paretti, 2021; Xu, 2017). Hence, campuses may need to support their students in processing these experiences and advocating for themselves. Further, colleges and universities hold privileged spaces in society and can often use their influence to effect change—we offer that colleges and universities can advocate for change with tech employers. At the same time, tech employers hold the responsibility to interrogate the negative experiences recent graduates face as they assume roles in the tech workforce and to seek to address structural and systemic barriers that may be driving them.

8.3. Computing Workplaces as Inclusive Spaces

Above all, the findings from our study assert the importance of a workplace in shaping an individual’s view of the field as a whole: the strongest predictor of recent graduates’ views of computing as an inclusive field was a welcoming environment in their current workplace. Indeed, when tech companies hire recent graduates, this job may well be their first exposure to a career in computing. As the differences between a college and workplace environment are palpable (Lutz & Paretti, 2021), employers should not take that power lightly. At the undergraduate level, research on best practices to broaden participation in undergraduate computer science underscores the importance of students’ introductory courses serving as many individuals’ first encounter with computing (Lazowska et al., 2013). Our findings suggest that an individual’s first job in the field plays a similarly important role. Relatedly, our study found an inverse relationship between recent graduates’ satisfaction with their supervisor/manager and their view of the field (i.e., those who were more satisfied with their supervisor were less likely to view computing as inclusive). Though counterintuitive, it connects to previous findings that suggest that college students who had mentors who discussed career options were less likely to aspire to a computing career (George et al., 2022). We arrive at a similar conclusion as George and her colleagues to explain the phenomenon in the present study: it is possible that recent graduates are seeking out their supervisor for support when experiencing challenging circumstances at the company more broadly. Alternatively, it is possible that supervisors are sharing stories about their own or others’ negative experiences in computing. Hence, more research is needed to clarify the role of supervision in new employees’ transition to the computing workforce.

As tech companies seek to recruit and retain more women, women of color, and people of color in their companies, particularly in technical roles (AnitaB.org, 2022), the findings from this study emphasize the need to increase support for employees new to the field. Given that recent college graduates will have participated in programs and initiatives that intentionally centered and supported their identities in STEM (e.g., student organizations, conferences such as the Grace Hopper Celebration, etc.), early career computing professionals should be provided with similar opportunities to engage during their initial transition into their new roles. This may look like mentoring and training programs both for new hires as well as for current employees to increase individuals’ computing identity and belonging and promote a welcoming environment. Further, employers should seek to identify and implement policies that promote workplace inclusivity, knowing that their employees’ perceptions of their workplace will translate to their view of the field writ large. CSM (Lent & Brown, 2013) emphasizes the cyclic nature of career decision-making. Early career professionals will continually reevaluate their belongingness in the field, and retaining diverse talent requires providing intentional and ongoing support.

9. Conclusions

While higher education institutions are charged with equipping their graduates with the skills and knowledge to thrive in the workplace, the findings from this study highlight the ways that undergraduate experiences can also shape students’ longer term career trajectories. Alongside their gender and racial identities, the identity of being a “computing person” is crucially important to early career professionals’ ability to thrive in the workplace. Computing departments play a key role in helping develop these identities through the ways that faculty interact with their students as well as the opportunities afforded to students through student organizations. As these students transition into the workplace as early career professionals, it is important to continue emphasizing the ways in which the field of computing not only needs, but also explicitly values, diversity.