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Promoting diversity: reactions to corporate actions that support anti-racism efforts

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Promoting Diversity: Reactions to Corporate Actions that Support Anti-Racism Efforts
by
Tamara Marie Horn

Rossier School of Education
University of Southern California
A dissertation submitted to the faculty
in partial fulfillment of the requirements for the degree of
Doctor of Education

May 2022

© Copyright by Tamara Marie Horn 2022
All Rights Reserved

The Committee for Tamara Marie Horn certifies the approval of this Dissertation

Monique Datta
Darline P. Robles
Paula M. Carbone, Committee Chair

Rossier School of Education
University of Southern California
2022
iv
Abstract
In the United States, throughout 2020 and into 2021, a series of events, such as the death of
George Floyd, Black Lives Matter marches, and the insurrection attempt on January 6, 2021,
resulted in a national uprising in support for racial justice and equity. Many Americans
responded by educating themselves about racism in the United States, and corporations publicly
displayed support for anti-racism activities. This study examined how U.S. citizens and residents
who were employees of technology companies perceived their corporations’ pro-diversity and
anti-racism activities since May 2020. Survey data from 397 participants and interview data from
11 participants were analyzed. The results showed that most participants, regardless of
racial/ethnic group, supported pro-diversity and anti-racism corporate-sponsored activities, such
as workplace diversity training. The recommendations presented were made in consideration of
the interests of technology corporations and their employees.
Keywords: bias, corporate diversity training, intergroup threat

v
Dedication
To everyone who reaches for the goal of bringing us closer to equity. And to all those who came
before us who never gave up.

vi
Acknowledgements
I am overwhelmingly grateful to everyone who has supported me during my doctoral
journey. My sincerest thanks to my dissertation chair Paula M. Carbone, Ph.D., for her support
of my research, expert advice, and guidance throughout my journey. I also wish to thank my
committee members, Monique Datta, Ed.D., for equipping me with the foundation I needed to be
successful. Your advice has benefited me throughout the program. Thank you, Darline P. Robles,
Ph.D., for sharing your subject matter expertise and expanding my understanding of the critical
concepts of diversity, equity, and inclusion. I wish to also thank my extremely talented cohort for
their collaboration and friendship. We did it! I am so grateful that I got to share this experience
with you. Furthermore, I wish to thank all participants who contributed to the study for their time
and candor.
My biggest thanks go to my family. Thank you to my amazing husband, Solomon, for
being my cheerleader, celebrating my milestones with me, and taking over most of our
household management needs, allowing me the time I needed to focus on my doctoral program.
Thank you to my incredible kiddo Elijah for his patience during those many nights that I needed
to sit behind a closed door to focus on writing. Elijah, you fill my world with joy and laughter,
and I am incredibly grateful to be your mom. Thank you to my wonderful mother, Alicia M.
Howard, Ph.D., for always being available to listen to my ideas, offer wise counsel, and for
helping me to make this journey fun. Thank you for being there for me. To Mrs. Crystal Lomax-
Parker, who is more than my best friend of nearly 30 years, but also a woman I consider to be my
sister. Thank you for your endless praise, encouraging words, and hilarious distractions when I
needed them most. Last but never least, to my cousin Mr. Dauron Cannon, who cared for my
mom so I did not have to worry. You are more than a cousin; you are my brother. Thank you for
vii
your brotherly love and support throughout my life. No matter the situation, I have always been
able to count on you in good times and in bad.
viii
Table of Contents
Abstract .......................................................................................................................................... iv
Dedication ........................................................................................................................................v
Acknowledgements ........................................................................................................................ vi
List of Tables ...................................................................................................................................x
List of Figures ............................................................................................................................... xii
Chapter One: Introduction to the Study ...........................................................................................1
Context and Background of the Field Study ........................................................................2
Importance of the Study .......................................................................................................3
Overview of Theoretical Framework and Methodology .....................................................4
Definitions............................................................................................................................5
Organization of the Dissertation ..........................................................................................6
Chapter Two: Literature Review .....................................................................................................7
Psychological Response to Demographic Shifts in the United States .................................8
Sources That Contribute to Anti-White Bias Beliefs .........................................................11
Chapter Three: Methodology .........................................................................................................39
Data Sources ......................................................................................................................40
Data Collection Procedures ................................................................................................50
Data Analysis .....................................................................................................................50
Ethics..................................................................................................................................54
The Researcher...................................................................................................................55
Limitations .........................................................................................................................56
Chapter Four: Findings and Results ...............................................................................................59
Categories ..........................................................................................................................61
Summary ............................................................................................................................91
ix
Chapter Five: Discussion and Recommendations..........................................................................92
Discussion of Findings and Results ...................................................................................92
Recommendations ..............................................................................................................95
Recommendations Summarized.......................................................................................106
Recommendations for Future Research ...........................................................................108
Conclusion .......................................................................................................................109
References ....................................................................................................................................111
Appendix A: Google’s Ideological Echo Chamber .....................................................................135
Appendix B: EEO-1 Reports .......................................................................................................142
Appendix C: Survey Protocols.....................................................................................................151
Appendix D: Interview Protocol ..................................................................................................158
Appendix E: Cronbach’s Alpha Computation to Test Reliability ...............................................161
Appendix H: EBIT reports ...........................................................................................................164

x
List of Tables
Table 1: Research Methods Used in the Study 39
Table 2: Interviewee Racial Distribution and Racial Distribution at High-Tech Corporations 44
Table 3: Matrix of Interview Questions 49
Table 4: Predefined A Priori Codes 52
Table 5: Racial/Ethnic Participant Makeup 60
Table 6: Five Categories Examined 62
Table 7: Sentiment Regarding Meritocracy From Interviewed Participants With Mixed
Perceptions 71
Table 8: Sentiment Regarding Affirmative Action From Interviewed Participants 76
Table 9: Participation in Corporate Diversity Training 82
Table 10: Voluntary Participation in Activities that Support Diversity and Anti-racism 83
Table 11: Levels of Support for Corporate Interventions 85
Table 12: Support for Pro-Diversity Corporate Interventions 87
Table 13: Support for Decreasing Corporate Interventions 89
Table 14: Support to Focus Resources on Existing Employees or Making Other Undefined
Interventions 90
Table 15: Predicted EBIT After Increasing BIPOC Executive/Senior Leadership 98
Table 16: Engaged Employees and Potential Gains in Profitability 102
Table 17: Disengaged Employees and Potential Loses in Profitability 103
Table 18: Approximate Cost of Diversity Training 105
Table B7: Oracle America Inc. 150
Table C1: Survey 152
Table C2: Additional Questions Added to Facebook and LinkedIn Survey 156
Table D1: Interview Questions 158
Table E1: Cronbach’s Alpha 161
xi
Appendix F: Lawshe’s Content Validity Ratio (CVR) to Test Validity of Interview Questions 162
Appendix I: Groupings 166
xii
List of Figures
Figure 1: Respondents Perceptions of Anti-White and Anti-Black Bias Over Six Decades 12
Figure 2: High-Tech Corporate Employment Data for All Job Categories 27
Figure 3: High-Tech Corporate Employment Data for Executive and Managerial Job
Categories 29
Figure 4: Leadership Ratios in High-Tech Corporations by Race 30
Figure 5: Conceptual Framework of Antecedents That Result in the Perception of Threats 35
Figure 6: Analysis for Awareness of Asian, BIPOC (underrepresented), and White Participants 64
Figure 7: Aggregate Analysis for Awareness of All Participants 65
Figure 8: Analysis for SLB of Asian, BIPOC (Underrepresented), and White Participants 67
Figure 9: Aggregated Analysis for Agreement with SLB of All Participants 68
Figure 10: Analysis for Threats of Asian, BIPOC (underrepresented), and White Participants 73
Figure 11: Aggregate Analysis for Threats of All Participants 74
Figure 12: Analysis for Support of Asian, BIPOC (underrepresented), and White Participants 78
Figure 13: Aggregate Analysis for Support of All Participants 79
Figure B1: Alphabet Inc. 142
Figure B2: Apple Inc. 143
Figure B3: Cisco Systems Inc. 144
Figure B4: Facebook Inc. 145
Figure B5: Intel Corporation 146
Figure B6: Microsoft Corporation 148
Appendix G: Census Regions and Divisions of the United States 163
1

Chapter One: Introduction to the Study
Anti-racism messages and activities that support diversity in corporations’ hiring and
promotional practices are found to contribute to expressions of racial bias by White American
employees (Dover et al., 2016; Wellman et al., 2016). While the U.S. government is not a
corporation, the reactions to the issue of diversity are comparable. A memo released on
September 4, 2020, by the U.S. Office of Management and Budget, which is the business
division of the Executive Office of the President of the United States, demonstrates that the
promotion of diversity generates adverse reactions. The opinion presented in the memo
essentially concluded that the efforts to promote diversity were forms of propaganda. In
particular, the memo focused on the training materials that were associated with “critical race
theory” and “white privilege”:
It has come to the President’s attention that Executive Branch agencies have spent
millions of taxpayer dollars to date “training” government workers to believe divisive,
anti-American propaganda…. all agencies are directed to begin to identify all contracts or
other agency spending related to any training on “critical race theory,” “white privilege,”
or any other training or propaganda effort that teaches or suggests either (1) that the
United States is an inherently racist or evil country or (2) that any race or ethnicity is
inherently racist or evil. In addition, all agencies should begin to identify all available
avenues within the law to cancel any such contracts and/or to divert Federal dollars away
from these un-American propaganda training sessions. (Vought, 2020, p.1)
Research has revealed that diversity programs can undermine a corporation’s efforts to mitigate
discrimination rather than support diversity (Kaiser et al., 2013). Following the death of George
Floyd (Fitz-Gibbon, 2020), several well-known companies publicly announced how they would
2

support anti-racism efforts in their corporations (Hessekiel, 2020). Though the intent is meant as
a positive step to promote change regarding racial disparities, corporations’ efforts might also
stimulate resistance among White American employees. The resistance then contributes to lower
levels of racial diversity in the workplace (Plaut et al., 2011).
White privilege and systemic racism have disproportionately impacted Black,
Indigenous, and people of color (BIPOC). Following the death of George Floyd in May 2020
(Fitz-Gibbon, 2020) and the nationwide protests that ensued in an uprising for racial justice and
equity, more Americans are educating themselves about racism in the United States. The purpose
of the study was to determine the perceptions of White employees towards corporate policy and
corporate support of diversity and anti-racism following events that occurred since May 2020.
The study also sought methods that could be developed to mitigate perceptions that impede the
efforts for diversity, particularly if the employees do not support diversifying the workforce. A
research question anchored this study: How do U.S. citizens and residents who are employees of
technology companies perceive their corporations’ activities to address racial injustice?
Context and Background of the Field Study
Employment discrimination based on race, color, religion, sex (including sexual
orientation and gender identity), and national origin is illegal according to Title VII of the Civil
Rights Act of 1964 (Bostock v. Clayton County, 2020; Hersch & Shinall, 2015). Equal
opportunity laws made discrimination illegal, but that fact was not the sole motivating factor for
creating diverse corporate environments. The benefits of diversity in corporations are tied to
increased profits, higher innovation, amplified creativity, healthier relations between leaders and
employees, and a better overall corporate reputation (Andrevski et al., 2014; Bear et al., 2010;
Eagly & Chin, 2010; Herring, 2017; Miller & Triana, 2009; Randolph-Seng et al., 2016). Thus,
3

interest convergence exists between corporations and people who are minoritized (Bell, 1980).
Clearly, increasing employment of people from underrepresented groups aligns with the interests
of corporate executives, who are predominately White (Bell, 1980; Delgado, 2015; Farnsworth
& Holtzblatt, 2016). As corporations recognize that increasing diversity is in a company’s best
interest, statements supporting a diversified workplace have become commonplace on U.S.
corporate websites. While not discounting the significance of publicly displayed corporate
messages that support diversity and anti-racism, simply publicly declaring support does not
directly address the barriers BIPOC faced due to internal corporate policies and procedures (Bell,
2003).
Guided by good intentions as well as legal obligations, corporations have implemented
diversity training programs. However, research reveals that messages promoting
multiculturalism through diversity programs elevate anxiety, fear, and anger among increasing
numbers of White Americans. Negative emotions are triggered, as many White Americans
interpret supportive diversity messages to mean that they, as Whites, are not as highly valued in
the workplace as non-Whites. The inference of being less appreciated than members of minority
groups can also lead White people to believe that they will become members of the
disadvantaged group (Dover et al., 2016; Outten et al., 2012). Fear and anger increase towards
BIPOC as White people feel in danger of losing political, economic, and social power (Dover et
al., 2016; Outten et al., 2012).
Importance of the Study
Companies want diverse and inclusive workplaces, and employees want equal
opportunities. However, if negative reactions among White American employees in response to
diversity efforts do exist, progress towards anti-racism, equity, and diversity in the workplace
4

could be at risk. Disparities at work could continue to exist in terms of hiring, career
advancement, and overall career satisfaction.
According to Farnsworth and Holtzblatt (2016), minoritized groups are significantly less
represented in the high-tech industry when compared to all industries in the U.S. private sector.
This finding is true despite existing strategies within tech companies to increase diversity.
Therefore, it is important to understand how dominant racial group members support the efforts
to increase diversity and if their support is critical for success.
Overview of Theoretical Framework and Methodology
Intergroup threat theory focuses on realistic threats (threats to the ingroup’s economic
and political power, resources, safety, or well-being) and symbolic threats (threats to the
ingroup’s values, culture, or worldview) (Stephan et al., 2009). Intergroup threat theory
emphasizes that realistic and symbolic threats do not need to be factual or correct to affect an
ingroup’s attitude. This theory offers a model that encompasses a comprehensive range of
potential threats a group may experience and provides parameters to examine how negative
perceptions about an outgroup can elicit adverse psychological consequences, which can result in
destructive intergroup relations (Riek et al., 2010; Rios et al., 2018; Stephan et al., 2015).
A mixed-methods approach to research combines elements of qualitative and quantitative
research approaches (Creswell & Creswell, 2018; Merriam & Tisdell, 2016). For this study, a
questionnaire was designed to obtain information about participants’ thoughts, perceptions, and
behavioral intentions. Additionally, participants were recruited for an interview intended to
uncover additional insights (Johnson & Christensen, 2015; Merriam & Tisdell, 2016). A basic
mixed-methods approach is suited to examine the efforts to promote diversity at technology
5

corporations because it allows relationships to be discovered between two sources of data
(Merriam & Tisdell, 2016).
Definitions
The following concepts are included in this study:
• Affirmative Action Policies Including Corporate Diversity and Inclusion Initiatives:
Policies or programs that seek to correct the effects of discrimination and racism through
active measures (American Association for Access, Equity and Diversity, n.d.).
• Anti-Racist: Conscious and deliberate behavior that works to reverse disparities caused
by racism (Tatum, 1992).
• Anti-White Bias: The belief that White people are oppressed or harmed by policies meant
to rectify past and current racial harms against minoritized groups (Wilkins & Kaiser,
2014).
• Colorblindness: A philosophy of ignoring racial and ethnic differences with the belief
that doing so fosters equality and is necessary to prevent discrimination (Glaser, 2005).
• Critical Multiculturalism: Structural analysis of unequal power and actively challenging
injustice (May, 2012).
• Discrimination: Actions based on unconscious or conscious prejudice (Richardson,
2017).
• Gaslighting: Psychological manipulation in which a person or group covertly propagates
skepticism and doubt into another or others, making them question their own perceptions
of reality, memory, or judgment, and getting them to believe what the manipulator claims
to be true instead (Fuchsman, 2019).
6

• Meritocracy: A social system by which status and advancement are obtained through a
system of rewards based on an individual’s merit and effort (Kluegel & Smith, 1986).
• Minoritized: “Groups that are different in race, religious creed, nation of origin, sexuality,
and gender and as a result of social constructs have less power or representation
compared to other members or groups in society” (Smith, 2019).
• Multiculturalism: Equality, respect, and acceptance of distinct cultures or ethnic groups
(Lexico Dictionaries, n.d.).
• Race: A socially constructed means of identifying people (Omi & Winant, 1986).
• Racism: A system of advantage based on race (Tatum, 1992).
• Status-Legitimizing Beliefs (also System Justifying Beliefs): Legitimizing myths, beliefs,
or opinions that collectively serve to justify and rationalize a status system by making it
appear fair and legitimate (Jost, 2018).
Organization of the Dissertation
Five chapters were used to organize this study. The first chapter introduces the dilemma
regarding how messages that support diversity can undermine efforts to mitigate discrimination
in the workplace. Chapter Two reviews the literature regarding the issue of diversity in the
workplace. Negative reactions of employees and issues that appear to impede progress are
explored. Chapter Three will provide the methods by which participants’ reactions to diversity
were examined. It includes information about how participants were chosen and how materials
were distributed and gathered. Chapter Four will demonstrate how participants’ responses were
analyzed, and the study’s final results will be given. Chapter Five will provide a detailed
summary of the results and recommendations.

7

Chapter Two: Literature Review
The civil rights movement was a struggle for social justice in the United States. Before
Title VII of The Civil Rights Act of 1964 was signed into law, employers could discriminate
against people for any reason. Businesses could choose not to hire someone, turn down an
employee for a promotion, or discriminate against people in some other way because of the
person’s race, religion, sex, or national origin, and it would all be legal (Arnesen, 2006).
Employment laws and policies, such as Affirmative Action, adopted through the Civil Rights
Act, began to address decades of struggle caused in part by job discrimination (Arnesen, 2006).
Obstacles and agitation felt by some people against policies like Affirmative Action exist
today, and opponents of affirmative action policies state that there is no longer a need for
programs designed to remedy racial inequality because racism no longer exists at the same levels
as it had in the past (Kaiser et al., 2009; Wilkins et al., 2017). However, the Civil Rights Act has
been in existence for over 50 years, and BIPOC continue to be underrepresented in sectors such
as high-tech (Farnsworth & Holtzblatt, 2016). The intent of the Civil Rights Act was to prevent
future discrimination. It did not dismantle the structures already in place that were born through
racist ideas and intentions. Thus, these systems essentially remained intact. Diversity training
and affirmative action policies have been met with resistance and resentment by some White
people. In addition, the research has demonstrated that racial progress has contributed to
perceptions of increased discrimination against Whites among some White Americans (Wilkins
& Kaiser, 2014; Wilkins et al., 2017).
This literature review discusses reactions that increasing numbers of White Americans
have about the demographic changes expected to occur in the United States. It illustrates the
impact that endorsing diversity can have in workplace settings and offers evidence indicating
8

how multicultural messages contribute to elevated anxiety levels in a growing number of White
people. Ideologies and policies that cultivate anti-White bias and differences between how
members of different racial groups perceive discrimination and diversity are highlighted. Recent
examples of perceived anti-White bias will be presented, and combined Equal Employment
Opportunity reports (EEO-1) that reveal employee racial demographics among the top 10
Fortune 500 U.S. technology corporations will be examined. References to White people in this
study are meant to reflect how White people were depicted in research and are not meant to
essentialize people who are White.
Psychological Response to Demographic Shifts in the United States
Increasing numbers of White people experience a heightened sense of anti-White bias as
the psychological response to demographic shifts in the United States. Although Whites are
predicted to be the majority in terms of wealth and power for decades to come, a growing
number of White people hold the conviction that racial progress will lead to a loss of White
Americans’ dominant positions in society (Asante-Muhammed et al., 2016; Phillips & Lowery,
2015; U.S. Census Bureau, 2014, 2015; Vespa et al., 2020). Additionally, Craig and Richeson
(2014) analyzed responses from White participants and found that prominently presenting
information that Whites will no longer be the majority by 2042 led to Whites revealing explicit
pro-White or antiminority sentiment.
Perceptions of group dominance and group advantage are often determined by group size,
so it is conceivable that some White people view population increases of minority groups as a
threat to the economic and political well-being of White people (Craig & Richeson, 2014;
Stephan, Ybarra, & Rios, 2015). Craig and Richeson (2014) conducted two studies to discover
how White participants would react after being presented with information showing that Whites
9

would no longer be the majority by 2042. In one study, the researchers used an online
crowdsourcing marketplace to recruit participants. Eighty-six White U.S. participants from 25
states were randomly assigned to read either an article that discussed U.S. Census Bureau racial
and ethnic projections by 2042 or 2010 U.S. population estimates by race and ethnicity. In the
2042 article, the researchers highlighted that non-Whites were destined to outnumber Whites. In
the 2010 article, only the current demographics were presented without indicating projected
population changes. After each participant read their assigned article, each was asked to
complete a survey to measure their racial bias. The researchers found that the participants who
read about the future decline of the White population in 2042 expressed more racial bias than
participants who read about U.S. racial demographics in 2010 (Craig & Richeson, 2014). Thus,
the study’s results suggested that the future U.S. projections (whereby non-White Americans
outnumbered White Americans) were threatening to some White participants and resulted in
negative attitudes.
In another study, Craig and Richeson (2014) sought to understand if the attitudes of
White people varied across different racial groups (Hispanics/Latinx, Blacks, and Asians). The
researchers partnered with Time-Sharing Experiments for the Social Sciences program and
Knowledge Networks to collect data from a nationally representative participant panel. Craig and
Richeson were only interested in the viewpoints of White people. So, from the original data pool
exceeding 900 participants, the researchers selected a subpopulation of 415 White participants
(212 women and 188 men). Half of the participants were assigned to read about the 2042 U.S.
racial demographic projections showing a decrease in the White population. The other half was
assigned to read a neutral article about the rate at which people changed residences within a year
in the United States. Following the reading assignments, each participant was asked to respond to
10

statements designed to measure their racial bias and indicate how warm or positive they felt
about Black, Hispanic/Latinx, Asian, and White people. Like previous studies, the researchers
found that the participants who read the U.S. 2042 racial population projections expressed more
racial bias. However, in addition to this finding, the new study demonstrated that the White
participants felt differently towards different races. Whites reported feeling the most positivity
toward Whites, followed by Asians, then Blacks. Whites reported feeling the least positivity
toward Hispanics/Latinx (Craig & Richeson, 2014). The researchers speculated that these
findings could serve to forewarn that racial bias and intolerance against non-Whites are likely to
increase as the population of Whites decreases in the United States.
The expression “All American” was created in the 1880s as a sports term associated with
American football (Norris, 2011). Decades later, during the 1930s, qualities associated with “All
American” evolved to include traits such as hard-working, resourceful, quick-thinking,
adventurous, and White (Danbold & Huo, 2015; Norris, 2011). Thus, the term recognized the
dominance of the White race in America while conveying a sense of White American pride. As
demographic shifts in the United States have continued since the 1930s, White people have
expressed that their group’s distinctiveness and acclaim as the prototypical American was
threatened (Danbold & Huo, 2015). Research has demonstrated that White Americans, indeed,
tend to become disturbed when considering how changing demographics might affect their
status. Outten et al. (2012) conducted an investigation and analyzed the responses of 209 White
U.S. college students at Purdue University (average age 20.3 years) to assess their reactions
about a future where their racial ingroup was no longer the numerical majority. The study found
that U.S. college students felt angrier and more fearful when considering themselves as the future
numerical minority.
11

Research has further shown that regular exposure to information about White Americans’
declining population led to decreased support for multiculturalism (Danbold & Huo, 2015; Yang
et al., 2015). Resistance to multiculturalism and support for assimilation from White people who
considered their ingroup to exemplify American qualities, values, and characteristics has been
further generated by [ongoing exposure] to the changing U.S. racial demographics (Craig &
Richeson, 2014; Danbold & Huo, 2015; Yang et al., 2015). Additionally, research has revealed
that increased intergroup bias has also emerged due to messages that promoted diversity in the
workplace (Dover et al., 2016).
Sources That Contribute to Anti-White Bias Beliefs
Strategies that aim to balance and improve the racial inequities of underrepresented
groups contribute to the animosity that White people feel towards BIPOC. Norton and Sommers
(2011) found that some White people view racism as a zero-sum game whereby progress towards
equality for Black people means increased inequality for Whites. The researchers recruited
participants through an online survey research company. Out of a pool of 2.5 million, 417
panelists were randomly selected for participation in the study, and they were each paid $5 for
their participation. The participant pool of 209 White people and 208 Black people matched the
demographic make-up of the 2000 U.S. census on age, gender, and educational level.
Participants were asked to indicate how much they thought Blacks were the victims of
discrimination in the United States in each decade from 1950 through the 2000s. They were also
asked the same question about Whites. From this study, Norton and Sommers found that all
participants perceived a decrease in anti-Black bias over several decades. However, Whites
perceived a link to the decrease in anti-Black bias with an increase in inequality for Whites
12

(zero-sum game), while Black respondents reported that anti-White bias has been relatively
absent over time. Figure 1 illustrates the perceptions shared by the respondents.

Figure 1
Respondents Perceptions of Anti-White and Anti-Black Bias Over Six Decades

Note. Using a 10-point scale (1 not at all; 10 very much), 209 White Americans and 208 Black
Americans were asked the extent to which they felt both Whites and Blacks were the target of
discrimination in each decade from the 1950s to the 2000s. From “Whites See Racism as a Zero-
Sum Game That They Are Now Losing,” by M. I. Norton and S. R. Sommers, 2011,
Perspectives on Psychological Science, 6(3), 215–218. doi:10.1177/1745691611406922

13

As noted in Figure 1, Whites responded to gains for one racial group as if they were
equivalent to losses for their own group (a zero-sum game interpretation). According to Dover et
al. (2015), beliefs of discrimination against Whites led to anger, anxiety, and a sense of injustice.
As Whites perceived that the U.S. had made significant progress toward racial equality, they
expressed that racial equality not only came at their expense but that they had become the target
of discrimination (Wilkins & Kaiser, 2014; Wilkins et al., 2015). Although researchers did not
definitively establish why there were increasing perceptions of racial victimization among
Whites, they suggested that the increase in minority populations, outward support for
multicultural movements, and racial progress were threatening and contributed to greater
perceptions of anti-White bias (Craig & Richeson, 2017; Dover et al., 2016; Outten et al., 2012;
Wilkins & Kaiser, 2014; Wilkins et al., 2015).
Corporations understand the positive impact of a racially diverse workforce, and virtually
every major U.S. corporation has integrated diversity management programs. Research has
shown that racial diversity within an organization is important because diversity improves the
organization’s overall performance (Fombrun & Gardberg, 2000; Sharma et al., 2020). Diversity
programs often include targeted recruitment and hiring strategies for practical, legal, and ethical
reasons (Lynch, 2017; Newman & Lyon, 2009). However, positive diversity statements in
corporate recruitment advertisements may inadvertently support concerns from White people
about anti-White bias. Dover et al. (2016) compared the responses of 644 participants in a study
that examined the effects of corporate recruitment materials that had supportive diversity
messages embedded within them. The study showed that Whites saw companies with positive
diversity messages in their recruitment materials as more likely to discriminate against Whites
despite the lack of information to support such perceptions.
14

Status-Legitimizing Beliefs Influence White American Attitudes
In the United States, differences in social and economic status are linked to racial group
membership, with minority group members being less well off (Glaser, 2005; Tyler, 2006;
Wellman et al., 2016). Status-legitimizing ideologies and beliefs reconcile these differences by
rationalizing inequality (Chow et al., 2013). Thus, race-based status hierarchical discrepancies
are legitimized (Chow et al., 2013; Glaser, 2005; Tyler, 2006).
The perception of anti-White bias in the workplace cannot be managed without
addressing the status-legitimizing beliefs (SLB) held by White Americans. SLB includes
viewpoints that support the idea that an individual’s actions solely determine the individual’s
position in society (Craig & Richeson, 2017; Wellman et al., 2016;). It is important to note that
for White Americans, the perceptions of anti-White bias can be triggered merely by exposing
status legitimizing statements to them (Wellman et al., 2016). Wellman et al. (2016) sought to
understand how SLB were related to anti-White bias, zero-sum game, and views on Affirmative
Action. The researchers examined responses from 123 White participants recruited through an
online crowdsourcing marketplace. Participants were randomly assigned to either an SLB prime
group or a control group. Each group received 20 sets of words and was told to create phrases
using these words. The SLB prime group received word sets that only allowed them to construct
phrases that supported status-legitimizing viewpoints. Thus, they were purposely primed to
become familiar with status-legitimizing statements. The control group received word sets that
were benign to status legitimizing viewpoints. The SLB prime group constructed phrases such as
“effort leads to prosperity” and “life is usually fair,” while the control group created phrases such
as “she likes fluffy cats” and “books open new worlds” (Wellman et al., 2016).
15

After the priming activity, participants engaged in the second part of the study.
Participants were asked to rate their agreement with statements that represented anti-White bias,
zero-sum game, and Affirmative Action. Wellman et al. (2016) found that participants who were
part of the SLB prime group perceived more anti-White bias, endorsed zero-sum beliefs to a
greater extent, and indicated less support for Affirmative Action after being primed. Therefore,
the study demonstrated that SLB tended to influence perceptions of anti-White bias for White
Americans.
If it is possible to influence perceptions of anti-White bias and zero-sum game reactions
through SLB, then one might ask if it is also possible to increase racial tolerance and support for
diversity through pro-tolerance and anti-injustice messages. If White people are informed about
the lack of evidence or the assumptive nature of beliefs that generate fear and anxiety, perhaps
new messages and substantiated information would work to penetrate underlying negative
reactions. Consequently, White people may become better prepared to recognize irrational
beliefs that contribute to racism and therefore be more willing to endorse diversity.
Colorblindness Principle
Colorblindness is an example of an SLB that influences some White American attitudes
about racial diversity (Glaser, 2005; Wilkins et al., 2013). The colorblindness principle
rationalizes the existing status hierarchy as legitimate because it presents that people are treated
equally irrespective of race (Glaser, 2005). Common claims recited by people who hold
colorblind racial attitudes are, “we treat everyone the same regardless of race,” “all lives matter,”
and “we hire the best person for the job” (Tawa et al., 2016). Thus, advocates of colorblind
philosophy believe that racial categories to ensure equal employment opportunities ought to be
ignored (Glaser, 2005). Further, advocates believe that ignoring race is necessary to prevent
16

discrimination (Glaser, 2005; Richeson & Nussbaum, 2004; Ryan et al., 2007). However,
perspectives that incorporate colorblind principles ignore the deeply entrenched systemic racism
that supports inequity for BIPOC.
Opponents of colorblindness cite research that has shown that adopting a colorblind
mindset leads to greater racial bias, making colorblind policies ill-advised in the workplace
(Glaser, 2005; Richeson & Nussbaum, 2004; Ryan et al., 2007). Disregarding a person’s race
undermines the cultural heritage of non-White people and may facilitate intolerance (Levy &
Hughes, 2009; Richeson & Nussbaum, 2004). Additionally, studies have shown that categorizing
people by race happens rapidly, implicitly, and effortlessly, so achieving a colorblind society is
unlikely because race cannot be ignored (Ito & Urland, 2003). Facts found in the U.S. Bureau of
Labor Statistics, 2018, show that race is related to socioeconomic status; therefore, race affects
people’s lives. For example, Black men earn 73.1% of the earnings of White men, and Black
women earn 86.1% of that of White women (Plaut, 2010). In the United States, minoritized
groups have lower incomes, experience longer durations of unemployment, have lower
educational attainment, and hold fewer managerial positions than Whites and Asians (American
Psychological Association, 2017; Noël, 2018).
Although psychological research contradicts colorblind ideology, colorblindness remains
a principle held by people who hold SLB (Glaser, 2005; Richeson & Nussbaum, 2004). Instead
of acknowledging and valuing differences, colorblindness ignores the existing social systems that
support inequity. Colorblindness justifies the status quo without recognizing personal bias and
the inequity that already exists. Thus, colorblindness is an SLB that allows the perpetuation of
the existing status hierarchy without addressing underlying issues of racism.
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The Myth of Meritocracy
In the United States, a system of meritocracy means that individuals achieve status in
society through merit and hard work (McCoy & Major, 2007; Newman et al., 2015). People are
impartially rewarded for their skills and initiative (Newman et al., 2015). In other words, people
with higher status have demonstrated that they are more talented and work harder than people
who have lower status (McCoy & Major, 2007). Meritocratic ideology seems fair because
meritocracy asserts that everyone has an equal opportunity to succeed. The reality is that in the
United States, many Americans have not reached high status meritoriously. Instead, they
achieved high status because they were born into privilege, had access to social, political,
educational, and economic resources that were not available to all people, or they simply got
lucky (Imbroscio, 2016). Additionally, social and economic systems that perpetuate racial
injustice and give racial advantages to White people remain in place. Such systems maintain
disadvantages for minority groups (Bonilla-Silva, 2001; Bonilla-Silva et al., 2015). Thus,
meritocracy in the United States is a myth.
Status-legitimizing beliefs (SLB) validate meritocracies and make systems appear fair
and legitimate (McCoy et al., 2013). Merit-based employee reward systems are examples of
meritocratically designed approaches to employee performance evaluation practices (Castilla,
2008). While some corporations implemented merit-based employee rewards believing that these
systems would eliminate inequity, research has found that meritocratically designed performance
systems mask inequality caused by performance evaluation bias (Castilla, 2008; Elvira & Town,
2001).
Corporate recruitment procedures also promote meritocracy. People tend to perceive
hiring based on a candidate’s potential to accomplish job tasks to be meritocratic and fair
18

(Walton et al., 2013). However, the dynamics that occur interpersonally between job
interviewers and candidates strongly influence hiring decisions (Kandola, 2004; Rivera, 2012).
According to Rivera (2012), subjective impressions of candidates are formed based on their
personal tastes and style, hobbies, and other interests and often carry more weight than the
candidate’s job qualifications. It is also not unusual for interviewers to consciously or
unconsciously look for and favor applicants who show signs of sharing cultural, racial, and
ethnic similarities during the interviews. The notion of determining a candidate’s aptitude,
capability to perform, and cultural compatibility is often thought of as ascertaining a candidate’s
fit (Rivera, 2012). Therefore, hiring for fit often translates to hiring recommendations based on
how similar or familiar a candidate is to their interviewers (Grensing-Pophal, 2017).
Researchers found that redefining “fit” to incorporate considerations about how
candidates could help to accomplish an organization’s overall mission is one way to alter the
behavior of seeking candidates who fit the status quo (Gaspar & Brown, 2015; Hunt et al., 2018).
Gaspar and Brown (2015) recommended including values-based criteria for evaluating
candidates. Human resource (HR) departments could include a way of evaluating the alignment
between corporate values and a candidate’s values (Gaspar & Brown, 2015). Additionally,
determining fit could incorporate considerations about how a candidate would add to a
corporation’s overall diversity (Gaspar & Brown, 2015).
White Americans who believe that anyone can improve their social status through hard
work, motivation, and talent (Wellman et al., 2016; Wilkins et al., 2013) are less likely to support
Affirmative Action or corporate diversity and inclusion programs because meritocracy assumes
that race, gender, or sexual orientation are not factors that contribute to achievement (Glaser,
2005; Wellman et al., 2016). Those who subscribe to meritocratic ideology do not recognize that
19

racism and oppression are obstacles that can limit the levels of achievement that minoritized
groups can obtain (Augoustinos et al., 2005; Kwate & Meyer, 2010). Thus, the actions taken by
people to abort support for Affirmative Action policies are examples of how meritocracies are
upheld (Kwate & Meyer, 2010).
Achieving Workplace Diversity Means Different Things
Black people and White people have different views about how Black people are treated
in various settings (Horowitz et al., 2019). According to a 2019 Pew Research study, 44% of
White people say that Black people are treated less equitably than Whites in hiring, pay, and
promotional situations compared with 82% of Blacks who shared that sentiment (Horowitz et al.,
2019). So, it is not surprising that White people and minoritized people define diversity in the
workplace to mean different things as well (Unzueta & Binning, 2012).
Unzueta and Binning (2012) looked at the responses from self-identified, monoracial
Asian Americans (82 participants), African Americans (161 participants), and White Americans
(290 participants). They found that non-Whites placed importance on numerical and hierarchical
representation of diversity to define diverseness. Conversely, participants who identified as
White defined diversity as only a numerical representation of BIPOC without considering the
hierarchical roles that BIPOC played. Thus, having diversity in leadership was equally important
to having workforce diversity for members of minoritized groups.
Subtle Acts of Discrimination in the Workplace
Increasing the racial diversity in leadership positions could help reduce the occurrence of
discrimination whereby workers feel isolated, targeted, or what authors Caver and Livers (2002)
describe as miasma. Miasma, in this context, refers to what many BIPOC experience when they
find that they need to tolerate added burdens and exert extra effort to maintain employment for
20

reasons that do not relate to their occupation (Caver & Livers, 2002). When working in a world
where non-Whites are judged based primarily on race, non-Whites persevere by finding ways to
cope with inequity and prejudice. As some BIPOC have experienced, “as a Black person in
White America, you’ve got to work twice as hard to get half as far” (DeSante, 2013, p. 342).
DeSante (2013) discussed how principled objections to racialized policies such as
Affirmative Action and welfare were related to racial resentment and modern racism. The
researcher conducted a study to determine whether Black people and White people were treated
equally for excellent work (DeSante, 2013). In an experiment that contained the responses of 931
participants (751 people who presented as White, 96 who presented as Black, and 84 who
presented as Hispanic,) the participants were asked to allocate $1,500 to two applicants for state
assistance. Each state aid applicant was said to need $900, and each research participant had the
option to also allocate some or all of the $1,500 to offset the state’s budget deficit. The
researcher leveraged four names (Laurie, Emily, Latoya, and Keisha) shown to strongly signal
racial identities from a study conducted by Bertrand and Mullainathan (2004). DeSante (2013)
also assigned worker quality assessments to the state aid applications that the participants would
review. Thus, the research participants were given state aid applications that contained one of the
following assessed worker quality conditions: no work history given, excellent worker (hard-
working), or poor worker (lazy). The experiment was designed to expose the participants to
subtle psychological cues that suggested the applicants’ race based on their names while also
including objective measures of the applicants’ work ethic (DeSante, 2013).
Through this study, DeSante (2013) found that state aid applicants who appeared to have
White names were rewarded with greater amounts of state assistance than those who appeared to
have Black names even when the worker quality assessments were the same. Also, Black aid
21

applicants were punished more than White aid applicants for the same level of perceived
laziness. In other words, the researcher found that “hard-working Blacks will be rewarded less,
and lazy Blacks would be punished more than their White counterparts, all else being equal”
(DeSante, 2013, p. 349).
DeSante’s (2013) study confirmed that prejudice affected White people’s behavior
towards Blacks. Not considered in the study was what might have happened if White people
became aware of their behavior. In its own way, one could say that the Black Lives Matter
(BLM) movement is an extension of studies such as DeSante’s in that BLM messages revealed
how racism has affected the lives of Black people in the United States. In response to undeniable
truths about discrimination, many White people became allies who stood in solidarity with Black
people. Therefore, it is conceivable that interventions designed to raise consciousness and reduce
discriminatory behavior through facts could mitigate anti-Black bias among White people
without also raising feelings of fear and threat.
Discriminatory acts can be hard to identify unless those acts are overtly offensive.
Blatant discrimination may be less prevalent in the workplace, but inconspicuous forms of
mistreatment continue to be experienced disproportionately by minority group members. Subtle
acts of discrimination encourage a miasma of despair and stress in the workplace for many Black
Americans (Caver & Livers, 2002; Deitch et al., 2003). Less overt forms of discrimination by
White co-workers include avoidance of BIPOC, insufficient and unfriendly communication
efforts, and failures to assist co-workers who are BIPOC (Deitch et al., 2003). Deitch et al.
(2003) used secondary data (initially presented by Ehrlich and Larcom in 1993) to investigate the
existence of subtle acts of discrimination and mistreatment of Blacks and their impact on job
satisfaction. The researchers examined information collected from personal interviews of 314
22

Black and White first-line employees at a U.S. corporation. The average age of the respondents
was 37, and 79.6% of the interviewees were White. The dataset assessed various forms of
mistreatment at work and assessed the frequency of these forms of mistreatment. Incidences of
mistreatment that participants were asked to rank in the study included, “Set you up for failure,”
“Gave others privileges you didn’t get,” “Treated you as if you didn’t exist,” “Damaged your
personal property,” or “Made insulting jokes or comments” (Deitch et al., 2003). Through the
examination of the dataset, Deitch et al. (2003) found that Blacks perceived more mistreatment
on the job than Whites, Blacks felt lower levels of workplace well-being, and being a member of
the Black race was significantly related to mistreatment in the workplace.
Additional examples of covert acts of discrimination at the workplace involve overly
harsh criticisms, exclusionary actions, and failures to recognize the promotability of individuals.
Covert acts have contributed to regular occurrences of discrimination because many people who
did not define themselves as racist failed to recognize their own actions of discrimination (Deitch
et al., 2003; Ikuenobe, 2011). Since many people defined racism as a class of actions that were
aggressive or violent, acts of racism that were subtle and done without conscious intent were
more likely to occur and went unnoticed by racial groups who were not the targets of those
assaults (Deitch et al., 2003; Ikuenobe, 2011). Thus, for members of minority groups, strategic
responses to coping with tension, distrust, and uncertainty at work interfered with feelings of
safety and belonging.
Like any other part of society, U.S. corporations are not immune to subtle acts of
discrimination by their employees. So, it can be assumed that discrimination within corporations
has occurred and remains unaddressed. As long as unrecognized discriminatory acts continue to
occur, they will undermine corporate missions and activities that promote diversity.
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Perception of Anti-White Bias in the Workplace
The Civil Rights Act of 1964 was put in place to level the playing field by making racial
discrimination illegal. Affirmative Action laws were then enacted to proactively remedy past
inequalities due to discrimination. Attitudes that stem from ideas that discrimination is not an
issue in present-day society can result in banning policies that were put in place to provide
equality of opportunity (Glaser, 2005; Myers, 2007). Those who oppose measures that promote
equality say that new laws and policies are not required because equality already exists.
However, Whites have used anti-discrimination laws to address perceptions of anti-White bias in
the workplace.
An incident involving former Google engineer, James Damore, presents as an example of
perceived anti-White bias. In a memo dated July 2017, Damore wrote inflammatory statements
criticizing diversity programs at Google and selected scientific evidence as a way to sway his
readers and confirm his perceptions (see Appendix A). Damore presented his belief that male
and female biological disparities made them differently suited for employee positions at Google,
and he also commented that both race and gender should be downplayed (Damore & Gudeman v.
Google, 2018; Gordils, 2018). After being fired from his position, Damore filed a lawsuit against
Google in 2017, alleging that the company discriminated against conservative White men
(Damore & Gudeman v. Google, 2018; Domonoske, 2018):
Google employees who expressed views deviating from the majority view at Google on
political subjects raised in the workplace and relevant to Google’s employment policies
and its business, such as “diversity” hiring policies, “bias sensitivity,” or “social justice,”
were/are singled out, mistreated, and systematically punished and terminated from
Google, in violation of their legal rights…Damore, Gudeman, and other class members
24

were ostracized, belittled, and punished for their heterodox political views, and for the
added sin of their birth circumstances of being Caucasians and/or males. This is the
essence of discrimination—Google formed opinions about and then treated Plaintiffs not
based on their individual merits, but rather on their membership in groups with assumed
characteristics. (Damore & Gudeman v. Google, 2018)
In 2020, a judge with the Superior Court in Santa Clara, CA, wrote that the matter was dismissed
in its entirety (Damore et al. v. Google, 2020). Details of the agreement between Google and
Damore to dismiss the case were not disclosed.
A lawsuit filed against Starbucks Coffee Company presents another example of perceived
anti-White bias. Shannon Phillips was a former Starbucks regional director who oversaw stores
in the Philadelphia region, where the highly publicized arrest of two Black men took place in
April 2018. Phillips was terminated from Starbucks on May 9, 2018. Following her termination,
she filed a complaint with the Equal Employment Opportunity Commission (EEOC) in May
2018, accusing Starbucks of racial discrimination. In July 2019, the EEOC dismissed Phillips’s
complaint, citing that,
Based upon its investigation, the EEOC is unable to conclude that the information
obtained establishes violations of the statutes. This does not certify that the respondent is
in compliance with the statutes. No finding is made as to any other issues that might be
construed as having been raised by this charge (p. 22).
The EEOC provided Phillips with a notice of her rights to sue Starbucks. Phillips filed a
lawsuit in Oct 2019. In court documents, Phillips stated, “I was terminated because I am White.
If I was Black, I would not have been terminated. I was terminated because I complained of and
objected to race discrimination” (Phillips v. Starbucks Corporation, 2019, p. 19). According to
25

the lawsuit, Phillips believed that her firing was done as an attempt to appease protestors and
community demonstrators (Phillips v. Starbucks Corporation, 2019). In response to Phillips’
complaint in March 2020, Starbucks presented, “All actions taken by Starbucks with respect to Ms.
Phillips were for legitimate, non-discriminatory, non-retaliatory reasons,” and,
Starbucks specifically denies that it engaged in any conduct in violation of Title VII of
the Civil Rights Act of 1964 (“Title VII”), 42 U.S.C. § 1981 (“Section 1981”), the New
Jersey Law Against Discrimination (“NJLAD”), or any other law, and that Ms. Phillips is
entitled to damages of any kind (Phillips v. Starbucks Corporation, 2020, p. 1).
As of January 27, 2021, the lawsuit was still pending. If the case is not settled by October 8,
2021, it will be listed for trial on October 29, 2021 (Phillips v. Starbucks Corporation, 2020).
Leslie Chislett, a former executive director of advanced academic access in the office of
equity and access, filed a lawsuit against the New York City Department of Education (DOE)
and the chancellor of the DOE. Chislett accused the chancellor and the DOE of “targeting
Caucasian employees on the basis of race and diminishing the roles of Caucasian employees at
DOE like Chislett, oftentimes to the benefit of less qualifies non-Caucasian employees.”
(Chislett v. New York City Department of Education and Richard Carranza, Chancellor of New
York City Department of Education, 2019, p. 4). In reaction to Chislett’s lawsuit, in his editorial,
education reporter Michael Elsen-Rooney reported that “sources in the Education Department
vehemently disputed Chislett’s account, alleging she was an inept and, at times, an outright racist
manager” (Elsen-Rooney, 2019, para 3). The lawsuit was pending as of this writing.
As industries work to incorporate policies that promote racial diversity, including zero-
tolerance against discriminatory attitudes, corporations may continue to face lawsuits that claim
discrimination against people who are White. Claims such as these are sometimes referred to as
reverse discrimination and regarded as discrimination claims under statutes of the Civil Rights
26

Act of 1964. As corporations continue to be outspoken about anti-racism, they will need to
balance their promotion of diversity with efforts to address feelings of anti-White bias and
reverse discrimination.
Racial Diversity in Technology Corporations
The Civil Rights Act, Affirmative Action, and corporate diversity training programs have
helped increase the hiring of applicants from minoritized groups and reduce bias in the
workplace (Kalev et al., 2016). However, empirical evidence that demonstrates correlations
between specific diversity programs in corporate organizations and the presence or absence of
racial diversity is essentially non-existent. What can be inferred based on U.S. Equal
Employment Opportunity reports is that minoritized groups continue to be underrepresented in
major U.S. economic sectors that fuel the U.S. economy (Farnsworth & Holtzblatt, 2016).
The analysis contained in the May 2016 U.S. EEOC report and individual equal
employment opportunity survey reports (EEO-1) revealed that racial diversity in the largest
North American high-tech companies remains low (Farnsworth & Holtzblatt, 2016). The
federally mandated EEO-1 reports contain corporate employment data categorized by
race/ethnicity, gender, and job category. EEO-1 reports from seven of the 10 largest United
States-based technology companies in the world listed by Forbes (2019) highlighted that
underrepresented minorities held disproportionately fewer positions in high-tech corporations
than Whites and Asians, especially in executive-level, mid-level, and first-level management
(Alphabet Inc., 2018; Apple Inc., 2018; Cisco System Inc., 2018; Facebook Inc., 2018;
Farnsworth & Holtzblatt, 2016; Intel Corp., 2018; Microsoft Corporation, 2018; Oracle America
Inc., 2018; Ponciano, 2019). Figure 2 shows the demographic information in terms of
27

employees’ race based on EEO-1 reports from Alphabet (parent company of Google,) Apple,
Microsoft, Cisco, Facebook, Intel, and Oracle (Appendix B).

Figure 2
High-Tech Corporate Employment Data for All Job Categories

Note. U.S. high-tech companies had a relatively larger share of Whites employees and a larger
share of Asian employees. Other racial groups were less represented by a significant margin in
the tech sector (Farnsworth & Holtzblatt, 2016). Underrepresented racial minority group
categories listed in EEO-1 reports are “Hispanic or Latino, Black or African American, Native
Hawaiian or Pacific Islander, American Indian or Alaska Native, and Two or More Races (Not
Hispanic or Latino).” All terms that reference race, such as Hispanic and Black, are the terms
that were used in the cited source.

28

In aggregate, Figure 2 illustrates that people of Hispanic or Latinx descent represented
8.05% of the employee population. Black or African Americans made up 4.94% of the employee
population. People who identified as being Two or More Races occupied 2.37%, and both Native
Hawaiian or Pacific Islander and Native American or Alaska Native represented less than 1% at
0.29% and 0.34%, respectively (Alphabet Inc., 2018; Apple Inc., 2018; Cisco System Inc., 2018;
Facebook Inc., 2018; Intel Corp., 2018; Microsoft Corporation, 2018; Oracle America Inc.,
2018).
Corporate EEO-1 reports presented evidence showing that racial minorities were
underrepresented in the technology sector. The same aforementioned EEO-1 reports also
revealed that disproportional rates were wider in leadership categories for underrepresented
groups. Figure 3 shows the demographic information in terms of the race of executives and
managers from Alphabet (parent company of Google), Apple, Microsoft, Cisco, Facebook, Intel,
and Oracle.

29

Figure 3
High-Tech Corporate Employment Data for Executive and Managerial Job Categories

Note. Executive and manager population calculated using EEO-1 2018 report data from
Alphabet, Apple, Cisco, Facebook Intel, Microsoft, and Oracle. All terms that reference race,
such as Hispanic and Black, are the terms used in the cited source.

Figure 3 illustrates that, in aggregate, people of Hispanic or Latinx descent represented
5.48% of employees who held a leadership position. Black or African Americans made up 2.70%
of employees in these positions. People who identified as being of two or more races occupied
1.82%, and both Native Hawaiian or Pacific Islander and American Indian or Alaska Native
represented less than 1% at 0.21% and 0.24%, respectively (Alphabet Inc., 2018; Apple Inc.,
2018; Cisco System Inc., 2018; Facebook Inc., 2018; Intel Corp., 2018; Microsoft Corporation,
2018; Oracle America Inc., 2018).
30

As depicted in Figure 2, Black or African Americans made up 4.94% of the employee
population while Whites made up 51.52%. If the concept of equal representation is applied, this
would mean that 4.94% of those in leadership would also be Black or African American.
However, this is not the case among the technology companies evaluated. Figure 4 illustrates the
ratio of leaders to employees by racial category.

Figure 4
Leadership Ratios in High-Tech Corporations by Race

Note. Ratios calculated using EEO-1 2018 report data from Alphabet, Apple, Cisco, Facebook,
Intel, Microsoft, and Oracle.

31

Adequate representation in leadership is absent for underrepresented minority groups.
The checkered bar in Figure 4 indicates the leadership representation for the race shown at the
top of that bar, and the solid bar represents the entire employee population who identified with
the race depicted. The checkered bar overlays the solid bar. A checkered bar that meets the 50%
level means that equal representation exists: a perfect 1:1 ratio. A checkered bar above the 50%
level means that leadership for that racial group is overrepresented, and a checkered bar below
the 50% line means that leadership among that race is underrepresented. Every company
examined had an overrepresentation of White employees in leadership positions relative to the
percentage of employees who are White. People who identify as Asian are also close to equal
representation. Hence, the data reveals that not only are all other racial groups underrepresented
in leadership, but it also shows that a White or Asian employee is twice as likely to be
represented in leadership positions than a Black employee.
All seven technology corporations examined disclosed their appreciation for diversity in
talent recruitment literature and on their corporate websites. That said, simply valuing corporate
racial diversity has not been enough for them to achieve a racially diversified corporate
environment. If corporations desire to have a more diversified workplace, then affirmative action
policies and clear goals regarding diversity need to be established for changes to occur (Motel,
2016). Additionally, strategies to address realistic and symbolic fears or other negative emotions
that some would experience in reaction to policies regarding diversity would need to be created.
For example, incorporating an all-inclusive multiculturalism (AIM) model could ensure that non-
minorities feel included while also recognizing the importance of differences, which is essential
for supporting minority employees (Stevens et al., 2008). Approaches such as AIM attempt to
quell resistance from non-minorities by emphasizing that diversity includes all employees and
32

explicitly acknowledging the critical role that non-minorities play. In this way, AIM can alleviate
perceived realistic and symbolic threats that some non-minorities might foster (Stevens et al.,
2008). While this may help mitigate the threats, it may also be perceived as appeasing those who
resist inclusion.
Conceptual Framework
Perceived threats have consequences. The degree to which threats are perceived will
impact cognitive, emotional, and behavioral outcomes, whether or not the perceptions of the
threats are factual or correct (Stephan et al., 2009). Intergroup threat theory is a model primarily
focused on perceptions of realistic and symbolic threats among social groups. An intergroup
threat is experienced when one group, or an individual member of one group, perceives that
members from a different group are in a position to cause them harm (Stephan et al., 2009).
Thus, an adaptation of intergroup threat theory is appropriate to guide this study because the
theory provides parameters to examine how perceptions about an outgroup can elicit
psychological consequences (Rios et al., 2018; Stephan et al., 2009).
Societal group memberships (race, ethnicity, political affiliation, social class, and more)
form our identities and shape our lives. Group memberships contribute to our sense of safety and
belonging. Values, beliefs, and other characteristics determine who is included and who is
excluded in group memberships. Because social group memberships are part of our identities, it
is not outlandish that people can become very sensitive to situations that represent symbolic or
realistic threats to their groups. In the United States, one of the greatest sources of intergroup
challenges is the belief that other races and cultures pose a threat to one’s own race and culture,
especially during contentions times (Stephan & Stephan, 2000; Stephan et al., 2009).
33

Triggers in this study refer to situations that stimulate feelings such as anxiety, fear,
anger, and aggression, resulting in emotional or physical reactions (Brader et al., 2008; Cuncic,
2020; Lawrence, 2006). Triggers or amplifications of prejudice result from both symbolic and
realistic threats. At the time of this study, anti-racism messages and activities that support
diversity have prompted fear and anxiety among some White Americans in the United States
(Dawkins, 2021; Norris, 2021). As discussed in the literature review, perceived threats to
economic and group power have influenced White American perceptions of anti-White bias in
the workplace. Anxiety related to U.S. racial demographic shifts (where White people eventually
become the minority) ignited perceived threats to the values and culture of White North
Americans. Even when symbolic or realistic threats lead to non-aggressive behaviors, the
cognitive and emotional reactions to threats are likely to be negative from people who are most
susceptible to feeling threatened by outgroups. For example, some White people who feel
threatened by BIPOC may take political positions that limit marginalized groups’ abilities to gain
social and economic opportunities.
Actions stemming from resentment, fear of disempowerment, racist attitudes, hate, and
the belief that personal economic issues resulted from corrupt elite politicians were displayed on
January 6, 2021, by supporters of Donald J. Trump, the 45th President of the United States. Pro-
Trump demonstrators broke into the U.S. Capitol in Washington, D.C. Their aim was to prevent
the 117th United States Congress from conducting the Electoral College vote count to formalize
Joseph R. Biden’s victory as the 46th U.S. President and overturn the defeat of Trump in the
2020 presidential election. The resulting violence led to the evacuation and lockdown of the U.S.
Capitol and five deaths (Healy, 2021; Leatherby et al., 2021; Petras et al., 2021). As Edsall
(2021) commented, “There is no question that out-and-out racism and a longing to return to the
34

days of White supremacy were high on the list of motivations of the pro-Trump mob that
ransacked the Capitol on Jan. 6.” As of January 26, 2021, the Federal Bureau of Investigation
had identified 400 suspects and had charged more than 140 people in connection with the Capitol
siege (Fazio, 2021; Mallin & Barr, 2021).
The storming of the U.S. Capitol demonstrates the phenomenon of intergroup threat
theory. The rioters wanted Trump reelected because they believed that he best represented their
interests. Fueled by misinformation, they believed that Trump won the election and that his win
was stolen through fraud and corruption (Jurkowitz, 2021). Though there was no evidence to
support allegations of fraud or corruption, Trump and other authority figures who asserted that
the election was stolen gaslighted the rioters. These were the triggers that cultivated the realistic
and symbolic threats to the belief systems of the pro-Trump group. Rioters refused to consider
information that invalidated their beliefs. They felt justified in taking actions that ranged from
protesting to assassinations and believed that they had the backing of the Trump administration.
The rioters also believed that Trump and his allies endorsed their desires to challenge the
confirmation of the U.S. presidential election results, and it was their patriotic duty to take their
government back. In reaction to the unsubstantiated perceptions of a corrupt government that
threatened their way of life and belief systems that often enough included values of White
supremacy, the rioters reacted by staging a violent insurrection at the U.S. Capitol (Dawkins,
2021; Edsall, 2021; Norris, 2021).
As previously mentioned, intergroup threat theory mainly focuses on perceptions of
threat. The conceptual framework used in this study is an adaptation of intergroup threat theory.
Figure 5 shows a graphical representation of triggers represented in the literature review.

35

Figure 5
Conceptual Framework of Antecedents That Result in the Perception of Threats

Note. Modified from “Does a common ingroup identity reduce intergroup threat?,” by B. M.
Riek, E. W. Mania, S. L. Gaertner, S. A. McDonald, and M. J. Lamoreaux, 2010, Group
Processes & Intergroup Relations, 13(4), 403–423.

Figure 5 depicts antecedents that can trigger feelings of risk to safety experienced as
realistic or symbolic threats to the ingroup. These threats result in negative feelings from fear and
anxiety. The subsequent responses to the threats are implicit and explicit intellectual, emotional,
and behavioral actions.
Intergroup threats can have destructive effects on intergroup relations. In the United
States, membership in the majority group is associated with dominance, power, and privilege. In
line with intergroup threat theory, messages and activities that attempt to promote diversity and
resolve past social and economic inequities can trigger fear and anxiety. The idea that outgroups
might be able to experience social and economic security becomes a threat to the security of
susceptible members of the ingroup. Rather than envisioning the possibilities of sharing social
and economic status, some members of the ingroup are likely to believe that members of the
outgroup cannot make gains unless these are taken away from the ingroup. Thus, susceptible
36

members of the ingroup are likely to experience feelings that resemble loss and resentment.
Responses could then vary from opposition to policies that support Affirmative Action,
rationalization of discriminatory acts against the outgroup, to violent acts of prejudice.
Atwell Seate and Mastro (2016) utilized intergroup threat theory as a frame to help
investigate the influence that media can have on intergroup attitudes through affective processes.
Affective processes include emotional feelings and reactions related to behavior or beliefs and
can alter perceptions of situations (Aberson, 2015; Atwell Seate & Mastro, 2016). The
researchers presented that media exposure is a primary source of contact for numerous social
groups and that threatening themes that associated racial minorities as the main culprit of the
threat were commonplace in both news and entertainment programming (Atwell Seate & Mastro,
2016). Atwell Seate and Mastro noted that coupling threatening messages with racial minority
groups had potentially harmful implications for intergroup relations and that “media that
emphasize an outgroup’s threatening nature toward other groups are likely to produce negative
intergroup attitudes and increase intergroup anxiety” (p. 199). The researchers identified that
immigration in the United States was a recurring topic in media. According to Pew Research
Center (2011), immigration in U.S. society was associated with realistic and symbolic threats.
Therefore, immigration was an appropriate topic to test the researchers’ hypotheses regarding the
influence of media on intergroup perceptions.
Atwell Seate and Mastro (2016) enlisted 444 participants from a university located on the
U.S.-Mexico border to examine intergroup perceptions regarding immigration. In the study,
immigration was positioned as the antecedent, and immigration threat was incited through
messages designed to convey how illegal immigration resulted in increased crime, threatened
U.S. culture, and threatened the U.S. economy. Because the researchers were exploring
37

intergroup perceptions about immigration from Mexico to the U.S., they excluded 20 non-U.S.
citizens and 50 Latinx participants from their final analysis; thus, the study’s results were based
on 374 participants’ perceptions.
For the experiment, the researchers guided the participants to first watch a benign news
segment about the weather and then watch either a pro-themed or anti-themed segment about
immigration. After that, the participants completed a questionnaire on their attitudes toward
undocumented immigrants. Among the findings, the researchers uncovered that participants in
the immigration threat condition conveyed more anxiety towards undocumented immigrants than
those in the no-threat condition. Hence, the messages delivered to the participants through media
exposure was an antecedent that drove increased anxiety and produced perceived threats that led
to consequential affective reactions.
Summary
Since the death of George Floyd in May of 2020 and the insurrection attempt at the U.S.
Capitol in Washington D.C. on January 6, 2021, more people have educated themselves about
racism in America. Newly informed Americans who have discovered the impact of systemic
racism seem unwilling to dismiss grim examples of prejudice. People of all races across the
United States have participated in anti-racism protests and continue to take actions that address
discrimination. It is not inconceivable to conclude that the civil unrest in the United States has
also contributed to actions undertaken by technology corporations in support of diversity.
Technology corporations, including Microsoft and Cisco, have publicly communicated their
stance on anti-racism through corporate social media platforms and openly donated to
organizations such as BLM to help fight injustice. While the civil unrest in the United States may
38

have acted as a catalyst, it would seem that some technology companies recognized that
promoting diversity and advocating anti-racism was worth the investment.
Some White Americans will continue to be susceptible to feelings of fear and anxiety in
response to the efforts to increase diversity in the workplace. Susceptible White Americans will
likely experience changes in diversity as realistic and symbolic threats. Many White Americans
now recognize that systemic racism is real. Time will tell if newly informed White Americans
will stay committed to actions against practices that promote systemic racism and if they will
support Affirmative Action policies that could lead to equity and opportunity for minoritized
groups.

39

Chapter Three: Methodology
Intergroup threat perceptions defined by intergroup threat theory have played a
significant role in U.S. society (Stephan et al., 2009). The literature suggested that messages
supporting diversity are enough to incite perceptions of both realistic and symbolic threats
(Dover et al., 2016; Stephan et al., 2009; Wellman et al., 2016). The intent of the research was to
evaluate the perceptions of employees from technology corporations towards corporate activities
that address racial equality. Information collected for the study offered insight into the extent to
which perceptions of threat had been affected by events such as the killing of George Floyd in
May 2020, knowledge of systemic racism, and required corporate diversity training. Table 1
depicts the research question that focused the study and the research methods used.

Table 1
Research Methods Used in the Study
Research question
Research method 1
Quantitative
Research method 2
Qualitative
How do U.S. citizens and residents who
are employees of technology companies
perceive their corporations’ activities to
address racial injustice?
Survey

Closed-ended
questions
Interview

Semi-structured
interview

40

A mixed-methods approach was chosen to examine how U.S. citizens and residents
employed at technology companies perceived their corporations’ activities to address racial
injustice. The survey (quantitative approach) provided ways to analyze and derive numeric
descriptions of phenomena included in the survey (Creswell & Creswell, 2018; Merriam &
Tisdell, 2016). The interview (qualitative approach) focused on gaining deeper understandings
from participants about specific insights uncovered through the survey, what meaning those
individuals attributed to those insights, and their personal experiences related to the insights
(Creswell & Creswell, 2018; Merriam & Tisdell, 2016).
Data Sources
Sources of data were surveys (Appendix C) and interviews (Appendix D). Two surveys
were used for this study: a survey distributed to a SurveyMonkey Audience panel and a survey
distributed via Facebook and LinkedIn. To protect the SurveyMonkey Audience participants’
anonymity, no identifying information was allowed to be released, so none of those participants
could have been contacted after they completed the survey. Therefore, participants who
completed the survey through SurveyMonkey Audience were not recruited for interviews. The
social media platforms Facebook and LinkedIn were used to recruit participants for interviews
through a second survey. This second survey was posted on Facebook and LinkedIn and was not
sent to the SurveyMonkey Audience panel. In addition to gathering more survey data,
participants were asked if they would agree to be interviewed and provide their contact
information.
Survey Distributed to a SurveyMonkey Audience Panel
A SurveyMonkey Audience panel was purchased through SurveyMonkey (an online
survey development software company). SurveyMonkey Audience panels represented a global
41

online population that voluntarily joined panels to take surveys. Survey Monkey provided a way
to ensure that the surveyed population met the required target audience criteria for this study.
The survey provider was also fairly confident that using the SurveyMonkey Audience panel
would result in over 300 qualified completed responses; therefore, this survey provider was used
to gather the bulk of the survey data. This survey was distributed through Survey Monkey’s
system to the SurveyMonkey Audience panel participants via email.
Survey Distributed Via Facebook and LinkedIn
The questions on the two survey protocols were identical. The only exception was that
the survey distributed via Facebook and LinkedIn also included a request to participate in an
interview and asked for demographic information. Apart from race, the survey protocol used
with the SurveyMonkey Audience panel did not request demographic information because
demographic information (gender, age, income level, and U.S. regional location) was collected
by SurveyMonkey prior to distributing the survey to the panel. Demographic information was
not automatically collected for the survey distributed via Facebook and LinkedIn. Therefore, it
was necessary to add items that requested demographic information to the Facebook and
LinkedIn survey protocol. Specifically, the additional demographic information sought in the
Facebook and LinkedIn survey was about gender, age, U.S. regional location, and income level.
Participant Sampling Strategy
A purposeful sampling strategy was used because the study deliberately focused on
targeting people who met specific criteria (Merriam & Tisdell, 2016). SurveyMonkey’s Target
Audience collector was used to target panel participants who met the specific participation
requirements. Additionally, SurveyMonkey provided the ages, gender, and location of the
participants who completed the survey through their services.
42

A second survey was used to solicit requests for interviews. The second survey used the
same criteria as the SurveyMonkey’s Target Audience survey to determine a participant’s
eligibility for participation. As previously mentioned, the second survey was distributed via
Facebook and LinkedIn and the only modifications to the second survey were to allow for the
collection of demographic information while inviting volunteers for interviews.
Participant Population
The purpose of this study was to discover how White American employees of technology
companies perceived the actions that their corporations had taken in support of racial diversity
and anti-racism since May of 2020. The research question for this study was focused on
employees who work for U.S.-based technology companies and who also resided in the United
States. Midsize to large companies (100 or more employees) tended to have a greater number of
resources at their disposal (Gartner, n.d.). These resources could have been put towards
employee training and other external corporate activities, which may have included pro-diversity
and anti-racism activities (Gartner, n.d.). Therefore, the size of the company for which each
participant worked was also part of the target audience criteria. Thus, the target population was
people who lived in the United States and were employed at a technology company that had 200
or more employees and was headquartered in the United States. The minimum sample size
recommended for getting meaningful results for this study was 377 (Raosoft, Inc., n.d.). The
margin of error for the survey was 5%, and the confidence level was 95%. The survey remained
open to the SurveyMonkey Audience panel until the recommended minimum sample size of 377
was surpassed (Raosoft, Inc., n.d.).
In aggregate, 885 people accessed one of the surveys. Of these people, 472 did not match
the intended target audience and were disqualified from completing the survey. Sixteen people
43

who were qualified to complete the survey abandoned the survey. In aggregate, the final
participant population who completed the survey consisted of 397 adults. Age, gender, and
location information was collected for 383 participants, and the remaining 14 participants did not
provide age, gender, or location demographic information. Among the 383 participants, 147
identified as female, and 236 identified as male. The ages of the participants ranged from 18 to
60+. The smallest proportion of participants came from the >60 age range (3%), the largest
percentage of participants (55%) came from the 30–44 age range, followed by 18–29 (25%), and
45–60 (17%). Participants of the survey resided in four different regions across the United States
(see Appendix G for Regions and Division of the United States). Thirty-seven percent of the
population was located in the South, 29% in the West, 20% in the Northeast, and 14% in the
Midwest. Among the participants, 389 disclosed their racial identity, and eight preferred not to
respond. The majority of the participants (61%) identified themselves as White, 13% of the
participants classified themselves as Asian, 12% as Black or African American, 11% as Hispanic
or Latino, 3% as Two or More Races, 1% as Native American or Alaska Native, and one
participant identified themselves as Native Hawaiian or Pacific Islander which equates to less
than 1% of the participant population.
As a reminder, the survey that was distributed via Facebook and LinkedIn social media
platforms was used to recruit participants for interviews. Eleven volunteers who expressed
interest in providing additional insights were interviewed (Johnson & Christensen, 2015;
Merriam & Tisdell, 2016). Table 2 depicts the number of interview participants who identified as
White, Asian, or underrepresented BIPOC. The 11 interview participants are included in the total
participant population of 397.

44

Table 2
Interviewee Racial Distribution and Racial Distribution at High-Tech Corporations
Racial category
Number of
interviewees
Interviewed
population %
Aggregate employee
population % from six major
U.S. tech corporations
Asian 3 27%
32%
BIPOC (underrepresented) 2 18%
16%
White 6 55%
52%

Note. U.S. high-tech companies had a relatively larger share of Whites employees and a larger
share of Asian employees. Other racial groups were less represented by a significant margin in
the tech sector (Farnsworth & Holtzblatt, 2016). Underrepresented racial minority group
categories listed in EEO-1 reports are “Hispanic or Latino, Black or African American, Native
Hawaiian or Pacific Islander, American Indian or Alaska Native, and Two or More Races (Not
Hispanic or Latino).” All terms that reference race, such as Hispanic and Black, are the terms
that were used in the cited source (Alphabet Inc., 2018; Apple Inc., 2018; Cisco System Inc.,
2018; Facebook Inc., 2018; Intel Corp., 2018; Microsoft Corporation, 2018; Oracle America
Inc., 2018).

White people represented the largest racial makeup in U.S. based technology companies
(Farnsworth & Holtzblatt, 2016; Plaut et al., 2011). Therefore, a population that approximated
the racial populations of U.S. based technology companies was interviewed (Alphabet Inc.,
2018; Apple Inc., 2018; Cisco System Inc., 2018; Facebook Inc., 2018; Farnsworth & Holtzblatt,
2016; Intel Corp., 2018; Microsoft Corporation, 2018; Oracle America Inc., 2018).

45

Instrumentation
Two surveys were designed to be completed in under 10 minutes because research
showed that data quality declined and abandonment rate increased for surveys that were longer
than 10 minutes (Guo et al., 2016). The survey distributed via Survey Monkey systems collected
the bulk of the survey responses. The survey sent via Facebook and LinkedIn collected survey
responses and personal contact information provided by the participant when they responded to
the survey question that requested an interview.
Surveys
Both survey protocols sent to the SurveyMonkey Audience panel and sent via Facebook
and LinkedIn explained the purpose of the study in accessible and friendly language and
presented the survey questions and instructions for completing the survey. Some of the survey
questions could have triggered sensitive feelings or incited feelings of threat as defined by
intergroup threat theory. In such situations, participants might have chosen to answer in a manner
that they felt portrayed them best or might have chosen not to answer. The desire to be portrayed
positively is known as social desirability bias (Robinson & Firth Leonard, 2019). To mitigate the
effects of social desirability bias, the survey contained closed-ended questions with a verbalized
unipolar rating scale (Höhne et al., 2020; Robinson & Firth Leonard, 2019). Unipolar scales
allowed participants to focus on the absence or presence of the item along one pole, such as the
degree of agreement (strongly agree, somewhat agree, moderately agree, hardly agree, not at all
agree; Höhne et al., 2020; Robinson & Firth Leonard, 2019). In comparison, a bipolar rating
scale contrasts opposite poles and contains an ambiguous midpoint (strongly agree, somewhat
agree, neither agree nor disagree, somewhat disagree, strongly disagree) (Höhne et al., 2020;
Robinson & Firth Leonard, 2019). For this study, participants were asked to assess the extent to
46

which they agreed instead of deciding to what extent they agreed or disagreed, thereby reducing
unwanted variance caused by social desirability bias (Höhne et al., 2020). Finally, to prevent
situations whereby participants could choose to withhold definitive answers by selecting don ’t
know as an answer, don ’t know and similar response options were not offered in questions about
the participants’ actions to address systemic racism (Robinson & Firth Leonard, 2019). Survey
questions used “I” statements sparingly and were crafted to seek opinions in an attempt to
intellectually distance the participants’ thoughts and help them not feel uncomfortable about
unpopular thoughts.
Both the SurveyMonkey Audience and the Facebook and LinkedIn surveys were
comprised of 13 questions (not including demographic questions), and some of the questions
were divided into multiple parts (Robinson & Firth Leonard, 2019). The total number of
subitems across the 13 questions was 39 subitems, and each question contained closed-ended
response options (Robinson & Firth Leonard, 2019). Nominally designed questions were used to
qualify or disqualify survey participation, collect demographic information, and allow
participants to choose more than one response from a list (Robinson & Firth Leonard, 2019).
Ordinal scaled questions informed the importance level or affinity level of a question to rate the
participants’ agreement or support of an item (Robinson & Firth Leonard, 2019). Ideas for the
items were inspired from existing surveys created by a market research company (Ipsos, 2020,
2021). Survey questions were designed to obtain information about the perceptions of employees
of technology corporations regarding diversity initiatives. The results of each item were
described in terms of tally counts and percentages.
Screening questions were included at the beginning of the survey to qualify or disqualify
people from the target audience depending on how they answered. Survey logic was added to the
47

screening questions that allowed qualified survey participants to complete the survey. People
who did not match the intended target audience were moved on to a disqualification page. One
question containing three items was used to determine the participants’ awareness of 2020 events
and assess if the events of 2020 helped shift the participants’ perceptions. Each of the subitems
was assessed using a 5-point scale ranging from not at all agree to strongly agree. Higher scores
indicated that the events of 2020 had a greater effect on participants. Lower scores indicated that
the 2020 events had less impact. Three questions containing 14 subitems were used to determine
a participant’s support for corporate activities that were being taken to promote diversity in the
workplace. Two of the 14 subitems were assessed using a 5-point scale ranging from not at all
agree to strongly agree. The remaining 12 subitems were coded based on a participant’s
response to the question, “In my opinion, my company needs to do the following…” Options that
represented support for corporate activities were coded higher than options that represented low
or zero support for corporate activities. Higher scores indicated greater agreement with corporate
activities that supported diversity. Two questions containing nine subitems were used to
determine the actions that a participant took in support of diversity and anti-racism.
Personal actions offered insights into the participant’s commitments to understand and
contribute to change. The levels of activity provided information about how the participant
responded to realistic and symbolic threats. The participants’ response choices to each question
were either yes, or no. “Yes” answers yielded higher scores than “no” answers. Higher scores
indicated that more personal actions had been taken. Four questions containing 10 subitems were
used to obtain information about the participants’ perceptions of how diversity could affect them
in the workplace. This information was required to understand if the participants’ held zero-sum
game beliefs, meritocracy beliefs, and SLB. Each subitem was rated by way of a 5-point scale
48

ranging from not at all agree to strongly agree. Higher scores indicated the presence of zero-sum
game beliefs, meritocracy beliefs, and SLB (Appendix C).
Interview
An interview protocol was designed in advance for use in each interview (Appendix D).
A semi-structured interview was designed because it allowed the flexibility to ask additional
questions based on participants’ responses to ensure an accurate understanding of their
perspectives (Merriam & Tisdell, 2016). Interview questions were open-ended, and probing
interview questions changed depending on the context of each interview. Participants did not see
the interview protocol. The introductory section of the interview protocol included a statement
that reminded the researcher to verbally restate the purpose of the interview explained in
concrete and accessible language, the anticipated length, and the general structure of the
interview. Participants were notified that they could decline to answer any question or withdraw
from the interview at any time (Creswell & Creswell, 2018). The interview began with warm-up
questions to put the participant at ease. The remaining questions in the interview were designed
to collect information regarding the research question. When deemed appropriate by the
researcher, probes were used to ask participants to elaborate and clarify their responses (Creswell
& Creswell, 2018).
Twenty questions (including three generic questions) were prepared for all participants.
Probing had commenced depending on the participant’s responses to the questions (Merriam &
Tisdell, 2016). Each question fell under a specific question type defined by Patton (2002) and
Merriam and Tisdell (2016). Table 3 depicts the interview question matrix.

49

Table 3
Matrix of Interview Questions
Question focus Past Present Future
Generic question 1 ,2, 3
Responses to racial diversity 4 5, 6, 18
Opinions regarding activities
to promote diversity in the
workplace
10a 7, 8, 17 10b
Experiences, beliefs, and
activities related to
diversity in the workplace
15a 9, 11, 12, 13, 14 15b, 16

Note. Adapted from “Qualitative interviewing,” by M. Q. Patton, 2002, in Qualitative Research
& Evaluation Methods (3rd ed., pp. 339–380), Sage Publications. The numbers listed in the table
correspond with the numbers assigned to the items in the interview protocol.

The interview questions were designed to elicit participant responses about their past,
present, or future perceptions and actions regarding their perceptions about messages and
activities that promote diversity at their workplaces. Four questions sought to uncover
information about how participants responded to diversity. Five questions asked participants to
give opinions regarding the promotion of diversity at their companies. Eight questions related to
diversity in the workplace were designed to learn about the participants’ experiences, beliefs, and
activities that have been taken or will be taken (Patton, 2002). The interview questions were
developed to measure perceptions of realistic and symbolic intergroup threat in accordance with
intergroup threat theory.
50

Data Collection Procedures
Survey and interview participants were provided with a brief statement about how their
responses would be used. They were told that their participation was voluntary, anonymous, and
confidential. Participants were also informed that they could abandon the survey or stop the
interview at any point. As noted previously, two distributions of surveys occurred. The first
survey was distributed to a SurveyMonkey Audience panel. The second survey was distributed
via Facebook and LinkedIn. The Facebook and LinkedIn survey participants were informed that
$5 would be donated to the non-profit organization Doctors Without Borders for every
completed survey. Three hundred and ninety-seven surveys were collected, and $1,985 was
donated to Doctors Without Borders.
An invitation for a one-on-one meeting was sent via email to 11 participants who
volunteered to be interviewed. The duration of each meeting was 45 to 60 minutes and took
place using Zoom conferencing technology. At the start of each interview, participants were
reminded about the purpose of the meeting and the anticipated length of the interview. Each
interview was recorded using audio and video recording features of conferencing technology to
ensure that the participant’s views were captured and analyzed correctly (Patton, 2002). All
video and audio recordings were destroyed after they were transcribed and analyzed (Creswell &
Creswell, 2018; Patton, 2002). Handwritten notes were taken during the interview to capture
noteworthy nuances and were also destroyed after those nuances had been added to the
transcripts.
Data Analysis
A mixed-methods approach for collecting data was used to obtain a reasonable
understanding of participant perceptions towards racial diversity, racial equality, and views about
51

corporate activities to address racial injustice. The data were available to be viewed and analyzed
by the researcher using SurveyMonkey Analysis tools and Qualtrics XM analysis tools.
Sequential steps for analysis outlined by Creswell and Creswell (2018) were followed after each
interview.
Surveys
Every survey was completed by an individual participant. Then, individual-level data
from the surveys were combined and analyzed using features provided by Qualtrics XM.
Nominal data depicted frequency distributions (Robinson & Firth Leonard, 2019; Salkind, 2014;
Spickard, 2016). Cross-tabulation was used to analyze data from ordinal and nominal scales, and
the mode was analyzed (Robinson & Firth Leonard, 2019; Salkind, 2014; Spickard, 2016). The
resulting analysis revealed information that answered the research question.
Interviews
Digital computer-generated transcriptions were created after each interview using the
transcription feature in Zoom conferencing software. Each computer-generated transcription was
checked against the associated recording for accuracy and completeness. Any errors that
appeared in the computer-generated transcriptions were corrected. The researcher reviewed
handwritten notes and incorporated notes regarding nonverbal communication and noteworthy
nuanced information into the transcription using a different font color and highlighted text to
distinguish these notes (Creswell & Creswell, 2018; Denham & Onwuegbuzie, 2013).
Once the transcriptions were completed, the data were coded based on a predefined a
priori coding scheme. Table 4 depicts the predefined a priori codes.

52

Table 4
Predefined A Priori Codes
Reference
A priori
code Decipher Definition
McCoy & Major, 2007;
Newman et al., 2015;
Participant phrased assertions
MR Meritocracy
Impartially rewarded for their skills
and initiative
Success is achieved through merit
and hard work
Those that work harder are more
successful
McCoy et al., 2013; Participant
phrased assertions
RS Rewards Reward systems are fair
Walton et al., 2013
RH
Recruitment and
hiring
Hiring is based on candidate
potential
Rivera, 2012; Participant
phrased assertions
Hiring for fit
Glaser, 2005; Wilkins et al.,
2013; Participant phrased
assertions
CB Colorblind
People are treated equally
irrespective of race
AS Assimilation
Racial categories to ensure EEO
ought to be ignored
DeSante (2013); Participant
phrased assertions
AA AA
Objections to racialized policies like
Affirmative Action
Caver & Livers, 2002; DeSante,
2013, p. 342; Participant
phrased assertions
OW Overworked
Extra effort, working twice as hard,
inconspicuous forms of
mistreatment
Motel, 2016; Participant
phrased assertions
AIM AIM
All-inclusive multiculturalism,
Perception of Anti-White Bias in
the Workplace
Participant assertions AW Awareness
Awareness of racism in the United
States
Stephan & Stephan, 2000;
Stephan, Ybarra, & Morrison,
2009; Participant phrased
assertions
TRT Threats
Feelings of anxiety, fear, anger,
aggression
Participant assertions
ACT Activist Voluntary actions (public)
GRW Growth Voluntary actions (private)
CM Corp money
Support of Black Lives Matter,
statements against racism,
financial support for anti-racism

53

New codes emerged as the researcher recognized patterns and repeated words, phrases,
and ideas expressed during the interviews. The new codes were also used when the researcher
coded the data. All of the codes were organized into categories, which were then synthesized into
generalized themes (Creswell & Creswell, 2018).
Validity and Reliability
The researcher examined the effect that messages supporting diversity, examples of racial
and social injustice issues such as the death of George Floyd, and knowledge about systemic
racism (independent variable) had on employee perceptions towards corporate support of
diversity and anti-racism (dependent variable) (Salkind, 2014). The strategy of comparing
multiple data collection and analysis methods to test the findings’ consistency and increase the
validity and reliability of the study (triangulation) were employed (Creswell & Creswell, 2018;
Merriam & Tisdell, 2016).
Internal consistency was used to test the survey’s reliability. Internal consistency
reliability determined that the survey would measure what the researcher intended to measure by
testing to see that the survey questions were reliable (Salkind, 2014). Cronbach’s alpha was used
to measure internal consistency reliability (Salkind, 2014; Tavakol & Dennick, 2011). Ten
participants were recruited from the USC Rossier School of Education and from non-tech
corporate sectors to pilot the survey. The researcher used Qualtrics Online Survey Software to
create and administer the pilot project. After gathering all of the data from the preliminary
survey, the alpha was calculated. A low alpha (below .70) would have indicated that questions in
the survey had a low correlation and were not appropriate for the survey (Tavakol & Dennick,
2011). A high alpha (above .80) would have meant that survey questions were highly correlated
54

(Tavakol & Dennick, 2011). The coefficient alpha for the pilot survey was calculated to be .852,
which meant that the survey was internally consistent and reliable (Appendix E).
Content validity was used to assess whether items intended for use during the interview
were representative of the intended research. A method developed by Lawshe (1975) called
content validity ratio (CVR) was used to quantify content validity (Ayre & Scally, 2014; Gilbert
& Prion, 2016). Subject matter experts in the areas of diversity and systemic racism were asked
to test the validity of each interview question. Diversity and inclusion subject matter experts
were recruited and asked to rate each item from the interview protocol instrument into one of
three categories: essential, useful, but not essential, or not necessary. According to Lawshe
(1975), if more than half of the subject matter experts perceived an item to be essential, it
indicated that the item had some degree of content validity. In other words, the more that subject
matter experts perceived an item as “essential,” the higher the degree of that item’s content
validity (Gilbert & Prion, 2016; Lawshe, 1975). For this study, CVR values from a panel of eight
experts were calculated. Values for individual items in the instrument that were .50 or above
were considered valid. Items with a value less than .50 were removed from the interview
protocol (Appendix F).
Ethics
The research for this study was conducted and disseminated ethically. Doing no harm to
others, protecting participants, and enabling beneficial research were top priorities (Merriam &
Tisdell, 2016; Whitney, 2016). Information about the content and goals of the research project
was reviewed by the Institutional Review Board (IRB) situated at the University of Southern
California (USC). The IRB process provided a set of standards for ethical compliance during
55

research (Creswell & Creswell, 2018; Whitney, 2016). While IRB approval did not guarantee
ethical methods, it acknowledged intent to conduct research without harming.
Transparency about the purpose of the study and data use was provided to each
participant prior to engaging in the survey or interview. Privacy for survey respondents was
protected through anonymity, and privacy for interviewees was protected through confidentiality.
Individuals who only participated in the survey through SurveyMonkey Audience had anonymity
because personally identifiable information was not automatically collected. Participants who
received the survey via Facebook or LinkedIn were invited to participate in a follow-up
interview. Those who volunteered were asked to provide their contact information. Participants
who were interviewed had their identities masked by pseudonyms in the transcriptions.
After disclosing the interview parameters, participants were reminded that their
involvement was voluntary and that they could opt out of the interview at any point. The goal of
the disclosure was to lower the risk of participants feeling pressured to consent, which is required
by IRB, before embarking on the interview (Creswell & Creswell, 2018).
The Researcher
I acted as the sole party to obtain survey data through SurveyMonkey Audience,
Facebook, and LinkedIn, and I conducted all of the participant interviews. Therefore, I
recognized that it was important and necessary that my personal values, identity affiliation, and
biases be noted (Creswell & Creswell, 2018). I identify as Black-mixed race. I am a member of
the underrepresented racial group within high-tech corporations categorized as of two or more
races (Farnsworth & Holtzblatt, 2016). I most closely align with the transformative worldview.
This worldview is focused on power, inequality, oppression, social change, and the needs of
people who are marginalized or disenfranchised (Creswell & Creswell, 2018). As a Black-mixed
56

race woman, I am directly and personally impacted by adverse reactions to diversity and
perceptions of threat prompted by anti-racist actions.
Although every effort was made to ensure objectivity, I acknowledge that I have biases
stemming from my employment at a high-tech corporation, my knowledge of systemic racism,
and my experiences of discrimination. I am aware that these biases could have impacted how I
viewed and understood the data. To help mitigate researcher bias, the survey was deployed to
people outside of my sphere of influence (Creswell & Creswell, 2018). Additionally, I followed
the interview protocol designed for this study, took care not to unconsciously influence the
responses of the interviewees, and used a qualitative data coding scheme to organize the data and
assist with data analysis (Creswell & Creswell, 2018).
Limitations
Limitations to the study that were out of my control also influenced the research. While
intergroup threat theory has been applied in other studies (Atwell Seate & Mastro, 2016; Atwell
Seate et al., 2018; Filindra & Pearson-Merkowitz, 2013; Riek et al., 2010), evidence that
powerfully demonstrated correlations between perceived threats as defined by intergroup threat
theory in technology corporations and the consequences of those perceptions were essentially
non-existent. Therefore, preexisting data was not included in the triangulation process as existing
research did not offer clear methods or indisputable conclusions to assess or address perceptions
of threat in corporations. To address the lack of triangulation, member checking was done with
the interview participants to help ensure that the meanings of the information shared through the
interviews were accurately understood. Another limitation could have been the mental state of
the participants at the time when they completed the survey and engaged in interviews. Both the
57

qualitative and quantitative aspects of the study relied on the truthfulness of the participants
during data collection.
Time was also a limiting factor. The study was conducted over a few weeks, which
impacted the number of survey responses received. The compressed period impacted the amount
of data available to include in the analysis.
Perceptions and biases that influence behavior in society also affect the types, qualities,
and conclusions in research. While I made every effort to prevent participant and researcher bias,
I acknowledge that bias cannot be completely eliminated and could have existed at any phase of
the study. Thus, bias also contributed to the limitations of the study.
It is worth noting that limitations about how the data were collected were also a factor. I
had hoped to collect data from a single corporation and explored corporations where this might
have been possible. One senior leader at a high-tech company supported my research proposal
and agreed to make employees available to me. When I approached the HR department of that
leader’s corporation, my request was denied. The reasons HR gave involved generic concerns
about security, privacy, and the protection of their employees.
Hopefully, in the future, HR departments will become excited about the prospects of in-
house research. By allowing research that would encourage people to understand how employees
perceive their companies’ activities to address racial injustice, companies could gain information
that would help them to modify their approaches and take credit for the work they are doing.
When research is prohibited, the possibility of uncovering uncomfortable truths is avoided,
change is prevented, and progress is unrecognized. I recognized this was a bias that I brought to
the research. “History is written by the winners” (Orwell, 2003, p. 14). Such is also true in
58

research. The same limitations, delimitations, and biases that influence behavior in society also
affect the parameters, qualities, and conclusions in research.

59

Chapter Four: Findings and Results
The purpose of this study was to uncover the perceptions of White employees towards
corporate policies and corporate support of diversity and anti-racism activities since May 2020.
Research revealed that some White people were more susceptible to feelings of threat in reaction
to supportive diversity workplace messages and actions (Dover et al., 2016; Kaiser et al., 2013;
Outten et al., 2012). If people who identified as White perceived corporate support for diversity
and anti-racism as threatening and were resistant to actions that supported diversity in the
workplace, diversification could be at risk. One research question was used to anchor this study:
How do U.S. citizens and residents who are employees of technology companies perceive their
corporations’ activities to address racial injustice? Among the findings, the results showed that
the majority of participants supported pro-diversity interventions in the workplace. At the same
time, the results revealed that most participants noted some level of agreement that pro-diversity
interventions might generate issues of anti-White or anti-Black bias.
A mixed-methods methodology was applied for the study. Two surveys were
administered, and the questions on the surveys were identical except that one of the surveys
included a request to interview participants. The first survey was distributed through a survey
vendor, and the vendor did not permit the recruitment of participants for interviews. Therefore, it
was necessary to distribute the second survey to recruit participants for interviews by way of
social media platforms. The survey data received from both surveys were combined before
analysis. The combined total of participants that completed the surveys was 397. Among those
397 participants, 11 people were interviewed. Data from different social groups were analyzed
separately to understand the perceptions of the different groups as well as in aggregate. Table 5
depicts the racial/ethnic groups of participants who responded to the surveys, the racial/ethnic
60

groups of interviewed participants, and the pseudonyms used when referencing the interviewees
in this study.

Table 5
Racial/Ethnic Participant Makeup
How participants identified racially/ethnically
Racial/ethnic category
# Of
survey
participants
# Of
interview
participants
Pseudonym of
interview
participants
White 239 6
Jack, Sue, Alan,
Morris, Dennis,
Charles
Asian 49 3 Lucy, Sarah, Kate

BIPOC (underrepresented) 101 2 Eli, Debra
Black or African American

Hispanic or Latino

Native American or Alaska Native

Two or More Races

Native Hawaiian or Pacific Islander

Prefer not to respond 8 0 n/a

Note: each
interviewee will
be referred to
with their
pseudonym and
parenthetically
with their self-
identified racial
descriptor;
White, Asian,
Black, or
Biracial.

61

Interview and survey data were analyzed to determine how different groups (White,
Asian, and underrepresented BIPOC) responded to corporate support of diversity and anti-
racism. The 2016 U.S. EEOC’s Diversity in High Tech special report did not define White or
Asian people as underrepresented in the technology industry. The underrepresented racial
minority group categories referenced in the special report and that also appear in EEO-1 reports
were defined as Hispanic or Latino, Black or African American, Native Hawaiian or Pacific
Islander, American Indian or Alaska Native, and Two or More Races (Not Hispanic or Latino).
Therefore, following suit, the responses from people who identified as White and Asian were
examined in separate groups for this study. The questions on the surveys were identical, and the
information from the surveys was combined. Survey data responses from eight participants who
did not identify their race/ethnicity were excluded from individual social group results, but the
responses from those eight participants were included in the aggregated results.
Finally, 11 interviews were conducted to uncover insights related to findings from the
survey data. The 11 interview participants were chosen from those who expressed interest in
being interviewed. As previously noted in chapter 3, the number of participants in each racial
category was purposely selected to approximate the racial demographic populations of U.S.-
based technology companies.
Categories
Individual items from the survey data and interview data that were related to each other
were grouped into categories. The category distinctions were influenced by the predetermined a
priori codes. Five groupings were defined to categorize information for this study (see Appendix
I for Groupings). Descriptive statistical analysis was then performed on the survey data collected
for each category (Robinson & Firth Leonard, 2019; Salkind & Frey, 2019). The medians,
62

means, and standard deviations for each of the five categories were obtained. Finally, insights
gathered from participant interviews were grouped into the five established categories. Table 6
depicts the five categories that were examined.

Table 6
Five Categories Examined
Awareness SLB Threats Support Actions
Increased
awareness
about race in
the United
States
Perceptions of
SLB in the
workplace
Perceptions of the
impact of
diversity in the
workplace
Support for
corporate
activities that
promote
diversity in the
workplace
Voluntary actions
taken in
support of
diversity and
anti-racism

63

The five categories that were established were awareness, SLB, threats, support, and
actions. The awareness category grouped data about participant perceptions of race after May
2020. The SLB category grouped information about SLB that justifies and rationalizes status
systems. The threats category grouped information that could have been seen as threats to a
participant’s social group, livelihood, or well-being. Intergroup threat theory was utilized to
define the threat category. Intergroup threat theory primarily focused on perceptions of realistic
and symbolic threats among social groups. In accordance with intergroup threat theory,
intergroup threat can be experienced when one group, or an individual member of one group,
perceives that members from different groups are in positions to cause them harm (Stephan et al.,
2009). The support category grouped data regarding activities that corporations took in support
of diversity training and education, recruitment and hiring and commitments made by
corporations towards anti-racism and social justice movements. Finally, the action category
grouped information regarding pro-diversity activities in which participants engaged voluntarily
and mandatorily.
Awareness of Race and Racism in the United States
Survey Data Regarding Awareness
Two survey items measured how the events from May 2020 onward affected the
participants’ perceptions of race and racism in the United States. There were no missing data
from participants who identified as Asian and underrepresented BIPOC (sample sizes 49 and
101, respectively). Data from two White participants were missing, which left a sample size of
237. Figure 6 shows the distribution of scores for awareness for participants who identified as
Asian, BIPOC (underrepresented), and White. Figure 7 shows the aggregated results for the total
64

population of participants, including individuals who preferred not to indicate their racial/ethnic
identity.

Figure 6
Analysis for Awareness of Asian, BIPOC (underrepresented), and White Participants

2%
5%
17%
42%
34%
5% 5%
1%
24%
49%
7%
9%
12%
27%
44%
0%
10%
20%
30%
40%
50%
60%
1 2 3 4 5
Participant responses
to awareness category
Responses from not at all agree (1) to strongly agree (5)
ASIAN BIPOC (underrepresented) WHITE
65

Figure 7
Aggregate Analysis for Awareness of All Participants

In Figure 6, the sum total of survey item scores was averaged to obtain categorical results
for awareness of Asian participants (M = 4, 𝑥 ̅ = 4, s = .86, N = 49), underrepresented BIPOC (M
= 4.5, 𝑥 ̅ = 4.06, s = .99, N = 101), and White participants (M = 4.5, 𝑥 ̅ = 3.9, s = 1.1, N = 237).
The results for the scores of the total population shown in Figure 7 were (M = 4.5, 𝑥 ̅ = 3.95, s =
1.05, N = 395). Higher scores represented greater agreement that awareness regarding race and
racism increased since May 2020. The median and mean scores showed that most participant
responses in each social group fell to the right of the scale midpoint (midpoint = 3). The
aggregate survey data revealed that 72% of participants somewhat agreed to strongly agreed that
the events since May 2020 raised their level of awareness.
6%
8%
14%
28%
44%
0%
5%
10%
15%
20%
25%
30%
35%
40%
45%
50%
1 2 3 4 5
Participant responses to awareness category
Responses from not at all agree (1) to strongly agree (5)
AWARENESS (after May 2020)
66

Interview Data Regarding Awareness
The sentiment shared by interviewed participants aligned with the survey data analysis.
During the interviews, participants were asked to reflect on the events since May 2020. Kate
(Asian) shared that she “thought things were getting better from [the] time when I was little all
the way up to now. It seems to be that it has gotten worse.” She “thought we were at a place in
time where racism isn’t as prevalent.” Charles (White) said, “I didn’t realize that things were as
bad and as unequal as it certainly appears they are, or that some of these attitudes were as deeply
rooted as they apparently are.” Eli (Black) reflected specifically on the death of George Floyd
and said, “What happens to my family if something like this happens to me? What position does
that put them in?” Sue (White) revealed that she felt “outraged and just unwell.”
Status-Legitimizing Beliefs
Survey Data Regarding Status-Legitimizing Beliefs
Status-legitimizing ideologies and beliefs rationalize systems that perpetuate inequality
(Chow et al., 2013). Two survey items measured participant agreement with SLB. There were no
missing data from participants who identified as Asian (sample size 49). Survey data from one
participant who identified as BIPOC (underrepresented) were missing, leaving a sample size of
100. Survey data from one White participant were missing, which left a sample size of 238.
Figure 8 shows the distribution of the results for SLB for participants who identified as Asian,
BIPOC (underrepresented), and White. Figure 9 shows the aggregated results for the total
population of participants, including individuals who preferred not to indicate their racial/ethnic
identity.

67

Figure 8
Analysis for SLB of Asian, BIPOC (Underrepresented), and White Participants

2%
8%
39%
33%
18%
8%
13%
19%
26%
34%
5%
8%
14%
28%
45%
0%
5%
10%
15%
20%
25%
30%
35%
40%
45%
50%
1 2 3 4 5
Participant responses to SLB category
Responses from not at all agree (1) to strongly agree (5)
SLB
ASIAN BIPOC (underrepresented) WHITE
68

Figure 9
Aggregated Analysis for Agreement with SLB of All Participants

Figure 8 shows the sum total of survey results for SLB of Asian participants (M = 3.5, 𝑥 ̅
= 3.57, s = .74, N = 49), underrepresented BIPOC (M = 3.5, 𝑥 ̅ = 3.64, s = 1.05, N=100), and
White participants (M = 4.5, 𝑥 ̅ = 4.01, s = .94, N = 238). The aggregate results for the total
population shown in Figure 9 were (M = 4.0, 𝑥 ̅ = 3.85, s = .96, N = 395). The survey data
showed that 73% of people who identified as White somewhat to strongly agreed with
perceptions aligned with SLB; 51% of the Asian participants and 60% of underrepresented
5%
9%
20%
28%
39%
0%
5%
10%
15%
20%
25%
30%
35%
40%
45%
1 2 3 4 5
Participant responses to SLB category
Responses from not at all agree (1) to strongly agree (5)
SLB
69

BIPOC participants also somewhat to strongly agreed. The aggregate survey data ultimately
showed that 67% of participants somewhat to strongly agreed with SLB, such as that hard work
leads to success and that people at their workplaces are colorblind, meaning they do not see
color.
Interview Data Regarding SLB
During one-on-one interviews, participants were asked to share their perceptions about
the colorblindness principle and about meritocracy at their places of work. The sentiment they
shared aligned with the survey data analysis. The interviewees provided insights regarding their
perceptions of SLB.
Colorblind ideology and meritocracy are SLB (Glaser, 2005; McCoy et al., 2013; Wilkins
et al., 2013). Ten participants denounced the colorblindness ideology, and Morris (White) stated
that he was unfamiliar with the philosophy. Sue (White) said, “I’m going to be blunt and say that
[colorblindness] is absolutely stupid. If you don’t see color, you’re not seeing people.” Dennis
(White) said, “I don’t think anyone can be colorblind or should be. It’s frankly a bit offensive
when I hear people say, ‘I’m colorblind.’ I’d much rather live in a world where people recognize
and appreciate people of different races and cultures.” Eli (Black) said, “If you tell me you’re
colorblind, you’re telling me you’re not appreciating me for who I am, and you’re ignoring part
of something that makes me who I am.”
Regarding meritocracy, participants were asked if they felt that everyone had an equal
chance to succeed at their company if they worked hard enough. No interviewee associated
success or lack of success with race. Two participants conveyed that their companies rewarded
employees equally. Kate (Asian) said, “I think our managers are very supportive of everyone that
works for them. If they see hard work, they help kind of raise that and provide opportunities for
70

you.” Sarah (Asian) offered, “my organization looks at, now more than anything, how do you do
the work and what is the work that you do.” Conversely, Lucy (Asian), Debra (Biracial), and Eli
(Black) both adamantly responded, “No.” Debra said, “It’s almost like the harder you work,
you’re kind of like a fool. Why are you doing that? Because you think you’re going to get ahead?
No, that’s not how this place is.” Eli explained, “Everybody doesn’t make friends the same way.
No matter how hard I work, no matter what I do, if the right person isn’t backing me or the right
person isn’t standing up for me, that [success] will never happen.” Finally, Lucy said, “It’s a lot
less of like how hard you work and how much you do so much as who you know and like who
you’re buddies with.”
Charles, Dennis, Morris, Alan, Sue, and Jack (who are all White) revealed that they felt
like everyone at their company had an equal chance at success, but also shared the sentiment
which suggested that relationships and “who you know” in the company made a big difference in
obtaining promotions, recognition, and success. Unlike the participants who were non-White,
each of the White participants expressed mixed feelings regarding meritocratic beliefs. For the
sake of clarity, sentiment offered from each of the White participants is presented in Table 7.

71

Table 7
Sentiment Regarding Meritocracy From Interviewed Participants With Mixed Perceptions
Do you feel that at your workplace, everyone has an
equal chance to succeed if they work hard enough? (participant quotes)
Participant yes… but…
Charles
(White)
It is a very results-based
company.
There are factors beyond your control. There are
individuals that you might work with that have an
impact on your career trajectory in a very positive
or a very negative way, so there’s certainly no
guarantee.
Dennis
(White)
I believe everyone has a
chance if they work
hard... smart and work
hard.
I also think, because of unconscious bias, certain
individuals will have an easier time of that than
others.
Morris
(White)
In my heart of hearts, in
my mind of minds, yes.
I also see, I’m gonna say, “favorites.” I see a bias
towards certain people. What I see is people that
have been together longer and working together in
the same team or the same company, I see kind of a
bias for what’s familiar, and if the biases for
familiar and you’re not part of that familiar or
favorite group than I, I think there’s there is a
limitation.
Alan
(White)
I definitely think if you’re
hard-working, it
definitely shows, and it
pays off.
I’ve been on teams where it’s really like if your direct
manager doesn’t like you, you’re not going to go
anywhere. Your manager has to like you. If your
direct manager isn’t that type of person, you’re
really not going to get anywhere. You can be doing
great things within your team, but nobody’s seeing
it, but your manager.
Sue
(White)
I think so.
It is true to a point. They make sure that the [hiring]
loops are diverse, but the winner was always like
the most educated and most experienced. White
male people have been the most educated and
experienced from the day they were born.
Jack
(White)
I truly do believe that. I
believe that
[participant’s company]
absolutely values the
quality of your work.
If you just pick yourself up by your bootstraps and
work hard, you’re going to have the same equal
opportunity, as everybody else is not true, and I
think data shows that that’s not true. Whether or not
you want to choose to actually acknowledge that is
far different.

72

As displayed in Table 7, each of the participants who identified as White agreed in the validity of
SLB while also conveying sentiment that SLB could not be relied upon due to factors outside of
one’s control.
Responses to Realistic and Symbolic Threats
Survey Data Regarding Threats
Intergroup threat theory posits that intergroup threat is experienced when one group
perceives that another group will cause them realistic or symbolic harm. Five survey items
measured the perceptions of how diversity could affect participants in the workplace (threats).
There was no missing data from participants who identified as Asian (sample size 49). Data from
two participants who identified as BIPOC (underrepresented) were missing, leaving a sample
size of 99. Data from one White participant were missing, which left a sample size of 238. Figure
10 shows the distribution of results for participant perceptions of threats among those
participants who identified as Asian, BIPOC (underrepresented), and White. Figure 11 shows the
aggregate results for the total population of participants, including individuals who preferred not
to indicate their racial/ethnic identity.

73

Figure 10
Analysis for Threats of Asian, BIPOC (underrepresented), and White Participants

23%
16%
28%
21%
12%
31%
13%
17%
20%
20%
19%
12%
16%
23%
30%
0%
5%
10%
15%
20%
25%
30%
35%
1 2 3 4 5
Participant responses to threat category
Responses from not at all agree (1) to strongly agree (5)
THREATS
ASIAN BIPOC (underrepresented) WHITE
74

Figure 11
Aggregate Analysis for Threats of All Participants

Figure 10 shows the sum total survey data results for the threats category of Asian
participants (M = 3.0, 𝑥 ̅ = 2.83, s = 1.13, N = 49), underrepresented BIPOC (M = 3.0, 𝑥 ̅ = 2.87,
s = 1.30, N = 99), and White participants (M = 3.5, 𝑥 ̅ = 3.32, s = 1.26, N = 238). The results for
the total population shown in Figure 11were (M = 3.2, 𝑥 ̅ = 3.13, s = 1.27, N = 394). The survey
data showed that 31% of White participants either hardly agreed or did not at all agree with the
survey items, which suggested that the promotion of diversity might not increase discrimination.
Thirty-nine percent of Asian participants and 44% of underrepresented BIPOC also hardly
agreed or did not at all agree. In contrast, 53% of people who identified as White somewhat to
strongly agreed with the survey items, which suggested that the promotion of diversity in the
workplace could increase discrimination. Thirty-two percent of Asian participants and 40% of
underrepresented BIPOC also somewhat to strongly agreed. In aggregate, almost half (47%) of
23%
13%
18%
22%
25%
0%
5%
10%
15%
20%
25%
30%
1 2 3 4 5
Participant responceses to threats
category
Responces from not at all agree (1) to strongly agree (5)
THREATS
75

the participants presented somewhat to strong agreement regarding the perceptions of threat that
promotion of diversity and anti-racist activity could lead to racial discrimination and job
insecurity.
Interview Data Regarding Threats
Survey data revealed that perceptions of threat existed in all social groups. To gain
additional insight, each interviewee was asked about antecedents that could be perceived as
realistically or symbolically threatening. The antecedents were about recruitment and
promotions, affirmative action policies that would require employers to hire more people of
color, and the personal impacts that increasing racial diversity would have on the participant. All
interviewees shared perceptions that they welcomed increasing diversity in their workplaces and
that messages promoting diversity were positive. Sarah (Asian) said, “always hire the best
person, but the best person sometimes has to be looked at more broadly and holistically.” Kate
(Asian) shared that based on her experience, the youngest generations entering the workforce
want to work for a company that “proudly says they are open and diverse” and actively hires for
diversity. At the same time, Eli (Black) stressed the importance of due diligence in hiring
qualified BIPOC candidates:
Do it the right way. Don’t do it to meet a number. You don’t want to do that and have
your turnover be just as high as the [number of] people you’re bringing in. If you’re
bringing people in and you meet a number, and over half of them leave, that means you
potentially didn’t bring the right people in. Or, you’re not equipping them enough to be
able to manage and handle the situation.
76

Support for policies such as Affirmative Action was evenly split between the participants
who fully supported the policies and those who somewhat supported the policies. Table 8
displays sentiment from each interviewed participant regarding Affirmative Action.

Table 8
Sentiment Regarding Affirmative Action From Interviewed Participants
Participant perceptions about affirmative action policies
Expressed full support (participant quotes)
Kate
(Asian)
I don’t have any negative opinions about it.
Sarah
(Asian)
I believe it’s a very positive thing, so long as the intent is positive. I think it’s a
good thing.
Jack
(White)
I personally believe that Affirmative Action or that that type of methodology is
important because I think it’s the only way you begin to actually level the
playing field, programs like Affirmative Action are forcing functions.
Alan
(White)
I definitely don’t see that negative in them. To me, all they do is give others
opportunity. You’re basically expanding your pool of candidates, and there’s
absolutely nothing wrong with that. The more diverse your team is, the better for
everybody.
Morris
(White)
Yeah, I agree with it. I’m in support of a workforce that, on a percentage basis, is
parity with what your population is. I absolutely believe that.
Charles
(White)
I support Affirmative Action in a way that I don’t think I did five years ago. I
would prefer that we didn’t need it, but as I have looked more and more at it, and
listened to people’s stories, and seen the way that the power structures react to
being challenged, I do think we need it. So yeah, I do support it.
Somewhat or cautiously support (participant quotes)
Lucy
(Asian)
Affirmative Action can backfire.
Eli
(Black)
I’m torn because I look at it two ways. I look at it as a great opportunity to bring in
the right talent. I also look at it as someone’s potential opportunity to bring in
people they know will fail and say, “See, I tried to do this, and it didn’t work
out.” Used the right way, I think it’s beneficial. Used incorrectly, it definitely
becomes a situation of, “see, I tried it your way, and it doesn’t work.”
Debra
(Biracial)
That’s the only way you’re gonna start getting people in those positions. People
who aren’t of color, they would not like it. They would just say, like with an
education, you’re only here because you are of a certain race.
77

Participant perceptions about affirmative action policies
Sue
(White)
I’m not opposed to Affirmative Action and encouraging those numbers and
whatnot because that’s how you measure progress, but I think that that’s not
going to solve the problem. I think that it starts much earlier, before the tech
sector, so I’d say I’m lightly in agreement that we should have some kind of
metrics or statements or policies.
Dennis
(White)
I think it seems like a policy like that would probably have good intentions behind
it, but I think it could have poor outcomes. It could result in unqualified
candidates getting positions, you know, that aren’t a good fit. I think that creates
problems people don’t think about. It’s not like someone’s magically going to
become a good fit.

Support of Corporate Activities Regarding Diversity
Survey Data Regarding Support
Two survey items measured participant support for corporate activities to promote racial
diversity in the workplace (support). Specifically, survey data revealed levels of participant
agreement of corporate diversity training and corporate participation in social/racial justice and
activism. There was no missing data from participants. Figure 12 shows the distribution of
results for participant support among those participants who identified as Asian, BIPOC
(underrepresented), and White. Figure 13 shows the results for the total population of
participants, including individuals who preferred not to indicate their racial/ethnic identity.

78

Figure 12
Analysis for Support of Asian, BIPOC (underrepresented), and White Participants

0%
2%
19%
37%
42%
2%
4%
12%
28%
53%
5%
3%
12%
27%
53%
0%
10%
20%
30%
40%
50%
60%
1 2 3 4 5
Participant responses to support category
Responses from not at all agree (1) to strongly agree (5)
SUPPORT
ASIAN BIPOC (underrepresented) WHITE
79

Figure 13
Aggregate Analysis for Support of All Participants

Figure 12 shows the sum total survey data results for support among Asian participants
(M = 4.0, 𝑥 ̅ = 4.18, s = .73, N = 49), underrepresented BIPOC (M = 4.5, 𝑥 ̅ = 4.26, s = .86, N =
101), and White participants (M = 4,5, 𝑥 ̅ = 4.19, s = 1.01, N = 239). The aggregate results for the
total population shown in Figure 13 were (M = 4.5, 𝑥 ̅ = 4.19, s = .94, N = 397). The data revealed
that 79% of surveyed participants somewhat to strongly agreed with corporate activities that
4%
3%
14%
28%
51%
0%
10%
20%
30%
40%
50%
60%
1 2 3 4 5
Participant responses to support category
Responses from not at all agree (1) to strongly agree (5)
SUPPORT
80

supported diversity and anti-racism. The median and mean scores showed that most participant
responses in each social group fell to the right of the scale midpoint (midpoint = 3).
Interview Data Regarding Support
Interviewees’ sentiment aligned with survey data analysis for support. Each interviewee
stated that they supported their companies’ efforts to drive education and training on topics
related to racial diversity. Participants who were aware of how their company supported anti-
racism outside of the workplace also supported the outside corporate efforts. Each interviewee
agreed that corporate diversity training was beneficial. All participants except Sue (White)
agreed that corporate diversity training was essential. When asked if the training was essential in
the workplace, Sue stated,
That’s where I kind of probably divert from some other peoples’ opinions. When you put
it in the work environment, that’s where I find myself drawing a line. It’s not essential for
me to partner with my teammates. It’s not essential for me to get my job done.
However, Sue also stated that training was essential, “to participate in the community, it’s
essential for people to get along in the world in general.”
The reasons that stated why training was beneficial and essential were similar. Jack
(White) said that he thought training was beneficial and “absolutely critical.” He shared,
I think that this is something that we have to face is a necessary step to start shaking
people out of a level of comfort. People aren’t willingly going to do this stuff unless it’s
something that, quote-unquote, impacts them directly. Maybe it’s something that gets
them to have that conversation or read that book or watch that documentary.
Like the other interviewed participants, Morris (White) said that he believed diversity
education was important. Morris stated, “knowing what I know as a result of my experiences at
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major technology companies, two of them now, I’d say it’s absolutely essential.” Morris also
expressed that diversity activities and programs were needed to ensure that some people were not
unintentionally disenfranchised. He conveyed that there was a need to recognize White people
who have not knowingly participated in discriminatory behavior:
Programs need to make sure that they’re not disenfranchising those that may not know
any better. Sometimes diversity, equity, and inclusion can disenfranchise those that may
not know better to be able to do better, it’s like, “what happened?” We were doing it this
way all these years, and now all of a sudden were wrong or in the wrong. I just say that
it’s not explained that well. There needs to be an equal amount of explanation for those
that might be in the majority, so they understand that they’re, you know, not doing
anything, you know, wrong. There’s no attack on them, it’s just trying to get things
aligned to the reality of the world that we live in.
Support for Mandatory Diversity Training in the Workplace
Survey Data Regarding Support for Mandatory Diversity Training
Most survey respondents indicated that their companies required all employees to
complete mandatory diversity training. The survey participants who indicated that diversity
training was mandatory also indicated that they had completed it. Table 9 depicts the numbers of
participants who worked for companies where diversity training was mandatory and for
companies where diversity training was not mandatory.

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Table 9
Participation in Corporate Diversity Training
Participant racial
identification
Employer does NOT
require all employees
to complete diversity
training
Employer DOES
require all employees
to complete diversity
training
Participant
count total
Participant
count
Participant
percentage
Participant
count
Participant
percentage
Asian 9 18% 40 82% 49
BIPOC (underrepresented) 23 29% 78 77% 101
White 36 15% 203 85% 239
Prefer not to respond 1 14% 7 88% 8

Interview Data Regarding Support for Mandatory Diversity Training
None of the interviewed participants provided a response that suggested disapproval of
mandatory corporate training that supported diversity. Kate (Asian) shared her reason for
supporting mandatory training was because she did not think that many White Americans had
experience with discrimination or racism. Kate said, “They [White Americans] have a lot of
privilege, and they don’t think from our [non-White] perspective or understand what we go
through. The only way [they will understand is] if it’s mandatory.” When Charles (White) was
asked about his thoughts on any adverse outcomes from mandatory training and corporate
support for anti-racism, he said, “you may lose some talented engineers here and there.” Then he
added, “I think the benefits outweigh the loss of any one individual.”
Demonstration of Support Through Voluntary Actions
Survey Data Regarding Voluntary Actions
Among the 397 participants of this study, 95% indicated that they voluntarily engaged in
activities in support of diversity and anti-racism. Twenty participants (5%) indicated that they
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did not take any actions voluntarily. Table 10 shows the number of participants who voluntarily
participated in an activity in support of diversity and anti-racism and the racial category in which
they defined themselves.

Table 10
Voluntary Participation in Activities that Support Diversity and Anti-racism
Racial identification
Did nothing Did something
Participant
count total
Participant
count
Participant
percentage
Participant
count
Participant
percentage
Asian 3 6% 46 94% 49
BIPOC
(underrepresented) 2 2% 99 98% 101
White 15 6% 224 94% 239
Prefer not to respond 0 0% 8 100% 8

The majority of the participants revealed that they engaged in multiple activities
voluntarily. Among the participants, 171 indicated that they engaged in voluntary diversity
training, 183 watched documentaries, films, or videos containing race-related information during
their own time, 172 engaged in discussions about racial issues at their workplace, 143 read books
regarding diversity, 123 donated monies to racial justice organizations, 177 voted for issues that
promoted social equality, and 139 did something else.
Interview Data Regarding Voluntary Actions
All interviewees were among those that engaged in activities voluntarily. Sarah and Kate
(who were Asian), and Dennis, Charles, and Jack (who were all White) revealed that the actions
they took were not done in a private setting. Sarah marched in a demonstration that supported
anti-racism with friends and family. Dennis described himself as an active advocate who wrote
blog posts, voiced support for BLM, and engaged in conversations with people who have
opinions opposite his own. Dennis said, “I’ve decided to become a more active advocate so that,
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you know, if I see something, I’m much more willing to say something and want to say
something.” Dennis explained, “I think there’s a need for anybody with a voice to do the right
thing. That includes racial justice, includes social justice, includes justice in general.” Kate
shared that she now would step in and help anyone she sees is being taken advantage of or being
ignored because of their race or abilities. She said, “I’m not the type of a protest person [who]
will go out and walk around and carry a big sign and saying all that. I’d rather take action and do
something about it.” She also said, “If I’m offended, I’m going to tell you I’m offended.” She
stated that some people think she is overly sensitive, and Kate’s response to that way of thinking
was, “well, someone has to be overly sensitive to bring it to the front of your mind.” Both
Charles and Jack described having tough conversations with family members and friends.
Charles shared a memory of what he did when his friend used terminology that was
discriminatory. Charles said, “I have felt obliged to push back. It’s not comfortable, and it’s not,
you know, doing any great benefit to the friendship, but I felt I had to say something.” Jack
became teary as he shared his experience when having conversations related to race with his
family members. Jack said, “we actually lost incredibly close family because of the
conversation.”
Levels of Support for Corporate Interventions
An item on the surveys allowed participants to select from a list of 12 proposed
interventions that corporations should take regarding diversity. Marking items indicated
agreement with the selected intervention. Four levels of support were measured for this survey
item (support for pro-diversity interventions, support to decrease interventions, support for both
pro-diversity interventions and decreasing interventions, and support on placing resources for
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existing employees or making other undefined interventions). Table 11 shows the aggregate
number of participants that indicated support for each level.

Table 11
Levels of Support for Corporate Interventions
Levels of support for corporate interventions
Number of
participants
% of
participants
Support diversity interventions 247 63%
Decrease interventions 26 7%
Both support and decrease interventions 99 25%
Support for placing resources for existing
employees or making other undefined
interventions
22 6%

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Table 11 depicts the levels of support at each intervention level. Data from three
participants were missing, which left a sample size of 394. Two hundred forty-seven participants
(63%) marked choices that supported pro-diversity interventions without marking any decreases.
Twenty-six participants (7%) marked choices that proposed decreases in diversity interventions.
Ninety-nine participants (25%) marked both pro-diversity interventions and decreasing diversity
intervention choices. Twenty-two participants (6%) marked neither level of support for the
interventions that were presented, but they marked support for interventions that would focus on
existing employees or for making other undefined interventions. Overall, when looking at all
participants who supported pro-diversity interventions, even if they also concurrently supported
to decrease interventions, 346 participants (88%) indicated support for one or more pro-diversity
corporate interventions.
Support for pro-diversity interventions and support to decrease interventions were
apparent in all social groups. Table 12 shows the results of how participants responded to pro-
diversity activities.

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Table 12
Support for Pro-Diversity Corporate Interventions

My company needs to do
the following:
Racial identification
Asian
BIPOC
(underrepresented) White
Preferred
not to
self-identify
Set goals for hiring more
people of color.
15 31% 45 45% 107 45% 2 25%
Set goals for promoting
more people of color.
20 41% 48 48% 102 35% 1 13%
Increase diversity
training.
20 41% 42 42% 100 35% 3 38%
Commit to partnerships
with minority-owned
businesses.
16 33% 38 38% 65 22% 2 25%
Invest in underserved
communities.
16 33% 31 31% 68 24% 4 50%
Donate money to
underserved
communities.
16 33% 27 27% 64 22% 4 50%

Table 12 shows how participants responded to items asking about how their companies
should support diversity. The table depicts responses in accordance with the participants’
identified social groups. Regarding setting goals for hiring more people of color, 31% of the
Asian population, 45% of the BIPOC (underrepresented) population, 45% of the White
population, and 25% of the population who preferred not to indicate their racial/ethnic identity
supported this point. Among the participants who answered that goals for promoting people of
colors should be set, 41% of the Asian population, 48% of the BIPOC (underrepresented)
population, 35% of the White population, and 13% of the population who preferred not to
indicate their racial/ethnic identity supported this point. Among the participants who answered
that diversity training should be increased at their companies, 41% of the Asian population, 42%
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of the BIPOC (underrepresented) population, 35% of the White population, and 38% of the
population who preferred not to indicate their racial/ethnic identity supported this point. Among
the participants who answered that their company should commit to partnerships with minority-
owned businesses, 33% of the Asian population, 38% of the BIPOC (underrepresented)
population, 22% of the White population, and 25% of the population who preferred not to
indicate their racial/ethnic identity supported this point. Among the participants who answered
that their company should invest in underserved communities, 33% of the Asian population, 31%
of the BIPOC (underrepresented) population, 24% of the White population, and 50% of the
population who preferred not to indicate their racial/ethnic identity supported this point. Among
the participants who answered that their company should donate money to underserved
communities, 33% of the Asian population, 27% of the BIPOC (underrepresented) population,
22% of the White population, and 50% of the population who preferred not to indicate their
racial/ethnic identity supported this point. Table 13 shows how participants responded to choices
to decrease pro-diversity activities.

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Table 13
Support for Decreasing Corporate Interventions

My company needs to do the
following:
Racial identification
Asian
BIPOC
(underrepresented) White
Preferred
not to self-
identify
Reduce diversity training
9 18% 10 10% 47 20% 1 13%
Stop diversity training
6 12% 14 14% 35 15% 0 0%
Disengage in social justice
activities
3 6% 12 12% 39 16% 1 13%
Prohibit political discussions
at work
8 16% 22 22% 39 16% 2 25%

Table 13 shows how participants responded to items asking about decreasing corporate
interventions regarding diversity. The table depicts responses in accordance with the
participants’ identified social groups. Regarding reducing diversity training, 18% of the Asian
population, 10% of the BIPOC (underrepresented) population, 20% of the White population, and
13% of the population who preferred not to indicate their racial/ethnic identity supported this
point. Among the participants who answered that their company should stop diversity training,
12% of the Asian population, 14% of the BIPOC (underrepresented) population, and 15% of the
White population supported this point. Among the participants who answered that their
companies should disengage in social justice activities, 6% of the Asian population, 12% of the
BIPOC (underrepresented) population, 16% of the White population, and 13% of the population
who preferred not to indicate their racial/ethnic identity supported this point. Among the
participants who answered that political discussions should be prohibited at their companies,
16% of the Asian population, 22% of the BIPOC (underrepresented) population, 16% of the
White population, and 25% of the population who preferred not to indicate their racial/ethnic
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identity supported this point. Table 14 shows the results of how participants responded to
concentrating resources on existing employees or taking some other undefined actions.

Table 14
Support to Focus Resources on Existing Employees or Making Other Undefined Interventions

My company needs to do the
following:
Racial identification
Asian
BIPOC
(underrepresented) White
Preferred
not to self-
identify
Invest in current employees
instead of outside businesses
14 29% 28 28% 86 36% 3 38%
My company needs to do the
following: Something else
3 6% 7 7% 19 8% 1 13%

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Table 14 shows how participants responded to items that asked if their companies should
invest in current employees instead of outside businesses. The table depicts responses in
accordance with the participants’ identified social groups. The results showed that 29% of the
Asian population, 28% of the BIPOC (underrepresented) population, 36% of the White
population, and 38% of the population who preferred not to indicate their racial/ethnic identity
supported this point. Among the participants who answered that their companies should
participate in some other unidentified intervention, 6% of the Asian population, 7% of the
BIPOC (underrepresented) population, 8% of the White population, and 13% of the population
who preferred not to indicate their racial/ethnic identity supported this point.
Summary
The majority of people who participated in the study conveyed that they became more
aware of racism in the United States after the death of George Floyd and the events that followed
May 2020. These participants revealed that they voluntarily engaged in activities that supported
issues surrounding diversity and anti-racism. They also revealed that they agreed with their
corporations’ efforts to support diversity training and diversity in the workplace. At the same
time, when responding to issues regarding race, the majority of participants across each social
group showed some level of uneasiness with the possibilities of being personally affected by pro-
diversity activities.

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Chapter Five: Discussion and Recommendations
Research prior to 2020 suggested that corporate support for diversity and anti-racism
might stimulate resistance to a diversified workplace among White American employees in the
United States (Dover et al., 2016; Kaiser et al., 2013; Wellman et al., 2016). Findings derived
from information collected through surveys and interviews for this study corroborated some of
the prior research. Data analysis revealed that the antecedents in the conceptual framework did
result in adverse perceptions of threat from the promotion of diversity and anti-racism corporate
activities. However, despite the perceptions of threat, the data collected also suggested that
events that occurred since May 2020 contributed to people’s awareness of racism in the United
States and to their willingness to support pro-diversity activities in the workplace.
Data analysis also revealed that meritocratic systems in corporations continued to be
acceptable, even though conflicts existed between the values of diversity and meritocracy
(Konrad et al., 2021). Meritocratic systems form discriminatory barriers because such systems
are subjective and inadvertently perpetuate exclusion. Conflicts arise because human biases play
a key role in evaluation within meritocratic systems (Konrad et al., 2021).
Discussion of Findings and Results
In the United States, throughout 2020 and into 2021, there were a series of events, such
as the death of George Floyd, BLM marches, and the insurrection attempt on January 6, 2021.
These were only three of many more events that occurred and were continuously reported
through mass media and social media in the United States and globally (Dixon & Dundes, 2020).
The common threads of these events were acts of discrimination, violence, and the death of
people of color in situations whereby the perpetrators of violence legitimized their actions
through socially acceptable parameters (Leonard, 2016; Liao et al., 2016; Vang & Myers, 2021).
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These parameters disguised underlying issues of racism that were confronted by the evidence
presented by the media (Dixon & Dundes, 2020). The existence of systemic racism in the United
States was exposed, and its history and ongoing prevalence were recognized (de Oca et al., 2020;
Fine, 2021). This information became almost inescapable to the average person (Cramer, 2020).
Thus, people in the United States shared the social experience of looking at themselves and
others in terms of race. People who may not have previously grasped the nature of racism had to
decide for themselves what these events meant (Jackson, 2011; Mendes, 2021). The results of
this study showed that the events influenced many people to seek a greater understanding of race
and social systems in the United States and how racism has affected BIPOC. In fact, 95% of the
participants in this study indicated that they voluntarily engaged in activities in support of
diversity and anti-racism (Table 9).
As can be anticipated by the tenets of intergroup threat theory, almost half of the
participants agreed that the efforts to increase diversity in the workplace could simultaneously
increase anti-White and anti-Black biases. The theory also allowed the prediction of the findings
regarding zero-sum game responses. The majority of participants did indeed agree that they
expected zero-sum-game issues whereby the gains for one group would result in losses for
another group (Norton & Sommers, 2011).
Intergroup Threat Theory
Intergroup threat theory presented that when threats are experienced, people will respond
with negative reactions. Here, the data deviated from the theory. The reasons for the deviation
fell outside of the scope of the data collected. While the results showed that almost half of the
participants perceived threats, they responded positively by taking actions that increased their
awareness, engaged voluntarily in activities to educate themselves about race in the United
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States, entered conversations that were sometimes uncomfortable, and invested in activities that
could resolve practices that perpetuated racism. Additionally, the majority of participants
supported pro-diversity and anti-racism corporate-sponsored activities, such as workplace
diversity training, and they perceived these activities to be beneficial and essential. Thus, the
research question was answered. Participants, indeed, positively perceived their corporations’
efforts to support diversity.
Prior research offered little empirical evidence that diversity training changed negatively
biased behavior of employees who were typically members of the group that held the most
power within organizations (Chang et al., 2019; Paluck, 2006). Results from this study revealed
that corporate diversity training was an important resource to most participants, regardless of the
racial/ethnic group of which they identified. It appeared that in conjunction with the social
experiences that occurred throughout 2020 and into 2021 that made the realities of racism
undeniable, corporate diversity training provided opportunities to discuss topics that were often
otherwise deemed problematic, offensive, or taboo. It appeared that most participants wanted to
broaden their awareness, gain objective information, and explore ideas about workplace diversity
and inclusivity. Thus, though prior empirical evidence about the effectiveness of corporate
diversity training was lacking, most participants in this study presented that it was a valued
resource.
Meritocracy as a Status-Legitimizing Belief
An SLB is a myth, belief, or opinion that serves to justify, rationalize, and legitimize a
status system (Jost, 2018). Meritocracy is an SLB. In the United States, a system of meritocracy
means that an individual’s status in society is achieved through merit and hard work (McCoy &
Major, 2007; Newman et al., 2015). Thus, people who have elevated prominence have
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demonstrated that they are more talented and hard-working than people who have lower status
(McCoy & Major, 2007). It is important to note that status-legitimizing ideologies and beliefs
rationalize systems that perpetuate inequality (Chow et al., 2013) and serve to validate
meritocracies (McCoy et al., 2013).
An incongruity in attitude from participants related to meritocracy emerged from the
data, and this uncertainty mirrored past research findings. Newman et al. (2015) noted that
survey data over the past 30 years “consistently reveal variation and ambivalence in public
attitudes toward meritocracy in America.” In this study, the majority of interviewed participants
from each of the racial/ethnic groups appeared to recognize that historically, the employment of
Black and Brown people had been affected by racism. Yet, at the same time, most interviewees
made no connection between race and success in employment. Most interviewees presented that
success or lack of success at work was not associated with race, and instead, success was
associated with whom you knew. Further, most of the interviewees supported their companies’
meritocratic systems. One explanation for the support of the SLB of meritocracy may be that
higher-income individuals may be more likely to defend a meritocratic economic system because
they personally benefit from it and desire its continuation (Newman et al., 2015). According to
Payscale.com, early in career salaries from U.S. based technology companies started at over
$80,000, and employees with 10 or more years of experience commonly earned over $125,000.
Thus, employees who believe themselves to be successful within the meritocratic system support
the system that is working for them.
Recommendations
Companies have recognized that increasing diversity is in their best interest, and
corporations have implemented pro-diversity and anti-racism programs within their
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organizations (Alegria, 2020). In addition to continuing pro-diversity actions, activities that
support inclusion and recognize the value each employee brings to their workplace should be
incorporated into corporate diversity strategies (Sabharwal, 2014). This study recognized the
weaknesses of meritocracy, such as subjectivity and bias in hiring or promoting. Based on the
results, equitable practices in the workplace will be suggested to address non-inclusive work
practices that ultimately protect the status quo.
Corporations are designed to be profit-making engines. As Milton Friedman (1970)
wrote, “there is one and only one social responsibility of business - to use its resources and
engage in activities designed to increase its profits so long as it stays within the rules of the
game” (para, 33). Therefore, recommendations for diversity and inclusion that were derived from
the analyses of the data considers the interests of corporations. Recommendations include
increasing underrepresented racial representation of executive and senior leaders, installing
inclusive practices without damaging perceptions of fairness among majority social groups, and
continuing corporate diversity training programs. Because the study focused on U.S.-based
corporations, EEO-1 report data from Apple, Cisco, Facebook, Google (Alphabet), Intel, and
Microsoft were used as examples in each recommendation.
Recommendation 1: Increase Racial Representation of Executive and Senior Leaders
Racially heterogeneous leadership teams can better represent multicultural organizations
and positively impact organizational outcomes (Creek et al., 2019). Research showed that
diverse leadership teams made up of individuals with varying experiences, values, and beliefs
outperformed homogeneous leadership teams. The data between 2001-2003 from 115 Fortune
1000 firms (across 57 industries) were analyzed to investigate the relationships between
managerial racial diversity and organizational performance. The researchers found a positive
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correlation between firms with racially diverse management and a higher probability that these
firms would increase market share and profit (Andrevski et al., 2014).
When perspectives and expertise were unique and varied across racially diverse
leadership teams, creativity and innovation were promoted. Miller and Triana (2009) analyzed
the demographic data of boards of directors (boards) from Fortune 500 companies and
discovered a relationship between racially diverse boards and company performance. The
researchers found that racially diverse boards were positively correlated with innovation in the
form of research and development expenditures. Racially diverse boards increased their
companies’ reputations by indicating that their board members understood the diverse
environment in which their companies operated (Bear et al., 2010; Miller & Triana, 2009).
Diversity in leadership has also been connected to financial gains. The research showed a
statistically significant correlation between diverse leadership teams and increased financial
performance. Researchers found that earnings before interest and taxes (EBIT) of public
companies in the U.S. rose 0.8% for every 10% increase in racial and ethnic diversity on the
senior executive team (Hunt et al., 2015). Table 15 depicts the predicted EBIT of six U.S.-based
technology companies if each company was to increase their executive/senior leadership racial
makeup by 10%.

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Table 15
Predicted EBIT After Increasing BIPOC Executive/Senior Leadership

Note. All six companies are headquartered in the United States. Each company’s EBIT for the 12
months ending December 31, 2020, was retrieved on March 13, 2021, from Macro Trends:
https://www.macrotrends.net/ (see Appendix H for EBIT reports). Average U.S. technology
executive salary information was retrieved on March 13, 2021, from Glassdoor:
(https://www.glassdoor.com/Salaries/technology-executive-salary-SRCH_KO0,20.htm)

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Table 15 presents that increasing executive/senior racial representation by 10% would
increase a company’s EBIT. The costs associated with increasing racial representation would be
covered by the increase in EBIT for each company. Most importantly, evidence based on
research supported the idea that each company depicted in Table 15 would gain multi-million
dollar increases in EBIT due to increasing diversity in their executive/senior leadership ranks
(Hunt et al., 2015). Thus, corporations with adequate racial/ethnic representation in leadership
achieved better financial performance (Hunt et al., 2015).
While the majority of interviewed participants did not associate success with race and
instead commented that success at work was mostly based on whom they knew, prior research
has suggested that differences in social and economic status were linked to racial group
membership (Glaser, 2005; Wellman et al., 2016). Given that corporations have stated that they
want to increase diversity, increasing racial representation in executive/senior leadership would
be required. Increasing representation can be accomplished by growing the number of
executive/senior leaders within each company. Companies do not have to terminate existing
members of their executive teams in order to increase diversity in executive/senior leadership.
Therefore, zero-sum game ideology whereby progress towards equity for BlPOC means
increased inequity for Whites should not be a reason for opposing this strategy (Norton &
Sommers, 2011).
Recommendation 2: Consider Employee Engagement in Corporate Diversity Goals and
Corporate Profit
Employee engagement represents the levels of passion, commitment, purpose, and
connection employees have with their organizations (Mone et al., 2011). Employee engagement
impacts workplace productivity, and productivity impacts corporate profitability. Therefore,
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employee engagement, involvement, and satisfaction should be of great concern for
organizations (Catteeuw, 2007; Mone et al., 2011). Thus, practical strategies that encourage
employee engagement are recommended, and these should be incorporated in corporate diversity
strategies. Policies that conflict with corporate diversity goals would have to be modified.
One approach that considers employee engagement in supporting corporate diversity
goals is corporate sponsorship for developing cross-social-group relationships. Engaging with
co-workers outside of one’s social group and establishing trust can be complex. Therefore,
programs could be designed to provide opportunities for employees of different backgrounds to
create relationships with other employees and gain a deeper understanding of unfamiliar cultures,
abilities, and more. One-to-one connections with others are the foundations for change, and
trusting relationships are the glue that holds people together (Glisson, 2019).
Examples of methods to support employee engagement are cross-cultural events and
mentoring programs. The overall intention of these events and programs would be to build and
support inclusive behaviors to increase a sense of belonging and inclusion. Ninety-five percent of
the participants in this study indicated that they voluntarily engaged in a learning activity in
support of diversity and anti-racism. Among this population, 59% were White. Based on data,
there is an opportunity to invite White people into situations to grow their allyship and learn how
to use their voice to bring about change. Opportunities to drive change may not be initially
apparent to White people in technology corporations in the United States, so cross-group work is
an important step for supporting organization diversity (Crary, 2017). Furthermore, cross-social-
group engagement programs designed to grow allyship could also work to address the trauma
that would-be White allies might experience as a result of recognizing and admitting that they
have benefited from racial oppression (Jackson, 2011). Corporate-sponsored engagements could
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result in coaching and training to increase White allyship competence and self-efficacy (Erskine
& Bilimoria, 2019). Ultimately based on the responses of the participants in this study, all groups
could benefit from opportunities to become allies and allow each to support change.
Hiring practices also play a part in employee engagement. As an example, employment
based on fit is a common practice (Higgins & Judge, 2004; Kristof, 1996). Unfortunately, hiring
for fit can translate to hiring recommendations being made based on how similar or familiar a
candidate was to their interviewers (Kristof-Brown, 2000). One modification that could be made
to the existing practice of hiring for fit is to include values-based criteria for evaluating
candidates (Elfenbein & O’Reilly, 2007). To alter the behavior of seeking candidates that fit the
status quo, HR departments could redefine fit and include a way of evaluating the alignment
between corporate values and a candidate’s values. Determining fit would incorporate
considerations about how a candidate values diversity, inclusion, and equity as well as how a
candidate would add to the overall diversity of a corporation (Elfenbein & O’Reilly, 2007;
Gaspar & Brown, 2015; Pollitt, 2008). The majority of the participants in the study indicated that
diversity training was important for creating an inclusive workplace environment. Therefore, it is
reasonable to suggest that diversity is perceived as a corporate value and should be considered in
hiring decisions. Hiring employees who share a corporations’ values enhances opportunities for
employees to form connections to their corporations, and this would contribute to employee
engagement.
Gallup (2017) conducted research and found that companies with high employee
engagement have 17% higher productivity and 21% greater profitability. Consequently, when
employees do not feel engaged and connected to their workplace, their productivity declines
(Gopal, 2006; Wolff, 2019). Table 16 depicts the potential gains that could be associated with
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high employee engagement. Table 17 depicts the potential losses that could be associated with
employee disengagement.

Table 16
Engaged Employees and Potential Gains in Profitability

Note. Employee population was obtained from EEO-1 2018 report data for Apple, Cisco,
Facebook, Google (Alphabet), Intel, and Microsoft (see Appendix A for EEO-1 reports).
Revenue per employee was calculated using the formula obtained from Good Calculators,
https://goodcalculators.com/revenue-per-employee-calculator/

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Table 17
Disengaged Employees and Potential Loses in Profitability

Note. Employee population was obtained from EEO-1 2018 report data for Apple, Cisco,
Facebook, Google (Alphabet), Intel, and Microsoft (see Appendix A for EEO-1 reports).
Revenue per employee was calculated using the formula obtained from Good Calculators,
https://goodcalculators.com/revenue-per-employee-calculator/

Table 16 shows that if 20% of employees become highly engaged and profitability
increased by 21% for each highly engaged employee, corporations would gain millions and
billions of dollars annually. Conversely, the reverse could be true. Disengaged employees have
lower productivity, which negatively impacts profitability. It is likely that employees who do not
feel included are susceptible to negative feelings and may become disengaged. Table 17 shows
that if 20% of the employees become disengaged and if revenue decreased by 10% for each
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disengaged employee, then their companies would lose millions and billions of dollars annually.
Based on data in this study, most employees supported corporate efforts regarding diversity in
the workplace, yet many participants also harbored concerns that these efforts might lead to anti-
White bias and anti-Black bias. Increasing employee engagement efforts could help quell
negative perceptions or concerns regarding pro-diversity efforts. Further, it is conceivable that if
concerns about the potential of racial biases are not addressed, such issues could lead to
employee disengagement.
Recommendation 3: Continue Diversity Training Programs
The data in this study showed that most surveyed participants (79%) supported corporate
pro-diversity and anti-racism activities, including diversity training. Eighty-three percent of the
surveyed participants indicated that diversity training was mandatory, and all of the interviewed
participants perceived that the diversity training they completed was beneficial. Therefore,
corporate diversity training programs should continue to be promoted, and diversity training
topics, such as recognizing bias, that focus on skill development should be assigned to
employees as mandatory training. Table 18 presents the approximate cost of diversity training.

105

Table 18
Approximate Cost of Diversity Training

Note. Employee population was calculated from information obtained from EEO-1 2018 report
data for Apple, Cisco, Facebook, Google (Alphabet), Intel, and Microsoft. Per-hour cost of
online diversity training ranges from $25 to $50 per employee. From “The 8 Best Diversity
Training Programs,” by The Balance Small Business (https://www.thebalancesmb.com/best-
diversity-training-programs-4843059)

Table 17 shows the potential costs of maintaining existing online diversity training.
While the estimated cost of diversity training for the large corporations depicted in Table 18
totals over $1 million annually, those costs would be less than half of one percent of EBIT. If
diversity training is no longer offered within corporations, employees might perceive its absence
as an indication that diversity is no longer valued. Given that most employees want the training,
if it is not available, its absence could become a source of employee disengagement. The
negative economic results for reasons related to employee disengagement (as seen in Table 17)
would outweigh the costs of continuing corporate diversity training.
Helping employees grow capabilities that align with internal corporate values can take
several years and requires constant reinforcement in order for changes to become permanent
106

(Wilhelm, 1992). In order to meet the goals of achieving anti-racism and anti-discriminatory
practices, corporations would need to adopt long term strategies that would help solidify new
practices. Companies may also have to consider that anti-racism and anti-discriminatory goals
might not be welcomed by all employees, and some employees may have difficulties adjusting to
corporate expectations. Leadership must be prepared to deal with such conflicts by providing
supportive services and clear expectations. Training should be designed as a step-by-step
evolution that begins with understanding bias. Training would then progress to exercising
empathy and embracing diversity before moving to topics regarding anti-racism and anti-
discrimination. To ensure new skills and behaviors become permanent, practicing awareness and
inclusion should be a daily occurrence in the workplace.
Recommendations Summarized
Corporate diversity training in and of itself is not enough to accomplish corporate
diversity goals (Bezrukova et al., 2012). The aforementioned recommendations are both practical
and profitable for corporations and consider the opinion that while corporations generate value
for society, they were not created to develop rules for the public good (Ramanna, 2020). Rightly
or wrongly, actions taken by corporations that support diversity and anti-racism should not be at
odds with the desires of corporate stakeholders if anti-racism and diversity goals are to be
achieved. Table 19 shows the potential economic benefit for companies if the aforementioned
recommendations are implemented.

107

Table 19
Potential Economic Benefit of Integrating Diversity Strategies

Note. Employee population was obtained from EEO-1 2018 report data for Apple, Cisco,
Facebook, Google (Alphabet), Intel, and Microsoft (see Appendix A for EEO-1 reports). All six
companies are headquartered in the United States. Each company’s EBIT for the 12 months
ending December 31, 2020, was retrieved on March 13, 2021, from Macro Trends
(https://www.macrotrends.net/) Average U.S. technology executive salary information was
retrieved on March 13, 2021, from Glassdoor (https://www.glassdoor.com/Salaries/technology-
executive-salary-SRCH_KO0,20.htm). Revenue per employee was calculated using the formula
obtained from Good Calculators, (https://goodcalculators.com/revenue-per-employee-calculator/)
Per-hour cost of online diversity training ranges from $25 to $50 per employee. From “The 8
Best Diversity Training Programs,” by The Balance Small Business,
(https://www.thebalancesmb.com/best-diversity-training-programs-4843059)
108

Independent of social and political concerns, Table 19 shows there are at least two
significant economic benefits related to increasing racial diversity in technology corporations.
The first is increased EBIT through increased racial diversity in the executive/leadership ranks.
The second is the increase in profitability that results from value-based hiring and fostering
cross-social-group engagement. Along with multimillion and billion-dollar gains, the
recommended strategies would cover costs associated with continuing existing diversity training.
Recommendations for Future Research
An unexpected result emerged from the qualitative data regarding personal experiences
and resolutions that were often made on a personal level. The events since May 2020 presented
dilemmas regarding how people wanted to participate in the social experience. The analysis
indicated that the dilemmas presented to BIPOC were different from the dilemmas presented to
people who were White. The dilemmas put both groups in positions where they could examine
anti-racism and pro-diversity efforts at their workplaces and examine whether or not they wished
to participate. Participation would, of course, offer opportunities to examine how these efforts
could affect their own lives. According to the data, most chose to contribute to change and
support corporate activities that promoted anti-racism. Future research could broaden knowledge
about how employees integrate their experiences with corporate anti-racism activities and
diversity training into their personal lives and how such experiences contribute to their choices
regarding change.
The shutdowns and quarantines caused by the COVID-19 pandemic contributed to the
unique environment during which this study was conducted. Many U.S.-based technology
companies had moved to a work-from-home model by 2020, which may have made it easier for
participants to be inundated by mass media and social media coverage of the social unrest in the
109

United States. The flooding of information regarding the issues of racism in the United States
was readily available, while almost unavoidable, to employees while they were working from
home. Future research undertaken with a population sample who work in-office (in-person)
versus remotely from home might present different results regarding support for diversity in the
workplace.
The events of 2020 and 2021 appeared to have encouraged support for racial equity from
the majority of the participants. In line with intergroup threat theory, most of the participants also
indicated that they were concerned that pro-diversity activities in the workplace would increase
discriminatory biases from others. Thus, these threats were identified. Yet, contradicting
intergroup threat theory, the participants did not respond to issues of threat in negative ways.
Rather, they continued to support pro-diversity and anti-racism goals, and they continued to
support diversity training. In the future, it would be important to obtain a greater understanding
of the variables that contributed to the participants’ abilities to overcome negative personal
perceptions of threats and then to respond by supporting activities that triggered these threats.
Conclusion
The recommendations demonstrate the notion that progress toward racial equality for
BIPOC is attainable when it converges with the interests of corporations whose executive and
senior leaders most often are people who identify as White. The opinion that striving to achieve
racial equity and equality in the workplace, whereby all people can participate or compete fairly
for the same opportunities, is the right thing to do might not be enough of a stimulus. However,
interest convergence can be a significant motivating factor for corporations. As Derrick Bell
(1980), lawyer, professor, civil rights activist, and author of critical race theory, noted, “The
110

interest of blacks in achieving racial equality will be accommodated only when it converges with
the interests of whites” (p. 523).
Eradicating systems within corporations that support racism while at the same time
increasing profitability within corporations can be accomplished. Based on the data, conscious
recognition of the issues of racism in the United States contributed to participants’ support for
pro-diversity and anti-racism activities within their workplaces. Participants employed at
technology corporations appeared to be in a state of readiness to become more involved in
corporate diversity goals. Maintaining this momentum and giving employees opportunities to
participate in change that is actionable, measurable, and evidence based is critical.
111

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Appendix A: Google’s Ideological Echo Chamber
How bias clouds our thinking about diversity and inclusion
go/pc-considered-harmful
James Damore - damore@
July 2017
Feel free to comment (they aren’t disabled, the doc may just be overloaded).
For longer form discussions see g/pc-harmful-discuss
Reply to public response and misrepresentation 1
TL;DR 2
Background 2
Google’s biases 2
Possible non bias causes of the gender gap in tech 3
Personality differences 4
Men’s higher drive for status 5
Non discriminatory ways to reduce the gender gap 5
The harm of Google’s biases 6
Why we’re blind 7
Suggestions 8

Reply to public response and misrepresentation
I value diversity and inclusion, am not denying that sexism exists, and don’t endorse using stereotypes.
When addressing the gap in representation in the population, we need to look at population level
differences in distributions. If we can’t have an honest discussion about this, then we can never truly
solve the problem.
Psychological safety is built on mutual respect and acceptance, but unfortunately our culture of shaming
and misrepresentation is disrespectful and unaccepting of anyone outside its echo chamber.
Despite what the public response seems to have been, I’ve gotten many personal messages from fellow
Googlers expressing their gratitude for bringing up these very important issues which they agree with but
would never have the courage to say or defend because of our shaming culture and the possibility of being
fired. This needs to change.

TL;DR
• Google’s political bias has equated the freedom from offense with psychological safety, but
shaming into silence is the antithesis of psychological safety.
• This silencing has created an ideological echo chamber where some ideas are too sacred to be
honestly discussed.
• The lack of discussion fosters the most extreme and authoritarian elements of this ideology.
o Extreme: all disparities in representation are due to oppression
o Authoritarian: we should discriminate to correct for this oppression
• Differences in distributions of traits between men and women may in part explain why we don’t
have 50% representation of women in tech and leadership.
• Discrimination to reach equal representation is unfair, divisive, and bad for business.

136

Background
People generally have good intentions, but we all have biases which are invisible to us. Thankfully, open
and honest discussion with those who disagree can highlight our blind spots and help us grow, which is
why I wrote this document 2. Google has several biases and honest discussion about these biases is being
silenced by the dominant ideology. What follows is by no means the complete story, but it’s a perspective
that desperately needs to be told at Google.

Google’s biases
At Google, we talk so much about unconscious bias as it applies to race and gender, but we rarely discuss
our moral biases. Political orientation is actually a result of deep moral preferences and thus biases.
Considering that the overwhelming majority of the social sciences, media, and Google lean left, we
should critically examine these prejudices:

___________________________________________________________________________
1 This document is mostly written from the perspective of Google’s Mountain View campus, I can’t speak
about other offices or countries.
2 Of course, I may be biased and only see evidence that supports my viewpoint. In terms of political
biases, I consider myself a classical liberal and strongly value individualism and reason. I’d be very happy to discuss
any of the document further and provide more citations.

Neither side is 100% correct and both viewpoints are necessary for a functioning society or, in this case,
company. A company too far to the right may be slow to react, overly hierarchical, and untrusting of
others. In contrast, a company too far to the left will constantly be changing (deprecating much loved
services), over diversify its interests (ignoring or being ashamed of its core business), and overly trust its
employees and competitors.

Only facts and reason can shed light on these biases, but when it comes to diversity and inclusion,
Google’s left bias has created a politically correct monoculture that maintains its hold by shaming
dissenters into silence. This silence removes any checks against encroaching extremist and authoritarian
policies. For the rest of this document, I’ll concentrate on the extreme stance that all differences in
outcome are due to differential treatment and the authoritarian element that’s required to actually
discriminate to create equal representation.

137

Possible non-bias causes of the gender gap in tech
At Google, we’re regularly told that implicit (unconscious) and explicit biases are holding women back in
tech and leadership. Of course, men and women experience bias, tech, and the workplace differently and
we should be cognizant of this, but it’s far from the whole story.

On average, men and women biologically differ in many ways. These differences aren’t just socially
constructed because:
• They’re universal across human cultures
• They often have clear biological causes and links to prenatal testosterone
• Biological males that were castrated at birth and raised as females often still identify and act like
males
• The underlying traits are highly heritable
• They’re exactly what we would predict from an evolutionary psychology perspective
Note, I’m not saying that all men differ from all women in the following ways or that these differences
are “just.” I’m simply stating that the distribution of preferences and abilities of men and women differ in
part due to biological causes and that these differences may explain why we don’t see equal
representation of women in tech and leadership. Many of these differences are small and there’s
significant overlap between men and women, so you can’t say anything about an individual given these
population level distributions.
____________________________________________________________________________
3 Throughout the document, by “tech,” I mostly mean software engineering.

Personality differences
Women, on average, have more:
• Openness directed towards feelings and aesthetics rather than ideas. Women generally also have a
stronger interest in people rather than things, relative to men (also interpreted as empathizing vs.
systemizing).
o These two differences in part explain why women relatively prefer jobs in social or
artistic areas. More men may like coding because it requires systemizing and even within
138

SWEs, comparatively more women work on front end, which deals with both people and
aesthetics.
• Extraversion expressed as gregariousness rather than assertiveness. Also, higher agreeableness.
o This leads to women generally having a harder time negotiating salary, asking for raises,
speaking up, and leading. Note that these are just average differences and there’s overlap
between men and women, but this is seen solely as a women’s issue. This leads to
exclusory programs like Stretch and swaths of men without support.
• Neuroticism (higher anxiety, lower stress tolerance).
o This may contribute to the higher levels of anxiety women report on Googlegeist and to
the lower number of women in high stress jobs.
Note that contrary to what a social constructionist would argue, research suggests that “greater nation-
level gender equality leads to psychological dissimilarity in men’s and women’s personality traits.”
Because as “society becomes more prosperous and more egalitarian, innate dispositional differences
between men and women have more space to develop and the gap that exists between men and women in
their personality traits becomes wider.” We need to stop assuming that gender gaps imply sexism.

Men’s higher drive for status
We always ask why we don’t see women in top leadership positions, but we never ask why we see so
many men in these jobs. These positions often require long, stressful hours that may not be worth it if you
want a balanced and fulfilling life.

Status is the primary metric that men are judged on4, pushing many men into these higher paying, less
satisfying jobs for the status that they entail. Note, the same forces that lead men into high pay/high stress
jobs in tech and leadership cause men to take undesirable and dangerous jobs like coal mining, garbage
collection, and firefighting, and suffer 93% of work-related deaths.
________________________________________________________________________
4 For heterosexual romantic relationships, men are more strongly judged by status and women by beauty.
Again, this has biological origins and is culturally universal.

Non-discriminatory ways to reduce the gender gap
Below I’ll go over some of the differences in distribution of traits between men and women that I outlined
in the previous section and suggest ways to address them to increase women’s representation in tech
without resorting to discrimination. Google is already making strides in many of these areas, but I think
it’s still instructive to list them:
• Women on average show a higher interest in people and men in things
o We can make software engineering more people-oriented with pair programming and
more collaboration. Unfortunately, there may be limits to how people-oriented certain
roles at Google can be and we shouldn’t deceive ourselves or students into thinking
otherwise (some of our programs to get female students into coding might be doing this).
• Women on average are more cooperative
o Allow those exhibiting cooperative behavior to thrive. Recent updates to Perf may be
doing this to an extent, but maybe there’s more we can do.
o This doesn’t mean that we should remove all competitiveness from Google.
Competitiveness and self reliance can be valuable traits and we shouldn’t necessarily
disadvantage those that have them, like what’s been done in education.
• Women on average are more prone to anxiety
o Make tech and leadership less stressful. Google already partly does this with its many
stress reduction courses and benefits.
• Women on average look for more work-life balance while men have a higher drive for status on
average
139

o Unfortunately, as long as tech and leadership remain high status, lucrative careers, men
may disproportionately want to be in them. Allowing and truly endorsing (as part of our
culture) part time work though can keep more women in tech.
• The male gender role is currently inflexible
o Feminism has made great progress in freeing women from the female gender role, but
men are still very much tied to the male gender role. If we, as a society, allow men to be
more “feminine,” then the gender gap will shrink, although probably because men will
leave tech and leadership for traditionally “feminine” roles.
Philosophically, I don’t think we should do arbitrary social engineering of tech just to make it appealing
to equal portions of both men and women. For each of these changes, we need principled reasons for why
it helps Google; that is, we should be optimizing for Google—with Google’s diversity being a component
of that. For example, currently those willing to work extra hours or take extra stress will inevitably get
ahead and if we try to change that too much, it may have disastrous consequences. Also, when
considering the costs and benefits, we should keep in mind that Google’s funding is finite so its allocation
is more zero-sum than is generally acknowledged.

The harm of Google’s biases
I strongly believe in gender and racial diversity, and I think we should strive for more. However, to
achieve a more equal gender and race representation, Google has created several discriminatory practices:
• Programs, mentoring, and classes only for people with a certain gender or race5
• A high priority queue and special treatment for “diversity” candidates
• Hiring practices which can effectively lower the bar for “diversity” candidates by decreasing the
false negative rate
• Reconsidering any set of people if it’s not “diverse” enough, but not showing that same scrutiny
in the reverse direction (clear confirmation bias)
• Setting org level OKRs for increased representation which can incentivize illegal discrimination6
___________________________________________________________________________
5 Stretch, BOLD, CSSI, Engineering Practicum (to an extent), and several other Google funded internal and external programs are for people with a
certain gender or race.
6 Instead set Googlegeist OKRs, potentially for certain demographics. We can increase representation at an org level by either making it a better environment for
certain groups (which would be seen in survey scores) or discriminating based on a protected status (which is illegal and I’ve seen it done). Increased representation
OKRs can incentivize the latter and create zero-sum struggles between orgs.

These practices are based on false assumptions generated by our biases and can actually
increase race and gender tensions. We’re told by senior leadership that what we’re doing is
both the morally and economically correct thing to do, but without evidence this is just veiled left
ideology7 that can irreparably harm Google.

Why we’re blind
We all have biases and use motivated reasoning to dismiss ideas that run counter to our internal values.
Just as some on the Right deny science that runs counter to the “God > humans > environment” hierarchy
(e.g., evolution and climate change), the Left tends to deny science concerning biological differences
between people (e.g., IQ8 and sex differences). Thankfully, climate scientists and evolutionary biologists
generally aren’t on the right. Unfortunately, the overwhelming majority of humanities and social sciences
lean left (about 95%), which creates enormous confirmation bias, changes what’s being studied, and
maintains myths like social constructionism and the gender wage gap9. Google’s left leaning makes us
blind to this bias and uncritical of its results, which we’re using to justify highly politicized programs.

In addition to the Left’s affinity for those it sees as weak, humans are generally biased towards protecting
females. As mentioned before, this likely evolved because males are biologically disposable and because
women are generally more cooperative and agreeable than men. We have extensive government and
Google programs, fields of study, and legal and social norms to protect women, but when a man
140

complains about a gender issue issue affecting men, he’s labelled as a misogynist and a whiner10. Nearly
every difference between men and women is interpreted as a form of women’s oppression. As with many
things in life, gender differences are often a case of “grass being greener on the other side”; unfortunately,
taxpayer and Google money is being spent to water only one side of the lawn.
____________________________________________________________________________
7 Communism promised to be both morally and economically superior to capitalism, but every attempt became morally corrupt
and an economic failure. As it became clear that the working class of the liberal democracies wasn’t going to overthrow their
“capitalist oppressors,” the Marxist intellectuals transitioned from class warfare to gender and race politics. The core oppressor-
oppressed dynamics remained, but now the oppressor is the “white, straight, cis-gendered patriarchy.”
8 Ironically, IQ tests were initially championed by the Left when meritocracy meant helping the victims of aristocracy.
9 Yes, in a national aggregate, women have lower salaries than men for a variety of reasons. For the same work though, women
get paid just as much as men. Considering women spend more money than men and that salary represents how much the
employee sacrifices (e.g. more hours, stress, and danger), we really need to rethink our stereotypes around power.
10 “The traditionalist system of gender does not deal well with the idea of men needing support. Men are expected to be strong,
to not complain, and to deal with problems on their own. Men’s problems are more often seen as personal failings rather than
victimhood, due to our gendered idea of agency. This discourages men from bringing attention to their issues (whether individual
or group-wide issues), for fear of being seen as whiners, complainers, or weak.”

This same compassion for those seen as weak creates political correctness11 , which constrains discourse
and is complacent to the extremely sensitive PC-authoritarians that use violence and shaming to advance
their cause. While Google hasn’t harbored the violent leftist protests that we’re seeing at universities, the
frequent shaming in TGIF and in our culture has created the same silent, psychologically unsafe
environment.

Suggestions
I hope it’s clear that I’m not saying that diversity is bad, that Google or society is 100% fair, that we
shouldn’t try to correct for existing biases, or that minorities have the same experience of those in the
majority. My larger point is that we have an intolerance for ideas and evidence that don’t fit a certain
ideology. I’m also not saying that we should restrict people to certain gender roles; I’m advocating for
quite the opposite: treat people as individuals, not as just another member of their group (tribalism).
My concrete suggestions are to:
• De-moralize diversity.
o As soon as we start to moralize an issue, we stop thinking about it in terms of costs and
benefits, dismiss anyone that disagrees as immoral, and harshly punish those we see as
villains to protect the “victims.”
• Stop alienating conservatives.
o Viewpoint diversity is arguably the most important type of diversity and political
orientation is one of the most fundamental and significant ways in which people view
things differently.
o In highly progressive environments, conservatives are a minority that feel like they need
to stay in the closet to avoid open hostility. We should empower those with different
ideologies to be able to express themselves.
o Alienating conservatives is both non-inclusive and generally bad business because
conservatives tend to be higher in conscientiousness, which is required for much of the
drudgery and maintenance work characteristic of a mature company.
• Confront Google’s biases.
o I’ve mostly concentrated on how our biases cloud our thinking about diversity and
inclusion, but our moral biases are farther reaching than that.
o I would start by breaking down Googlegeist scores by political orientation and
personality to give a fuller picture into how our biases are affecting our culture.
• Stop restricting programs and classes to certain genders or races.
o These discriminatory practices are both unfair and divisive. Instead focus on some of the
non-discriminatory practices I outlined.
141

• Have an open and honest discussion about the costs and benefits of our diversity programs.
o Discriminating just to increase the representation of women in tech is as misguided and
biased as mandating increases for women’s representation in the homeless, work-related
and violent deaths, prisons, and school dropouts.
o There’s currently very little transparency into the extent of our diversity programs which
keeps it immune to criticism from those outside its ideological echo chamber.
o These programs are highly politicized which further alienates non-progressives.
o I realize that some of our programs may be precautions against government accusations
of discrimination, but that can easily backfire since they incentivize illegal
discrimination.
• Focus on psychological safety, not just race/gender diversity.
o We should focus on psychological safety, which has shown positive effects and should
(hopefully) not lead to unfair discrimination.
o We need psychological safety and shared values to gain the benefits of diversity.
o Having representative viewpoints is important for those designing and testing our
products, but the benefits are less clear for those more removed from UX.
• De-emphasize empathy.
o I’ve heard several calls for increased empathy on diversity issues. While I strongly
support trying to understand how and why people think the way they do, relying on
affective empathy—feeling another’s pain—causes us to focus on anecdotes, favor
individuals similar to us, and harbor other irrational and dangerous biases. Being
emotionally unengaged helps us better reason about the facts.
• Prioritize intention.
o Our focus on microaggressions and other unintentional transgressions increases our
sensitivity, which is not universally positive: sensitivity increases both our tendency to
take offence and our self censorship, leading to authoritarian policies. Speaking up
without the fear of being harshly judged is central to psychological safety, but these
practices can remove that safety by judging unintentional transgressions.
o Microaggression training incorrectly and dangerously equates speech with
o violence and isn’t backed by evidence.
• Be open about the science of human nature.
o Once we acknowledge that not all differences are socially constructed or due to
discrimination, we open our eyes to a more accurate view of the human condition which
is necessary if we actually want to solve problems.
• Reconsider making Unconscious Bias training mandatory for promo committees.
o We haven’t been able to measure any effect of our Unconscious Bias training and it has
the potential for overcorrecting or backlash, especially if made mandatory.
o Some of the suggested methods of the current training (v2.3) are likely useful, but the
political bias of the presentation is clear from the factual inaccuracies and the examples
shown.
o Spend more time on the many other types of biases besides stereotypes. Stereotypes are
much more accurate and responsive to new information than the training suggests (I’m
not advocating for using stereotypes, I just pointing out the factual inaccuracy of what’s
said in the training).
____________________________________________________________________________
11 Political correctness is defined as “the avoidance of forms of expression or action that are perceived to exclude, marginalize, or insult groups
of people who are socially disadvantaged or discriminated against,” which makes it clear why it’s a phenomenon of the Left and a tool of
authoritarians.

142

Appendix B: EEO-1 Reports
Figure B1
Alphabet Inc.

143

Figure B2
Apple Inc.

144

Figure B3
Cisco Systems Inc.

145

Figure B4
Facebook Inc.

146

Figure B5
Intel Corporation

147

Figure B6
Microsoft Corporation

148

149
150

Table B7
Oracle America Inc.

151

Appendix C: Survey Protocols
Welcome

Thank you for taking the time to complete this brief survey. Your opinion is extremely valuable.
The feedback collected in this survey will be used as part of a research study intended to
understand employee perceptions about corporate activities regarding diversity in the workplace.

The survey will clouse on [time and date will be added]. Please respond prior to then.

This Survey Is Confidential and Anonymous.

Reports that will be generated are intended to summarize responses from the entire surveyed
population and all responses will be reported in aggregate. You will remain anonymous, and
your responses will be kept confidential.

Please answer all of the questions. Incomplete surveys will not be included in the research.
Hmmm…is there literature showing that this would skew the analysis? You might get interesting
responses before someone quit? For qualitative analysis?

In appreciation of your time, the researcher will donate $5 to Doctors Without Borders for
every completed survey. The researcher anticipates donating $1350 in total.

Instructions

This brief survey will take 10 minutes to complete.

Answer each question by selecting the response category that best describes your opinion. Your
answers should reflect your own experience.

152

Table C1
Survey

Question
Level of
measurement Response options
Concept being
measured
I work for a technology company
such as Apple, Cisco,
Facebook, Google, Intel,
Microsoft, Oracle, IBM, Dell,
etc. that has headquarters in the
United States.
Nominal Yes
No
Screening question
(1 item)
I work for a telecommunications
company such as AT&T,
Verizon, T-Mobil, Comcast,
etc. that has headquarters in the
United States.
Nominal Yes
No
Screening question
(1 item)
I work for an internet company
such as Netflix, eBay, etc. that
has headquarters in the United
States.
Nominal Yes
No
Screening question
(1 item)
After the death of George Floyd,
Black Lives Matter marches in
2020, and the January 6, 2021
insurrection attempt, I have
been affected in the following
ways:
I became more aware of
racism in America.
I became more concerned
about how others perceive
me because of my race.
My views on race in America
have not changed.
Ordinal Strongly agree
Somewhat agree
Moderately agree
Hardly agree
Not at all agree
1- Awareness of
2020 Events and
Racism in the
United States
(3 items)
My employer requires all
employees to complete
mandatory diversity training.
Nominal Yes
No
3- Personal
Actions Taken
(1 item)
Diversity training is essential for
creating an inclusive workplace
environment.
Strongly Agree
Somewhat Agree
Moderately Agree
Hardly Agree
Not at all agree
2- Support for
Corporate
Actions Being
Taken to
Increase
153

Question
Level of
measurement Response options
Concept being
measured
Diversity in the
Workplace
(1 items)
Since May 2020, I have done the
following:
Completed voluntary diversity
training.
Watched documentaries,
films, or videos containing
race-related information
during on my own time.
Completed mandatory
diversity training at my
workplace.
Engaged in discussions about
racial issues at my
workplace.
Donated money to racial
justice organization(s).
Voted for issues that
promoted social equality.
Did something else.
No actions taken.
Nominal Yes
No

3- Personal
Actions Taken
(8 items)
Technology corporations such as
Amazon, Microsoft, Facebook,
and Google should participate
in activities that support
social/racial justice and
activism.
Ordinal Strongly agree
Somewhat agree
Moderately agree
Hardly agree
Not at all agree
2- Support for
Corporate
Actions Being
Taken to
Increase
Diversity in the
Workplace
(1 items)
What is your opinion about the
following:
Diversifying the workplace
would not lead to
discrimination against
people who are White.
Prioritizing the hiring and
promotion of Black and
African Americans would
not discriminate against
White employees.
My company focuses too
much on diversity.
Ordinal Strongly agree
Somewhat agree
Moderately agree
Hardly agree
Not at all agree
4- Reactions to
Perceived
Intergroup
Threat
(3 items)
154

Question
Level of
measurement Response options
Concept being
measured
What is your view about the
following:
Diversity training is divisive
and promotes
discrimination.
Workplace diversity
programs promote anti-
White bias.
Workplace diversity
programs promote anti-
Black bias.
Strongly agree
Somewhat agree
Moderately agree
Hardly agree
Not at all agree
4- Reactions to
Perceived
Intergroup
Threat (3 items)
What is your opinion about the
following:
Where I work, anyone
can succeed if they
work hard enough
regardless of race.
People at my work are
colorblind (they don’t
see color).
Affirmative Action
programs discriminate
against White people.
Ordinal Strongly agree
Somewhat agree
Moderately Agree
Hardly agree
Not at all agree
4- Reactions to
Perceived
Intergroup
Threat (3 items)
If diversity is increased at my
workplace, my employment
might be at risk.

Ordinal Strongly agree
Somewhat agree
Moderately agree
Hardly agree
Not at all agree
4- Reactions to
Perceived
Intergroup
Threat
(1 item)
In my opinion, my company needs
to do the following:
Set goals for hiring more people
of color.
Set goals for promoting more
people of color.
Increase diversity training.
Reduce diversity training.
Stop diversity training.
Commit to partnerships with
minority-owned businesses.
Invest in current employees
instead of outside businesses.
Invest in underserved
communities.
Nominal Check all that apply 2- Support for
Corporate
Actions Being
Taken to
Increase
Diversity in the
Workplace (12
items)
155

Question
Level of
measurement Response options
Concept being
measured
Disengage in social justice
activities.
Donate money to underserved
communities.
Prohibit political discussions at
work.
Something else (please type
your response in the open text
field). [Open Text Field]
How do you identify
racially/ethnically?

Nominal White
Asian
Hispanic or Latino
Black or African
American
Native Hawaiian or
Pacific Islander
Native American or
Alaska Native
Two or More Races
Prefer not to respond
Demographic
information
(1 item)

156

Table C2
Additional Questions Added to Facebook and LinkedIn Survey
With which gender do you
identify?
Female
Male
Non-Binary
Identify as [Open
Text Field]
Prefer not to respond
Demographic
information
(1 item)
In which U.S. region do you reside?
New England
Middle Atlantic
South Atlantic
East South Atlantic
East North Central
West North Central
West South Central
Mountain
Pacific
Will use a graphic
and ask participants
to select their state.
Prefer not to respond
Demographic
information
(1 item)
What is your household income? $0 to $49,999
$50,000 to $99,999
$100,000 to $149,999
$150,000 to $199,999
$200,000 and up
Prefer not to respond
Demographic
information
(1 item)
In what age range are you? 18–29
30–44
45–59
60+
Prefer not to respond
Demographic
information
(1 item)

157

Thank you for taking this survey.

Would you be willing to interviewed to further participate in this study about employee
perceptions of corporate activities regarding diversity in the workplace? The online interview
would take 45 – 60 minutes and require your written permission.

All materials associated with the interview would be kept confidential. Pseudonyms would
be used to disguise the identities of all participants.

Please provide your email address to be contacted if you are interested or would like to receive
more information. [Open Text Field]

158

Appendix D: Interview Protocol
Introduction to the Interview:
Thank you for taking time to meet with me today. The additional insights you will share
will contribute to a research study intended to understand employee perceptions about corporate
activities regarding diversity in the workplace. Your opinions are extremely valuable. As a
reminder, you may decline to answer any question and you can also withdraw from the interview
at any time. During our conversation, you might see me write a few notes. I want to let you know
that my notes are simply intended to make sure that I capture your ideas and come back to them
if I need to. I will also record our time together to make sure I capture your perspectives
accurately. I will be the only person who will see the recording and I will destroy the recording
once I complete the transcription of our conversation. Your identity will be kept confidential.
This interview will take 45 to 60 minutes. Do you have any questions before we begin?

Table D1
Interview Questions
Interview questions Potential probes
Are you comfortable?
Take a minute to get comfortable and let me
know when you are ready to start.
Please tell me the name of the company your
work for?
Warm-up generic question
If the participant is from a company that I
don’t know.
I’m not familiar with that company. In what
industry or sector is the company?
If the company does not match the criteria for
the study.
“I am so sorry, but I can’t continue, and I don’t
wish to waste your time. The problem is that
your company falls outside of the criteria that
is needed for this study. I am going to have to
end the interview, but do you have any
questions or thoughts before you leave the
meeting? Thank you goodbye.”
How long have you been at the company and
what is your current role? Warm-up generic question
Many people were affected by the events
surrounding George Floyd’s death, Black
Lives Matter marches in 2020, and the January
6, 2021 insurrection attempt. Could you tell me
how you personally were affected?
You mentioned…Could you say more about…
If participant is triggered by a memory,
address and ask if they need a moment? Are
you to continue?
Were your opinions about race in America
changed?
What were your opinions before these events
happened? How did they change?
159

I am interested in hearing
about your experience
with diversity training
at work within the last
year and am going to
ask a few questions
about that.
Did you complete a
class?
And was it mandatory?
Can you tell me about it?
IF NO:
Would you want to
participate in such
a class?

Have you ever
participated in a
class?

What are your
thoughts on what
these types of
classes might
accomplish, if
anything?

If no, SKIP TO #11 after #6
Did you think that the training was beneficial or
not? Could you say more about why?
Do you think that everyone experienced the
training in the same ways? Could you say more?
Do you think people of different races
experienced the training in the same ways? Could you say more?
Would you be willing to share how you
personally experienced the class? Could you say more?
Do you know if your
company is involved in
any activities about
social and racial
justice?
11a. Could you tell me
what the company is
doing?
IF NO: Do you think
they should be?
What would you
suggest they do? If no, SKIP TO #14 after #11
Do you have any opinions about those activities?
Could you say more?
Have you always felt this way?
Could you say how these activities affect you
personally? Could you say more?
Do you think that recruitment messages, hiring
advertisements that highlight diversity, and
diversity training change the way people think
about race?
If yes, can you say how?
If no, can you say more about how they don’t
change the way that people think?

160

What difference do you think diversity training
and recruitment advertisements that highlight
diversity might make in racial equity?

What problems might they present?

(Only used if not already presented)
Do you have any opinions about Affirmative
Action policies that require employers to hire
more people of color? Could you say more?
You may have said something about this
already, but I’d like to know what your opinion
is about increasing racial diversity at your
workplace.
How did you arrive at this opinion(s)?
Do you think you have changed as a person in
the past year in regards to your thoughts and
feelings about race and social justice? Tell me more.
Have you done anything in the past year to make
public your opinions about race and social
justice? For example, posting your opinions on
social media, sharing your views in group
conversations, etc.?

Could you tell me more about what did you
did?
*If looking for examples-Did you read books
or watch anything?
Are you planning to do something else in the
future?

Do you have any ideas about what your
company should do about racial diversity in the
workplace?
Could you say more?
Is there anything you think they should stop
doing?
Before we close Is there anything that you would
like to add?

Conclusion to the Interview:
Thank you again for taking time to speak with me. I appreciate it your participation in
this interview. I anticipate that the research study will be conducted and completed next year.
The findings I will gather from the interviews will be used as part of the study. May I contact
you if I need any clarifications? Do you have any questions for me?

161

Appendix E: Cronbach’s Alpha Computation to Test Reliability
Preliminary data for 10 people who took the pilot survey for scales where the response
options were degrees of agreement (strongly agree, somewhat agree, moderately agree, hardly
agree, not at all agree).

Table E1
Cronbach ’s Alpha
Cronbach’s alpha formula is 𝑎 = (
𝑘 𝑘 − 1
)(
𝑠 𝑦 2
− ∑𝑠 𝑖 2
𝑠 𝑦 2
) 𝑎𝑙𝑝 ℎ𝑎 = .852

Note. Adapted from “Chapter 6: Just the truth,” by N. J. Salkind, 2014, in Statistics for People
Who (think they) Hate Statistics, (pp. 105–128). The number of items included in calculation is
15. The sum of the item variances is 17.07. The total score of the variances is 83.25. The
coefficient alpha is 0.852.

162

Appendix F: Lawshe’s Content Validity Ratio (CVR) to Test Validity of Interview
Questions
Lawshe’s Content
Validity Ratio is
𝐶𝑉𝑅 =
𝑛 𝑒 − ( 𝑁 ∕ 2)
𝑁 ∕ 2

Note. CVR values from a panel of eight experts were calculated.Adapted from “Critical Values
for Lawshe’s Content Validity Ratio: Revisiting the Original Methods of Calculation,” by C.
Ayre and A. J. Scally, 2014, in Measurement and Evaluation in Counseling and Development,
47(1), 79–86.

163

Appendix G: Census Regions and Divisions of the United States

Note. Census Regions and Divisions of the United States from
https://www2.census.gov/geo/pdfs/maps-data/maps/reference/us_regdiv.pdf

164

Appendix H: EBIT reports
Alphabet Inc (parent company of Google)
Alphabet annual and quarterly EBIT history from 2006 to 2020. EBIT can be defined as earnings
before interest and taxes.
• Alphabet EBIT for the quarter ending December 31, 2020 was $15.651B, a 68.91%
increase year-over-year.
• Alphabet EBIT for the twelve months ending December 31, 2020 was $41.224B, a
20.43% increase year-over-year.
• Alphabet 2020 annual EBIT was $41.224B, a 20.43% increase from 2019.
• Alphabet 2019 annual EBIT was $34.231B, a 24.37% increase from 2018.
• Alphabet 2018 annual EBIT was $27.524B, a 5.14% increase from 2017.

Apple Inc
Apple annual and quarterly EBIT history from 2006 to 2020. EBIT can be defined as earnings
before interest and taxes.
• Apple EBIT for the quarter ending December 31, 2020 was $33.534B, a 31.15%
increase year-over-year.
• Apple EBIT for the twelve months ending December 31, 2020 was $74.253B, a 12.24%
increase year-over-year.
• Apple 2020 annual EBIT was $66.288B, a 3.69% increase from 2019.
• Apple 2019 annual EBIT was $63.93B, a 9.83% decline from 2018.
• Apple 2018 annual EBIT was $70.898B, a 15.57% increase from 2017.

Cisco Systems Inc
Cisco annual and quarterly EBIT history from 2006 to 2021. EBIT can be defined as earnings
before interest and taxes.
• Cisco EBIT for the quarter ending January 31, 2021 was $3.223B, a 4.64% decline year-
over-year.
• Cisco EBIT for the twelve months ending January 31, 2021 was $12.454B, a 12.06%
decline year-over-year.
• Cisco 2020 annual EBIT was $13.62B, a 4.21% decline from 2019.
• Cisco 2019 annual EBIT was $14.219B, a 15.52% increase from 2018.
• Cisco 2018 annual EBIT was $12.309B, a 2.81% increase from 2017.

165

Facebook Inc
Facebook annual and quarterly EBIT history from 2009 to 2020. EBIT can be defined as
earnings before interest and taxes.
• Facebook EBIT for the quarter ending December 31, 2020 was $12.775B, a 44.22%
increase year-over-year.
• Facebook EBIT for the twelve months ending December 31, 2020 was $32.671B, a
36.21% increase year-over-year.
• Facebook 2020 annual EBIT was $32.671B, a 36.21% increase from 2019.
• Facebook 2019 annual EBIT was $23.986B, a 3.72% decline from 2018.
• Facebook 2018 annual EBIT was $24.913B, a 23.31% increase from 2017.

Intel Corporation
Intel annual and quarterly EBIT history from 2006 to 2020. EBIT can be defined as earnings
before interest and taxes.
• Intel EBIT for the quarter ending December 31, 2020 was $5.884B, a 13.43% decline
year-over-year.
• Intel EBIT for the twelve months ending December 31, 2020 was $23.678B, a 7.46%
increase year-over-year.
• Intel 2020 annual EBIT was $23.678B, a 7.46% increase from 2019.
• Intel 2019 annual EBIT was $22.035B, a 5.49% decline from 2018.
• Intel 2018 annual EBIT was $23.316B, a 29.17% increase from 2017.

Microsoft Corporation
Microsoft annual and quarterly EBIT history from 2006 to 2020. EBIT can be defined as
earnings before interest and taxes.
• Microsoft EBIT for the quarter ending December 31, 2020 was $17.897B, a 28.84%
increase year-over-year.
• Microsoft EBIT for the twelve months ending December 31, 2020 was $60.155B, a
21.96% increase year-over-year.
• Microsoft 2020 annual EBIT was $52.959B, a 23.28% increase from 2019.
• Microsoft 2019 annual EBIT was $42.959B, a 22.54% increase from 2018.
• Microsoft 2018 annual EBIT was $35.058B, a 20.79% increase from 2017.

166

Appendix I: Groupings
Category a priori code
Awareness
I became more aware of racism in America.
I became more concerned about how others perceive me
because of my race.
AW
SLB
Where I work, anyone can succeed if they work hard enough
regardless of race.
People at my work are colorblind (they don’t see color).
MR, RS, CB,
AS
Threats
If diversity is increased at my workplace, my employment
might be at risk.
Workplace diversity programs promote anti-White bias.
Diversity training is divisive and promotes discrimination.
Workplace diversity programs promote anti-Black bias.
Affirmative action programs discriminate against White
people.
AA, OW,
AIM, TRT,
Actions
Completed voluntary diversity training.
Watched documentaries, films, or videos containing race-
related information during on my own time.
Engaged in discussions about racial issues at my workplace.
Read books regarding diversity
Donated money to racial justice organization(s).
Voted for issues that promoted social equality.
Did something else (in support of).
ACT, GRW,
CM
Support (of corporate activities)
Technology corporations such as Amazon, Microsoft,
Facebook, and Google should participate in activities that
support social/racial justice and activism.
Diversity training is essential for creating an inclusive
workplace environment.
RH

Abstract (if available)

Abstract In the United States, throughout 2020 and into 2021, a series of events, such as the death of George Floyd, Black Lives Matter marches, and the insurrection attempt on January 6, 2021, resulted in a national uprising in support for racial justice and equity. Many Americans responded by educating themselves about racism in the United States, and corporations publicly displayed support for anti-racism activities. This study examined how U.S. citizens and residents who were employees of technology companies perceived their corporations’ pro-diversity and anti-racism activities since May 2020. Survey data from 397 participants and interview data from 11 participants were analyzed. The results showed that most participants, regardless of racial/ethnic group, supported pro-diversity and anti-racism corporate-sponsored activities, such as workplace diversity training. The recommendations presented were made in consideration of the interests of technology corporations and their employees.

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Asset Metadata

Creator Horn, Tamara Marie (author)

Core Title Promoting diversity: reactions to corporate actions that support anti-racism efforts

School Rossier School of Education

Degree Doctor of Education

Degree Program Organizational Change and Leadership (On Line)

Degree Conferral Date 2022-05

Publication Date 03/05/2022

Defense Date 02/18/2022

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag bias,corporate diversity training,intergroup threat,OAI-PMH Harvest

Format application/pdf (imt)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Carbone, Paula M. (committee chair), Datta, Monique (committee member), Robles, Darline P. (committee member)

Creator Email tamarahorn@gmail.com,tamarahorn@live.com

Permanent Link (DOI) https://doi.org/10.25549/usctheses-oUC110768173

Unique identifier UC110768173

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Document Type Dissertation

Format application/pdf (imt)

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Type texts

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