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Ageism and ingrained stereotypes: a qualitative study of systemic injustices in hiring practices

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Ageism and ingrained stereotypes: a qualitative study of systemic injustices in hiring practices

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Ageism and Ingrained Stereotypes: A Qualitative Study of Systemic Injustices in Hiring
Practices
by
Fredrica L. Crowe

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

August 2021

© Copyright by Fredrica L. Crowe 2021
All rights reserved

The Committee for Fredrica L. Crowe 2021 certifies the approval of this Dissertation

Jennifer Phillips
Courtney Malloy
Eric A. Canny, Committee Chair

Rossier School of Education
University of Southern California
2021

iv
Abstract
The present study investigated the effects of managers interviewing protocols to understand if
bias hiring decisions in the technology industry, if any, were implicit or explicit. Unconscious
bias occurs automatically when the mind makes judgments based on past experiences and
encounters. Because the world is inundated with stereotypes; a distorted image of an older
person, oversimplification of health issues, their behavior or their ideals, has been shown that
continued exposure to these negative stereotypes results in unconscious biases in the short-term.
Deeply internalized and ingrained stereotypes from media portrayals, or from cultural settings
inculcate and influence unconscious behavior even when a person consciously believes
discrimination is wrong.
In this field study, age bias in the workplace and its influences on hiring decisions were a
culmination of internalized negative perceptions on aging which manifested from childhood
exposures. While participants were older and had a median age of 52, they internalized the
societal views of older people and believed younger workers to be more productive. This attitude
demonstrated self-directed ageism when choosing to avoid the hire of older workers believing
they were prone to illness. Going into the study, the researcher‟s belief was that younger
employees were hired based on similarity attraction theory where managers hired workers based
on likeness or resemblance. When participant‟s ages were revealed, the tenets of the theory based
on attachment styles or age proximity as the reason for hire were deflated. Further research into
the study revealed there was a strong current of implicit bias and ageism in the workforce that
was made evident by the preference of younger versus older workers.
Keywords: ageism, unconscious bias, self-directed ageism, implicit bias, digital ageism

v
Dedication
“Each of us has the power to inspire or depress, to lift others or to push them down. We should
look well to our words” - Wilferd A. Peterson
To my departed mother, Evelyn P. Hankins, my heart, who always saw the best in me; who
never denied me, who loved me without conditions and was a persistent guide and conductor of
education throughout my life. It was under my mother‟s tutelage that I came to understand the
value of communication, the beauty of words, the love of people, the significance of learning and
the benefit in reaching and teaching others. The kind, indomitable spirit and everlasting love
from my mother is carried with me daily and throughout each undertaking. I am forever grateful
for her teachings, her humanity, her gift of love and her joyful soul.
To my father, Fredric A. Crowe, I offer a special sentiment of appreciation and love whose
words of encouragement, praise and reassurance in the completion of my Doctorate in Education
in Organizational Change and Leadership was unwavering. It is through my father‟s lens that I
came to recognize that despite institutional inequalities to be steadfast in my doctrine and to
always lead with dignity. His words of, “Always be a lady,” will forever resonate and have
significance.
To the study participants in the technology industry, thank you for your time, for removing your
defenses, for standing on your truth and for becoming transparent while being interviewed.
Without your contribution my dissertation would not have been complete.
To workers over the age of 40 who encounter closed doors and discouraging excuses in your
pursuit of employment, keep knocking, new doors are opening each day; remain steadfast and do
not tire, a door will open.

vi
Acknowledgements
“Everyone who remembers his own education remembers teachers, not methods and techniques.
The teacher is the heart of the educational system.” Sidney Hook
I wish to thank my committee members who were generous with their time, their
expertise and their proficiency in guidance. I extend a special thanks to my mentor, and my
dissertation chair, Dr. Eric A. Canny, for his uncountable hours of reflecting, redirecting,
reading, encouraging and patience from the beginning until the end. To Dr. Courtney Malloy, I
offer overwhelming gratitude for profoundly shaping my thinking, for hours of instruction and
for providing me with support from the onset of my dissertation focus. To Dr. Jennifer Phillips, I
offer a heartfelt thank you for agreeing to serve on my committee; for your sage advice, your
detailed examination of my study and for the supportive suggestions you gave that allowed me to
center my analysis. Your coaching has been incalculable. And to Dr. Carey Regur, I thank you
for perceptively inspecting my dissertation and offering viewpoints to help align my study with
my problem of practice.
Finally I would like to acknowledge and thank the professors who left a lasting impact on
my development at the University of Southern California, from Dr. Derisa Grant, Dr. Jane
Rosenthal, Dr. Esther Kim and Dr. Wayne Combs who served as both mentors and
administrators. Their collective enrichment and feedback along with their willingness to provide
opinion made the completion of this research an enjoyable experience. To the many that were not
mentioned though who were supportive, I say, thank you. I did not get to this point as an island
and will not move forward as one.

vii
Table of Contents
Abstract .......................................................................................................................................... iv
Dedication ........................................................................................................................................v
Acknowledgements ........................................................................................................................ vi
List of Tables ...................................................................................................................................x
List of Figures ............................................................................................................................... xii
Chapter One: Introduction ...............................................................................................................1
Organizational Context and Mission ...................................................................................3
Field Background .................................................................................................................4
Importance of the Evaluation .............................................................................................13
Stakeholders‟ Description ..................................................................................................15
Stakeholder Performance Goals .........................................................................................15
Purpose of the Project and Questions ................................................................................16
Methodological Framework ...............................................................................................17
Definitions..........................................................................................................................19
Organization of the Project ................................................................................................20
Chapter Two: Literature Review ...................................................................................................22
The Foundation of Ageism ................................................................................................25
The Media‟s Influence on Visual Ageism .........................................................................31
Stereotype Embodiment and Childhood Development ......................................................33
Women and Workplace Hiring Biases ...............................................................................34
Joblessness Creates Health Disparities ..............................................................................46
Bronfenbrenners Ecological Systems Theory Conceptual Framework .............................46
Literature Search Strategy..................................................................................................56
Chapter Three: Methods ................................................................................................................58

viii
Demographics ....................................................................................................................60
Participating Stakeholders .................................................................................................60
Interview Sampling Criteria and Rationale........................................................................61
Role of the Researcher .......................................................................................................66
Instrumentation and Data Collection .................................................................................66
Procedures for Data Collection ..........................................................................................68
Limitations and Delimitations ............................................................................................71
Positionality .......................................................................................................................72
Research Setting.................................................................................................................74
Chapter Four: Findings ..................................................................................................................75
Research Method ...............................................................................................................75
Introduction to the Questionnaire Study ............................................................................76
Questionnaire Summary.....................................................................................................82
The Interview Sample ........................................................................................................83
The Themes that emerged from the Interview Process ......................................................85
Defining Identified Themes ...............................................................................................86
Summary ..........................................................................................................................111
Chapter Five: Discussion, Conclusions and Recommendations ..................................................113
Discussion of Findings .....................................................................................................115
Unexpected Findings .......................................................................................................121
Recommendations for Practice ........................................................................................121
Conclusion .......................................................................................................................129
References ....................................................................................................................................131
Appendix A: Interview Questions ...............................................................................................156
Appendix B: Questionnaire..........................................................................................................158

ix
Appendix C: Introduction Letter to Management Personnel Participants ...................................161

x
List of Tables
Table 1: Ecological Systems Framework on Aging 18
Table 2: Explanation of Bronfenbrenner‟s Ecological System 48
Table 3: Micro Influences 53
Table 4: Macro Influences 55
Table 5: Responses on Perception From the Mesosystems Addressing Research Question One
(n =18) 77
Table 6: Responses on Environmental Setting From the Micro and Macrosystems Addressing
Research Questions Two and Three (n = 18) 79
Table 7: Responses on Stereotypes/Implicit Bias From the Chronosystem Addressing Research
Question Four (n = 18) 81
Table 8: Participant Demographics 84
Table 9: Themes and Definitions That Emerged From the Interview 85
Table 10: Has the Technology Industry Been Impacted by Discrimination? 91
Table 11: What Is the Impression of Workers Over the Age of 50 in the Workplace? (When
Interviewing Candidates for Hire) 92
Table 12: How Can You Tell if Someone Is Older or Younger in the Workplace? 93
Table 13: Are There Advantages (Benefits) or Are There Disadvantages to Aging? 96
Table 14: Tell Me About Your Interactions With Older Members of Your Family During Your
Developmental Years 97
Table 15: Can You Tell Me About a Time You Initially Became Aware of Age Gaps or Age
Differentiation at Work or at Home? 98
Table 16: Do You Believe Age Bias Is Intentional or Unintentional in the Workplace? 100
Table 17: Do You Find There Is a Difference in the Levels of Energy With Older Adults Versus
the Younger Workers? 102
Table 18: Do You Believe There Should Be an Expiration or Cut-Off Age for Working and
Why? 103
Table 19: Would You Train Adults Over the Age of Forty Differently Than Those Ages 35 and
Under? 104

xi
Table 20: Given Your Current Age What Do You Think You Will Be Doing in 15–20 Years? 105
Table 21: Tell Me How Your Interactions Change, if at All, When Working With People Your
Own Age Compared to Older Workers or Those Younger Than Yourself? And Is
There a Reluctance to Hire Older Workers? 106
Table 22: Can You Tell Me About a Time When You Made a Hiring Decision Based on an
Applicant‟s Age Instead of Their Experience 107

xii
List of Figures
Figure 1: Workforce by Age Demographics in the Tech Industry 5
Figure 2: Median Employee Age in the Technology Industry 36
Figure 3: Labor Force Employment Participation From 1992 Through 2022 38
Figure 4: Comparisons of Job Applicant Callback Rates by Age 40
Figure 5: Conceptual Framework for the Study: Bronfenbrenner‟s Ecological System and
Ageism 52
Figure 6: Over a Third of IT Workers Have Encountered Age Discrimination 113
Figure 7: Workers Surveyed First Started to Experience Ageism at the Age of 41: IT and Tech
Workers First Experienced Ageism at Age of 29 114
Figure 8: PEACE Model (Levy, 2018) 127

1

Chapter One: Introduction
Feeble-minded! Out of touch with technology, slow, sickly and too old are the negative
epithets and labels that people assign to older people and the descriptors employers have when
adults over the age of 50 seek employment. For more than 50 years, psychologists have provided
meaning to the word ageism and the effects it has in its discriminatory practices in employment,
housing and medicine (Fasbender & Wang, 2017).
Ageism is the act of discrimination against older people. It is an incipient issue that is
growing and has touched every area of life (Palmore, 2015). Ageism has a long history and yet
has not been given the dialogue necessary to palliate the effects of its dominance in society.
While ageism has been given meaning, research is only beginning to emerge about the effects of
workforce age-based discrimination (Ayalon & Tesch-Römer, 2017). Regulations put in place to
mollify the effects of discriminatory practices in hiring have not been successful as evidenced by
the 20,000 complaints are filed annually with the Equal Employment Opportunity Commission,
EEOC (Barnes, 2019). According to Donizetti, 2019, there are profound changes in the
progressive aging of society that brings with it a need for change as ageism is encountered. The
multidimensional challenges will affect individuals and society if the problem of ageism is not
corrected (Donizetti, 2019). Individual challenges include increased financial hardships due to
unemployment, lack of medical insurance, secondary health effects due to depression and the
inability to sustain one‟s life (Azulai, 2014). Societal impacts include disparity in hiring, lack of
promotions, discrimination, and joblessness for its most “vulnerable citizens” (Dionigi, 2015).
This research dissertation is on ageism in the workforce focused on biases in hiring
practices within the technology industry. The field study examined a perspective on the effects
that ageist attitudes and ageism have on hiring in the workforce for workers over the age of 50.
2

The literature review will not only synthesize the origin of ageism and how it is embedded in the
United States but will also view the manifestations of ageism on an institutional, structural and
interpersonal level. As one decisively examines systemic ageism, and how age discrimination is
fostered, one begins to learn how within the macro systems, a child‟s social ecology begins to
develop (Noffsinger, et al., 2012). In 1979, Bronfenbrenners micro ecological system
interconnecting groups and communities along with the family and parent/child relationships
play influential roles that influence social factors in childhood development (Noffsinger, et al.,
2012). Systemic ageism is not only responsible for negative self-perceptions in the elderly, but it
is a learned behavior that children subconsciously practice throughout life and carry from
adolescence to adulthood (Dittman, 2003). Age stereotypes are often internalized at a young age,
long before they are even relevant to people (Dittman, 2003).
Childhood socializations factor into one‟s perspectives when viewing physical features
and limitations of elderly people. The connections and associations carried between an older
person and their characteristics contribute to furthering childhood generalizations and ageist
attitudes that carry into adulthood (Perez-Felkner, 2013). The researcher‟s examination into this
paper studied ingrained discrimination that affects older adults when trying to re-enter the
workforce or remain in the workforce. The research studies show how power and consequences
of ageism impact cultural perceptions leading to inequity in the workforce. Further exploration
into ageism in hiring practices discusses the Federal Age Discrimination Employment Act
(ADEA) of 1967, which prohibits discrimination based on the basis of age against anyone 40,
and older (von Schrader & Nazarov, 2015).
The problem of ageism and disparities in the workforce is important as the workplace as
a small scale of society echoes the stereotypes and biases that make up the social environment.
3

By the year 2030, there will be over 71.5 million people who will be considered “older.” Adults
age 65 and older who remain interested in the workforce are expected to grow to become 20% of
the population by the year 2030 (Eglit, 2009). Twenty-nine percent of households in the United
States run by an adult 55 or older do not have retirement savings and must remain in the
workforce (Kita, 2019). The problem is that older people are marginalized in the workplace and
do not feel useful in the work environment (Kita, 2019). Without adequate employment adults
over the age of 50 cannot sustain their lives and the crisis of ageism remains a problem in spite
of laws intended to protect older workers.
Organizational Context and Mission
GAFE, an illusory acronym and pseudonym used for the benefits of the research describe
companies-located within Silicon Valley such as Google, Amazon, Facebook, and EBay that
were the focus of this field study. The general mission for these organizations is to organize the
world‟s information and make it universally accessible; to give people the power to build
community and bring the world closer to together (Zuckerberg, 2017). Since the early 1990s the
technology industry has focused on developing its proprietary algorithms to maximize
effectiveness in organizing online information (Wilson et al., 2016). Each of these technology
businesses have focused on socially connecting users and to making resources available through
ecommerce, consumer-to-consumer or through business-to-consumer. GAFE continues to focus
on ensuring people‟s access to the information they need; this supports their mission to a
utilitarian benefit that the business provides to its users. In this regard, the following are the
primary elements of the technology field:
 G: To share new ideas, this is the largest technology giant providing a forum for
consumers and businesses to have global access to limitless sources of information.
4

 G: Global accessibility allowing users to have organized algorithms that can track user
trends and personalize information.
 G: Boosting the organization, having a dominant position in the market and occupying a
position of influence because of its unique brand.
 A: Customer obsession, frugality, with a high bar for talent and innovation.
 F: Bringing the world closer together as the Largest Social Online Network, with over
2.7 billion monthly active users as of 2020, Facebook is the biggest social network
worldwide (Zuckerberg, 2017).
 E: Enabled by people, powered by technology, providing an online marketplace where
anyone can trade anything enabling economic opportunity.
Field Background
Many of the technology industries within GAFE originated in the mid-1990s following
the downfall of the dotcom crash when venture capitalists and banks were looking to maximize
revenue through the internet (Gascon & Karson, 2017). With a core function in digital, internet
products and social media, the median hiring age of 29 years is well below the median age of all
U.S. workers (Swanner, 2018). According to the U. S. Bureau of Labor Statistics (2018), the
median hiring age in the United States is 41.9 years which makes Baby Boomers and Generation
X most vulnerable for age discrimination (Wilkie, 2018). According to a Market Watch
periodical, Baby Boomers (aged 56 to 74) are 60% less likely to be hired than their workforce
representation in tech and non-tech fields within Silicon Valley (Fottrell, 2017). Sixty-eight
percent of Baby Boomers have stated a reluctance to seek employment feeling discouraged due
to age disparities while 40% of Generation X (aged 40 to 55) believe ageism has affected an
ability to earn a living (Swanner, 2018). Silicon Valley is where the world‟s largest tech
5

companies reside and not only does discrimination exist in other forms but according to Barnes
(2015), women and ethnic minorities are underrepresented and subjected to age-based
discrimination. As a result of an ageism problem within the technology industry, the Equal
Employment Opportunity Commission (EEOC) issued a thinly veiled threat to all employers
who solicit employees for digital natives, a synonym for post millennial workers who are under
the age of 40 (Barnes, 2015).
Figure 1 illustrates the median hiring age in the IT Workforce of 29 which is below the
National hiring age of 41.5.

Figure 1
Workforce by Age Demographics in the Tech Industry

6

The disparity in hiring and ageism creates a problem in technology industries with the
shift in age demographics in the workforce as a result of the first group of Baby Boomers
reaching the age of 65 in 2011causing joblessness and a lack of financial security (Anderson et
al., 2012). In a Dice Diversity and Inclusion Survey (Swanner, 2018), directed by the technology
industry, it was found Baby Boomers and Generation X as being most at risk for age
discrimination. Out of 4,000 technology workers responding to the survey, 68% of Baby
Boomers stated they were discouraged from applying to jobs due to age, 40% of Generation X
believed ageism affected their ability to be financially solvent and 29% of respondents said they
had experienced or witnessed ageism within the technology industry (Swanner, 2018).
Though most organizations have human resources and compliance programs to counter
unfair employment practices, age biases persist within the technology industries where the
majority of employment opportunities are available (Bellis, 2019). It is necessary to highlight
jobs in IT as the majority of jobs in the technology industry operate within the digital and social
media arena, which means that a large percentage of employment opportunities exist in a field
where age bias is present (Wilkie, 2018). Discrimination in all areas of employment is forbidden
though the EEOC informed IT employers for biased hiring posts. The notification of the age
discriminatory hiring posts came after it was noted that Facebook was filtering hiring
announcements intended for a younger population which was in violation of fair employment
laws (Swanner, 2018). In its initial stages, Facebook was utilized primarily by a younger
population making advertisements and recruitment efforts by corporations on evident of hiring
biases.

7

Related Literature
Research suggests one of the most prevalent fears of older workers remains a loss of
employment due to age (Perron & McCann, 2018). Discrimination is seen in polls indicating
three out of five older workers are subjected to age discrimination, 40% believe age
discrimination is common and the other 60% believe it is somewhat common (Perron &
McCann, 2018). Older workers are discriminated against for being viewed as less productive in
their work performance compared to younger workers though scientific literature does not
support the notion that older workers are necessarily less productive (Perron & McCann, 2018).
In Literature from Ayalon and Tesch-Römer, (2018) it is suggested that while some researchers
have concluded that a person‟s age has a harmful effect on productivity within the labor market,
there are other researchers who studied employees at the corporate level that believe older
workers produce a higher level of productivity (Ayalon & Tesch-Römer, 2018). Ageism in the
labor market can manifest in situations where older employees are not re-trained or considered
for jobs that require skill sets in jobs such as the high-tech or IT industries (Ayalon & Tesch-
Römer, 2018). Ageism becomes more of a concern given the technological advancements and
emergence of the digital market which can hold older adults from obtaining new skills and the
knowledge necessary to acquire better opportunities (Ayalon & Tesch-Römer, 2018).
There is a shrinking population of the working age in the United States and an increase in
older workers who have chosen to remain in the workplace which means it is necessary that
policies and practices to harness the strength of the aging worker be established (Ayalon &
Tesch-Römer, 2018). On-going workforce implementations that begin with communication to
identify egregious behaviors and determinants must be used to decrease inequities (Jones et al.,
2017). Earlier research has shown that when older employees experience stereotypes and
8

discrimination in the work environment, it can impact productivity (Thorsen et al., 2012),
retirement (Schermuly et al., 2014), organizational commitment (Snape & Redman, 2003), and
work satisfaction (Orpen, 1995).
As part of the theoretical framework to this study, the theory of stereotype embodiment
proposes that exposure to cultural messages of ageism lead to an internalization of an ageist
construct and once internalized becomes part of an implicit, subconscious set of beliefs about
older people (Levy, 2009). Empirical research on ageism predictors are linked to the way older
persons are treated, stereotypical age beliefs, negative self-perceptions about their own aging and
age discrimination which describes the detrimental treatment of older persons (Levy, 2009).
Again, these findings suggest that corporations should dissuade negative stereotypes that cause
older workers to disengage from work losing interest in productivity (Chiesa et al., 2019).
In a design methodology approach, with a sample of over 4,500 workers, ages 18 to 94,
Boone-James et al. (2013), examined the relationship between perceptions that older workers are
less likely to be promoted by using multilevel mixed effects linear regression models. The
authors also examined whether the relationship is different if older workers were seen as fit for
promotion, and whether discrimination is intentional (fit, but less likely to be promoted) or
unintentional (unfit, and less likely to be promoted). Results indicated perceived discrimination
is related to lower levels of employee engagement among workers of all ages. Findings also
suggested that for older workers, there is a more negative relationship between unintentional
discrimination and employee engagement, while for younger workers the relationship is more
negative for intentional discrimination (Boone-James et al., 2013).
While there may be a number of reasons why older adults are not promoted or hired into
the workplace, there remains a need to understand the barriers that exclude older adults and to
9

recognize the challenges in order to interpret the obstacles. Age discrimination is demonstrated
in organizational practices where older workers have been denied job responsibilities and
therefore a lack of access to career opportunities. Understanding intentional biases, origins of
ageism, the lack of employee engagement at certain ages and areas that point to unfair treatment
is necessary to recognize to address this problem (Boone-James et al., 2013).
Environmental Influences
The negative stereotypical imaginings of older people as frail evoke ideations of
dependence along with biased media representations which can draw destructive slants and
perpetuates ageism (Marques et al., 2020). Observing aging adults in poor health along with
stereotypical images of older people with less mobility than someone younger or unable to
maneuver technology will yield a negative impression supporting stereotype behavior (Marques
et al., 2020). With older adults being on the negative side of the digital divide, a term used to
describe who uses digital technology and who does not, the perceptions of digital exclusion
position older people at a disadvantage (McDonough, 2015). New technology was introduced in
the late 70s and 80s during a time when Baby Boomers born between 1946 and 1964 were in
school establishing careers and raising families (Golden, 2017).
Manifestations of Aging
According to the World Health Organization (2015), aging often requires the need to
make significant lifestyle changes, such as taking new medications, following a different diet or
changing an exercise regimen. Making lifestyle changes could benefit older people and help
lessen discrimination experienced in the workplace which causes financial strains and depressive
symptoms in the mental health of all people, especially women (Eglit, 2009). Because women
tend to live longer than men, women tend to experience ageism on a greater level (Chrisler et al.,
10

2016). From micro-aggressions to blatant negative remarks about age, women are under
additional pressure to appear attractive and to conceal age (Chrisler et al., 2016).
In 2015, The World Health Organization published a world report on ageism and health
stating public health action on population aging is urgently needed. This comprehensive report
advocated for a functional lifestyle capacity for the elderly as a primary goal of life instead of
concentrating on mortality as people age (Woo, 2017). The report was important as it suggested
how self-perception could change the aging process if older adults made environmental and
physical changes into their lifestyle by adapting healthy behaviors and increasing physical
activity (Woo, 2017). Ageism is an emergent problem embedded in global culture and a problem
that existed before the word was created (Bengtson & Whittington, 2014). It is a problem that
has permeated the workforce and is an age-based inequity that is presumptively the primary
barrier for unemployment for adults age 50 and older (Wilson et al., 2007). The global problem
of age biases in hiring has been condoned by corporations due to older adults being perceived as
less adaptable when the reality is that age-based discrimination weakens the economy with
institutional prejudices and impedes the growth and production of organizations (Wilson et al.,
2007). Older workers possess a knowledge and experience that is not easily replaced after years
of service on the job. The long-term knowledge workers bring is essential to the company‟s
success, and if not captured and transmitted to the next generation, organizations lose capital and
resources (Kita, 2019).
Age discrimination takes many forms from refusal to hire and promote or assigning
menial jobs or exclusion from activity (Stypinska & Nikander, 2018). The problem is that there
is a persistence of ageism in the labor market and particularly in the technology industry and
while this has been a subject of interest since the twentieth century, the problem of unequal
11

treatment of older workers has continued (Zacher & Steinvik, 2015). Research suggests two
divergent definitions of ageism: ageist ideology and ageist behaviors (Stypinska & Nikander,
2018). Ageist ideology incorporates negative stereotypes and attitudes, whereas ageist behaviors
references people who have been excluded and placed in disadvantaged situations because of
their age category (Stypinska & Nikander, 2018.)
Prohibiting Age Discrimination
The Age Discrimination in Employment Act (ADEA) was legislated in 1967 in order to
prohibit age-based discrimination for adults over the age of 40 within the workplace and to
support employment of older workers. The act legislated on the strength of congressional reports
that reflected how adults over the age of 40 were withheld equal employment opportunities
because of stereotypes ascribed to workers of a certain age demographic (EEOC, 2018). Though
workers in current times continue to challenge unsupported suppositions about age
discrimination, negative perceptions persists and employers continue to ignore ADEA
regulations resulting in federal law reports that 6 out of 10 older workers have seen or
experienced age discrimination in the workplace and 90% of those say it is common (EEOC,
2018). Conversely, the ADEA permits employers to favor older workers based on age even when
doing so adversely affects a younger worker who is 40 or older (EEOC, 1997). The ADEA was
written to apply to private employers with 20 or more employees, state and local governments,
employment agencies, labor organizations and the federal government. The ADEA was a
legislative milestone in the 1960s where its enactment was to ensure equality for workers and
was to co-exist with the 1963 Equal Pay Act as well as the Civil Rights Act of 1964 (EEOC,
2018). This was a time in U.S. history where laws were being created for civil rights to address
prior obstacles that hindered progression, equality and uniformity for vulnerable groups of
12

people. Within the ADEA was the proviso that employers consider individual ability, rather than
assumptions about age, in making an employment decision. Years after the ADEA was leislated,
a special Senate committee on aging stated the act was put into place to provide data that would
change attitudes, not just to enforce the law (EEOC, 2018).
Aside from the protection of age, promotion, discharge, compensation, or privileges of
employment, the ADEA has provisions that safeguards advertisements and job notices from
making unlawful age preferences or limitations. Additionally, the ADEA protected
apprenticeship programs, and pre-employment inquiries prohibiting employers to ask an
applicant‟s age or date of birth. In 1990, The Older Workers Benefit Protection Act (OWBPA)
amended the ADEA to prohibit employers from denying benefits to older employees. Congress
identified that the cost of providing certain benefits to older workers was greater than the cost of
providing the same benefits to younger workers. It was further determined that the greater costs
would create a disincentive to hire older workers.
It is important to look at the history of the ADEA because the share of the aging
population in the United States is projected to rise and therefore the act‟s role in maintaining and
encouraging employment of older workers should also grow in its importance (Ho, 2010).
How the ADEA Failed Workers
Since the passage of the ADEA in 1967, ageism continues to be a severe problem that
causes obstructions to employment resulting in financial ruin and health disparities (Rothenberg
& Gardner, 2011). The ADEA is an inherently flawed act as a result of poor designing,
enforcement and effectiveness intended to heighten economic interests that do not intersect with
the interests of older workers (Rothenberg & Gardner, 2011). The ADEA was directed towards
the promotion of employment of older persons based on one‟s ability rather than age and it was
13

put into law to prohibit age discrimination in employment and to help employers and workers
find ways of meeting problems that arise from the impact of ageism in employment (Rothenberg
& Gardner, 2011). It has been 50 years since the passage of the ADEA and the act did not meet
its objective in reducing discrimination in hiring and therefore ageism and discrimination in the
workplace remain serious impediments to employment with over 20,000 cases filed each year
(Rothenberg & Gardner, 2011). Problems with the ADEA arose when supporters failed to
recognize that age discrimination was as an institutional discrimination that intersects with wage
labor. Instead of having a plan in place to address underlying causes of age-based discrimination,
the act was intended to improve economic rights and improve workforce productivity
(Rothenberg & Gardner, 2011).
Importance of the Evaluation
It is important to evaluate ageism and the level of age-based discrimination in work place
hiring due to the growing world age population. It is projected that individuals over the age of 60
will increase worldwide bringing the estimates to two billion people over the age of 60 by the
year 2050 (Engel, 2014). According to Wilkie, in the year 2019, the labor force resembled a
millennial-focused workforce, more than 50 years after Congress signed into law the ADEA,
illustrating more than half of older workers had been eliminated from long-time jobs before
retirement (Wilkie, 2019). Despite efforts to legislate age discrimination, 35% of workers in the
United States are Millennials and from 1992 until 2016, 56% of workers over the age of 50 lost
jobs at least once resulting in financial hardship (Wilkie, 2019).
According to the Centers for Disease Control and Prevention due to unforeseen
circumstances, adults have found the need to remain in the workforce longer than in years past
(White et al., 2018). As a result of being in the workforce, barriers such as stereotyping and
14

marginalizing become challenges older adults face while working beyond 50 years of age (White
et al., 2018). While the attention on ageism is not where it should be, awareness has increased as
Baby Boomers who are those born in 1946 to 1964 have elected to continue working. Despite
labor laws that have long-sense prohibited workplace discrimination, workplace inequities based
on age exits in several forms; negative performance reviews, the denial of promotions and age-
related intimidation each which have resulted in forced retirements or resignations. Amendments
were added to current laws that prohibit employers from denying benefits and for barring forced
resignations, though laws do not enforce the hiring of older workers. Current laws protect
employees once hired, but laws do not exist that would encourage corporations to hire workers
over the age of 40.
While choosing applicants on the basis of age is not a new problem, the pattern of hiring
selection undermines the respect for employees and erodes the organizational environment by
designing and enforcing a negative work culture by being complicit in hiring biases. Hiring
workers on the basis of age instead of skill set prohibits cognitive diversity and damages the
global economy (Bersin & Chamorro-Premuzic, 2019). Though scientific data supports evidence
that recruitment efforts and assessment systems are biased against older adults, knowledge and
expertise are predictors of job performance which increases after the age of 80 (Bersin &
Chamorro-Premuzic, 2019). Awareness on ageism and biases in hiring practices is a valuable
analysis because of the increasing number of older adults who choose to remain in the workforce
after the age of 60. By creating an inclusive workforce, organizations become innovative and
profitable while intentional limitations placed on older workers is limiting and is an act of
ageism (Harris et al., 2018).

15

Stakeholders’ Description
The stakeholders that comprise GAFE include the employees, human resource personnel,
middle management and directors. HR personnel and management workers are a commodity to
the organization because their actions and performances help to increase the sustainability of the
company by expanding its value. These stakeholders specifically help to safeguard profits and by
ensuring capital and principal investments are available via other stakeholders. Finally,
employees are important stakeholders within GAFE, because of their reputable character and
reputation in the industry as being the best in technology.
Stakeholder Performance Goals
A complete analysis would involve each of the stakeholder groups in management. The
study on ageism in hiring in the workplace focuses on age-based hiring biases. As internal
stakeholders, the management team has a function in how the direction and vision of the
company will evolve. Management‟s initial contact is with human resources, human resources
then interacts with the hiring body from management to discuss final decisions on the direction
forecasted for the organization. Directives from operations management team is channeled to the
hiring personnel where responsibilities are carried out. An inclusive environment of age diversity
and one that depicts the real world is what the stakeholder goal entails. The goal is that each
prospect will be assessed by their experience, credentials, and knowledge and not based on age,
skin color or gender. The primary goal is to increase the median age of workers in the tech
industry in accordance with the U.S. labor force age of 41.9 (U. S. Bureau of Labor Statistics,
2020).

16

Purpose of the Project and Questions
The purpose of this field project is to determine if age bias during the hiring process in
the tech industry is intentional or unintentional for persons seeking work after the age of 50. The
study evaluated if a manager‟s cultural environment during childhood development or the
relationship within the community they resided in impacted or influenced hiring behaviors as
they transitioned into adulthood. The study will further examine how the micro ecological
systems and environments impelled biased attitudes in hiring practices against older adults that
have resulted in ageist behavior within the workforce. The analysis will focus on the
interconnection of relationships, roles and varying degrees of stereotype embodiment and social
identity formation, media consumption and environmental events that might have prejudiced or
induced biases in hiring or prompted negative perception of older adults in the tech industry. The
aim is to understand reluctance in hiring older workers; behavioral factors and why negative
perceptions exist and how it is aligned to the socio-historical events in the ecological systems
that have influenced development.
The research questions that guide this study are the following:
1. To what extent are hiring decisions among GAFE managers influenced by age across the
mesosystems?
2. To what extent do micro and macrosystem affect GAFE managers when making hiring
decisions about older adults?
3. What effect does the microsystem have on GAFE managers in hiring older workers?
4. What role has the media played in the perception of older adults when aligned to the
chronosystem if any, and to GAFE managers hiring of workers over the age of 50?

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Methodological Framework
Ecological systems theory (Bronfenbrenner, 1979) and stereotype embodiment theory
(Levy, 2009) formed the theoretical and conceptual foundation for this study. Bronfenbrenner‟s
ecological systems theory provided a paradigm to examine the various environmental and
individual influences on the development of ageism, the adaptations and bi-directional
interactions of those influences, their consequences, and how those consequences influence the
individual and their environment. The primary dimensions of ageism relate to fear, lower
affective solidarity, uncertainty and ambivalence (Birditt et al., 2009) suggesting limited
interaction with or a negative relationship between the micro-, meso-, and exosystems associated
with the ecological systems theory. The stereotype embodiment theory provided an explanation
in how perceptions of aging negatively impact adults and how ageism is internalized and
becomes self-relevant (Swift et al., 2017). This field study utilized a qualitative data analysis to
gather insights into the manager‟s environment from adolescence to adulthood and how learned
behavior in the micro and macro levels connect to ageism and hiring biases as an adult within
management. Through secondary research, questionnaires and interviews helped to formulate the
assessment on ageism endeavoring to understand behaviors and why the roots of ageism in hiring
practices exist. Using Urie Bronfenbrenner‟s ecological model, the study drew on relevant
literature in the micro and meso level to understand factors that suggest environmental influences
in the workforce being seen as pathological rather than healthy and how it relates to the
environmental systems.
Table 1 illustrates Bronfenbrenner‟s ecological system framework on aging and how each
of the settings contain a system that have influence across one‟s development, their relations
with each other and the roles they play.
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Table 1
Ecological Systems Framework on Aging
Microsystem Mesosystem Exosystem Macrosystem
The individual and
their family and
work influence
behavior
Family, peers,
workplace make up
this ecological
system.
Mass Media plays as
much a role in
behavior as the
environment.
Laws that govern
ageism and social
conditions. The
long-standing
history of age bias.

In order to
understand
behaviors a
person‟s
environment;
developmental
climate needs to be
understood.

Segregation in the
workplace due to
ageist attitudes and
behavior leads to
failure without a
cooperative group to
interact with.

Ageism is a process
of systematic
stereotyping that
takes place in
human ecosystems.

Human capital is
loss without
processes in place.

Bronfenbrenner says
ageism is inherent
in proximal
processes such as in
the workplace.

Under diagnosis of
depression in older
adults as
manifestations of
ageism in the
mesosystem.

Repository of images
that do not reflect
diversity.

Cultural norms and
inter-relatedness
between ageism
and belief systems.

When one looks at the ecological systems theory and the determinants in the micro and
macro level factors of development, ageist behaviors are uncovered from childhood in the micro-
level where psychological structures of deep-rooted prejudices and biases affect all age
categories (Stypinska & Nikander, 2018). On a meso level, it is the social interactions of family
and close relationships that play a role in social change and acceptance (Stypinska & Nikander,
2018). On a macro level, we would consider the entire population of employees as a whole and
how behaviors as major stakeholders can shape the environment in which they work. On a
micro-level, we consider how each employee interacts with another on a one-on-one basis; social
19

interactions, individual behaviors and relationships (Naegele et al., 2018). Each of these
dynamics provides an in-depth understanding of the employee‟s central role as the key
stakeholder and how the impact on behaviors is pivotal to the ageism study. When it comes to
strategizing, planning and directing the courses of action within an organization, the role of the
employee is often an initial consideration.
The reason the focus is on management as primary stakeholders is because they are the
principal decision makers for hiring. The aging population of employees changed the structure of
the work environment and is the cause of the enduring frequency of biases and discriminatory
behavior that compromises productivity. Individual perceptions of age discrimination that are
amplified through the interaction with co-workers on a micro-level foster the prevalence of
ageism at the organizational level through stakeholders (Naegele et al., 2018).
Definitions
 Age discrimination: Unfair or unequal treatment of an employee by an employer because
of the employee‟s age
 Ageism: Prejudice or discrimination on the grounds of a person‟s age.
 Ageist: (n) a person with ageist views. Characterized by or showing prejudice or
discrimination on the grounds of a person‟s age.
 Chronosystem: The fifth and final level of the ecological systems theory. This system
consists of all of the experiences that a person has had during his or her lifetime,
including environmental events, major life transitions, and historical events.
 Ecological Systems Theory: Defines how a child‟s development is affected by their social
relationships and the world around them.
20

 Exosystem: This system refers to a setting that does not involve the person as an active
participant, but it still affects them. This includes decisions that have bearing on them.
(Ex. A child being affected by a parent who receives a promotion at work or losing their
job)?
 Macrosystem: Cultural Values, health and public policy and laws are part of the
macrosystem.
 Mesosystem: This system consists of the interactions between the different parts of a
person‟s microsystem. The mesosystem is where a person‟s individual microsystems do
not function independently, but are interconnected and assert influence upon one another.
 Microsystem: The first of the ecological systems, the microsystem is the system closest to
the person and the one in which they have direct contact. Examples are: home, school,
daycare, work. A microsystem includes family, peers or caregivers.
 Older: Age 50+
Organization of the Project
The dissertation study covers five chapters where through an exhaustive exposition of
scholarly literature sources research and investigation was on the emerging problem of ageism
and how it has fractured the workplace environment with an acceptance of exclusion. Through
the micro and meso ecological systems theory, factors in one‟s environmental influences during
early childhood development reveal how there is a connection in the decoding of cultural
messages and the alignment with ageism and biased hiring practices.
Chapter Two provides a history of ageism and its origin as well, the Age Discrimination
in Employment Act (ADEA) of 1967. Next was a discussion on negative perceptions, age bias in
hiring practices and why ageism has become an emerging problem. Finally, the general literature
21

explores financial hardships through unemployment and how ageism in the work culture is
embedded within the IT culture.
Chapter Three offers a description of the sample used in understanding discriminatory
practices in the workforce and how the discoveries have been made through questionnaire and
interviews. Chapter three also details the design rationale, participating stakeholders and helps to
develop the theory of childhood development and the interconnection of the ecological system
and adult behavior. Within Chapter three the theoretical foundations of Bronfenbrenner were
reexamined through data collection and instrumentation. Also covered is how credibility and
trustworthiness is employed throughout the research and too, how steps to ensure validity and
reliability during the analytical procedures was reflected during the recording phase, and during
the preparation.
Chapter Four provides findings from the research utilizing Bronfenbrenner‟s 1979
ecological theory to the problem. Findings have been aligned to research questions and explore
how the levels of Bronfenbrenner‟s model are found to have an effect on hiring practices.
Chapter Five provides recommendations, conclusions, solutions and the summary based
upon the study findings. Within this chapter, the researcher has given implications by addressing
how the study brings awareness to the problem. Findings are linked to the literature and also
provided within Chapter five is future research needed as a result of the findings.
22

Chapter Two: Literature Review
As the world population continues to age older individuals are remaining in the
workforce longer than in years past (Fasbender & Wang, 2019). Individuals over the age of 50
are facing challenges in acquiring sustainable income to support one‟s life while the larger
problem older adults face is an acceptance into the technology industry that is millennial-driven
and youth-oriented (Myers & Sadaghiani, 2010). The increasing ratio of retired to employed
workers is attributed to ageism in the workforce which indicates an increasing loss of productive
capacity in our nation (Palmore, 2015). Because age discrimination is difficult to prove, only 2%
of the 20,000 cases filed each year with EEOC have enough evidence to file a lawsuit (Wilkie,
2018). There is little attention given to why hiring biases in the workforce exist with the
exception of the technology industry where it is a youth-obsessed culture (Wilkie, 2018). Prior
research on age-based discrimination tends to discuss social inequalities and emotional
manifestations due to impartial hiring, but seldom do studies address why hiring biases exist for
workers over the age of 50 who have the right qualifications for the job. In a research study by
Krings et al. (2010), biased beliefs toward older people led to age discrimination at the hiring
selection among human resources personnel.
Drawing on the theories of stereotype content and role congruity, research explored the
role of stereotypes for age-based employment discrimination with a focus on warmth and
competence as the primary dimensions in social judgment. According to Krings et al., 2010,
hiring biases exist because of stereotypical inferences about older people presenting warmth but
low competence (Krings et al., 2010). Researchers investigated how the two variables interact
with job requirements to predict hiring biases in the workforce (Krings, 2010). Findings were
23

that older people were less trainable, less adaptable, less effective and less competent than
younger workers (Krings, 2010).
Conversely, they were believed to be more reliable, more loyal, more stable,
interpersonally skilled and more prone to illness (Krings et al., 2010). In conclusion, the results
showed age bias was robust or more pronounced during the hiring phase (Krings et al., 2010).
Older candidates despite qualifications were found to be discriminated against even if the
position required warmth-related qualities illustrating bias (Krings et al., 2010).
What remains unclear is how a bias such as ageism that affects health, finances, and
hiring practices is socially accepted into the work culture. Information is vague as to how ageism
and workplace biases involving persons over the age of 50 persists in areas of employment. And
it is unclear whether ageism across the spectrum emerges from one‟s environmental systems in
their social setting, during the developmental stages or as a result of societal impulses. It is also
unclear if negative stereotypes and views on aging is a reflection on those interviewed in the
technology industry or if findings of ageism would parallel in other sectors of employment. The
purpose of this dissertation was guided by four questions:
1. To what extent is the hiring decision of GAFE influenced by age across the mesosystem?
2. To what extent do the micro and macrosystem affect GAFE managers when making
hiring decisions about older adults?
3. What effect does the microsystem have on hiring older workers?
4. What factor has media played when aligned to the chronosystem if any, to older adults
and GAFE hiring of workers over the age of 40?
Within chapter two is a thorough review into the origin of ageism and its founder Robert
Butler (1989). It is important to know who the pioneer of gerontology is and the man that
24

invented the term ageism in order to understand his research, his determination in unveiling
ageism and how the fear of aging is looked upon as daunting and associated with deterioration
and decline (Awang et al., 2018). By bringing awareness to ageism, Butler believed
misconceptions and intergenerational tensions would be lifted in order to create less proscriptive
attitudes towards age.
The general literature review is organized into four major sections: (a) age-driven
inequalities in the work culture, (b) unfair hiring practices in the labor force and the negative
complexities and consequences in hiring biases, (c) how the media impels negative perceptions
on aging, (d) how the failure of the Age Discrimination Employment Act (ADEA) failed its most
vulnerable population. Next the review will turn to Bronfenbrenner‟s 1979, ecological systems
theory where the microsystems level and the macrosystems level influences the general literature
linking environmental and societal exposures to hiring practices in employment. Finally, the
conceptual framework including, stereotype embodiment theory illustrated how stereotypes and
cultural messages are internalized from the host culture at a young age and carried into
adulthood, directly effecting hiring practices. Each section is linked to unfair hiring practices and
the prevalence of ageism and how it has led to an internalization of ageist construct. The
stereotype embodiment theory reinforces Bronfenbrenner‟s ecological theory by presenting the
manifestations of ageism and how they sit at the root of fears. As children frequently become
exposed to age stereotypes by their environment these stereotypes continue beyond childhood
(Levy, 2009). Bronfenbrenner‟s theory defines the complexities and the various layers within the
environment that affect development from childhood and are manifested in adulthood
(Bronfenbrenner, 1979). During the developmental stages, individuals have exposure to the
ageist paradigm before it is related to one‟s experience (Levy, 2009). The exposure one has to
25

the ageist paradigm is assumed as part of the collected expectation of human development
throughout one‟s life. Once an individual receives the message that older people display
forgetfulness or cognitive decline, it has become a concept (Levy, 2009). Once the same message
has been observed or heard several times, it is inculcated and becomes a fact of aging (Levy,
2009).
The Foundation of Ageism
When Washington Post journalist Carl Bernstein interviewed psychiatrist, Robert Butler
in 1969 about the public outcry and opposition involving the development of public housing
units in an affluent section of Washington, D.C. the discussion changed to a conversation on age
bias (Achenbaum, 2015). A civic project intended to promote good-will was met with challenges
when neighbor‟s fueled hostility after learning an apartment complex within their district was
being transformed to accommodate Black people, lower-income individuals and aging adults.
When Butler was asked, if the animus from neighbors was racially motivated, he replied that he
thought it was more a function of ageism, and from that conversation, the origin of the word
ageism was coined (Achenbaum, 2015).
Definitions and conceptual understandings of ageism often vary across contexts, cultures,
and spheres of influence within the social-scientific environment (Snellman, 2018). There is little
consensus on how ageism is defined or theoretically understood (Tornstam, 2006). Conceptually
ageism is considered to be a bias that is barely 50 years old and has not had the same study, and
research as other discriminations (Demby, 2014). The difference between discriminations such
as racism, sexism and heterosexism where the target is part of a subpopulation, is that everyone
becomes vulnerable to ageism if they live long enough (Palmore, 2015). Ageism is often thought
26

of as the same type of prejudice as gender discrimination, sexism or racism but one‟s gender and
race is typically who you are for a lifetime whereas ageism occurs later in life (Palmore, 2015).
According to Donizetti 2019, gerontologist Robert Butler was the first person to use the
term ageism to describe prejudice against the elderly, defining it as a process of systematic
stereotyping and discrimination against people because of age. Unlike Palmore, Robert Butler,
saw structural discrimination as being equivalent to racism, sexism or other constructs of
bigotries since he believed each of these biases is a form of systemic stereotyping (Bengtson &
Whittington, 2014). It is important to note that as the first Director of the National Institute on
Aging, Butler witnessed an ever-growing problem of derision for the elderly by his medical
colleagues and a disapproval of the elderly that brought about negative comments from the
medical community (Bengtson & Whittington, 2014).
Powered by a determination to help broaden the understanding of aging Americans,
Butler‟s commitment was to bring understanding of aging adults to his colleagues by revealing to
them that the basis of their fears and cause of ageism was rooted in personal fears of growing old
(Bengtson & Whittington, 2014). Robert Butler‟s belief was that when people held negative
perceptions and spoke about the elderly with disdain they were speaking about their feared future
selves (Nelson, 2005). As people age they foster stereotypes about aging; and one‟s future self of
aging is not characterized in print or media in a glowing manner but rather a show of social
isolation, aloneness, and health disparity (Nelson, 2005).
While studying at Columbia in the 1960s Butler was aware of the discriminations older
adults faced and became an advocate for the aging population. He was disappointed when he
learned there was a lack of education in the field of gerontology and a greater lack of knowledge
by his medical colleagues who held negative stereotypes associated with aging adults (Bengtson
27

& Whittington, 2014). As a supporter against discrimination, Butler observed partialities and
poor treatment of elderly people by professionals which prompted him to challenge conventional
attitudes (Bengtson & Whittington, 2014).
What Butler learned was that there were three distinct sides to the problem of ageism and
yet they were interconnected (Butler, 1980). First, there is “Prejudicial attitudes toward the aged,
toward old age, and towards the aging process” which included attitudes held by the older adults
themselves (Butler, 1980). Second, there were discriminatory practices against older adults,
predominantly in employment, and in areas of social roles where there was a specific language
towards aging adults where adults reverted to talking down to seniors when communicating
(Butler, 1980). Third, were institutional practices and policies, which often without malice
perpetuates stereotypic beliefs about the older adults and a process where older people
flagellated themselves for being older (Butler, 1980). With the understanding of the distinct sides
to the problem of ageism, Butler wanted to give meaning to this bias and worked with colleagues
to dismiss myths and stereotypes and to train healthcare professionals on how to treat older
patients with compassion (Butler, 1980). Though combatting ageism remained the paramount
goal, Butler‟s attention was replaced with promoting healthy aging (Butler, 1989).
As a medical forerunner in the science of gerontology, Butler wanted to remove ageist
attitudes as he saw it as a debilitating form of discrimination. He sought to dispel age bigotry and
stereotypical myths about the aging by invoking a call to action after observing elderly patients
he felt were being willfully ignored by physicians (Bengtson & Whittington, 2014). Butler
believed that one day a new intervention that could significantly delay the aging process would
be pioneered and a new way of thinking about the diseases of aging and what predisposes older
individuals to disease would be found (Bengtson & Whittington, 2014).
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Age-Driven Inequalities
Continued age-driven inequalities fueled Butler to work on behalf of elderly adults where
he identified and wrote about the pervasive issues of contempt for older people in the workforce.
With abiding research to understand and perpetuate healthy aging, Butler wrote that damaging
perceptions toward aging adults is a profoundly embedded problem in the United States
(Achenbaum, 2015). Through a career spanning five decades, Butler was able to influence
research on aging by bringing national attention to public policy in a theme he called longevity
revolution (Bengtson & Whittington, 2014). After Butler observed his grandmother managing a
farm during the depression after his grandfather‟s death, Butler saw elderly adults as persevering
survivors (Bengtson & Whittington, 2014). He believed that chronological age was not directly
correlated with functional performance in terms of physical and mental health and a predictor
that in years to come, more individuals will be seen as old in years but functionally young
(Bengtson & Whittington, 2014).
Ageism Is Socially Accepted
Nelson (2005) reported that the challenge with ageism is that it is socially accepted and
has been part of society for over 50 years. Frequent tales of growing old is a comedic narrative as
greeting cards amplify the consequences of age to perpetuate stereotypes (Nelson, 2005). There
was once a time when older people were revered and well-regarded, but it is common to find
witticisms being made about the elderly where parody is levied about physical inadequacies
(Ellis & Morrison, 2005). These behaviors are damaging to older adults‟ overall image and
opinion of older adults when trying to gain access into the workforce as negative stereotypes can
be internalized when seen often (Zebrowitz, 2003).
29

The Baby Boom Generation (1946–1964) has seen both sides of ageism. During the
1960s and the era of Woodstock, Flower Power, and the Vietnam war, Baby Boomers wore afros
and donned longer hair becoming symbols of rebellion to employers (Herrick, 2006). As a result
of longer hair being a connection to drugs and free love, it too became a contentious issue in the
workplace where workers were harassed, and unions failed in defending men‟s rights wearing
shoulder-length or longer hair (Herrick, 2006). From job suspensions and job refusal,
dehumanization and workforce alienation biases based on looks is not a new occurrence for
many Baby Boomers who find themselves in the job market (Herrick, 2006). The gray wave, a
name given to workers over the age of 65 is impacting the workforce with institutional
knowledge and professional contacts that can be difficult to find among younger talent (Taylor,
2019).
With the collection of negative images being attached to the state of being old, even
adults in their 40s are subjected to scrutiny and denied hiring opportunities. Because ageism
targets the vulnerability and defenselessness of older people, it can be internalized if one has low
self-esteem (Bengtson & Whittington, 2014). If an individual is conditioned to look at aging
adults positively these viewpoints may serve as a barrier against internalized ageism and
dependency at advancing ages, especially as the thought of aloneness and fear begins to mount
(Bengtson & Whittington, 2014).
Ageism is rooted in American society‟s laws and its customs (Eglit, 2009). The populace
in the United States is instructed to aspire to qualities associated with youth according to popular
print and visual media. At the same time, older people are presented as victims or resistant to
change (Eglit, 2009). Though negative views of adults 55+ are viewed as legitimate by
30

government rules, customs, social norms, and physical conditions, these views spread to the
workplace and effects careers (James et al., 2007).
In a recent study for Deloitte (Bersin & Chamorro-Premuzic, 2019), approximately
10,000 companies were asked, “Is age a competitive advantage or competitive disadvantage in
your organization?” The reply from two-thirds of the respondents was that they viewed older age
as a competitive disadvantage, numbers consistent with AARP that show adults between the ages
of 45 and 74 have experienced age discrimination (Bersin & Chamorro-Premuzic, 2019). Ruth
Finkelstein, associate director of the Robert N. Butler Columbia Aging Center at Columbia
University says that we are comfortable making fun of old people and do it routinely as
individuals. Much like gender and race individuals rely on physical cues for categorizing people
based on age. Ageism is alive and well. Old people are shown as decrepit, unattractive and
forgetful, yet the most powerful people in the world are old; the pope, most presidential
candidates, members of Congress, the Senate and the Supreme Court Justices and yet age
continues to be a disadvantage in the corporate arena (Pyrillis, 2016).
For 40 years, scholars have interpreted ageism as a negative or positive stereotype, a
prejudice or discrimination against someone based on a chronological age or on the basis of a
perception of older adults as being old or elderly (Donizetti, 2019). When reviewing positive
stereotypes, one looks at the contradiction of the two motivations: Positive stereotypes are
intended to be perceived as complimentary. They offer ideas about the elderly that directly or
indirectly connotes favorability, yet they also serve to justify existing intergroup inequality
(Czopp et al., 2015). An example of intergroup inequality as it pertains to ageism in hiring is
stereotyping an older person by stating they lack the ability to move quickly or describing them
as having little intellectual capacity as justification in a refusal to hire. Then later, referencing
31

older persons as having new emerging stereotypes intended to celebrate what society deems an
achievement by describing older adults in the workforce as offering traditional virtues;
benevolence, compassion, thoughtfulness, traits requiring little academic aptitude but are
considered positive stereotypes (Czopp et al., 2015).
The Media’s Influence on Visual Ageism
Visual ageism is an axiom defined as a media practice of visually underrepresenting older
people or misrepresenting them in a prejudiced way (Ivan et al., 2020). Depicting older people in
marginal illustrations or demeaning roles exaggerates and distorts accurate portraits of older
people. While it is a fact that people age, negative images create obstacles to ones way of
thinking about aging at the micro-level and at a more project-oriented level (Ivan et al., 2020).
An enduring history of research established media portrayals of older groups of people in
society can be biased and have the ability to promote stereotypes that influence decisions when it
comes to hiring personnel (Kroon et al., 2018). The message that the media sends to society
about aging adults is through a stereotypical lens of declining health and diminished value
(Milner et al., 2012). Media portrayals of aging adults not only reflect the prevalence of ageism
in society, but it largely reinforces negative stereotypes (Milner et al., 2012).
From observation and the imitation of relevant role models to the media, social images
and negative perceptions towards aging adults are formed during the developmental stage
(Mendonça et al., 2018). The media makes older individuals susceptible to negative messaging
when it presents older adults as unattractive and unwelcomed (Vickers, 2007). The cultural
message of slanted media confirms negative perceptions that have seeped into everyday culture
prompting ideas that older age is a negative (Vickers, 2007). Individuals judge cognitive and
physical attributes according to what is seen and accepted on television. If the media can break
32

an image by showing older adults as incapable and fragile, then it can build an image by
exhibiting elderly people in a desirable manner. Positive perceptions of older adults could likely
ensue and mitigate hiring biases (Ellis & Morrison, 2005). Instead, what society faces are
negative perceptions socialized from youth and carried into adulthood (Nelson, 2005).
While there are fundamental causes of ageism in the United States that cause hiring
problems for adults over the age of 50, it is the negative stereotyping and media‟s portrayal of
older adults that contribute to the debasement of older people (Radović-Marković, 2013). People
over the age of 50 face discriminatory problems based on stereotypes and perceptions about the
inadequacy of older workers (Radović-Marković, 2013). In hiring decisions, references of age
discrimination is implied by the use of crude proxies in personnel decisions, terminology that is
both impolite and improper as it relates to promotions, training, hiring, termination and
mandatory retirement (Radović-Marković, 2013). Destructive consequences of ageism in
employment also include obstructions in recruitment, diminished conditions of work and
employment, limited career development and, in the absence of legislation, diminished
employment protection and rights (Radović-Marković, 2013). Recent reviews state ageism in
hiring practices occurs when there is partiality and decisions are not based on job performance,
merit, credentials or education but rather based on age (Radović-Marković, 2013). Ageism
leaves individuals with feelings of being devalued and degraded in the eyes of society (Palmore,
2015). Aging should be celebrated though advancing in age in the United States and in other
countries is a life cycle not looked upon favorably when employment cannot be attained due to
age. Through research the effects that ageist attitudes and ageism have on workers resulted from
media depictions can have damaging results in the workforce (Kroon et al., 2019). The
psychosocial issues, judgments and stereotypic beliefs about ageism are rooted in negative myths
33

throughout the years and have an influence on the lifestyles of aging adults (Singh & Misra,
2009).
Stereotype Embodiment and Childhood Development
The impression the media has on children is profound and has the ability to produce
destructive outcomes such as negative images of aging after hours of repeated messages being
shown during developmental years (Francoeur, 2003). The negative age stereotype exposure
received during one‟s developmental years extends into older age and is internalized because of
held beliefs extended beyond childhood (Levy, 2009). A dominating example is television,
which frequently presents older people in a demeaning manner. Older individuals with greater
lifetime television viewing would tend to hold more negative age stereotypes because of the
repeated messages seen. When integrating the theoretical framework of stereotype embodiment
theory, during childhood development, a critical observation is how youth assimilate and
internalize self-perception based on those exposed to them within their environment (Levy,
2009). Children are able to recognize age differentiations by the time they are six, it‟s at this age
when they form group memberships and when children learn discrimination (Burke, 1982). The
same tendency to discriminate emerges with verbiage describing older adults with descriptors
such as “lonely,” sad, ill, “doesn‟t know a lot” which become words children use as they become
in-tuned to their personal identity and to the identity of others (Burke, 1982, p. 205). The same
way a child learns hostility from observation they also learn to discriminate and encode behavior.
In later years, children emulate negative behavior observed by continuing to reside within
settings where discrimination exists (Nabavi, 2012). Prior research studies show that the roots of
ageism in adulthood may be traced back to one‟s early life experiences (Kennison & Byrd-
Craven, 2018). The results of a multiple regression analysis showed predictors of ageism in
34

relationships with parents during early life influence attachment. The findings indicated that
there is a relationship between attachment and levels of ageism in adulthood. Researchers tested
the hypothesis that parent-child relationships during childhood would be related to ageism in
young adults either directly or mediated by attachment. It was further hypothesized that
predictors of ageism would coincide for men and women. The outcomes of multiple regression
analyses indicated that avoidant attachment and a negative relationship with mothers during
childhood were significant predictors of ageism. The relationship between negative mother
relationship during childhood and ageism was partially mediated by avoidant attachment. The
relationship between anxious attachment and ageism was not significant when controlling for
avoidant attachment. These results indicate that social development processes occur in early
childhood and can predict ageism later in life (Kennison & Byrd-Craven, 2018).
Women and Workplace Hiring Biases
For women, age discrimination starts at the age of 40 in the workplace (Cook, 2018). The
idea that organizations should be more inclusive of older workers is supported by recent research
by the Harvard Business Review where it cites diversity is critical in incorporating innovation in
a business (Cook, 2018). When women experience age prejudices due to joblessness the feelings
of hostility create both psychological and physiological emotions that contribute negatively to
their health (Chrisler et al., 2016). For women, ageism on the job is yet another disadvantage as
the media is a beauty-oriented industry that controls perceptions and defines what the acceptable
look for employment resembles (Slevin, 2015). Studies such as, You look “Mahvelous”: The
Pursuit of Beauty and the Marketing Concept, conducted by Bloch and Richins (1992), have
been steered to show the effects of media on women and results indicate the media negatively
35

affects self-image (Britton, 2012). The barriers to economic gender equality for women over the
age of 50 demonstrate how the media impairs judgments that hinder employment (Britton, 2012).
Demonstrations of Hiring Biases
Age discrimination in the workforce is not only a salient problem but it is the primary
fear that older adults have about their place of employment (Perron & McCann, 2018). Age
discrimination in hiring practices has placed boundaries on the livelihood and financial
achievements of life for adults over the age of 50 (Perron & McCann, 2018). The perception is
that older workers are not wanted and not hired within the workforce (Perron & McCann, 2018).
When in pursuit of work, people over the age of 50 suffer from longer unemployment periods
despite organizational hiring practices being challenged for unfair treatment on the grounds of
age. So, while it is unlawful to discriminate against workers because of their age, it remains a
common practice in hiring (Fasbender & Wang, 2017). One justification for the disparity in
hiring practices has been the negative perceptions and behavior that exists within organizations
leading to age discrimination (Fasbender & Wang, 2017).
While Silicon Valley is known for having a youth-obsessed culture, the market research
firm Statista surveyed the median ages within the top technology industries (refer to Figure 2) in
the United States and assessed the leading tech companies; AOL, Apple, Amazon, Facebook and
Google‟s average employee age is between 27 and 31 years (Pelissons & Hartmans, 2017).

36

Figure 2
Median Employee Age in the Technology Industry

Note. Reprinted from “The Average Age of the Employees at All the Top Tech Companies, in
One Chart” by A. Pelisson & A. Hartmans, 2017, Business Insider.
(https://www.businessinsider.com/median-tech-employee-age-chart-2017-8)

37

Prior Research
While still new compared to other biases and discriminatory behavior, research
supporting age bias is emerging. For example, there are study‟s that investigate hiring decisions
and negative behavior towards older workers and reports that discuss how hiring decisions were
made and reasons for age discrimination in the workforce (Fasbender & Wang, 2017). The
monumental demographic shift has shown adults age 65 and older are remaining in the
workforce (illustrated in Figure 3) and as a result, the number of age discrimination cases filed
with the Equal Employment Opportunity Commission has increased 47% since 1999,
representing 20% of all discrimination charges (Rockwood, 2018).

38

Figure 3
Labor Force Employment Participation From 1992 Through 2022

Note. Reprinted from TED: The Economics Daily by US. Bureau of Labor Statistics, 2014.
(https://www.bls.gov/opub/ted/2014/ted_20140106.htm?view_full)

Relation to Ageism Study
In the research correspondence study titled, Numark, Burn and Button, there was
evidence of age discrimination involving applicants who forwarded resumes for various
occupations. The study was titled the Neumark, Burn, and Button Resume-Correspondence Study
which measured ageism in hiring practices (Neumark et al., 2019a). While this type of study is
costly because it requires hiring actors and actresses in order to create fictional resumes for real
jobs, when comparing the response rate by age it was proven that age-based hiring disparities
39

exist (Neumark et al., 2019a). In the Neumark et al. (2019a) study, in order to look closer at
issues that confront age discrimination in hiring, researchers created a large comprehensive study
with 40,000 fictional resumes responding to over 13,000 job positions in 11 states and 12 cities,
for the age categories of 29–31, 49–51, and for the senior group of 64–66. Careful attention was
paid to isolate age differences and forward resumes from seniors with the exact experience as
younger applicants. The chart (Figure 4) indicates that throughout each of the occupations and
gender categories that the age group of 64–66 received fewer responses than younger applicants
including jobs that were for unskilled positions for men (Neumark et al., 2019).

40

Figure 4
Comparisons of Job Applicant Callback Rates by Age

Note. Reprinted from Population Aging, Age Discrimination, Age Discrimination Protections at
the 50th Anniversary of the Age Discrimination Act by D. Neumark, I. Burn, & P. Button, 2019b,
IZA Institute of Labor Economics.

The Neumark et al. (2017) study is significant as it indicates evidence from the field
experiment how age discrimination makes it more difficult for older individuals, especially
women to get hired. This is important as seniors age 65 and over in the U.S. working population
is projected to rise from 19% to 29% in the year 2060 (Neumark et al., 2017). According to
Neumark, if there continues to be a decrease in employment for workers aged 50 and over,
41

public policy challenges will be expected as the dependency ratio of non-workers to workers will
rise and labor force will slow down (Neumark et al., 2017). Currently, public policy efforts are
being put in place to increase the labor supply of older workers in the United States focusing on
Social Security by reducing benefits for those claiming subsidies at the age of 62. If corporations
fail to respond to labor supply in a refusal to hire older workers, the likelihood of harsher policy
reforms for older adults will follow (Neumark et al., 2017) Should this happen then age
discrimination in hiring may reach a critical stage as to whether older adults can work longer
considering many transition to part-time retirement at the end of their careers (Neumark et al.,
2017).
From invoking mandatory retirement policies to imposing digital work initiatives that are
not indigenous to senior employees, hardships are often placed on older employees without
regard to their individual abilities to perform (Eglit, 2009). While ageism is a global problem,
placing hardships on older employees runs unbridled within organizations (Eglit, 2009). It is a
practice that is supported by many plausible theories as the process helps to increase
opportunities for younger employees in order for them to grow. Conversely this process has
abrogated employees with work deficiencies relieving companies of workers who affect the
performance of others (Eglit, 2009). When assessing the impact of ageism in the workforce one
is mindful that there are nearly 76 million people who were born between 1946 and 1964 which
make up the Baby Boomers in the United States. Workers between the ages of 65 and 74 and
those aged 75 and older were once projected to increase by more than 70% in 2016, while the
forecast fell slightly short, it remains a telling sign for what predictors are expecting in the years
ahead (Eglit, 2009). More recent forecast predicts that as a result of healthier lifestyles in the
United States, the number of people aged 65 and older will rise from 46 million to over 98
42

million by the year 2060 which will call for more reform and legislation in hiring practices (Jin
& Baumgartner, 2019).
When it comes to financial stress, older Americans say that job insecurity is their
number-one concern (Parramore, 2013). People over the age of 50 have greater difficulty in
securing employment as they are aware that corporations view their positions as expendable and
their worth as less valuable than young colleagues (Parramore, 2013). According to an AARP
survey, more than 37% of older workers lack the confidence that they would find work if
terminated from their employment. Of the 37%, one in five or 19% say they lack confidence in
finding work because of age discrimination (Parramore, 2013).
In addition to the removal of age identifying questions that denote age, workplace
policies and programs need expansion in order to facilitate opportunities that would extend labor
force participation for older workers.
Hiring Biases in the Tech Industry
In the Tech industry sector employers place a high premium on digital natives, those who
are of a specific age demographic who were reared with online technology. The drive to reach
young employees who are young have prompted employers to trade words such as seasoned and
experienced for energetic and high-potential, words that are codes for youth and words that
raised concern about Verizon, Amazon, Facebook, Goldman Sachs and Target for spearheading
hiring campaigns where they limited advertisements to specific age demographics and college
campuses (Rockwood, 2018). Though the percentage of workers over the age of 50 continues to
grow as adults attempt to remain in the workforce, a large percentage of viable and high-cost
paying work is within the tech industry, an industry that prefers 24- to 34-year-olds and not 50-
to 65-year-old workers (Lucas, 2016). Based on the hiring manager‟s premise, a younger worker
43

will work longer hours than that of older workers (Lucas, 2016). Before the field study was
conducted, the researcher posited that if the hiring manager is between the ages of 24 and 34,
then another theory to consider for the lack of older workers in the tech industry would be social
identity theory. Based on this theory, self-esteem and social identity would be generated based
on one‟s group membership or group category. According to McLeod (2019), by viewing the
group you belong to as the in-group; members outside of the group would be the out-group. In
this instance, the out-group would be older workers. If the hiring managers within the tech sector
were choosing to hire workers based on social identity theory; then this would be the basis of
hiring biases (McLeod, 2019). The current study debunked the idea of social identity theory
when the age membership of its participants was revealed. Additional research would have to be
conducted with a different age category in order to determine hiring biases based on social
identity theory or the attraction theory.
Studies show that because older workers are typically unemployed for longer periods of
time, once they accept work, they take on jobs with low wages, contractual jobs, part-time work
or lower-skilled work (Harris et al., 2018). In addition to being defined as older, experienced
candidates are also likely to expect a higher salary, benefits and costs that younger workers do
not expect (Harris et al., 2018). Research reveals that even if the job role is low status, a younger
stereotype profile is preferred and the older stereotype profile is considered only when the role is
casted as a subordinate to the younger employee stereotype (Abrams et al., 2016).
The systematic stereotyping of older adults contributes to injustices and inequality. On
the individual level, older people internalize ageist behaviors that erode self-esteem and
confidence levels leading to feelings of inadequacies affecting their drive to seek employment
opportunities (Harris et al., 2018).
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Hiring Inequities Are Not Exclusive to the United States
Workplace discriminations in hiring based on age is not a problem indigenous to the
United States but a far-reaching obstruction where people's social, health and economic
contributions have been impacted around the globe (O‟Loughlin et al., 2017). Australian Human
Rights Commission (AHRC) conducted a campaign focused on improving attitudes to aging and
combatting ageism within the workplace. Australia has age discrimination legislation in place at
the national level as well as within states and territories though discriminatory hiring practices
have persisted. While the presence of age discrimination in Australia is present across all
industries, research has shown that hiring biases is blamed on the reluctance of employers to
change negative attitudes and portrayals of the elderly as seen in the media (O‟Loughlin et al.,
2017). Within the United States, age discrimination is a part of the social fabric of American life
where the workplace as Laurie McCann, attorney for AARP states is a microcosm of society and
part of our social environment (Dennis & Thomas, 2007). The barriers to workplace diversity in
the United States cannot be pointed solely at employers as it encompasses many facets such as
organizational function, education, cognitive styles, ethnicity and more. A lack of diversity not
only hinges on how individuals see others but also how they see themselves which affects
personal interactions and communication in the workplace (Patrick & Kumar, 2012).
According to an English Longitudinal Study of Ageing, an observational population
analysis with a sample of 7,731 men and women participants aged 50 years or older reported
experiences of age discrimination through face-to-face computer-assisted personal interviews
(Jackson et al., 2019). In a self-completed questionnaire over a period of 6 years, between July,
2010, and June, 2011, and May 2016 and June 2017 participants were rated on health, chronic
health conditions, and depressive symptoms. The study methods used logistic regression to
45

measure cross-sectional associations between perceived age discrimination and baseline health
status and prospective associations between perceived age discrimination and incident ill health
(Jackson et al., 2019). The findings concluded perceived age discrimination was reported by
1,943 (25·1%) participants. Patients who perceived age discrimination were more likely to self-
report fair or poor health (odds ratio [OR] 1·32 [95% CI 1·17–1·48]) and to have coronary heart
disease (1·33 [1·14–1·54]), chronic lung disease (1·37 [1·11–1·69]), arthritis (1·27 [1·14–1·41]),
limiting long-standing illness (1·35 [1·21–1·51]), and depressive symptoms (1·81 [1·57–2·08])
than those who did not perceive age discrimination (Jackson et al., 2019). Follow-up data
collected 6 years after the baseline assessment was available for 5,595 participants.
Longitudinally, perceived age discrimination was associated with the deterioration of self-rated
health (OR 1·32 [95% CI 1·10–1·58]) and incident coronary heart disease (1·66 [1·18–2·35]),
stroke (1·48 [1·08–2·10]), diabetes (1·33 [1·01–1·75]), chronic lung disease (1·50 [1·10–2·04]),
limiting long-standing illness (1·32 [1·10–1·57]), and depressive symptoms (1·47 [1·16–1·86])
(Jackson et al., 2019).
The interpretation of the study from 2010 to 2017 was that older adults living in England
perceived age discrimination is associated with increased odds of poor self-rated health and the
serious health problems over a period of 6 years. These findings underscore the need for
effective interventions to combat age stigma and discrimination in the workforce (Jackson et al.,
2019).
Since hiring biases and population aging affect all countries, and all income groups, The
World Health Organization (WHO) promotes an Age-Friendly City movement where nearly 300
countries are participants in advocating for the functional capacity of aging adults (Woo, 2017).
What each participant shares is the desire to promote healthy and active aging, and better self-
46

esteem in order to garner employment as it has been confirmed that our sense of self and the
environment we live in determines our mental outlook throughout one‟s life (Woo, 2017). WHO.
states that aging can offer challenges and opportunities; it can increase demand for primary
health care and long-term care, and it can require an improved workforce which would
strengthen the need for environments to be made more age-friendly (Woo, 2017). Making
lifestyle changes was echoed and the belief was that it would benefit older persons and help to
lessen negative perceptions and discrimination experienced in the workplace which causes
financial strains and depressive symptoms in women‟s mental health (Eglit, 2009).
Joblessness Creates Health Disparities
As research continues to evolve, it becomes more pervasive how joblessness due to hiring
biases aid in presenting health inequalities and tension among older adults. Unemployed workers
tend to avoid health care due to a lack of finances and they tend to have high levels of
depression, anxiety and stress (Pharr et al., 2012).According to researchers at the Yale School of
Public Health, consequences of ageism found evidence that ageism leads to worse outcomes in
mental health, physical health and shortens life expectancy (Greenwood, 2020). While everyone
becomes vulnerable to ageism, not everyone will have a decrease in their quality of life or have
their lives affected or influenced by the loss of work (Nelson, 2016).
Bronfenbrenners Ecological Systems Theory Conceptual Framework
Bronfenbrenner‟s ecological systems theory provides a framework by which ageism in
the work culture can be explored. Scholars perceive that age discrimination and the perception of
age discrimination on a micro-level are developed from social interactions in the immediate
environment (Voss et al., 2018). From this theoretical point, stereotype embodiment suggests age
stereotypes began in childhood and become visible once stereotypes are relevant to one‟s
47

personal identity (Levy, 2009). As Levy states, stereotype embodiment does not stop in
childhood, it continues throughout one‟s adult years, so as one ages, stereotype embodiment
becomes a self-stereotype. Age stereotypes that were once pointed outwardly to others who were
perceived as old eventually points inwardly as one ages and self-stereotypes begin to develop
(Levy, 2009). Throughout the aging process, the longer one is exposed to negative stimuli such
as the media where age stereotypes exists, then the greater age stereotypes will be internalized
(Levy, 2009).
Bronfenbrenner‟s Ecological systems theory comprises the conceptual framework as it
describes childhood environmental exposures and proposes that stereotypes and the aging
process is in part a social construct (Bronfenbrenner, 2000). The different environmental systems
influence development within the social setting (refer to Table 2), making it safe to theorize that
the environment where one has interacted and developed has direct influence on one‟s outlook
and behavior. The conceptual framework and the injustices in hiring suggest development is a
transactional process where a person‟s development is influenced by their environmental
interaction (Shelton, 2019). Bronfenbrenner theorizes that people exists within a system of
relationships and settings that are each interwoven. The theory is fixed in the belief that
development occurs as an individual ages and becomes familiar with their set of experiences
within the system they live in (Bronfenbrenner, 1977).

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Table 2
Explanation of Bronfenbrenner’s Ecological System
Bronfenbrenner‟s ecological system
Microsystem
level
Immediate Family, household; caregivers, grandparents residing within the
home, how these groups interact with the child has an effect on how the child
develops. The more encouraging the more nurturing these relationships, the
better the child will be able to develop. How a child reacts to those in the
microsystem will affect how they interact with others.

Mesosystem
level
Informal networks: cultural groups, friends; parents, surrounding environments
The mesosystem describes how the different parts of a child‟s microsystem
works together for the sake of the child. Ex: If the parent takes an active role
in the child‟s school such as parent teacher conferences then this will help to
ensure overall growth. Conversely, if the child‟s two sets of caregivers
disagree on how to rear the child and give the child conflicting lessons this
can hinder growth in different channels.

Exosystem
level
Local environment; safety, playground, sports, schools, childcare.
The exosystem level includes the other people different from the microsystem
where one interacts with often but has an effect on them such as a
neighborhood, extended family members, or parent‟s workplace. Example: If
a child‟s parent loses their job that could have a negative effect on the child
if the parent is unable to buy purchase necessities.

Macrosystem
level
Broader economic, policy, social welfare, housing policy, demographic
change, social norms and attitudes.
Within the macrosystem you find the largest and most remote set of people
and “things” to a child but it has the greatest influence over the child in this
level.
This system includes freedoms permitted by the National government, cultural
values, beliefs, and the economy.

For the sake of this study, the microsystems level and the macrosystems level will
provide an ecological synthesis of research into cultural changes and why hiring biases in the
workforce exist. The ecological theory in 1979 was divided into four levels with the microsystem
49

being the most influential and has the closest relationship to the person because it is where direct
contact occurs (Shelton, 2019). The macrosystem level is composed of values, dominant beliefs
and cultural influences. This is the largest most distant culture with the most significant influence
that includes wealth, poverty and ethnicity (Shelton, 2019). Bronfenbrenners framework
provides for the study of the layers in the developmental process and aligns with ageism in the
workplace by explaining in the ecological systems how everything in one‟s environment affects
how they grow and develop (Bronfenbrenner, 2000).
The study on human development in the ecological system endeavors to advance the
theoretical understanding of how one‟s exposures in society play a role in perceptions and
discernments in the hiring process with a focus on age.
In 1979 Bronfenbrenner defined the primary levels of the ecological environment to offer
a framework to examine relationships within communities and how intrinsic behaviors and
qualities of children and their environment influence how they grow (Ettekal & Mahoney, 2017).
Consistent with his assertion, the ecological system‟s theory is a framework based on influences
by the various nested systems he or she is a part of, whether bi-directional and those that have no
direct interaction with the individual (Bronfenbrenner, 2000). Bronfenbrenner‟s ecological
system underlines the significance of one‟s environmental systems and how historical influences
during the developmental stages factor into one‟s overall development and empower
psychological views into adulthood that can drive workplace hiring decisions (Schaie, 2011).
Associations with family and friends affect attitudes and behaviors at the microsystems and
macrosystems level while stigmas and ageist attitudes accumulated during developmental stages
carry into behavior into one‟s adulthood (Jackson, 2014).
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The microsystem level is where personal relationships with caregivers, teachers and
family are developed (Schaie, 2011). This is the system that includes family and child and those
that influence the child‟s immediate environment. Within the microsystem observations can give
rise to negative or positive perceptions within the family nucleus that have an impact on opinions
of older adults creating biases that obstruct hiring decisions due to rooted ideas (Schaie, 2011).
As one observes aging grandparents or limitations placed on elders, cognitive decline, retirement
and death are each factors that are potential contributors to the development of ageist
assessments within this stage as suggested by the stereotype embodiment theory. During the
developmental stage one can witness cultural and behavioral ideas of aging and began to discern
what that means to them which help to form opinions within the environment in which they live
(Schaie, 2011). Bronfenbrenner‟s fourth level of the ecological system is the macrosystem where
the bodies of knowledge, customs, and lifestyle are entrenched and where beliefs guide behavior.
According to Shelton (2019) the macrosystem level is thought of as a societal blueprint for
cultures and subcultures.
From lifestyle changes to environmental factors, enduring and collective experiences
from life have an effect on one‟s behavior within the micro and macrosystem levels (Shelton,
2019). While the microsystem level is where the family influences take place, the macrosystem
shape how we behave, how our microsystems operate and what we experience as we grow up; it
shapes our view of the world and how we participate in it (Shelton, 2019). Within the
macrosystem the facilitation of development occurs; how people relate to each other and what
roles there are and how those roles will be defined exists. According to Bronfenbrenner, each of
the ecosystems invariably interacts with each other (refer to Figure 5), as the microsystem is
influenced by its macrosystem. The macrosystem is the overarching pattern of several
51

microsystems; therefore, joblessness is a microsystem in the macrosystem of hiring
discrimination. Bronfenbrenner‟s framework affirms how environmental factors affect
development and that social interactions and close relationships in the family influence thinking
and impact emotions (Rosa & Tudge, 2013). Determinants of age-based hiring discrimination
include social and societal factors of the individual and their interaction with others, culminating
in the macrosystem level. Bronfenbrenner‟s theory is attractive as a theoretical tool for
understanding age bias in hiring practices given that many social disorders and preconceptions
are within the developmental stages (Eriksson et al., 2018).
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Figure 5
Conceptual Framework for the Study: Bronfenbrenner’s Ecological System and Ageism

Microsystem
The relationships that one has within their immediate environment when interacting in
environmentally specific roles are referred to as the microsystem (Bronfenbrenner, 1977). The
53

parent-child relationship that arises within the home and the employer-employee relationship that
exists within the workplace are examples of microsystem relationships (see Table 3).

Table 3
Micro Influences
Micro influences in the ecological system Ecological model
Exposure to negative age-based stereotypes in the home
led to stereotypes in hiring.

Micro
Media influences: Prejudices in hiring decisions.

Micro
Observation of older adults/teachers in school: Prejudicial
hiring decisions.

Micro
Perceived inadequacies in older parents with technology;
translate to the prejudice that all older people have
limited ability with technology.

Micro
Childcare and differentiation of older people, stereotype
imagery from childhood transfers to adulthood causing
hiring disparities

Micro
School: Peer‟s influence, teasing older people, ideas
becomes reality when internalized making hiring
decisions prejudiced

Micro
Mental map: Recollection of or negative perception of old
age images from developmental stages leads to a
reluctance to hire one‟s grandparent who is ill or feeble.

Micro

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Mesosystem
The mesosystem defines the relationships between the microsystems that the individual is
a part of (Bronfenbrenner, 1977). Relationships between one‟s individual family and
organization the individual is a member of, such as a church or a board membership.
Exosystem
The exosystem is the third level of Bronfenbrenner‟s theory and contains settings where
the child does not actively participate in, though this level has a profound effect on a child‟s
development. This level includes the life of a parent or guardian which can impact family
members even though their life is indirectly impacted. Formal and informal structures such as
government, media, and communities and neighborhoods are also among the structures
Bronfenbrenner (1977) indicated which are included in the exosystem, which is an extension of
and encompasses the mesosystems.
Macrosystem
The macrosystem is a collection of prototypes and stereotypes that guide the widely held
beliefs about individuals and the structures within each of the preceding ecological systems
(Bronfenbrenner, 1977). Beliefs perceived by members of the cultures and subcultures within a
society shape the norms surrounding expectations of, and responses to individuals and groups
(Bronfenbrenner, 1977). Ageism is an exemplar within the set of beliefs that can occur across
ecological systems that have influence on the ability of one to thrive in various social settings
(See Table 4). Because ageism is a manifestation of stereotypes pertaining to individuals of a
certain age, it can exist on multiple systems within the ecological framework including the
macrosystem (Iversen et al., 2009).

55

Table 4
Macro Influences
Macro influences in the ecological system Ecological model
Stereotypes of age as being feeble-minded or slow
possessing inabilities to perform. Belief that this is the
norm for all older adults causing prejudices in hiring
decisions.

Macro

Beliefs within cultural surrounding that old age is inferior
to youth, leads to feeling justified hiring decisions.

Macro

Ageism; belief in slanted views perpetuated by social
memberships or groups within a personal arena.
Assumption all views could not be wrong, avoidance in
hiring older people.

Macro

Chronosystem
Ecosystems change for various reasons; change can move slowly or it can move swiftly
but whenever change does exist, people adapt to it and other components of the ecosystem
(Shelton, 2019). The chronosystem represents the changes in society and the understanding of
the environment (Shelton, 2019). The chronosystem was the last addition to Bronfenbrenner‟s
ecological framework as the initial model in 1979 did not incorporate time as an element
(Shelton, 2019).

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Literature Search Strategy
My research strategy includes a broad review of topics using empirical data, peer
reviews, Google Scholar and multinational databases to support and identify elements of the
ecological systems theory and stereotype embodiment theory which are the theoretical
foundations and conceptual framework for the study. When seeking to uncover how these
theories aligned with ageism early key words used to identify findings were: Ageist attitudes,
ageism in American culture, ecological systems theory and ageism, social perceptions of aging,
Bronfenbrenner and ageism, Stereotype embodiment theory, Social identity theory, Robert
Butler, age discrimination in the workforce, the media and ageism, visual ageism, ageism in
society, the effects of ageism, and workplace harassment, ADEA, age discrimination in the
workforce, the microsystem and childhood development, ageism and hiring, the effects of
discrimination, long-term effects of job rejection, macrosystem and childhood environment.
An exhaustive review of the literature indicates that hiring practices within the corporate
structure is a complicated experience due to the various environmental encounters each hiring
personnel within management has been exposed to. The multifaceted factors identified by
Bronfenbrenner‟s ecological systems theory pose numerous concerns by the population of
individuals who are responsible for hiring adults over the age of 50. Stereotype embodiment
theory explains why there is an objective of young and middle-aged groups who distinguish their
identity and elevate their positions from older age groups (Levy et al., 2018). The explanation
that supports the theory hinges on the belief that distinctions are made in order to create
individualism unique to their age category (Levy et al., 2018). Stereotype embodiment focuses
on expressions of ageism in various age groups and focuses on prejudice and discrimination and
how the bias is directed to ones future self which denotes a fear of one‟s demise. To better cope
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with fear people detach emotions and remove themselves from the group in order to identify with
a different group association (Levy et al., 2018). The research pertaining to alleged
discrimination and the plight of long-term unemployment of protected groups continues to
emerge as approaches to intervention become paramount (Butler & Berret, 2011). Lawmakers
look at the ethical crossroads and practitioners review the obligation of employers to handle
hiring practices in an ethical manner (Butler & Berret, 2011).

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Chapter Three: Methods
The core of this study was to identify why age-based hiring biases exist in the technology
industry and how such behavioral factors influenced hiring decisions among managers. The drive
behind the field study was to evaluate if age-based employment choices by management in the
technology industry were intentional or unintentional. Qualitative research methods were utilized
in this study to allow managers to address questions as to why ageism is rampant in the
technology industry and why jobs are skewed toward youth. The study aspired to uncover
motives behind hiring biases, if any, while aligning the study to the socio-historical context
within Bronfenbrenner‟s 1979 ecological systems framework. The researcher also pursued
guiding questions thought to unveil closely held biases that might have developed from one‟s
surrounding culture. The theorized biases were based on Levy‟s theory of stereotype
embodiment where it is believed stereotypes become ingrained across a lifespan and can operate
unconsciously (Levy, 2009)
The goal was to bring awareness into the evolving issues of ageism in the technology
industry by illuminating how hiring personnel and leadership have permitted ageism to exist
within the workplace (Berger, 2017). The intention was to determine if the micro and the macro
ecological systems induced biased behavior in the hiring process and what environmental
influences, if any and developmental experiences impacted these decisions. Of particular concern
was the influence on management‟s ideations that lead to ageism and suggested hiring biases
within the workforce. With this objective in mind, the qualitative research strategy focused on
the interconnection of relationships, media influence, environmental settings, and stereotype
embodiment where either could contribute to behaviors suggestive of ageism.
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The study sought to use trustworthiness in the qualitative research throughout the process
of data collection, analysis, and interpretation. The necessary steps were taken to lessen the
influence of researcher bias. An unbiased, sampling from a social media outlet, LinkedIn,
provided a catalog of managers, regional directors and system and operation engineers each with
a minimum of five years of hiring responsibility. Participants selected came from various
technology giants across the United States and were selected based on management job title and
whether they have or had hiring experience in the field of technology. There was no omission
bias in the study and no selection bias as it referenced age or gender. During the original
selection of recruits for the study, LinkedIn provided a job title and a facial image though facial
image had no bearing on the study. Methods associated with increasing trustworthiness for
qualitative research was employed throughout the process of data collection, analysis, and
interpretation.
The conceptual framework that guided the study was a reflection from Bronfenbrenner‟s
ecological model (Bronfenbrenner, 1979). The following research questions guided the study:
1. To what extent is the hiring decision of GAFE influenced by age across the mesosystem?
2. To what extent do the micro and macrosystem affect GAFE managers when making
hiring decisions about older adults?
3. What effect does the microsystem have on hiring older workers?
4. What factor has media played when aligned to the chronosystem if any, to older adults
and GAFE hiring of workers over the age of 40?

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Demographics
There were 250 potential prospects from various technology industries across the United
States that were invited to participate in the study and were forwarded an invitation. Out of the
250 prospects, 45 prospects responded to the emailed request and were forwarded a
questionnaire. Eighteen out of the 45 respondents returned a completed questionnaire within two
weeks and four out of the 18 participants declined the invitation to be interviewed citing it was
against their company policy to discuss hiring processes. Of the respondents who returned their
questionnaire, 17 were male and one was a female. Of the 14 who agreed to be interviewed for
the study, all were men. The median age for each of the interviewed subjects was 52 years.
When recruiting participants for the study, the email forwarded to each of the 250
prospects stated, “My dissertation is on hiring disparities for people age 50+ in the tech
industry.” Because of the wording in the email, the belief by participants was that the
requirement for the study was minimally age 50 years which establishes the reason participants
had a median age of 52. Had the wording been altered to reflect, “I‟m seeking managers who
have interviewed candidates age 50+ in the tech industry,” then it is believed the findings in the
study would have supported the similarity attraction theory.
Participating Stakeholders
The participating titles of the Stakeholders within the completed analysis included IT
Managers, IT Engineering Operation Managers and Regional IT Managers from the technology
industry. Associated businesses under the GAFE pseudonym that were a part of the technology
industry participated in completing the questionnaire. The research study included participants in
leadership roles responsible for hiring, evaluations, training and recruitment of staff. Creswell
and Creswell (2018) recommended five to 25 participants for interviewing. Despite the
61

researcher‟s initial projection of 20 interview participants, 14 participants were interviewed and
18 participants returned completed questionnaires.
Interview Sampling Criteria and Rationale
Within the tech industry the median age is 29 (Thibodeau, 2015). While it was expected
that participants would reflect the median age based on the hiring scale in technology, the
manager‟s interviewed ranged in age from 39 to 66. What was not considered when selecting
participants were the ages of manager‟s being interviewed. Based on the median age in the
technology industry, the preconception was that hiring decisions were impacted by social
identity theory. When the question of age was broached and the researcher expressed
astonishment that recruits were over the age of 50, participants explained ages were higher for
management personnel based on the level of experience expected for their job categories. This
was an acceptable answer though the belief remains that had wording in the recruitment email
reflected either a specific age category or had the wording been more precise, then the analysis
and qualitative findings would have been different.
Out of the 250 potential LinkedIn prospects for the study, the observation was that there
were not as many women in management to choose from that were listed on LinkedIn or who
were in management positions. The response rate from those recruited was nearly 25% with 64
members in the technology industry acknowledging receipt of the researcher‟s request to
participate in the study. It is believed that if more time was allotted the response rate would have
been greater as several respondents forwarded questionnaires after coding was completed. The
questionnaire was emailed to each participant who agreed to contribute to the study and 28%
(18) of the respondents submitted their completed questionnaire within the 2-week time allotted
for the study completion. Out of the 18 respondents that returned their questionnaire, 14 agreed
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to be interviewed while four respondents submitted their questionnaire but stated interviewing
for the study went against their corporate policy despite the confidentiality agreement. The
participants who stated interviewing was against company policy each came from the same
organization, though others who worked for the organization did not display unwillingness. Out
of the 14 respondents that agreed to interview for the study, all were male. Out of the 18
participants that submitted their completed questionnaire, 17 were men and one was a female.
In order to determine causation of implicit or explicit bias, it was essential that
participants have minimally five years of leadership experience with practice and involvement in
the hiring process. It was also necessary to have participants who were knowledgeable about the
technology industry who could relate to questions as it pertained to skills.
Criterion 1
Men or women needed minimally five years of workforce exposure in the technology
industry. Five years of experience would ensure grounded knowledge and general business
awareness.
Criterion 2
Men or women needed minimally five years of experience in a managerial role in the
technology industry. Five years of experience in management would allow a participant
understanding of the hiring processes, better knowledge of the technology industry‟s inner-
workings, and it would allow the participant to provide seasoned content.
Criterion 3
The participants had to work within the United States.

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Interview Strategy and Rationale
When engaging in the interview process, the goal was to determine if age was a
determining factor in the hiring process. The interview strategy focused on unveiling potential
biases, stereotypes and the manager‟s perception of the older interviewee as a candidate for hire
(Ryan et al., 2009). Through the interview process there were explicit, clear unambiguous
questions along with some flexibility in the event there were unanticipated responses and
questions to emerge from respondents. The interview was structured with each participant being
asked the same questions in the same order to understand unfolding perspectives and to generate
responses to address the problem (Ryan et al., 2009). The researcher‟s objective was to make
clear each question was fully understood and answered completely. It was important that each
respondent addressed questions in their own words, with as much time as needed allowed. This
method was helpful in gathering detailed information. The interview process lasted from 27
minutes to 72 minutes. Another objective was for the researcher to allow time for questions to be
asked after the interview process concluded. The researcher announced the estimated length of
time the interview was expected to last although it was made clear that respondent‟s answers
would not be rushed. The role of the interviewer was also to ensure that the participant did not
feel threatened and that their environment was conducive for an interview without interruptions
(Ryan et al., 2009).
The researcher exercised their duty in informing respondents that their participation was
voluntary and confidential (Merriam & Tisdell, 2016). Respondents were also made aware of the
confidentiality of the interview along with the answers they would give. Confidentiality was
reiterated to participants to give assurance of the protection of their identity. In general, subject‟s
names and organizations will be kept confidential in order to protect the integrity of the data and
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the confidentiality of the participants. Interview respondents were made aware that data
collection would not be shared with other participants nor shared within their business entity.
Records have been secured through the use of password protected files and participant‟s
communication has been kept private from others who participated within the study. Language
has been coded to protect identities, geographical locations, organizations, names, dates or
assessments of findings all which will remain confidential. Through a series of 20 open-ended
questions, participants were asked to provide answers in their own words and to raise points
believed to be germane to the study. The interview involved accounting for personal biases that
can influence or distort findings.
Biases that could have impacted the study were as follows:
1. Selection or Sampling bias; if participants selected were not reflective of the industry.
2. Design bias; if considerations and sensitivities for the design and questions of the study
were not considered (De Vries, 2017).
3. Inclusive bias; if the net was for selection of participants was not widely casted and was
based on convenience (De Vries, 2017).
4. Confirmation bias; processing information to support personal beliefs. (De Vries, 2017).
5. Interviewer bias; this could have occurred if the researcher inadvertently influenced
responses from the participants (De Vries, 2017).
6. Response bias; if a participant responded to a question based on giving an answer they
believed to be expected or correct (De Vries, 2017).
7. Reporting bias; when the researcher gives findings and results either more critical than
what was stated by a participant or more positive than what was stated in order to
influence perceptions or tilt the study in favor of their hypothesis.
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The interview analysis included verbatim descriptions of participant‟s accounts to support
findings while demonstrating clarity in terms of thought processes during data analysis and
subsequent interpretations, data triangulation, whereby different methods and perspectives to
help produce a more comprehensive set of findings (McGrath et al., 2019).
Because employees within the GAFE business framework were located within various
parts of the United States, taped telephone conversations replaced face-to-face meetings
according to COVID-19 restrictions. The telephone was an effective way to communicate with
participants because of their location in multiple states. Introductions to the ageism study to
management participants were made through LinkedIn correspondence (Appendix C). In the
recruitment specifications to management, the researcher requested an allotted time of 30 to 60
minutes to interview each potential respondent who would address questions. The email noted
the criteria for participants noting management experience of minimally five years with a request
to speak with members involved in the hiring process only and those who resided in the United
States. Interview questions are shown in Appendix A, which is aligned to feedback needed to
uncover hiring practices. Participants were given a three week notice before the interviewing
processes commenced to ensure proper scheduling. Interviewing participants had the opportunity
to schedule a selected time period between mornings to evenings between the hours of 7:00 am
EST to 10:00pm EST to accommodate schedules on the West Coast should it be requested.
Audio tape recorded interviews were utilized using 20 predetermined open-ended questions that
relate to ageism within the ecological systems context. An audio tape was utilized to ensure
clarity of voice response and to make sure information was audible, in the event a malfunction
should occur during taping.

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Role of the Researcher
The researcher‟s role in this study was to collect, analyze and articulate the information
gathered from participants in the interview and questionnaire study in a manner that is objective
and free of researcher bias. As a human instrument of the research and dissemination process, the
researcher‟s role was to be the conduit of the participants while delivering a channel for readers
to retrieve the encounters and perceptions of the participants involved. The qualitative researcher
intended to follow the prescriptive measurements in the least biased and most objective manner
possible. The use of open-ended questions was asked during the interview process in order to
minimize the potential bias related to question formulation. The researcher‟s preconceived ideas
about the manifestations associated with ageism may influence researcher interpretations and
conclusions about inequities in hiring practices. To avoid researcher bias, strictly planned
parameters related to the research topic and the problem of practice guided the study and the
respondent‟s answers to questions as it related to the participant‟s environment, family, media
influences and perceptions of aging adults.
Instrumentation and Data Collection
The main instrument in this study was the researcher who in addition to steering
competencies in communication theory used two instruments in this study: interview questions
and a questionnaire. Originally in-person observation was a consideration for the study which no
longer presented an option due to COVID-19. Interviews were conducted through audio
recording communication, and via taped recording. The respondents were assured privacy and
confidentiality of the recording before and after consent was received. Research included
qualitative research methods with a minimum of 2-week duration between conducting the
interviews and questionnaires. There were no age-specific demographic information
67

requirements though the median age of the interview respondents was later revealed to be 52
years. Age specifics were casually addressed in order to minimize potential influence either
positively or negatively on the interview, though information was gleaned during the course of
discussion. The research focus hinged on Bronfenbrenner‟s ecological systems theory,
adaptations to one‟s environment and the complications of human development along with the
various layers that shape human growth and expansion. The study focused on four primary levels
of development: (a) the microsystem based on participants daily interactions, family influence,
peers and work environment, (b) the exo-system, their acquaintances, neighbors, city and state
laws (c) the macrosystem which is the mainstream culture of where one lives, works, their
cultural beliefs, worldview, how they identify, political ideologies and where they get their
understanding of the world and (d) the chrono-system which involves the socio-historical and
environmental events that arise throughout a child‟s life.
Through a qualitative research method the researcher‟s interviews detailed impressions
and opinions of older workers, interactions, perceptions based on energy levels and media
influences. Because attention was placed on interactions and focused on perceptions of age,
biases and stereotypes in one‟s life, the research will lend itself to creating new theories using the
inductive method allowing the researcher to test with additional research later.
The questionnaire was administered electronically to leadership respondents.
Respondents answered 17 closed-ended questions with a level of measurement of interval,
nominal, ordinal or ratio (see Appendix B). The responses were (a) Always (b) Often (c)
Sometimes (d) Never. The scale was used to collect respondent‟s attitudes and opinions and to
understand perspectives about ageism and biases in hiring. The intent was to psychometrically
measure attitudes and hiring perceptions and to gauge ageist behaviors as it relates to
68

preconceived ideas about older individuals and then to compare and contrast with the interview
results (McLeod, 2019). The decision to omit a neutral Likert measurement was to avoid opinion
neutrality which could affect the distribution of responses and would lead to unreliable outcomes
(McLeod, 2019).
Procedures for Data Collection
The procedural plan below guided this researcher and served as the first step towards the
research interviews.
1. Contact each recruited participant that responded from: LinkedIn.
2. Provide the participants contacted with information pertinent to the study, addressed
questions and reminded each that the study was confidential and voluntary.
3. Forwarded through email transmission the questionnaire asking each to comment whether
they would agree to being interviewed following completion of the questionnaire.
4. Contact each participant individually to confirm participation and to determine best time
for interviews and appointments.
5. During the meeting via telephone, explain purpose of the research, explain confidentiality
agreement and interview process. Verify potential participant‟s desire to be included in
the study.
6. Schedule interviews to occur within two weeks.
7. Communicate with participants to confirm scheduled appointments and reiterate the
voluntary nature of participation.
8. Conduct recorded interviews via telephone, at their designated place of privacy.
9. Schedule a follow-up appointment to verify information two weeks later.
10. Transcribe audiotaped interviews.
69

11. Verify accuracy of transcribed interviews.
12. Analyze data according to the Colaizzi and interpretative phenomenological analytical
methods (Wirihana, et al., 2018)
13. Review and code for each participant.
Data Analysis
Elements from the Narrative Inquiry Approach interview method (Hunter, 2011) and The
Interview Protocol Refinement framework (Castillo-Montoya, 2016) were merged to define the
interview analysis. Each of the interviews was recorded via digital recording and reviewed for
accuracy. Audio recordings of the interviews were reviewed multiple times to garner a general
sense of the participants collectively and as individuals. Scrutinized review was then conducted
to identify recurrent themes and salient participant statements. This was followed by a review of
the audio recordings to find statements that addressed the research questions. Responses relevant
to the research questions were highlighted and compared to identify recurring themes among
them. The researcher used inductive coding to develop concepts and themes for a more complete,
unbiased look at the categories throughout the data. After reviewing each of the interview
responses from words, phrases and opinions, each was labeled according to the relevance of the
interview. The relevance of each statement was predicated on whether it was repeated throughout
the interviewing process. Statements were assessed based on phraseology or repetition, age bias,
perceptions and perceived stereotypes (Elliott, 2018). As the interpreter of the phenomena, the
methodology was clearly delineated for readers understanding. The categories were based on
ageism in common parlance and how descriptors were utilized. This information is the core of
the study. Through the interview process participants gave personal perspectives and addressed
questions on whether age-based hiring biases existed within the technology industry and whether
they believed hiring biases were intentional or unintentional (Elliott, 2018). The interpretations
70

and results were written based on the guiding research questions and concepts revealed from the
field study.
Validity and Reliability
In order for the researcher to ensure there was secured optimum standards of validity and
reliability, usage of methods was performed where the findings accurately reflected data and
reliability denoting consistency within the employed analytical procedures. The researcher
maximized credibility by using three main phases: preparation, organization, and reporting of
results. The researchers‟ understanding lends itself to the importance of scrutinizing the
trustworthiness of every phase of the analysis process from preparation, collecting suitable data
for content analysis to the findings. The organization phase included open coding, creating
categories, and abstraction which were carried out in a non-bias manner. And in the reporting
phase, results were described by the content of the categories describing the study using an
inductive approach. The aim of trustworthiness in the researcher‟s study supports the argument
that the inquiry‟s findings are worth paying attention to. Also, from the perspective of
establishing credibility, the researcher ensured that participants were identified without names
but described accurately based on gender, (Noble & Smith, 2015).
As the research is grounded in valid and reliable knowledge, information has been
presented in an ethical manner and has been conducted according to well-established guidelines
(Siebenhofer et al., 2016). In order to ensure participants were sufficiently secure in giving
accurate responses, prior discussion during the introductory phase was discussed letting
participants know there would be no right or wrong answer to the questions. It was re-
emphasized how there would be no outside influence and no identification of the participants
individually. Complete transparency on participant‟s willingness to respond were made clear and
documentation has been administered for clarity and in order to secure confidentiality.
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Ethics
Age biases, uncovering the power struggle in hiring practices and unveiling
discrimination in a workplace were the important considerations for the researcher‟s field study.
The potential challenges were mitigated by being transparent, by inculcating the message of
confidentiality, honesty and a questionnaire and interview process intended to be non-
judgmental. The objective during the interview process was to uncover rooted messages that
revealed why biases if any existed. The only way to overcome any of the aforementioned issues
was through clear, decisive plans of discussion that were not hidden. As one who witnessed
ageism in the employment sphere, the researcher found it to be an uncomfortable and debasing
experience to realize that one is being marginalized because of age. During a telephone
interview, an HR representative informed the researcher how their preference was to hire
millennial workers. As presumptive as the outburst was in addition to the subsequent line of
questioning, what it illustrated was age bias and a discrimination that signals problems that need
attention in the labor force. Because of a personal experience, the researcher brought familiarity
into the study after being exposed to ageism both directly and indirectly. Though much of
qualitative research is based on assumption, the researcher‟s goal was to uncover information in
an ethical manner which is what the researcher accomplished in order to establish
trustworthiness of the study (Merriam & Tisdell, 2016).
Limitations and Delimitations
The expected limitations in the field study were due to the coronavirus pandemic which
prevented face-to-face interviews. The usage of tape recorded telephone calls became the
alternative method in addition to utilizing email correspondence. The option of interviews via
telephone recorded transmission was necessary due to COVID-19 though this prevented the
researcher the benefit in assessing body language for an observational study. Making certain
72

there were enough participants in order to receive feedback became a limitation when four
respondents stated it was against their corporate policy to address questions about their hiring
processes. Truthfulness and transparency from the participants was a concern as the presumptive
guilt of being bias and showing any form of discrimination is not a characteristic anyone wanted
to be assigned. Going into the study the researcher believed management participants were going
to be in their late twenties or early thirties since the median hiring age is 29 in the technology
field. However, the age of experienced leadership personnel was not considered and the
participants ranged in age from their late thirties to their late sixties. None of the biases around
the researcher‟s age were a deterrent as the participants were in the same age range as the
researcher/interviewer. Questions were not raised as to the reliability of the study from the subset
of participants though research suggested young participants might question the reliability of a
study as they are generally less trusting of older adults (Gramlich, 2019).
Because the scale of the study centered on the technology industry, the purpose was to
unveil if biases were intentional or unintentional from leadership in this sector of business. In
addition to Social Identity theory, prior thoughts justifying age biases leaned towards the
similarity/attraction theory that hypothesizes people are attracted to others who are similar rather
than divergent to themselves (Michinov, 2020).
Positionality
Influencing the practice of research was the approach from the paradigm of
constructivism. The worldview of the constructivist asserts that people construct their own
understanding and knowledge of the world (Creswell & Creswell, 2018). Ageism is socially
accepted because it has been justified on many terms; as being a joke, as a rite of passage after a
certain milestone has been reached and then in the workforce where it has been accepted because
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of perceptions that have permeated for years. People have constructed a worldview of ageism
because they have been allowed to construct their measurement methods when determining
whether it is discriminating, offensive or customary.
Going into the study the researcher‟s premise was that management‟s hiring decisions
were made based on one‟s microsystem. That hiring manager‟s relationships within their
communities, their family, and their close proximities gave reason for biases that existed. But
much like Bronfenbrenner‟s ecological system, there are layers as to why biases exist. The
constructivist paradigm goes along well with ageism because it is known but seldom contested.
Yet the value and meanings of ageism have been challenged but never mitigated because of the
diverse layers and interpretations. Because the study of ageism is diverse and can be intentional
or unintentional or it can also yield itself to biases against youth, it is a philosophical paradigm.
In the constructivist paradigm individual‟s base analogy or constructs from what they have
learned or experienced (Adom et al., 2016). The conceptual framework for the study on ageism
leans on Bronfenbrenner‟s belief that a person‟s development is affected by everything that
occurs in their surrounding environment (Bronfenbrenner, 1977). This is likened to the
constructivist paradigm because each experience one has in their life setting or within their
environment affects their development and shapes who they are. During the interview process
the researcher came to understand the historical and cultural settings of respondents while
attempting to make sense of the meaning‟s respondents had of the world (Creswell & Creswell,
2018).

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Research Setting
Initial consultations and formal interviews took place at sites according to the
participant‟s choosing. Fourteen interviews were administered with no interruptions and were
each conducted at the participant‟s homes where connections were via telephone communication.

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Chapter Four: Findings
The qualitative research study focused on whether hiring biases existed and if so, were
they intentional or unintentional. To that end, four primary research questions were developed
focusing on perceptions, implicit bias, stereotyping and the environmental settings. A purposive
sample of 17 men and one woman who lived in the United States and who worked in the
technology industry completed questionnaires; out of 18 participants, 14 men agreed to be
interviewed and four participants declined the invitation, citing corporate policy. The findings of
the research are presented in this chapter.
Research Method
The research method for the study was a qualitative phenomenological field study
methodology. Phenomenological research was chosen in order to examine opinions, realities,
biases if any, and factors that contribute and have impacted hiring disproportions in the
technology industry (Creswell, 2014).
The questionnaire (see Appendix B) consisted of 17 closed-ended questions designed to
force the opinions of respondents and measure frequency. Some questions were iterative which
was designed to expose inconsistencies for validity in responses, which were not evident in the
specific questions addressed. Structured Interviews (see Appendix A) were recorded on a digital
recorder and over the telephone for accuracy. The researcher designed an interview protocol
based on researched literature and was culled for questions for the qualitative field study.
Questions within the interview were based on themes of experience and were broad and open-
ended allowing for the participant‟s viewpoints to be expressed extensively (Brevan, 2014).

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Introduction to the Questionnaire Study
The questionnaire used a forced 4-point Likert scale for specific responses with no
neutrality value. Questions were close-ended and ranged from: Always, Often, Sometimes, and
Never. Based on collective responses in the questionnaire, paradigms were formed and assigned
descriptions that fall under the following categories, Perceptions, Implicit bias/Stereotypes and
Environmental Settings. Each of the headings aligned with the guiding research questions from
the study:
1. To what extent is the hiring decision of GAFE influenced by age across the
mesosystems?
2. To what extent do the micro and macrosystem affect GAFE managers when making
hiring decisions about older adults?
3. What effect does the microsystem have on hiring older workers?
4. What factor has media played when aligned to the chronosystem if any, to older
adults and GAFE hiring of workers over the age of 40?
Perception
The grouping of perception addresses the guiding research question of the extent to
which the hiring decision of GAFE is influenced by age across the mesosystem.
Because the mesosystems is a combination of two or more microsystems that can be
long-lasting, occurrences in either home or socialization experiences that could influence
interactions when engaging with others during the hiring process (Bronfenbrenner, 1979). The
mesosystems contains the relationship between the microsystems and proposes influences within
the familial environment or surrounding environments can influence development that can
transfer into adulthood. The Perceptions category offered participants the opportunity to reflect
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on opinions, personal perceptions of workers, and sensitivities as these categories related to the
participants‟ judgment when interviewing older workers for employment.
Table 5 includes data and addresses questions on what extent is hiring decisions among
GAFE managers influenced by age across the mesosystem is in Table 5. When addressing
questions in this category, participant responses indicated stereotypical age-based hiring biases
were absent based on questions in the perception grouping. When questions were posed asking if
there should be a mandatory cut-off age for working in the technology area and if there should be
a mandatory age for retirement, respondent‟s answers were consistent, demonstrating a lack of
age-based biases in the perception of older workers.

Table 5
Responses on Perception From the Mesosystems Addressing Research Question One (n =18)
Question Always Often Sometimes Never
Do negative stereotypes of aging adults
affect your perception of older workers
abilities when it pertains to hiring?

2

16
Based on your experience, do older
workers have an increased amount of
illnesses that limit production?

1

2

15
Have you found that older workers are
slower workers?

2

16
Do you believe there should be a cut-off
age for working in the technology
industry?

1

17
Should there be a mandatory retirement
age for workers in the corporate sphere?

1

17
Do you find older workers to be more
reliable than younger workers?

6

10

2
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The thoughts participants held of older workers were based on perceptions emanating
from their surrounding environments in the microsystem where opinions were formed. The
observations indicated participants had positive perceptions of older workers. When questions
were asked whether negative stereotypes affect the perception of older workers, 16 out of 18
managers responded Never indicating there was no implicit bias. When questions were asked, if
older workers had an increased amount of illnesses, 15 out of 18 participants responded never,
indicating a positive perception of older workers. Each of the questions under the heading of
perception showed very low levels of implicit bias. In summary, a majority of the participant‟s
responded and showed no signs of ageist attitudes or negative perceptions towards older workers.
Environmental Setting
The findings under Environmental Setting addressed two of the guiding research
questions: To what extent do micro and macro systems affect GAFE managers when employing
older adults? What effect does the microsystem have on hiring older workers?
The macrosystem is where cultural elements such as status, ethnicity, poverty, and ideologies of
a culture reside. Influences, customs and laws originate from the macrosystem as well. The
microsystems and the macrosystems are interrelated. While the microsystem has the most direct
effect between the individual and the environment, the macrosystem affects cultural values and
one‟s belief system (Bronfenbrenner, 2000). Under the grouping of Environmental Setting
participants‟ belief systems were reflected in responses given to questions (Table 6). The one
manager who responded in the questionnaire that age should be a criterion when hiring, reflected
on child labor laws and the Fair Labor Standards Act (FLSA) that stipulates minors are not
allowed to work before 7am or after 7pm (Doyle, 2019). In the ecological systems, there are
many characteristics within the context of the system that forms and influences decisions. The
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exposures that occur in an individual‟s primary environment contribute to their development
(Paquette & Ryan, 2001). Each of the interactions that transpire during the maturation process
occurs daily and over an extended period which steers development in the microsystem
(Bronfenbrenner, 2000).

Table 6
Responses on Environmental Setting From the Micro and Macrosystems Addressing Research
Questions Two and Three (n = 18)
Question Always Often Sometimes Never
Have you ever witnessed
age-based
discrimination from co-
workers?

6

9

3
Is an inclusive
departmental
environment important
for productivity?

18

Do you believe one‟s age
should be a barrier to
hiring?

1

17
Is an applicant‟s job
experience the primary
criteria you seek for
employment

2

15

1

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The Manager‟s responses in this group demonstrated a heightened awareness of age-
based discrimination, meaning they witnessed ageism or the maltreatment of others based on
their age category. The responses also implied explicit bias was not demonstrated based on 18
out of 18 Managers responding that Always an inclusive environment was important for
productivity. Further evidence showed no explicit or intentional bias when 17 out of 18
Managers responded age should not be a barrier to hiring.
Stereotypes/Implicit Biases
The Stereotypes/Implicit Biases category offered participants the chance to reflect on the
role media played in the perception of older adults when aligned to the Chronosystem if any, and
to GAFE managers hiring of workers over the age 40? It is in the Chronosystem where patterns
of environmental events occurred in the developmental process where negative effects can
transition over the course of life. This system consists of major life transitions, environmental
events and historical events that occur during identity development. The influences that have
cumulative effects on the entire sequence of conversions during the life course can include
normative or non-normative life transitions such as negative media representations during
identity development and in later years could influence hiring decisions.
Overgeneralized and distorted character beliefs about older workers based on their age
category denotes stereotyping and ageism (Toomey & Rudolph, 2015). Categorizing older
workers as less capable and unable to perform job functions are labels that impact restrictions in
hiring when interviewing older candidates for employment. The questionnaire supports findings
that suggest biases when participants asserted responses that are aligned with reported
stereotypes. The patterns of thinking under the category group of Perception, eliminates the act
of discriminatory behaviors with the same group of managers suggesting uncontrolled thinking
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or implicit bias (Table 7). The responses did not signify a prejudiced automatic association but
rather indications of past experiences or encounters.

Table 7
Responses on Stereotypes/Implicit Bias From the Chronosystem Addressing Research Question
Four (n = 18)
Question Always Often Sometimes Never
Do you think there could be
reluctance, if any, to hire older
workers because they expect too
much money?

17

1

Do you believe negative stereotypes
on aging affect hiring decisions?

15

3

Do older workers complain on the
job more than younger workers?

14

2

2
Do older workers have difficulty
when working with technology?

16

1

1
Do younger workers work more
efficiently than older workers?

16

2
Are older workers taking jobs that
should go to a younger work force?

13

1

4
Do you think stereotypes of older
people are realistic? I.e. inability to
use technology, grumpy, fatigue,
frail illnesses?

2

12

4

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Questionnaire Summary
After data collection from the questionnaire, evaluations were assessed to determine if
responses had an objective dimension, a level of agreement or disagreement or an intermediate
response. The findings from the questionnaire indicated there were inconsistent responses given
in addressing a few of the questions suggesting unconscious biases. In particular, 18 out of 18 of
the responses from participants believed an inclusive work environment was always necessary
for productivity and 17 out of 18 Managers believed there is always a reluctance to hire older
workers because of higher pay expectations. When stereotypes are allowed to influence the way
thoughts are processed then unconscious bias occurs. Just as explicit and conscious biases are
real, so is implicit or unconscious bias. There is a tendency for thoughts to spontaneously cross
the mind that resemble a confirmation of stereotypical beliefs which is what psychologists call
implicit bias (Payne & Vuletich, 2019). Implicit bias leads to overgeneralization and
discrimination even when one believes they are being nondiscriminatory. The immediate
environment manifests itself into a belief system (microsystem) and dictates how individuals
react.
How individuals respond to their social environment is based on what has occurred in a
person‟s ecological systems during development (Hernandez & Blazer, 2006). Bronfenbrenner‟s
2000, ecological systems theory identifies the multifaceted layers of development that impact
individuals and their cognitive structures as they advance in life. Each of the accepted
explanations from the five ecological systems; the microsystem to the chronosystem, explains an
individual‟s social environment on human development.
Respectively, the context in which an individual lives in has a bearing on relationships,
discrimination, belief systems, and social environments in an individual‟s life (Bronfenbrenner,
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2000). In the questionnaire, respondents believed age should not be a barrier to hiring, but in
turn believed younger workers worked more efficiently than older workers. The contrasting
answers demonstrated evidence of implicit bias suggesting some of the managers held implicitly
biased views when it pertained to older workers despite their previous belief that age should not
be a factor in hiring. There appeared to be an unconscious yet perceptual illusion from Managers
who believed there should not be a mandatory cut-off age for workers though believed
stereotypes about older people were existent. Some of the results showed conflicting answers
demonstrating automatic and involuntary associations. Based on responses, implicit bias existed
in several answers.
Overall, the questionnaire aligned with the theory that there is a modicum of ageism that
exists in the technology area though biases were not intentional. While the questionnaire did
unveil unintentional biases towards ageism, it did not demonstrate that biases were deliberate.
Entering the study the researcher believed that based on an overwhelming disparity in age-based
hiring that findings would identify objectionable and prejudicial responses indicating intentional
bias. After analyzing and interpreting data from the questionnaire, implicit bias was determined
based on collective responses in the stereotype heading.
The Interview Sample
The interview sample was comprised of 14 participants (Table 8), who addressed 20
open-ended questions. Participants were recruited from the questionnaire study where each
consented in furthering the investigation into age-based hiring biases.

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Table 8
Participant Demographics

Participant
Job title/Role Years in
management

Gender

Age

Race/Ethnicity
1 IT manager 7 Male 39 White

2
Corporate IT
manager

15

Male

54

White

3
Regional IT
manager

21

Male

63

White

4
IT operations
manager

5+

Male

52

White
5 IT manager

5+ Male 52 White

6
VP digital
automation

35

Male

66
African
American
7 IT manager

5+ Male 52 White
8 IT manager

5+ Male 50 White
9 IT manager

9 Male 47 White
10 IT manager

12 Male 52 White
11 IT operations

5+ Male 58 White
12 VP engr.

18 Male 52 White
13 IT manager

9 Male 56 White

14
Sr. regional
manager

25

Male

52

White

Note. It was mandatory that each participant had minimally 5 years hiring experience in IT
management.

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The Themes That Emerged From the Interview Process
Four themes emerged during the interviewing process and have been associated with each
of the research guiding questions. Participant roles varied from IT Managers to VP Digital
Automation. A prerequisite for study participation was minimally five years of hiring experience
in the technology industry in order to allow participants to provide content from a knowledgeable
viewpoint. Each of the nine themes gave salience to the study. When interviewing, the
researcher‟s objective was to determine if conscious or unconscious biases were present and if
any, how they influenced management‟s hiring decisions, and how if biases, if any, developed?
Table 9 illustrates the themes and definitions for the findings that emerged during the interview.

Table 9
Themes and Definitions That Emerged From the Interview
1 Perceptions of digital ageism are present in hiring
2 Family influences affect environmental settings which have an effect on hiring
outcomes

3 Managers are led by belief systems
4 Media perpetuates stereotypes and influences hiring outcomes

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Defining Identified Themes
This study identified four themes in relationship to hiring biases in the workforce;
Perception of Digital Ageism is identified and is best described as ageism in the digital
platform; it is an embedded discrimination from aged perceptions with a disregard for applicants
over the age of 40. This theme was recognized by remarks respondents made when addressing
candidates for hire.
Digital ageism operates through cultural representations and through generational
segregation, an outlook that was distinct based on discussions during the interview process with
participants. When hiring managers exclude certain groups of people in the digital arena, then it
is known as digital exclusion (McDonough, 2015). When creating digital exclusion, managers
also create what is termed, digital divide. Viewing older adult‟s through stereotypes, as a group
or as monolithic, can cause negative assessments to form based on a population of older people
and widely held biases.
The second theme identified was on, Family Influences and its effect on the
Environmental Settings and hiring outcomes. This theme is closely related to the microsystem;
family connections, home environment and relationships with peers and those in the nesting
system. What emerged during the interview discussion was interviewee‟s had close relationships
with family members who were a constant in their lives. Interview participants detailed
characteristics of family bonding and parental closeness involving the presence of grandparents
and older members of their family when vacationing. Descriptions were given illustrating family
connectedness during holidays and movements when riding the transit as family members began
to age. Also discussed were illnesses, migraine headaches a participant‟s mom experienced and
how it affected him watching her in pain and, the lack of value society seemed to have for older
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adults. One interviewee stated, “You can‟t keep up with youth when you‟re older.” As an older
participant in the study, I imagined his statement was a self-reflection. Because unconscious bias
is an inherent part of being human, management‟s hiring decisions have likely been influenced
by environmental networks and what has been observed and internalized about older adults. The
negative perceptions of aging gleaned from the family environment or the microsystem
potentially manifested in hiring decisions after witnessing progressions of age. The relationship
between family, environmental settings and hiring outcomes is dependent upon what is observed.
The third theme detected was how managers are led by their belief system. The belief
system is where the root cause of biases begins which is at the individual level. A belief system
is a set of principles which form the basis for a meaning or a philosophy. During the interview,
two managers expressed how they were hesitant in hiring older workers because of the belief that
they would only work for a few years before retirement. While nine out of 14 managers stated
they chose to pass over older candidates because they believed they would have higher salary
expectations. These thought processes or belief systems were cultivated from experience and
carried throughout life; however, they are personal points of view, not factual and are part of
one‟s belief systems which have influenced hiring outcomes.
The fourth theme was recognized was based on how Media perpetuates stereotypes and
influences hiring outcomes. This theme is related to the actual force exerted by a media message
that results in a measurable effect (Phoon, 2017). This theme was deemed essential because of
the overwhelming media content and stereotypes that media perpetuates on a daily basis.
Negative stereotypes on aging are influenced by persistent exposure based on media
identification. The biased communications and use of stereotypes observed by individuals shapes
and reinforces attitudes on aging that is collected from the media (Milner, et al, 2012). Media is
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either saturated with negative representations of older adults or there is under representation
portrayed, which has a profound effect on aging ideology, perceptions and hiring outcomes.
Summary on Themes
Belief Systems and Media Influences and Stereotypes were the most compelling themes
as all decisions commence and are influenced from one‟s beliefs, values, and expectations
acquired through the growth process (Rettig, 2017). It is the influences from the Microsystem
and the Macrosystem where the belief system originates and exposes individuals to what is
acceptable and what is not acceptable. A large percent of behavior is without conscious guidance
and is based on unconscious biases and beliefs, rather than intentional rational action. The fact
that we are often unaware of the stimuli or motivations for the decisions that we make is further
complicated by the impact of bias (Wheeler, 2015). Judgments are made based on what has been
absorbed in the microsystem from family, and peers, through the Macrosystem which is an
amalgamation of each of the exposures in the context of the Bronfenbrenners‟ ecological
systems. Media Influences and Stereotypes are powerful in how it frames what is acceptable and
what is not. Media content on television and in magazines is a continuous reflection of societal
practices. It influences interactions and affects how individuals relate to older people and the
way people see themselves as they age (Loos & Ivan, 2016).
Research Question 1: To What Extent Are Hiring Decisions Among GAFE Managers
Influenced by Age Across the Mesosystem?
Guiding Research Question 1 focused on the discovery in how hiring decisions were
made by managers in the technology industry and how age biases, if any, may have been
influenced across the Mesosystem. Reports of ageism in the tech industry have impacted who is
allowed entry into high-tech jobs and how older adults are on the negative side of the digital
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divide (McDonough, 2015). Internal characteristics of older adults have been reported as being
disengaged from internet usage and having a lack of computer literacy that has resulted in an
exclusion from prime jobs (McDonough, 2015). However, from the years 2000 to 2013, internet
usage in the United States rose from 50% to 86% with usage among older adults rising from 13%
to 59% (McDonough, 2015). To determine if hiring decisions among GAFE managers was
influenced by age across the Mesosystem, managers addressed questions during the interview
process on their impression of workers over the age of 50 in the workplace.
The mesosystem links to relationships as well as communication channels within the
different microsystem influences that each impacts the individual. Bronfenbrenner described that
every individual is shaped by the direct influences in their immediate and external sphere of
influence (Bronfenbrenner, 1994). While the microsystem involves relationships with family,
peers and schools, the mesosystem is linked to the microsystem illustrating how influences from
relationships can affect hiring decisions. When participants addressed the question of their
impression of workers over the age of 50 in the workplace and how they could detect age in an
individual, responses were moderately implicit. Because an unbiased person does not exist, there
was an intersection of implicit bias and micro-aggressions. Research has indicated as a result of a
life of conditioning by friends, institutions and schools, implicit biases exists in everyone and can
influence decisions (Royer et al., 2010).
Managers also addressed the question of, “Has age discrimination impacted the
technology industry?” This question was asked to gauge the level of perception and awareness of
age bias, if any in technology. While the numbers of respondent‟s belief‟s about the impact of
discrimination were not overwhelming, 43% or nearly half of the participants were aware of
digital ageism and familiar with instances where it existed. Digital ageism in addressing
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questions showed a preference and an attitude towards stereotypes without being deliberate.
Answers appeared to be measured, though reactions seemed more commonplace without the
realization of what was being stated which denotes implicit bias. As a result of the average age of
employees in technology the age category illustrates a measure of implicit bias when six out of
14 respondents stated a belief that the technology industry was impacted by discrimination based
on their personal selection of hires based on age.
Theme 1: Perceptions of Digital Ageism Are Present in Hiring
The interview discussion presented the first theme to emerge that was directed towards
the perception of digital ageism from the managers which is aligned to Research question one.
Digital ageism refers to prejudices faced by older adults in the digital world and was identified
based on respondent‟s reluctance in hiring candidates whose age category was over 40 years.
While digital ageism is a form of discrimination that is amplified in the digital realm, the idea is
that there is a disregard for older adults (McDonough, 2015). Many actions occur without
conscious thought that allows people to exist in a complicated world, though implicit actions
steer conscious values and leads to ideas of categorizations (Blair et al., 2011). The biases that
were demonstrated by a segment of the participants interviewed revealed a consciousness in the
selection of candidates for digital positions. The answers given generally began with “we”
denoting managers were speaking for themselves and others which could imply inferences made
through environmental exposure and interactions (See Table 10).While conscious thoughts
would normally denote explicit bias, managers in the hiring process formed opinions based on
experiences influenced across the Mesosystem (Table 11).

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Table 10
Has the Technology Industry Been Impacted By Discrimination?
Implicit bias Explicit bias Direct quotes from interviewees

X
“Yes. I believe it has. We focus on a band of workers
new out of college and who are up and coming.” The
focus is on youth because we know they will be
around longer.”

X “I had 110 sales people who reported to me in
operations and I noticed we did skew younger.”

X
“We look at someone with 15–20 years and we don‟t
want to hire them in a junior level.” “One of the key
factors we look for is growth potential and you don‟t
find that in older people as much.”

X
“We look closely at college graduates, 29 and
younger.”
“You look at a resume and see someone with 30 years
of experience and you realize that someone with 10
years of experience is more adequate for the
company.” “They are more adequate because they
will not have high salary expectations.”

X

“If you [sic] talking about age, then yeah, its
discrimination. We hire more men than women but a
younger person you can pay less.” We don‟t mean
any harm, I know I don‟t, but we are looking at the
bottom line.”

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Table 11
What Is the Impression of Workers Over the Age of 50 in the Workplace? (When Interviewing
Candidates for Hire)
Implicit bias Explicit bias Direct quotes from interviewees

X
“Often times I think they‟re not someone who can do
the job.”

X “They look like me and they can bring maturity to the
job. They‟ve seen a lot of what‟s going on and they
know how the corporate office works.”

X “If they‟re looking at an entry level position, it gives
me pause and I question why haven‟t they gotten
farther.”

X “I usually wonder if they can fit in. It depends on how
they answer the questions; I don‟t care about the
age.”

Theme one further aligns to Guiding Research Question 1 based on how respondent‟s
answers aligned to influences across the mesosystems that impact hiring decisions. When six out
of 14 interviewee‟s answers indicated there was a lack of preference in hiring candidates
perceived to be older, the interviewer probed for an explanation. The respondent stated, “I know
it might sound wrong, but I just want to get the job done and let‟s face it, older people are not
always able.” Interviewees were then asked to describe how they defined the word “older?” The
word was described as a person wearing a certain style of clothing, a person‟s gait, or by
physical limitations and through general assessment. Some participants stated they could detect
age by a person‟s skin texture, while another remarked age could be detected based on posture
which are each based on age stereotypes and the progressive deterioration during the adult period
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of life. Findings (illustrated in Table 12), showed responses were based on judgments that have
been influenced by age across the Mesosystems.

Table 12
How Can You Tell if Someone Is Older or Younger in the Workplace?
Implicit bias Explicit bias Direct quotes from interviewees

X
“If a guy walks in and he‟s dressed like the 60s, not
current, not groomed properly…”

X
“Two ways; visually, their skin, if you‟re looking at
resumes and you can look at how long they‟ve been at
an organization.”

X “Just by looking at the face.” “Maybe more looking at
resumes.”

X “I can tell by the amount of years on a resume.”

X
“You can tell a person‟s age by how they walk and how
their posture is.”

X

“It‟s easy to tell when they start talking; the words and
language they use and then when they start asking for
more money, negotiating…showing experience.”

Guiding Research Question 2: To What Extent Do Micro and Macrosystem Affect GAFE
Managers When Making Hiring Decisions About Older Adults?
Guiding Research Question 2 centered on Bronfenbrenner‟s theory that propose
relationships within the immediate environment in the microsystem and relationships and events
in the macrosystem have relevance on decisions and ideas within the lifecycle. The
environmental setting in the microsystem impacts social interactions within an individual‟s
immediate surroundings (Backonja et al., 2014). The extent to which the micro and
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macrosystems affect employers when employing older adults is based on the hiring manager‟s
environmental factors. Research shows performance differences do not exist between younger
and older workers though negative stereotypes persist (Malinen & Johnston, 2013).
Bronfenbrenner‟s ecological systems theory suggests development involves a system of complex
relationships that affect multiple levels of the surrounding environment (Guy-Evans, 2020).
The core of Bronfenbrenner‟s ecological systems exists within the ecological influences
beginning with the microsystem which is at the innermost nested level and includes colleagues,
friends and family. The last level of influence involves the macrosystem which contains belief
systems, resources, and knowledge. When the two systems interlock you have colleagues, friends
and family (micro), influencing the belief systems (macro) which can either positively or
negatively influence hiring decisions (Backonja, et al., 2014).
Theme 2: Family Influences Affect Environmental Settings Which Have an Effect on hiring
Outcomes.
Findings during the interview process with participants indicated environmental settings
within the family are influential and embedded in roles and therefore have an effect on hiring
outcomes. Theme two references the microsystem which is the most influential environmental
level in an individual‟s life because it is where an individual has immediate contact with family
and friends that influence decisions (Bronfenbrenner, 1994). Based on responses given by
interviewees, 12 of the 14 respondents viewed growing old as a disadvantage indicating implicit
bias. The responses given were based on witnessing a decline in health from their parents and in
two instances where interviewee‟s stated, “Sometimes, the mind is willing but the body is not
able.” Negative stereotypes mentioned were based on perception. It was perceived that one‟s
worth was not as valuable, it was perceived that society discounts the value of older people and it
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was perceived that “everyone believes you forget everything,” as one participant stated. The
statements given about immediate declines in health were the predicting objectives of aging. The
negative stereotypes were through observation and internalized since childhood where beliefs
were held about those observed (Robertson, et al., 2015). The perception interviewee‟s had about
older people were sometimes self-directed but more often referenced aging parents or
grandparents observed (Table 13).

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Table 13
Are There Advantages (Benefits) or Are There Disadvantages to Aging?
Implicit bias Explicit bias Direct quotes from interviewees

X

“Ageism is the disadvantage, because society as a whole
discounts the value of older people.” “When you get
old it‟s like your worth begins to go down to society.
Not your family cause that‟s when you usually get
closer to family.

X “…Health is the main disadvantage in growing older.” I
think I can do a lot of the same things I did when I was
younger but in reality I can‟t, but in my mind I can.
(laughter). “Neither parent is [sic] working and both
retired in their sixties.

X

“My mom has had migraines [sic] her entire life, she
forgets things.” Her migraines have nothing to do with
age but it has given her problems.”

X
“They‟re advantages and disadvantages. Maybe not in
the workplace but in life there are advantages. More
respect comes with age because of the wisdom.”

X “I think it‟s all in the mind. You [sic] old as you feel.”

X “The disadvantage is everyone thinks you begin to
forget everything.” It‟s a disadvantage if people do not
see you for what you are.”

X

“Perceived ageism is the negative, it doesn‟t matter how
you view yourself, its how others view you.”

X
“The biggest disadvantage is keeping up with younger
people on the job.”

X

“Growing old is a disadvantage because of health.
Wanting to do more than your body allows.”

X “Getting‟ sick, watching people around you go down in
life, you know what I mean?” That‟s the
disadvantage.”

X
“It‟s some positives like you get to retire at age 60 or so,
but knowing death is around the corner, man, old
sucks.”
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What was interesting to the researcher was how the median age of the participant‟s was
52 years and management‟s preference in hiring was adults under the age of 30. Table 14 and
Table 15 point to evidence that implicit bias existed and was based on responses in the interview
where respondents measured age based on physical consciousness, perception and long-held
observations about aging.

Table 14
Tell Me About Your Interactions With Older Members of Your Family During Your
Developmental Years
Implicit bias Explicit bias Direct quotes from interviewees

X

“I had a good relationship with my parents. Right
now, neither one of my parents is still working.
They both taught classes and were professors.”

X
“…Mother was an achiever. I was around very
influential older people who worked hard and had
prestigious jobs. My father was my hero.”

X
“My dad worked at NASA and I have older sisters
that also worked well into their late sixties...”

X
“I‟m around older people who are effective and „with
it.‟” No one just sat around when they got up there
in age. My grandfather still golfs and fishes and he‟s
over 75. I grew up around people in my family who
had very active lives.”

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Table 15
Can You Tell Me About a Time You Initially Became Aware of Age Gaps or Age Differentiation
at Work or at Home?
Implicit bias Explicit bias Direct quotes from interviewees

X

“I observed as a child that my grandparents looked
older. They moved a little slower than my parents
and it seems my parents did small things like serve
them first at dinner.”

X
“…when I was with my grandmother early on there
were seats on the transportation for the elderly or
those with a disability…my parents would always let
someone older get in front of them in line.” I knew
they were older because of how they looked.
Something in their eyes or sometimes how they
would move.”

X “I became aware of physical changes; my mother
started wearing glasses and her hair color changed. I
knew she was getting older even though she acted the
same.”

X “I became aware of age about 8 years ago when I saw
older workers being pushed out of work or denied
training.”

X
“…Probably about 10 years ago when my senior
managers talked about how much money the older
workers were going to make compared to the
younger workers…then when I saw older workers
being pushed out of work.”

X

“I was around my grandmother and she was 98 when
she passed away…I have very long-lived [sic] family
members, dad got his second PhD at age 72.” I knew
my grandmother was older because we all listened
when she talked. I became aware of age because of
the respect shown to her. “Not that no [sic] one else
was given respect but they gave her more attention.”

X
“I grew up around extended family going back
generations…I found, at least in my family is [sic]
that the older a person was the more accomplished
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Implicit bias Explicit bias Direct quotes from interviewees
they were.”

X

“I didn‟t pay attention to age until I started hearing
family talk about retirement; downsizing the living
arrangements, aches and pains and when I was told I
was over the hill at age 50.”

Guiding Research Question 3: What Effect Does the Microsystem Have on GAFE
Managers in Hiring Older Workers?
The microsystem is where communications and connections take place, therefore
information, observations and experiences gleaned in the environmental dwelling will have an
effect on GAFE managers. Because of one‟s experiences, beliefs are formed through
interactions, social relationships and cognitive structures that affect life and the decisions one
will make. What is witnessed in this ecological system directly and indirectly impacts actions
because of the bi-directional influences. In addressing the guiding research question of what
effect does the microsystem have on GAFE managers? The effect it has is the consequences of
the immediate relationships the manager has had with his direct circle; home, parents, friends,
and colleagues.
Theme 3: Managers Are Led by Belief Systems
When exploring the workplace impact of intentional and unintentional age
discrimination, responses varied on whether it was believed if biases were implicit or explicit.
Participants shared observations of older workers intentionally being marginalized; not being
given training opportunities and being forced out of their positions (Table 16). When a
respondent stated how he often hired employees based on age rather than experience, he also
lamented how his intent was only to get the best candidates that could also save the company
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money. The interviewee was quick to explain how older candidates looking for employment had
a higher salary expectation and younger candidates would seldom negotiate salaries offered. The
belief that harm was not being done to older applicants nor intended was evident when the
participant remarked, “the intent is not to discredit people but you want the best employee…,” as
if to say, older individuals would not be the best employee.

Table 16
Do You Believe Age Bias Is Intentional or Unintentional in the Workplace?
Implicit bias Explicit bias Direct quotes from interviewees

X
“There are definitely biases in tech…” “I‟ve been in
meetings when the request for new work will go to
those 27 and under because the belief is they will pick
up the work quick.”

X “I think there are some age biases that are intentional.
Given two resumes with identical information, the
person with fewer years is more likely to get hired.”

X “Age bias is intentional. Because younger people can be
worked harder. They get new ideas and they don‟t
have to give them long-term benefits.”

X “I think age bias is unintentional, you don‟t mean to
discredit people but you want the best employee for
the job and sometimes that‟s someone younger.”

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Implicit biases affect decisions and actions in an unconscious manner. When the
respondent alluded to his intent in “not to discredit people”, he was not showing explicit bias, his
actions were not intentional and his intent was not to injure, demonstrating clearly an implicit
bias was being manifested. When discussing energy levels of younger workers compared to older
workers, no indications of bias existed. Respondents addressed questions identifying by
observance how older workers displayed more energy. Energy was described as workers who
often went to game rooms to play Ping-Pong at lunch, workers who often “horsed around” and
by the liveliness and vigor that youth exhibited (Table 17). What the researcher noted during the
interview were the respondent‟s dialectal responses and their attempts to conceal language that
would be perceived as being unfair. Each question addressed was methodically spoken with
some hesitations and the assuredness of establishing they were being non-bias even in instances
when there was an appearance of bias.

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Table 17
Do You Find There Is a Difference in the Levels of Energy With Older Adults Versus the
Younger Workers?
Implicit bias Explicit bias Direct quotes from interviewees

X
“Yes…Older workers try to get to work in the time
allotted and younger people like to jerk off a lot. I
guess they have a lot more energy to do silly things.
Younger workers will stay after work longer…while
older workers get the job done and go home.”

X
“I don‟t know if there is a difference in energy but
there is a difference in stamina. How long can you
maintain a high energy level?”

X
“I‟ve seen younger guys with less energy than older
workers.”

X
“Younger workers will go play Ping-Pong after lunch
or sometimes forfeit their lunch hour to shoot pool
or play table games. Older people go to lunch and
seem to use the time to relax.”

Each participant believed there should not be a cut-off age for working which
demonstrated a positive evaluation for older workers (Table 18). Conversely, when addressing
alternative training methods for younger versus older employees, implicit bias was confirmed by
six participants. Responses indicated an age-based stereotype when assertions were made that
older workers were slow or “have trouble connecting the dots” as one respondent stated (Table
19). When referencing current age and what the future looked like in 15 to 20 years for
participants, implicit bias was shown when 10 respondents believed, at 55 to 65 years old, they
would be retired and enjoying grandchildren, while four respondents believed they would still be
working (Table 20). Participant remarks showing explicit bias were demonstrated in the remark
“I have made hiring decisions based on age before,” and “Honestly, I don‟t think I‟m being
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prejudiced against older people but my preference is to hire someone young.” Each noted remark
in (Tables 21 and 22), revealed consciously processed and deliberate statements where
preferences for hiring were drawn according to a chronological age rather than experience which
confirms explicit age bias (Kleissner & Jahn, 2020).

Table 18
Do You Believe There Should Be an Expiration or Cut-Off Age for Working and Why?
Implicit bias Explicit bias Direct quotes from interviewees

X

“It depends on someone‟s financial situation.” If
someone is working then they must need the money.
Why stop someone from working if they are able to
still do the job?

X
“I don‟t think there should necessarily be a cut-off age
for working but I think people should take health
considerations into effect when working.”

X
“I don‟t think there should be a cut-off age for working
but people should consider their health?” “If you can‟t
do what your co-workers are doing and you are
messing with production then they should not work.”

X
“…my dad still works at age 84 and is still savvy. Me
and my brother would like to think we will still be as
strong.”

X
“I don‟t think there should be a cut-off age but I do think
people should be growing and making room for those
coming behind them and that‟s the trade-off you get.
You move over and let someone else work. But I do
not think people should be forced out.”

X

“No. I don‟t think there should be a cut-off age.” I think
the decision to work should be a personal decision.”

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Table 19
Would You Train Adults Over the Age of Forty Differently Than Those Ages 35 and Under?
Implicit bias Explicit bias Direct quotes from interviewees

X
“I would train older adults with more confirmations to make
sure everything was understood because they might have
some difficulty.”

X
“I would probably watch someone older a little closer to just
make sure they can do the job but the training would be
the same.”

X
“Older people might have problems connecting the dots so I
would train them slower.”

X
“I would assess their capabilities whether they were young
or old and then train them based on their knowledge.”

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Table 20
Given Your Current Age What Do You Think You Will Be Doing in 15–20 Years?
Implicit bias Explicit bias Direct quotes from interviewees

X

“I‟m already over 65; I hope I‟m retired, living on my
savings and retirement away from the rat race.”

X

“Breathing.” I‟m in my fifties now so I will be 72 in 20
years. I will be getting on up there so I hope I have saved
enough by then to stop working. In 15 years I will be 67
so. I hope I have my health.”

X

“I think I will still be working hard. Setting strategic
visions. I would like to balance fun, and enjoy work „cause
the thought of retirement sounds boring.”

X “In 15–20 years I will be doing the same thing that I‟m
doing now only slower.”

X “Relaxin‟ [sic] enjoying the fruits of my labor and hopefully
doing whatever I want.”

X

“I do believe I will stop working by 60.”

X

“In 15 years I will be living in Florida and probably retired
and playing with grandkids.”

X
“I hope I will still be working and maybe in Information
Securities.”

X “Same thing I‟m doing now: leadership. No changes
expected in 15 years.”

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Table 21
Tell Me How Your Interactions Change, if at All, When Working With People Your Own Age
Compared to Older Workers or Those Younger Than Yourself? And Is There a Reluctance to
Hire Older Workers?
Implicit bias Explicit bias Direct quotes from interviewees

X
“My interactions do not change when working with anyone. I
get along well with everybody. Of course, I‟m the boss so
they want to get along with me.” I do listen to a lot of the
ideas that young workers have because [sic] they are always
thinking about how we can make something quicker or more
efficient.” “I‟m in my 50s and there is no one here older than
me.”
“…No reluctance to hire older people, but it‟s not my
preference because they come in saying that‟s not how I use
to do it...no time for that”

X
“My interactions only change if they (younger workers) cannot
relate to what I‟m talking about.”

X
“If a person is over 60 then you may not want to hire them.
Most times they are looking for a cushion job. Sometimes
you hire people knowing they will only be there 5 years.”
“You question if the energy level will be there.” I don‟t think
I change, but maybe around younger workers I might show
more bravado.”

X
“I grew up in a country where you did not work past the age of
65. I don‟t discriminate but I would not hire someone over
55.” “Rotation in the workforce is healthy.”

X

“The reluctance to hire is only because you know they will be
leaving soon.” I probably do change when I‟m around
someone younger. They might be in my age category so I can
relate better.” “I think that‟s normal.”

X

“No interactions with older workers. I have no problems
against anyone but we do not have a lot in common.”

X

“Younger workers have something to prove, but I like what
they bring to the table. They‟re still trying to learn.” “I would
hire someone younger any day of the week but I have
nothing against older workers.”
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Table 22
Can You Tell Me About a Time When You Made a Hiring Decision Based on an Applicant’s Age
Instead of Their Experience?
Implicit bias Explicit bias Direct quotes from interviewees

X
“I made hiring decisions based on age before. If
someone had years and years of experience then I‟d
skip over them and go to someone younger assuming
they want more money.”

X
“I made the decision to hire someone younger before,
but I don‟t think that judgment was overt.”

X “Older people are more responsible and take off less, but
younger people stay after work when older people hit
the door at five o‟clock because of family
responsibilities.”

X

“It usually boils down to money.”

X
“Younger people listen more. An older person
sometimes won‟t shut the hell up because they want to
prove what they know.”

X

“I don‟t discriminate against older people, I‟m older too
but I need the work to get done and if I want someone
here on Saturdays, older people want to be home with
families and they are ready to wind down.”

X

“Honestly, I don‟t think I‟m being prejudiced against
older people but my preference is to hire someone
younger who will be around for a while. “They have a
lot of good ideas too...”

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Guiding Research Question 4: What Role Has the Media Played in the Perception of Older
Adults When Aligned to the Chronosystem, if Any, and to GAFE Managers Hiring of
Workers Over the Age of 50?
The time-based and progressive changes that are experienced in life have an effect on
what a person‟s worldview resembles. Ideas that are formed from youth, or the moment a person
is born affects much of what a person does, their understanding, outlook on life and the decisions
that are made through adulthood (Yelland, 2020). What individuals perceive in media have
contextual influences based on slanted media representations of older adults according to Kroon,
et al. (2019). The impact of visibility and media stereotypes in magazines along with what is
seen on television are triggers that signal age discrimination (Kroon et al., 2019).
The media is a large part of the development of individuals from youth to adulthood and
provides a framework for conceptualizing ecological influences on individuals (McHale, 2015).
From Bronfenbrenner‟s ecological assessment, daily activities have an influence on identity
development and building social ties (McHale, 2015). The Chronosystem consists of an
individual‟s lifetime experiences; major life transitions as well as historical events. When aligned
to the Chronosystem, the media‟s role in shaping hiring decisions is founded in the
environmental experiences one has had during childhood development (McHale, 2015).
Theme 4: Media Perpetuates Stereotypes and Influences Hiring Outcomes
Answers received from interviewees illustrate identity development through the media
and support Theme 4 demonstrating how media perpetuates stereotypes and influences hiring
outcomes. Though it was expressed by three of the 14 participants they rarely watched television,
each participant acknowledged they participated in social media activity; had LinkedIn accounts
or were active on Facebook or other sites which allows access to age-based media stereotypes.
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From magazines, commercials, movies or television, media impacts the way society sees the
world. Erroneous portrayals and images of older adults is debilitating when descriptions embrace
a mind that disgraces the elderly. When interviewees were asked, “How do you believe media
perceptions of older adults have influenced the way you view older candidates for hire?”
Respondent‟s pointed blame at older people for allowing themselves to become targets of
ridicule (Table 23). In one instance, a participant brought up the, Where’s the beef, commercial.
“Let‟s face it he said, old people are funny and we like to laugh at them but not in a negative or
demeaning way.” Another interviewee remarked, “I bet Betty White is laughing all the way to
the bank.” While none of the participant‟s conceded negative media portrayals of older adults
steered their hiring decisions, it has been widely known that frequent negative stimuli about
aging adults has harmful consequences (Dionigi, 2015).
Implicit biases do not always align with an individual‟s actions; it is the uncontrolled
thought processes towards older adults that have negative impacts. Explicit cues demonstrating
ageism were demonstrated when an aversion to hire older candidates was made by participants
based on a movie image. As blameless as remarks sounded when participant‟s expressed sorrow
at age disparities in hiring, it was apparent interviewee‟s biases had become internalized across
the chronosystem lifespan (Kleissner & Jahn, 2020).
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Table 23
How Do You Believe Media Perceptions Have Influenced the Way You View Older Candidates
for Hire?
Implicit bias Explicit bias Direct quotes from interviewees

X
“I think based on how they have old people look in
movies no one would want to hire someone that
appears frail when you can hire a younger person?”
But that‟s the movies and it‟s fictional.” It‟s not just
me but no one would hire someone that looked real
old. That‟s a fact.”

X
“A lot of old people are self-deprecating about their age.
Media does influence my view…”

X
“Back in the 80s and 90s older people were bubbling
[sic] fools but not as much today. More recently seeing
older actors like Sean Connery and Meryl Steep and
others. The media has done better.”

X
“I don‟t watch television much. You don‟t see a lot of
old people on TV anyway.”

X
“I‟ve lived in the U.S. for over 20 years. This is difficult
to believe that people in the states are treated poorly
due to age considering the respect given to those in my
country.”

X

“Let‟s face it he said, old people are funny and we like
to laugh at them but not in a negative or demeaning
way.”

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Summary
Individuals subconsciously look for points of similarity in those they hire which is called
cultural matching (Rivera, 2012). The process of cultural matching is when firms seek candidates
who are competent but also culturally similar to themselves in terms of self-presentation (Rivera,
2012). The impulse of hiring in the technology industry presumed inequality in hiring was based
on the same premise. The theory was that younger employers dismissed older workers in
exchange for candidates resembling themselves. This idea would also have meant based on the
sampling of managers interviewed, the absence of older workers in the tech field was intentional
implying explicit bias. The problem is that, in my findings, the current study revealed the median
age of hiring managers was 52 years and their decision was to hire workers who were under the
age of 30 exposing implicit bias. Bronfenbrenner‟s ecological system reasoned the environment
you grow up in affects every facet of your life (Bronfenbrenner, 1979). Research postulates
implicit bias can be influenced by background, experiences and environment and may result in
behaviors one is not aware of (Borchelt & Smith, 2019). While there were instances when the
manager stated older workers were more responsible, there were also discussions where
managers deliberately chose younger workers illustrating implicit bias. The same managers also
showed no biases when addressing follow-up questions pertaining to noticeable differences in
energy levels for older workers versus younger workers.
When referencing candidate selection, the choices the managers made revealed implicit
stereotype when, rather than being neutral, the employment preference was younger candidates
(Staats, 2015). An alternate motive for choosing younger candidates may rely on research data
that says internalized age stereotypes do not only point towards others but can also be self-
directed (Ayalon & Tesch-Römer, 2017). The negative societal views of older people influence
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perceptions of aging over a lifespan which is how social inequalities and hiring biases are
reinforced (Ayalon & Tesch-Römer, 2017). The aversion for older workers to hire older workers
may uncover ingrained stereotypes that have been manifested from childhood causing an
imbalance in age in the technology field (Cox, 2016).
Despite well-intentioned individuals, the manner in which technology engineers in the
sampling have made hiring decisions is not in good practice when choosing to hire workers
based on age instead of experience. This demonstrates there is implicit bias with a conscious
awareness for decisions made. While 11 out of 14 of the respondents were clear in stating their
hiring decisions were made based on qualifications and not age, this idea does not seem to
measure the statistics of candidates across the industry. Conversely, 12 out of 14 respondents
specified that in the final decisions with Human Resources, older workers were denied
employment for several reasons including the expectation of more money.
Research was unable to point definitively to a single reason for inequities in hiring.
Reasons varied and ranged from, “older people may not work as hard,” to “older people have
problems adjusting to new technology.” The study concluded implicit bias existed in the
technology field based on decisions managers made in hiring and a majority consensus on a
preference for younger workers.

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Chapter Five: Discussion, Conclusions and Recommendations
The literature review uncovered age-based hiring inequities that contribute to hiring
imbalances in the technology industry. While ageism belongs to a long history of discrimination,
the growing sound of voices alleging a widespread culture of bias in the technology industry has
been observed by 41% of sector workers (Sevilla, 2019). The perceptions of age and attitude
delineating implicit bias align with Hummert et al. (2002), who found older adults had more age
stereotypes than younger adults and their stereotypes carried more negative categories
(Hummert et al., 2002).

Figure 6
Over a Third of IT Workers Have Encountered Age Discrimination

Note. Reprinted from Everyday ageism in the tech industry by C. Sevilla, 2019. Totaljobs Group.
(https://www.cwjobs.co.uk/advice/ageism-in-tech)
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Figure 7
Workers Surveyed First Started to Experience Ageism at the Age of 41: IT and Tech Workers
First Experienced Ageism at Age of 29

Note. Reprinted from Everyday ageism in the tech industry by C. Sevilla, 2019. Totaljobs Group.
(https://www.cwjobs.co.uk/advice/ageism-in-tech)

Chapter Five will discuss findings as it relates to systemic injustices in hiring practices
within the technology industry. Discoveries found during research will relate to the conceptual
framework of Bronfenbrenner‟s ecological systems theory and how the complexities of one‟s
environmental settings in the five ecosystems; microsystem, mesosystem, exosystem,
macrosystem, and chronosystem influence development. The researcher will discuss how every
aspect of one‟s surroundings and the environment they grow up directly influences their
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behavior, their human development and the decisions they make. This Chapter will discuss
Stereotype Embodiment theory as the theoretical framework hypothesized by psychologist Becca
Levy to describe the process in which age stereotypes impact older adults. When giving
recommendations, the researcher will cover environmental influences and media representations
and how they influence ideas, behaviors and decisions and allows for stereotypical slants to
continue. Also mentioned will be the digital divide that hinders employment for older workers
and diplomacies and approaches to remedy the problem. Chapter Five will discuss participant
responses and how various iterations by respondents indicated implicit bias and how the
researcher concluded implicit biases existed.
Segments of this reading will allude to how age discrimination and its many forms
weakens the economy and how reforms on ADEA are necessary in order to meet the rising
impact of ageism and to assist employers in reducing discrimination. Within Chapter five is a
series of recommendations aligned to the findings and the guiding research questions and how
the existing problem will be addressed within the technology industry.
Discussion of Findings
The research study investigated the four emerging themes found when interviewing
participants. Each of the 14 participants validated through open dialogue how their employment
outcomes evolved by referencing one or more of the developing themes that have shaped the
manner in which they made hiring decisions.
1. Perceptions of digital ageism in hiring
2. How family influences affect environmental settings which have an effect on hiring
outcomes.
3. How managers are led by belief systems
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4. How media perpetuates stereotypes and influences hiring outcomes.
When looking at the overall constructs, the central theme revealed participants had strong
family attachments in the microsystem whether they were discussing family illnesses or if there
were pronouncements of admiration and reverence when discussing parental accomplishments.
When addressing relationships with older members of their family, participants offered
discussions about their social functioning and activities shared with their parents. Participants
also expressed sensitivity to their parents when remarking how age has affected one or both
members along with their upbringing with older family members. Findings demonstrated deep
connections and emotional ties for older relatives within the microsystem and family nucleus
suggesting influences whether direct or indirect were positive and would not contribute to age-
based hiring disparities across the age continuum.
The interaction of structures between layers is a key proponent to Bronfenbrenner‟s
theory as relationships and interactions in the microsystem affect immediate surroundings
(Paquette & Ryan, 2011). Findings across all data sources revealed perceptions of security when
100% of participants stated if at any time mistreatment of an employee were witnessed they
would report the occurrence or take measures to immobilize the abuse. Statements on safety and
guarding others are reflective of the microsystem and speaks to the immediate relationships or
organizations the individual interacts with; peers, family or organizations. Admissions of
wanting to intercede to arrest negative behavior revealed two microsystem variables, cognitive
stimulation and emotional support which exhibit an ease in making friends and positive peer
influence (Hong, 2014). The qualities demonstrated did not lean towards an individual who
would intentionally show bias in hiring. The characteristics demonstrated they were learned
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while in the microsystem and show an encouraging and nurturing personality with a willingness
to protect and safeguard others.
Discoveries into historical occurrences within participant‟s family structure in the
Chronosystem were indicated when respondents addressed the question, “Can you tell me a time
when you initially became aware of age gaps or age differentiations?” The Chronosystem
reasons that when a person faces major occurrences and transitions, then these controls can
influence and shape a person‟s life. It is further noted how events could come from an illness,
disasters or even a pandemic. However, it is the impact of time and the environmental transitions
and milestones that are the turning points that have the most impact (Mulcahy, 2020). Rosa and
Tudge (2013) write that Bronfenbrenner asserted, human development is “powerfully shaped by
conditions and events occurring during the historical period through which the person lives”
(Rosa & Tudge, 2013, p 254). The range of answers by participants made findings clear in how
people interact within their environmental surroundings long after it has passed; even as the
environment changes (Backonja et al., 2014). A participant cited the death of a 98 year old
grandmother as a time when he became aware of age and when a 70 year old father received his
second PhD at the age of 70. Other remarks detailed watching older employees being pushed
aside because of age while another replied watching his grandfather gradually lose his hearing as
an indication to him that he was growing older. Each pivotal moment in time could have altered
behaviors and attitudes by inspiring compassion towards older adults or a fear of aging
depending on other transitions within the life cycle (Backonja et al., 2014).
Conversely, due to influences and environmental factors we operate within, occurrences
had the potential to impact the respondent‟s lives either positively or negatively during a hiring
process. It is unclear how events discussed shaped the respondents to be more sensitive to older
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adults or impervious to them. Because implicit biases are without conscious knowledge,
participants may have associated older adults with memories of a dying grandmother or a
grandfather losing his hearing without knowing. The possibility exists that these influences
interrupted hiring decisions as implicit biases often predict how individuals behave more
accurately than conscious values (De Houwer, 2019).
Other findings from the study indicated respondent‟s awareness in how technology was
impacted by age-based biases in the pursuit of a youth-oriented environment. Had participants
denied awareness of biases existing in the technology industry, it would have further established
unconscious association as hiring biases in technology is widely known (Abrams et al., 2016).
The admission of having the awareness of negative forces within ones surrounding environment
defines the mesosystem, where influences exist and are influential on one‟s development. The
structure in this ecological system is where a person‟s worldview expands and leads to new
levels of cognitive complexity (Bronfenbrenner, 1977). Respondents were aware of existing
biases though when addressing follow-up questions, they failed to see how their thought
processes contributed to censuring aging applicants from employment. It is not uncommon for
individuals to express explicit disapproval of certain attitudes or belief such as the exclusion of
older workers or mandatory cut-off ages, while still harboring biases on a more unconscious
level (Cherry & Marsh, 2020).
While unconscious biases were noted during the interview, it was also observed how
negative media perceptions, languages and age bias was ubiquitous and therefore to participants
it seemed customary. The failure to acknowledge media bias was essentially observed by
omission when participants seemed oblivious to how distorted media representations affect
perceptions and judgments in the hiring of older adults. Cherry and Marsh (2020) say
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experiences and social conditioning play a role in implicit biases. From cultural conditioning,
media portrayals and how one is reared contribute to the implicit associations, from the
microsystem to the macrosystem. What was described by participants were features they
believed to distinguish age when asked, “How can you tell when someone is older?” Remarks
ranged from, “loose skin,” “aged attire,” how they speak,” “how they walk” to “how older
people resolve problems.” These terminologies are a form of “visual ageism”: the social practice
of visually underrepresenting older people or misrepresenting them in a prejudiced way (Loos &
Ivan, 2016). These replies also demonstrate how biased discernments operate both within and
outside of consciousness (Chopik & Giasson, 2017).
When judgments operate outside of consciousness, it is an unconscious or implicit bias; a
social stereotype that is formed outside of an individual‟s awareness (Chopik & Giasson, 2017).
When referencing the appearance and detection of older workers, while implicit bias was
detected, participants could not see the same discriminations in how media portrays older adults.
Situations such as these occur because individuals have been influenced by their environmental
settings from birth, as a result, stereotypes that have existed in the public domain make it
difficult to separate thinking from the influence of society (Cherry & Marsh, 2020).
Moskowitz proffers, once a stereotype is evoked, negative and imprecise triggers about
that social group emerge. For many social groups, the negative components of the stereotype are
more dominant to the group, causing the overall reaction to the group to be unintentionally
negative (Moskowitz, et al, 2012). This is not the only unintentional causation in the negative
categorization of groups. When negative stereotypes rise to a conscious level, they influence
perception, judgment and behavior (Moskowitz, et al. 2012).
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Despite 10 out of 14 participants being unconscious of biased messages and the distorted
images of older adults, the findings further demonstrate how ageist stereotypes in the media have
subconsciously influenced hiring decisions. Because media is a source of information; if people
agree with what is seen or heard in the media then it leads to conformation bias where there is an
inability to see conditions objectively (Christensen, 2016). As individuals grow and become
accustomed to environments and societies they interact in, they absorb and become exposed to
images and ideas which guides the way they see the world (Luscombe, 2019). This
developmental process is what occurs in the macrosystem, the largest culture that individuals are
immersed in which can influence beliefs.
When examining the question addressed by participants on, “how do your interactions
change, if at all, when working with people your own age versus older adults,” the interview
question should have asked, how does your behavior change? The researcher was seeking
answers about conduct therefore the question should have been aligned to behavior instead of
interactions. Twelve out of 14 respondents stated, because they had nothing in common with
older workers and there was no communication with them, therefore there was limited
interaction. What the respondents implied was a behavioral change when choosing to disregard
the presence of older workers. Without intention, respondents displayed the tendency for
stereotype confirming thoughts. When stereotypical and ageist replies are spontaneously
confessed without forethought and awareness, this exhibits what psychologists call implicit bias
(Payne et al., 2018).
The researcher believes that none of the participants consciously entertained negative
thoughts about older workers and were unaware of their implicit biases however their natural
inclination was to overlook these members of society because of an age assignment. Further
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study would be needed to gather data on if a lack of relevant awareness or implicit attitudes and
the purported reduced controllability of thought is justification for discriminatory behavior: If
adults are said to be conscious of explicit attitudes and behavior then how would implicit bias be
absent of intention? Still, stereotype messaging is prevalent and harms others when unconscious
behavior is condoned and thought to be customary. The participants were implicit in the
judgment of older adults, yet 10 of the 14 participants preferred to hire younger workers when
interviewing, though unconsciously.
Unexpected Findings
The researcher expected the participant‟s responding to the study would have an average
age of 40 years and younger, however, the median age for respondents was 52 years. Because the
hiring age in technology is skewed towards younger workers, the hypothesis going into the study
was that management hired people that resembled themselves. Researchers attribute the
similarity effect in hiring to be an unconscious decision when they select candidates that mirror
their own biographies to define accomplishment (Rivera, 2013).
Recommendations for Practice
Ageism and Systemic injustices in hiring practices is derived from negative perceptions
of older people; a lack of intergenerational contact and skewed media illustrations of older
members of society. In order to reduce ageism in the workforce, education combined with
training and strategies to improve perceptions will serve as the basis of effective interventions.
Stereotypes and negative perceptions of older adults are as a result of having a lack of contact
between different generations (Abrams et al., 2015).
Because of the social constructionism of ageism; how it is ingrained and observed, how
variations in age is presented in day-to-day life and how ageism has been an acceptable „ism‟ in
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society, open communication is needed for management to learn where employees levels of
understanding is. The introduction to employees when presenting recommendations to improve
the problem of hiring inequities will involve an overview of hiring biases the technology industry
has faced. It will in addition allow for employee feedback, opinions and concerns on how the on-
going process is presented. It is vital that all employees be involved in the communication
process in order to have successful outcomes.
Recommendation 1: Employ the Implicit Association Test to Determine the Level of
Implicit Biases of All Hiring Managers
During interviews, perceptions of digital ageism were noted during the hiring process
when six of the 14 managers discussed their beliefs that the technology industry was impacted by
ageism in hiring practices. While 43% of managers confirmed there was an awareness of
discrimination in hiring, nine of the 14 managers demonstrated implicit bias when addressing
detections of age in older adults. The managers in this study had a median age of 52 years which
aligns with examinations of the interaction between managers and age where it has been held
that older managers have more negative beliefs about older workers than younger managers
(Hassell & Perrewe, 1995). The current study not only revealed managers saw older applicant‟s
age as a disadvantage but 11 of the 14 hiring managers affirmed hiring decisions were made
based on youth instead of a candidate‟s qualifications. Ageism has been said to have two distinct
targets: it can be directed towards other individuals or it can be directed towards oneself. In this
study with the median age of interviewees at 52 years, the admission of bias in hiring supported
the behavioral dimensions of ageism (Marques et al., 2020).
Despite reports that state unreliability with IAT, it is the most well-known implicit
measure in psychology (Fazio & Olson, 2003). Researchers in recent years have documented the
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implicit bias test as being unreliable in racial bias detections, scoring below reliability against
predictors of behaviors in real life. Though studies also conclude IAT is a predictor of
subsequent behavior other than explicit responses (Nguyen, 2019). The recommendation for IAT
is the first step in detecting age biases among managers and is not to be used as an inclusive
remedy but instead, as an initial tool intended for insights into worker‟s implicit biases. IAT will
measure the strength of association of age-based biases. It will test the relative favorableness
toward two concepts. The outcomes will detect if there is a preference for A over B, not whether
the worker dislikes B or is neutral toward B (Brunel, Tietje, & Greenwald, 2004).
The Implicit Association Test (IAT) is the first recommendation to determine implicit
biases held by management and later employees. Stereotype IATs measure the relationships
between concepts that reflect the strength to which a person holds a societal bias. Attitudes and
beliefs about topics are reported to gain self-awareness of the bias that is being measured. Once
the test is taken, it will expose implicit behaviors that allow management to recognize implicit
biases that exist. The IAT test is secure using the same hypertext as banks, and there is strong
security for data transfer and IP addresses that are confidential. Results of the IAT test will state
interpretations that have a basis in research conducted at the University of Washington, Yale
University and Harvard University. The IAT test allows for a sense of awareness which is
needed in identifying age-based hiring biases in technology.
Studies that summarize data across many people find that the IAT predicts discrimination
in hiring, education, healthcare, and law enforcement. The IAT test is not 100% but will give
management a benchmark on where areas of bias are located. Implementing the test would take
approximately one month. After all of management has taken the test, the test will then be rolled
out to all employees.
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Recommendation 2: Instituting a Structured and Defined Interview Protocol
The second recommendation to mitigate age-based hiring biases would be a structured
and defined interview protocol. During the interview, none of the interviewee‟s reflected on the
manner in which interview questions were addressed, however, on one occasion it was said that
older applicant‟s act as if they have something to prove by talking too much. It is unclear as to
when this occurs during the interview process.
A principal reason given by 10 of the applicants for hiring younger workers was that
older workers have higher salary expectations compared to younger applicants. It was mentioned
that older workers want to negotiate salaries and younger workers accept the salary that is
offered. Managers also stated younger applicants bring fresh ideas to the table, but again, the
interviewee did not state if this was during the interview or what may have prompted the
discussion of ideas.
It is vital to have a defined interview protocol to allow managers to stay on course when
interviewing and avoid personal discussions to take place. During the interview process
participants relied on stereotypical depictions of age to determine the hiring outcome. Potential
candidates for hire were categorized not by answers given during the interview on skill but rather
on clothing, skin texture and mannerisms. The biased judgments manager‟s used when
eliminating candidates for hire were based on ideations about older adults and preconceptions
carried throughout adulthood. Twelve of the 14 managers stated they had nothing in common
with older workers though by definition of the word older, being over the age of 50 placed them
in the same category. For interviewees to state they had little in common with older workers
suggested conversations beyond work performance took place during the interview process.
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A defined or structured interview protocol will eliminate preconceptions based on one‟s
age during an interview that leads to charges of age-based discrimination. Revisions that will be
needed include a fair scoring system in accordance to the job specifications criteria to allow
candidates to be selected based on skills and knowledge. According to Brecher, et al, 2006,
research indicates traditional interviews do not suggest a candidate‟s potential however a
structured interview will increase fairness, reduce biases and allow candidates to each address
the same questions (Brecher, et al, 2006).
Prior to giving management the resume of applicants, each resume should be reviewed
with precursors to job qualifications. If an applicant has the desired years for performance then it
should be noted, there should be salary transparency and blind testing to avoid hiring biases. In
order to achieve a culture that is diverse and inclusive it is necessary that management hires
candidates based on ability and not age. Rather than hiring those deemed as being able to fit into
the organizational culture, hire workers that can offer different perspectives. As globalization
increases, a diverse and multicultural workforce environment will increase work productivity,
combat discrimination, and promote inclusiveness (Farnsworth, et al, 2015).
The ADEA‟s objective is to promote and include employees based on abilities. This is a
creed that should be emphasized and embraced by management. Confronting the rising impact of
age-based hiring inequities and reducing discrimination is the focus of the recommendation. The
new focus will be on employing workers who foster an inviting workplace; those that will
complement the organization‟s goals and vision regardless of age. The organizational culture is
the collective behavior of the employees; their values, norms, visions and customs.
Communication and a broad cultural framework throughout the organizational process offer
sustainability for employees over the age of 40; and a refined and controlled interviewing format
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affords management the opportunity to have a defined structure. The objective is to find the
employees in an ethical and coordinated manner, and to have employees address questions in the
same way to determine the best candidate selection (Clark &Estes, 2004).
Recommendation 3: Introducing Intergenerational Communication and Connections and
Adopting Levy’s PEACE Model
The third recommendation is taken from Levy‟s PEACE (Positive Education about Aging
and Contact Experiences) model, where the objective is to promote equal status and share
information. The model proposes the two factors that will reduce ageism is education to mitigate
myths and, positive contact and experience with older adults (Levy, 2018).
The interview demonstrated there was a lack of exposure to older candidates when
interviewee‟s described older adults by their gait; by not having the same energy level, skin
texture and as having a lack of motivation. Interventions to change the negative self-perceptions
of aging would include exposing workers to intergenerational groups to also improve self-
perceptions of aging.
Levy‟s idea corresponds with the researcher‟s that interactions and exposure with older
adults would create a unified and cohesive environment for employees to dispel negative images
and perceptions of older workers. Inviting older successful employees from outside of the
organization to affiliate with workers in the technology field will broaden the understanding of
diversity and create an inclusive workforce. Bringing a diverse group of personnel together is
expected to give employees an opportunity to engage with older workers; to communicate and
see first-hand capabilities of those previously shunned and to witness the efficiency and business
acumen of older members of society.
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The workforce in 2021 is comprised of five generations of workers; Traditionalists born
1927 to 1946, Baby Boomers born 1947 to 1964, Generation X born 1965 to 1980, Millennial
born 1981 to 2000 and Generation Z born 2001 to 2020. Intergenerational exposure outside of
the family structure is an important component in understanding age diversity.
The efficacy of the PEACE model in reducing ageism and intergenerational relations is
undetermined, though college students who participated in intergenerational interactions rate
their experience with older adults to be positive (Bousfield & Hutchison, 2010).

Figure 8
PEACE Model (Levy, 2018)

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Recommendation for Future Research
This study has demonstrated that an individual‟s age identity has an important effect on
hiring outcomes. While I found some evidence to support the theory that hiring biases are
intentional, the study also indicates that biases are sometimes unintentional. The study indicates
that further research is required to understand why older managers were reluctant to hire older
workers and to determine how they may perceive themselves against their chronological age.
Because the median age of hiring managers in the study was 52 years, the theory is that
managers were self-directing. Future research would include modifying the initial research letter
to reflect younger management personnel is needed for the study and also to include a longer
time frame to allow for more participants to contribute to the study. There should have also been
a cross-section of interviewees that would include employees who could attest to management
treatment and biases witnessed, human resources personnel, and again, a broader age selection.
A full study involving human resources could provide additional information which would be
vital to the study in uncovering hiring inequities. As a result of the low number of participants
within this study, and the higher age category, the researcher could not give a full representation
of the hiring processes in the technology industry. More time would be needed to allow for all
recruitments to return questionnaires or in order to receive replies on availability for interviews.
Additional study involving older workers who were denied advancements; their perceptions and
experiences could further give significance to treatment experienced along with reintegration and
technology acceptance by management. In order to have more grounded information in the
study, more persuasive knowledge and underpinnings that exist in technology are needed.
While the researcher would like to believe participants were forthright in responses
given, the replies were not indicative of a market that has experienced overwhelming criticism
for failure to hire older workers. Statistics suggest explicit biases exist and illustrate a story that
129

suggests workers over the age of 40 face career worries as they fear losing jobs. Problems of
ageism in the technology industry are at a critical stage. Society is aging at an unprecedented rate
and by the year 2025, 103 million people, 30% of the U.S. population is projected to be aged 55+
(Thompson & Mayhorn, 2012).
Conclusion
The study makes clear the relationship between age-based hiring inequities and the
technology industry is complex for those interviewed. Findings showed a strong basis for
concluding implicit biases exists in the technology industry and that ageism plays a central role.
Indications of the hiring inequalities are paramount in understanding the disadvantages and lack
of opportunity for advancement to older members of society. Computers and Information
Technology (IT) touch nearly every aspect of modern life (Csorny, 2013) and is a significant
component for economic growth, and international competitiveness, though everyone is not
being included. Older members of society have been left out of a growing job market while there
remains a high-demand for services. The technology field is vital and while it serves to connect
people, it has dismissed an older segment of society as they confront illicit age barriers late in
their careers. Negative stereotypes on aging have been promulgated within corporate spheres of
influence and, the technology industry‟s preference for younger workers as being more future-
focused than older workers has continued to grow (Loten, 2019). As the study pointed out, there
are many factors that contribute to stereotypes and a great many have been inflicted by society
while others have been self-directed by aging adults. Self-perception and internalizing ageist
attitudes have contributed to the institutional manifestations of ageism. While perceptions of age
change as people grow older, the interview study confirmed that negative self-stereotypes affect
cognitive decisions (Levy et al., 2011). Participants were reluctant to hire older adults yet 80% of
130

the participants in this study were over the age of 50. The avoidance demonstrated how older
adults avoid stereotypes of their own groups by distancing themselves from their age group
(Horovitz, 2019). The study also illustrated how media is acknowledged as a potential source of
stereotypical views that promote biases towards older workers and influences judgments when
hiring.
A unification of some of the most influential ideas and creative minds globally work
within an industry where there has not been a distribution of equitable employment. While hiring
youth has been the hallmark for Silicon Valley since its inception, it is necessary for change to
begin. What started during the Great Depression in Silicon Valley with two college students,
William Hewlett and David Packard, having jobs created for them has culminated into a brand
where only youth is sought in technology (Amadeo & Kelly, 2020).
131

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Appendix A: Interview Questions
1. In recent years has the tech industry been impacted by age discrimination within your
workforce or in previous employments in the tech field?
2. Tell me about a time, if any, when you believed employees were being treated unfairly
and what made you believe this?
3. How do your interactions change, if at all, when working with people your own age versus
older adults? Are you aware of the change? If at all, and is it a conscious change?
4. What is your initial impression of older workers over the age of 50 in the workplace? Do
you believe they are taking jobs away from younger workers?
5. Explain if you would train older adults between the age of 40 – 60 differently, if at all,
than someone between the ages of 25- 35. How would you alter the training and why
would your methods change?
6. How would you define ageism?
7. How can you typically tell if someone is older or younger?
8. Describe the difference in the energy level, if any, between older workers and younger
ones?
9. Can you tell me about a time, if any, when you made a hiring decision based on an
applicant‟s age or their youth, instead of their experience?
10. How would you respond if at all, if you witnessed or observed age discrimination on the
job or outside of work?
11. Do you believe there should be a cut-off age for working and why or why not?
12. Are there any advantages (benefits) or disadvantages to growing older? If so, what might
they be?
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13. At what point should one stop working and why?
14. Tell me how your interactions or relationships with older members of your family
influence, if any, hiring decisions?
15. What does the saying “act your age” mean to you? Do you believe there is a certain way
each age group should act or respond? If so, why do you believe this? How should a 25
year old act compared to a 55 year old? Why do you believe this?
16. Do you believe televisions image of older people have distorted your view of older
people? And if so, how do you believe media perceptions have influenced the way you
view older adults, if at all, as it relates to hiring?
17. Once you have received a resume, what are the primary criteria you look for in an
applicant?
18. Can you tell me how you can tell if a person is older or younger? Tell me about a time
when you may have become aware of their age? Was it based on their
actions/behavior/attire or something else?
19. Tell me about a time, if any, when a person‟s age may have factored into your decision for
hiring?
20. Do you believe age biases in hiring are intentional or unintentional and why?

158

Appendix B: Questionnaire

Question

Open or
closed

Level of
measurement.
(nominal,
ordinal
interval, ratio)

Response
closed-ended or
open-ended

Research
question
and
influence
1. Do negative stereotypes
of aging adults affect
your perception of older
workers abilities when it
pertains to hiring?
Closed Interval a) Always
b) Often
c) Sometimes
d) Never
Macro
2. Based on your
experience, do older
workers have an
increased amount of
illnesses that tend to limit
productivity?
Closed Ordinal a) Always
b) Often
c) Sometimes
d) Never
Macro
3. Have you found that
older workers are slower
workers?
Closed Interval a) Always
b) Often
c) Sometimes
d) Never
Micro
4. Do you believe there
should be a cut-off age
for working in the Tech
industry?
Closed Ratio a) Always
b) Often
c) Sometimes
d) Never
Macro
5. Should there be a
mandatory retirement age
for workers in the
corporate sphere?
Closed Ratio a) Always
b) Often
c) Sometimes
d) Never
Meso
6. Do you find older
workers to be more
reliable?
Closed Ordinal a) Always
b) Often
c) Sometimes
d) Never
Micro
7. Do you think stereotypes
of older people are
realistic? , i.e. inability to
use technology, fatigue,
grumpy, frail, illness?
Closed Interval a) Always
b) Often
c) Sometimes
d) Never
Macro
159

Question

Open or
closed

Level of
measurement.
(nominal,
ordinal
interval, ratio)

Response
closed-ended or
open-ended

Research
question
and
influence
8. Do you think there could
be reluctance, if any, to
hire older workers
because they expect too
much money?
Closed Interval a) Always
b) Often
c) Sometimes
d) Never
Micro
9. Are older workers taking
jobs that should go to a
younger workforce?
Closed Ratio a) Always
b) Often
c) Sometimes
d) Never

Macro
10. Do younger workers
work more efficiently
than older workers?
Closed Interval a) Always
b) Often
c) Sometimes
d) Never
Micro
11. Do older workers
complain on the job more
than younger workers?
Closed Ordinal a) Always
b) Often
c) Sometimes
d) Never
Macro
12. Do older workers have
difficulty when working
with technology?

Multiple

a) Always
b) Often
c) Sometimes
d) Never

Micro
13. Have you ever witnessed
age-based discrimination
from co-workers?
Multiple

a) Always
b) Often
c) Sometimes
d) Never
Meso
14. Do you believe negative
stereotypes on aging
affect hiring decisions?
Multiple a) Always
b) Often
c) Sometimes
d) Never
Macro

160

Question

Open or
closed

Level of
measurement.
(nominal,
ordinal
interval, ratio)

Response
closed-ended or
open-ended

Research
question
and
influence
15. Is an applicant‟s job
experience the primary
criteria you seek?

Multiple

a) Always
b) Often
c) Sometimes
d) Never
Macro

16. Do you believe one‟s age
should be a barrier to
hiring?

Open a.) Yes
b.) No
Meso
17. Is an inclusive
departmental
environment important
for productivity?
Open a.) Yes
b.) No
Exo

Short-answer questions
18. If there was a cut-off age for working in the tech field, what age should that be?
________________________________________________________________________
________________________________________________________________________
________________________________________________________________________

19. What is the primary criteria/quality you seek in job applicants?
_________________________________________________________________________
_________________________________________________________________________

161

Appendix C: Introduction Letter to Management Personnel Participants
Greetings! My name is Fredrica Crowe:
I am a Doctoral student at the University of Southern California, located in Los Angeles. I am
studying age biases and hiring practices in the work culture but specifically in the technology
industry. The purpose of the study is to learn whether age bias is intentional or unintentional,
rooted from childhood or from environmental experiences. I chose the tech industry because the
median hiring age is 29 and your company fits the profile of the study. I am reaching out to you
because I am interested in speaking with you along with members of your leadership personnel
for 30–60 minute interview to address 20 questions. The interview will be completely
confidential and is strictly voluntary. There is no right or wrong answer in the study and my only
request would be that respondents feel comfortable expressing how they feel. No self-
identification is necessary and no names will be reported. I will be tape recording the interview
but again, no names will be included and I am the only person who will hear the answers
therefore identification of respondents will be strictly confidential between me and my
dissertation chair – the purpose is so that I can contact you for follow-up questions if necessary.
I will be happy to provide additional information and address questions should you have any.
Thank you for your time and understanding. I look forward to speaking with you and your staff.
Sincerely,
Fredrica Crowe
Doctorate Student
University of Southern California

Abstract (if available)

Abstract The present study investigated the effects of managers interviewing protocols to understand if bias hiring decisions in the technology industry, if any, were implicit or explicit. Unconscious bias occurs automatically when the mind makes judgments based on past experiences and encounters. Because the world is inundated with stereotypes; a distorted image of an older person, oversimplification of health issues, their behavior or their ideals, has been shown that continued exposure to these negative stereotypes results in unconscious biases in the short-term. Deeply internalized and ingrained stereotypes from media portrayals or from cultural settings inculcate and influence unconscious behavior even when a person consciously believes discrimination is wrong. ❧ In this field study, age bias in the workplace and its influences on hiring decisions were a culmination of internalized negative perceptions on aging which manifested from childhood exposures. While participants were older and had a median age of 52, they internalized the societal views of older people and believed younger workers to be more productive. This attitude demonstrated self-directed ageism when choosing to avoid the hire of older workers believing they were prone to illness. Going into the study, the researcher’s belief was that younger employees were hired based on similarity attraction theory where managers hired workers based on likeness or resemblance. When participant’s ages were revealed, the tenets of the theory based on attachment styles or age proximity as the reason for hire were deflated. Further research into the study revealed there was a strong current of implicit bias and ageism in the workforce that was made evident by the preference of younger versus older workers.

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Asset Metadata

Creator Crowe, Fredrica L. (author)

Core Title Ageism and ingrained stereotypes: a qualitative study of systemic injustices in hiring practices

School Rossier School of Education

Degree Doctor of Education

Degree Program Organizational Change and Leadership (On Line)

Degree Conferral Date 2021-08

Publication Date 08/02/2021

Defense Date 06/03/2021

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

Tag Ageism,digital ageism,implicit bias,OAI-PMH Harvest,self-directed ageism,unconscious bias

Format application/pdf (imt)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Canny, Eric A. (committee chair), Malloy, Courtney (committee member), Phillips, Jennifer (committee member)

Creator Email fcrowe@usc.edu

Permanent Link (DOI) https://doi.org/10.25549/usctheses-oUC15673814

Unique identifier UC15673814

Legacy Identifier etd-CroweFredr-9973

Document Type Dissertation

Format application/pdf (imt)

Rights Crowe, Fredrica L.

Type texts

Source University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

Access Conditions The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the author, as the original true and official version of the work, but does not grant the reader permission to use the work if the desired use is covered by copyright. It is the author, as rights holder, who must provide use permission if such use is covered by copyright. The original signature page accompanying the original submission of the work to the USC Libraries is retained by the USC Libraries and a copy of it may be obtained by authorized requesters contacting the repository e-mail address given.

Repository Name University of Southern California Digital Library

Repository Location USC Digital Library, University of Southern California, University Park Campus MC 2810, 3434 South Grand Avenue, 2nd Floor, Los Angeles, California 90089-2810, USA

Repository Email cisadmin@lib.usc.edu