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Well-being, employee effectiveness, and organizational support: community college administrators during the COVID-19 pandemic

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Well-being, employee effectiveness, and organizational support: community college administrators during the COVID-19 pandemic

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Well-Being, Employee Effectiveness, and Organizational Support:
Community College Administrators During the COVID-19 Pandemic
by
Nan Ho

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 2022

© Copyright by Nan Ho 2022
All Rights Reserved

The Committee for Nan Ho certifies the approval of this Dissertation

Kimberly Hirabayashi
Helena Seli
Bryant Adibe, Committee Chair

Rossier School of Education
University of Southern California
2022

iv
Abstract
The COVID-19 pandemic has created high levels of occupational stress in organizations, which
has led to a negative impact on employee well-being. This study examined organizational and
leadership practices implemented during the COVID-19 pandemic that positively affected the
well-being of higher education administrators who serve in community colleges. These
administrators were responsible for leading their institutions’ efforts to serve their students
effectively during the pandemic. Using a social cognitive conceptual framework, this study
examined how emotional exhaustion, fatigue, and burnout—as measures of well-being—affected
administrators’ perceptions of their effectiveness during the pandemic and whether these beliefs
differed based on demographic characteristics. In addition, the study identified what practices,
policies, and strategies were effective at supporting administrator well-being. A review of the
literature on well-being, occupational stress, and burnout revealed a gap in recent research on
higher education administrator occupational stress and burnout. The study sample included more
than 300 California community college administrators, from managers to chancellors. An online,
anonymous survey with items adapted from existing instruments alongside novel questions was
distributed through professional organizations and networks. Both quantitative and qualitative
data from surveys were analyzed. The study found emotional exhaustion, burnout, fatigue, and
lower well-being during the pandemic were correlated to administrators’ perception of low
effectiveness. Significant differences emerged between certain demographic groups in their
perceptions of effectiveness, fatigue, and degree of organizational support. These included age,
gender, race and ethnicity, characteristics of dependents, and experience. In addition,
administrators identified various policies and practices implemented by organizations and
strategies implemented by supervisors that were effective at supporting their well-being. Based

v
on the findings, recommendations for practice are presented to help community colleges and
their leaders better support the well-being of administrators in service of students.
Keywords: well-being, burnout, effectiveness, community college, administrators,
COVID-19

vi
Dedication
To my parents, husband, and children. We did this together. I miss you, Dad.

vii
Acknowledgements
Thank you, Dr. Bryant Adibe, for serving as chair of my committee and for your support
and encouragement throughout this journey. Thank you and Dr. Kimberly Hirabayashi for giving
our thematic group the opportunity to share in our exploration of well-being across our diverse
disciplines. I appreciated our many conversations that strengthened my research. Thank you, Dr.
Helena Seli, for serving on my dissertation committee. I am profoundly grateful to you for
helping me cross the finish line. Thank you, Dr. Alexandra Wilcox, for serving on my proposal
defense committee, encouraging me to consider different facets of my research, and being an
inspiring leadership professor. Thank you, Dr. Carey Regur, for your thoughtful and helpful
feedback. Each of you provided valuable input and encouragement.
Thank you, Dr. Dennis Hocevar, for your extraordinary generosity in helping guide my
quantitative analysis. I am deeply grateful to you for your patience, kindness, support, and
humor. It was my privilege to learn from a master teacher.
Thank you, Dr. Monique Datta and Dr. Doug Lynch, my first professors in this program,
for your outstanding teaching that set high standards and prepared me well. I have fond
memories of our time together during Immersion One.
Thank you to the many community college administrators who generously shared their
time and experiences to participate in this study. Thank you to the Association of California
Community College Administrators for your support.
Thank you, Cynthia and Amy. There are not enough words that can express my gratitude
and appreciation to you for being by my side, nudging, budging, pulling, pushing, and always
loving through this incredible journey. Thank you, David, Steven, and Ellie. I treasure our time
together, our dedication to one another each week, and our mutual celebrations of milestones. I

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am thankful we found each other. Thank you, Cohort 16, for your friendship and support. I could
not have done this without you all.
Thank you to my dear family for your incredible support. My dear husband, who
surrounded me with love and care every step of the way, and my darling children, who grew into
beautiful adults in the blink of an eye.

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Table of Contents
Abstract .......................................................................................................................................... iv
Dedication ...................................................................................................................................... vi
Acknowledgements ....................................................................................................................... vii
List of Tables ................................................................................................................................. xi
List of Figures .............................................................................................................................. xiii
Chapter One: Introduction to the Study .......................................................................................... 1
Context and Background of the Problem ............................................................................ 1
Purpose of the Project and Research Questions .................................................................. 2
Importance of the Study ...................................................................................................... 3
Overview of Theoretical Framework and Methodology .................................................... 3
Definitions ........................................................................................................................... 4
Organization of the Dissertation ......................................................................................... 5
Chapter Two: Literature Review .................................................................................................... 6
Well-Being .......................................................................................................................... 6
Occupational Stress ........................................................................................................... 20
Burnout ............................................................................................................................. 27
Organizational and Leadership Support for Well-Being in the Workplace ...................... 41
Challenges Related to Well-Being, Stress, and Burnout During the Pandemic ............... 49
Conceptual Framework ..................................................................................................... 52
Summary ........................................................................................................................... 54
Chapter Three: Methodology ........................................................................................................ 55
Research Questions ........................................................................................................... 55
Overview of Design .......................................................................................................... 55
Research Setting ................................................................................................................ 56

x
The Researcher .................................................................................................................. 56
Data Sources ..................................................................................................................... 57
Method .............................................................................................................................. 58
Reliability and Validity ..................................................................................................... 74
Ethics ................................................................................................................................. 74
Chapter Four: Findings ................................................................................................................. 76
Research Question 1 ......................................................................................................... 77
Research Question 2 ......................................................................................................... 97
Research Question 3 ....................................................................................................... 108
Summary of Overall Findings ......................................................................................... 123
Chapter Five: Recommendations ................................................................................................ 125
Discussion of Findings .................................................................................................... 125
Recommendations for Practice ....................................................................................... 135
Limitations and Delimitations ......................................................................................... 141
Recommendations for Future Research .......................................................................... 145
Conclusion ...................................................................................................................... 146
References ................................................................................................................................... 148
Appendix A: Demographic Characteristics of Participants ........................................................ 196
Appendix B: Survey Instrument Items ....................................................................................... 200
Appendix C: Comparison of Participant Personal Demographics .............................................. 202
Appendix D: Comparison of Participant Work Demographics .................................................. 207

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List of Tables
Table 1: Quantitative Survey Questions: Reverse Coding, Original Instruments, and
Composite Variables 71
Table 2: Distribution of Survey Responses Related to Burnout (N = 308) 79
Table 3: Distribution of Survey Responses Related to Fatigue (N = 308) 80
Table 4: Distribution of Survey Responses Related to Effectiveness (N = 308) 82
Table 5: Distribution of Survey Reponses Related to Organizational Support (N = 308) 83
Table 6: Distribution of Survey Responses Related to Relative Effectiveness and Well-
Being (N = 308) 85
Table 7: Item-Total Statistical Analysis for Low Effectiveness 86
Table 8: Item Statistical Analysis for Lower Effectiveness During the COVID-19
Pandemic 87
Table 9: Comparison of Descriptive Statistics for Two Measures of Effectiveness 88
Table 10: Item-Total Statistical Analysis for Burnout 91
Table 11: Item-Total Statistical Analysis for Fatigue 91
Table 12: Descriptive Statistics for Single-Item Independent Variables Related to
Emotional Exhaustion, Organizational Support, and Relative Well-Being During the
Pandemic 92
Table 13: Comparison of Descriptive Statistics for Emotional Exhaustion, Burnout,
Fatigue, Measures of Organizational Support, and Lower Well-Being During the
COVID-19 Pandemic 93
Table 14: Correlations Between Independent Variables 95
Table 15: Correlations Between Independent and Dependent Variables 96
Table 16: Comparison of Participant Personal Demographics for Measures of
Effectiveness 99
Table 17: Comparison of Participant Work Demographics for Measures of Effectiveness 102
Table 18: Organizational Policies and Practices That Support Administrator Well-Being
(N = 311) 110
Table 19: Supervisor Strategies That Support Administrator Well-Being (N = 311) 113

xii
Table 20: Barriers to Participation 117
Table 21: Categories of Responses When Policies, Practices, or Strategies Are Not
Identified 121
Appendix A: Demographic Characteristics of Participants 196
Appendix B: Survey Instrument Items 200
Table C1: Comparison of Participant Personal Demographics for Emotional Exhaustion,
Burnout, and Fatigue 202
Table C2: Comparison of Participant Personal Demographics for Lower Well-Being and
Measures of Organizational Support 205
Table D1: Comparison of Participant Work Demographics for Emotional Fatigue,
Burnout, and Fatigue 207
Table D2: Comparison of Participant Work Demographics for Lower Well-Being and
Measures of Organizational Support 209

xiii
List of Figures
Figure 1: Relationship of Areas of Work Life to Burnout Dimensions and Well-Being 34
Figure 2: Conceptual Framework 53
Figure 3: Low Effectiveness 89
Figure 4: Lower Effectiveness: Self-Reported Comparison of Work Effectiveness During
the COVID-19 Pandemic 90

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Chapter One: Introduction to the Study
This study addressed the problem of high levels of occupational stress during the
COVID-19 pandemic leading to negative impacts on employee well-being (D. S. Johnson et al.,
2021; Kniffin et al., 2021; Teixeira da Silva, 2021). In a survey taken during the pandemic,
responses from 1,500 people representing 46 countries and different industries, job roles, and
seniority levels revealed that 89% felt their work lives had worsened in the pandemic, 85%
indicated a decline in well-being, and 62% experienced burnout “often” or “extremely often” in
the previous 3 months (Moss, 2021). The evidence also shows COVID-19-related work stressors,
such as high levels of ambiguity and uncertainty and changes in work demands, structures, and
processes can negatively affect employees’ mental health (AXA Asia and Columbia University
World Health Organization Centre for Global Mental Health, 2020). Furthermore, based on
research from more than 300 companies and surveys of more than 40,000 employees, McKinsey
& Company (2020) concluded the pandemic crisis has created unsustainable pressure on the
workforce. Specifically, employees cite burnout and mental health among their greatest
challenges, yet few organizations have adjusted norms and expectations that are the underlying
causes of burnout. This problem is important to address because stress and burnout negatively
affect employee well-being and job and organizational performance (Griffin & Clarke, 2011;
Maslach & Leiter, 2017; Maslach et al., 2001).
Context and Background of the Problem
This study focused on community college administrators. Community colleges in the
United States are public postsecondary institutions that typically offer subbaccalaureate and
some baccalaureate training and education. Approximately 1,000 community colleges serve
nearly 10 million to 12 million students and award more than 1.5 million degrees and certificates
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annually (American Association of Community Colleges, 2021; Fink & Jenkins, 2020, April 30).
The COVID-19 pandemic has presented multiple challenges to community colleges in how they
operate and serve students. These include mental health and well-being for students, faculty, and
staff; enrollment declines; decreased revenues and increased costs; possible layoffs; and new
responsibilities to ensure the safety of employees and students (Centers for Disease Control and
Prevention, 2020; Floyd, 2021; Phillippe, 2020; Turk, Soler, & Ramos, 2020; Vasquez, 2020).
Administrators, who are responsible for leading and managing through these challenges, include
presidents, vice presidents, deans, directors, and managers (Association of California
Community College Administrators, n.d.; Centers for Disease Control and Prevention, 2020;
VanDerLinden, 2004). Previous studies in higher education settings have found increased
demands caused by external pressures and significant organizational change can lead to
decreased well-being and increased burnout (Coll et al., 2019; Kolomitro et al., 2020; Tabakakis
et al., 2020).
Purpose of the Project and Research Questions
The purpose of this study was to examine organizational and leadership practices
implemented by community colleges during the COVID-19 pandemic that positively affected the
well-being of administrators. The research questions that guided this study are as follows:
1. How have emotional exhaustion, burnout, and fatigue impacted, if at all,
administrators’ beliefs about their effectiveness throughout the COVID-19 pandemic?
2. What demographic differences, if any, exist for administrator perceptions of
emotional exhaustion, burnout, fatigue, organizational support, well-being, and
effectiveness at work during the COVID-19 pandemic?
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3. What specific strategies, if any, have community colleges and their leaders
implemented that support the well-being of administrators during the COVID-19
pandemic?
Importance of the Study
COVID-19 has created broad-scale disruptions to the workplace, including in community
colleges, and amplified stress and introduced new challenges for administrators (Brazeau et al.,
2020; Floyd, 2021; International Labour Organization, 2020; Maqsood et al., 2021). This
problem is important to address because higher levels of stress and burnout are associated with
negative impacts on the individual, ranging from job performance to health, and the organization
(Maslach & Leiter, 2017; Maslach et al., 2001). Outcomes of burnout include lower productivity
and job satisfaction, decreased work effectiveness and work commitment, and increased intent to
leave and turnover (Maslach et al., 2001; Mullen, Malone, et al., 2018; Sabagh et al., 2018;
Tabakakis et al., 2020). Organizational and leadership practices and interventions that
successfully address stress and support well-being can help improve performance and prepare
organizations for change and unexpected crises (Daniels, Watson, et al., 2021; Gallup, 2020;
Ginger, 2021; Griffin & Clarke, 2011; McKinsey & Company, 2020).
Overview of Theoretical Framework and Methodology
The theoretical framework for this study is social cognitive theory (SCT). SCT proposes
a triadic reciprocal causation model of human agency (Bandura, 1988, 1989b, 2005). The three
parts of the triad are behavior, person, and environment, each with a bidirectional influence on
the others (Bandura, 1988). Thus, SCT connects a person’s internal beliefs and cognitive and
affective processes (Bandura, 1989b) with their behaviors and external environment. For
example, social influences in the environment, such as social learning and modeling, act on the
4

person. At the same time, the person’s role or status activates different responses from the
environment (Bandura, 1989a, 2005). Similarly, behaviors alter the environment, which in turn,
affects behaviors, such as through social and performance feedback (Bandura, 1988).
When SCT is applied to this study, the person is the community college administrator
who holds perceptions about their work and effectiveness as an administrator. Those beliefs
affect their behaviors and environment, both of which reciprocally affect their beliefs. The
environment is complex and includes the organization, its leaders, the administrator’s colleagues,
the social workplace, and the COVID-19 pandemic. Strategies, policies, and practices
implemented by the organization to support the well-being of administrators are also part of the
environment that affects the person and their behaviors. SCT can be used to model a pathway
toward well-being (Bandura, 1998, 2004).
This study employed a quantitative methodology. A survey instrument was developed by
adapting existing instruments that address the variables of exhaustion, fatigue, and burnout. The
survey elicited data about specific strategies used by organizations and their leaders to support
the well-being of their employees. Correlational statistical analysis was used.
Definitions
This section defines key terms to help the reader understand this study: burnout,
emotional exhaustion, fatigue, occupational stress, and well-being.
Burnout is the prolonged response to chronic workplace stressors, both emotional and
interpersonal (Maslach et al., 2001; Schaufeli, Leiter, et al., 2009; World Health Organization,
2019). Burnout has three dimensions: exhaustion, cynicism about and distancing from work, and
reduced professional efficacy.
5

Emotional exhaustion refers to the stress dimension of burnout, when a person feels
overextended and depleted of emotional and physical reserves, often from frequent and intense
interpersonal contact at work (R. T. Lee, 2015; Leiter & Maslach, 1988; Maslach, 2003, 2015).
Fatigue is both a process and a mental state that arises from “increased effort to maintain
task goals and protect performance during periods of demanding work” (Hockey, 2013, p. 14)
and includes the urge to reduce task engagement (Hockey, 2013; van der Linden, 2011).
According to Hockey (2013), work fatigue has three patterns: a decline in performance,
increased effort to maintain performance, and sustained high effort that leads to work strain.
Occupational stress is the interaction between stressors in the work environment and the
strains caused by an individual’s responses to those stressors (Griffin & Clarke, 2011; Sliter &
Yuan, 2015; World Health Organization, 2020).
Well-being is a complex construct that indicates a state of optimal experiences and
function across multiple dimensions (Danna & Griffin, 1999; Deci & Ryan, 2008; Diener et al.,
2018b; C. D. Fisher, 2014; Rath & Harter, 2010; Ryan & Deci, 2001; Schultz et al., 2015).
Organization of the Dissertation
This dissertation is organized into five chapters. This chapter introduced the problem of
practice, purpose of the study and its importance, research questions, theoretical framework, and
key definitions for the study. Chapter 2 provides a review of the literature related to well-being,
occupational stress, and burnout, particularly in the higher education setting. Chapter 3 explains
the methodology for participant selection, instrument development, data collection, and analysis.
Chapter 4 presents and analyzes the findings of this study. Finally, Chapter 5 offers
recommendations based on the findings.

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Chapter Two: Literature Review
This chapter provides a comprehensive review of the literature relate to well-being,
occupational stress, and burnout in the higher education setting. First, this review examines the
wide range of definitions and models of well-being and applies those concepts to work-related
well-being and its importance to individuals and organizations. Measures of well-being also are
reviewed. Next, the chapter defines occupational stress, highlights how research in other areas
informs the study of occupational stress in higher education settings, and describes the effects of
occupational stress on individuals and organizations. The chapter then provides a comprehensive
review of aspects of burnout, including its definitions, relationship to work engagement,
predictors, and models. In addition, the importance of addressing burnout, the effects of burnout
in higher education, and how burnout is measured are reviewed. The chapter continues with a
review of how organizations and leadership support well-being. Finally, this chapter concludes
with a review of recent literature related to the challenges of well-being, stress, and burnout
during the COVID-19 pandemic.
Well-Being
In general, well-being refers to how people are doing in their lives, but definitions and
descriptions of well-being vary widely and are inconsistent in the literature (Campbell, 2016;
Clark, 2016; Coulthard et al., 2018; Danna & Griffin, 1999; Diener et al., 2018b; C. D. Fisher,
2014; M. Fisher, 2019; Rath & Harter, 2010; Ryan & Deci, 2001; Waterman, 2008). Definitions
of overall well-being typically refer to optimal experiences and function across health, social,
psychological, emotional, material, and subjective dimensions. However, well-being often
overlaps with other concepts such quality of life, life satisfaction, or happiness (Diener, Lucas, et
al., 2018; Henriques et al., 2014). Taking a different approach, Dodge et al. (2012) posited that
7

well-being is the homeostatic point that individuals seek amid the ongoing fluctuation between
challenges they face and their resources. When higher education professionals were asked to
define well-being, this focus on balance appeared as a major theme even though their specific
responses showed wide-ranging and myriad interpretations of well-being (O’Brien & Guiney,
2018), mirroring in practice the lack of consensus about well-being concepts found in academic
research. The next sections first examine definitions and models of well-being, then explore
well-being and the importance of work-related well-being. Finally, measures of well-being are
discussed.
Definitions of Well-Being
To lay the foundation to understand models of well-being, the next sections review the
different and sometimes conflicting or overlapping ways that well-being is discussed in the
literature. First is an approach that describes well-being from two perspectives: hedonic and
eudaimonic. Next is the concept of subjective well-being, a highly active area of research, which
is sometimes used as a convenient proxy for overall well-being (Diener, Lucas, et al., 2018),
followed by a brief overview of related well-being concepts. These frames of well-being are not
mutually exclusive.
Hedonic and Eudaimonic Well-Being
Two perspectives arising from Greek philosophy have been used extensively to define
well-being: hedonic and eudaimonic (Campbell, 2016; De Simone, 2014; Deci & Ryan, 2008;
Dodge et al., 2012; C. D. Fisher, 2014; M. Fisher, 2019; Gallagher et al., 2009; Panel on
Measuring Subjective Well-Being in a Policy-Relevant Framework, 2014; Ryan & Deci, 2001).
The hedonic view originates from the 4th century B.C.E. Greek philosopher Aristippus, who
equated pleasure and happiness with well-being. Hedonic well-being, thus, refers to the
8

emotional (affective) experiences that make a person evaluate their life for happiness or as
pleasant or unpleasant (Diener et al., 2018a; C. D. Fisher, 2014; Panel on Measuring Subjective
Well-Being in a Policy-Relevant Framework, 2014; Ryan & Deci, 2001). Affective components
of well-being range from positive emotions, such as contentment, joy, or happiness, to negative
ones, such as sadness, stress, or suffering (Diener et al., 2018a; C. D. Fisher, 2014; Panel on
Measuring Subjective Well-Being in a Policy-Relevant Framework, 2014; Waterman, 2008).
In comparison, the eudaimonic perspective of well-being originates from the 4th century
B.C.E. Greek Aristotelian concept of eudaimonia, holding that happiness is achieved when
people have value, purpose, and meaning in their lives. Eudaimonic well-being occurs when
individuals experience challenge, autonomy, competence, mastery, identity, relatedness, personal
growth, and self-actualization (Diener et al., 2018a; C. D. Fisher, 2014; Gallagher et al., 2009;
Panel on Measuring Subjective Well-Being in a Policy-Relevant Framework, 2014; Ryan &
Deci, 2001; Ryff & Singer, 2008; Waterman, 2008). Thus, eudaimonic well-being refers to the
subjective perception of a good life, not only a pleasant one, when a person acts in a way that is
true to their self (Diener et al., 2009; Ryff & Singer, 2008; Waterman, 2008). A person can
believe their life is going well, regardless of objective measures such as income, material
comforts, or pleasure (Diener, Lucas, et al., 2018; Diener et al., 2009).
Even though hedonic and eudaimonic well-being may appear to be a dichotomous pair,
they are correlated and interconnected (C. D. Fisher, 2014; Ryan & Deci, 2001; Waterman,
2008). For instance, self-actualizing (eudaimonic) behaviors can be pleasant and satisfying
(hedonic; Waterman, 2008). These two concepts are also connected to and often overlap with
another important construct in the well-being literature, that of subjective well-being.
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Subjective Well-Being
Subjective well-being is a multidimensional phenomenon that refers to the global
evaluation people make of their experiences in all important domains of their life (Deci & Ryan,
2008; Diener, Lucas, et al., 2018; Diener et al., 2009; Diener, Oishi, et al., 2018; Panel on
Measuring Subjective Well-Being in a Policy-Relevant Framework, 2014; Ryan & Deci, 2001;
Sonnentag, 2015). A person’s subjective well-being is determined by various subjective
evaluations, both affective and cognitive. This includes attitudinal judgments, assessments of
needs and desires, positive and negative affect (emotions and moods), and self-reported
happiness and overall life satisfaction and fulfillment (De Simone, 2014; Deci & Ryan, 2008;
Diener et al., 2018a, 2009, 2018b; C. D. Fisher, 2014; Keyes et al., 2002; Panel on Measuring
Subjective Well-Being in a Policy-Relevant Framework, 2014; Ryan & Deci, 2001). A state of
subjective well-being occurs when a person’s life matches their ideals, positive affect is present
or frequent, negative affect is absent or infrequent, and there is positive cognitive judgment of
overall life satisfaction (Deci & Ryan, 2008; Diener et al., 2018a, 2009; C. D. Fisher, 2014;
Keyes et al., 2002; Ryan & Deci, 2001).
Clearly articulating the meaning of subjective well-being remains a challenge. Subjective
well-being is sometimes associated with hedonic well-being (C. D. Fisher, 2014; Ryff & Singer,
2008), but Deci and Ryan (2008) considered it distinct because cognitive evaluation of life
satisfaction is not hedonic. Subjective well-being is also sometimes associated with eudaimonic
well-being, but Diener, Lucas, et al. (2018) noted some aspects of eudaimonic well-being are
predictors, rather than components, of subjective well-being. Demonstrating the lack of clarity
about subjective well-being, a report by the National Research Council (Panel on Measuring
Subjective Well-Being in a Policy-Relevant Framework, 2014) described subjective well-being
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as having three dimensions: eudaimonic, evaluative, and experienced. The authors explained that
evaluative well-being is understandably the focus of most research on subjective well-being, yet
their report titled Subjective Well-Being is about experienced well-being.
Other Definitions of Well-Being
Two other related concepts of well-being are also important to mention. The first is
objective well-being, which could be viewed as the opposite of subjective well-being. The
literature in this area is focused on objective human goods or objectivist lists that are worth
pursuing or benefit people (Campbell, 2016; Ferdman, 2019; Hurka, 2016; Kagan, 2009; Rice,
2013). The second is psychological well-being, which is described as having six dimensions:
purpose in life, personal growth, self-acceptance, environmental mastery, autonomy, and positive
relations (Ryff & Keyes, 1995). Given that some of these constructs may overlap with other
perspectives of well-being, psychological well-being is sometimes equated with eudaimonic
wellbeing (Diener et al., 2018a; Keyes et al., 2002; R. Lucas, 2016; Page & Vella-Brodrick,
2009; Ryff & Singer, 2008; Waterman, 2008). Ryff and Keyes (1995) proposed that
psychological well-being is “related but distinct” (p. 1009) from yet complementary to subjective
well-being. However, Lucas (2016) noted that subjective well-being is based solely on
evaluation, whereas psychological well-being is based on characteristics independent of whether
a person finds them valuable.
The wide array of well-being concepts in the literature is not limited to academic
research. In their study of higher education staff, O’Brien and Guiney (2018) found that
participants’ descriptions of well-being were a “complex, contested and slippery concept” (p. 7).
Their lay definitions ranged widely across physical, emotional, social, psychological, cognitive,
mental, and financial features, fully reflecting the breadth and intersecting concepts found in
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definitions of well-being in the research literature. This multitude of definitions is likewise
reflected in different models of well-being.
Models of Well-Being
Models of well-being differ in their mixture and degree of hedonic, eudaimonic,
subjective, and objective perspectives. Some concepts of well-being extend beyond eudaimonic
(a meaningful life) and hedonic (a pleasant life) in how they add components and dimensions.
For example, social well-being can be considered a third component of overall well-being,
demonstrating the importance of frequent and stable social relationships that exchange care or
the significance of broader societal participation (Baumeister & Leary, 1995; Coulthard et al.,
2018; Diener & Seligman, 2002; C. D. Fisher, 2014; Gallagher et al., 2009). Social well-being is
composed of social acceptance, social actualization, social coherence, social contribution, and
social integration (C. D. Fisher, 2014; Gallagher et al., 2009). Interestingly, Diener, Lucas, et al.
(2018) noted that although subjective well-being that is assessed via self-reports is consistently
associated with strong social relationships, the correlations are not as strong when objective
measures are used. Rath and Harter (2010) also considered social well-being important. Their
model includes social well-being as one of the five domains of an individual’s life that are
essential to overall well-being: career, social, financial, physical, and community. The
importance of social relationships is reflected in these models that identify social well-being as a
component of overall well-being.
Another five-dimensional model is Seligman’s (2011) theory of well-being, which
includes hedonic (positive emotion) and eudaimonic (meaning) elements and adds the elements
of engagement, accomplishment, and positive relationships. These five elements are each
pursued for its own sake and measured independently of one another, some subjectively and
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some objectively (C. D. Fisher, 2014; Seligman, 2011). Alternately, Huppert and So (2013)
developed a multidimensional model that equates positive well-being with positive mental health
and that captures both positive feeling (hedonic) and positive function (eudaimonic). Based on
their surveys of 43,000 people across Europe, Huppert and So (2013) identified 10 features that
they grouped into three dimensions of well-being to form an operational definition of flourishing.
The first dimension is positive appraisal, which includes life satisfaction. The next dimension is
positive functioning, which includes meaning, competence, engagement, and positive
relationships, thus mixing concepts of eudaimonic and social well-being. Finally, the third
dimension is positive personal characteristics, which are emotional stability, optimism, positive
emotion, resilience, self-esteem, and vitality (C. D. Fisher, 2014; Huppert & So, 2013). This
model proposes that measures of life satisfaction that are used to assess subjective well-being do
not adequately represent well-being.
Some models of well-being combine subjective well-being, social well-being, and health
with objective or relational elements (Coulthard et al., 2018; M. Fisher, 2019; Henriques et al.,
2014; Jayawickreme et al., 2012; Loveridge et al., 2020; Seligman, 2011). For example, a
socioecological model adds security and freedom of choice and action to material well-being,
health, and social relations, whereas another reframes well-being as a suite of seven abilities to
engage with and respond constructively to the environment (Corvalan et al., 2005; Diener,
Lucas, et al., 2018; M. Fisher, 2019; Loveridge et al., 2020). To integrate multiple well-being
frameworks, Jayawickreme et al. (2012) created the engine model with three variables: inputs,
processes, and outcomes. Input variables are either endogenous personality variables, such as
optimism, values, neuroticism, strengths, and positive affectivity, or exogenous environmental
factors, such as income, genetics, green space, and education. Process variables affect individual
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choices through cognition, motives, and feelings, hence bringing in subjective well-being.
Outcome variables are the voluntary behaviors of well-being, such as accomplishments,
engagement, autonomy, and meaning.
Furthermore, Henriques et al. (2014) reconceptualized well-being with a nested model
wherein subjective well-being is the first domain at the center of four concentric layers. Building
outward, the second layer is called the health and functioning domain, which includes biological
and psychological (personality) features, and the third layer is called the environmental domain,
which includes the social and material context of a person’s life. The outermost domain is
composed of the values and ideology of the evaluator. Finally, other models incorporate other
features: ecosystems; sustainable human, natural, social, and economic capital; local indicators;
and other objective dimensions that are beyond the scope of this study (Boarini et al., 2014;
Coulthard et al., 2018; Schleicher et al., 2018).
These models of well-being consider how individuals are doing across various aspects
across their lives. Yet well-being can also be defined in different domains, including the work
setting (Diener, Lucas, et al., 2018). The next section examines work-related well-being and its
importance.
Work-Related Well-Being
There is a lack of uniformity in how work-related well-being is described. Work-related
well-being adopts constructs from the broader fields of well-being and subjective well-being,
tailored to the work setting. These models adopt both positive features such as job satisfaction,
organizational commitment, work engagement, thriving, and meaning and negative features such
job tension, lowered productivity, decreased effectiveness, alienation from work, and burnout
(Charalampous et al., 2019; Danna & Griffin, 1999; De Simone, 2014; C. D. Fisher, 2014;
14

Keeman et al., 2017; R. T. Lee, 2015; Sonnentag, 2015). Like those of overall well-being,
models of work-related well-being differ in how they package and conceptualize the components
of well-being (Danna & Griffin, 1999; De Simone, 2014; C. D. Fisher, 2014; Keeman et al.,
2017; Rath & Harter, 2010; van Horn et al., 2004).
Conceptualizations of Work-Related Well-Being
For example, work-related well-being can be approached by adding work-related aspects
to the three-pronged approach to overall well-being (hedonic, eudaimonic, and social), although
some aspects span two of the three areas (De Simone, 2014; C. D. Fisher, 2014). Specifically,
subjective work-related well-being, closest to hedonic, includes work-related features such as job
satisfaction, organizational commitment, and positive and negative affect. Second, eudaimonic
job-related well-being refers to meaning, growth, motivation, and identity at work. Third, social
well-being at work includes social support, trust, good relationships with supervisors, satisfaction
with coworkers, and feelings of belonging to the work community. Work engagement spans two
types of well-being, with elevated levels of both hedonic pleasantness and eudaimonic meaning
(De Simone, 2014; C. D. Fisher, 2014). C. D. Fisher’s (2014) model of work-place well-being
places positive affect at the center of a multilayer conceptual framework. This first layer includes
happiness, or pleasant moods and emotions, while working. The next layer is also subjective and
includes both negative affect (negative moods and emotions) and cognitive judgments (work
satisfaction). Eudaimonic and social well-being at work comprise the outermost circle.
Van Horn et al. (2004) developed and tested a model for occupational well-being with
five dimensions: affective, professional, social, cognitive, and psychosomatic. The affective
dimension applied to the work setting reflects a hedonic perspective through mood, job
satisfaction, organizational commitment, and emotional exhaustion. The professional dimension
15

reflects eudaimonic concepts such as competence, autonomy, and aspiration. The social
dimension relates to both depersonalization and social functioning toward others at work. The
cognitive dimension refers to the ability to receive information and concentrate, whereas the
psychosomatic dimension relates to health. The affective, professional, and social dimensions
were found to form the core of occupational well-being for teachers. However, in a systematic
review of the application of this model to remote worker well-being, Charalampous et al. (2019)
showed these three dimensions were also the most widely studied, suggesting the need for
further study of the other two dimensions.
Models of job-related well-being also identify different antecedents and consequences
(Danna & Griffin, 1999; De Simone, 2014; Griffin & Clarke, 2011). Danna and Griffin (1999)
defined well-being in the workplace as life satisfaction, work-related satisfaction, and all aspects
of health (mental, physical, and general). Three antecedents lead to well-being in the workplace:
the work setting, occupational stress, and the individual’s personality traits. The category of
occupational stress is further elaborated to include intrinsic job factors, workload, role
ambiguity, level of responsibility, work relationships, career development, organizational climate
and structure, and the home–life interface. The consequences of workplace well-being include
individual ones that are physical, psychological, or behavioral, alongside organizational ones that
range from productivity and absenteeism to increased health insurance costs (Danna & Griffin,
1999; De Simone, 2014).
Other conceptualizations of work-related well-being posit intersecting dimensions (R. T.
Lee, 2015; Sonnentag, 2015). For example, according to a two-dimensional model by Warr
(1978, 1990), one dimension spans pleasure to displeasure, and the other spans levels of mental
arousal or activation. When displeasure is coupled with low activation, the result is cynicism,
16

whereas when displeasure is coupled with high activation, the result is job anxiety. Likewise,
pleasure with low activation leads to job satisfaction, and pleasure with high activation leads to
job involvement.
In another example of how different definitions and constructs of general well-being are
recombined, Page and Vella-Brodrick (2009) proposed a three-component model of employee
well-being with subjective, psychological, and workplace well-being. Additionally, constructs of
satisfaction and affect are attached to both subjective and work well-being. Specifically, life
satisfaction and dispositional affect are related to subjective well-being, whereas job satisfaction
and work-related affect are related to workplace well-being. The authors posited that employee
well-being contributes to organizational well-being through its impacts on employee retention
and performance.
These various conceptualizations and constructs of work-related well-being lay the
groundwork for studies on the importance of well-being to both the individual and the
organization.
Importance of Work Well-Being
Work well-being is important because of its many individual and organizational impacts
(Danna & Griffin, 1999; Diener, Oishi, et al., 2018; Tenney et al., 2016). For the individual,
benefits of work well-being include better physical and mental health. This is reflected in
physiological measures, increased longevity, and better choices about health, such as the
increased likelihood to take part in health-promoting activities and decreased likelihood to drink
and smoke (Diener & Chan, 2011; Diener, Lucas, et al., 2018; Diener, Oishi, et al., 2018; Frey,
2011; Kansky & Diener, 2017; Pfeffer, 2018a, 2018b; Tenney et al., 2016). Work-related well-
being is also associated with increased self-regulation, stronger motivation, and increased
17

resilience, which is the ability to bounce back from negative events and recover more quickly
from stress (Diener, Lucas, et al., 2018; Diener, Oishi, et al., 2018; Kansky & Diener, 2017;
Tenney et al., 2016). Increased life satisfaction also spills over to home and family life (Grebner
et al., 2005; Kossek et al., 2012). In contrast, lower levels of work well-being can lead to
decreased mental and physical health, which leads to multiple negative impacts on absenteeism,
job satisfaction, and work engagement (De Witte et al., 2016; Tenney et al., 2016).
Work-related well-being also influences work performance and the organization. Both
life satisfaction and job satisfaction have been shown to be positively associated with job
performance in multiple studies, assessed based on both objective and subjective measures,
including longitudinal studies (Diener & Chan, 2011; Diener, Oishi, et al., 2018; Kansky &
Diener, 2017; Kossek et al., 2012; Luna-Arocas & Danvila-del-Valle, 2021). People with high
levels of well-being are more likely to be more collaborative, effective, creative, and productive;
have higher levels of work performance and work engagement; and have less absenteeism, lower
turnover intent, and less actual turnover (Danna & Griffin, 1999; Diener, Oishi, et al., 2018;
Erdogan et al., 2012; Figueroa, 2015; Hakanen et al., 2006; Kansky & Diener, 2017; Kossek et
al., 2012; Pfeffer, 2018a, 2018b; Tenney et al., 2016). These individuals also have better social
relationships, which are strongly related to work performance and work engagement (Diener,
Oishi, et al., 2018; Guidetti et al., 2018; Pfeffer, 2018a, 2018b; Tenney et al., 2016). Finally,
organizations benefit beyond better work performance of employees. Company earnings,
shareholder benefits, business unit outcomes, and customer satisfaction have been shown to be
related to work-related well-being, although results are mixed (Harter et al., 2002; Kansky &
Diener, 2017; Tenney et al., 2016).
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It is important to note that the degree of the impact of work-related well-being and its
consequences are complex and nuanced, and they are also moderated by specific situations, such
as the task, industry, company, or environmental factors (Diener, Oishi, et al., 2018; Tenney et
al., 2016). For example, the connection between lower absenteeism and better performance is
stronger when the individual works in teams or has a high level of expertise. Lower turnover
may lead to better organizational performance when there is a need for high levels of expertise
but not when employees are weak or the organization needs fresh perspectives. Similarly,
positive social relations may lead to better performance when social interactions are part of the
job yet may also distract people from their jobs.
Measures of Well-Being
The many ways that well-being is conceptualized are reflected in relatively few types of
well-being measures. Well-being is assessed using different approaches and instruments, ranging
from self-reports to global level assessments. Subjective well-being is assessed from the
perspective of the person, so its measurement has typically been based on different forms of self-
reports (Danna & Griffin, 1999; Diener, Lucas, et al., 2018; Diener et al., 2009; Kansky &
Diener, 2017; Krueger & Stone, 2014; Tenney et al., 2016). The Satisfaction with Life Scale is
widely used and has been extensively validated (Diener et al., 1985; Emerson et al., 2017;
Erdogan et al., 2012; Esnaola et al., 2017; Jang et al., 2017). Two other self-report approaches
are the experience sampling method, which collects real-time information on people’s feelings
during the day, and the more efficient day reconstruction method used to construct the U-Index,
which indicates the relative time a person spends in an unpleasant or undesirable state
(Kahneman & Krueger, 2006; Krueger & Stone, 2014).
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Reports by informants, such as coworkers or peers, or trained experts are also used to
assess well-being (Kansky & Diener, 2017). In addition, there are cognitive, physiological, and
behavioral measures not based on self-reports; however, because their theoretical links to well-
being are indirect, they are unlikely to replace self-report measures (Danna & Griffin, 1999;
Diener, Lucas, et al., 2018; Diener, Oishi, et al., 2018; Krueger & Stone, 2014). Other measures
of well-being include the proprietary Well-Being Index, jointly administered by a health care
provider and the Gallup Organization (Diener et al., 2009); its current iteration is the Gallup
Sharecare Well-Being Index, which encompasses five individual well-being factors: physical,
community, purpose, social, and financial. Another measure specific to a profession is the
Medical Student Well-Being Index developed by Dyrbye et al. (2010)
In addition to individual measures of well-being, population-level characteristics can also
be examined (Diener, Lucas, et al., 2018). For example, components of well-being, such as
evaluative, eudaimonic, and hedonic, are already assessed on a collective scale through national
or cross-national surveys, such as the German Socio-Economic Panel Study, Canadian General
Social Survey, National Opinion Research Center General Social Survey (United States), and the
European Social Survey (Clark, 2016; Diener et al., 2009; Krueger & Stone, 2014). Global and
regional indexes and measures (Global Wellness Institute, n.d.; Organisation for Economic Co-
operation and Development, n.d.) may also provide data on subjective indicators of life
satisfaction and perceived social network support.
Workplace measures of well-being are also available, though their use is limited. The
Job-Related Affective Well-Being Scale was developed by van Katwyk et al. (2000) to study
university employees. Warr (1990) developed an instrument to measure competence, aspiration,
and negative job carryover in workers. More recently, Sorensen et al. (2018) created the
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Workplace Integrated Safety and Health assessment to examine features of the workplace that
contribute to well-being: “leadership commitment; participation; policies, programs, and
practices that foster supportive working conditions; comprehensive and collaborative strategies;
and adherence to federal and state regulations and ethical norms; and data driven change” (p.
430). Finally, the Work-Related Quality of Life scale (Easton & Van Laar, 2018) includes
subscales that assess general well-being, control at work, and stress at work. As this instrument
suggests, well-being can be negatively affected by work-related or occupational stress.
Occupational Stress
This section provides an overview of occupational stress, beginning with its definition
and relationship to strain, which negatively affects well-being. Next, this section identifies a gap
in the recent literature about stress among higher education administrators and briefly describes
the literature in other educational settings and for other types of higher education employees. In
addition, literature related to higher education administrators from 20 to 30 years ago is briefly
reviewed. Finally, the effects of occupational stress are addressed.
Definition of Occupational Stress
Stress is an interaction between stressors in the external environment and the strains
(outcomes) caused by the person’s responses (Ganster & Rosen, 2013; Griffin & Clarke, 2011;
Sliter & Yuan, 2015; World Health Organization, 2020). Occupational stress, or job stress,
extends this definition to the workplace, where employees encounter, manage, and respond to
ongoing demands and job stressors (Griffin & Clarke, 2011; Sliter & Yuan, 2015), yet their
knowledge, ability, and capacity to cope are not properly matched (World Health Organization,
2020). Stressors may arise from intrinsic job characteristics; organizational or management
issues; workload or time pressure; role conflict or ambiguity; and relationships with colleagues
21

and supervisors (Danna & Griffin, 1999; Lamb & Kwok, 2016; Park, 2008; Sliter & Yuan, 2015;
von Onciul, 1996; World Health Organization, 2020). Strains include anxiety, job dissatisfaction,
burnout, disengagement, and physical and mental symptoms (Griffin & Clarke, 2011; Park,
2008; Sliter & Yuan, 2015).
A person’s responses to stress can be psychological, physiological, behavioral, cognitive,
emotional, or physical (American Psychological Association, 2009; Griffin & Clarke, 2011;
Sliter & Yuan, 2015). How an individual perceives stress also influences its effects (Griffin &
Clarke, 2011; Lepine et al., 2005; Sliter & Yuan, 2015). If stress is sustained, a cascade of
responses can become maladaptive and lead to exhaustion, health effects, and decreased
performance (Danna & Griffin, 1999; Liu et al., 2017; von Onciul, 1996).
Occupational Stress in Higher Education Administration Is Informed by Other Research
There is a gap in recent literature about occupational stress in higher education
administration. Hence, this topic is informed by studies about occupational stress in other
settings and in two other bodies of literature. First, research on stress among higher education
employees typically focuses on faculty members or other employees rather than administrators
(Coetzee & Rothmann, 2005; Gillespie et al., 2001; Gmelch et al., 1984, 1986; Jacobs et al.,
2007; Malik et al., 2017; Mountz, 2016; Shrivastava, 2021; Tan, 2017; Tijdink et al., 2014; Van
Katwyk et al., 2000). The situation is similar for studies conducted during the pandemic (Jelińska
& Paradowski, 2021b; MacIntyre et al., 2020; Odriozola-González et al., 2020; Oducado et al.,
2021; Sahu, 2020). Second, the literature about educational administrators has focused on the K-
12 setting rather than the higher education setting (DeMatthews et al., 2021; Gmelch & Swent,
1984; Mullen, Blount, et al., 2018; Poirel et al., 2012; Sarros, 1988; Suleman et al., 2018; Torelli
& Gmelch, 1992). Overall, these studies revealed that occupational stress can lead to components
22

of emotional exhaustion, depersonalization, burnout, lower levels of job satisfaction, lower levels
of job commitment, increased turnover rates, and lower psychological well-being.
Much of the literature on stress for higher education administrators is from the 1990s and
early 2000s (Bailey, 2008; Blix & Lee, 1991; Carlton & Bennett, 1980; Houston et al., 2006;
Rasch et al., 1986; Royal, P. & Grobe, 2008; Torelli & Gmelch, 1992; Tytherleigh et al., 2005;
Wild, 2002; Wild et al., 2003; Wolverton et al., 1998). For example, Rasch et al. (1986) and
Wild et al. (2003) classified distinct types of work stressors: role-based, task-based, conflict-
mediation, and boundary-spanning stress. The literature has also identified both general work
stressors and those specific to education: role conflict, role ambiguity, work overload, difficulty
with management, issues with students, issues with supervisees, external demands, increased
accountability, more initiatives, job insecurity, and lack of resources (Bailey, 2008; Blix & Lee,
1991; Carlton & Bennett, 1980; Houston et al., 2006; Tytherleigh et al., 2005; Wolverton et al.,
1998).
A few recent studies have examined stress for specific administrator roles or
demographics or have included administrators in their samples (Bedford, 2016; Bradshaw, 2020;
Coll et al., 2018; Kersh, 2018; Kolomitro et al., 2020; Mattson, 2012; Mohammed et al., 2019;
Mullen, Malone, et al., 2018; Tabakakis et al., 2020). For example, student affairs professionals
deal with crises and address the emotional needs of others, deans are increasingly given the
responsibility to lead organizational change and interact with external constituencies, and
research administrators face increased accountability and institutional complexity (Bailey, 2008;
Coll et al., 2018; Mullen, Malone, et al., 2018; Tabakakis et al., 2020; Wild et al., 2003). A study
of COVID-19’s impact on academic department chairs offered insights into how pandemic-
associated stressors affected those with leadership roles in higher education. Specifically,
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Gigliotti (2021) found chairs, who often serve as front-line managers and provide advocacy and
emotional support, faced more intense challenges during the pandemic. For example, one
chairperson shared they felt they had “no real way out of … an intensely painful and stressful
period” (p. 8), whereas another noted that the pandemic had taken an already “hard job and
ma[de] it much harder” (p. 8).
Effects of Occupational Stress
Occupational stress negatively affects well-being and health (Eriksson et al., 2013;
Ganster & Rosen, 2013; Gmelch & Swent, 1984; Grebner et al., 2005; Guimont et al., 2006;
Kersh, 2018; Markovitz et al., 2004; Shen & Slater, 2021). In higher education institutions,
where levels of stress have increased during the COVID-19 pandemic, 82% of academic staff
members report moderate to high levels of stress and nearly half report poor levels of well-being
(Shen & Slater, 2021). In a study of the psychological effects of the pandemic, Odriozola-
Gonzalez et al. (2020) found moderate to high levels of stress, anxiety, and depression in more
than half of students and staff members at one university. Studies of stress in higher education
during the pandemic have shown the effects of stress vary according to demographic groups and
coping strategies (D. S. Johnson et al., 2021; MacIntyre et al., 2020).
Before the pandemic, Kersh (2018) found that 83% of higher education women
administrators had experienced a stressful event at work the prior week and 31% were at
increased risk of developing mental illness. Even though the results showed that the women
effectively employed active coping strategies to deal with daily stress, Kersh (2018) suggested
that they face chronic and sustained stressors, such as high work strain, workload, and work–
family imbalance. In a study of college presidents, Royal and Grobe (2008) found job-related
stress compromised sleep quality. In a study of faculty members and administrators, Mohammed
24

et al. (2019) found participants had high levels of stress that were associated with poor health.
Similarly, academic and support staff members surveyed by Coetzee and Rothman (2005)
experienced major occupational stressors related to workload and job control. They also reported
higher levels of both physical and psychological ill health than the general population.
Studies in settings outside higher education also demonstrated the negative impacts of
stress on well-being and health. In a study of more than 1,000 school administrators, Gmelch and
Swent (1984) showed that increases in each of seven stressors led to poor self-reported physical
health. In a study of more than 4,000 workers with a 1-year follow-up, Smith (2000) found stress
was associated with impaired physical and mental health and negative health-related behaviors
such as more drinking or smoking. In a two-wave study, Grebner et al. (2005) found job stressors
predicted negative impacts on general and job-related well-being, such as irritability and feeling
resigned at work. Spillover effects outside the work domain include a decreased ability to switch
off after work.
Longitudinal studies that spanned many years with thousands of participants further
demonstrated the long-term negative effect of stress on well-being and health. A prospective
study by Eriksson et al. (2013) followed more than 5,400 middle-aged women and men for 8 to
10 years. Women who had greater levels of work stress and job strain had an increased risk of
developing Type 2 diabetes. In similar longitudinal studies that looked at the effects of
cumulative job strain, Guimont et al. (2006) found increased risk of higher blood pressure for
nearly 8,400 public-sector employees, and Markovitz et al. (2004) found increased rates of
hypertension among 3,200 employees. Meta-analyses showed chronic exposure to work stressors
has psychological, physiological, and psychosomatic effects, such as hypertension, that progress
25

from primary stress responses toward disease endpoints such as cardiovascular disease, diabetes,
and depression (Ganster & Rosen, 2013; Liu et al., 2017; Schwartz et al., 1996).
Occupational stress can negatively affect work outcomes (Griffin & Clarke, 2011; Sliter
& Yuan, 2015; Wolverton et al., 1998). In higher education settings, studies have shown that as
work stress increases, job satisfaction decreases (Bradley & Eachus, 1995; Mullen, Malone, et
al., 2018; Wolverton et al., 1998). General work-related outcomes affected by stress include
disengagement, withdrawal, burnout, increased turnover intent, decreased productivity,
decreased organizational commitment, perceived lack of organizational support, and increased
absenteeism (Coetzee & Rothmann, 2005; Jacobs et al., 2007; Mostert et al., 2008; Sliter &
Yuan, 2015; Tytherleigh et al., 2005; Yusoff & Khan, 2013).
It is important to note the effects of stress on work outcomes are not uniform. For
instance, Barkhuizen and Rothman (2008) found even when stress contributed significantly to ill
health, academic staff members remain committed to their organization. Heiden et al. (2021)
found higher levels of work-related stress among those who teleworked at least several days a
week compared to those who worked outside their conventional workplace less than once a
month. Yet no significant effects were found between telework hours and health, stress, and
work motivation. Also, Jacobs et al. (2007) found that although all stressors tested had a negative
linear relationship with job performance measures, the association between stress and
performance varied depending on the category of the staff job, suggesting that job factors are
involved.
Numerous studies outside the higher education setting also have shown that stress can
lead to negative strain outcomes such as motivation, productivity, and performance (Griffin &
Clarke, 2011; Imtiaz & Ahmad, 2009; Okeke et al., 2016; Olafsen et al., 2017; Prada-Ospina,
26

2019; Sliter & Yuan, 2015; Tentama et al., 2019). A four-wave study by Olafsen et al. (2017)
showed that work stress can occur when needs for autonomy, competence, and relatedness
(connection to others) are not met. This leads to somatic symptom burdens, which are associated
with emotional exhaustion, turnover intent, and absenteeism. Moreover, stress-induced somatic
symptoms can make work tasks more challenging and hence, more stressful.
The nature and stability of work stressors determine whether they positively or negatively
affect work performance. Specifically, challenge and hindrance stressors differ in how they
influence work (M. A. Cavanaugh et al., 2000; Crawford et al., 2010; Griffin & Clarke, 2011;
LePine et al., 2004; Lepine et al., 2005; Rosen et al., 2020). Challenge stressors are appraised as
demands that provide opportunities to grow, achieve, and demonstrate mastery and competence,
whereas hindrance stressors are considered as demands that act as barriers to goal attainment and
rewards (M. A. Cavanaugh et al., 2000; Crawford et al., 2010). Challenge stressors include time
pressure, high levels of job responsibility, and high levels of workload. Challenge stressors are
positively related to job satisfaction and work engagement but negatively related to job search,
which means employees were less likely to seek another job. In contrast, hindrance stressors
include administrative hassles, resource inadequacies, organizational politics, emotional conflict,
role conflict, and role overload. These hindrance stressors are negatively associated with job
satisfaction and work engagement and positively associated with both job search and turnover
(M. A. Cavanaugh et al., 2000; Crawford et al., 2010). Importantly, Rosen et al. (2020) showed
that the stability of challenge stressors also matters. Stable ones have positive indirect effects on
work performance and well-being, but fluctuating ones show negative effects. Concepts from the
study of occupational stress and strain responses are highly relevant to the study of burnout,
which is one outcome of prolonged exposure to work-related stress.
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Burnout
This section reviews the construct of burnout and shows how stress can be a predictor. It
also shows that work engagement is considered a separate but related and sometimes opposite
construct relative to burnout. Then, different models of work-related well-being and burnout are
described. Next, the importance of addressing burnout is examined, with a focus on higher
education settings. Finally, different ways to measure these constructs are compared.
Definitions of Burnout
Three definitions of burnout from more than 50 years of research demonstrate refinement
of the concept is ongoing and unfinished (Bakker & de Vries, 2021; Freudenberger, 1974;
Guseva Canu et al., 2021; Maslach & Jackson, 1984; Mayo Clinic, 2021; W. Schaufeli, 2021).
Freudenberger (1974) first identified burnout among clinic staff members who had become
“inoperative” (p. 160), as manifested through physical and behavioral symptoms. He described
these symptoms as exhaustion, fatigue, being “too somatically involved with one’s bodily
functions” (p. 160), irritation, frustration, and being less amenable to constructive change. He
also noted those most dedicated to serve the needs of others are prone to burnout because they
experience both internal and external pressure, and thus work “too much, too long and too
intensely” (p. 161). Concepts from this early description continue to inform the field of burnout.
Much of the current burnout literature uses Maslach’s work or its variations to define
burnout as a psychological state caused by the prolonged response to chronic interpersonal and
emotional workplace stressors that have not been successfully managed (Bakker & de Vries,
2021; R. T. Lee, 2015; Maslach, 2015; Maslach & Jackson, 1981; Maslach & Leiter, 2016a;
Maslach et al., 2001; W. B. Schaufeli et al., 2008; Sonnentag, 2015; World Health Organization,
2019, 2020, 2022). Maslach and Jackson’s original concept of burnout was created for those with
28

frequent and intense interpersonal contact with those they serve (R. T. Lee, 2015; Maslach &
Jackson, 1981, 1984).
According to its original definition, burnout has three dimensions (Maslach & Jackson,
1981), the first two of which are found in other definitions used in the field (Bakker & de Vries,
2021; Demerouti et al., 2001; Schaufeli et al., 2020). The first is emotional exhaustion, also
referred to simply as exhaustion, which is the stress, or individual, dimension of burnout. The
precise description of exhaustion varies greatly in the literature: overwhelming exhaustion;
chronic exhaustion; fatigue; feelings of overextension; being depleted of emotional reserves,
energy, or physical resources; and emotional and physical exhaustion (Bakker & de Vries, 2021;
Guidetti et al., 2018; R. T. Lee, 2015; Maslach, 2015; Mayo Clinic, 2021; Sonnentag, 2015;
Tijdink et al., 2014).
The second dimension of burnout is depersonalization, reconceptualized more generally
as cynicism, which is the motivational and interpersonal dimension of burnout. Alternate
descriptions of this dimension range from negative or callous responses to work or career to
excessive mental or unhealthy detachment or distance from work. The third dimension is feelings
related to personal or professional accomplishment, which is the self-evaluative dimension of
burnout. Other descriptions of this dimension include feelings of professional ineffectiveness,
reduced professional efficacy, lack of accomplishment or achievement, lack of productivity,
incompetence, decline in competence, inefficacy, low efficacy, or lack of efficacy (Bakker & de
Vries, 2021; R. T. Lee, 2015; Maslach, 2015; Maslach & Leiter, 2016a; Sonnentag, 2015; World
Health Organization, 2019, 2022).
Though prevalent, Maslach’s definition is only one of several used in the burnout field
(Bakker & de Vries, 2021; Demerouti et al., 2001; Schaufeli et al., 2019), indicating that there is
29

not yet a consensus definition. For example, Demerouti et al. (2001) proposed that
disengagement, rather than depersonalization, better represents how individuals distance
themselves from their work. The term burnout is also used differently in different countries, in
some cases being understood as the equivalent of exhaustion, rather than including exhaustion as
one dimension of burnout. In some languages, burnout is considered too strong because it
conveys a “psychological death sentence” (Schaufeli, Leiter, et al., 2009, p. 210). Similarly,
Kristensen et al. (2005) noted that the term may be misleading because it implies a one-way
process, yet individuals can recover from burnout. In addition, the degree of burnout among
individuals can fluctuate over time. Burnout is also considered a clinical diagnosis in some
countries (Schaufeli, Leiter, et al., 2009).
In a recent systematic review, Guseva Canu et al. (2021) identified 11 original definitions
of burnout in the literature that had given rise to 88 distinct definitions. Semantic analysis and
Delphi consensus were used to generate a harmonized definition of occupational burnout: “In a
worker, occupational burnout or occupational physical AND emotional exhaustion state is an
exhaustion due to prolonged exposure to work-related problems” (p. 104). Like those of
Freudenburger (1974) and Maslach and Jackson (1981), the consensus definition of burnout
emphasizes the central role of exhaustion.
Researchers continue to examine the definition of burnout. For example, recent work
challenged the premise that burnout is a job-induced syndrome (Bianchi & Brisson, 2019;
Guseva Canu et al., 2021). Schaufeli (2021) also noted the most recent consensus definition from
(Guseva Canu et al., 2021) does not adequately address several outstanding questions in the
field: whether the exhaustion central to the definition of burnout is emotional, physical,
30

cognitive, or mental; whether exhaustion is equivalent to burnout; and if exhaustion is necessary
but not sufficient for burnout.
An important part of the history of the study of burnout includes its relationship with and
influence on the study of work engagement (Bakker & Demerouti, 2008). Schaufeli and Bakker
(2004) described work engagement as the “positive antipode” (p. 294) of burnout, and other
authors have continued to refer to work engagement as the opposite of burnout (R. T. Lee, 2015;
Maslach et al., 2001; W. B. Schaufeli et al., 2008; Sonnentag, 2015). In addition, burnout is often
conceptualized as a stress phenomenon (Angerer, 2003; Griffin & Clarke, 2011; Leiter &
Maslach, 1988; Maslach, 2015; Maslach et al., 2001; Pines & Keinan, 2005). Thus, the literature
about burnout and stress overlaps with and extends into the literature about work engagement.
Definition of Work Engagement
Work engagement is defined as a work-related state of persistent, positive emotional
attachment to work and includes three components: vigor, dedication, and absorption (Demerouti
et al., 2001; R. T. Lee, 2015; Leon et al., 2015; Maricuțoiu et al., 2017; W. B. Schaufeli &
Bakker, 2004; W. B. Schaufeli et al., 2002; Taris et al., 2017). Much like the situation for the
three dimensions of burnout, the dimensions of work engagement are described in a multitude of
ways in the literature. Vigor is variously represented as high energy, increased (mental)
resilience and effort, and persistence. Dedication is described as strong job involvement,
enthusiasm about the job, and feelings of pride, inspiration, challenge, and significance. Finally,
absorption is characterized as having full concentration, efficacy, immersion in the profession, or
being deeply engrossed in work that makes it difficult to detach.
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Relationship Between Burnout and Work Engagement
The relationship between the constructs of burnout and work engagement is described in
several ways, such as two ends on a continuum of work well-being, separate but related
constructs that may even co-occur, or overlapping constructs (R. T. Lee, 2015; Maricuțoiu et al.,
2017; Maslach & Leiter, 2016a, 2008; W. B. Schaufeli et al., 2020; W. Schaufeli & De Witte,
2017; Sonnentag, 2015; Taris et al., 2017). If burnout and engagement are considered opposites
on a scale of work well-being, then burnout means poor well-being and engagement means good
well-being (R. T. Lee, 2015; Maricuțoiu et al., 2017; Maslach & Leiter, 2008; Sonnentag, 2015).
Moreover, when work engagement declines and burnout occurs, vigor, dedication, and
absorption transform into direct opposites: exhaustion, cynicism, and professional inefficacy,
respectively (Maslach et al., 2001; Taris et al., 2017).
In contrast, other researchers argued that burnout and engagement are not opposites but
rather independent, negatively related ones. For instance, Schaufeli and Bakker (2004) posited
that burnout and engagement have different predictors and outcomes. Moreover, engagement and
burnout are not mutually exclusive; a person can be engaged but also burned out, or they could
be neither burned out nor engaged (Leon et al., 2015; Schaufeli & De Witte, 2017; Taris et al.,
2017). Leon et al. (2015) suggested a dialectical perspective whereby burnout and work
engagement are measured simultaneously, followed by examination of how the constructs
interact when both are present. A recent study (Taris et al., 2017) that suggested that burnout and
engagement are overlapping concepts added to the continuing discussion about these constructs.
Predictors of Burnout Incorporate Concepts of Well-Being, Stress, and Work Engagement
Discussions of burnout have shown how it overlaps with work engagement, well-being,
and stress (Bakker & Demerouti, 2008; Bakker et al., 2014; Demerouti et al., 2001; Griffin &
32

Clarke, 2011; R. T. Lee, 2015; Maslach, 2015; Maslach et al., 2001; W. B. Schaufeli et al., 2008;
Sonnentag, 2015). Though models differ, they focus, to various degrees, on both situational and
individual factors that are correlates of burnout (Maslach, 2015; Maslach et al., 2001; Schaufeli
& Buunk, 1996; Sonnentag, 2015). Situational factors and stressors include job characteristics
(Schaufeli & Buunk, 1996) and the interpersonal environment (Leiter & Maslach, 1988).
Individual factors focus on personality, personal resources, and self-efficacy (Bakker & de Vries,
2021; Schaufeli & Buunk, 1996).
For example, high job demands such as sustained physical, cognitive, and emotional
effort can lead to burnout (Demerouti et al., 2001). Demands could include work overload, time
pressure, role conflict, role ambiguity, lack of autonomy, high job demands, low job resources,
lack of social support (both at and away from work), lack of information, lack of feedback, and
low levels of control (Kronos, 2018; Leiter & Maslach, 1988; Maslach, 2015; Maslach et al.,
2001; Sonnentag, 2015). Other situational factors are occupational characteristics, such as job
categories or professions that involve intensive caregiving or teaching roles, and organizational
characteristics such as climate, culture, and leadership (K. J. Cavanaugh et al., 2020; Kronos,
2018; Maslach & Jackson, 1984; Maslach et al., 2001; Moczydłowska, 2016; Schaufeli &
Buunk, 1996). Gallup (2020) identified five root causes of burnout that reflect some of these
situational factors: unfair treatment at work, unmanageable workload, lack of communication
from managers, lack of manager support, and unreasonable time pressure.
Individual factors also affect burnout, though to a lesser degree than situational factors.
Those in certain demographic groups or with certain personality characteristics (external locus of
control, poor self-esteem; neuroticism) are more prone to burnout (K. J. Cavanaugh et al., 2020;
Marchand et al., 2018; Maslach & Jackson, 1984; Maslach et al., 2001; Sonnentag, 2015).
33

Among demographic factors, age has been most studied, with employees younger than 30 being
at higher risk of burnout; however, Maslach et al. (2001) cautioned there may be survival bias
because burned out employees may have quit. Nevertheless, the demographic correlation to age
is not consistent among studies. Whereas Marchand et al. (2018) found younger men and a
bimodal distribution of women were more susceptible to burnout, Ahola et al. (2008) found
middle-aged men and aging women had higher rates of burnout. Furthermore, a meta-analysis
study of the relationship of both age and years of experience with emotional exhaustion showed
small negative correlations for both (Brewer & Shapard, 2004). The self-regulation strategies
adopted by employees also vary (Bakker & de Vries, 2021). When job strain is higher, the use of
maladaptive strategies such as self-undermining and inflexibility increases, whereas the use of
adaptive strategies such as job crafting and stress recovery decreases.
Several moderators of burnout have been found; similar to predictors, moderators can
also be individual or organizational. Individual factors include emotional stability and self-
efficacy beliefs (Alessandri et al., 2018). Organizational factors are also important. For example,
a perceived motivational climate that emphasizes performance and social comparison may
enhance burnout tendencies, whereas a mastery climate may moderate burnout (Nerstad et al.,
2019). Supervisor support and job control have also been shown to moderate the higher rates of
burnout associated with organizational change stressors (Day et al., 2017).
Theoretical Models That Relate Work-Related Well-Being, Stress, and Burnout
The next section briefly describes different theoretical models that relate work-related
well-being, stress, and burnout. These include the Maslach and Jackson model, conservation of
resources model, and person-fit model. The job demands–resources model and how it has
evolved are also described.
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Maslach and Jackson Model
The original theoretical framework for burnout by Maslach and Jackson (1981) had three
dimensions: emotional exhaustion, depersonalization, and diminished personal accomplishments;
the latter two were later generalized to cynicism and lower professional efficacy, respectively.
These three dimensions of burnout are shown in Figure 1. The theory posits that work stressors
lead to exhaustion, which causes employees to distance from work. Over time, withdrawal leads
to decreased efficacy; however, this third dimension has not been shown to be consistently
sequential (Griffin & Clarke, 2011; Maslach & Jackson, 1981; Maslach & Leiter, 2016; Maslach
et al., 2001).

Figure 1
Relationship of Areas of Work Life to Burnout Dimensions and Well-Being

Note. Adapted from Maslach and Leiter (2016). Areas of work life that are out of balance affect
burnout dimensions, which negatively affect well-being and health and work outcomes.

Areas of Work Life
Work overload
Lack of control
Insufficent reward or
recognition
Loss of community
Lack of fairness
Conflicting values
Burnout Dimensions
Emotional exhaustion
Depersonalization
(cynicism)
Professional inefficacy
–
Outcomes
Well-being
Health outcomes
Work outcomes
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Conservation of Resources Model
The conservation of resources model proposes that employees desire to acquire and retain
resources and minimize resource loss. In this model, workers make judgments on what resources
to expend on job demands or invest based on expectations of positive outcomes (Hobfoll, 1989).
When resources are not restored, well-being declines (Griffin & Clarke, 2011; Hobfoll, 1989; R.
T. Lee, 2015). Similarly, higher levels of job demands, lower levels of job resources, and lower
adaptive organizational attitudes are associated with burnout (Alarcon, 2011).
Person-Fit Model
The person-fit model considers that mismatch, or imbalance, between job demands and
the individual negatively affects work well-being (Angerer, 2003; R. T. Lee, 2015; Maslach,
2015; Maslach & Leiter, 2016a, 2008; Maslach et al., 2001). The six areas of work life in this
model are workload, control, reward, community, fairness, and values. When these are out of
balance, the areas transform into work overload, lack of control, insufficient reward or
recognition, loss of community, lack of fairness, and conflicting values, respectively. Figure 1
shows the connection of burnout with the influence of job demands such as work overload and
lack of job resources such as control, reward, and community. The greater the mismatch, the
greater the likelihood of burnout, which in turn, leads to negative work-related outcomes such as
incivility, absenteeism, poor work, client dissatisfaction, and higher costs.
Job Demands–Resources Model
The job demands–resources model of burnout (Demerouti et al., 2001) is an extension of
the job demand–control model (Griffin & Clarke, 2011; Karasek, 1979; Sonnentag, 2015). Job
demands include high workload and time pressure. In contrast, job resources include job control,
feedback, reward, participation, social support, and supervisor support, among others. The areas
36

of work life in Figure 1 encompass the demands and resources of this model. Job demands are
more related to the exhaustion dimension of burnout, whereas lack of job resources is associated
more with the disengagement dimension (Crawford et al., 2010; Demerouti et al., 2001).
Subsequent research has further refined the job demands–resources model. For example,
Schaufeli and Bakker (2004) demonstrated burnout and work engagement are negatively related.
Bakker and Demerouti (2008) added personal resources to the model, showed job resources can
buffer the effect between job demands and well-being, and found job stress and motivation can
both predict and be an outcome of job demands and resources. Furthermore, Bakker et al. (2014)
showed that burnout is related closely to health outcomes, whereas engagement is related closely
to motivational outcomes. Bakker and De Vries (2021) later proposed the addition of self-
regulation to the model. Specifically, consistently high job demands and low job resources, when
combined with poor self-regulation, such as maladaptive coping strategies, can lead to burnout.
Importance of Addressing Burnout
Burnout has significant consequences for the individual, their job performance, and the
organization (Bakker & de Vries, 2021; Kristensen et al., 2005; R. T. Lee, 2015; Maslach, 2015;
Maslach & Jackson, 1984; Maslach & Leiter, 2016a; Maslach et al., 2001; W. B. Schaufeli et al.,
2008; Sonnentag, 2015). For example, when a person experiences the dimension of burnout
whereby they distance from the job, outcomes may include absenteeism, turnover intent, and
actual turnover. Furthermore, for those who stay on the job, outcomes include lower
productivity, effectiveness, task performance, job satisfaction, and commitment to the job and
organization. Individuals can also feel exploited. Individuals who are burned out can also
negatively influence others at work due to impacts on work tasks and personal relationships.
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Burnout, especially its exhaustion component, is also related to stress-related health
outcomes (Kristensen et al., 2005; Maslach & Leiter, 2016; Maslach et al., 2001; Schaufeli et al.,
2008). These include stress, headaches, sleep disturbances, muscle tension, chronic fatigue, use
of pain medications, gastrointestinal disorders, cardiovascular disease, type 2 diabetes, and
increased susceptibility to illness, among others. Longitudinal studies of cognitive impairment
due to burnout show some recovery can occur, although cognitive performance is still affected
up to 2 years from clinical diagnosis and intervention (Mayo Clinic, 2021; Oosterholt et al.,
2016; van Dam et al., 2011, 2012). Forms of substance abuse are also more related to the
exhaustion component of burnout. Spillover effects outside of work are also a consequence of
burnout (Brotheridge & Lee, 2006; Maslach & Jackson, 1984).
Burnout in Higher Education Settings
Studies about burnout have shown similar findings in the higher education setting
(Gillespie et al., 2001; Khan et al., 2019; Kolomitro et al., 2020; Naidoo-Chetty & du Plessis,
2021; Tabakakis et al., 2020; Tijdink et al., 2014; Watts & Robertson, 2011). Recent research on
administrator burnout is scarce. Tabakakis et al. (2020) found high levels of burnout among
research administrators and a strong association with intent to leave the profession. Coll et al.
(2019) found a general pattern indicating that the more a dean was involved in organizational
change, the greater their emotional exhaustion and depersonalization and the lower their personal
accomplishment. All three are components of Maslach and Jackson’s burnout theory. Mullen,
Malone, et al. (2018) found a link between burnout and lower job satisfaction and increased
turnover intent, but administrators only made up 4.4% of the sample.
The literature from other segments (nonadministrator) of higher education provides
further insights about burnout in this setting. Demonstrating that intensive caring professions
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such as teaching may be more susceptible to burnout, Watts and Robertson (2011) showed that
high levels of exposure to students predicted burnout. Moreover, Gillespie et al. (2001) found
stress-related burnout among academic staff members led to greater absenteeism, more job
seeking, lower cognitive effort, and decreased commitment, as seen in “closing down” (p. 65)
from their various roles. Staff participants also reported psychological consequences such as
anxiety and irritability and detrimental impacts on family life.
Demographic factors also seem to be associated with burnout among administrators in
higher education (Tabakakis et al., 2020; Tijdink et al., 2014; Watts & Robertson, 2011). For
example, in their study of research administrators, Tabakakis et al. (2020) identified three types
of burnout: personal burnout, work-based burnout, and client-related burnout. Women and those
who had been in their positions for 5 or more years had higher levels of personal and work-
related burnout, whereas research administrators between ages 20 and 29 had higher levels of
client-related burnout, but not personal or work-related burnout than other age groups. Tijdink et
al. (2014) found a similar pattern for the risk of burnout among early career and younger medical
professors. Finally, in their review of literature on empirical studies on burnout among
nonclinical, full-time university teaching staff members, Watts and Robertson (2011) found
younger age was associated with greater vulnerability to emotional exhaustion and that men
scored higher for depersonalization whereas women scored higher for emotional exhaustion.
However, the authors noted that not all studies showed a significant gender difference.
Two recent systematic reviews of higher education institutions further illustrated the
factors and consequences associated with burnout. Naidoo-Chetty and Plessis (2021) found that
increased job demands increased emotional exhaustion, psychological distress, intent to leave,
and work–family conflict. Increased job demands also decreased work engagement and job
39

satisfaction. Conversely, increased job resources led to the opposite finding for each. Khan et al.
(2019) also found multiple individual and organizational sources of burnout that lead to negative
impacts on mental and physical health, job satisfaction, performance, work engagement, and
commitment to the organization. The development of different constructs and models of burnout
has been accompanied by a proliferation of instruments to measure burnout.
Measurement of Burnout
Several tools are available to measure burnout. The most used (Demerouti et al., 2001;
Schaufeli et al., 2008) is the Maslach Burnout Inventory (MBI; Maslach & Jackson, 1981). The
original version was designed to assess three components of burnout syndrome: emotional
exhaustion, depersonalization, and reduced personal accomplishment and involvement, with
involvement removed in the second version (Maslach & Jackson, 1981; McCormack & Cotter,
2013). Different versions of the MBI have been adapted for specific or general work domains:
MBI-Human Services Survey, MBI-Human Services Survey for Medical Personnel, MBI-
Educators Survey, MBI-General Survey, and MBI-General Survey for Students (Demerouti et
al., 2001; Maslach, 1998; McCormack & Cotter, 2013; Wheeler et al., 2011). The general survey
developed for people in professions without a significant human services aspect restates the
original three dimensions as exhaustion, cynicism, and reduced professional efficacy (Maslach et
al., 2001; McCormack & Cotter, 2013; Schaufeli & Taris, 2005). Maslach (2015) also referred to
reduced professional efficacy as a “sense of ineffectiveness and lack of accomplishment” (p.
930).
Other tools to measure burnout reflect the underlying model of burnout of the author,
with some elaborating on exhaustion and others adding different dimensions (McCormack &
Cotter, 2013). Three instruments that focus on exhaustion are the Burnout Measure (Guseva
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Canu et al., 2021; Malach-Pines, 2005), the Shirom-Melamed Burnout Measure (Guseva Canu et
al., 2021; Shirom & Melamed, 2006), and the Copenhagen Burnout Inventory (Kristensen et al.,
2005). The Burnout Measure assesses mental, emotional, and physical exhaustion. The Shirom-
Melamed Burnout Measure measures different manifestations of exhaustion: physical fatigue,
emotional exhaustion, tension, listlessness, and cognitive weariness (McCormack & Cotter,
2013). Interestingly, the Copenhagen Burnout Inventory reframes “physical and psychological
fatigue and exhaustion” (Kristensen et al., 2005, p. 197) across three areas: personal, work-
related, and client-related burnout.
Other instruments include both the exhaustion and disengagement factors of the MBI. For
example, the Oldenburg Burnout Inventory (Demerouti et al., 2001) reflects the job demands–
resources model with two burnout factors: exhaustion related to job demands and disengagement
related to (the lack of) job resources. Exhaustion in this measure differs from the MBI because it
includes physical and cognitive strain in addition to affective strain. Schaufeli et al. (2020)
propose the Burnout Assessment Tool with four factors, incorporating both the exhaustion and
mental distance aspects of the MBI and adding emotional disturbance and cognitive trouble.
Measurement of Work Engagement
Instruments used to measure work engagement reflect two underlying assumptions about
how engagement is conceptually related to burnout. For example, if burnout and work
engagement are considered opposites, MBI scores can be used to measure engagement as energy,
involvement, and efficacy, which are the respective opposites of exhaustion, cynicism, and
ineffectiveness (Maslach et al., 2001). Furthermore, engagement as assessed through the MBI
should correlate to a better match between the person and the job. This, in turn, can be assessed
with the Areas of Worklife Survey that was developed to evaluate job stressors that contribute to
41

burnout (Leiter & Maslach, 2003). Six areas of work life are described: workload, control (role
conflict and role ambiguity), reward (material reward and intrinsic satisfaction), community
(quality of social interactions at work, support, and teams), fairness, and values (meaning,
motivation, and ideals that attracted them to the job). In contrast, even though Schaufeli et al.
(2002) agreed that burnout and engagement are the antithesis of each other, they argued that
work engagement is not adequately measured by simply using the opposite of MBI scores.
Specifically, burnout and engagement are opposite concepts that should be measured
independently with the use of different instruments. Thus, they created the Utrecht Work
Engagement Scale, which is now the most widely used instrument to measure work engagement
and is available in three successively shorter versions (Leon et al., 2015; Schaufeli et al., 2016,
2002, 2019; Schaufeli & De Witte, 2017).
Organizational and Leadership Support for Well-Being in the Workplace
The next section provides a review of the literature on how leadership and organizational
practices support well-being at work. For leaders, this support includes their behaviors,
leadership style, and own well-being. For organizations, approaches to support well-being
include interventions that span different timeframes relative to the onset of stressors and burnout.
Organizational approaches also vary in whether they are directed toward individuals or the
organization. Recent literature related to leadership and organizational support for well-being
during the COVID-19 pandemic is reviewed.
Leadership Contributes to Work-Related Well-Being
Leadership behaviors and styles influence employee well-being. Constructive leadership
behaviors, such as support and consideration toward employees, recognition of contributions,
and good relationships with employees, are positively related to affective well-being and lower
42

stress levels among employees (Kaluza et al., 2020; O’Brien & Guiney, 2018; Skakon et al.,
2010). In contrast, autocratic behaviors, abusive supervision, hostility, and negative affect have
deleterious effects on employee tension, stress, and well-being.
Certain leadership styles are more likely to be positively associated with overall well-
being. Transformational leadership, which is oriented toward change, has been related to better
job satisfaction, lower stress, less burnout, and better affective well-being, although the
relationship may be mediated by other factors such as employee and team self-efficacy (Arnold,
2017; Kaluza et al., 2020; Skakon et al., 2010). Inclusive leadership similarly positively affects
employee well-being and innovative behavior, especially employee development, respect,
fairness, and recognition of the employees’ value (L. Chen, 2020; Choi et al., 2017; O’Brien &
Guiney, 2018). Participatory leadership also predicts employees’ helping behavior and thriving
at the workplace (Usman et al., 2021). The results for task-oriented transactional leadership are
mixed; some studies showed no effect, whereas others showed a decrease in burnout among
employees (Kaluza et al., 2020; Skakon et al., 2010). In general, positive leader behaviors are
positively related to employee being and low stress levels, whereas negative behaviors are
negatively related (Skakon et al., 2010).
Importantly, a leader’s well-being has been shown to influence employee well-being. In a
systematic review of 49 studies, Skakon et al. (2010) found leaders’ high levels of stress and
poor affective well-being were associated with similar levels of employee stress and affective
well-being. Based on their review, the authors proposed that stressed leaders more often exhibit
negative leader behaviors, which may have a negative effect on employees. This is important
because when leaders care for others, they experience a stressor. In fact, relationship-based
43

leadership is not as strongly associated with well-being as change-oriented leadership, suggesting
that the stress of the relationship affects the leader and employees (Kaluza et al., 2020).
Leadership During the Pandemic
The pandemic required leaders to identify, strengthen, and implement certain leadership
practices to meet the immediate and future needs of their organizations, including higher
education institutions (Bersin, n.d.; Dirani et al., 2020; Logan & Rodriguez, 2020; Shufutinsky et
al., 2020; Wedell-Wedellsborg, 2020). The literature suggests that exhausted leaders must first
become well and address the challenge of sustaining their energy and that of their teams (Logan
& Rodriguez, 2020; Wedell-Wedellsborg, 2020). Among leadership competencies identified as
important during the pandemic and times of crises are supervisor and human resources support;
clear communication; management that values employee expertise; recognition, respect, and
appreciation; and space for emotional stability and well-being (Dirani et al., 2020; Logan &
Rodriguez, 2020; O’Brien & Guiney, 2018). Best practices with a human-centered approach to
support well-being include modeling the way, inspiring a shared vision, challenging the process,
enabling others to act, and encouraging the heart (Bersin, n.d.; Cherkowski et al., 2021; Dirani et
al., 2020; Kossek et al., 2012; O’Brien & Guiney, 2018; Wedell-Wedellsborg, 2020).
Organizational Approaches to Support Well-Being
Organizational approaches to address employee stress and well-being differ in the timing
and organizational level of interventions (Daniels, Watson, et al., 2021; Maslach, 2015; Nielsen
et al., 2017; Quick & Henderson, 2016; Sonnentag, 2015). One approach is to categorize
interventions from prevention to mitigation based on the stage of a negative effect. Primary
interventions attempt to prevent workplace stresses, secondary approaches promote effective
management of stressors that are already present, and tertiary approaches respond to negative
44

outcomes that have already occurred, such as burnout. Combinations of these interventions are
considered multifocal. A different approach reflects a structural focus, such as whether well-
being programs should be centered on the individual or workplace (or a combination), or an even
finer level of emphasis on the individual, group, leader, or organization (Angerer, 2003; Daniels,
Watson, et al., 2021; R. T. Lee, 2015; Nielsen et al., 2017; Sliter & Yuan, 2015; Sonnentag,
2015). The preventive stress management system proposes a framework to address interventions
at different levels (organizational and individual) and different stages (primary, secondary, and
tertiary; Quick & Henderson, 2016).
Primary, Secondary, and Tertiary Interventions
Primary approaches that have been found effective in preventing or removing workplace
stressors include many aspects that reflect the demands and resources in the job demands–
resources model and have been found to decrease levels of depression and absenteeism (Daniels
et al., 2017; Daniels, Watson, et al., 2021; Kawakami et al., 1997; Quick & Henderson, 2016;
Sliter & Yuan, 2015). Promotion of positive social support and work–life balance adds to a
person’s job resources. Work redesign, increased autonomy, and increased decision-making
abilities have been shown to reduce stress. Yet primary interventions such as job redesign are not
always possible in some professions such as law enforcement.
Secondary interventions are designed to manage stressors. Examples shown to reduce the
strain caused by stressors include training employees in coping strategies, meditation, relaxation,
and health promotion efforts related to fitness, eating, and sleep (Daniels et al., 2017; Quick &
Henderson, 2016; Sliter & Yuan, 2015). Some interventions also contribute to general life
satisfaction and improve relationships outside work. However, a meta-analysis of controlled
45

interventions for burnout showed interventions were effective for only the exhaustion dimension
of burnout when assessed 6 months later (Maricuţoiu et al., 2016).
Studies, including some from higher education institutions or during the pandemic,
showed that coping strategies can mitigate stress (Blix & Lee, 1991; de los Santos et al., 2018;
Jelińska & Paradowski, 2021a; Kersh, 2018; MacIntyre et al., 2020; Park, 2008; Poirel et al.,
2012; Shen & Slater, 2021; Tan, 2017). For example, mindfulness has been associated with
positive well-being outcomes, although the quality of studies varies (Daniels et al., 2017; Lomas
et al., 2017). In a qualitative study, Mahfouz (2018) found mindfulness training improved well-
being among school administrators and was associated with better self-care, better relationships,
and improved leadership skills. Specifically, school principals who participated in a multiweek
professional development program that incorporated mindfulness and awareness practices,
emotion skills, and caring and compassion skills reported positive outcomes when they applied
practices they had learned. These included improved regulation of emotions, reduced stress,
greater effectiveness as leaders, better decision making, and awareness of the impact of their
actions on staff morale and productivity. Other studies showed that mindfulness is related to
increased job satisfaction and work engagement and decreased emotional exhaustion and
turnover intent, and it can moderate negative effects of controlling work environments (Dane &
Brummel, 2014; Hulsheger et al., 2013; Schultz et al., 2015).
Tertiary interventions attempt to heal negative effects that have already occurred, and
their success is difficult to assess (Daniels, Watson, et al., 2021; Sliter & Yuan, 2015). These
include counseling and referrals, often through an employee assistance plan (EAP), or
rehabilitation. Deloitte (2021) found that although employers provided employees with stress and
mental health resources, 40% of C-level executives felt the services were either “not adequate or
46

simply OK” (p. 15). In a recent survey of college and university presidents about their
institutional response to the pandemic, more than a third reported an expansion of mental health
services through an EAP and use of listening sessions with faculty and staff members about
mental health and well-being (Turk, Soler, Chessman, et al., 2020). Yet well-being remained a
concern, with 60% rating the mental health of the faculty and staff as the most pressing issue due
to COVID-19, second only to the mental health of students (Turk, Soler, Chessman, et al., 2020).
Demonstrating the prolonged effect of the pandemic on well-being, the results of a similar
survey only 3 months prior showed the mental health of the faculty and staff was rated most
pressing issue by 42% of presidents, behind long-term financial viability and the mental health of
students (Turk, Soler, & Ramos, 2020).
Daniels, Watson, et al. (2021) found primary, secondary, and tertiary interventions
among workplace health and well-being practices differed in their degrees of success and
sometimes worked through mechanisms that had not been planned. They identified three
characteristics in the most effective practices. First is continuity from implementation to
adaptation and sustaining of the intervention. Second is the presence of intervention-supporting
structures such as coaching, workshops, and problem solving. Third, functional governance
structures are required to promote functional learning structures, which in turn, facilitate
adaptation of interventions. The results showed the importance of organizational context and
successful navigation of conflicts that arise between implementation and existing processes.
Individual and Organizational Approaches
Individual-oriented approaches help employees with their perceptions of work situations
or provide direct support for psychological and physical well-being. These include meditation,
cognitive behavioral techniques, muscle relaxation, psychotherapy, counseling, adaptive skills
47

training, and communication skills training (Angerer, 2003; R. T. Lee, 2015; Maslach, 2015;
Sonnentag, 2015). In a meta-analysis of 25 primary intervention programs from health care,
education, engineering, telecommunications, and other industries, Awa et al. (2010) found
person-directed interventions were effective against the emotional exhaustion dimension of
burnout during the short term (up to 6 months). However, when person-directed interventions
were combined with organization-directed ones, the effects lasted more than a year.
Workplace interventions include various strategies that echo several constructs from the
field of well-being (Angerer, 2003; Daniels et al., 2017; Kossek et al., 2012; R. T. Lee, 2015;
Maslach, 2015; Sonnentag, 2015). These include increases in job control, job redesign, role
clarity, and job resources; reductions in job stressors; improvements in work procedures; and
better interpersonal and social relations. However, effect sizes vary, indicating approaches may
need to be tailored to the situation (Sonnentag, 2015). For example, those most at risk of burnout
may need targeted programs to address their motivational and resource needs, and adjustments to
demands and resources to encourage work engagement should be tailored to the individual
(Daniels et al., 2017; van den Berg et al., 2015). Additionally, the success of a well-being
promotion program or intervention requires support from managers who are trained and
empowered to support employees in the program (Passey et al., 2018; Wieneke et al., 2019).
Recent literature has emphasized a positive approach to well-being that fosters work
engagement and addresses root organizational causes of work stress and burnout, rather than
focusing on the negative aspects of burnout (Galaiya & Arulampalam, 2020; Maslach, 2015;
Moss, 2020, 2021; Quick & Henderson, 2016; Schaufeli et al., 2008). Merely asking employees
to demonstrate grit or resilience places the responsibility and blame on the individual (Galaiya &
Arulampalam, 2020; Moss, 2019).
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Promotion of Well-Being by Organizations and Leaders
Key strategies are available in the literature regarding how organizations and leaders can
more actively promote well-being. In particular, the literature identifies the importance of
prioritizing well-being as part of the workplace culture (AXA Asia and Columbia University
World Health Organization Centre for Global Mental Health, 2020; Bersin, n.d.; Deloitte, 2020;
Gallup, 2020; Ginger, 2021; Kolomitro et al., 2020; Kossek et al., 2012; Moss, 2021; O’Brien &
Guiney, 2018; Pfeffer, 2018a, 2018b). Examples include leaders who speak about their own
well-being (and struggles) and promote and model self-care; organizations that allow time and
space for rest; normalization of discussions of anxiety and stress; increased training related to
well-being; and support for and encouragement of discussions about mental health. However, in
a revealing study, 98% of CEOs reported that their companies provide sufficient mental health
support, yet only 69% of employees felt similarly (Ginger, 2021). Also, CEOs were twice as
likely as employees (69% vs. 35%, respectively) to consider themselves as accepting of mental
and emotional health challenges in the workplace. Furthermore, even though 58% of the CEOs
indicated that talking about their mental health makes them better leaders, they reported holding
back from doing because of perceived loss of credibility or employee confidence. In one study in
higher education, employees report they want a “less ‘ad hoc’ or ‘DIY’ or ‘piece-meal’”
approach to well-being (O’Brien & Guiney, 2018, p. 11).
Other strategies that promote well-being are institutional policies, procedures, and
processes that modify work demands, pressures, structures, and processes; increase autonomy,
agency, and flexibility; and promote and model flexibility (AXA Asia and Columbia University
World Health Organization Centre for Global Mental Health, 2020; Gallup, 2020; McKinsey &
Company, 2020; Moss, 2021; O’Brien & Guiney, 2018; Pfeffer, 2018a, 2018b). Frequent
49

bidirectional communication allows managers to share information, plans, and vision; foster a
sense of purpose; and demonstrate supervisor support for professional growth (AXA Asia and
Columbia University World Health Organization Centre for Global Mental Health, 2020; Bersin,
n.d.; Gallup, 2020; Kossek et al., 2012, 2011; Moss, 2021; O’Brien & Guiney, 2018). Promotion
of social support systems and celebration of accomplishments with peers, teams, and managers
help build engagement and meaningful connections (Albrecht, 2012; AXA Asia and Columbia
University World Health Organization Centre for Global Mental Health, 2020; Bersin, n.d.;
Chanana & Sangeeta, 2020; Gallup, 2020; Guidetti et al., 2018; Kolomitro et al., 2020; Kossek et
al., 2012; Maslach, 2015; Mayo Clinic, 2021; Moss, 2021; Pfeffer, 2018a, 2018b). Many of these
organizational strategies to support well-being had been identified prior to the pandemic. But
COVID-19 exerted significant pressures on all aspects of how organizations operate, including
challenges to employee well-being (Teixeira da Silva, 2021).
Challenges Related to Well-Being, Stress, and Burnout During the Pandemic
The effect of the COVID-19 pandemic on well-being and occupational stress has
implications for individuals and organizations, including in higher education settings (Brazeau et
al., 2020; Floyd, 2021; Jelińska & Paradowski, 2021a, 2021b; D. S. Johnson et al., 2021; Kniffin
et al., 2021; M. Lee et al., 2022; MacIntyre et al., 2020; Maqsood et al., 2021; Odriozola-
González et al., 2020; Suppawittaya et al., 2020; Syrek et al., 2022). More Americans now report
that work is a significant source of stress than prior to the pandemic, 89% report that their work
life is worse during the pandemic, and 85% report their well-being has declined (American
Psychological Association, 2020; Moss, 2020). Employees have identified job security, burnout,
and mental health as among their greatest stressors and challenges (AXA Asia and Columbia
University World Health Organization Centre for Global Mental Health, 2020; McKinsey &
50

Company, 2020; Moss, 2020). Studies related to the higher education setting predicted impacts
on mental, psychological, and socioemotional well-being (Brazeau et al., 2020; International
Labour Organization, 2020; Maqsood et al., 2021).
Multiple studies have shown the pandemic has intensified predictors of stress and
burnout. Increases or changes in job demands, declines in work conditions or resources, and high
levels of anxiety increase the likelihood of burnout (AXA Asia and Columbia University World
Health Organization Centre for Global Mental Health, 2020; Kniffin et al., 2021; Moss, 2020).
Social isolation and social distancing decrease opportunities for social support, an important
component of well-being, thus posing psychological and professional challenges (AXA Asia and
Columbia University World Health Organization Centre for Global Mental Health, 2020;
Jelińska & Paradowski, 2021a; Kniffin et al., 2021; Moss, 2020; Suppawittaya et al., 2020).
Remote work also presents challenges to maintenance of a boundary between work and home
and the support and development of employees by supervisors (McKinsey & Company, 2020).
The impact of the pandemic has disproportionately affected women, including among
educators (Jelińska & Paradowski, 2021a; McKinsey & Company, 2020, 2021). In a study
during the pandemic with more than 800 higher education educators from more than 90
countries, Jelińska and Paradowski (McKinsey & Company, 2020) found female teachers
reported significantly stronger negative moods than male teachers; however, the authors also
noted that research in other countries relating gender to stress, anxiety, or mental health has
shown varying degrees of differences. In a study of 40,000 employees, McKinsey & Company
(2020) found senior-level women, relative to men, were more likely to feel exhausted (54% vs.
41%, respectively) and burned out (39% vs. 29%). Thirty-six percent reported the pressure to
work more during the pandemic, compared to 27% of senior-level men. Moreover, even though
51

women were more likely during the pandemic to be spending an additional 3 or more hours a day
on housework and childcare, they were less likely to share they faced work–life challenges
(McKinsey & Company, 2020). Mothers were also more than twice as likely as fathers to
experience discomfort sharing that they were a parent, worry that their performance would be
judged based on their caregiving, and consider leaving the workforce or reducing their work
hours or responsibilities (McKinsey & Company, 2020).
The disproportionate impact of the pandemic on women also extends across different
demographic groups. For example, family responsibilities during the pandemic have
disproportionately affected Latina and Black mothers. Not only were they more likely to be
either the sole income source for their family or have a partner who worked away from home
during the pandemic. but they were also up to twice as likely to be responsible for all childcare
and housework when compared to White mothers (McKinsey & Company, 2020). Asian
American women, particularly South Asian women, experienced the greatest increase in
domestic responsibilities during the pandemic when compared to other demographic groups
(McKinsey & Company, 2020). This applied to both childcare and household responsibilities.
The disproportionate effect on some demographic groups is not limited to family responsibilities.
For instance, Asian American and Black women reported feeling higher rates of constant stress,
exhaustion, and burnout than other demographic groups (McKinsey & Company, 2021). Black
women were more likely to feel less supported and more excluded at work (McKinsey &
Company, 2020). Specifically, they were less likely to report that their supervisor asked about
their workload or ensured their work–life needs were met and more likely to feel uncomfortable
sharing their thoughts about current events and racial inequity.
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Conceptual Framework
The conceptual framework for this study is based on SCT, which posits a triadic model
whereby person, environment, and behavior act in reciprocal causation with one another
(Bandura, 1977, 1989a; Hirabayashi, 2020). Triadic reciprocity connects people’s behaviors,
their external environment, and their internal beliefs and mental processes.
The SCT framework allows study of the interplay of relationships among person,
behavior, and environment. For this study, the person in the triad is the community college
administrator, who has individual characteristics and experiences and holds beliefs about their
well-being and effectiveness at work. The individual may experience stress, emotional
exhaustion, fatigue, and burnout. These beliefs affect their behaviors, which also affect their
environment. The environment, in turn, which includes policies, practices, and strategies
implemented to support well-being, influences the administrator’s beliefs. The environment also
affects the administrator’s behaviors, such as when, where, and how they work, and if any efforts
by the organization to support their well-being affected their behaviors. The environment also
includes people at work, such as the administrator’s supervisor and direct reports, and those at
home, such as family members or dependents. Each of these, in turn, affects both the person and
their behaviors. For senior executives such as chancellors and presidents without a direct
supervisor, the environment also includes the board of trustees to which they report and the
entire organization. Finally, the COVID-19 pandemic, as part of the environment, affects the
administrator’s beliefs and behaviors.
Figure 2 depicts the conceptual framework triad of person, behavior, and environment.
The environment includes the organization, supervisor, policies, practices, strategies, people at
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work and home, and COVID-19. This study focused on the effects of the environment on the
person and their behaviors.

Figure 2
Conceptual Framework

54

Summary
Although definitions and models of well-being vary widely and are inconsistent in the
literature, well-being generally refers to how well people are doing in their lives. Various
descriptions of well-being incorporate concepts that range from happiness to purpose, objective
to subjective, physical to psychological, among others. Even though conceptualizations of well-
being in the work domain similarly lack uniformity, workplace well-being has measurable
benefits for both employees and organizations. In contrast, occupational stress occurs when
employees respond to ongoing workplace demands and stressors, causing strains such as
dissatisfaction, disengagement, and burnout. These can lead to negative impacts on both
employees and organizations.
Burnout, like well-being, has different definitions. The most widely used measure of
burnout, the MBI, includes three dimensions: emotional exhaustion, cynicism and detachment
toward the job, and decreased professional efficacy. Work engagement, considered in some
models to be the opposite of burnout, also has three dimensions: vigor, dedication, and
absorption in work. Burnout is important to address because of its significant negative
consequences for individuals, their job performance, and their organizations. There is a gap in
the current literature about both stress and burnout among higher education administrators.
The COVID-19 pandemic has led to decreased well-being and increased stress and
burnout among employees, including those in higher education. Interventions at different levels
and points vary in their success at addressing stress and burnout. Leadership behaviors and
organizational practices play important roles in the support of employee well-being.

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Chapter Three: Methodology
This chapter restates the purpose and introduces the proposed methodology of this
research study. The purpose of the study was to examine organizational and leadership practices
implemented by community colleges during the COVID-19 pandemic that positively affected the
well-being of administrators. The chapter begins with a statement of the research questions and
an overview of the research design, followed by descriptions of the research setting and the
researcher. The next section explains the proposed data sources, including how data were
collected and analyzed. Finally, the chapter concludes with analysis of the study’s validity,
reliability, ethics, limitations, and delimitations.
Research Questions
The research questions that guided this study are as follows:
1. How have emotional exhaustion, burnout, and fatigue impacted, it at all,
administrators’ beliefs about their effectiveness throughout the COVID-19 pandemic?
2. What demographic differences, if any, exist for administrator perceptions of
emotional exhaustion, burnout, fatigue, organizational support, well-being, and
effectiveness at work during the COVID-19 pandemic?
3. What specific strategies, if any, have community colleges and their leaders
implemented that support the well-being of administrators during the COVID-19
pandemic?
Overview of Design
This study used a quantitative design. A study should take a quantitative approach when
it seeks to examine the effect of a variable or the relationships among variables, the design is
predetermined, the approach is deductive, and standardized instruments are used (Creswell &
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Creswell, 2018; Samkian, 2021). Because the purpose of this study was to determine the
interrelationships among variables, a quantitative approach is appropriate. Standardized
instruments with descriptive sections are also appropriate because the study solicited
descriptions, through the use of open-ended questions, of organizational and leadership practices
and strategies. Thus, a survey was the data source for both research questions.
Research Setting
The setting for this study was public 2-year community colleges in California.
Community colleges in the United States serve more than 5.5 million students, conferring more
than 1.1 million certificates and degrees in 2017–2018 (de Brey et al., 2021). Of more than 2.5
million staff members, 31,000 are classified as management. The subset of these administrators
who work in the California community college system, numbering more than 4,000, composed
the target population (R. B. Johnson & Christensen, 2015; Pazzaglia et al., 2016) for this study,
which is appropriate because both research questions were specific to administrators in
community colleges.
The Researcher
I was an administrator in a community college at the time of the study. My position and
perceived power (Burkholder et al., 2019; Creswell & Creswell, 2018) in this role could have
introduced bias into the research design and its survey questions. Personal experiences related to
well-being, burnout, and administrative effectiveness may have made me prone to expect that
other administrators had similar experiences or to write survey questions that favor certain
answers. I might have recruited respondents unevenly and overrepresented or underrepresented
my organization or job classification, especially if the results of the study reflected well or poorly
on my institution.
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My positionality as a woman may have socialized me to be more attentive and responsive
to emotional well-being. This could have raised concerns about whether research on well-being
is considered too feminine, if male colleagues would notice or spend energy on this problem, and
if this research would be marginalized. These concerns could have led me to adopt only certain
approaches to understanding the problem. As an Asian American woman, stereotype threat
(Casad & Bryant, 2016) and microaggressions (Sue et al., 2009) may have affected how I framed
the research questions or interacted with and recruited participants in the study.
Because I personally attempted to address well-being and burnout for my team and
organization during the pandemic, I could have been biased in my interpretation of the survey.
For example, the open-ended answers inquired about strategies taken by the respondents’
organizations and leaders to support well-being during the pandemic. As a member of a
leadership team at my college, I bore responsibility for timely implementation of effective
strategies that support employee needs. Thus, the research study could have been an attempt to
justify my actions or hold someone else accountable for inaction. Moreover, I might have coded
or given more weight to answers that included approaches I employed in my leadership efforts
yet ignored or discounted strategies I did not use. Strategies to address issues of power and
positionality included reflexivity to check for bias, pilot testing, and peer review of the results
(Creswell & Creswell, 2018; Merriam & Tisdell, 2016).
Data Sources
The data source for this research study was a survey with both closed and open-ended
questions. Items were either adopted or adapted from existing instruments or created for this
study. Personal and work demographic information was collected. The online self-administered
survey was created using Qualtrics (http://www.qualtrics.com). Data from closed-ended
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questions were analyzed quantitatively, whereas those from open-ended questions were analyzed
qualitatively.
Method
This study used an anonymous online survey of California community college
administrators to investigate emotional exhaustion, fatigue, burnout, well-being, and
organizational support during the COVID-19 pandemic. The instrument, created specifically for
this study, utilized items from four existing instruments and novel questions. Both statistical
analysis software (Salkind & Frey, 2020) and computer-assisted qualitative data analysis (Gibbs,
2018) software were used.
Participants and Setting
The population for this study included managers and administrators who serve in
community colleges. The Integrated Postsecondary Education Data System (National Center for
Education Statistics, n.d.) defines management function as those who “plan, direct, or coordinate
policies, programs, and may include some supervision of other workers.” According to the U.S.
Bureau of Labor Statistics (2022b), postsecondary education administrators “oversee student
services, academics, and faculty research.” Estimates of the number of administrators who serve
in more than 900 public 2-year institutions in the United States (American Association of
Community Colleges, 2021) range from more than 31,900 (de Brey et al., 2021) to
approximately 38,300, as reported by the U.S. Bureau of Labor Statistics (2022a). The bureau
reported approximately 16,500 work in California, with approximately 4,200 in community
colleges (S. Bray, personal communication, March 25, 2022). In fall 2021, community college
districts reported staffing headcounts of 4,436 executive, administrative, or managerial
administrators to the state chancellor (California Community Colleges Chancellor’s Office, n.d.).
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For this study, the participant inclusion criteria were to be a current administrator in a California
community college and at least 18 years old. Demographic data collected about participants
included job title, length of experience in their current role and in higher education, scope of
responsibility, age, gender, race, ethnicity, household income, household size, and information
about their dependents.
A multistage sampling approach, as described by Creswell and Creswell (2018), was used
to recruit administrators (directors, managers, deans, and executives) through use of social media
and professional networks, during conferences, and appeals to organizations that serve
community college administrators. Nonprobability convenience sampling that chooses
participants based on availability was used in those entities. To mitigate any potential effects due
to perceived power or association of the researcher with organizations or networks, the
researcher shared institutional review board (IRB) guidance with organizations and professional
network members who recruited additional participants.
The final sample included 308 participants with broad representation of roles, settings,
experience levels, and job and work characteristics. Detailed personal and work demographic
information about the participants is provided in Appendix A. The most commonly reported job
role was dean (38.0%), which also included assistant, associate, and senior dean roles. Similar
gradations also applied to other positions. The next most common roles were at the level of vice-
president and director, each representing 23.7% of the participants. Managers, CEOs (chancellor,
superintendent, or president), and vice-chancellors each represented between 3.6% and 5.5% of
the sample. More than 8% of the sample were senior executives at the CEO and vice-chancellor
levels. Most participants (88.3%) worked at colleges or campuses of either single or multicollege
districts. The other 11.7% worked at corresponding district offices. The number of years
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participants had served in their current role ranged from less than 1 year to 20 years (M = 4.34,
Mdn = 3.74). Years worked in higher education extended to 30 years (M = 11.11, Mdn = 7.66).
Nearly half (49.4%) of the participants had doctoral degrees, and 44.8% had master’s degrees.
The number of direct reports for participants ranged from none (2.3%) to 150 or more
(2.6%). Nearly 30% of participants indicated having between six and 10 direct reports, with
51.2% of all participants having 10 or fewer direct reports. Responses revealed 15% to 19% each
for the categories of 11 to 20, 21 to 50, and 50 or more direct reports. More than 28% of
participants reported working an average of 46 to 50 hours per week at work and remotely
combined. More than 18% each reported working either 51 to 55 or 56 to 60 hours. Sixteen
percent reported working 40 to 45 hours per week or less, and 3.6% worked more than 70 hours
per week.
Participant personal demographics are also shown in Table 1. More than two thirds of the
participants were currently married, 62.3% identified as female, and 73.1% were non-Hispanic or
non-Latino. For race, 65.6% reported their race as White, 9.4% as Black or African American,
and 7.8% as Asian, with 4.9% choosing more than one race. The average age was 49.6 years; the
most common age ranges were 40 to 49 (37.6%) and 50 to 59 (35.1%), with the remainder
evenly split between those younger than 40 and those 60 or older. The most common household
income range was between $150,000 and $199,999 (28%). An additional 31.8% reported
incomes within $50,000 of this range. Household sizes ranged from one (only the participant) to
more than 10. Overall, households were relatively small. A two-person household was the most
commonly reported (31.2%), followed by slightly more than 20% each having either three or
four household members. The number of dependents ranged from none to eight. Reflecting the
corresponding small household size data, 41.9% reported having no dependents, 21.4% had one
61

dependent, and 23.7% had two dependents. Dependents ranged in age from less than 1 year old
to 24 years old.
Participant demographics for gender, age, race and ethnicity were compared to data
reported for fall 2021 to the state chancellor’s office (California Community Colleges
Chancellor’s Office, n.d.). Participants who identified as female represented 62.3% of the
sample, whereas state records indicated women make up 54.3% of executives, administrators, or
managers. The average age of participants was 49.6 years, whereas the chancellor’s office data
indicated the average ages of educational administrators and classified administrators are 50.9
years and 48.9 years, respectively. Moreover, the percentage of participants in 10-year age
ranges differed from state data by less than 1% (ages 50 to 59) up to 4.9% (ages 40 to 49). For
race and ethnicity, the distribution of study participants who were Asian (9.1%), Black or
African American (10.4%), or Hispanic (21.1%) closely matched state data (8.9%, 10.0%, and
22.7%, respectively). However, the proportion of White participants was 64.3%, compared to
48.5% in the state data. Interestingly, the participation of certain groups in the study was higher
compared to official reports of their prevalence: Native American (2.6% vs. 0.6%, respectively),
Native Hawaiian or Pacific Islander (1.3% vs. 0.5%), and multiethnic or more than two races
(4.9% vs. 2.0%).
It is important to look at participants who chose not to select among given categories for
survey questions designed for inclusiveness (see Robinson & Leonard, 2019, p. 148). The
percentage of respondents who chose “prefer not to answer” in response to personal
demographic questions ranged from 1.3% to 1.6% for number of dependents, household size,
and age. Between 3.2% and 6.5% chose “prefer not to answer” for marital status, gender,
ethnicity, and household income. Slightly more than 10% of participants chose “prefer not to
62

answer” for race, and 6.5% chose to self-describe their race. Less than 1% of participants chose
to self-describe their gender.
Power analysis was performed using SPSS for both Pearson product-moment correlations
and comparison of means. The power for correlation was calculated as 1.000, with parameters
set to a sample size of 308, .05 significance, and .30 Pearson correlation, suggesting the sample
size for this study was sufficient. Furthermore, although not done a priori, a power analysis using
SPSS to determine the minimum sample size to achieve 80% power for detecting a moderate
Pearson correlation of greater than .30 at a significance level of .05 resulted in a sample size of
84. Thus, the final sample size of 308 was adequate for this study. In addition, the observed
power for comparison of means was calculated for an independent-samples t-test, with
assumptions set to estimate power for a sample size of 154 each for two groups, a population
mean difference of .50, and a pooled standard deviation of 1.0 that was equal for two groups.
The result for this calculation was .992 (p = .05), which indicates a large effect size (Salkind &
Frey, 2020).
Instrumentation and Instrument Reliability
The survey instrument was designed to examine concepts from the research questions
including well-being, burnout, and employee effectiveness using a combination of closed and
open-ended questions. The instrument adopted and adapted questions from existing measures
and included original questions. Closed survey questions addressed the areas of emotional
exhaustion, burnout, fatigue, and effectiveness as part of Research Question 1 and demographic
differences, if any, as part of Research Question 2. Open-ended questions addressed Research
Question 3 to identify strategies that participants’ organizations and their supervisors utilized to
support well-being. The full instrument is provided in Appendix B.
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Survey Design
The survey was designed to maximize response rates following principles of good survey
design (Robinson & Leonard, 2019). Items related to the demographic characteristics that are
part of Research Question 2 were ordered in the survey dependent on potential sensitivity. Less
potentially sensitive work demographic questions were placed at the beginning of the survey,
whereas more potentially sensitive personal demographic questions were placed at the end. The
option to choose “prefer not to answer” was offered in most questions to encourage survey
respondents to move forward to the next question. The option “prefer to describe” was offered as
recommended by Robinson and Leonard (2019) for some questions such as those pertaining to
gender and race. The open-ended questions related to Research Question 2 preceded the personal
demographic questions to maximize response rates before potentially more sensitive questions.
The most important survey questions were placed at the beginning to ensure an answer in case a
respondent quit the survey. Finally, following Robinson and Leonard (2019), the mechanics of
the survey were pilot tested extensively to ensure the survey worked as intended. During
piloting, some volunteers reported the “I have been more callous since I took this job” item
adopted from the MBI did not align well with the possible responses (never to always), with
several suggesting that an agree-to-disagree scale would be more appropriate for a statement
regarding someone’s attitude. However, because the MBI has extensive psychometric data
supporting its reliability and validity, the response options were retained.
The reliability of an instrument includes measures of internal consistency such as
Cronbach’s alpha, originally described as coefficient alpha (Cronbach, 1951). Optimal values
range between .70 and .90 (Creswell & Creswell, 2018; Salkind & Frey, 2020). The next section
describes each instrument used to develop the survey and includes reliability data.
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Burnout and Emotional Exhaustion
Two questions related to burnout were adopted from the 22-question MBI, which was
modified from a 25-question version originally developed by Maslach and Jackson (1981). This
instrument assesses three domains of burnout—emotional exhaustion, depersonalization, and
personal accomplishment (or professional efficacy)—using a scale ranging from 1 (a few times a
year) to 6 (every day) with a value of 0 if the respondent indicated they never experience the
feeling or attitude described. West et al. (2012) and Li-Sauerwine et al. (2020) demonstrated two
items from the MBI correlated well with the 22-question MBI. Specifically, the item “I feel
burned out from my work” corresponded to the risk of high burnout in the emotional exhaustion
domain, whereas “I have become more callous toward people since I took this job” corresponded
to risk in the depersonalization domain (West et al., 2009).
Cronbach’s alpha has been reported as psychometric data for the MBI (Maslach &
Jackson, 1981), which includes the three subscales the authors hypothesized to be aspects of
burnout syndrome: emotional exhaustion (nine questions), depersonalization (five questions),
and personal accomplishment (eight questions). Cronbach’s alpha analysis yielded internal
consistency coefficients of .83 and .84 for frequency and intensity, respectively. Subscale
coefficients were .89 (frequency) and .86 (intensity) for emotional exhaustion, .74 (both
frequency and intensity) for personal accomplishment, and .77 (frequency) and .72 (intensity) for
depersonalization. The authors also reported Cronbach’s alphas of .59 (frequency) and .57
(intensity) for involvement. In the same year, Iwanicki and Schwab (1981) showed similar
results in a cross-validation study of the MBI used with a helping profession, teachers. The MBI
has since undergone revision to 22 questions, become proprietary, and been validated extensively
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for content and construct validity (Loera et al., 2014; Maslach et al., 2001; Salkind, 2014;
Schutte et al., 2000).
Although the use of multiple questions for each component of burnout could increase
internal consistency and reliability (Salkind, 2014), the use of two items from the MBI has been
shown to be a useful surrogate for the 22-question instrument when the latter is not feasible due
to length (Li-Sauerwine et al., 2020; West et al., 2012, 2009). In a comparison of predictive
models applied to longitudinal and cross-sectional data representing more than 10,000 medical
professionals, West et al. (2012) demonstrated strong associations between both questions and
key outcomes. Furthermore, Li-Sauerwine et al. (2020) showed the emotional exhaustion item “I
feel burned out from my work” correlated to the MBI emotional exhaustion subscale with a
Spearman’s rho of .81. In addition, the depersonalization item “I have become more callous
toward people since I took this job” correlated to the MBI depersonalization subscale with a
Spearman’s rho of .73. A summative score of the two questions greater than 3 most closely
correlated with the definition of burnout, with a Spearman’s rho of .65 (Li-Sauerwine et al.,
2020).
Fatigue
Four questions related to fatigue were adopted from the 10-question Fatigue Assessment
Scale (FAS; Michielsen et al., 2003), which was derived from other fatigue questionnaires. The
questions chosen for this study originated from either the Checklist Inventory Strength or the
World Health Organization Quality of Life energy and fatigue subscale and included one
question original to the FAS. The scale for the FAS ranges from 1 (never) to 5 (always).
Cronbach’s alpha for the FAS was .87, with underlying values of .96 and .88 for the
Checklist Inventory Strength and World Health Organization Quality of Life energy and fatigue
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subscale, respectively (Michielsen et al., 2003, 2004). Questions for physical fatigue and mental
fatigue had factor loading values .77 and .74, respectively, whereas the questions related to
energy and concentration had values of .57 and .63. In a comparison of the psychometric
qualities of six fatigue instruments, De Vries et al. (2003) showed the FAS had the highest factor
loading for one factor identified across all scales. In addition, the FAS had a Cronbach’s alpha
of .90. The Gallup Workplace Audit instrument had a Cronbach’s alpha of .91 when using a
business-unit level of analysis (Harter et al., 2002).
Effectiveness
Survey questions that address work effectiveness were derived from two instruments.
Three questions were adopted and adapted from the original 14-question Perceived Stress Scale
(PSS) developed by Cohen et al. (1983) to measure the degree that life situations are appraised as
stressful. The original scale ranged from 0 (never) to 4 (very often). Two questions were
modified to specify the work setting, in alignment with the purpose and research questions of this
study. Additionally, two questions related to effectiveness were adopted from the Gallup
Workplace Audit (Harter et al., 2002). This instrument has evolved into the proprietary Q12
Employee Engagement Survey (Gallup, n.d.; Harter et al., 2009). Its scale ranges from 1
(strongly disagree) to 5 (strongly agree).
Alpha reliability coefficients for the PSS were determined to be .84, .85, and .86 for each
of three samples tested (Cohen et al., 1983). Test–retest correlation was .85. In a review of
psychometric properties of the PSS, E.-H. Lee (2012) showed its Cronbach’s alpha was greater
than .70 and test–retest reliability was greater than .70 among studies that used either the 14-
question or 10-question versions of the PSS. However, the reported alpha for studies that used
the 4-question version was less than .70 in three of six studies.
67

Effectiveness and Well-Being Relative to the COVID-19 Pandemic
Four original questions were developed. For Research Questions 1 and 2, two closed-
ended questions examined how survey respondents evaluated their current effectiveness and
well-being compared to prior to the pandemic. Five options on the scale ranged from 1
(significantly worse) to 5 (significantly better). For Research Question 3, two open-ended
questions solicited the policies, practices, and strategies used by the respondents’ organizations
or direct supervisors to support well-being. Each was followed by an option for the respondent to
identify the most effective of the strategies.
Demographic Questions
To address Research Question 2, demographic questions were carefully chosen to
describe the characteristics of the sample and understand differences, if any, among people in
different groups. Work-related demographic survey questions were placed at the beginning of the
survey, whereas personal demographics were placed at the end of the survey. Items related to the
work setting included job title, number of direct reports, and length of time in current role at the
current organization and in higher education. Personal demographic variables included marital
status, gender, age, race, ethnicity, highest level of education, size of household, household
income, and number and age of dependents in the household. Specific categories were adopted
from census categories and other studies (see Robinson & Leonard, 2019 for examples).
Data Collection Procedures
The study was reviewed and approved by the IRB at the University of Southern
California (IRB UP-21-01051). An online anonymous self-administered survey was delivered
via Qualtrics in early 2022. This timeframe minimized the effect on response rates of external
events such as major holidays, as recommended by Pazzaglia et al. (2016) and Salkind (2014).
68

Other aspects of data collection followed suggestions of Robinson and Leonard (2019),
Creswell and Creswell (2018) and Pazzaglia et al. (2016). Multiple points of contact with
potential respondents were attempted through distribution of the survey through different
professional networks. Uniform resource locators (or web addresses) and quick response (known
as QR) codes were distributed through postcards, emails, and social media (e.g., Facebook) and
at conferences, including one hosted by the Association of California Community College
Administrators. A compelling invitation to participate included information about why the
survey was important, how the results would matter, details about confidentiality, and what to do
if participants had questions. Invitations were sent from a trusted organization, followed by
reminders, if possible, with salient information about the importance of the study. Approximately
94% of respondents who completed the survey used the anonymous web address, whereas 6%
used the QR code. Response rates were monitored regularly, as suggested by Pazzaglia et al.
(2016) to ensure that the logistics were working. No apparent logistical issues arose.
Recommendations for how long a survey should stay open vary. Pazzaglia et al. (2016)
recommended keeping the survey open for up to 6 to 8 weeks, Creswell and Creswell (2018)
recommended 4 weeks for a mailed survey, and Malloy (2021) recommended a shorter
timeframe of 3 to 4 weeks. If response rates are lower than expected, the survey timeframe may
be extended (Robinson & Leonard, 2019).
This survey remained open for 8 weeks, starting January 31, 2022. During this period,
reminders were sent to participants if possible. The decision to close the survey was based on
two factors. First, the rate of new surveys with 100% completion had dropped to two during the
last 2 weeks of the survey period. In addition, responses in progress were reviewed for their
percentage of completion and last activity date. All responses in progress were less than 70%
69

complete and more than 10 days had elapsed since any activity had occurred, suggesting that
closing those responses posed a minimal risk of loss of data.
Data Analysis
This study gathered both quantitative data and qualitative data. Quantitative analysis
includes descriptive and inferential statistics as described by Creswell and Creswell (2018) and
Salkind and Frey (2020). Qualitative analysis includes coding and thematic analysis as described
by Merriam and Tisdell (2016), Gibbs (2018), and Creswell and Creswell (2018). Because the
survey was anonymous, response rates could not be determined. Descriptive analysis of
quantitative variables in the study included frequencies, measures of central tendency, ranges,
standard deviations, and correlational analysis and one-way analysis of variance (ANOVA) to
relate variables and constructs. Inferential statistical tests were used to draw inferences from the
sample to the population.
Software
Survey data were collected in Qualtrics. Quantitative data was analyzed using SPSS
(version 28.0.0.0 [190]). Qualitative data was analyzed using ATLAS.ti (version 22.0.2 [3332]).
Data Cleaning
Data were exported from Qualtrics and imported into SPSS for cleaning, transformation,
and statistical analysis. Thirty-four responses were removed. One response did not meet the
inclusion criteria, and 33 responses were less than 97% complete. Of the incomplete responses,
25 were less than one-quarter complete. The one response that was 97% complete included an
answer to the final question about the number of dependents, although it did not specify their
ages; this response was retained. After cleaning, the final sample size for quantitative analysis
was 308. Three incomplete responses (68% to 85% complete) provided answers to the open-
70

ended questions; those responses were qualitatively analyzed. After cleaning, the final sample
size for qualitative analysis was 311.
Data Transformation
Some survey questions were reverse coded in SPSS. Data transformation of items into
composite variables was based on the reliability data as described in the next section. Composite
variables were computed with SPSS for burnout (two items), fatigue (four items), and low
effectiveness (three items). Fatigue had two reverse-coded items and low effectiveness had one.
Reverse coding and composite variables are shown in Table 1.

71

Table 1
Quantitative Survey Questions: Reverse Coding, Original Instruments, and Composite Variables
Order
of item
in
survey
Survey item Reverse
coded
(R)
Original
instrument,
if adopted
or adapted
Composite
variable, if
applicable
1 I have the materials and equipment
I need to do my work right.
R Gallup Q12
2 I feel burned out from my work. MBI Burnout
3 I have enough energy for everyday
life.
R FAS Fatigue
4 I have become more callous toward
people since I took this job.
MBI Burnout
5 Physically, I feel exhausted. FAS Fatigue
6 Mentally, I feel exhausted. FAS Fatigue
7 When I am doing something, I can
concentrate quite well.
R FAS Fatigue
8 In the last month, how often have
you felt that you were unable to
control the important things in
your life?
PSS Low effectiveness
9 In the last month, how often have
you felt difficulties at work were
piling up so high that you could
not overcome them?
PSS Low effectiveness
10 In the last month, how often have
you felt confident about your
ability to handle your problems
at work?
R PSS Low effectiveness
11 My supervisor or someone at work
seems to care about me as a
person.
R Gallup Q12
12 How do you compare your current
effectiveness at work relative to
your effectiveness before the
pandemic?
R
13 How do you compare your current
wellbeing relative to your
wellbeing before the pandemic?
R

72

Reliability and Statistical Analysis
Reliability and correlational analysis were conducted using IBM SPSS.
Reliability. Cronbach’s alpha (internal reliability) was determined for each set of
questions adopted or adapted from existing instruments. Two sets had Cronbach’s coefficients
exceeding .70, considered the threshold for reliability (Salkind & Frey, 2020). The four questions
adopted from the FAS designed to measure exhaustion had a Cronbach’s alpha of .79, with one
item identified as improving reliability if removed. Removal of the items “When I am doing
something, I can concentrate quite well” increased Cronbach’s alpha to .80. However, because
Research Question 1 focused on fatigue and the reliability of the FAS questions was high, all
four questions were retained. The three questions adopted and adapted from the PSS designed to
assess effectiveness had a reliability of .73. No items were identified that would increase the
alpha if removed.
Neither sets of questions derived from the MBI and the Gallup Q12 had Cronbach’s
alphas of .70 or higher. However, the reliability coefficient for the pair of questions from the
MBI was .62, considered acceptable for this study (D. Hocevar, personal communication, April
17, 2022). The alpha for the two questions adopted from the Gallup Q12 was much lower (.246).
Thus, each item was treated as a separate variable. Finally, reliability analysis for the two
original questions that compared both effectiveness and well-being during the pandemic showed
a Cronbach’s alpha of .70.
Statistical Analysis. Descriptive statistical analysis (means and standard deviations) was
performed for each survey question. Data were analyzed for skewness and kurtosis. Two items
had an absolute value of kurtosis greater than 1.0: mental exhaustion and concentration. Thus,
73

these two variables were not normally distributed. The sample size (greater than 100) allowed
the use of Pearson’s correlation, even with a nonnormally distributed outcome (Hocevar, 2022c).
Correlational analysis was performed in SPSS with two measures for effectiveness that
were considered dependent variables: the composite “low effectiveness” derived from PSS
questions and the “lower effectiveness during COVID-19” question about the respondents’
perceived relative effectiveness during the pandemic. Independent variables included the
composites of burnout and fatigue, the single comparative well-being question, and the two items
from the Gallup Q12. Comparisons of means for work-related and personal demographic
characteristics were conducted using one-way ANOVA with the F-test statistic as outlined in
Salkind and Frey (2020). Significant ANOVA results with more than three groups were analyzed
post hoc with a Fisher’s least significant difference test in SPSS. Results of these analyses are
discussed in Chapter 4.
Qualitative Data Analysis
The answers to open-ended questions were inductively analyzed using methods described
in Gibbs (2018) and Merriam and Tisdell (2016), including computer-assisted qualitative data
analysis software, specifically ATLAS.ti version 22.0.2 (3332). A priori and open codes
developed during the first cycle of coding were further developed into axial and comparative
codes. A codebook was developed, revised, and continuously refined, as explained by DeCuir-
Gunby et al. (2011), as more participant responses were analyzed. Some codes were renamed or
removed during subsequent cycles of coding and thematic analysis. More than 1,400 quotes were
coded with 142 final codes.
Of the sample of 311 respondents, 257 (82.6%) wrote at least one answer to one of the
four open-ended questions. Some respondents provided more than one policy, practice, or
74

strategy for a question, including those that asked about the most effective policy, practice, or
strategy. All policies, practices, or strategies mentioned in multiple answer responses were coded
independently. Analytic themes were identified, and different comparative strategies were used,
following methods recommended in the literature (Corbin & Strauss, 2008; Gibbs, 2018;
Merriam & Tisdell, 2016; Ravitch & Carl, 2019). Codes were developed independently, as were
emergent categories and themes, for organizational and supervisor responses.
Reliability and Validity
To increase reliability, strategies suggested by Creswell and Creswell (2018), Robinson
and Leonard (2019), and Salkind (2014) were employed, including pilot testing, testing of survey
mechanics and logistics, and estimating of survey duration. Unclear items were removed or
revised and retested, and instructions were standardized and clear across all settings. Because the
instrument was modified from existing instruments, Creswell and Creswell (2018) cautioned that
original validity and reliability measures may not apply. Exploratory factor analysis was run in
SPSS to test the overall instrument, but results were not further analyzed because there were not
at least three variables from each instrument (D. Hocevar, personal communication, April 6,
2022).
Internal validity was supported through use of validated survey items and recruitment of
a large sample to address the threat of attrition, whereas internal reliability was supported by use
of standardized measures and instruments. Transparency about sampling and study methods
helped minimize threats to external reliability.
Ethics
The researcher received certification through the Collaborative Institutional Training
Initiative program, submitted protocols and instruments for review by the University of Southern
75

California IRB, and did not begin the study until approved. Changes to the recruitment process
were approved by the IRB. Participants received an informed consent statement following
guidelines recommended by Creswell and Creswell (2018) and Glesne (2011) that provided
sufficient information about the study and possible risks and explained they did not have to
participate and could withdraw at any time. Additionally, anonymity was protected by
configuring Qualtrics to not collect internet protocol addresses, and data will be discarded after 5
years. Pseudonyms were used if necessary to prevent linkage of any unique organizational
descriptors and names to participants. Disclosure of information that would harm participants
was avoided.
Survey questions may have caused participants to relive personal and professional losses
experienced during the pandemic. They may have also revealed or reinforced feelings that the
participant or their organization did not effectively support well-being during a time of high
occupational stress. In addition, participants may have experienced social desirability bias to
report positive answers (Robinson & Leonard, 2019). To mitigate the effects of these potentially
sensitive questions, the survey used primarily closed questions and the qualifier “if any” for
open-ended questions about strategies, practices, and policies. The dissertation chair and IRB
were consulted when ethical questions arose.

76

Chapter Four: Findings
The purpose of this study was to examine organizational and leadership practices
implemented during the COVID-19 pandemic that positively influenced the well-being of
community college administrators. The study utilized an anonymous online survey instrument
that adopted and adapted items from existing instruments and also included novel questions. The
instrument included quantitative Likert-style items, demographic questions, and open-ended
questions. Descriptive and inferential statistical analyses were conducted for the quantitative
sections, and coding and thematic analysis were conducted for the open-ended qualitative
questions. Three research questions guided this study:
1. How have emotional exhaustion, burnout, and fatigue impacted, if at all,
administrators’ beliefs about their effectiveness throughout the COVID-19 pandemic?
2. What demographic differences, if any, exist for administrator perceptions of
emotional exhaustion, burnout, fatigue, organizational support, well-being, and
effectiveness at work during the COVID-19 pandemic?
3. What specific strategies, if any, have community colleges and their leaders
implemented that support the well-being of administrators during the COVID-19
pandemic?
More than 300 community college administrators, from managers to chancellors,
participated in the study. The findings for each research question follow; each begins with an
overview of the findings and then moves into a presentation of key findings. The chapter closes
with a summary of the overall findings of the study.
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Research Question 1
This study examined whether the beliefs of California community college administrators
about their effectiveness were related to emotional exhaustion, burnout, and fatigue. Two
measures of effectiveness were used. The first is referred to as “low effectiveness” and is a
composite variable. The second measure of effectiveness derives from asking administrators how
their current effectiveness at work compared to their effectiveness before the pandemic. This
variable is referred to as relatively lower effectiveness throughout COVID-19, or “lower
effectiveness.”
Results of this study show that emotional exhaustion, burnout, and fatigue among
California community college administrators were strongly correlated with self-reported low
work effectiveness and moderately correlated to perceived lower levels of work effectiveness
during the pandemic. Furthermore, emotional exhaustion, burnout, and fatigue were correlated
with lower levels of well-being during the pandemic, which was correlated with the two
measures of low effectiveness. Finally, measures of organizational support were found to be
related to emotional exhaustion, fatigue, burnout, and low effectiveness.
This section on Research Question 1 first presents descriptive statistics for survey item
responses, then reports the results of statistical analysis of items measuring effectiveness as a
dependent variable. Next, reliability analysis is presented for the dependent variable of
effectiveness and the independent variables of burnout and fatigue. Correlational analysis among
variables follows.
Descriptive Results for Items
This section provides descriptive statistics of the 13 Likert-style survey items organized
by the original survey instrument from which questions were adopted or adapted. These include
78

a 2-item measure of burnout, which is a version of the MBI (Li-Sauerwine et al., 2020; West et
al., 2012, 2009); one of these questions has also been shown to effectively measure emotional
exhaustion. Four questions to measure fatigue were from the FAS (De Vries et al., 2003;
Michielsen et al., 2004) and three questions related to effectiveness were from the PSS (Cohen et
al., 1983). Distributions of responses, means, and standard deviations are shown in Tables 2
through 6.
Emotional Exhaustion and Burnout: Administrators Feel Burned Out at Work but Have Not
Become More Callous
Table 2 shows the distribution of participant responses to questions related to emotional
exhaustion and burnout. Overall, survey respondents (N = 308) reported feeling burned out from
work. The mean for the question “I feel burned out from my work” was 4.80 (SD = 1.58) on a
scale from 1 (never) to 7 (every day). Overall, nearly 80% reported feeling burned out at work at
least a few times a month. More than half (54.9%) reported feeling burned out at least once a
week, including 27.9% who feel burned out several times a week and 14.3% who feel burned out
every day. Because the item “I feel burned out from my work” also serves as a measure of
emotional exhaustion (West et al., 2012), these results show that many community college
administrators felt emotional exhaustion at least weekly.

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Table 2
Distribution of Survey Responses Related to Burnout (N = 308)
Survey
question
Never A few
times a
year or
less
Once a
month
or less
A few
times a
month
Once a
week
A few
times a
week
Every
day
M SD % % % % % % %
I feel
burned
out
from
my
work.
4.80 1.58 1.9 7.5 10.7 25.0 12.7 27.9 14.3
I have
become
more
callous
toward
people
since I
took
this job.
3.00 1.70 24.0 21.8 17.2 18.2 6.5 10.4 1.9

In contrast, when asked about whether they “have become more callous toward people”
since they took their job, 45.8% of administrators responded never (24.0%) or only a few times a
year or less (21.8%). The mean was 3.00 (SD = 1.70). The proportion that reported becoming
more callous at least once a week was 18.8%, and only 1.9% reported the frequency as every
day.
Fatigue: Administrators Feel Exhausted, Sometimes Have Enough Energy for Everyday Life,
but Retain Their Ability to Concentrate on Their Work
The distribution of responses to survey items related to fatigue is shown in Table 3.
Participants reported feeling exhaustion regularly. On a 5-point scale (1 = never, 2 = sometimes,
80

3 = regularly, 4 = often, 5 = always), the item “Physically, I feel exhausted” had a mean of 2.92
(SD = 1.03). The proportion reporting physical exhaustion either often or always was 30.5%,
with an additional 23.7% feeling regularly physically exhausted. The proportions for mental
exhaustion were higher (M = 3.29, SD = 1.04). Those reporting mental exhaustion either often or
always represented 41.8% of the sample, with an additional 30.2% feeling regularly mentally
exhausted. Thus, participants regularly felt exhausted both physically (54.4%) and mentally
(72.4%).

Table 3
Distribution of Survey Responses Related to Fatigue (N = 308)
Survey question Never Sometimes Regularly Often Always
M SD % % % % %
Physically, I feel
exhausted.
2.92 1.03 1.3 44.5 23.7 22.1 8.4
Mentally, I feel
exhausted.
3.29 1.04 0.6 26.9 30.2 27.6 14.6
I have enough
energy for
everyday life.
2.58 0.90 4.2 54.2 24.4 13.6 3.6
When I am doing
something, I
can concentrate
quite well.
2.97 0.94 0.6 39.3 27.3 28.2 4.5

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When asked if they “can concentrate quite well” when they are doing something, 50.0%
of administrators replied that this was regularly, often, or always true; the mean was 2.97 (SD =
0.94). The mean was lower (M = 2.58, SD = 0.94) and a smaller percentage (47.6%) answered
similarly to the item “I have enough energy for everyday life.”
Effectiveness: Administrators Reveal Lack of Control and Piling Up of Work yet Feel
Confident in Their Abilities to Handle Problems at Work
Table 4 shows the distribution of responses for items related to effectiveness.
Respondents were asked to report on a 5-point scale (1 = never, 2 = almost never, 3 = sometimes,
4 = fairly often, 5 = very often) how often in the last month they felt “unable to control the
important things in their lives.” More than 30% of administrators responded either fairly often
(26.3%) or very often (4.5%), whereas 46.8% responded sometimes (M = 3.11, SD = 0.85). The
responses shifted toward more often (M = 3.23, SD = 1.04) when asked, in the last month, how
often they felt “difficulties at work were piling up so high that [they] could not overcome them,”
nearly tripling the percentage that answered very often (12.3%), whereas those indicating fairly
often remained at 26.3% and those responding sometimes fell to 37.2%. More than three quarters
of responses were in combined categories of very often, fairly often, and sometimes for being
unable to control the important things in their lives (77.6%) and having difficulties at work piling
up so high that they could not overcome them (75.9%).

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Table 4
Distribution of Survey Responses Related to Effectiveness (N = 308)
Survey question Never Almost
never
Sometimes Fairly
often
Very
often
M SD % % % % %
In the last month,
how often have
you felt that you
were unable to
control the
important things
in your life?
3.11 0.85 2.3 20.1 46.8 26.3 4.5
In the last month,
how often have
you felt
difficulties at
work were
piling up so high
that you could
not overcome
them?
3.23 1.04 4.2 19.8 37.3 26.3 12.3
In the last month,
how often have
you felt
confident about
your ability to
handle your
problems at
work?
3.77 0.80 0.6 2.9 33.4 44.8 18.2

Nearly two thirds (63%) of administrators, in contrast, indicated they felt confident in
their ability to handle their problems at work very often (18.2%) or fairly often (44.8%; M =
3.77, SD = 0.80). About a third responded they sometimes felt confident, and only 3.5% reported
almost never or never.
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Organizational Support: Administrators Feel Someone Cares About Them and They Have the
Equipment and Materials They Need
Participant responses to questions related to organizational support are shown in Table 5.
Based on a 5-point scale (1 = strongly disagree, 2 = disagree, 3 = neither agree nor disagree, 4 =
agree, 5 = strongly agree), almost three fourths (73.4%) of administrators reported that their
“supervisor or someone at work seems to care about [them] as a person,” with approximately
half of these indicating strongly agree and half indicating agree (M = 3.94, SD = 1.08). Only 12%
disagreed or strongly disagreed. It is notable that more than one fourth of respondents did not
report having a supervisor or someone at work who cared about them. The results were similar
when asked if they “have the materials and equipment [they] need to do [their] job right,” with
69.1% in agreement, but the proportion in agreement was 51.6%, and strong agreement was
lower at 17.5%. Those that disagreed or strongly disagreed made up 21.7%.

Table 5
Distribution of Survey Reponses Related to Organizational Support (N = 308)
Survey question Strongly
disagree
Disagree Neither
agree nor
disagree
Agree Strongly
agree
M SD % % % % %
My supervisor or
someone at work
cares about me as
a person.
3.94 1.08 3.6 8.4 14.6 37.7 35.7
I have the materials
and equipment I
need to do my
work right.
3.59 1.12 5.5 16.2 9.1 51.6 17.5

84

COVID-19: Effectiveness and Well-Being at Work Worsened During the Pandemic
Table 6 shows the distribution of responses for items related to participant perceptions of
the effect of the pandemic. Survey respondents were asked to compare their current effectiveness
at work and well-being relative to before the pandemic on a 5-point scale (1 = significantly
worse, 2 = somewhat worse, 3 = about the same, 4 = somewhat better, 5 = significantly better).
Effectiveness at work worsened for 50% of the survey participants during the pandemic (M =
2.17, SD = 0.92), with 43.2% reporting the change was somewhat worse and 6.8% reporting the
change was significantly worse. Those reporting that effectiveness had stayed about the same
accounted for 30.5% of the responses. Nearly one fifth (19.5%) reported their effectiveness was
either somewhat (15.9%) or significantly (3.6%) better.

85

Table 6
Distribution of Survey Responses Related to Relative Effectiveness and Well-Being (N = 308)
Survey question Significantly
worse
Somewhat
worse
About
the
same
Somewhat
better
Significantly
better
M SD % % % % %
How do you
compare your
current
effectiveness
at work
relative to
your
effectiveness
before the
pandemic?
2.66 0.95 6.8 43.2 30.5 15.9 3.6
How do you
compare your
current
wellbeing
relative to
your
wellbeing
before the
pandemic?
2.17 0.92 21.4 52.3 16.9 7.1 2.3

Well-being worsened during the pandemic to a greater extent. Nearly three quarters
(73.7%) of administrators reported their well-being was worse relative to before the pandemic,
either somewhat worse (52.3%) or significantly worse (21.4%; M = 2.66, SD = 0.95). Only 9.4%
felt their well-being was somewhat (7.1%) or significantly (2.3%) better.
Statistical Analysis of Measures of Effectiveness
Effectiveness as the dependent variable was assessed using two independent measures.
The first was a composite called low effectiveness, composed of three items. Cronbach’s alpha
was .727, which falls in the optimal range between .70 and .90 (Creswell & Creswell, 2018;
86

Salkind & Frey, 2020). Removal of any item would reduce reliability. Details are shown along
with item means and standard deviations in Table 7.

Table 7
Item-Total Statistical Analysis for Low Effectiveness
M SD Corrected item-
total correlation
Cronbach’s
alpha if item
deleted
In the last month, how often have you felt
that you were unable to control the
important things in your life?
3.11 .853 .532 .661
In the last month, how often have you felt
difficulties at work were piling up so high
that you could not overcome them?
3.23 1.037 .557 .647
In the last month, how often have you felt
confident about your ability to handle
your problems at work? (reverse coded)
2.23 .800 .581 .614

Note. N = 308. Scale was 1 = never, 2 = almost never, 3 = sometimes, 4 = fairly often, 5 = very
often.

87

A single item was used to examine relative work effectiveness during the COVID-19
pandemic. The item mean and standard deviation are shown in Table 8.

Table 8
Item Statistical Analysis for Lower Effectiveness During the COVID-19 Pandemic
M SD
How do you compare your current effectiveness at work relative to your
effectiveness before the pandemic? (reverse coded)
3.34 0.95

Note. N = 308. Scale was 1 = significantly better, 2 = somewhat better, 3 = about the same, 4 =
somewhat worse, 5 = significantly worse.

88

The two measures of effectiveness are compared in Table 9. Skewness and kurtosis were
within acceptable ranges, indicating parametric statistical tests were appropriate (Hocevar,
2022c). Pearson’s correlation between the two measures of effectiveness was .356 (p < .001),
indicating a moderate relationship (Salkind & Frey, 2020). Figures 3 and 4 show histograms for
the low effectiveness and lower effectiveness measures, respectively.

Table 9
Comparison of Descriptive Statistics for Two Measures of Effectiveness
Low effectiveness Lower effectiveness during
COVID-19 pandemic
M 2.86 3.34
Mdn 2.67 3.50
Mode 2.67 4.00
SD 0.75 0.95
Variance .527 .895
Skewness .024 -.489
SE skewness .139 .139
Kurtosis -.472 -.243
SE kurtosis .277 .277

Note. N = 308. Scale ranged from 1 to 5. Low effectiveness was a composite measure. Lower
effectiveness was a single-item measure.

89

Figure 3
Low Effectiveness

Note. Distribution for low effectiveness (N = 308, M = 2.86, SD = 0.73), a composite measure.

90

Figure 4
Lower Effectiveness: Self-Reported Comparison of Work Effectiveness During the COVID-19
Pandemic

Note. Percentage of responses (N = 308, M = 2.66, SD = .946) to the question, “How do you
compare your current work effectiveness relative to your effectiveness before the pandemic?”
Scale ranged from 1 (significantly worse) to 5 (significantly better).

Statistical Analysis of Independent Variables
Independent variables for burnout and fatigue were tested for internal reliability. Table 10
shows the statistical analysis for the 2-item burnout composite. Cronbach’s alpha was .616.

6.8%
43.2%
30.5%
15.9%
3.6%
Significantly
worse
Somewhat
worse
About the same Somewhat
better
Significantly
better
% of Respondents
Relative Work Effectiveness
91

Table 10
Item-Total Statistical Analysis for Burnout
M SD Corrected item-
total correlation
Cronbach’s
alpha if
deleted
I feel burned out from my work. 4.80 1.58 .447 Not applicable
I have become more callous toward people
since I took this job.
3.00 1.70 .447 Not applicable

Note. N = 308. Scale was 1 = never, 2 = a few times a year or less, 3 = once a month or less, 4 =
a few times a month, 5 = once a week, 6 = a few times a week, 7 = every day.

Table 11 shows the reliability analysis for the 4-item composite for fatigue. Its alpha
coefficient was .786, comparable to the .87 reliability coefficient from the 10-item FAS (Ginger,
2021; Moss, 2021), from which these items were adopted.

Table 11
Item-Total Statistical Analysis for Fatigue
M SD Corrected item-
total correlation
Cronbach’s
alpha if deleted
Physically, I feel exhausted. 2.92 1.03 .689 .680
Mentally, I feel exhausted. 3.29 1.04 .720 .662
I have enough energy for everyday life.
(reverse coded)
3.42 0.90 .523 .766
When I am doing something, I can
concentrate quite well. (reverse coded)
3.03 0.94 .452 .799

Note. N = 308. Scale was 1 = never, 2 = sometimes, 3 = regularly, 4 = often, 5 = always.

92

Table 12 shows four single-item measures that were also tested as independent variables.
Emotional exhaustion was adopted from the MBI. Two were originally adopted from the Gallup
12 survey but had a low Cronbach’s alpha of .246 and thus, were treated as independent
variables. The third item examined how administrators perceived their well-being had changed
during the pandemic.

Table 12
Descriptive Statistics for Single-Item Independent Variables Related to Emotional Exhaustion,
Organizational Support, and Relative Well-Being During the Pandemic
M SD
Emotional exhaustion: I feel burned out from my work. 4.80 1.58
Organizational support: My supervisor or someone at work seems to care
about me as a person. (reverse coded)
2.06 1.08
Organizational support: I have the materials and equipment I need to do my
work right. (reverse coded)
2.41 1.12
Relative well-being: How do you compare your current wellbeing relative to
your wellbeing before the pandemic? (reverse coded)
3.83 0.92

Note. N = 308. Emotional exhaustion had a 7-point scale; the others had a 5-point scale.

93

Table 13 compares the descriptive statistics for each independent variable. The first two
are the composite variables of burnout and fatigue. The others are the single-item variables
related to organizational support and relative well-being.

Table 13
Comparison of Descriptive Statistics for Emotional Exhaustion, Burnout, Fatigue, Measures of
Organizational Support, and Lower Well-Being During the COVID-19 Pandemic
Emotional
exhaustion
Burnout Fatigue Lack of
supervisor or
someone at
work who
seems to care
about me as a
person
Not having
the materials
and
equipment I
need to do
my work
right
Lower well-
being during
COVID-19
pandemic
M 4.80 3.90 3.16 2.07 2.41 3.83
Mdn 5.00 4.00 3.25 2.00 2.00 4.00
SD 1.56 1.39 0.76 1.08 1.12 0.92
Skewness -.374 .089 -.128 .970 .807 -.923
SE skewness .139 .139 .139 .139 .139 .139
Kurtosis -.735 -.688 -.603 .309 -.249 .881
SE kurtosis .277 .277 .277 .277 .277 .277

Note. N = 308. Emotional exhaustion and burnout had a 7-point scale; the others had a 5-point
scale.

94

Correlational Analysis of Variables
Correlations Between Independent Variables Show Moderate to Strong Relationships With
Emotional Exhaustion, Burnout, Fatigue, and Lower Well-Being
Correlation coefficients between independent variables are shown in Table 14. The
correlation was strongest between emotional exhaustion and the burnout composite to which it
belongs (r = .838, p = < .05). In addition, emotional exhaustion was strongly correlated with
fatigue (r = .642, p < .05). Fatigue and burnout also demonstrated a strong correlation (r = .568,
p < .05). Lower well-being during COVID-19 also had a strong correlation with fatigue (r
= .436, p < .05) and a moderate to strong correlation with both emotional exhaustion (r = .374, p
< .05) and burnout (r = .384, p < .05). The lack of a supportive supervisor or someone else at
work who cares about the person was moderately correlated with burnout (r = .300, p < .05) and
weakly to moderately correlated with emotional exhaustion (r = .255, p < .05), fatigue (r = .240,
< .05), and lower well-being during COVID-19 (r = .227, p < .05). Though statistically
significant, the correlations were weak (r < .20, p < .05) between not having needed materials
and equipment and all four other independent variables.

95

Table 14
Correlations Between Independent Variables
1 2 3 4 5
1. Emotional exhaustion

2. Burnout .838*

3. Fatigue .642* .568*

4. Lack of supervisor or someone at work who
seems to care about me as a person
.255* .300* .240*

5. Not having the materials and equipment I need to
do my work right
.115* .127* .118* .140*

6. Lower well-being during COVID-19 pandemic .374* .384* .436* .227* .125*

Note. N = 308.
*p < .05 (two-tailed).

Emotional Exhaustion, Burnout, Fatigue, and Lower Well-Being Are Significantly Correlated
With Both Measures of Low Effectiveness
Both measures of effectiveness were correlated with emotional exhaustion, burnout, and
fatigue, as shown in Table 15. Low effectiveness was strongly correlated with emotional
exhaustion (r = .558, p < .05) burnout (r = .518, p < .05), and fatigue (r = .677, p < .05), whereas
lower effectiveness was moderately correlated with emotional exhaustion (r = .332, p < .05),
burnout (r = .322, p < .05), and fatigue (r = .293, p < .05).

96

Table 15
Correlations Between Independent and Dependent Variables
Lower effectiveness
during COVID-19
pandemic
Low
effectiveness
Emotional exhaustion .332* .558*
Burnout .322* .518*
Fatigue .293* .677*
Lack of supervisor or someone at work who seems to
care about me as a person
.151* .277*
Not having have the materials and equipment I need to
do my work right
.079 .176*
Lower well-being during COVID-19 pandemic .528* .370*

Note. N = 308.
*p < .05 (two-tailed).

Similarly, both measures of effectiveness were also correlated with lower well-being.
Lower effectiveness showed a strong correlation (r = .528, p < .05), whereas low effectiveness
showed a moderate correlation (r = .370, p < .05).
The lack of a supervisor or someone at work who seems to care about the administrator
as a person showed a weak to moderate relationship with low effectiveness (r = .277, p < .05)
and a weak relationship with relative lower effectiveness (r = .151, p < .05). Not having the
materials and equipment needed for the job showed a similar weak, though significant,
relationship to low effectiveness (r = .176, p < .05). The relationship between access to materials
and equipment to relatively lower effectiveness was not statistically significant (r = .079, p
> .05).
97

Summary of Significant Findings for Research Question 1
This study addressed Research Question 1 by showing that emotional exhaustion,
burnout, and fatigue were all strongly correlated with administrators’ perception of low
effectiveness at work and moderately correlated with perceptions of relative lower effectiveness
during the pandemic. Emotional exhaustion, burnout, and fatigue were also all moderately to
strongly related to self-reported lower levels of well-being during the pandemic. Lower well-
being during the pandemic, in turn, correlated moderately to strongly with both measures of
effectiveness. In addition, one measure of organizational support, the lack of a supervisor or
someone at work who cares about the administrator, was moderately related to burnout and
weakly to moderately related to low effectiveness, emotional exhaustion, and fatigue.
Research Question 2
This study examined whether administrators’ perceptions of emotional exhaustion,
burnout, fatigue, organizational support, well-being, and effectiveness at work differed based on
demographic factors. Statistically significant differences were found between certain
demographic groups for levels of fatigue, perceptions of levels of organizational support,
decreased well-being during the pandemic, decreased work effectiveness during the pandemic,
and overall low effectiveness as an administrator. However, emotional exhaustion, when
measured separately from burnout, did not vary significantly among demographic groups. This
section provides a comparison of means for all variables, including those that significantly
differed between demographic groups of administrators.
Comparison of Effectiveness Across Different Demographic Groups
The demographic characteristics of participants were analyzed using one-way ANOVA
for statistically significant differences among groups in measures of effectiveness. This section
98

discusses differences in the two measures of effectiveness between groups, first based on
personal demographic characteristics, then based on work-related demographic characteristics.
Effectiveness Differs Based on Demographic Variables of Educational Level, Dependents, and
Age
Table 16 reports the means and standard deviations for participant demographics. The
data revealed two instances in which relative lower effectiveness during the pandemic differed
significantly between groups. First, those whose highest education was a bachelor’s degree had
significantly lower mean relative lower effectiveness than those with a master’s degree, F(2,
301) = 4.336, p < .05. That means effectiveness did not decrease as much in the bachelor’s
degree group as in those with a higher degree. Second, those with either no dependents or two
dependents had statistically higher means than those with one or three dependents, F(4, 299) =
2.510, p < .05. Thus, they assessed a greater decrease in their effectiveness during the pandemic.

99

Table 16
Comparison of Participant Personal Demographics for Measures of Effectiveness
Characteristic

Lower effectiveness during the
pandemic
Low effectiveness

n M SD F p M SD F p
Gender

0.008 .927

2.212 .138
Female
a
192 3.34 0.95

2.89 0.67

Male
b
106 3.35 0.97

2.76 0.79

Race and ethnicity

1.583 .179

1.832 .123
Asian
a
24 2.96 0.91

2.72 0.62

Black or African
American
b

29 3.48 0.95

3.16 0.78

White
c
162 3.37 0.96

2.80 0.73

Multiracial or
multiethnic
d

15 3.20 1.21

2.76 0.81

Hispanic or
Latino
e

55 3.49 0.81

2.90 0.74

Marital status

1.600 .190

0.852 .467
Previously
married
43 3.33 0.89

3.01 0.76

Domestic
partnership
16 2.88 1.15

2.77 0.72

Never married 29 3.28 0.96

2.78 0.84

Now married 207 3.40 0.93

2.84 0.70

Age

0.245 .865

4.920 .002*
Under 40
a
37 3.35 1.06

3.12
acd
0.73

40–49
b
116 3.29 0.96

2.93
bd
0.71

50–59
c
108 3.39 0.92

2.80
acd
0.72

60 or older
d
40 3.28 0.93

2.54
abcd
0.64

Highest education

4.336 .014
*

0.709 .493
Bachelor’s
a
10 2.70
ab
1.06

2.93 0.38

Master’s
b
142 3.47
ab
0.77

2.90 0.72

Doctorate
c
152 3.26 1.05

2.80 0.75

Household income

2.026 .052

0.530 .812
< $100,000 9 3.11 0.78

2.96 0.51

$100,000–
$149,999
50 3.58 0.88

3.00 0.78

$150,000–
$199,999
88 3.33 0.93

2.88 0.75

$200,000–
$249,999
48 3.10 1.02

2.75 0.70

$250,000–
$299,999
45 3.20 0.94

2.83 0.71

100

Characteristic

Lower effectiveness during the
pandemic
Low effectiveness

n M SD F p M SD F p
$300,000–
$349,999
24 3.75 0.79

2.94 0.80

$350,000–
$399,999
12 3.42 0.90

2.75 0.64

$400,000 or
more
12 3.67 0.98

2.86 0.72

Household size

1.906 .093

1.749 .123
1 37 3.46 0.73

2.92 0.87

2 96 3.40 0.99

2.72 0.70

3 64 3.06 0.96

2.89 0.58

4 63 3.30 0.99

3.04 0.80

5 24 3.63 0.77

2.78 0.75

6 or more 19 3.53 1.02

2.74 0.57

Number of
dependents
2.510 .042
*
3.074 .017*
None
a
129 3.43
abd
0.90

2.78
ac
0.71

1
b
66 3.12
abc
1.03

2.90
be
0.73

2
c
73 3.48
bcd
0.80

3.05
acde
0.73

3
d
24 3.00
acd
1.14

2.72
cd
0.71

4 or more
e
12 3.50 1.09

2.44
bce
0.57

Age of dependents

1.584 .181

3.191 .015*
5 or younger
a
9 3.11 1.17

3.41
acde
0.49

6 to 12
b
78 3.38 0.86

3.04
bc
0.68

13 to 17
c
25 3.48 0.92

2.61
abc
0.78

18 to 22
d
41 2.98 1.11

2.80
ad
0.73

23 or older
e
21 3.33 1.06

2.84
ae
0.76

Note: Significant ANOVA with at least three groups were analyzed using Fisher’s least
significant difference test. Statistically different groups are indicated by shared superscripts.
*p < .05.

For the second measure of effectiveness—namely, overall low effectiveness at work—the
number of dependents mattered, F(4, 299) = 3.074, p < .05. Those with two dependents had
significantly higher means—thus, lower levels of perceived effectiveness—than those with no,
three, or four or more dependents. Those with one dependent also had significantly higher means
101

than those with four or more dependents. Significant differences were also found between groups
depending on the age of their dependents. Specifically, those with dependents aged 5 or younger,
F(4, 169) = 3.191, p < .05, had more pronounced low effectiveness (higher mean) compared to
those with dependents aged 6 to 12, 18 to 22, and 23 or older. Those with elementary school age
children between 6 and 12 also had significantly lower perceived effectiveness than those with
older children between 13 and 17.
Low effectiveness also significantly differed according to the age range of the study
participants, F(3, 297) = 4.920, p < .05. The group with the highest mean—and thus, lowest
effectiveness—was for those younger than 40, differing significantly from those who were 50 to
59 or 60 or older. The lowest mean—and thus, the highest effectiveness—was for those aged 60
or older, differing from all other age ranges. Notably, the mean dropped as the age range
increased, indicating that perceived effectiveness increased as age increased.
Other demographic variables exhibited no significant differences in effectiveness; these
included gender, race and ethnicity, marital status, household income, and household size.
Effectiveness Differs According to Work Demographics Related to Higher Education
Experience and Number of Direct Reports
When work-related demographics were analyzed for levels of effectiveness, two
variables showed statistically significant differences in means among groups, as seen in Table
17. There were no differences between groups for relative lower effectiveness.

102

Table 17
Comparison of Participant Work Demographics for Measures of Effectiveness
Characteristic n Lower effectiveness during
COVID-19 pandemic
Low effectiveness

M SD F p M SD F p
Role

0.794 .555

1.829 .107
Manager 17 3.24 0.90

2.75 0.66

Director,
including
senior,
associate, or
assistant
73 3.47 0.91

3.04 0.68

Dean, including
senior,
associate, or
assistant
117 3.26 0.99 2.77 0.76
Vice president or
provost,
including
associate or
assistant, or
other CEO
cabinet
73 3.32 0.90 2.89 0.76
Vice chancellor,
including
deputy,
associate, or
assistant, or
other chancellor
cabinet
11 3.64 1.03 2.94 0.47
CEO, chancellor,
president, or
superintendent
14 3.50 0.85 2.57 0.56
Worksite

0.284 .595

0.089 .766
College 272 3.33 0.96

2.85 0.74

District 36 3.42 0.87

2.89 0.65

Years in role

2.142 .095

0.293 .830
1 or less 61 3.20 0.96

2.85 0.74

2–4 144 3.26 0.96

2.86 0.72

5–10 79 3.51 0.89

2.89 0.77

11 or more 24 3.58 0.93

2.74 0.64

Years in higher
education

0.350 .844

2.948 .021*
1 or less 14 3.21 1.05

2.90 0.71

103

Characteristic n Lower effectiveness during
COVID-19 pandemic
Low effectiveness

M SD F p M SD F p
2–4
a
56 3.29 0.91

3.00
ad
0.70

5–10
b
109 3.35 0.93

2.92
bd
0.72

11–20
c
82 3.30 1.01

2.82
cd
0.75

21 or more
a
47 3.47 0.91 2.55
abcd
0.66
Number of direct
reports

0.618 .714

2.315 .034*
2 or fewer
a
30 3.53 0.90

3.23
abcd
0.61

3–5 38 3.37 0.94

2.89 0.60

6–10
b
90 3.31 0.93

2.74
ab
0.70

11–20
c
58 3.34 0.89 2.75
ac
0.77
21–50 47 3.38 0.92 2.94 0.76
51–100 22 3.32 0.95 2.95 0.65
More than 100
d
23 3.04 1.26 2.71
ad
0.88
Hours worked per
week

0.246 .960

2.012 .064
40–45 41 3.34 1.06

2.76 0.81

46–50
a
88 3.30 0.90

2.67 0.60

51–55
b
56 3.29 0.99

2.98 0.73

56–60
b
56 3.39 0.89 2.96 0.82
61–65
b
28 3.50 1.00 3.01 0.78
66–70
e
25 3.40 0.96 3.03 0.58
More than 70 11 3.27 1.01 2.79 0.60

Note. Significant ANOVA with at least three groups were analyzed using a Fisher’s least
significant difference test. Statistically differing groups are indicated by shared superscripts.
*p < .05.

For low effectiveness, however, those with 21 or more years of experience in higher
education had significantly lower means than three other groups: 2 to 4 years, 5 to 10 years, and
11 to 20 years, F(4,303) = 2.948, p < .05. This means those with more experience perceived
themselves as more effective than those in other groups. Those who had been in higher education
for 1 year or less were the only other ones who showed a similar lower mean and thus, a higher
level of perceived effectiveness.
104

The number of direct reports also showed significant differences for those who had two
or fewer direct reports. The mean for this group was higher, meaning the participants perceived
themselves as less effective than those who had six to 10, 11 to 20, or more than 100 direct
reports, F(6, 301) = 2.315, p < .05.
Overall Pattern of Effectiveness
As demonstrated in Tables 16 and 17, measures of effectiveness differed significantly for
certain groups. Perceptions of effectiveness affected these groups to the greatest or least extent:
• Perceptions of effectiveness were highest in the group aged 60 or older.
• Perceptions of effectiveness were highest for those who had spent 21 or more years in
higher education.
• Perceptions of effectiveness were lowest in the group younger than 40.
• Perceptions of effectiveness were lowest among those with two or fewer direct
reports.
• Perceptions of effectiveness were lowest in the group reporting two dependents.
• Perceptions of effectiveness were lowest in the group reporting dependents aged 5 or
younger.
• Perceptions of lower effectiveness were greatest in the groups reporting no or two
dependents.
• Perceptions of lower effectiveness during the pandemic were the least for the group
without a graduate degree.
105

Comparison Across Demographic Groups for Emotional Exhaustion, Fatigue, Burnout,
Lower Well-Being, and Measures of Organizational Support
Demographic characteristics of participants were analyzed by one-way ANOVA for
statistically significant differences, if any, in emotional exhaustion, fatigue, burnout, lower well-
being, and organizational support. This section first describes differences found across personal
demographic characteristics, then proceeds to describe those related to work characteristics.
Differences Between Demographic Groups
Analysis of differences between means for demographic groups found significant
differences between certain groups for fatigue, lower well-being, and measures of organizational
support. The personal demographic categories were gender, race and ethnicity, age, highest
education level, and number of dependents. Neither emotional exhaustion nor burnout differed
significantly between groups for any personal demographic category. Detailed analyses of
differences in means between personal demographic groups are shown in Appendix C.
Fatigue Varies According to Gender, Age, and Number of Dependents
Fatigue (Table C1) was the only variable for which differences between means of groups
were found in more than one demographic category. First, women reported greater fatigue than
men, F(1, 296) = 12.504, p < .001). Second, those aged 60 or older showed significantly lower
levels of fatigue compared to all other age groups, F(3, 297) = 4.000, p = .008. Overall,
perceived fatigue decreased with increasing age. Finally, those with dependents aged 5 or
younger or between 6 and 12 had a significantly higher level of fatigue than those whose
dependents were older, specifically aged 13 to 17 and 18 to 22, F(4, 169) = 2.989, p = .020.
106

Lower Well-Being During COVID-19 Varies by Education Level
Lower well-being (Table C2) differed between groups only for the variable of highest
level of education. Specifically, those whose highest education was a bachelor’s degree assessed
a lesser decrease in their well-being during COVID-19 than those with a master’s degree or
doctoral degree, F(2, 301) = 3.562, p = .03.
Measures of Organizational Support Show Differences by Race and Ethnicity and Gender
Significant differences between groups in lack of supervisor care (Table C2) were found
for race and ethnicity, F(4, 285) = 4.404, p = .02. Participants who were Black or African
American assessed lower levels of supervisor care (higher mean) compared to Asian Americans,
Whites, and Hispanics or Latinos. Similarly, those who were multiracial or multiethnic had a
significantly higher mean for lack of supervisor care than Asian Americans and Whites.
A statistically significant difference for lack of materials and equipment (Table C2) was
found for gender. Women reported this lack of organizational support more than men, F(1, 296)
= 6.726, p = .01.
Differences Between Work-Related Demographic Groups Are Limited to Organizational
Support
Work-related demographics showed less variation according to group characteristics (see
Appendix D, Tables D1 and D2). Statistically significant differences were found for only one
variable, lack of materials and equipment, between groups based on the average number of hours
worked (Table D2). The group working 46 to 50 hours a week did not experience a lack of
materials and equipment (lower mean), F(1, 296) = 3.245, p = .004, as much as those in all other
categories, ranging from 40 to 45 hours to 70 hours or more.
107

The lack of supervisor care, the second measure of organizational support, showed no
differences among groups. Finally, analysis of emotional exhaustion, fatigue, burnout, and lower
well-being also showed no significant differences among work-related demographic groups.
Overall Pattern of Demographic Differences
As demonstrated in Tables C1, C2, D1, and D2, fatigue and measures related to
organizational support differed significantly for certain groups. The groups affected to the
greatest or least extent were as follows:
• Fatigue was higher for women.
• Fatigue was highest for those with dependents aged 5 or younger.
• Fatigue was lowest for those aged 60 and older.
• Women were more likely to report lack of materials and equipment to do their work.
• Those who worked 46 to 50 hours per week were least likely to report lack of
materials and equipment to do their work.
• Blacks and African Americans were most likely, and multiracial or multiethnic
respondents the next most likely to report the lack of a supervisor or other person at
work who cares about them as a person.
• Those without a graduate degree were least likely to report a decline in well-being
during the pandemic.
Summary of Significant Findings for Research Question 2
Statistically significant differences were shown between groups for certain demographics.
Perceived effectiveness at work was highest for those aged 60 or older and those who had the
most experience in higher education. In contrast, perceived effectiveness at work was lowest for
those with two dependents, those with dependents aged 5 or younger, and those with two or
108

fewer direct reports. The perception of decreased effectiveness during the pandemic was most
pronounced among those with no or two dependents and least pronounced for those without a
graduate degree.
Fatigue was most pronounced for women and those with dependents aged 5 or younger.
Fatigue was least pronounced for those aged 60 or older. Black or African American participants
were most likely and multiracial or multiethnic next most likely to report the lack of a supervisor
who cares about them. Women were more likely and those who worked 46 to 50 hours a week
were least likely to report the lack of necessary equipment and materials to do their work. The
group least likely to report a decline in well-being during the pandemic were those without a
graduate degree.
Research Question 3
This study examined what specific strategies, if any, were implemented by community
colleges and their leaders that support the well-being of administrators during the COVID-19
pandemic. Four open-ended questions asked the following:
• What policies or practices, if any, did your organization implement to support your
well-being during the pandemic?
• Which of these organizational policies did you think was most effective?
• What strategies, if any, did your direct supervisor implement to support your well-
being during the pandemic?
• Which of these direct supervisor strategies did you think was most effective?
Of 311 respondents survey respondents, 257 responded to at least one of the four open-ended
questions. Their answers were analyzed qualitatively for themes.
109

Results of this study show that organizations implemented more than a dozen categories
of policies and practices and direct supervisors implemented a similar number of strategies that
supported the well-being of California community college administrators during the COVID-19
pandemic. The most common categories for organizational policies and practices, which
respondents also found to be the most effective, related to remote and flexible work, support and
resources for well-being, COVID-19 policies and practices, and necessary tools and technology
support. In contrast, administrators reported the most common categories of strategies
implemented by their direct supervisors to support well-being were primarily interpersonal.
This section on Research Question 3 first describes the organizational policies and
practices and supervisor strategies that were implemented and perceived to be effective in
supporting administrator well-being. Next is the presentation of emerging findings related to how
study participants perceived remote and flexible work and their workload. The final section
highlights what participant responses reveal about ineffective policies, practices, and strategies.
Organizational and Supervisor Support of Administrator Well-Being
Organizational Policies and Practices Implemented During the Pandemic
Following successive cycles of coding and thematic analysis, a dozen categories were
inductively determined for organizational policies or practices that supported administrator well-
being during the pandemic. Because some respondents provided multiple answers, Table 18
shows the number of unique mentions among all responses. The most common response was
remote work (n = 81). Representative quotes are also shown in the table. The next most common
practices were flexible work (n = 37); well-being training, activities, and resources (n = 37);
COVID-19 policies for vaccines, masks, and testing (n = 26); and necessary tools and technology
support (n = 21). Other categories with more than five instances are also shown in the table.
110

Table 18
Organizational Policies and Practices That Support Administrator Well-Being (N = 311)
Categories of
organizational
policies and
practices
What policies
or practices, if
any, did your
organization
implement to
support your
wellbeing
during the
pandemic?
Which of these
organizational
policies did you
think was most
effective?
Representative quotes
n n
Remote work 81 47 Remote work.
They did move to remote work though
for employee wellbeing, which was
appreciated.
Allowed remote working—significantly
improved wellbeing and productivity
as it reduced lengthy daily travel
time.
Flexible work 38 26 Flexible schedules.
Flexibility in work location.
Allowed staggered scheduling for staff
members—social distancing was
possible.
Well-being
training,
activities, and
resources
37 12 Offer wellness workshops.
We offered a mental health training and
series of trainings to support selfcare.
There are opportunities to engage in
activities for handling stress and
encouraging mindfulness.
COVID-19
policies for
vaccines,
masks, and
testing
26 14 The college implemented a vaccine
mandate and frequent on-campus
Covid [sic] testing, which makes me
feel more secure.
Vaccination and masking requirements.
Necessary tools
and
technology
support
21 9 My organization allowed us to take
home laptops and computers.
Technology support for remote work.
COVID-19
safety
protocols and
equipment
16 4 Pandemic safety protocols and supplies.
Additional safety precautions and covid
[sic] regulations enforced so that they
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Categories of
organizational
policies and
practices
What policies
or practices, if
any, did your
organization
implement to
support your
wellbeing
during the
pandemic?
Which of these
organizational
policies did you
think was most
effective?
Representative quotes
n n
can ensure employee safety as much
as possible.
Professional or
on-call
services
13 5 My college hired a third-party company
to host sessions … that focus on
mental health practices.
Brought in clinicians to work with
managers to help them… with faculty
and staff angst.
Encourage use
of EAP or
similar
11 5 EAP Support.
The college has an EAP for mental
health and wellness.
Change in work 10 2 Fewer meetings.
No meeting Fridays. This helped me
feel like I had a Friday to play catch
up.
Fewer emails after work.
Promote self-
care, balance,
and time off
9 6 Reminders by senior leadership to be
kind to ourselves, to take time if we
needed it.
Encourage use of leaves, vacations, and
holidays.
Opportunities
for social
connection
8 5 Informal zoom meetings to get to know
one another better and connect.
Launching an employee engagement
campaign.
Leadership and
climate
6 5 Caring and loving campus.
Leading and working with compassion
as much as possible.
Communication 6 1 Continuous and regular communication.
More open forums, where people can
ask questions and connect virtually.

Note. Responses were provided by 257 participants. n = number of unique mentions among total
responses.
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For the survey item regarding which policy or practice was most effective, the top five
categories were also the same, although the order of the frequencies was slightly different. As
before, remote work (n = 47) and flexible work (n = 26) were most frequent. The next two
switched relative rank: well-being training, activities, and resources (n = 12) and COVID-19
policies for vaccines, masks, and testing (n = 14).
Interestingly, three policies and practices identified by fewer than 10 study participants
were also noted as most effective at a relatively high rate: six of nine reported promoting self-
care, balance, and time off; five of eight chose opportunities for social connection; and five of six
chose leadership and climate.
Some organizational policies and practices were mentioned fewer than five times. These
are not shown in Table 18. These policies and practices included supervisor support,
compensation, recognition and praise, meetings, staffing levels, support for childcare, training
for managers, generic training, and modeling of behavior. Four of these were also reported by
survey respondents as the most effective policy or practice that supported their well-being:
supervisor support, compensation, recognition and praise, and staffing levels.
Direct Supervisor Strategies Implemented During the Pandemic
Qualitative thematic analysis inductively generated 13 categories for supervisor strategies
that support administrator well-being. In contrast to organizational categories, all but one most
commonly reported supervisor strategies were relational. Table 19 shows the most common
responses (n = 59) were categorized as encouraging self-care, balance, and time off.
Representative quotes are also shown. The next two most common strategies were check-ins (n =
38) and remote or flexible work (n = 38). These were followed by the categories of offering
support and encouragement (n = 25), caring and empathy (n = 19), and communication (n = 19).
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Whereas COVID-19-related categories were the fourth and sixth most common organizational
policy or practice (Table 18), this category was more broadly defined among supervisor
strategies and ranked eighth (n = 8). Other categories with fewer mentions are also shown.

Table 19
Supervisor Strategies That Support Administrator Well-Being (N = 311)
Categories of
supervisor
strategies
What
strategies, if
any, did your
direct
supervisor
implement to
support your
wellbeing
during the
pandemic?
Which of
these direct
supervisor
strategies did
you think was
most
effective?
Representative quotes
n n
Encourage self-
care, balance,
time off
59 24 She reminded us weekly to take care of
ourselves.
My manager is understanding of work-life
balance.
Asking “Why are you on this meeting?”
when I was supposed to be off of work.
Check-ins 38 21 Weekly check ins.
Periodic check ins.
Weekly phone calls (sometimes daily) to
check in offer support.
Remote or
flexible work
37 26 Remote work.
Allowed greater flexibility working
remotely.
Hybrid work schedule.
Offer support
and
encouragement
25 15 Being very supportive.
His encouraging words of support and
following up on how I am doing.
My direct supervisor has always been
available to support my area needs and
that has made a huge difference.
Supporting my area’s needs meets my
needs for wellbeing at work.
Communication 19 19 Open communication.
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Categories of
supervisor
strategies
What
strategies, if
any, did your
direct
supervisor
implement to
support your
wellbeing
during the
pandemic?
Which of
these direct
supervisor
strategies did
you think was
most
effective?
Representative quotes
n n
More patience, listened better.
More time for compassion/communication
during meetings.
Caring and
empathy
19 14 Empathetic.
Empathy and concern.
Simply showed he was understanding of
the extreme stress he/we were all under.
Opportunities for
social
connections
9 3 Retreats.
She also planned some social events for us
to be in community and exhale.
Occasionally do lunch or drinks together.
COVID-19
safety
protocols and
equipment
8 3 Getting the vaccination clinics to campus
early on so that I could get vaccinated
along with my colleagues.
Provided administrative support and
financial resources to implement
COVID-19 response.
Make sure that work environment was kept
safe and all covid [sic] resources were
made available and implementation of
guidelines were followed.
Recognition and
praise
7 3 Continuous praise for successful tasks
large and small.
Verbal acknowledgements and support
with work efforts.
More consistent positive feedback.
Advocacy 6 3 My direct supervisor acts a buffer to
higher administration.
Pushing back above her at times when I
pushed back and said we needed more
support.
Bringing my problems up, getting me into
important meetings.
Connection 6 0 More frequent group meetings and drop-in
hours.
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Categories of
supervisor
strategies
What
strategies, if
any, did your
direct
supervisor
implement to
support your
wellbeing
during the
pandemic?
Which of
these direct
supervisor
strategies did
you think was
most
effective?
Representative quotes
n n
Encouraging connection via zoom events
and trainings.
Support for team
meetings
5 3 Created a space for colleagues to discuss
challenges and successes.
We had an adhoc [sic] dean’s meeting with
the VPI/VPSS to discuss any concerns
we were having.
To have more frequent meetings with our
core group.
Work autonomy 5 2 Did not micro-manage.
He left me alone.
Allowing me to run my area in the way
that best allowed me to be more efficient
while continuing to maintain safety
protocols.

Note. Responses were provided by 257 participants. n = number of unique mentions among total
responses.

The same six categories remained the most common when evaluated for the supervisor
strategy that respondents found to be most effective, but the order changed. Remote work (n =
26) was most common, followed by encouraging self-care, balance, and time off (n = 24). The
next three were check-ins (n = 21), communication (n = 19), offering support and encouragement
(n = 15), and caring and empathy (n = 14).
Table 19 does not show supervisor strategies that were mentioned fewer than five times.
These included celebrations, modeling of behavior, change in work, professional services,
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training, compensation, and EAP. Three of these were mentioned only once each as most
effective: compensation, resources, and professional services.
Other Emerging Qualitative Findings
Administrators Refer to Remote Work and Flexible Work in Different Ways
Participants described remote work and flexible work in diverse ways, sometimes clearly
separating their meaning, whereas other times using them to mean similar things. For example,
some respondents drew a distinction between remote and flexible by indicating the organization
supported “remote work and flexible schedules” or “remote work, flexible hours.”
Flexibility was also used to refer to how remote work is done, such as having
organizational support for “for working remotely if needed,” “flexibility in work location,” and
“flexibility in when I have to be on-site.” Similarly, respondents indicated that supervisors
supported “flexibility to work remotely and independently” and “flexibility in schedules and
locations to serve the new varying needs of students.” One respondent noted their supervisor
“encourage[d] [them] to be fluid in [their] on-site and remote hours.”
Barriers to Participation by Administrators
Several respondents identified policies, practices, or strategies that had been
implemented, but also noted barriers to their participation, with nearly all referring to workload
as a reason. For organizational policies and practices, 10 respondents wrote of not being able to
participate in strategies primarily related to well-being trainings and programs. In contrast, for
supervisor strategies, seven respondents referred to being encouraged to take time off or form
boundaries for work, but being unable to do so effectively. Table 20 shows representative quotes.

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Table 20
Barriers to Participation
Policy, practice, or strategy Representative quote
Organizational policy or
practice
I have not been able to attend either of these programs due to
workload, but it makes me feel a little better to know
someone is at least trying to support wellbeing
To be honest, I was too busy to attend of the trainings offered.
I know colleagues who felt the same way. However, for
those who were able to attend, they felt they were beneficial.
None, I was not able to participate in any.
With no time to attend webinars, it was difficult to make time
for them. The few I attended helped.
Supervisor strategy My supervisor encourages me to take time off if I need it but
the amount of work that I come back to is so large that I
don’t take time off.
Supporting a shut down time, although that has been great in
concept but in reality is nearly impossible to do if wanting to
stay afloat.
Told me to take time off, then added three more things for me
to do—so not much.
Allowed me to take time off when needed, though I can never
be gone from work (which is also at home) so it’s not
realistic to get a break.

Workload Has Increased
As previously discussed, organizations and supervisors implemented and supported ways
to support the well-being of administrators. However, an emerging finding based on qualitative
analysis of this study suggests that administrators faced increased workload due to the pandemic.
Eleven respondents in the study offered a glimpse into this issue. One administrator noted that
“workload has increased significantly as a result of covid [sic] planning and management” and
another stated their organization did nothing and instead “only added to workload, stress and
expectations without much support.” One person wrote the pandemic had “overwhelmed and
tripled my responsibilities.” Furthermore, remote work, which was perceived by administrators
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to be one of the most effective strategies that supported their well-being, may have had negative
effects. For example, one administrator wrote, “remote work … which had led to MORE WORK
and LONGER WORK HOURS!!!” Finally, another administrator shared:
I’m not trying to be negative, but I feel the district could have done more to support my
wellbeing during the pandemic—or at least not continue to pile stuff on so high that I
continually feel like I’m drowning.
Policies, Practices, and Strategies Might Not Address Needs of Administrators
Eleven respondents added commentary that although the organization or supervisor
implemented policies, practices, or strategies to support well-being, they felt that administrators
were not the intended focus of those efforts and thus, might not be able to benefit. Examples of
these responses included, “We seem to be concerned about everyone else’s wellbeing but not our
own,” “Administrators were not really targeted. … We were more focused on the wellbeing of
our instructors and staff,” and “Not much has been done to support administrators but there have
been programs for faculty and staff.” Other responses included, “Sometimes, I wish they would
do more to support middle managers” and “There was relatively little focus on the needs of
administrators.” Another person responded that there were no strategies for “MY wellbeing as a
VP. I implemented policies for faculty,” reflecting a theme that administrators were busy during
the pandemic addressing the needs of others with no “consideration given to management
wellbeing.”
Implemented Policies, Practices, and Strategies That Were Ineffective
Some respondents articulated policies, practices, or strategies that the organization or
supervisor had implemented to support well-being, yet their answers to the follow-up question
about the effectiveness of those efforts suggested they were not effective. Among 42 responses
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from 36 individuals who had already identified efforts by their organization or supervisor,
answers included “none,” a version of “not applicable,” or something more descriptive, such as
“this did not help” and “nothing has been effective organizationally.” One respondent listed two
organizational policies and practices as effective, but also responded that neither was most
effective. Another indicated the organization provided mental health hotlines but responded
“none” for what was most effective. In another case, a strategy implemented by the supervisor
was actually considered “counter-effective.”
Interestingly, two respondents indicated that their organization did not implement
anything, and that they implemented strategies themselves. For instance, one individual wrote,
“My managers and I put things together for our department to help with staff wellbeing.”
Another wrote, “As colleagues, several of us reached out to each other to provide support for one
another.”
Categories of Responses When Policies, Practices, or Strategies Were Not Identified
Responses that did not specify or identify a policy, practice, or strategy that was
implemented to support well-being, whether by the organization or the supervisor, were nuanced.
Table 21 shows five categories and representative quotes. The first category, “cannot think of
anything,” represents answers that suggested the person actively considered the question, but
then could not or did not recall any that were effective. Next, three distinct types of responses
indicated variations of “none.” First, some respondents simply wrote “none” or “nothing.” Other
answers included commentary or text that suggested “none” or “nothing.” Another category
encompassed responses that included commentary or text that can be interpreted as negative in
tone. No responses categorized as “none” or “nothing” had a commentary that was positive.
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Finally, the table shows the distribution of responses by question that indicated some version of
“not applicable” or were left blank.

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Table 21
Categories of Responses When Policies, Practices, or Strategies Are Not Identified
Response when
policies,
practices, or
strategies are
not identified
Number of responses Representative quotes
Organizational Supervisor
Strategies Most
effective
Strategies Most
effective

Cannot think of
anything
7 6 1 1 Nothing comes to
mind.
None that I am aware
of.
Nothing specific.
None or nothing 40 20 44 15 None.
Nothing.
None or
nothing,
indicated by
commentary
or text
21 8 15 3 The college did not
implement any
policies to support
well being.
My direct supervisor
didn’t do anything
else.
No formal policies
have been
established.
None or
nothing,
indicated by
commentary
or text that is
negative in
tone
17 6 8 5 None-completely
inflexible and not
human centered in
my district.
I feel there was little
to no support for
my well being
other than lip
service.
Don’t make me
laugh.
Not applicable 2 32 9 33 N/A.
NA.
Not applicable.
Blank 67 112 76 121

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Summary of Findings for Research Question 3
This study addressed Research Question 3 by identifying organizational and leadership
practices implemented during the pandemic that supported the well-being of administrators. The
most common of these approaches at the organizational level were:
• Remote work
• Flexible work
• Well-being-related training, activities, and resources
• COVID-19 safety policies, protocols, and equipment
• Necessary tools and technology support for remote work
In contrast, the most common and most effective supervisor strategies were mostly interpersonal
in nature:
• Encouragement of self-care, balance, and time off
• Regular check-ins
• Remote or flexible work
• Caring and empathy
• Communication
Remote or flexible work was the only overlapping strategy with both high frequency and high
effectiveness between organizational and supervisor levels.
Additional findings based on analysis of open-ended responses emerged. First, although
both remote work and flexible work were identified by survey respondents, the terms remote and
flexible were sometimes used to describe different things or the same thing. Second,
administrators may not have had the time to take advantage of organizational and supervisor
efforts to support their well-being, especially because their workload had increased during the
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pandemic. In addition, the policies, practices, and strategies implemented during the pandemic
may not have been designed to meet the specific needs of administrators, and some were not
effective. Finally, even when respondents identified no policies, practices, or strategies, their
responses were varied, nuanced, and informative.
Summary of Overall Findings
In addressing Research Question 1, this study showed emotional exhaustion, burnout, and
fatigue were strongly correlated with perceptions of low effectiveness at work and moderately
correlated with relative lower effectiveness at work during the pandemic. All three factors were
also moderately to strongly correlated with perceived lower well-being during the pandemic,
which was moderately to strongly correlated with both measures of low effectiveness. They were
also similarly correlated to the lack of organizational support—specifically, the lack of someone
such as a supervisor at work who cares about the administrator as a person.
Results for Research Question 2 revealed statistically significant differences between
certain demographic groups. Perceptions of low effectiveness was significantly elevated among
those younger than 40 and those with the youngest dependents, two dependents, and the fewest
direct reports. Perceptions of low effectiveness were significantly lower for older and more
experienced administrators. Perceived declines in effectiveness during the pandemic were
greatest for those with no or two dependents and lowest for those without a graduate degree.
Women and those with young dependents reported more fatigue and those aged 60 or
older reported the least fatigue. Black and African American participants were most likely, and
multiracial or multiethnic participants were next most likely to report the lack of a supervisor
who cares about them. The lack of needed materials and equipment was reported by significantly
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more women and least by those who worked 46 to 50 hours per week. Perceived declines in
well-being were lowest for those without a graduate degree.
In addressing Research Question 3, this study found the strategies implemented to
support employee well-being differed between the organization and supervisor levels. Remote
work, flexible work, well-being activities, COVID-19 policies and practices, and providing tools
and technology were the most common and effective organizational efforts. In contrast, the most
common and effective supervisor strategies focused mostly on the interpersonal: encouragement
of self-care and well-being, regular check-in, remote or flexible work, caring and empathy, and
communication. Other findings suggest that some efforts made by organizations and supervisors
did not benefit administrators due to their pandemic-related increased workload or because the
efforts were directed toward others in the organization.

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Chapter Five: Recommendations
This chapter first provides further analysis of the findings presented in Chapter 4 and
discusses how the results connect to literature in the field and the social cognitive conceptual
framework that guided this study. Next, the chapter turns to recommendations for practice based
on the study findings, followed by a discussion of limitations and delimitations. The chapter
closes with recommendations for future research and concluding reflections.
Discussion of Findings
This study found that emotional exhaustion, burnout, and fatigue were strongly correlated
with the perception of low work effectiveness overall and moderately correlated with the
perception of lower work effectiveness during the pandemic. Each variable reflects a belief or
perception of an individual, who makes up one part of the triad of the SCT conceptual
framework. In addition, nearly 55% of participants in the study reported feeling burned out at
work at least once a week, and more than 72% reported feeling mentally exhausted regularly.
These findings are in alignment with other studies during the pandemic, including one that
showed 65% of 1,500 respondents across multiple industries and countries felt burned out often
or always (Moss, 2021).
Discussion of Findings Related to Work Effectiveness, Emotional Exhaustion, Burnout,
Fatigue, Well-Being, and Measures of Organizational Support
Work effectiveness was evaluated in two ways, with a composite variable and a single
question designed to capture perceived changes in work effectiveness during the pandemic. The
composite was strongly correlated with emotional exhaustion, fatigue, and burnout. Both were
also correlated with lower perceived well-being during the pandemic. These relative measures
should be interpreted with caution because the internal reliability of single-item measures cannot
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be measured and retest reliability (R. E. Lucas & Donnellan, 2012; Wanous & Reichers, 1996)
was not possible based on the study design.
Although the results showed that half of administrators perceived their effectiveness had
decreased during the pandemic, it is notable that the other half perceived their effectiveness had
remained the same (30.5%) or even improved (19.5%). This aligns with the finding that even
though administrators reported feeling burned out, fatigued, a lack of control, and high levels of
demands at work, they still expressed confidence in their ability to handle their problems at
work. The sustaining of effectiveness for half of the administrators contrasts markedly with
nearly three quarters who reported decreased well-being during the pandemic.
This study examined burnout using a 2-item construct, with one question correlated to
emotional exhaustion and the other to depersonalization, which was designed as a rapid
screening tool for those in the medical field (Li-Sauerwine et al., 2020; West et al., 2012, 2009).
In this study, Cronbach’s alpha was .616, not reaching the ideal benchmark of .70. The rate of
respondents in this study who reported feeling burned out at work more than once a week
(54.9%) contrasts with the low rate who felt they had become more callous since taking their job
(12.3%). This contrasts with the results found by Li-Sauerwine et al. (2020). In their study
conducted prior to the pandemic, which compressed the 7-item MBI scale to six items, they
found 37% of medical residents felt burned out from their work at least once a week and 46.7%
felt they have become more callous toward people since they took their job. Notably, for
community college administrators, even though they felt burned out at work (emotional
exhaustion) at a higher rate, they felt callousness (depersonalization) at a lower rate. One
possible reason is that the higher education setting buffers more against the development of
callousness toward the people served. Instead of directly serving patients, community college
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administrators directly serve faculty members, classified professionals, other administrators, and
to a lesser degree, students. Alternately, the Maslach and Jackson model of burnout (Maslach &
Jackson, 1981) posits that emotional exhaustion leads to depersonalization; over time, that can
lead to decreased professional efficacy or sense of personal accomplishment. Given that nearly
20% of participants had been in their current job for 1 year or less and 47% were in their current
role for 2 to 4 years, it could be that in a higher education setting, the high level of reported
burnout at work (emotional exhaustion) has not yet led to callousness (depersonalization). A
longitudinal study could address whether that progression of emotional exhaustion to
depersonalization applies to this population or setting.
As a measure of perceived organizational support, the lack of a supervisor who cares
about the administrator as a person was correlated with burnout to a moderate degree and with
emotional exhaustion, fatigue, and low effectiveness to a lesser degree. These results align with
the SCT conceptual framework, wherein the environment influences the individual’s beliefs. For
this study, this included whether the administrator perceived there is a direct supervisor or other
person at work who cares about them as an individual (Harter et al., 2002). Qualitative analysis
further highlighted the importance of a caring supervisor to the well-being of administrators.
Namely, supervisor strategies that emerged as both common and effective include encouraging
self-care, offering support and encouragement, and being caring and empathetic. However, it is
of concern that more than one fourth of administrators in this study did not agree that their
supervisor or someone at work cared about them as a person.
Discussion of Findings Related to Demographic Differences
This study found statistically significant differences between certain demographic groups
for some variables. For instance, perceptions of effectiveness at work were highest for those aged
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60 or older and those with 21 or more years of experience in higher education, whereas those
younger than 40, those with two dependents, and dependents aged 5 or younger had the lowest
perceptions of effectiveness at work. This suggests perceived effectiveness was lower among
administrators who were younger, less experienced, and had young children.
Interestingly, administrators with two or fewer direct reports had significantly higher
levels of ineffectiveness. One possibility is that these individuals had fewer direct reports
because they were earlier in their career. Another interesting finding was that administrators who
did not have a graduate degree reported the least decline in both effectiveness and well-being
during the pandemic. Although the sample was small, this was a significant difference. Further
analysis of the data could provide insight into these demographic differences. One notable
finding is the lack of statistically significant differences among groups based on work
demographics such as role and years in the current role.
In contrast to the relatively few differences found between groups according to work
demographics, there were significant differences related to certain personal demographics. For
example, those aged 60 or older reported significantly lower levels of fatigue, echoing the
finding that this same demographic group perceived significantly higher levels of effectiveness
than all other age groups. This could be related to being less likely to be raising young children,
having more career experience, or being in positions of leadership or greater influence. The latter
is supported by Sherman et al. (2012), who found an inverse relationship between leadership
level and stress, measured physiologically, and Korman et al. (2022), who showed that managers
at higher hierarchical levels have less risk of burnout.
One key finding of this study is that women reported significantly higher levels of fatigue
than men. In addition, those with dependents aged 5 and younger also showed significantly
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higher levels of fatigue. These two demographic groups also reported the lowest perceived
effectiveness. These characteristics may be related to the higher fatigue among women with
families.
These results reflect some of the literature published at the beginning of the pandemic
and more recent literature. For example, McKinsey & Company (2020) showed women (54%)
were much more likely than men (41%) to feel exhausted and also more likely to be burned out
(39% vs. 29%, respectively). In addition, women were more likely to feel pressured to work
more and spend additional time on housework and childcare while also being less likely to share
their work–life challenges. Moreover, in another study among faculty members, women were
more likely than men to report the deterioration of their work–life balance (74% vs. 63%,
respectively) and increased workload (82% vs. 70%; Chronicle of Higher Education, 2020). A
recent study of those who worked at home due to the pandemic found predictors for stress and
depressive symptoms included being female or younger than 45 (Platts et al., 2022), and a recent
industry survey of 1,001 employees showed 66% of women reported feeling burned out in the
past seven days, compared to 50% of men (Elsesser, 2022; Willis, 2022). This prior study also
showed that only 50% of women were likely to ask for help if they feel burned out (comparable
results were not reported for men). This suggests that organizational or supervisor check-ins may
help reveal dimensions of burnout among administrators. Because employees may be reluctant to
share mental health issues openly (Ginger, 2021; Moss, 2021), a supervisor’s interpersonal
strategies identified in this study as effective supports for well-being, such as check-ins, offering
of support and encouragement, and caring and empathy, may help reveal and address exhaustion
and burnout.
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One measure of organizational support showed differences according to race and
ethnicity. Importantly, administrators who identified as Black or African American were more
likely to report that they lacked a supervisor or other person who cared about them compared to
those who identified as Asian American, White, and Hispanic or Latino. A similar statistically
significant difference existed for those who identified as multiracial or multiethnic when
compared to administrators who were Asian American or White. These results are especially
concerning given that more than one fourth of all administrators did not feel their supervisor or
someone at work cared about them as a person.
The disproportionate impacts based on gender and race and ethnicity found in this study
add to the literature regarding how the pandemic affected certain demographic groups more than
others. For example, both Black and Asian American women have reported higher rates of stress,
exhaustion, and burnout than other demographic groups (McKinsey & Company, 2021). Black
women, in particular, felt less supported and more excluded at work and were less likely to
report their supervisor had asked about their workload or work–life needs (McKinsey &
Company, 2020). The present study showed that administrators who identified as Black or
African American or as multiethnic or multiracial were significantly less likely to feel they had a
supervisor or person at work who cared about them at work. However, this difference was not
further disaggregated according to gender. Further investigation is recommended.
The second measure of perceived organizational support was the lack of the necessary
materials and equipment needed to do their work. The correlation was weak between this and
other variables: emotional exhaustion, burnout, fatigue, and both measures of work effectiveness.
However, women were significantly more likely to report the lack of materials and equipment to
do their job, which suggests an area for additional research. The importance of necessary
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materials and equipment was also supported by qualitative data in this study, which found a
theme of “necessary tools and technology support” among the more common organizational
practices and policies that positively influenced the well-being of administrators. These results
suggest that providing adequate technology equipment and technical support for remote and
flexible work can positively affect well-being.
Discussion of Findings Related to Policies, Practices, and Strategies That Positively
Influenced Well-Being
Participants in this study identified a wide range of policies, practices, and strategies
implemented by their organization or direct supervisor that they found effective in supporting
their well-being. When viewed relative to the conceptual framework, these policies, practices,
and strategies are part of the environment’s influence on the individual and their behavior. These
organizational and supervisor efforts also align with the literature in three areas: strategies that
are successful at promoting workplace well-being, types of approaches to supporting well-being,
and areas of work life that contribute to burnout.
This study found many examples of practices, policies, and strategies that reflect findings
in the literature about promoting well-being in the workplace during the pandemic. These include
increased flexibility, modification of work demands, and promotion of self-care (AXA Asia and
Columbia University World Health Organization Centre for Global Mental Health, 2020;
McKinsey & Company, 2020; Moss, 2021; Sull et al., 2020). Specifically, participants identified
remote work, flexible work locations and schedules, management of work demands by a
supervisor, active encouragement of self-care by a supervisor, and communication, among
others. Clear, frequent, high-quality, and bidirectional communication; promotion of social
support systems; and celebration of accomplishments, which have been identified in the
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literature (AXA Asia and Columbia University World Health Organization Centre for Global
Mental Health, 2020; Bersin, n.d.; Gallup, 2020; Sull et al., 2020), also emerged among
categories of practices and strategies that administrators found effective. In this study, these
included check-ins, recognition and praise by supervisors, and communication and opportunities
for social connections offered by both the organization and the supervisor. Finally, the
importance of overall manager support (Gallup, 2020) was reflected in different categories of
supervisor support: offering support and encouragement, caring and empathy, and advocacy.
The approaches identified in this study represent a variety of primary, secondary, and
tertiary interventions to support workplace well-being that have been described in the literature
(Daniels, Fida, et al., 2021; Daniels et al., 2017; Quick & Henderson, 2016; Sliter & Yuan,
2015). Participants identified several effective primary interventions, defined as approaches that
prevent or remove workplace stressors: work redesign through remote work; access to necessary
technology and technical support; work autonomy through flexible work locations and
schedules; efforts to provide social support, especially check-ins by a supervisor; and active and
regular promotion of balance, again by a supervisor. In addition, some participants noted
maintaining boundaries between work and home such as not sending emails after work,
suggesting these types of work redesigns were effective. A direct supervisor’s management of
demands placed on administrators was also effective, which spans work redesign, social support,
and promotion of balance.
Secondary interventions such as meditation, coping training, and health promotion are
designed to manage existing stressors. In addition, some secondary approaches have been found
to be effective for reducing emotional exhaustion (Maricuţoiu et al., 2016). Participants
identified several secondary approaches as helpful: well-being workshops, trainings, webinars,
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and activities. However, the findings also suggest that increased workloads served as a barrier to
participation in these secondary approaches. Next, some participants identified tertiary
approaches, which are defined as those that address negative well-being that has already
occurred (Sliter & Yuan, 2015). Among two dozen mentions of access to EAPs, direct
counseling, and other on-call professional services, 10 responses also considered these as the
most effective practice or policy implemented by their organization to support their well-being.
Given the recent expansion of EAP offerings among higher education institutions as a response
to the COVID-19 pandemic (Turk, Soler, Chessman, et al., 2020), it would be informative to
determine if EAPs were similarly expanded or promoted among community colleges.
Finally, many participants mentioned more than one effective practice, policy, or strategy
that had differing influences on their well-being. Moreover, some participants referred to
interventions that were no longer in effect, especially related to remote or flexible work. The
literature suggests that certain combinations of interventions may have longer lasting effects,
such as on emotional exhaustion (Awa et al., 2010), and that the success of interventions depends
on aspects of how interventions are implemented and sustained through organizational structures
(Daniels, Fida, et al., 2021). Although beyond the scope of this study, the coordinated and
sustained implementation of practices, policies, and strategies may have affected how
administrators perceived the impact of those efforts.
It is also important to note that one of the most commonly cited and effective strategies
did not fit clearly into one of these three intervention levels. Specifically, the implementation of
various COVID-19-related policies and protocols has not yet been categorized in the literature
regarding its level of intervention. COVID-19 safety practices, equipment, and policies could be
considered primary approaches because they prevent or remove stressors; alternately, they could
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be considered secondary approaches because they manage existing stressors—namely, the threat
of COVID-19 to well-being and health.
In addition to categorizing practices, policies, and strategies as one of three levels of
intervention, the literature also has classified them according to whether they are organization or
person directed (Daniels, Fida, et al., 2021; Daniels et al., 2017; Nielsen et al., 2017; Sonnentag,
2015). In this study, organization-directed policies and practices that were mentioned and
considered as most effective related to remote work and COVID-19 safety. In contrast,
individual-oriented approaches that focus on how an individual perceives or responds to their
work situation included meditation, yoga, and coping. These examples were included in the third
most frequently most mentioned organizational category: well-being training, workshops, and
resources. In addition, this study aligns with the literature showing an important role of a
supervisor (Wieneke et al., 2019) who encouraged the administrator to participate in these
individual-oriented approaches, especially self-care.
The organizational and supervisor approaches to supporting well-being that emerged
from this study can also be evaluated relative to areas of work life that influence burnout:
workload, control, reward, community, fairness, and values (Leiter & Maslach, 2003). Examples
of all six of these areas of work life emerged in the study. For instance, several study participants
noted the negative impact of increased workload. Increased workload has been described during
the pandemic (Chronicle of Higher Education, 2020) and is a primary predictor of burnout
(Moss, 2021). Also, control emerged as a major theme in the study; many participants cited
remote work and flexible work frequently and ranked them as most effective. Others wrote about
the role conflict that arose because managers focused on supporting the well-being of others and
were not the focus of that type of support themselves. Reward, as seen in praise and recognition
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by a supervisor, was also mentioned as supportive of well-being. Community and other social
connections were found to be effective at the organization and supervisor levels. In terms of
fairness, several administrators wrote of different standards for policies related to returning to
campus, either among other administrators or relative to other classifications of employees.
Leadership, culture, and values were also mentioned as supportive of well-being. It is important
to note that some of these data arose not as the direct answer to the question of what was
effective, but rather through narratives that suggested what was not effective.
The nuanced responses to open-ended questions provided a compelling look into the
lived experiences of community college administrators as they led their organizations through
the COVID-19 pandemic. Answers often expanded beyond the questions asked, in some cases
several sentences long and reflective in nature, indicating a need for voices to be heard. Some
participants provided detailed descriptions of how their supervisors had cared for them or
specific organizational policies improved their well-being. Others provided various examples of
well-being efforts yet indicated none was effective. Some expressed frustration with increased
workload and not having the time to engage in well-being efforts. These responses suggest that
practices, polices, and strategies implemented to support well-being during the pandemic
differed in their ability to reach the targeted audience with the desired outcomes.
Recommendations for Practice
This section describes recommendations for practice that address the problem of high
levels of occupational stress during the COVID-19 pandemic that have had negative impacts on
the well-being of community college administrators. These recommendations are based on
findings that administrators perceived worsening of their well-being and effectiveness during the
pandemic and that they regularly felt burned out. Research has shown that work-related well-
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being and burnout affect work engagement (Bakker & Demerouti, 2008) and burnout is
associated with greater intent to leave a profession, including in higher education settings
(Chronicle of Higher Education, 2020; Tabakakis et al., 2020). Both work engagement and
burnout affect health and other well-being measures (Schaufeli, Bakker, et al., 2009). In addition,
the study provided indirect data on turnover, showing nearly 20% of administrators have been in
their current role for 1 year or less and 47% had been in their roles for only 2 to 4 years. Also,
qualitative answers indicated turnover had occurred for the direct supervisor of some study
participants, with subsequent changes to the level of support for well-being. Three
recommendations support the well-being of the administrator by focusing on the influences of
the direct supervisor and organization. A fourth recommendation is centered on addressing the
demographic differences in perceptions of well-being, organizational support, and effectiveness.
Recommendation 1: Implement a Training Program for Supervisors to Become Coaches
The first recommendation is to implement a program that trains the direct supervisors of
administrators on effective coaching, including skills such as building trust and effective
communication. According to the findings of this study, community college administrators
reported lower well-being and lower work effectiveness during the pandemic. Certain strategies
implemented by the administrators’ direct supervisors positively influenced their well-being,
suggesting the significant role of the supervisor in administrator well-being. The strategies
mentioned and identified as effective were mostly interpersonal, such as regular check-ins,
caring and empathy, support and encouragement, and communication. Furthermore, this focus on
training for supervisors would help address the finding that more than one fourth of
administrators did not report having a supervisor or person who cared about them. Of particular
concern is the statistically significant finding that Black and African American and multiracial
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and multiethnic administrators were more likely to report the lack of a supervisor who cared
about them as individuals, making the focus on the supervisor even more important. This
recommendation aligns with the triad of the SCT conceptual framework, with the direct
supervisor as part of the environment that influences the individual’s perception of their well-
being and effectiveness.
The effects of the supervisor extend further. The presence of a supervisor who cares
about the employee as a person is positively correlated with an employee’s job performance
(Harter et al., 2010). Moreover, both the relationship between the individual and their supervisor
or manager and the behavior of the manager are key factors in the decision of employees to leave
their job (Gallup, 2017). Leaders can reduce employee turnover intent if they adapt their
leadership styles to the needs of their employees (Reed, 2021). A coaching program aligns with
the findings of Wieneke et al. (2019) that showed supervisors feel they can support well-being by
serving as a role model and encouraging employees to engage in wellness activities. Training
programs could be developed internally or delivered through third-party platforms.
Recommendation 2: Develop Sustainable and Equitable Policies and Practices for Remote
and Flexible Work
The second recommendation is for colleges to develop sustainable and equitable policies
and practices regarding remote and flexible work. This study strongly supports this
recommendation. An equitable remote work policy would adequately address two types of needs.
First, to support effective remote work, administrators need adequate technology, equipment, and
technical support. Results of this study revealed the most commonly mentioned practices, also
noted as most effective at supporting well-being, included remote work and flexible work.
Qualitative data also showed administrators considered having necessary tools and technological
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support to do their work remotely to be effective at supporting their well-being. Furthermore,
quantitative data showed that nearly 80% of administrators perceived that their organizations
provided them the necessary equipment and materials to do their job; a lack of access, in
contrast, was correlated with low work effectiveness. When viewed in the SCT framework, this
recommendation focuses on the environment—specifically, the remote and flexible work
policies and technology (equipment and support) provided to the person. The organization
creates this environment, which affects the perceptions and belief of the person, including their
sense of well-being and work effectiveness.
An equitable remote work policy would also address a second need, the equitable
implementation of practices and policies related to remote and flexible work. This study found
flexible work to be among the most common and most effective organizational practices and
policies that positively influenced administrator well-being. Flexible work was also perceived as
distinct from remote work, with multiple mentions of flexibility regarding family and other
obligations and when and where the administrator worked as supporting their well-being. This
suggests that flexibility is a key component of an equitable practice because it provides support
according to the specific and dynamic needs of each administrator.
It is also important to pay attention to the equity of access to technology and technical
support and how remote and flexible work policies are applied. For example, more than 20% of
administrators in this study disagreed that they had the equipment and materials to do their job.
Significant differences emerged for certain demographic groups about whether they lacked the
materials and equipment to do their job, suggesting that remote work, flexible work, or both
could have different impacts on these groups. In addition, some administrators remarked on how
different classifications of employees at their organizations were subject to different practices or
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policies related to remote or flexible work and that administrators had less flexibility than other
groups. Policies for flexible work that are equitably applied support the fairness component of
the areas of work life that foster well-being and help prevent burnout (Leiter & Maslach, 2003).
Development of policies and practices regarding remote and flexible work requires
attention to several factors. For example, remote work implemented at the beginning of the
pandemic created challenges such as how to maintain regular and effective communication
among teams, sustain engagement and productivity, and remain aligned with strategic goals (Sull
et al., 2020). Research on the effect of remote work during the pandemic showed both negative
effects on certain workers (Platts et al., 2022) and correlations with positive well-being via
fulfillment of hedonic needs (Marikyan et al., 2021). Support for administrators working
remotely should address both equipment, technology, and technical support needs and social
needs. When leaders ensure employees have the necessary materials, equipment, and
development opportunities to meet the goals of the organization, the effectiveness of the
organization increases (Waters et al., 2004).
Recommendation 3: Implement a Well-Being Program and Ensure Its Accessibility and
Responsiveness to Administrators’ Needs
The third recommendation is to implement a well-being program in colleges and districts
and ensure its accessibility and responsiveness to the needs of administrators. In this study,
administrators reported the most common and most effective way that their direct supervisor
supported their well-being was through the encouragement of self-care, balance, and time off.
Similarly, well-being training, activities, and resources were the second most common category
of practices or policies at the organizational level that administrators found supported their well-
being. However, administrators were not always able to participate in well-being activities due to
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competing demands. Demographic differences also highlight that well-being was not
experienced the same by all administrators, such as the higher fatigue reported by women and
those with young dependents. Employer-sponsored workplace well-being or wellness programs
support employees “to adopt and sustain behaviors that reduce health risks, improve quality of
life, enhance personal effectiveness, and benefit the organization’s bottom line” (Berry et al.,
2011, p. 4). Thus, these types of programs align with the SCT conceptual framework as part of
the environment that affects both the individual and their perceptions of well-being and their
behaviors.
The specific design of a well-being program should consider the needs and goals of the
college or district and the specific needs of administrators. This study found many specific
examples under the category of well-being activities and resources that administrators considered
most effective, suggesting that well-being programming should offer a wide array of options.
Well-being programs vary widely in design, implementation, and evaluation (Goetzel et al.,
2014). In a study of 64 community colleges, Thornton and Johnson (2010) found 42% had
employee wellness programs, as evidenced by factors such as a wellness program coordinator or
administrative body or health-promoting activities such as walking, nutrition, or a health fair. In
a study of seven university wellness programs, Hill and Korolkova (2014) found differences in
objectives, components, implementation and participation, and outcomes tracking.
Well-being programs can also benefit organizations. In a survey conducted by the RAND
Corporation, employers were confident that wellness programs generate financial benefits by
reducing absenteeism, productivity, and medical costs, although few formally evaluated or
reported actual savings (Mattke et al., 2013). One example of a successful worksite wellness
program that generated cost savings is described by Merrill and LeCheminant (2016). Because
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the setting was a multisite school district, a similar program may be adaptable to the structure of
community college districts.
Recommendation 4: Identify and Address Demographic Differences in Perceived Well-
Being, Organizational Support, and Effectiveness
The fourth recommendation is for colleges and districts to identify and address the
demographic differences in administrators’ perceptions of their well-being, organizational
support, and effectiveness. This requires measurement and longitudinal tracking of well-being
and analysis of structures and processes that affect well-being. This study found differences
based on age, gender, race and ethnicity, and age and number of dependents, suggesting that
efforts to support well-being were not experienced equitably. Given the correlation of well-being
with effectiveness, it was not surprising that perceptions of low effectiveness also showed
demographic differences.
In the SCT context, these perceptions held by administrators were influenced by their
environment and behaviors, including those identified in this study. The environment, for
example, included the demands of their work and interpersonal interactions with supervisors and
various organizational policies designed to support well-being. Behaviors such as remote or
flexible work or participation in well-being activities also influenced their perceptions.
Limitations and Delimitations
Limitations
Limitations related to quantitative research include several beyond those discussed in
Chapter 3. First, the reliability of the study was affected by measuring constructs with one or two
items, such as the 2-item MBI, rather than the recommended three or more items, potentially
affecting the reliability of the instrument and its correlations (Hocevar, 2022a). The composite
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variable for burnout also had an alpha coefficient less than .70. In addition, 1-item scales, such as
those used in the study for emotional exhaustion and lower effectiveness, likely have lower
reliability. Measures with low reliability could result in measurement error, such as attenuated
correlations and group differences that are less than they should be (D. Hocevar, personal
communication, May 10, 2022). This is a threat to statistical conclusion validity (Creswell &
Creswell, 2018). Due to the level of exploratory demographic analysis, type 1 error was possible
(D. Hocevar, personal communication, May 10, 2022), wherein the null hypothesis was rejected
even when there was no true difference between groups (Salkind & Frey, 2020).
Next, this study examined the correlation of variables. Correlational studies have a low
degree of internal validity, making it more difficult to draw inferences about the population
(Creswell & Creswell, 2018; Hocevar, 2022a). For example, emotional exhaustion, fatigue, and
burnout were found to be correlated with effectiveness, but that does not imply causation.
The external validity of this study is limited. Findings may not be generalizable to other
contexts. Participants were not randomly selected, the setting was specific to community colleges
in California, and the timing was 2 years into the pandemic (Creswell & Creswell, 2018;
Hocevar, 2022a). The use of an anonymous survey distributed through networks led to the
possibility of a small sample size or a nonrepresentative sample, which also affected the
generalizability of the findings (Creswell & Creswell, 2018; Hocevar, 2022b). As discussed in
Chapter 3, the ethnicity and age demographics of study participants were similar to those
reported by college districts to the state chancellor’s office (California Community Colleges
Chancellor’s Office, n.d.), but gender was less so.
Limitations of a survey include whether participants can comprehend, retrieve, judge, and
respond to survey questions accurately and completely (Robinson & Leonard, 2019).
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Standardized surveys also assume that respondents accurately reveal knowledge. However, these
instruments act as proxies based on operational definitions (Parris, 2015, Jan 5) that index what
is preconsidered as acceptable knowledge. Other limitations could include lower response rates
due to survey fatigue or in the case of the open-ended questions, survey respondents not being
willing or able to write detailed responses (Robinson & Leonard, 2019).
Another possible limitation of the study is decreased accuracy and completeness of
survey responses if participants felt a power discrepancy (Creswell & Creswell, 2018; Merriam
& Tisdell, 2016; Robinson & Leonard, 2019; Rosenberg, 2017). Because the researcher recruited
participants through acquaintances and organizations and networks to which they belong,
potential respondents could have felt pressure to participate or answer in certain ways. Even
though the survey was confidential and anonymous, participants may have been wary that their
employer or the researcher would have access to survey answers that revealed the participants’
state of well-being or burnout or their effectiveness as an administrator. Social desirability bias
could also have skewed the results (Robinson & Leonard, 2019). For example, an administrator
may have answered questions to make them look good to the researcher, rather than provide
authentic answers about their effectiveness during the pandemic or callousness toward their
colleagues. Others may have refrained from providing candid answers regarding exhaustion or
burnout because of concern that their answers would be extrapolated to represent a demographic
group, reflecting stereotype threat (Casad & Bryant, 2016; Steele & Aronson, 1995).
Because the survey adopted and adapted some, but not all, questions from multiple
existing instruments, the statistical reliability of the final survey instrument was not known at the
beginning of the study (Creswell & Creswell, 2018). In addition, the two novel Likert-style
questions were created for this study and have not been validated. Results could have also been
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affected by the order of questions (Robinson & Leonard, 2019) or the self-report nature of the
survey items, which could have generated response sets (Cronbach, 1946) or method effects (D.
Hocevar, personal communication, June 15, 2022; Maul, 2013). In addition, it was not possible
to follow up with participants to confirm the data.
An important limitation is related to Research Question 1. The use of both emotional
exhaustion and burnout as variables is problematic because emotional exhaustion is also
considered a component of burnout (Maslach et al., 1997). Thus, it is not surprising that the
findings showed emotional exhaustion is strongly correlated with burnout, as Maslach et al.
(1997) suggested. Emotional exhaustion, examined separately, and burnout, which includes
emotional exhaustion is its construct, both showed the same degree of correlation with both low
effectiveness and lower work effectiveness during the pandemic. Additionally, two items that
were part of the fatigue construct also utilized the term exhaustion—namely, physical exhaustion
and mental exhaustion.
Delimitations
The most important delimitation of this study is that the research questions limited its
focus. The study focused on two of three parts of the SCT triad, the person and the environment.
Furthermore, the emphasis was on one direction of influence, namely from the environment
toward the person. For example, the study examined the strategies, practices, and policies that
supported the well-being of administrators, rather than how administrators influenced those
efforts. In addition, the research question sought practices that positively influenced
administrators, but not those that may have had negative influences. The study also did not
address whether administrators implemented any strategies or practices. In addition, the data for
the study were collected from self-reports of states of exhaustion, burnout, callousness, fatigue,
145

and effectiveness, rather than from direct observations of the administrators’ behavior, objective
measures of their well-being or effectiveness, or surveys of their supervisors or direct reports.
The survey instrument adapted only certain questions from various instruments, which
limited the scope of the data collected. Another delimitation was that the study asked individuals
which polices, practices, or strategies supported their personal well-being, but it was not
structured to collect objective data about participation rates or whether these efforts were
effective for all. Also, the study was designed to be inductive regarding open-ended questions,
requiring respondents to recall what their organizations or supervisors had done. Thus, it was
possible that participants could not remember or were not aware of policies or strategies earlier
in the pandemic and whether they were effective.
Recommendations for Future Research
The findings of this study, its conceptual framework, and its limitations and delimitations
suggest several avenues of further research. The first recommendation is to repeat the study with
the opportunity for respondents to participate in follow-up interviews. Many responses to open-
ended questions went beyond the specific question; participants offered additional descriptive
narratives, suggesting that they wanted their voices heard. Some answers were long and
reflective about whether the organization’s or supervisor’s efforts were or were not effective. On
the other hand, the variety of written answers that indicate some form of none or not applicable
suggests meaning that was not explored. Thus, a follow-up study with semistructured interviews
could triangulate or expand the findings (Creswell & Creswell, 2018). Because this study asked
for effective strategies, additional research could explicitly examine why given policies,
practices, and strategies were effective and by extension, which ones were not effective and why
that was the case.
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The second recommendation arises from demographic ANOVA analysis, which found
certain groups perceived significantly different levels of effectiveness, well-being, fatigue, and
organizational support. In addition to this quantitative analysis, it would be informative to
conduct a qualitative comparison of responses disaggregated by these demographic
characteristics. For example, did administrators who identified as women or had dependents aged
5 or younger, all of whom experienced significantly more fatigue than other groups, have
qualitatively different experiences and perceptions about how organizational policies and
procedures affected their well-being? Importantly, for administrators who identified as Black or
African American, what do the qualitative answers about their supervisor reveal about their
significantly different perceptions regarding a supervisor or person at work who cares about
them as a person?
The third recommendation is based on the delimitation that the study focused on only
parts of the SCT framework. For example, the study examined the influence of the environment
on the person, but not the reciprocal influence of the person on the environment. Further research
into how community college administrators influenced their workplace to support their well-
being, including how they may have shaped policies and practices of their organizations, may
reveal novel practices that are effective in the community college setting.
Conclusion
During the COVID-19 pandemic, community college administrators experienced declines
in well-being. Emotional exhaustion, fatigue, and burnout negatively affected their perceptions
of their effectiveness at work, yet they did not feel calloused toward those they served. It is
important to pay attention to these levels of exhaustion and self-reported burnout because they
can lead to depersonalization, decreased professional efficacy, lower work engagement, and
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other negative impacts on the person and the organization. The study also identified demographic
differences related to gender, race and ethnicity, age, and dependents for measures of
effectiveness, fatigue, and organizational support, suggesting immediate areas of emphasis for
targeted support for administrator well-being. To support well-being, organizations implemented
various practices and policies. The most effective were interpersonal at the supervisor level and
related to remote and flexible work, well-being activities, and COVID-19-related practices and
policies at the organizational level. Recommendations for practice emphasize the role of the
supervisor, the effectiveness of remote and flexible work, the importance of accessible well-
being resources, and the urgency of understanding demographic differences in perceived well-
being, organizational support, and effectiveness.
Administrators have led their colleges and districts through more than 2 years of the
COVID-19 pandemic. Community colleges have developed and delivered programs and services
to support students and employees under dynamic and challenging conditions, with the
responsibilities for implementation falling on administrators. In a recent survey of more than 200
managers, 44% considered team burnout and workload balance among their top priorities
(Humu, 2022), yet this present study suggests managers are themselves exhausted and at risk of
burnout. In addition, the perception of employees that their organization cares about them has
declined steeply since the early months of the pandemic, with a markedly higher decline among
managers (Harter, 2022). The findings of this study can be used to address the well-being of
administrators more systematically and positively so they can continue to effectively lead their
teams and organizations and ultimately, benefit students.

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196

Appendix A: Demographic Characteristics of Participants
Characteristic n %
Role
Manager 17 5.5
Director, including senior, associate, or assistant 73 23.7
Dean, including senior, associate, or assistant 117 38.0
Vice president or provost, including associate or assistant or other CEO
cabinet
73 23.7
Vice chancellor, including deputy, associate, or assistant, or other
chancellor cabinet
11 3.6
CEO, chancellor, president, or superintendent 14 4.5
Worksite
College 272 88.3
District office 36 11.7
Years in current role
1 or less 61 19.8
2–4 144 46.8
5–10 79 25.6
11 or more 24 7.8
Years as administrator in higher education
1 or less 14 4.5
2–4 56 18.2
5–10 109 35.4
11–20 82 26.6
21 or more 47 15.3
Number of direct reports
None 7 2.3
1–2 23 7.5
3–5 38 12.3
6–10 90 29.2
11–20 58 18.8
21–50 47 15.3
51–100 22 7.1
101–150 15 4.9
More than 150 8 2.6
197

Characteristic n %
Hours worked per week
Less than 40 3 1.0
40–45 41 13.3
46–50 88 28.6
51–55 56 18.2
56–60 56 18.2
61–61 28 9.1
66–70 25 8.1
71–75 1 0.3
76–80 7 2.3
More than 80 3 1.0
Gender
Female 192 62.3
Male 106 34.4
Prefer to describe 2 0.6
Prefer not to answer 8 2.6
Race (all that apply)
American Indian or Alaska Native 8 2.6
Asian 28 9.1
Black or African American 31 10.1
Native Hawaiian or other Pacific Islander 4 1.3
White 198 64.3
Prefer to describe 20 6.5
Prefer not to answer 32 10.4
Ethnicity
Hispanic or Latino 65 21.1
Non-Hispanic or non-Latino 225 73.1
Prefer not to answer 18 5.8
Marital status
Divorced 36 11.7
Domestic partnership 16 5.2
Never married 29 9.4
Now married 207 67.2
Separated 4 1.3
198

Characteristic n %
Widowed 3 1.0
Prefer not to answer 13 4.2
Age
Younger than 30 3 1.0
30–39 34 11.0
40–49 116 37.7
50–59 108 35.1
60–69 38 12.3
70 or older 2 0.6
Prefer not to answer 7 2.3
Highest level of education
Some college 1 0.3
Associate degree 1 0.3
Bachelor’s degree 10 3.2
Master’s degree 141 45.8
Doctoral degree 152 49.4
Other 2 0.6
Prefer not to answer 1 0.3
Household income
$50,000–$99,999 9 2.9
$100,000–$149,999 50 16.2
$150,000–$199,999 88 28.6
$200,000–$249,999 48 15.6
$250,000–$299,999 45 14.6
$300,000–$349,999 24 7.8
$350,000–$399,999 12 3.9
$400,000–$449,999 7 2.3
More than $450,000 5 1.6
Prefer not to answer 20 6.5
Household size
1 (respondent) 37 12.0
2 96 31.2
3 64 20.8
4 63 20.5
199

Characteristic n %
5 24 7.8
6 15 4.9
7 1 0.3
8 0 0.0
9 2 0.6
More than 10 1 0.3
Prefer not to answer 5 1.6
Number of dependents
None 129 41.9
1 66 21.4
2 73 23.7
3 24 7.8
4 7 2.3
5 3 1.0
7 1 0.3
8 1 0.3
Prefer not to answer 4 1.3

Note. N = 308.

200

Appendix B: Survey Instrument Items
Question Response options RQ Construct Citation
I feel burned out from my
work.
Never, a few times a
year or less, once a
month or less, a few
times a month, once a
week, a few times a
week, every day
1 Burnout;
emotional
exhaustion
Maslach and
Jackson
(1981);
West et al.
(2009)
I have become more
callous toward people
since I took this job.
Never, a few times a
year or less, once a
month or less, a few
times a month, once a
week, a few times a
week, every day
1 Burnout Maslach and
Jackson
(1981);
West et al.
(2009)
Mentally, I feel
exhausted.
Never, sometimes,
regularly, often,
always
1 Fatigue Michielsen et
al. (2003)
Physically, I feel
exhausted.
Never, sometimes,
regularly, often,
always
1 Fatigue Michielsen et
al. (2003)
I have enough energy for
everyday life.
Never, sometimes,
regularly, often,
always
1 Fatigue Michielsen et
al. (2003)
When I am doing
something, I can
concentrate quite well.
Never, sometimes,
regularly, often,
always
1 Fatigue Michielsen et
al. (2003)
In the last month, how
often have you felt that
you were unable to
control the important
things in your life?
Never, sometimes,
regularly, often,
always
1 Effectiveness Cohen et al.
(1983)
In the last month, how
often have you felt
confident about your
ability to handle your
problems at work?
Never, sometimes,
regularly, often,
always
1 Effectiveness Cohen et al.
(1983)
In the last month, how
often have you felt
difficulties at work
were piling up so high
that you could not
overcome them?
Never, sometimes,
regularly, often,
always
1 Effectiveness Cohen et al.
(1983)
201

Question Response options RQ Construct Citation
I have the materials and
equipment I need to do
my work right.
Strongly disagree,
disagree, neither agree
nor disagree, agree,
strongly agree
1 Organizational
support
Harter et al.
(2002)
My supervisor or
someone at work seems
to care about me as a
person.
Strongly disagree,
disagree, neither agree
nor disagree, agree,
strongly agree
2 Organizational
support
Harter et al.
(2002)
How do you compare
your current
effectiveness in your
work relative to before
the pandemic?
My current effectiveness
is: significantly worse,
somewhat worse,
about the same,
somewhat better, and
significantly better
1 Effectiveness
How do you compare
your current wellbeing
relative to before the
pandemic?
My current wellbeing is:
significantly worse,
somewhat worse,
about the same,
somewhat better, and
significantly better
2 Well-being
What policies or
practices, if any, did
your organization
implement to support
your wellbeing during
the pandemic?
2 Strategies
Which of these
organizational policies
or practices did you
think was most
effective?

What strategies, if any,
did your direct
supervisor implement
to support your
wellbeing during the
pandemic?
2 Strategies
Which of these direct
supervisor strategies
did you think was most
effective?

Note. RQ = research question.

202

Appendix C: Comparison of Participant Personal Demographics
Table C1
Comparison of Participant Personal Demographics for Emotional Exhaustion, Burnout, and Fatigue
Characteristic

Emotional exhaustion Burnout Fatigue

n M SD F p M SD F p M SD F p
Gender

0.355 .552

0.098 .754 12.504* < .001
Female
a
192 4.85 1.57

3.93 1.36

3.27
ab
0.73
Male
b
106 4.74 1.58

3.88 1.45

2.95
ab
0.76
Race and ethnicity

1.498 .215

0.730 .572 1.451 .217
Asian
a
24 4.25 1.62

3.52 1.44

3.05 0.71
Black or African
American
b

29 5.00 1.65

3.95 1.69

3.46 0.82
White
c
162 4.87 1.54

3.96 1.35

3.14 0.74
Multiracial or
multiethnic
d

15 4.47 1.73

3.60 1.21

2.98 0.84
Hispanic or Latino
e
55 4.81 1.58

3.95 1.40

3.18 0.78
Marital status

1.187 .315

0.955 .415 1.463 .225
Previously married 43 4.84 1.59

3.88 1.49

3.32 0.78
Domestic partnership 16 4.06 1.62

3.41 1.56

3.06 0.78
Never married 29 4.86 1.38

4.14 1.44

3.29 0.89
Now married 207 4.82 1.60

3.91 1.35

3.09 0.75
Age

0.563 .640

0.870 .457 4.000* .008
Younger than 40
a
37 4.86 1.40

3.92 1.27

3.30
ad
0.76
40–49
b
116 4.81 1.61

3.94 1.43

3.28
bd
0.75
50–59
c
108 4.9 1.58

4.01 1.41

3.12
cd
0.71
60 or older
d
40 4.53 1.63

3.60 1.29

2.84
abcd
0.83
Highest education

0.751 .473

2.934 .055 1.826 .163
Bachelor’s
a
10 4.80 1.62

3.70 1.25

3.08 0.41
Master’s
b
142 4.92 1.56

4.11 1.40

3.26 0.74
Doctorate
c
152 4.69 1.58

3.72 1.36

3.09 0.79
203

Characteristic

Emotional exhaustion Burnout Fatigue

n M SD F p M SD F p M SD F p
Household income 0.424 .887 0.894 .511 0.876 .526
Less than $100,000 9 5.11 1.17 4.22 1.06 3.58 0.71
$100,000–$149,999 50 4.56 1.72 3.59 1.47 3.25 0.81
$150,000–$199,999 88 4.86 1.43 4.01 1.38 3.23 0.74
$200,000–$249,999 48 4.96 1.75 4.22 1.59 3.04 0.82
$250,000–$299,999 45 4.98 1.5 4.02 1.19 3.17 0.78
$300,000–$349,999 24 4.71 1.60 3.77 1.25 3.19 0.79
$350,000–$399,999 12 4.83 1.34 3.96 1.14 2.94 0.50
$400,000 or more 12 5.08 1.68 4.04 1.51 3.13 0.72
Household size 1.051 .388 0.588 .709 1.624 .153
1 37 5.03 1.42 4.12 1.34 3.39 0.88
2 96 4.57 1.69 3.79 1.49 3.04 0.77
3 64 4.8 1.46 3.81 1.26 3.18 0.73
4 63 5.03 1.51 4.09 1.42 3.28 0.74
5 24 4.54 1.77 3.83 1.43 3.04 0.63
6 or more 19 5.05 1.58 3.95 1.39 3.05 0.75
Number of dependents 0.740 .565 0.382 .821 0.503 .733
None
a
129 4.79 1.55 3.95 1.36 3.16 0.76
1
b
66 4.76 1.68 3.80 1.44 3.14 0.82
2
c
73 4.93 1.42 4.03 1.35 3.26 0.72
3
d
24 4.38 1.77 3.73 1.55 3.09 0.75
4 or more
e
12 5.17 1.85 3.79 1.51 3.00 0.75
Age of dependents 0.637 .637 1.048 .384 2.989* .020
5 or younger
a
9 5.33 1.32 4.17 1.06 3.69
acd
0.60
6–12
b
78 4.94 1.51 4.08 1.36 3.29
bcd
0.69
13–17
c
25 4.68 1.65 3.64 1.58 2.92
abc
0.75
18–22
d
41 4.59 1.79 3.61 1.45 2.99
abd
0.86
23 or older
e
21 4.67 1.68 3.83 1.49 3.24 0.80

204

Note. Significant ANOVA with at least three groups were analyzed using Fisher’s least significant difference test. Statistically
different groups are indicated by shared superscripts.
*p < .05.

205

Table C2
Comparison of Participant Personal Demographics for Lower Well-Being and Measures of Organizational Support
Characteristic

Lower well-being Lack of supervisor care Lack of materials and equipment

n M SD F p M SD F p M SD F p
Gender

0.656 .419

0.702 .403 6.726* .010
Female
a
192 3.86 0.88

2.11 1.07

2.52
ab
1.13
Male
b
106 3.77 1.02

2.00 1.09

2.18
ab
1.06
Race and ethnicity

1.573 .181

4.404* .002 0.510 .729
Asian
a
24 3.46 0.88

1.83
abd
0.87

2.46 1.02
Black or African
American
b

29 3.97 0.87

2.69
abce
1.20

2.41 0.91
White
c
162 3.88 0.88

1.92
bcd
1.04

2.31 1.10
Multiracial or multiethnic
d
15 3.60 1.30

2.53
acd
1.36

2.20 0.86
Hispanic or Latino
e
55 3.87 0.90

2.09
be
1.01

2.53 1.25
Marital status

0.532 .661

0.049 .985 1.214 .305
Previously married 43 3.91 0.92

2.12 1.14

2.67 1.11
Domestic partnership 16 3.56 1.21

2.00 1.21

2.25 1.18
Never married 29 3.83 0.97

2.10 1.08

2.38 1.01
Now married 207 3.83 0.91

2.08 1.07

2.33 1.11
Age

0.536 .658

0.985 .400 0.750 .523
Younger than 40
a
37 3.73 1.02

2.32 0.97

2.51 1.10
40–49
b
116 3.85 0.93

2.07 1.07

2.34 1.13
50–59
c
108 3.90 0.82

1.97 1.08

2.35 1.10
60 or older
d
40 3.73 1.06

2.10 1.22

2.60 1.15
Highest education

3.562* .030

0.069 .933 1.826 .163
Bachelor’s
a
10 3.10
abc
1.10

2.00 0.82

2.50 0.97
Master’s
b
142 3.88
ab
0.83

2.09 1.02

2.35 1.07
Doctorate
c
152 3.86
ac
0.95

2.05 1.14

2.44 1.16
Household income

0.585 .768

1.324 .239 1.098 .365
Less than $100,000 9 4.00 0.50

2.56 0.88

2.11 0.93
$100,000–$149,999 50 3.82 0.87 2.10 0.97 2.70 1.18
206

Characteristic

Lower well-being Lack of supervisor care Lack of materials and equipment

n M SD F p M SD F p M SD F p
$150,000–$199,999 88 3.92 0.83 2.06 1.17 2.33 1.10
$200,000–$249,999 48 3.90 0.99 2.02 1.04 2.38 1.10
$250,000–$299,999 45 3.64 1.07 1.78 0.85 2.16 0.90
$300,000–$349,999 24 4.00 0.88 2.38 1.10 2.54 1.22
$350,000–$399,999 12 3.75 1.06 1.83 0.94 2.33 0.89
$400,000 or more 12 3.75 0.97

2.42 1.51

2.50 1.09
Household size

1.244 .288

1.995 .079 1.129 .345
1 37 4.03 0.80

2.43 1.19

2.65 1.01
2 96 3.69 1.01

1.91 1.03

2.33 1.01
3 64 3.77 0.92 2.06 1.11 2.19 1.08
4 63 3.84 0.97

2.10 1.10

2.35 1.18
5 24 4.00 0.66

1.88 0.80

2.63 1.31
6 or more 19 4.05 0.78

2.47 1.12

2.42 1.12
Number of dependents

1.308 .267

0.724 .576 0.086 .987
None
a
129 3.83 0.88

1.99 1.08

2.39 1.01
1
b
66 3.71 0.99

2.12 1.18

2.36 1.20
2
c
73 3.89 0.87

2.12 0.99

2.36 1.10
3
d
24 3.75 1.15 2.04 1.16 2.50 1.38
4 or more
e
12 4.33 0.49

2.50 0.90

2.42 1.08
Age of dependents

0.447 .775

0.510 .728 0.549 .700
5 or younger
a
9 3.78 1.20

2.33 1.22

2.33 1.00
6–12
b
78 3.94 0.86

2.22 1.03

2.40 1.17
13–17
c
25 3.80 0.87

2.20 1.15

2.24 1.27
18–22
d
41 3.71 1.08 1.98 1.08 2.32 1.19
23 or older
e
21 3.76 1.04

2.00 1.18

2.71 1.10

Note. Significant ANOVA with at least three groups were analyzed using Fisher’s least significant difference test. Statistically
different groups are indicated by shared superscripts.
*p < .05.
207

Appendix D: Comparison of Participant Work Demographics
Table D1
Comparison of Participant Work Demographics for Emotional Fatigue, Burnout, and Fatigue
Characteristic

Emotional exhaustion Burnout Fatigue

n M SD F p M SD F p M SD F p
Role

2.105 .065

2.152 .059 1.170 .324
Manager 17 4.76 1.25

3.65 1.20

3.10 0.78
Director, including senior,
associate, or assistant
73 4.84 1.52

3.91 1.31

3.25 0.66
Dean, including senior,
associate, or assistant
117 4.65 1.65

3.81 1.42

3.10 0.80
Vice president or provost,
including associate or
assistant, or other CEO
cabinet
73 5.03 1.53

4.13 1.45

3.26 0.82
Vice chancellor, including
deputy, associate, or
assistant, or other
chancellor cabinet
11 5.82 0.75 4.77 0.82 3.27 0.48
CEO, chancellor, president,
or superintendent
14 4.07 1.73

3.25 1.34

2.84 0.72
Worksite

1.607 .206

3.010 .084 0.363 .547
College 272 4.76 1.58

3.85 1.39

3.15 0.77
District 36 5.11 1.53

4.28 1.36

3.24 0.72
Years in role

2.429 .065

2.313 .076 1.379 .249
1 or less 61 4.43 1.61 3.52 1.34

3.16 0.79
2–4 144 4.76 1.51

3.92 1.34

3.11 0.78
5–10 79 5.14 1.58

4.14 1.44

3.31 0.73
11 or more 24 4.83 1.71 3.96 1.52 3.05 0.71
Years in higher education

2.189 .070

1.868 .116

1.086 .364
208

Characteristic

Emotional exhaustion Burnout Fatigue

n M SD F p M SD F p M SD F p
1 or less 14 4.07 1.86 3.21 1.40

3.13 0.78

2–4
a
56 4.73 1.53 3.91 1.37

3.24 0.79

5–10
b
109 4.81 1.53 3.89 1.28

3.20 0.74

11–20
c
82 5.13 1.48 4.15 1.40

3.19 0.75

21 or more
a
47 4.49 1.72 3.67 1.59

2.96 0.79

Number of direct reports

0.713 .640

0.494 .813

0.727 .628
2 or fewer
a
30 4.83 1.53 3.87 1.41

3.24 0.65

3–5 38 4.55 1.41 3.88 1.28

3.07 0.66

6–10
b
90 4.66 1.55 3.77 1.40 3.15 0.73
11–20
c
58 4.78 1.61 3.84 1.32 3.05 0.82
21–50 47 5.11 1.59 4.06 1.38 3.31 0.86
51–100 22 4.86 1.67 3.98 1.48

3.18 0.78

More than 100
d
23 5.09 1.83 4.24 1.70

3.26 0.84

Hours worked per week

2.101 .053

1.847 .090

1.651 .133
40–45 41 4.49 1.83 3.49 1.48

3.06 0.78

46–50
a
88 4.59 1.39 3.74 1.17

3.03 0.71

51–55
b
56 4.68 1.61 3.91 1.41

3.18 0.86

56–60
b
56 4.91 1.70 4.01 1.58 3.26 0.77
61–65
b
28 5.36 1.34 4.29 1.35 3.46 0.69
66–70
e
25 5.24 1.51 4.38 1.42 3.18 0.73
More than 70
f
11 5.64 1.29 4.23 1.17

3.39 0.48

Note. Significant ANOVA with at least three groups were analyzed using Fisher’s least significant difference test. Statistically
different groups are indicated by shared superscripts.
*p < .05.

209

Table D2
Comparison of Participant Work Demographics for Lower Well-Being and Measures of Organizational Support
Characteristic

Lower well-being Lack of supervisor care Lack of materials and equipment

n M SD F p M SD F p M SD F p
Role

1.585 .164

0.895 .484 1.260 .281
Manager 17 3.53 0.94

1.65 0.70

2.12 1.05
Director, including
senior, associate, or
assistant
73 3.85 0.72

2.21 1.09

2.45 1.07
Dean, including senior,
associate, or
assistant
117 3.75 0.99

2.07 1.15

2.56 1.19
Vice president or
provost, including
associate or
assistant, or other
CEO cabinet
73 3.95 0.97

1.99 1.02

2.22 1.06
Vice chancellor,
including deputy,
associate, or
assistant, or other
chancellor cabinet
11 4.36 0.81 2.00 0.89 2.09 1.04
CEO, chancellor,
president, or
superintendent
14 3.93 0.62

2.21 1.19

2.43 1.16
Worksite

2.995 .085

0.868 .352 0.288 .592
College 272 3.80 0.94

2.04 1.06

2.39 1.12
District 36 4.08 0.77

2.22 1.20

2.50 1.13
Years in role

1.049 .371

0.492 .688 2.049 .107
1 or less 61 3.85 0.93

2.07 1.11

2.38 1.08
2–4 144 3.74 0.95

2.03 1.06

2.56 1.15
210

Characteristic

Lower well-being Lack of supervisor care Lack of materials and equipment

n M SD F p M SD F p M SD F p
5–10 79 3.96 0.87

2.18 1.16

2.25 1.06
11 or more years 24 3.92 0.88 1.92 0.83 2.08 1.18
Years in higher
education

0.224 .925

2.071 .085

1.059 .377
1 or less 14 3.86 1.17 2.29 1.38

2.64 1.15

2–4
a
56 3.80 0.94 2.14 1.10

2.64 1.23

5–10
b
109 3.89 0.82 1.95 0.99

2.35 1.02

11–20
c
82 3.77 1.03 2.28 1.14

2.35 1.15

21 or more
a
47 3.85 0.86 1.81 0.99

2.28 1.14

Number of direct reports

0.805 .567

0.663 .680

1.420 .207
2 or fewer
a
30 3.93 0.83 2.17 1.18

2.50 1.01

3–5 38 3.79 0.99 2.18 1.11

2.61 1.03

6–10
b
90 3.81 0.99 1.96 0.99 2.26 1.02
11–20
c
58 3.81 0.87 1.95 0.94 2.17 1.13
21–50 47 4.00 0.78 2.11 1.07 2.53 1.14
51–100 22 3.91 0.68 2.09 1.19

2.73 1.42

More than 100
d
23 3.52 1.20 2.35 1.43

2.57 1.31

Hours worked per week

1.939 .064

1.523 .170

3.245* .004
40–45 41 3.63 0.94 1.90 1.11

2.37 1.02

46–50
a
88 3.70 0.92 1.84 0.93

2.03
abcdef
0.98

51–55
b
56 3.82 1.03 2.14 1.03

2.44
ab
1.14

56–60
b
56 3.98 0.90 2.27 1.18 2.66
ac
1.21
61–65
b
28 4.25 0.89 2.32 1.25 2.60
ad
1.10
66–70
e
25 3.96 0.61 2.08 1.08 2.72
ae
1.17
More than 70
f
11 3.91 0.54 2.27 1.01

3.00
af
1.26

Note. Significant ANOVA with at least three groups were analyzed using Fisher’s least significant difference test. Statistically
different groups are indicated by shared superscripts.
*p < .05.

Abstract (if available)

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University of Southern California Dissertations and Theses

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Asset Metadata

Creator Ho, Nan (author)

Core Title Well-being, employee effectiveness, and organizational support: community college administrators during the COVID-19 pandemic

School Rossier School of Education

Degree Doctor of Education

Degree Program Organizational Change and Leadership (On Line)

Degree Conferral Date 2022-08

Publication Date 07/28/2022

Defense Date 07/07/2022

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

Tag administrators,Burnout,community college,COVID-19,effectiveness,OAI-PMH Harvest,well-being

Format application/pdf (imt)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Adibe, Bryant (committee chair), Hirabayashi, Kimberly (committee member), Seli, Helena (committee member)

Creator Email Nan_ho@comcast.net,nanho@usc.edu

Permanent Link (DOI) https://doi.org/10.25549/usctheses-oUC111375571

Unique identifier UC111375571

Legacy Identifier etd-HoNan-11038

Document Type Dissertation

Format application/pdf (imt)

Rights Ho, Nan

Type texts

Source 20220729-usctheses-batch-963 (batch), University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

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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