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The Relation Among Employee Alignment, Perceived Organizational Support, and Employee Engagement
by John George Meier III
B.S. in Business Administration, February 1986, Michigan Technological University M.S. in Management (Transportation Logistics), March 1994, Naval Postgraduate School M.S. in National Resource Strategy, June 2003, Industrial College of the Armed Forces,
National Defense University
A Dissertation submitted to
The Faculty of The Graduate School of Education and Human Development
of The George Washington University in partial fulfillment of the requirements
for the degree of Doctor of Education
January 8, 2021
Dissertation directed by
Ellen F. Goldman Professor of Human and Organizational Learning
ii
The Graduate School of Education and Human Development of The George Washington
University certifies that John George Meier III has passed the Final Examination for the
degree of Doctor of Education as of November 3, 2020. This is the final and approved
form of the dissertation.
The Relation Among Employee Alignment, Perceived Organizational Support, and Employee Engagement
John George Meier III
Dissertation Research Committee:
Ellen F. Goldman, Professor of Human and Organizational Learning, Dissertation Director
David R. Schwandt, Professor Emeritus of Human and Organizational Learning, Committee Member
Vijay Krishna, Senior Director, Credentialing Programs, ANSI National Accreditation Board, Committee Member
iii
© Copyright 2020 by John George Meier III All rights reserved
iv
Dedication To my wife, Wanda. You have been the love of my life, soulmate, and best friend
for over 37 years. Your unfailing support, optimism, encouragement, and tough love over
the past three and a half years have made it possible for me to complete this dissertation
and the doctoral program. I could not have done this without you. Thank you.
v
Acknowledgments The completion of this dissertation and my doctoral studies was only possible
with the love, support, and encouragement of many people along the way. I would first
like to thank Drs. Ellen Goldman, David Schwandt, and Vijay Krishna, the members of
my dissertation committee. To Dr. Goldman, my dissertation chair, a simple “thank you”
cannot express the level of appreciation I have for your continued support and mentorship
throughout this journey. I will be forever grateful for your encouragement, insights,
thoughtful feedback, and patience in helping me to continually improve my work. My
sincere thanks and appreciation to my committee members, Drs. David Schwandt and
Vijay Krishna, for your invaluable advice and support throughout the entire process.
Your insightful questions and comments helped me properly frame my research and
challenged me in ways that helped me conduct the best study I possibly could. I also want
to thank my additional examiners, Drs. Andrea Casey and Russel Robinson, for your
willingness to serve and support my research. To all of you collectively, your time,
encouragement, and thought-provoking questions are very much appreciated. It was an
honor to have my work reviewed and approved by you. Thank you!
I am so thankful for my Cohort 29 family. Each of you have supported,
encouraged, and inspired me. I have been fortunate to have friends who have become
family. My heartfelt gratitude and appreciation to each of you who have made this
journey so meaningful. The journey was made special by the memories and successes
that we have shared together.
I would be remiss if I did not acknowledge the tremendous efforts of the
Executive Leadership Program (ELP) faculty and staff for your continuous
vi
encouragement, guidance, and support over the past three and a half years. Thank you to
Dr. Andrea Casey, Dr. Ellen Goldman, Dr. Sherry Kennedy-Reid, Dr. Russell Korte, Dr.
Catherine Lombardozzi, Dr. Michael Marquardt, Dr. Martha Miser, Dr. Ellen Scully-
Russ, Dr. Julia Storberg-Walker, Dr. Susan Swayze, Dr. Brandi Weiss, Dr. Nicole
Dillard, Jaudat Ashraf, Amanda Ray Ennis, Larry Hoffman, Tara Patterson, Danielle
Tope, and Ximena Vidal De Col. Collectively, you make the ELP an amazing learning
experience.
Without the cooperation of the research site, this study would not have been
possible. A special thanks to Maura for your assistance in helping me gain access to the
research site and to Carol who coordinated the interactions with the research participants.
To the study participants, thank you for generously sharing your precious time and
perceptions related to employee engagement.
Most of all, I want to thank my family for their constant love, encouragement, and
support. Wanda, you were always there for me, encouraging me to take it one step at a
time and believing that we could do this together. Chloe, Daddy is finally done. My
parents instilled in me a love of lifelong learning, the importance of setting goals, and the
confidence to pursue my dreams. Mom and Dad, thank you for your unconditional love
and support in this and all endeavors. To my countless other family members and friends,
I wish I could name each of you. Please know that your words of encouragement,
prayers, love, and support helped me through this entire journey to pursue my doctorate.
Thank you all!
vii
Abstract of Dissertation
The Relation Among Employee Alignment, Perceived Organizational Support, and Employee Engagement
As organizations struggle to become and remain competitive, the engagement of
employees may be a critical enabler in achieving organizational goals, enhancing
organizational competitiveness, and improving employee well-being. To this end,
scholars have identified a continuing need for research focused on organizational factors
within the purview of managers to improve the engagement of employees (Alagaraja &
Shuck, 2015; Coyle-Shapiro & Shore, 2007; Eldor & Vigoda-Gadot, 2017; Oswick,
2015; Whittington et al., 2017; Whittington & Galpin, 2010).
Using the employee engagement framework proposed by Shuck and Reio (2011),
this research examined the relation among employee alignment, perceived organizational
support, and employee engagement in an organizational context. The research site was
the human resources department of a not-for-profit health care organization located in the
southern region of the United States. Census sampling was used to identify the actual
sample (Fritz & Morgan, 2010) of 109 full-time nonsupervisory employees whose data
was used in the analysis. Three self-report survey instruments were used: (a) the
Employee Engagement Scale (Shuck, Adelson, et al., 2017), (b) the Stringer Strategic
Alignment Scale (Stringer, 2007), and (c) the Survey of Perceived Organizational
Support (Eisenberger et al., 1986). Bivariate correlation and multiple regression analyses
were used to test the research hypotheses.
The results provided evidence of partial support for the researcher’s hypotheses,
with four of the seven hypotheses supported. Evidence was found for a positive relation
viii
among employee alignment, perceived organizational support, and employee
engagement, as well as the statistically significant contribution of employee alignment in
explaining unique variance in employee engagement (i.e., 23.4%). Contrary to
expectations, the results did not provide evidence that perceived organizational support
had a statistically significant direct effect on employee engagement. Additionally, the
results did not provide statistically significant evidence of either a moderation or
mediation effect of perceived organizational support on the relation between employee
alignment and employee engagement. This study provides preliminary evidence that
suggests that employee alignment, and to a lesser extent perceived organizational
support, are two factors within the purview of managers that can be useful in creating the
requisite organizational environment in which engagement may thrive.
ix
Table of Contents
Page
Dedication ..................................................................................................................... iv
Acknowledgments ......................................................................................................... v
Abstract of Dissertation .............................................................................................. vii
List of Figures .............................................................................................................. xv
List of Tables ............................................................................................................. xvii
Chapter 1: Introduction ................................................................................................... 1
Overview ........................................................................................................................ 1
Background of the Research Problem .............................................................................. 3
Statement of the Research Problem ................................................................................. 6
Purpose of the Study ....................................................................................................... 8
Research Questions and Hypotheses ................................................................................ 8
Potential Significance of the Study .................................................................................. 9
Conceptual Framework ................................................................................................. 11
Employee Engagement ............................................................................................ 13
Employee Alignment ............................................................................................... 15
Perceived Organizational Support ............................................................................ 17
Overview of the Methodology ....................................................................................... 18
Population, Sampling, and Study Sample................................................................. 19
Data Collection........................................................................................................ 20
Pilot Study............................................................................................................... 22
Data Analysis .......................................................................................................... 23
x
Study Limitations and Delimitations ............................................................................. 24
Limitations .............................................................................................................. 24
Delimitations ........................................................................................................... 26
Definitions of Key Terms .............................................................................................. 28
Chapter Summary ......................................................................................................... 32
Chapter 2: Literature Review ......................................................................................... 34
Methods of the Literature Review ................................................................................. 35
Engagement of Employees ............................................................................................ 37
The Importance of Engagement for Managers in Organizations ............................... 37
Developing an Understanding of Engagement ......................................................... 45
Antecedents of Engagement .................................................................................... 61
Alignment of Employees ............................................................................................... 64
The Importance of Alignment for Managers in Organizations .................................. 65
Conceptualizing and Defining Alignment ................................................................ 68
Related Constructs: Person-Organization Fit and Person-Job Fit .............................. 71
Relation Between Employee Alignment and Employee Engagement ....................... 72
Relation Between Employee Alignment and Perceived Organizational Support ....... 77
Perceived Organizational Support ................................................................................. 78
The Importance of Perceived Organizational Support for Managers in
Organizations .......................................................................................................... 78
Conceptualizing and Defining Perceived Organizational Support ............................ 80
Relation Between Perceived Organizational Support and Employee
Engagement............................................................................................................. 80
xi
Perceived Organizational Support as a Moderating and/or Mediating Variable ........ 83
Inferences for the Current Study .................................................................................... 86
Chapter Summary ......................................................................................................... 90
Chapter 3: Methods ....................................................................................................... 91
Research Design ............................................................................................................ 92
Purpose of the Study ................................................................................................ 93
Research Questions and Hypotheses ........................................................................ 93
Conceptual Framework, Research Model, and Analysis Models .............................. 94
Population ..................................................................................................................... 97
Sample ........................................................................................................................ 100
Sample Size and Power Analysis ........................................................................... 101
Sampling and Study Sample .................................................................................. 103
Data Collection ........................................................................................................... 105
Level of Analysis .................................................................................................. 105
Survey Research Design ........................................................................................ 105
Survey Instrumentation.......................................................................................... 108
Pilot Study............................................................................................................. 122
Data Collection Procedures and Survey Administration ......................................... 127
Data Storage .......................................................................................................... 131
Preanalysis Data Handling........................................................................................... 131
Data Handling ....................................................................................................... 131
Checking Assumptions .......................................................................................... 147
Data Analysis .............................................................................................................. 158
xii
Threats to Validity ...................................................................................................... 161
Internal Validity Threats ........................................................................................ 161
External Validity Threats ....................................................................................... 163
Human Participants and Ethics Precautions ................................................................. 163
Chapter Summary ....................................................................................................... 166
Chapter 4: Results ....................................................................................................... 168
Participant Demographics ........................................................................................... 168
Level of Analysis .................................................................................................. 169
Participant Demographic Descriptive Statistics ...................................................... 169
Survey Questionnaire Scale Reliability and Validity.................................................... 170
Reestablishing Questionnaire Scale Reliability ...................................................... 171
Reestablishing Questionnaire Scale Validity .......................................................... 173
Descriptive Statistics of Study Variables ..................................................................... 187
Research Questions and Hypothesis Testing ................................................................ 189
Research Question 1 .............................................................................................. 190
Research Question 2 .............................................................................................. 205
Chapter Summary ....................................................................................................... 208
Chapter 5: Interpretations, Conclusions, and Recommendations .................................. 210
Research Problem ....................................................................................................... 210
Interpretation of the Study Findings ............................................................................ 211
Finding 1: Correlations Between Employee Alignment, Perceived
Organizational Support, and Employee Engagement.............................................. 212
xiii
Finding 2: Accounting for Statistically Significant Unique Variance in
Employee Engagement .......................................................................................... 214
Finding 3: Perceived Organizational Support as a Moderating/Mediating
Variable in the Relation Between Employee Alignment and Employee
Engagement........................................................................................................... 216
Conclusions................................................................................................................. 218
Conclusion 1: Employee Alignment is Critical to Employee Engagement .............. 218
Conclusion 2: Perceived Organizational Support is Affected by Individual
Employee Perceptions of Their Unique Work Context ........................................... 219
Conclusion 3: The Study of Employee Engagement Requires a Systems
Thinking Approach................................................................................................ 221
Recommendations for Theory, Research, and Practice ................................................. 222
Recommendations for Theory ................................................................................ 222
Recommendations for Research ............................................................................. 224
Recommendations for Practice .............................................................................. 228
Researcher Reflections on the Research Study............................................................. 232
Chapter Summary ....................................................................................................... 233
References .................................................................................................................. 235
Appendix A: Introduction and Site Access Request Email ........................................... 280
Appendix B: Research Site Permission Letter .............................................................. 282
Appendix C: A Priori Calculation off Minimum Sample Size for Statistical Power ..... 283
Appendix D: Permission to Use Instruments ............................................................... 284
Appendix E: Study Survey Questionnaire Instrument .................................................. 288
xiv
Appendix F: Institutional Review Board Approvals ..................................................... 294
Appendix G: Communications to Study Sample Participants ....................................... 296
Appendix H: Informed Consent for Participation in a Research Study ......................... 300
Appendix I: Comparative Analysis of Missing Value Imputation Techniques .............. 303
Appendix J: Research Study Overview ........................................................................ 306
Appendix K: Inter-Item Correlation Matrix for Survey Questions ............................... 307
Appendix L: Item-Factor Correlations and Factor Loadings for The 28 Scale
Questions .................................................................................................................... 308
Appendix M: Calculating Average Variance Extracted and Composite Reliability ...... 309
Appendix N: Descriptive Statistics by Survey Questionnaire Question ........................ 311
Appendix O: SPSS Hierarchical Multiple Regression Moderation Analysis Output ..... 313
Appendix P: SPSS PROCESS Macro Multiple Regression Mediation Analysis
Output ......................................................................................................................... 315
Appendix Q: SPSS Simultaneous Multiple Regression Analysis Output ...................... 317
xv
List of Figures
Page
1.1. Conceptual Framework of the Hypothesized Relation Between Employee
Alignment, Perceived Organizational Support, and Employee Engagement ....... 12
2.1. Hypothesized Moderation Model ....................................................................... 85
2.2. Hypothesized Mediation Model ......................................................................... 86
2.3. Conceptual Framework of the Hypothesized Relation Between Employee
Alignment, Perceived Organizational Support, and Employee Engagement ....... 89
3.1. Simplified Conceptual Framework of the Hypothesized Relation Among
Employee Alignment, Perceived Organizational Support, and Employee
Engagement ....................................................................................................... 95
3.2. Conceptual Research Model Incorporating the Research Hypotheses ................. 96
3.3. Analysis Models ................................................................................................ 97
3.4. Representative Relationship Among the Theoretical Population,
Accessible Population, Selected Sample, and Actual Sample ............................. 99
3.5. Relationship Among the Theoretical Population, Accessible Population,
Selected Sample, and Actual Sample ............................................................... 104
3.6. Data Outliers ................................................................................................... 135
3.7. Testing the Linear Relationship Between Variables ......................................... 152
3.8. Normal Quantile-Quantile Plot of the Unstandardized Residual ....................... 153
3.9. Testing the Assumption of Homoscedasticity................................................... 155
3.10. Scatterplot of the Studentized Residuals vs. Unstandardized Predicted
Values of the Outcome Variable ...................................................................... 156
xvi
4.1. Hierarchical Multiple Regression Analysis Models .......................................... 194
4.2. Simple Mediation Model ................................................................................. 197
4.3. Total Effect...................................................................................................... 198
4.4. Mediation Model Tested .................................................................................. 200
5.1. Graphical Representation of the Results of the Tests of the Study
Hypotheses ...................................................................................................... 212
xvii
List of Tables
Page
2.1. Summary of Correlations Between Engagement and Positive
Organizational Outcomes ................................................................................... 43
2.2. Summary of Correlations Between Engagement and Employee Well-
Being Outcomes ................................................................................................ 44
2.3. Summary of the Seminal Definitions and Associated Measures of
Engagement ....................................................................................................... 55
2.4. Summary of Correlations Between Engagement and Positive
Organizational Outcomes with Engagement Construct ....................................... 56
2.5. Summary of Correlations Between Engagement and Employee Well-
Being with Engagement Construct ..................................................................... 57
2.6. Summary of Alignment Definitions ................................................................... 68
2.7. Summary of Correlations Between Alignment and Engagement ........................ 76
2.8. Summary of Correlations Between Perceived Organizational Support and
Engagement ....................................................................................................... 82
3.1. Summary of Correlations Between Demographic Variables and
Engagement ..................................................................................................... 120
3.2. Summary of Variables and Instruments Used in the Study ............................... 122
3.3. Summary of Pilot Study Measures of Internal Reliability ................................. 127
3.4. Summary of the Data Collection Timeline ....................................................... 129
3.5. Descriptive Statistics of Participant Data Set.................................................... 137
3.6. Missing Value Analysis: Summary by Data Variable ....................................... 139
xviii
3.7. Missing Value Analysis: Summary by Data Record ......................................... 140
3.8. Normality Statistics for Explanatory and Outcome Variables ........................... 150
3.9. Normality Statistics for the Unstandardized Residual ....................................... 154
3.10. Collinearity Statistics for Explanatory Variables .............................................. 158
3.11. Alignment of Research Question, Hypotheses, Variables, Data, and
Statistical Analysis .......................................................................................... 160
4.1. Participant Demographic Descriptive Statistics ................................................ 170
4.2. Empirical Research Demonstrating Scale Reliability and Validity ................... 171
4.3. Summary of Measures of Internal Reliability ................................................... 172
4.4. Kaiser-Meyer-Olkin and Bartlett’s Test ........................................................... 175
4.5. Summary of Item-Factor Correlations and Factor Loadings for the 28
Scale Questions ............................................................................................... 177
4.6. Construct Correlation Matrix ........................................................................... 185
4.7. Construct Shared Variance............................................................................... 186
4.8. Comparison of Construct Average Variance Extracted to Shared Variance ...... 186
4.9. Descriptive Statistics of Study Explanatory and Outcome Variables ................ 187
4.10. Bivariate Correlation Matrix of Study Explanatory and Outcome Variables ..... 191
4.11. Hierarchical Multiple Regression Moderation Analysis: Model Summary ....... 194
4.12. Total, Direct, and Indirect Effects – POS as a Mediating Variable ................... 204
4.13. Simultaneous Multiple Regression Analysis: Model Summary ........................ 206
4.14. Simultaneous Multiple Regression Analysis: Model Coefficients..................... 207
4.15. Summary of Hypothesis Testing ...................................................................... 209
xix
I.1. Missing Value Imputation: Descriptive Statistics for Regression
Imputation ....................................................................................................... 303
I.2. Missing Value Imputation: Descriptive Statistics for Expectation-
Maximization Imputation ................................................................................. 303
I.3. Missing Value Imputation: Descriptive Statistics for Multiple Imputation........ 304
L.I. Summary of Item-Factor Correlations and Factor Loadings for the 28
Scale Questions ............................................................................................... 308
M.1. Calculating Average Variance Extracted and Composite Reliability ................ 310
N.1. Descriptive Statistics by Survey Questionnaire Question ................................. 311
O.1. Hierarchical Multiple Regression Analysis Results: Model Summary .............. 313
O.2. Hierarchical Multiple Regression Analysis Results: ANOVA .......................... 313
O.3. Hierarchical Multiple Regression Moderation Analysis Results:
Coefficients ..................................................................................................... 314
1
Chapter 1: Introduction
Overview
As organizations struggle to become and remain competitive, the engagement of
employees may be a critical enabler for successfully achieving organizational goals.
Today, many have observed that organizations—more accurately, the managers within
organizations1—often find themselves operating in environments that are increasingly
global in scope, highly competitive, changing at an increasing rate, and becoming ever
more interrelated (Ireland & Hitt, 2005; McCann, 2004; Nicolaides & McCallum, 2013;
Tarique & Schuler, 2010; Uhl-Bien et al., 2007; Wheatley, 2006; World Economic
Forum, 2016). These environments are also often characterized as being volatile,
uncertain, complex, and ambiguous (Bennett & Lemoine, 2014; Gerras, 2010; Jacobs,
2009; Johansen, 2012) and lead to a multitude of adaptive challenges that uniquely
challenge our individual and collective assumptions, attitudes, and approaches to problem
solving as never before (Byrd, 2007; Kegan, 2009; Kegan & Lahey, 2009; Mumford et
al., 2000; Nicolaides & McCallum, 2013; Uhl-Bien et al., 2007; Wheatley, 2006).
In such competitive and uncertain environments, many, if not most, managers
often struggle to improve the alignment of people (i.e., their knowledge, skills, abilities,
and effort), processes, and performance in order to achieve organizational goals
1 In response to Coyle-Shapiro and Shore’s (2007) criticism that the organizational behavior and human resource literatures “rarely if ever specify what is meant by the organization” (p. 167), this study used the term manager rather than organization to denote individuals from an organization’s managerial hierarchy who, as “organizational representatives, or agents” (p. 168), are assumed to “act in concert with the interests of the organization” (p. 168) while “carrying out the directives from the principals (i.e., owners)” (p. 168). While management styles may differ (Mintzberg, 1994), the fundamental function of managers is to organize, align, and integrate human and material resources to achieve desired organizational goals, objectives, and outcomes (Drucker, 2012; McGregor, 2000; Mintzberg, 1994).
2
(Alagaraja & Shuck, 2015; Bakker, 2011; Salanova et al., 2005). In these settings,
managers are increasingly turning to their employees for innovative and creative
solutions to the problems they face (Bakker, 2017; Boswell et al., 2006; Boudreau &
Ramstad, 2005; Luthans & Youssef, 2004; Pfeffer, 2005; Simon, 1991; Stringer, 2007).
They need employees who have a clear understanding of how their individual
contributions and efforts support the goals of the organization and are willing to expend
discretionary effort, proactively working towards achieving those goals (Alagaraja &
Shuck, 2015; Boswell, 2000a, 2006; Boswell et al., 2006; Boswell & Boudreau, 2001;
Chalofsky & Krishna, 2009; Kahn, 2010; Masson et al., 2008; Stringer, 2007).
Within this context, a greater understanding of the employee2-organization
relationship may be increasingly important in gaining, and maintaining, a competitive
advantage and achieving desired organizational goals (Coyle-Shapiro & Shore, 2007). As
defined by Shore et al. (2004), the employee-organization relationship is “the relationship
between the employee and the organization” (p. 292). Greenberg and Baron (2003)
further elaborated, noting that the employee-organization relationship includes an
employee’s “lasting feelings, beliefs, and behavioral tendencies toward various aspects of
the job itself” (p. 148), as well as “the setting in which the work is conducted, and/or the
people involved” (p. 148). This study focused on one aspect of the employee-
organization relationship, employee engagement; specifically, this research sought to
2 This study used Drucker’s (2001) definition of an employee as an individual who “works for an organization” (pp. 17-18). Additionally, Coyle-Shapiro and Shore (2007) noted a “duality of employment relationships” (p. 169) for managers, noting that “managers are party to two employment relationships: as employees they have their own employment relationship with the organization and at the same time, they represent the employer in managing the employment relationship with their employees” (p. 169).
3
better understand the relation among employee alignment, perceived organizational
support, and employee engagement in an organizational context.
This chapter presents an overview and rationale for this study. It discusses the
background of the research problem, the specific research problem addressed, the
purpose of the study, research questions and hypotheses, potential significance of the
study, the conceptual framework, overview of the methodology, limitations and
delimitations, and definitions of key terms.
Background of the Research Problem
Scholars have noted the revolutionary, disruptive, and pervasive effects of
technology and globalization on organizations, managers, and employees (Alheit, 2009;
Burke & Ng, 2006; Tarique & Schuler, 2010; Uhl-Bien et al., 2007; World Economic
Forum, 2016). As a result, managers often find their traditional business models disturbed
by competitive environments characterized by complexity, uncertainty, and disruptive
change (Ireland & Hitt, 2005; McCann, 2004; Nicolaides & McCallum, 2013; Tarique &
Schuler, 2010; Uhl-Bien et al., 2007; Wheatley, 2006; World Economic Forum, 2016).
Some have also observed that the nature of the challenges that managers face is changing
(Kegan, 2009; Kegan & Lahey, 2009; Luthans & Avolio, 2003; Nicolaides & McCallum,
2013; Uhl-Bien et al., 2007), with a growing recognition that the manager can no longer
be the sole source of answers and solutions to the problems confronting organizations
(Nicolaides & McCallum, 2013). Rather, managers must increasingly rely on the
knowledge, skills, abilities, and effort of employees throughout an organization to
address the challenges they face (Bakker, 2017; Boswell et al., 2006; Boudreau &
Ramstad, 2005; Luthans & Youssef, 2004; Pfeffer, 2005; R. Robinson & Shuck, 2019).
4
Concerning employees, Simon (1991) observed, “doing the job well is not mainly a
matter of responding to commands, but is much more a matter of taking initiative to
advance organizational objectives” (p. 32), and organizational success requires “that
employees take initiative and apply all their skill and knowledge to advance the
achievement of the organization’s objectives” (p. 32).
Research suggests that engaged employees can play a critical role in achieving
organizational (i.e., managerial) goals, improving organizational effectiveness, and
helping organizations become and remain competitive (Alagaraja & Shuck, 2015;
Bakker, 2011, 2017; Bakker & Demerouti, 2008; Burke & Cooper, 2006; Burke & Ng,
2006; Eldor, 2016; Eldor & Harpaz, 2016; Frank et al., 2004; Harter et al., 2002; Rich et
al., 2010; Saks, 2006; Saks & Gruman, 2014; Shuck, Rocco, et al., 2011; Shuck & Reio,
2011; Shuck & Rose, 2013; Shuck & Wollard, 2008; Stallard & Pankau, 2010). Studies
have shown a positive relation between engagement and outcomes often desired by
managers such as creativity (Bae et al., 2013; Reijseger et al., 2017; Toyama & Mauno,
2017), discretionary effort (Shuck, Reio, & Rocco, 2011), innovation (Bhatnagar, 2012;
Gomes et al., 2015), job (or task) performance (Reijseger et al., 2017; Rich et al., 2010;
Shantz et al., 2013), job satisfaction (Biswas & Bhatnagar, 2013; Saks, 2006), open-
mindedness (Reijseger et al., 2017), organizational commitment (Biswas & Bhatnagar,
2013; Saks, 2006), personal initiative (Reijseger et al., 2017), and productivity (Kataria et
al., 2013), as well as a negative relation to turnover intention (Bhatnagar, 2012; Saks,
2006; Shuck et al., 2014; Shuck, Reio, et al., 2011).
Engaged employees are more likely to be enthusiastic about their work, perform
better, expend discretionary effort to help accomplish the goals of the organization, and
5
be more committed to the success of the organization than those who are disengaged
(Alagaraja & Shuck, 2015; Bakker, 2011; Bakker et al., 2011; Bakker & Demerouti,
2008; Shuck, Reio, et al., 2011; Shuck & Reio, 2011). As noted by Robinson et al.
(2004), “An engaged employee is aware of the business context and works with
colleagues to improve performance within the job for the benefit of the organization” (p.
9). In addition to the organizational outcomes of engagement, studies have also found a
positive relation between engagement and individual employee health and well-being
outcomes such as job satisfaction (Biswas & Bhatnagar, 2013; Saks, 2006), feelings of
personal accomplishment (Shuck & Reio, 2014), psychological well-being (Shuck &
Reio, 2014), and overall quality of life (Freeney & Fellenz, 2013), and a negative relation
between engagement and feelings of depersonalization (Shuck & Reio, 2014), emotional
exhaustion (Shuck & Reio, 2014), and turnover intention (Bhatnagar, 2012; Saks, 2006;
Shuck et al., 2014; Shuck, Reio, et al., 2011).
Reflecting a growing recognition of the potential for gaining a competitive
advantage through engagement and engagement’s potential significance in helping
managers achieve organizational goals, a Harvard Business Review Analytic Services
(2013) study of 550 global executives found that “71 percent of respondents rank
employee engagement as very important to achieving overall organizational success”
(p. 1) and “a top-three business priority” (p. 3). However, approximately one-third (U.S.
Office of Personnel Management, 2018) to two-thirds (Gallup, 2017)3 of the U.S.
workforce remains disengaged, with an estimated impact on the U.S. economy, due to
3 Employee engagement numbers are from a Gallup Daily tracking study of 63,249 U.S. adults, aged 18 and older, conducted between January and September 2016 (Gallup, 2017, p. 202).
6
lost productivity, between $483 billion and $605 billion per year (Gallup, 2017, p. 19). In
addition to these reported levels of employee disengagement (Gallup, 2017) and the
reported recognition of the importance of engaged employees by managers (Harvard
Business Review Analytic Services, 2013), it has been estimated that U.S. companies
spend over $720 million annually on employee engagement efforts (LaMotte, 2015).
With approximately one-third (U.S. Office of Personnel Management, 2018) to two-
thirds (Gallup, 2017) of the U.S. workforce disengaged, a situation some have
characterized as employees who have “mentally ‘checked out’” (Seijts & Crim, 2006,
p. 1), there are opportunities for managers to improve both organizational and employee
well-being through the engagement of employees.
Statement of the Research Problem
Scholars have identified a need for research focused on the organizational
elements (Coyle-Shapiro & Shore, 2007), or factors (Whittington et al., 2017;
Whittington & Galpin, 2010) within the purview of managers that can improve the
engagement of employees and organizational effectiveness (Alagaraja & Shuck, 2015;
Coyle-Shapiro & Shore, 2007; Eldor & Vigoda-Gadot, 2017; Oswick, 2015; Whittington
et al., 2017; Whittington & Galpin, 2010). Two such factors are alignment (CEB
Corporate Leadership Council, 2015b, 2015c; Harter & Rigoni, 2015; Rao, 2017; Ray et
al., 2014; Stallard & Pankau, 2010) and perceived organizational support (Seijts & Crim,
2006; Shuck et al., 2014; Shuck, Rocco, et al., 2011; Wollard & Shuck, 2011).
Previous studies have separately examined alignment and perceived
organizational support as individual antecedent variables of engagement. However, the
relation among employee alignment, perceived organizational support, and employee
7
engagement, and how employee alignment and perceived organizational support interact
to contribute to employee engagement, remains relatively unexplored. Previous studies
have specifically examined the relation between alignment and work engagement
(Albrecht et al., 2018; Biggs et al., 2014b) and engagement at work (Stringer, 2007), with
a lack of empirical work examining the relation between alignment and the employee
engagement framework proposed by Shuck and Reio (2011). Similarly, a review of the
literature failed to identify any previous research that examined the effect of perceived
organizational support, as it affects employee perceptions of the work environment, on
the relation between employee alignment and engagement. This research addresses the
practical problem of how managers can create conditions that may increase employee
engagement in organizations and the theoretical problem of better understanding the
relation among employee alignment, perceived organizational support, and employee
engagement and how employee alignment and perceived organizational support interact
to contribute to employee engagement.
The goal of the study was to better understand the relation among employee
alignment, perceived organizational support, and employee engagement among full-time
nonsupervisory individuals in an organizational context (i.e., employed in organizations
in the United States). Better understanding of this relation can assist researchers,
managers, and human resources professionals in identifying and developing strategies to
improve employee engagement, which, in turn, should contribute to achieving
organizational goals, enhancing organizational competitiveness, and improving employee
well-being.
8
Purpose of the Study
The purpose of this study was to explore the relation among employee alignment,
perceived organizational support, and employee engagement and how employee
alignment and perceived organizational support interact to contribute to employee
engagement among full-time nonsupervisory individuals employed in organizations in
the United States. The intent of this examination was to seek a better understanding, or
explanation, of an outcome—in this study, the phenomenon of employee engagement. As
such, this study used the terms explanatory instead of independent or predictor variable
(Keith, 2015; Robson & McCartan, 2016) and outcome instead of dependent variable
(Keith, 2015; Kelley & Maxwell, 2019; Robson & McCartan, 2016). To achieve the
study’s purpose, this research examined a hypothesized employee engagement model to
explore the relation among the two explanatory variables of employee alignment and
perceived organizational support and the outcome variable of employee engagement.
Research Questions and Hypotheses
In support of the study’s purpose, the following research questions (RQ) guided
this inquiry:
RQ1: To what extent is there a statistically significant relation among employee
alignment, perceived organizational support, and employee engagement in an
organizational context?
RQ2: To what extent do employee alignment and perceived organizational support
explain a statistically significant proportion of the unique variance in employee
engagement?
9
In answering the two research questions, the following seven hypotheses were
tested:
H1a: There is a statistically significant positive correlation between employee
alignment and employee engagement.
H1b: Employee alignment explains a statistically significant proportion of the unique
variance in employee engagement after controlling for perceived organizational
support.
H2: There is a statistically significant positive correlation between employee
alignment and perceived organizational support.
H3a: There is a statistically significant positive correlation between perceived
organizational support and employee engagement.
H3b: Perceived organizational support explains a statistically significant proportion of
the unique variance in employee engagement after controlling for employee
alignment.
H4: Perceived organizational support positively moderates the relation between
employee alignment and employee engagement in an organizational context.
Specifically, as perceived organizational support increases, the relation between
employee alignment and employee engagement becomes more positive.
H5: Perceived organizational support mediates the relation between employee
alignment and employee engagement in an organizational context.
Potential Significance of the Study
This research was designed to explore the relation among employee alignment,
perceived organizational support, and employee engagement in an organizational context.
10
By examining employee perceptions of alignment and organizational support as
antecedents of employee engagement, this study extends the theoretical and practical
understanding of conditions under which employee engagement may occur in
organizations.
The conceptual significance of this inquiry is an enhanced understanding of the
relation among the constructs of employee alignment, perceived organizational support,
and employee engagement in an organizational context. Specifically, this study makes
three primary contributions to research and theory. First, this study complements and
supports existing models of antecedents of employee engagement by providing
corroborating empirical evidence for the positive relation between employee alignment
and employee engagement and between perceived organizational support and employee
engagement in an organizational context. Second, the study adds to the body of
knowledge by testing a model of employee engagement that focuses on a previously
unexamined combination of antecedents (employee alignment and perceived
organizational support), adding to the understanding of how these variables interact with
one another and affect employee engagement. The third contribution is an examination of
perceived organizational support as a moderating and/or mediating variable of the
employee alignment-engagement relation.
From a practice perspective, this study increases awareness and understanding of
the significance of employee alignment and perceived organizational support as
antecedent variables of employee engagement in organizations. In turn, greater awareness
and understanding can assist managers in developing strategies to facilitate and nurture
employee engagement, which should contribute to achieving organizational goals,
11
enhancing organizational competitiveness, and improving employee well-being. For
example, the study confirms a positive relation between alignment and engagement,
which should indicate to managers the importance of helping employees understand how
their efforts contribute to achieving the goals of an organization. Similarly, the results
provide limited evidence of a positive relation between perceived organizational support
and employee engagement, suggesting that managers focus attention on organizational
practices that affect employee perceptions of the extent to which the organization values
employee contributions and is concerned for employee well-being. As managers strive
for organizational competitiveness and survival in environments of complexity,
uncertainty, and change, better understanding of the relation among employee
engagement, employee alignment, and perceived organizational support may be a critical
enabler in achieving organizational goals, enhancing organizational competitiveness, and
improving employee well-being.
Conceptual Framework
Simply stated, a conceptual framework can be thought of as the lens through
which a researcher views the research problem (Roberts & Hyatt, 2019). Maxwell (2013)
defined a conceptual framework as “the systems of concepts, assumptions, expectations,
beliefs, and theories that supports and informs” (p. 39) the research. Further, Miles et al.
(2014) stated that a conceptual framework explains “the main things to be studied—the
key factors, variables or constructs—and the presumed interrelationships among them”
(p. 20).
This study was based on the foundations provided by the engagement, alignment,
and organizational support literature. As antecedents of engagement, alignment (Albrecht
12
et al., 2018; Biggs et al., 2014b; Stringer, 2007) and perceived organizational support
(Biswas & Bhatnagar, 2013; Mahon et al., 2014; Rich et al., 2010; Saks, 2006; Wang et
al., 2017; Wollard & Shuck, 2011; Zhong et al., 2016) provide a theoretical basis that
may help to further explain and better understand the engagement of employees within an
organizational context. Figure 1.1 depicts the conceptual model of the relation among the
constructs of employee alignment, perceived organizational support, and employee
engagement for the study.
Figure 1.1
Conceptual Framework of the Hypothesized Relation Between Employee Alignment,
Perceived Organizational Support, and Employee Engagement
13
Employee Engagement
While scholars have yet to agree upon a single definition and theoretical
framework for engagement (B. Little & Little, 2006; Macey & Schneider, 2008; Saks,
2006; Shuck, Adelson, et al., 2017; Shuck, Osam, et al., 2017; Shuck & Wollard, 2010,
2009), the academic literature has frequently identified the seminal conceptualizations of
engagement to include (a) personal engagement (Kahn, 1990); (b) job engagement
(Maslach et al., 2001); (c) work engagement (Schaufeli et al., 2002); (d) employee
engagement (Harter et al., 2002); (e) engagement at work (May et al., 2004); (f) employee
engagement (consisting of both job engagement and organization engagement) (Saks,
2006); (g) job engagement (Rich et al., 2010); and (h) employee engagement (Shuck &
Reio, 2011) (Eldor, 2016; Saks, 2008; Saks & Gruman, 2014; Serrano & Reichard, 2011;
Shuck, 2011; Shuck, Adelson, et al., 2017; Shuck, Osam, et al., 2017).
While the various definitions and conceptualizations of engagement may appear
similar, Shuck, Osam, et al. (2017) emphasized the distinctions among three common
conceptualizations of engagement often referenced in the literature—employee
engagement, job engagement, and work engagement—noting that each had a unique
definition, theoretical construct, and scale of measurement and the terms were not meant
to be used interchangeably. All three conceptualizations of engagement concern how and
where employees direct their energy and effort (i.e., engagement), where (a) employee
engagement is focused on employees’ active role in working towards desired
organizational outcomes within the full experience of their work (i.e., including their
work, job, team, and organization) (Shuck, Adelson, et al., 2017; Shuck, Osam, et al.,
2017; Shuck & Wollard, 2010); (b) job engagement is focused on the job and job
14
activities (Rich et al., 2010; Shuck, 2019; Shuck, Adelson, et al., 2017; Shuck, Osam, et
al., 2017); and (c) work engagement is focused towards the work and work activities
(Schaufeli et al., 2002; Shuck, 2019; Shuck, Adelson, et al., 2017; Shuck, Osam, et al.,
2017). In the same article, Shuck, Osam, et al. (2017) cautioned researchers to ensure
clarity of the engagement construct used in a research design—i.e., the alignment of
definition, theoretical framework, and measure. As such, the definition of employee
engagement offered by Shuck, Osam, et al. (2017) was used in this study, where
employee engagement is “a positive, active, work-related psychological state
operationalized by the maintenance, intensity, and direction of cognitive, emotional, and
behavioral energy” (p. 269).
Aligned with this definition, the theoretical framework of engagement that
underpins this study is that proposed by Shuck and Reio (2011), who conceptualized a
framework for employee engagement consisting “of three separate facets: cognitive
engagement, emotional engagement, and behavioral engagement” (p. 421). Expanding on
this framework, Shuck and Reio (2011) noted that cognitive engagement “revolves
around how an employee thinks about and understands his or her job, company, and
culture and represents his or her intellectual commitment to the organization” (p. 422);
emotional engagement “revolves around the emotional bond one feels toward his or her
place of work and represents a willingness to involve personal resources such as pride,
belief, and knowledge” (p. 423); and behavioral engagement involves “increased levels
of discretionary effort” as “the physical and overt manifestation of cognitive and
emotional engagement” (p. 423). Based on this conceptual framework and definition of
employee engagement, Shuck et al. (2014) noted that “those who felt that their work
15
mattered, that they were supported in their work, and that their well-being was considered
fairly were likely to embrace and engage” (p. 245). Employee engagement is
conceptualized and operationalized as a cognitive, emotional, and behavioral
phenomenon measured at the individual level of analysis.
Employee Alignment
The premise of alignment theory is that when there is agreement, cooperation, or
harmony among an organization’s strategy, structure, processes, culture, and employees,
there is a greater likelihood that the organization will successfully achieve its goals
(Alagaraja & Shuck, 2015; Ayers, 2013, 2015; Biggs et al., 2014b; Boswell, 2000a, 2006;
Boswell et al., 2006; Boswell & Boudreau, 2001; CEB Corporate Leadership Council,
2015c; Herd et al., 2018; Powell, 1992; Semler, 1997; Wollard & Shuck, 2011). A well-
aligned organization creates a clear linkage among the goals of the strategy, processes,
functional departments, workgroups, and individuals (Alagaraja & Shuck, 2015; Powell,
1992; Semler, 1997). At the individual employee level, an understanding of the
organization’s goals can be a critical determinant for achieving desired organizational
outcomes (Alagaraja & Shuck, 2015; Boswell, 2000a, 2006; Boswell et al., 2006;
Boswell & Boudreau, 2001; Gagnon & Michael, 2003; Kaplan & Norton, 2001; Stallard
& Pankau, 2010; Stringer, 2007; Wollard & Shuck, 2011). Alagaraja and Shuck (2015)
noted the importance of alignment with respect to individual employee roles and
responsibilities, whereby managers must connect the “overarching goals at the individual
level, such that this individual connection generates emotion, drives behavioral intention
and resulting performance” (p. 29).
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The alignment literature reveals multiple labels, definitions, and
conceptualizations of the alignment construct to include alignment (Labovitz &
Rosansky, 1997, 2012), employee alignment (Ayers, 2013, 2015; Gagnon et al., 2008;
Gagnon & Michael, 2003), employee strategic alignment (Gagnon et al., 2008; Gagnon &
Michael, 2003; Ouakouak & Ouedraogo, 2013a, 2013b), goal alignment (Beehr et al.,
2009; De Graaf, 2012), goal congruence (Ayers, 2013), line of sight (Boswell, 2000a,
2006), organizational alignment (Alagaraja et al., 2015; Alagaraja & Shuck, 2015;
Powell, 1992; Semler, 1997), and strategic alignment (Albrecht et al., 2018; Biggs et al.,
2014a, 2014b; Henderson & Venkatraman, 1991, 1993; Prieto & de Carvalho, 2011;
Stringer, 2007).
For conceptual and definitional clarity, this study used the term employee
alignment to denote the alignment construct of interest. Based on Boswell's (2000a,
2006) original conceptualization of employee line of sight and subsequent work on line of
sight by Boswell et al. (2006), as well as work on the alignment of employees by Ayers
(2013, 2015), Gagnon and Michael (2003), and Stringer (2007), employee alignment is
defined in this study as the extent to which employees understand the organization’s
goals and understand how their work and job responsibilities contribute to achieving the
organization’s goals. As such, employee alignment is conceptualized and operationalized
as a cognitive phenomenon measured at the individual level of analysis.
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Perceived Organizational Support
Organizational support theory posits that employees enter into reciprocal social
exchange relationships with organizations4 based on the extent to which they perceive an
organization’s support and commitment to them (Eisenberger et al., 1986; Kurtessis et al.,
2017; Rhoades & Eisenberger, 2002). Eisenberger et al. (1986) defined perceived
organizational support as the degree to which “employees develop global beliefs
concerning the extent to which the organization values their contributions and cares about
their well-being” (p. 501). Perceived organizational support is thus a view of the
employee-organization relationship from an individual employee’s perspective (Kurtessis
et al., 2017).
Perceived organizational support is based on the concept of reciprocity and an
employee’s effort-outcome expectations (Eisenberger et al., 1986; Kurtessis et al., 2017;
Rhoades & Eisenberger, 2002). As Kurtessis et al. (2017) observed, perceived
organizational support “should elicit the norm of reciprocity” (p. 1856) and “initiates a
social exchange process wherein employees feel obligated to help the organization
achieve its goals and objectives and expect that increased efforts on the organization’s
behalf will lead to greater rewards” (p. 1855). Overall, perceived organizational support
is expected to result in outcomes that are favorable to both employees (e.g., recognition
of efforts, increased job satisfaction, heightened positive mood, and reduced job strains)
4 Although perceived organizational support theory focuses on the relationship between an individual (e.g., an employee) and “organizations,” the concept is actually describing perceptions of interactions between and among individuals within an organization (Eisenberger et al., 1986; Rhoades & Eisenberger, 2002). Perceived organizational support recognizes an individual’s tendency to personify the organization, whereby an employee views actions taken by a member of the organization who controls the employee’s access to resources (e.g., a manager) as representing the organization’s intent towards them rather than solely the other member’s personal actions (Eisenberger et al., 1986; Rhoades & Eisenberger, 2002).
18
and managers (e.g., increased commitment, increased effort and performance towards
achieving organizational goals, reduced turnover, and engagement) (Eisenberger et al.,
1986, 2016; Eisenberger & Stinglhamber, 2011; Kurtessis et al., 2017; Rhoades &
Eisenberger, 2002). As an antecedent of engagement, perceived organizational support is
believed to affect employee perceptions of the organizational work environment, where
higher levels of perceived support from the organization will result in higher levels of
engagement (Rana et al., 2014; Rich et al., 2010; Saks, 2006). Perceived organizational
support is conceptualized and measured at the individual level of analysis.
Overview of the Methodology
This study was designed to enhance understanding of employee engagement in an
organizational context. The focus of the study was to (a) examine the relation among the
variables of employee alignment, perceived organizational support, and employee
engagement, (b) determine the extent to which employee alignment and perceived
organizational support explain a statistically significant proportion of the unique variance
in employee engagement, and (c) examine how employee alignment and perceived
organizational support collectively interact to contribute to employee engagement. This
section provides an overview of the methodology, discussing the population and sample,
data collection, pilot study, and data analysis.
This study used a quantitative methodology, specifically a nonexperimental,
cross-sectional, survey research design (Creswell, 2014; Dannels, 2019; Robson &
McCartan, 2016) with a self-completion, or self-reported, internet-based survey
questionnaire (Robson & McCartan, 2016). Creswell (2014) noted that a quantitative
approach is appropriate for “examining the relationship among variables” (p. 4) and when
19
the research problem calls for “the identification of factors that influence an outcome”
(p. 20). This quantitative methodology was situated in a realist ontology (Burrell &
Morgan, 1992; Huff, 2009) and postpositivist epistemology (Butin, 2010; Creswell, 2013,
2014; Robson & McCartan, 2016).
Population, Sampling, and Study Sample
When conducting empirical research, it is necessary to differentiate between the
study’s population and sample of interest (L. Cohen et al., 2011; Litt, 2010; Lomax &
Hahs-Vaughn, 2012; Robson & McCartan, 2016). Population can be defined as “all
members of a well-defined group” (Lomax & Hahs-Vaughn, 2012, p. 5) and “the entire
collection of entities one seeks to understand or, more formally, about which one seeks to
draw an inference” (Litt, 2010, p. 1053). The research site for the study was the human
resources department of a not-for-profit health care organization located in the southern
region (U.S. Census Bureau, n.d.) of the United States. The population (N = 229)5—the
accessible population (Fritz & Morgan, 2010)—consisted of all employees of the
research site who met the inclusion criteria of being a full-time and nonsupervisory
employee (i.e., an employee who does not directly supervise others).
As a reference point for identifying a sample for the current study, an a priori
power analysis was conducted using G*Power (Version 3.1.9.4) (Faul et al., 2007, 2009;
Keith, 2015; Lomax & Hahs-Vaughn, 2012). The minimum sample size required to
achieve statistical power for this study was computed based on a significance level of .05
5 Overall, 268 employees were invited to participate, with 150 initial responses (i.e., clicking on the survey link). Of the 150 initial responses, 39 records were excluded due to the participants not meeting the inclusion criteria (i.e., being a full-time, nonsupervisory employee) or having uncertain eligibility, resulting in an accessible population/selected sample of 229 employees.
20
(a = .05) (J. Cohen, 1988; J. Cohen et al., 2003; D. George & Mallery, 2020; Hinkle et
al., 2003), a level of statistical power (1 – b) of .80 (J. Cohen, 1988), an effect size of .15
(f 2 = .15) (Shuck, 2010), and three predictor variables. This analysis showed that a
minimum of 77 participants was required to achieve statistical power for the study.
A sample is simply a subset of a population (L. Cohen et al., 2011; Lomax &
Hahs-Vaughn, 2012; Robson & McCartan, 2016). In identifying the sample, this study
used a census sampling approach (L. Cohen et al., 2011; Creswell, 2012; Fraenkel et al.,
2015; Fritz & Morgan, 2010; Robson & McCartan, 2016; Stapleton, 2019) in that all full-
time and nonsupervisory individuals employed at the research site were invited to
participate in the study, which constituted the selected sample (Fritz & Morgan, 2010).
With census sampling, the selected sample consists of the same individuals as the
accessible population (Fritz & Morgan, 2010). The actual sample (Fritz & Morgan,
2010) consisted of 109 individuals who agreed to participate in the study, responded to
the survey questionnaire, and whose data was used in the analysis.
Data Collection
This study used three self-report survey instruments to measure the variables of
interest: (a) the Employee Engagement Scale (Shuck, Adelson, et al., 2017), (b) the
Stringer Strategic Alignment Scale (Stringer, 2007), and (c) the Survey of Perceived
Organizational Support (Eisenberger et al., 1986). Limited demographic information was
also collected on the participants in this study (age, gender, and organizational tenure).
Employee engagement, employee alignment, and perceived organizational
support were conceptualized and operationalized at the individual level of analysis. This
study collected data from individual employees and computed total scores for each of the
21
three constructs of interest for each individual participant. Subsequent data analysis was
performed using the participant total scores.
The Employee Engagement Scale (Shuck, Adelson, et al., 2017) is a 12-item scale
consisting of three subscales (cognitive engagement, emotional engagement, and
behavioral engagement) of four items each (Shuck, Adelson, et al., 2017). All scale items
are measured on a 5-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly
agree) (Shuck, Adelson, et al., 2017), where a higher numeric response indicates a higher
level of engagement. Shuck, Adelson, et al. (2017, p. 968) found “strong internal
consistency reliability” for each of the three subscales, with Cronbach’s alphas of .94 for
the cognitive engagement scale, .88 for emotional engagement, and .91 for behavioral
engagement.
The Stringer Strategic Alignment Scale is an 8-item scale measured on a 5-point
Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree) (Stringer, 2007),
where a higher numeric response indicates a higher level of alignment. Stringer (2007)
reported a Cronbach’s alpha of .95 (p. 81) from the data obtained in the original study.
The Survey of Perceived Organizational Support (Eisenberger et al., 1986) is an
8-item scale measured on a 7-point Likert scale ranging from 0 (strongly disagree) to 6
(strongly agree) (Eisenberger et al., 1986), where a higher numeric response indicates a
higher level of perceived support. Studies using the 8-item version of the survey
instrument have found Cronbach’s alphas of .88 and .89 (Neves & Eisenberger, 2012, p.
456) and .88 (Simmons, 2013, p. 68).
To test for moderation or an interaction effect, a cross-product, or interaction
effect, variable was created and tested for statistical significance when entered into the
22
regression equation (Keith, 2015). The interaction effect variable was computed by
multiplying the two explanatory variables of interest: employee alignment and perceived
organizational support (Keith, 2015).
In minimizing the amount of personally identifiable information collected from
participants (Lee & Schuele, 2010), the demographic information collected in this study
was limited to three characteristics shown to influence the outcome variable of
engagement: age (Avery et al., 2007; Bhatnagar, 2012; Gomes et al., 2015; Toyama &
Mauno, 2017), gender (Bae et al., 2013; Bhatnagar, 2012; Gomes et al., 2015; Mauno et
al., 2005; Toyama & Mauno, 2017), and organizational tenure (i.e., years employed by
the current organization) (Avery et al., 2007; Bae et al., 2013; Gomes et al., 2015).
Pilot Study
A pilot study was conducted prior to administering the survey questionnaire to the
main study’s participants. The purpose of the pilot study was to assess (a) the survey
questionnaire with respect to clarity of instructions, layout, ease of use, and completion
time (L. Cohen et al., 2011; Creswell, 2014; Dillman et al., 2014; Roberts & Hyatt, 2019)
and (b) the internal reliability (i.e., Cronbach’s alpha) of the three survey instruments
used in this study—the Stringer Strategic Alignment Scale, the Survey of Perceived
Organizational Support, and the Employee Engagement Scale—compared against the
psychometric data obtained by previous studies.
The pilot study was conducted in November 2019 with a convenience sample of
individuals drawn from the researcher’s professional network. Seventeen usable
responses were obtained that were not part of the sample for the main study. The survey
questionnaire was administered online, with SurveyMonkey. Findings resulted in three
23
changes to the survey questionnaire: (1) rewording the four reverse-worded questions on
the Survey of Perceived Organizational Support to reflect an affirmative, rather than
negative, orientation, (2) replacing “company” and “organization” with “human resources
department,” and (3) replacing “business unit” with “team.” As a measure of internal
reliability of the survey instrument, the computed Cronbach’s alphas from the pilot study
data compared favorably to the results of previous studies examining employee
alignment, perceived organizational support, and engagement.
Data Analysis
Statistical analysis of the data was conducted using IBM SPSS Statistics (Version
26.0.0.1 for Mac). Descriptive statistics were reported on the collected data including
mean and standard deviations for each variable (employee alignment, perceived
organizational support, and employee engagement) and participant demographics,
including age, gender, and number of years employed by the organization.
To test Hypotheses 1a, 2, and 3a, bivariate correlations, using the Pearson product
moment correlation coefficient, were computed (J. Cohen et al., 2003; Hinkle et al.,
2003; Keith, 2015; Lomax & Hahs-Vaughn, 2012). To test Hypotheses 1b, 3b, 4, and 5,
multiple regression analysis was conducted to test the statistical significance and extent to
which employee alignment and perceived organizational support explain unique variance
in employee engagement (J. Cohen et al., 2003; Keith, 2015), as well as to test whether or
not perceived organizational support moderates and/or mediates the relation between
employee alignment and employee engagement (Baron & Kenny, 1986; J. Cohen et al.,
2003; Keith, 2015).
24
A significance level of .05 (a = .05) was used in all hypothesis tests to determine
statistical significance (J. Cohen, 1988; J. Cohen et al., 2003; D. George & Mallery,
2020; Hinkle et al., 2003). Additionally, Cohen's (1988) benchmarks were used to
describe the magnitude of correlations and effect sizes.
Study Limitations and Delimitations
All studies have limitations and delimitations that affect the interpretation and
generalizability of the findings (Creswell, 2012, 2014; Roberts & Hyatt, 2019). As
defined by Creswell (2012), limitations “are potential weaknesses or problems with the
study identified by the researcher” (p. 199). Limitations are often associated with aspects
of a study that the researcher may have little or no control over, such as population,
sample size, response rate, and constraints associated with data collection and analysis
methods (Creswell, 2012; Roberts & Hyatt, 2019). Similarly, Roberts and Hyatt (2019)
discussed limitations as characteristics of a study that may affect the results or the ability
to generalize the findings to the larger population. Unlike limitations, delimitations are
primarily under the control of the researcher and consist of the parameters that define and
clarify the boundaries and scope of the study by indicating what is included and excluded
(Creswell, 2014; Roberts & Hyatt, 2019).
Limitations
This study has six main limitations: explanation not causation, self-report
questionnaire, close-ended questions, cross-sectional research design, participant self-
selection, and sample size.
Explanation Not Causation. The statistical analysis of the data consisted of
bivariate correlation and multiple regression techniques, with a goal to better understand
25
the relation among the three variables of interest. However, it is important to note that the
results do not, in and of themselves, infer causation in the relation among the explanatory
variables (employee alignment and perceived organizational support) and the outcome
variable (employee engagement) (Hinkle et al., 2003; Keith, 2015; Lomax & Hahs-
Vaughn, 2012). Rather, the results simply confirm a relation, in this case a positive
relation, among the variables (J. Cohen et al., 2003; Hinkle et al., 2003; Keith, 2015;
Lomax & Hahs-Vaughn, 2012).
Self-Report Questionnaire. Responses to the survey instruments—Stringer
Strategic Alignment Scale (Stringer, 2007), Survey of Perceived Organizational Support
(Eisenberger et al., 1986), and Employee Engagement Scale (Shuck, Adelson, et al.,
2017)—are self-reported, which introduces a limitation of the accuracy of the responses
provided by participants. For example, the data may be susceptible to issues of social
desirability response bias, the tendency of participants to respond in a manner that they
believe is more socially acceptable or desirable (Constantine & Ponterotto, 2006; Larson,
2019; Nederhof, 1985). Additionally, self-report questionnaires rely on the clarity of
survey questions and participants’ interpretation and understanding of the survey
questions. Two issues include understanding the intent of the question (i.e., what the
question is actually asking), as well as participants interpreting the question in the same
manner (Constantine & Ponterotto, 2006).
Closed-Ended Questions. The survey instruments utilized closed-ended survey
questions (i.e., Likert scale), which does not provide respondents an opportunity to
expound upon their responses.
26
Cross-Sectional Research Design. This study used a cross-sectional research
design. As a result, temporal aspects of the relation among the study variables were not
considered. That is, the data collected only reflected employee opinions at a specific
point in time (i.e., a snapshot) and did not account for how employee attitudes toward
engagement, alignment, and support may emerge and/or change over time.
Participant Self-Selection. Given that survey participation was voluntary, this
study may be susceptible to self-selection bias. For example, employees with higher (or
lower) levels of engagement may be more (or less) willing to participate.
Sample Size. A potential limitation of the current study was the sample size.
Based on the a priori power analysis with a minimum required sample size of 77, the
sample size of 109 was adequate for exploring the relation among the study variables.
However, as discussed in Chapter 4, a larger sample size may have provided additional
evidence to support the reestablishment of convergent validity for the employee
engagement and employee alignment scales within the context of the unique application
and resulting data set of this study.
Delimitations
This study has five main delimitations: limited scope of the variables of interest,
operationalization of engagement, population and sample, sampling, and timeframe for
completing the survey.
Limited Scope of the Variables of Interest. This study was purposefully scoped
to focus solely on two explanatory variables (employee alignment and perceived
organizational support) as antecedents to employee engagement (outcome variable). In
limiting the scope of potential antecedent variables, this study excluded other variables
27
that may enhance or diminish employee engagement in an organizational context. For
example, individual employee personality traits (Ford, 2012; Stringer, 2007; Wollard &
Shuck, 2011) and environmental factors such as work-life balance (Wollard & Shuck,
2011) or culture (Ford, 2012; R. Robinson, 2018; Wollard & Shuck, 2011) were not
considered.
Operationalization of Engagement. As discussed in Chapter 2, this study
recognized that there are many definitions and measures of the construct of engagement
(for example, see Table 2.3). By operationalizing employee engagement as defined by
Shuck, Osam, et al. (2017) and as measured by the Employee Engagement Scale (Shuck,
Adelson, et al., 2017), this study focused on a single unique operationalization of
engagement. As such, other operationalizations of engagement were not considered. This
is considered a delimitation in recognition that “a way of seeing is also a way of not
seeing” (Poggi, 1965, p. 284).
Population and Sample. The data for this study were limited to the responses
from employees from a single department within a single not-for-profit health care
organization located in the southern region of the United States. Additionally, potential
participants were limited to full-time nonsupervisory employees. There is no evidence
that the data in this study are representative of data that may be obtained from a different
setting or group of employees.
Sampling. This study used a nonprobabilistic census sampling approach (L.
Cohen et al., 2011; Creswell, 2012; Fraenkel et al., 2015; Fritz & Morgan, 2010; Robson
& McCartan, 2016; Stapleton, 2019). As such, caution must be used when attempting to
generalize the findings to settings or groups beyond the research site and the specific
28
participants—i.e., the actual sample (Fritz & Morgan, 2010)—of this study (L. Cohen et
al., 2011; Creswell, 2012; Fraenkel et al., 2015; Robson & McCartan, 2016; Stapleton,
2019).
Timeframe for Completing the Survey. A final delimitation of the study was
the timeframe for the questionnaire data collection. Specifically, potential participants
were given 4 weeks to respond and complete the survey questionnaire (from January 27,
2020, to February 21, 2020). By limiting the time available to respond, there may have
been unidentified factors that affected the ability of individuals in the accessible
population (Fritz & Morgan, 2010) to participate in the study.
Definitions of Key Terms
The terminology used in this research is derived from the disciplines of
management, organizational science, psychology, and sociology. For the purpose of this
study, the following definitions are provided:
Accessible population. Fritz and Morgan (2010) identified an accessible population as
those potential participants who are a subset of the theoretical population which a
researcher has access to.
Actual sample. The actual sample consists of the “individuals who agree to participate
and whose data are actually used in the analysis” (Fritz & Morgan, 2010,
p. 1304).
Antecedents. Antecedents of engagement refer to the factors and conditions believed to
provide a necessary foundation from which engagement may develop (Rana et al.,
2014; Wollard & Shuck, 2011). The antecedents of engagement examined in this
study were employee alignment and perceived organizational support.
29
Competitive advantage. An organization could be “said to have a competitive advantage
when it is implementing a value creating strategy not simultaneously being
implemented by any current or potential competitors” (Barney, 1991, p. 102), or
more simply, the state of one organization being situated in a more favorable
position than its competitors (Barney, 1991; Barney & Wright, 1998).
Discretionary effort. Discretionary effort is an employee’s voluntary effort, over and
above the minimum job responsibilities and performance required, which is
directed towards organizational goals (Lloyd, 2008; Shuck, Reio, et al., 2011).
Employee. This study used Drucker's (2001) definition of an employee as an individual
who “works for an organization” (pp. 17-18). Additionally, Coyle-Shapiro and
Shore (2007) noted a “duality of employment relationships” (p. 169) for
managers, where “managers are party to two employment relationships: as
employees they have their own employment relationship with the organization
and at the same time, they represent the employer in managing the employment
relationship with their employees” (p. 169).
Employee alignment. Employee alignment is defined in this study as the extent to which
an employee understands the organization’s goals and understands how his or her
work and job responsibilities contribute to achieving the organization’s goals
(Ayers, 2013, 2015; Boswell et al., 2006; Gagnon & Michael, 2003; Stringer,
2007). This study used the Stringer Strategic Alignment Scale (Stringer, 2007) to
measure employee alignment.
Employee disengagement. Kahn (1990) defined personal disengagement as “the
uncoupling of selves from work roles; in disengagement, people withdraw and
30
defend themselves physically, cognitively, or emotionally during [work] role
performances” (p. 694). Kahn (1990) further noted that this withdrawal and
defense result in “behaviors that promote a lack of connections, physical,
cognitive, and emotional absence, and passive, incomplete role performances” (p.
700) and where employees “become physically uninvolved in tasks, cognitively
unvigilant, and emotionally disconnected from others in ways that hide what they
think and feel, their creativity, their beliefs and values, and their personal
connections to others” (p. 701).
Employee engagement. This study used the definition of employee engagement offered
by Shuck, Osam, et al. (2017), where employee engagement is “a positive, active,
work-related psychological state operationalized by the maintenance, intensity,
and direction of cognitive, emotional, and behavioral energy” (p. 269). This study
used the Employee Engagement Scale (Shuck, Adelson, et al., 2017) to measure
employee engagement.
Employee-organization relationship. Shore et al. (2004) defined the employee-
organization relationship as “an overarching term to describe the relationship
between the employee and the organization” (p. 292). Greenberg and Baron
(2003) noted that the employee-organization relationship included an employee’s
“lasting feelings, beliefs, and behavioral tendencies toward various aspects of the
job itself” (p. 148), as well as “the setting in which the work is conducted, and/or
the people involved” (p. 148).
31
Goal. A goal is “an open-ended statement of what one wants to accomplish, with no
quantification of what is to be achieved and no time criteria for completion”
(Wheelen & Hunger, 2012, p. G-4).
Manager. As defined by Coyle-Shapiro and Shore (2007), a manager is an individual
from an organization’s managerial hierarchy who, as “organizational
representatives, or agents” (p. 168), are assumed to “act in concert with the
interests of the organization” (p. 168) while “carrying out the directives from the
principals (i.e., owners)” (p. 168).
Objective. An objective is “the end result of planned activity stating what is to be
accomplished by when and quantified if possible” (Wheelen & Hunger, 2012, p.
G-6).
Organization. An organization can be defined as “a group of people who collectively
pursue a common goal or fulfill an agreed-upon purpose” (Hatch, 2018, p. 384).
Perceived organizational support. Eisenberger et al. (1986) observed that perceived
organizational support results from employees’ perception of the organization’s
commitment to them and is defined as the degree to which “employees develop
global beliefs concerning the extent to which the organization values their
contributions and cares about their well-being” (p. 501). This study used the
Survey of Perceived Organizational Support (Eisenberger et al., 1986) to measure
perceived organizational support.
Selected sample. Fritz and Morgan (2010) defined the selected sample as the “smaller
group of individuals selected from the accessible population” (p. 1304) and the
individuals who “are asked by the researcher to participate in the study” (p. 1304).
32
Theoretical population. As defined by Fritz and Morgan (2010), the theoretical
population includes “all of the participants of theoretical interest to the
researcher” (p. 1304) and consists of “the individuals about which the researcher
is interested in making generalizations” (p. 1304).
Work. Budd (2011) defined work as “purposeful human activity involving physical or
mental exertion that is not undertaken solely for pleasure and that has economic
and symbolic value” (p. 2). Additionally, Shuck (2010) defined work as “a goal-
directed activity for social, economic, or other desired outcomes” (p. 17).
Chapter Summary
As organizations struggle to become and remain competitive, the engagement of
employees may be a critical enabler to successfully achieving organizational goals.
Research suggests that engaged employees can play a critical role in achieving
organizational (i.e., managerial) goals, improving organizational effectiveness, and
helping organizations become and remain competitive (Alagaraja & Shuck, 2015;
Bakker, 2011, 2017; Bakker & Demerouti, 2008; Burke & Cooper, 2006; Burke & Ng,
2006; Eldor, 2016; Eldor & Harpaz, 2016; Frank et al., 2004; Harter et al., 2002; Rich et
al., 2010; Saks, 2006; Saks & Gruman, 2014; Shuck, Rocco, et al., 2011; Shuck & Reio,
2011; Shuck & Rose, 2013; Shuck & Wollard, 2008; Stallard & Pankau, 2010). However,
approximately one-third (U.S. Office of Personnel Management, 2018) to two-thirds
(Gallup, 2017) of the U.S. workforce remains disengaged, with an estimated impact on
the U.S. economy, due to lost productivity, between $483 billion and $605 billion per
year (Gallup, 2017, p. 19).
33
Scholars have identified a need for additional research focused on the factors
within the purview of managers that can improve the engagement of employees and
organizational effectiveness (Alagaraja & Shuck, 2015; Coyle-Shapiro & Shore, 2007;
Eldor & Vigoda-Gadot, 2017; Oswick, 2015; Whittington et al., 2017; Whittington &
Galpin, 2010). Two factors identified as critical to creating conditions from which
employee engagement may arise are alignment (CEB Corporate Leadership Council,
2015b, 2015c; Harter & Rigoni, 2015; Rao, 2017; Ray et al., 2014; Stallard & Pankau,
2010) and perceived organizational support (Seijts & Crim, 2006; Shuck et al., 2014;
Shuck, Rocco, et al., 2011; Wollard & Shuck, 2011).
This study examined the relation among employee alignment, perceived
organizational support, and employee engagement in an organizational context and how
employee alignment and perceived organizational support interact to contribute to
employee engagement in an organizational context. This chapter presented an overview
and rationale for the study, with discussion of the research problem and the study’s
purpose, research questions, potential significance, conceptual framework, methodology,
limitations, delimitations, and key terms. The next chapter reviews the literature related
to the three constructs of the study’s conceptual framework: employee engagement,
employee alignment, and perceived organizational support.
34
Chapter 2: Literature Review
In support of the study’s purpose to better understand the relation among
employee alignment, perceived organizational support, and employee engagement and
how employee alignment and perceived organizational support interact to contribute to
employee engagement, this literature review was limited in scope to focus primarily on
the constructs (i.e., topics) of employee engagement, employee alignment, and perceived
organizational support, and the relation among them. Following the frameworks
identified by Butin (2010), Creswell (2014), Machi and McEvoy (2016), and Roberts and
Hyatt (2019), the purpose of this literature review was fivefold: to (a) provide a baseline
to ground this study; (b) synthesize the relevant academic literature; (c) identify a gap in
the literature; (d) position the study within the existing research; and (e) build a
foundation for this study to contribute to the existing literature.
This literature review explores the constructs of employee engagement, employee
alignment, and perceived organizational support, and the relations among them, within a
framework organized around five main areas. This chapter begins with a summary of the
methods used in the literature search strategy to scope and bound the literature review.
Second, is a synthesis of the engagement literature, to the importance of engagement,
seminal conceptualizations and definitions, engagement’s potential “dark side,” and
antecedents of engagement. Next, the discussion focuses on a synthesis of the alignment
literature, to include the importance of alignment to managers, the various
conceptualizations and definitions of the alignment construct, alignment as an antecedent
of engagement, and the relation between alignment and perceived organizational support.
The fourth section synthesizes the perceived organizational support literature, to include a
35
discussion of the importance of perceived organizational support to managers, its
conceptualization and definition, and perceived organizational support as an antecedent
of engagement. The chapter concludes with a summary of the literature review and a
discussion of inferences for the current study.
Methods of the Literature Review
For this review, the literature on engagement, alignment, and perceived
organizational support was identified primarily from searches conducted through The
George Washington University Gelman Library using the following library databases:
ABI/Inform, Academic Search Complete, Business Source Complete, JSTOR, ProQuest,
ProQuest Dissertations, PsycINFO, and Web of Science. Acknowledging that multiple
terms and labels are often used interchangeably in the literature for both engagement
(Carasco-Saul et al., 2015; Harter et al., 2002; Kahn, 1990; Maslach et al., 2001; May et
al., 2004; Saks, 2006; Schaufeli et al., 2002; Shuck, Osam, et al., 2017; Shuck & Reio,
2011) and alignment (Alagaraja et al., 2015; Alagaraja & Shuck, 2015; Albrecht et al.,
2018; Ayers, 2013, 2015; Biggs et al., 2014a, 2014b; Boswell, 2006; De Graaf, 2012;
Gagnon & Michael, 2003; Henderson & Venkatraman, 1991, 1993; Labovitz & Rosansky,
1997, 2012; Ouakouak & Ouedraogo, 2013b; Prieto & de Carvalho, 2011; Semler, 1997;
Stringer, 2007), numerous search terms and combinations of terms were used:
• Engagement and alignment: [“engagement” OR “employee engagement” OR
“engagement at work” OR “job engagement” OR “personal engagement” OR
“work engagement”] AND [“alignment” OR “employee alignment” OR
“employee strategic alignment” OR “goal alignment” OR “goal congruence” OR
“line of sight” OR “organizational alignment” OR “strategic alignment”].
36
• Engagement and perceived organizational support: [“engagement” OR “employee
engagement” OR “engagement at work” OR “job engagement” OR “personal
engagement” OR “work engagement”] AND “perceived organizational support.”
• Alignment and perceived organizational support: [“alignment” OR “employee
alignment” OR “employee strategic alignment” OR “goal alignment” OR “goal
congruence” OR “line of sight” OR “organizational alignment” OR “strategic
alignment”] AND “perceived organizational support.”
• Engagement and alignment and perceived organizational support: [“engagement”
OR “employee engagement” OR “engagement at work” OR “job engagement”
OR “personal engagement” OR “work engagement”] AND [“alignment” OR
“employee alignment” OR “employee strategic alignment” OR “goal alignment”
OR “goal congruence” OR “line of sight” OR “organizational alignment” OR
“strategic alignment”] AND “perceived organizational support.”
Searches were limited to peer-reviewed scholarly journals and doctoral
dissertations written in English, with the search term(s) appearing in the title, subject,
indexing, or keywords. Recognizing that Kahn (1990) is credited as the first to define
engagement in the academic literature (Bakker, 2017; Eldor, 2016; Saks & Gruman,
2014; Shuck, Osam, et al., 2017; Shuck & Wollard, 2009), the searches were further
limited to those works published in 1990 or later. Additionally, the bibliography and
reference sections of the works reviewed provided additional sources of references for
this literature review.
37
Engagement of Employees
In synthesizing the relevant academic literature on engagement, this section first
discusses why engagement is, or should be, important to managers within organizations
and the reasoning for the focus of inquiry on engagement in an organizational context.
Next is a discussion of what engagement is, to include how engagement has been
conceptualized and defined in the scholarly literature, as well as a discussion of
engagement’s “dark side.” Having discussed the “why” and the “what,” the discussion
then turns to the “how,” or the antecedents of engagement.
The Importance of Engagement for Managers in Organizations
A growing body of research suggests that engaged employees can play a critical
role in achieving organizational goals, improving organizational effectiveness, and
helping organizations become and remain competitive (Alagaraja & Shuck, 2015; Bakker,
2011, 2017; Bakker & Demerouti, 2008; Burke & Cooper, 2006; Burke & Ng, 2006; Eldor,
2016; Eldor & Harpaz, 2016; Frank et al., 2004; Harter et al., 2002; Saks, 2006; Saks &
Gruman, 2014; Shuck, Rocco, et al., 2011; Shuck & Reio, 2011; Shuck & Rose, 2013;
Shuck & Wollard, 2008; Stallard & Pankau, 2010). In addressing why engagement can be
important for managers in organizations, this section addresses (a) the changing nature of
management challenges; (b) competitive advantage; (c) organizational and employee
outcomes of engagement; and (d) the state of engagement in organizations.
The Changing Nature of Management Challenges
Many scholars have reflected on the revolutionary, often disruptive, and pervasive
effect that technological advances and globalization have had, and continue to have, on
individuals and managers within organizations (Alheit, 2009; Burke & Ng, 2006; Tarique
38
& Schuler, 2010; Uhl-Bien et al., 2007; World Economic Forum, 2016). As a result,
managers frequently find their traditional business models unsettled by competitive
environments characterized by complexity, uncertainty, and disruptive change (Ireland &
Hitt, 2005; McCann, 2004; Nicolaides & McCallum, 2013; Tarique & Schuler, 2010;
Uhl-Bien et al., 2007; Wheatley, 2006; World Economic Forum, 2016). These are
environments that are also often characterized as being volatile, uncertain, complex, and
ambiguous (Bennett & Lemoine, 2014; Gerras, 2010; Jacobs, 2009; Johansen, 2012).
In addition to, or possibly as a consequence of, the noted increase in complexity,
uncertainty, and change, some have observed that the nature of the challenges that
individuals and managers face is also changing (Kegan, 2009; Kegan & Lahey, 2009;
Luthans & Avolio, 2003; Nicolaides & McCallum, 2013; Uhl-Bien et al., 2007).
Specifically, as originally identified by Heifetz (1995, as cited in Kegan, 2009), we
increasingly face two types of challenges, technical and adaptive (Kegan, 2009; Kegan &
Lahey, 2009; Nicolaides & McCallum, 2013; Uhl-Bien et al., 2007). Technical
challenges are those that, while not simple or easy, can be solved with known knowledge,
methods, procedures, and skills (Kegan & Lahey, 2009; Nicolaides & McCallum, 2013;
Uhl-Bien et al., 2007). However, unlike technical challenges, adaptive challenges exist
where “the rules that have guided how we operate no longer work” (Luthans & Avolio,
2003, p. 242) and “there are no clear set of guidelines, rules, or direction for action”
(Luthans & Avolio, 2003, p. 242), and they cannot be solved with known knowledge,
methods, procedures, and skills (Kegan & Lahey, 2009; Nicolaides & McCallum, 2013;
Uhl-Bien et al., 2007). Rather, adaptive challenges require “unlearning old assumptions
39
and attitudes” (Nicolaides & McCallum, 2013, p. 248) to allow for “new learning,
innovation, and new patterns of behavior” (Uhl-Bien et al., 2007, p. 300).
With this changing nature of the problems managers face, there is growing
recognition that the “leader,” or manager, can no longer be the sole, or even the primary,
source of answers and solutions (Nicolaides & McCallum, 2013). Rather, managers must
rely on the knowledge, skills, abilities, and effort of employees throughout an
organization to address the adaptive challenges they face (Bakker, 2017; Boswell et al.,
2006; Boudreau & Ramstad, 2005; Luthans & Youssef, 2004; Pfeffer, 2005).
Competitive Advantage
In defining competitive advantage, Barney (1991) noted that an organization
could be “said to have a competitive advantage when it is implementing a value creating
strategy not simultaneously being implemented by any current or potential competitors”
(p. 102), or more simply, when an organization is situated in a more favorable position
than its competitors (Barney, 1991; Barney & Wright, 1998). For an organizational
resource, such as human capital, to have the potential to result in a competitive
advantage, the resource must (a) enable the organization to improve its efficiency and
effectiveness (Barney, 1991); (b) be limited in supply without readily available
substitutes (Barney, 1991; Barney & Wright, 1998); and (c) not be easily imitated by
other organizations (Barney, 1991; Barney & Wright, 1998).
The knowledge, skills, abilities, and effort of employees can be a “resource” from
which a competitive advantage may be realized within an organization. To remain
competitive, managers must increasingly nurture, encourage, and utilize the creativity,
initiative, commitment, innovativeness, and effort of their employees to address the
40
problems and challenges they face in order to achieve organizational goals, objectives,
and organizational success (Bakker, 2017; Boswell et al., 2006; Boudreau & Ramstad,
2005; Luthans & Youssef, 2004; Pfeffer, 2005; Simon, 1991; Stringer, 2007).
As workplace demands often increasingly require employee efforts beyond those
specific job tasks and responsibilities formally identified in a job description—task
requirements that are often difficult to foresee and define in advance—scholars have noted
the critical importance of employee discretionary effort to achieve organizational goals
(Boswell et al., 2006; Eldor & Harpaz, 2016; Masson et al., 2008; Shuck, Reio, et al.,
2011). As such, managers should strive to create conditions that nurture employees who
have a clear understanding of how their individual contributions and efforts support the
goals of the organization and expend discretionary effort, proactively working towards
achieving those goals (Alagaraja & Shuck, 2015; Boswell, 2000a, 2006; Boswell et al.,
2006; Boswell & Boudreau, 2001; Chalofsky & Krishna, 2009; Kahn, 2010; Masson et al.,
2008; Stringer, 2007). As Simon (1991) observed, “Doing the job well is not mainly a
matter of responding to commands, but is much more a matter of taking initiative to
advance organizational objectives” (p. 32), and organizational success requires “that
employees take initiative and apply all their skill and knowledge to advance the
achievement of the organization’s objectives” (p. 32). Rather than technology, capital, or
infrastructure, it is the knowledge, skills, and abilities of its people that are increasingly
recognized as a primary source of competitive advantage for an organization (Barney &
Wright, 1998; Baruch, 2006; Eldor, 2016; Luthans & Youssef, 2004; Pfeffer, 2005;
Tarique & Schuler, 2010; Whittington & Galpin, 2010).
41
In addition to the growing realization of the critical importance of human capital
to organizational success (Boudreau & Ramstad, 2005; Burke & Cooper, 2006; Eldor,
2016; Luthans & Youssef, 2004; Nicolaides & McCallum, 2013; Saks, 2006; Saks &
Gruman, 2014; Shuck & Reio, 2011; World Economic Forum, 2016), there has been
recognition of the challenges managers currently face, and are expected to continue to
face, in recruiting, developing, and retaining the talent necessary to address the
challenges and opportunities of the 21st century (Beechler & Woodward, 2009; Burke &
Ng, 2006; Frank et al., 2004; Tarique & Schuler, 2010; World Economic Forum, 2016).
Employees are important for all organizations, but attracting and retaining engaged
employees may be increasingly critical in gaining and maintaining a competitive
advantage and achieving organizational success (Eldor, 2016; Eldor & Harpaz, 2016;
Rich et al., 2010; Shuck, Reio, et al., 2011; Shuck & Rose, 2013; Stallard & Pankau,
2010; Whittington et al., 2017; Whittington & Galpin, 2010).
Organizational and Employee Outcomes of Engagement
A growing body of research has suggested that the engagement of employees has
benefits for both the organization and the individual. Studies have shown a positive
relation between engagement and outcomes often desired by managers such as creativity
(Bae et al., 2013; Reijseger et al., 2017; Toyama & Mauno, 2017), discretionary effort
(Shuck, Reio, & Rocco, 2011), innovation (Bhatnagar, 2012; Gomes et al., 2015), job (or
task) performance (Reijseger et al., 2017; Rich et al., 2010; Shantz et al., 2013), job
satisfaction (Biswas & Bhatnagar, 2013; Saks, 2006), open-mindedness (Reijseger et al.,
2017), organizational commitment (Biswas & Bhatnagar, 2013; Saks, 2006), personal
initiative (Reijseger et al., 2017), and productivity (Kataria et al., 2013), as well as a
42
negative relation with turnover intention (Bhatnagar, 2012; Saks, 2006; Shuck et al.,
2014; Shuck, Reio, et al., 2011). As noted by Robinson et al. (2004), “An engaged
employee is aware of the business context and works with colleagues to improve
performance within the job for the benefit of the organization” (p. 9). Conversely, an
employee who is not engaged will often withdraw, lose interest, and engage in behavior
that, at best, is only marginally beneficial to an organization and, at worst, is
counterproductive to organizational goals (Kahn, 1990; Pech & Slade, 2006; Saunders &
Tiwari, 2014; Seijts & Crim, 2006). These are employees who, as Seijts and Crim (2006)
observed, have “mentally ‘checked out’” (p. 1) and are essentially “sleepwalking through
their workday and putting time—but not passion—into their work” (p. 1). Table 2.1
provides a summary of correlations between engagement and the positive organizational
outcomes often desired by managers from a sample of recent empirical studies.
43
Table 2.1
Summary of Correlations Between Engagement and Positive Organizational Outcomes
Engagement outcome Correlation statistics a Study reference Creativity r = .39, p < .01, n = 304 Bae et al. (2013) r = .39, p < .001, n = 186 Reijseger et al. (2017) r = .30, p < .001, n = 489 Toyama and Mauno (2017)
Discretionary effort r = .43, p < .001, n = 283 Shuck et al. (2011)
Innovation r = .28, p < .01, n = 291 Bhatnagar (2012) r = .28, p < .01, n = 337 Gomes et al. (2015)
Job (or task) performance r = .44, p < .001, n = 186 Reijseger et al. (2017) r = .35, p < .05, n = 245 Rich et al. (2010) r = .12, p < .05, n = 283 Shantz et al. (2013)
Job satisfaction r = .32, p < .05, n = 246 Biswas and Bhatnagar (2013) r = .52, p < .001, n = 102 Saks (2006)
Open-mindedness r = .42, p < .001, n = 186 Reijseger et al. (2017)
Organizational commitment r = .47, p < .05, n = 246 Biswas and Bhatnagar (2013) r = .53, p < .001, n = 102 Saks (2006)
Personal initiative r = .51, p < .001, n = 186 Reijseger et al. (2017)
Productivity r = .27, p < .01, n = 304 Kataria et al. (2013)
Turnover intention r = –.09, p < .01, n = 291 Bhatnagar (2012) r = –.41, p < .001, n = 102 Saks (2006) r = –.56, p < .001, n = 283 Shuck et al. (2011) r = –.34, p < .001, n = 209 Shuck et al. (2014) a According to Cohen (1988), correlation coefficient effect size can be classified as (a) small, r = ± .10; (b) medium, r = ± .30; or (c) large, r = ± .50.
In addition to the organizational outcomes of engagement, studies have found a
positive relation between engagement and individual employee health and well-being
outcomes such as job satisfaction (Biswas & Bhatnagar, 2013; Saks, 2006), feelings of
personal accomplishment (Shuck & Reio, 2014), psychological well-being (Shuck &
Reio, 2014), and overall quality of life (Freeney & Fellenz, 2013), as well as a negative
relation between engagement and feelings of depersonalization (Shuck & Reio, 2014),
emotional exhaustion (Shuck & Reio, 2014), and turnover intention (Bhatnagar, 2012;
Saks, 2006; Shuck et al., 2014; Shuck, Reio, et al., 2011). Table 2.2 provides a summary
44
of correlations between engagement and individual employee well-being outcomes from
a sample of recent empirical studies. The engagement outcomes of job satisfaction and
turnover intention have been identified in the literature as both organizational and
employee well-being outcomes of engagement (Angle & Perry, 1981; Caruth et al., 2010;
Porras & Silvers, 1991) and are included in both Table 2.1 and Table 2.2.
Table 2.2
Summary of Correlations Between Engagement and Employee Well-Being Outcomes
Engagement outcome Correlation statistics a Study reference Depersonalization r = –.41, p < .01, n = 216 Shuck and Reio (2014)
Emotional exhaustion r = –.30, p < .01, n = 216 Shuck and Reio (2014)
Job satisfaction r = .32, p < .05, n = 246 Biswas and Bhatnagar (2013) r = .52, p < .001, n = 102 Saks (2006)
Personal accomplishment r = .48, p < .01, n = 216 Shuck and Reio (2014)
Psychological well-being r = .37, p < .01, n = 216 Shuck and Reio (2014)
Quality of life r = .19, p < .01, n = 158 Freeney and Fellenz (2013)
Turnover intention r = –.09, p < .01, n = 291 Bhatnagar (2012) r = –.41, p < .001, n = 102 Saks (2006) r = –.56, p < .001, n = 283 Shuck, Reio, et al. (2011) r = –.34, p < .001, n = 209 Shuck et al. (2014) a According to Cohen (1988) correlation coefficient effect size can be classified as (a) small, r = ± .10; (b) medium, r = ± .30; or (c) large, r = ± .50.
State of Engagement in Organizations
As a reflection of the growing recognition of the potential significance of
engagement in helping managers achieve organizational goals, a Harvard Business
Review Analytic Services (2013) study of 550 global executives found that “71 percent
of respondents rank employee engagement as very important to achieving overall
organizational success” (p. 1) and consider it “a top-three business priority” (p. 3). As
another indicator of engagement’s presumed importance to managers, it has been
45
estimated that U.S. companies spend over $720 million annually on employee
engagement efforts (LaMotte, 2015).
However, Gallup (2017) reported that only 33% of U.S. employees are engaged in
their job (p. 17), 51% are not engaged (p. 61), and 16% are actively disengaged (p. 61),
with an estimated impact on the U.S. economy, due to lost productivity, between $483
billion and $605 billion per year (p. 19). Additionally, the U.S. Office of Personnel
Management (2018) reported that approximately 32% (p. 34) of the over 1,473,870 (p. 1)
federal civilian employees surveyed as part of their annual Federal Employee Viewpoint
Survey were disengaged. With approximately one-third (U.S. Office of Personnel
Management, 2018) to two-thirds (Gallup, 2017) of the U.S. workforce disengaged, there
appears to be an opportunity for managers to improve both organizational and employee
well-being through increasing engagement of employees.
Having considered why engagement is important for managers, the next section
examines how engagement has been conceptualized and defined in the scholarly
literature. The intent of the discussion is to build a foundation for understanding what
engagement is in order to better understand and potentially address the state of
engagement found in organizations today.
Developing an Understanding of Engagement
In synthesizing how the academic literature has discussed “what” engagement is,
this section first looks at how the literature has conceptualized and defined engagement.
In an effort to balance both the positive and the negative, the discussion then turns to
what some have characterized as the “dark side,” or the potential adverse outcomes, of
engagement.
46
Conceptualizing and Defining Engagement
One of the observations, often a criticism, found in the academic engagement
literature is the lack of a common definition and theoretical construct for engagement (B.
Little & Little, 2006; Macey & Schneider, 2008; Saks, 2006; Shuck, Adelson, et al.,
2017; Shuck, Osam, et al., 2017; Shuck & Wollard, 2010, 2009). Scholars have also
noted that the various terms for engagement—e.g., employee engagement, job
engagement, and work engagement—are used interchangeably in the academic literature
(Carasco-Saul et al., 2015; Shuck, Osam, et al., 2017), sometimes even within a single
study, which can lead to confusion and lack of conceptual clarity (Shuck, Osam, et al.,
2017).
While scholars have yet to agree upon a single definition and theoretical
framework for engagement, the academic literature has frequently identified the seminal
conceptualizations of engagement to include (a) personal engagement (Kahn, 1990); (b)
job engagement (Maslach et al., 2001); (c) work engagement (Schaufeli et al., 2002); (d)
employee engagement (Harter et al., 2002); (e) engagement at work (May et al., 2004); (f)
employee engagement (consisting of both job engagement and organization engagement)
(Saks, 2006); (g) job engagement (Rich et al., 2010); and (h) employee engagement
(Shuck & Reio, 2011) (Eldor, 2016; Saks, 2008; Saks & Gruman, 2014; Serrano &
Reichard, 2011; Shuck, 2011; Shuck, Adelson, et al., 2017; Shuck, Osam, et al., 2017). A
discussion of each seminal conceptualization, to include the theoretical framework,
definition, and means of measurement, follows.
Kahn (1990). In developing a conceptual model of personal engagement and
personal disengagement in the context of work, Kahn (1990) grounded his framework on
47
the premise that individuals “occupy roles at work” (p. 692) and that they use “varying
degrees of their selves, physically, cognitively, and emotionally” (p. 692) as they perform
these roles. Kahn's (1990) focus was on the person-work role relationships and the
psychological conditions of work and the work environment that affected the extent to
which individuals engaged and disengaged at work, or “how psychological experiences
of work and work contexts shape the processes of people presenting and absenting their
selves during task performances” (p. 694). Kahn (1990) defined personal engagement “as
the harnessing of organization members’ selves to their work roles; in engagement,
people employ and express themselves physically, cognitively, and emotionally during
role performances” (p. 694) and personal disengagement “as the uncoupling of selves
from work roles; in disengagement, people withdraw and defend themselves physically,
cognitively, or emotionally during role performances” (p. 694).
Kahn (1990) identified three psychological conditions thought to influence an
individual’s level of personal engagement or disengagement: psychological
meaningfulness, psychological safety, and psychological availability. In Kahn's (1990)
framework, an individual essentially asks (consciously or unconsciously) questions
around these three psychological conditions, with the answers shaping their individual
level of engagement: (a) “how meaningful is it for me to bring myself into this
performance?” (p. 703), (b) “how safe is it to do so?” (p. 703), and (c) “how available am
I to do so?” (p. 703). In measuring engagement, Kahn (1990) used a qualitative approach
of observation, document analysis, and interviews. Of note, Kahn (1990) is often credited
as the first to define engagement in the academic literature (Bakker, 2017; Eldor, 2016;
Saks & Gruman, 2014; Shuck, Osam, et al., 2017; Shuck & Wollard, 2009).
48
Maslach, Schaufeli, and Leiter (2001). Situated in the job burnout literature,
Maslach et al. (2001) defined job engagement as the “positive antithesis” (p. 416) of
burnout. Maslach et al. (2001) identified burnout as an “individual-level construct,
specific to a given work context” (p. 407), whose three core dimensions were “an
overwhelming exhaustion, feelings of cynicism and detachment from the job, and a sense
of ineffectiveness and lack of accomplishment” (p. 399). As the antithesis of burnout,
Maslach et al. (2001) characterized job engagement as consisting of “energy,
involvement, and efficacy—the direct opposites of the three burnout dimensions”
(p. 416). Maslach et al. (2001) also noted that engagement was distinct from constructs
such as organizational commitment, job satisfaction, and job involvement. Maslach et al.
(2001) proposed using the Maslach Burnout Inventory (MBI) as an instrument to measure
job engagement, where the measure of engagement would be “assessed by the opposite
profile of MBI scores” (p. 417).
Schaufeli, Salanova, Gonzalez-Roma, and Bakker (2002). While similar to
Maslach et al. (2001) in conceptualizing engagement as the opposite of burnout,
Schaufeli et al. (2002) differed in their view that engagement could be “adequately
measured by the opposite profile of MBI scores” (p. 75) as had been proposed by
Maslach et al. (2001). Rather, Schaufeli et al. (2002) indicated that engagement was
“operationalized in its own right” (p. 75) and should be measured with a different
instrument. Schaufeli et al. (2002) also observed that “rather than a momentary and
specific state, engagement refers to a more persistent and pervasive affective cognitive
state that is not focused on any particular object, event, individual, or behavior” (p. 74).
49
Schaufeli et al. (2002) defined engagement “as a positive, fulfilling, work-related
state of mind that is characterized by vigor, dedication, and absorption” (p. 74).
Expanding on this definition, Schaufeli et al. (2002) characterized vigor as “high levels of
energy and mental resilience while working, the willingness to invest effort in one’s
work, and persistence even in the face of difficulties”; dedication as “a sense of
significance, enthusiasm, inspiration, pride, and challenge” (p. 74); and absorption as
“being fully concentrated and deeply engrossed in one’s work, whereby time passes
quickly and one has difficulties with detaching oneself from work” (p. 75). Work
engagement is conceptualized as relating uniquely to work and is concerned with the
focus of employees’ energies—their vigor, dedication, and absorption—towards the work
and work activities (Schaufeli et al., 2002; Shuck, 2019; Shuck, Adelson, et al., 2017;
Shuck, Osam, et al., 2017). It is worth noting that while Schaufeli et al. (2002) originally
used the term engagement, the definition they offered would subsequently become
associated with work engagement (Bakker, 2011; Bakker et al., 2008, 2011; Bakker &
Demerouti, 2008; Schaufeli et al., 2006). Work engagement is operationalized and
measured using the Utrecht Work Engagement Scale (Schaufeli et al., 2006; Schaufeli &
Bakker, 2004).
Harter, Schmidt, and Hayes (2002). Differing from previous studies that
focused on engagement at the individual employee level of analysis (e.g., Kahn, 1990;
Maslach et al., 2001; and Schaufeli et al., 2002), Harter et al. (2002) conducted a meta-
analysis on the effect of employee satisfaction and employee engagement aggregated at
the business-unit level for desired business outcomes. The analysis used a database of 42
previous studies from The Gallup Organization, consisting of 36 different companies
50
from the financial, manufacturing, retail, services, transportation, and public utilities
industries, with 7,939 business units and 198,514 total respondents (Harter et al., 2002).
The studies in the meta-analysis contained “considerable variation in type of business
unit, ranging from stores, to manufacturing plants, to departments” (Harter et al., 2002, p.
271). Harter et al. (2002) defined employee engagement as “the individual’s involvement
and satisfaction with as well as enthusiasm for work” (p. 269). In the analysis, employee
engagement was measured using the Gallup Workplace Audit instrument (Harter et al.,
2002). Harter et al. (2002) found small to medium (J. Cohen, 1988) positive correlations
between employee engagement and business-unit–level desired business outcomes of
customer satisfaction (r = .33), employee safety (r = –.32), employee turnover (r = – .30),
productivity (r = .25), and profitability (r = .17).
May, Gilson, and Harter (2004). May et al. (2004) quantitatively tested Kahn's
(1990) conceptualization of engagement, examining how the three psychological
conditions of psychological meaningfulness, psychological safety, and psychological
availability shaped an individual’s engagement in his or her work. May et al. (2004)
conceptualized engagement as engagement at work, defined as “the harnessing of
organizational members’ selves to their work roles; in engagement, people employ and
express themselves physically, cognitively, and emotionally during role performances”
(p. 12)—the same definition used by Kahn (1990) to define personal engagement.
In the analysis, engagement at work was measured as psychological engagement
using a scale developed by the authors for the study (May et al., 2004). The site for the
field study was a large insurance company (n = 213) located in the Midwest (May et al.,
2004). May et al. (2004) found that all three psychological conditions had a medium to
51
large (J. Cohen, 1988) positive correlation with engagement at work at the .05 level of
significance: psychological meaningfulness (r = .63), psychological safety (r = .35), and
psychological availability (r = .36).
Saks (2006). Saks (2006) was one of the first to provide an empirical basis for a
relation between antecedents and consequences of employee engagement. Saks (2006)
conceptualized employee engagement as role related, consisting of two types of
engagement (job engagement and organization engagement), defined as the “extent to
which an individual is psychologically present in a particular organizational role” (p.
604). Saks (2006) further noted that “the two most dominant roles for most organizational
members are their work role and their role as a member of an organization” (p. 604) and
that “the model explicitly acknowledges this by including both job and organization
engagements” (p. 604).
In the analysis, job and organization engagement were measured using scales
developed by the author for the study (Saks, 2006). The study data were collected from
102 voluntary participants working in a variety of organizations; sample participants
were identified and recruited by graduate students from a research methods class (Saks,
2006). The results showed a large (J. Cohen, 1988) positive correlation between job and
organization engagement (r = .62, p < .001); although related, job and organization
engagement were found to be distinct constructs (t(101) = 2.42, p < .05) (Saks, 2006).
Rich, Lepine, and Crawford (2010). Unlike Maslach et al. (2001), who situated
their definition of job engagement in the job burnout literature, Rich et al. (2010) based
their conceptualization of job engagement on the work of Kahn (1990). In building on
Kahn (1990), Rich et al. (2010) focused on exploring how the construct of engagement
52
represents an individual’s investment of “cognitive, affective, and physical energies into
role performance” (p. 617) and the relation between these investments of effort (i.e.,
engagement) and employee job performance. Job engagement was conceptualized as role
related and concerned with an employee’s focus of cognitive, affective, and physical
energies towards the job and job activities (Rich et al., 2010; Shuck, 2019; Shuck,
Adelson, et al., 2017; Shuck, Osam, et al., 2017). Rich et al. (2010) defined job
engagement as “a multidimensional motivational concept reflecting the simultaneous
investment of an individual’s physical, cognitive, and emotional energy in active, full
work performance” (p. 619).
In the study (Rich et al., 2010), job engagement was measured using an 18-item
scale—the Job Engagement Scale—developed by the authors. The scale consisted of
three subscales of 6 items each, measuring physical engagement, emotional engagement,
and cognitive engagement (Rich et al., 2010). The sample for the study consisted of full-
time firefighters and their supervisors from four municipalities (n = 245) (Rich et al.,
2010). Rich et al. (2010) found a medium (J. Cohen, 1988) positive correlation between
job engagement and two key aspects of job performance: task performance (r = .35, p <
.05) and organizational citizenship behavior (r = .35, p < .05). In addition to a
conceptualization of engagement focused on the job and job activities, Rich et al. (2010)
also provided empirical evidence supporting the frequently claimed argument that
engaged employees can create a competitive advantage for an organization.
Shuck and Reio (2011). Shuck and Reio (2011) conceptualized a framework for
employee engagement consisting “of three separate facets: cognitive engagement,
emotional engagement, and behavioral engagement” (p. 421). Shuck and Reio (2011)
53
characterized these facets, noting that cognitive engagement “revolves around how an
employee thinks about and understands his or her job, company, and culture and
represents his or her intellectual commitment to the organization” (p. 422); emotional
engagement “revolves around the emotional bond one feels toward his or her place of
work and represents a willingness to involve personal resources such as pride, belief, and
knowledge” (p. 423); and behavioral engagement is “the physical and overt manifestation
of cognitive and emotional engagement . . . [and] can be understood as increased levels of
discretionary effort” (p. 423).
In developing their conceptual framework, Shuck and Reio (2011) used the
definition of employee engagement proposed by Shuck and Wollard (2010): “an
individual employee’s cognitive, emotional, and behavioral state directed toward desired
organizational outcomes” (p. 103). Shuck, Osam, et al. (2017) subsequently updated the
associated definition of employee engagement as “a positive, active, work-related
psychological state operationalized by the maintenance, intensity, and direction of
cognitive, emotional, and behavioral energy” (p. 269). Based on this conceptual
framework and definition of employee engagement, Shuck et al. (2014) noted that “those
who felt that their work mattered, that they were supported in their work, and that their
well-being was considered fairly were likely to embrace and engage” (p. 245).
Employee engagement is conceptualized as relating uniquely to employees’ active
role in directing their cognitive, emotional, behavioral energies towards desired
organizational outcomes within the full experience of their work, to include the work,
job, team, and organization (Shuck, Adelson, et al., 2017; Shuck, Osam, et al., 2017;
Shuck & Wollard, 2010). This framework and definition of employee engagement reflect
54
that it is an individual-level construct where each type of engagement—cognitive,
emotional, and behavioral—is “separate, definable, and builds from one another” (Shuck
& Reio, 2011, p. 422). Lastly, this conceptualization of employee engagement is intended
to be measured using the Employee Engagement Scale (Shuck et al., 2017).
Summary of Seminal Definitions and Conceptualizations of Engagement.
While definitions and conceptual frameworks vary, the academic literature seems to
agree that engaged employees are more likely to be enthusiastic about their work,
perform better, expend discretionary effort to help accomplish the goals of the
organization, and be more committed to the success of the organization than those who
are disengaged (Alagaraja & Shuck, 2015; Bakker, 2011; Bakker et al., 2011; Bakker &
Demerouti, 2008; Shuck, Reio, et al., 2011; Shuck & Reio, 2011). Table 2.3 summarizes
the definitions and associated measures of engagement identified from the eight seminal
conceptualizations. Returning to the organizational outcomes of engagement shown in
Table 2.1, Table 2.4 contextualizes these outcomes within the seminal conceptualizations
of engagement—as reflected in the “engagement construct” column. Similarly, Table 2.5
contextualizes the employee well-being outcomes of engagement from Table 2.2 within
the seminal conceptualizations of engagement.
55
Table 2.3
Summary of the Seminal Definitions and Associated Measures of Engagement
Engagement construct
Study reference Definition Measurement
Personal engagement
Kahn (1990)
“The harnessing of organization members’ selves to their work roles; in engagement, people employ and express themselves physically, cognitively, and emotionally during role performances” (p. 694).
Qualitative – ethnographic study
Job engagement Maslach et al. (2001)
The “positive antithesis” of burnout, “characterized by energy, involvement, and efficacy—the direct opposites of the three burnout dimensions” (p. 416).
Maslach Burnout Inventory
Work engagement a
Schaufeli et al. (2002)
“A positive, fulfilling, work-related state of mind that is characterized by vigor, dedication, and absorption” (p. 74).
Utrecht Work Engagement Scale (Schaufeli et al., 2006; Schaufeli & Bakker, 2004).
Employee engagement
Harter et al. (2002)
“The individual’s involvement and satisfaction with as well as enthusiasm for work” (p. 269).
Gallup Workplace Audit (Harter et al., 2002)
Engagement at work
May et al. (2004)
“The harnessing of organizational members’ selves to their work roles; in engagement, people employ and express themselves physically, cognitively, and emotionally during role performances” (p. 12).
Author-developed scale (May et al., 2004)
Employee engagement (two types, job engagement and organization engagement)
Saks (2006)
“A distinct and unique construct that consists of cognitive, emotional, and behavioral components that are associated with individual role performance” (p. 602).
Author-developed scales (Saks, 2006)
Job engagement Rich et al. (2010)
“A multidimensional motivational concept reflecting the simultaneous investment of an individual’s physical, cognitive, and emotional energy in active, full work performance” (p. 619).
Job Engagement Scale (Rich et al., 2010)
Employee engagement b
Shuck and Reio (2011)
“An individual employee’s cognitive, emotional, and behavioral state directed toward desired organizational outcomes” (p. 103).
Employee Engagement Scale (Shuck, Adelson, et al., 2017)
a While Schaufeli et al. (2002) originally used the term “engagement,” the definition they offered has subsequently become associated with work engagement (Shuck, Adelson, et al., 2017; Shuck, Osam, et al., 2017). b In developing their conceptual framework, Shuck and Reio (2011) used the definition of employee engagement proposed by Shuck and Wollard (2010). Shuck, Osam, et al. (2017) subsequently updated the associated definition of employee engagement as “a positive, active, work-related psychological state operationalized by the maintenance, intensity, and direction of cognitive, emotional, and behavioral energy” (p. 269).
56
Table 2.4
Summary of Correlations Between Engagement and Positive Organizational Outcomes
with Engagement Construct
Engagement outcome Correlation statistics a Engagement construct b Study reference
Creativity r = .39, p < .01, n = 304 Work engagement 1 Bae et al. (2013) r = .39, p < .001, n = 186
Work engagement 1 Reijseger et al. (2017)
r = .30, p < .001, n = 489
Work engagement 1 Toyama and Mauno (2017)
Discretionary effort
r = .43, p < .001, n = 283
Employee engagement 2 Shuck et al. (2011)
Innovation r = .28, p < .01, n = 291 Work engagement 1 Bhatnagar (2012) r = .28, p < .01, n = 337 Work engagement 1 Gomes et al. (2015)
Job (or task) performance
r = .44, p < .001, n = 186
Work engagement 1 Reijseger et al. (2017)
r = .35, p < .05, n = 245 Job engagement 3 Rich et al. (2010) r = .12, p < .05, n = 283 Work engagement 1 Shantz et al. (2013)
Job satisfaction
r = .32, p < .05, n = 246 Employee engagement 4 Biswas and Bhatnagar (2013)
r = .52, p < .001, n = 102
Job engagement 4 Saks (2006)
Open- mindedness
r = .42, p < .001, n = 186
Work engagement 1 Reijseger et al. (2017)
Organizational commitment
r = .47, p < .05, n = 246 Employee engagement 4 Biswas and Bhatnagar (2013)
r = .53, p < .001, n = 102
Job engagement 4 Saks (2006)
Personal initiative
r = .51, p < .001, n = 186
Work engagement 1 Reijseger et al. (2017)
Productivity r = .27, p < .01, n = 304 Work engagement 1 Kataria et al. (2013)
Turnover intention
r = –.09, p < .01, n = 291
Work engagement 1 Bhatnagar (2012)
r = –.41, p < .001, n = 102
Job engagement 4 Saks (2006)
r = –.56, p < .001, n = 283
Employee engagement 2 Shuck et al. (2011)
r = –.34, p < .001, n = 209
Employee engagement 5 Shuck et al. (2014)
a According to Cohen (1988), correlation coefficient effect size can be classified as (a) small, r = ± .10; (b) medium, r = ± .30; or (c) large, r = ± .50. b For the engagement construct used in the associated study, Table 2.3 provides additional specifics on the definition and measurement instruments: 1Work engagement (Schaufeli et al., 2002); 2Engagement at work (May et al., 2004); 3Job engagement (Rich et al., 2010); 4Employee engagement / job engagement (Saks, 2006) (study does define job engagement); 5Employee engagement (Shuck & Reio, 2011) (study used the Job Engagement Scale (Rich et al., 2010) to measure employee engagement)
57
Table 2.5
Summary of Correlations Between Engagement and Employee Well-Being with
Engagement Construct
Engagement outcome
Correlation statistics a
Engagement construct b
Study reference
Depersonalization r = –.41, p < .01, n = 216
Employee engagement1
Shuck and Reio (2014)
Emotional exhaustion
r = –.30, p < .01, n = 216
Employee engagement1
Shuck and Reio (2014)
Job satisfaction
r = .32, p < .05, n = 246
Employee engagement2
Biswas and Bhatnagar (2013)
r = .52, p < .001, n = 102
Job engagement3 Saks (2006)
Personal accomplishment
r = .48, p < .01, n = 216
Employee engagement1
Shuck and Reio (2014)
Psychological well-being
r = .37, p < .01, n = 216
Employee engagement1
Shuck and Reio (2014)
Quality of life r = .19, p < .01, n = 158
Work engagement4 Freeney and Fellenz (2013)
Turnover intention
r = –.09, p < .01, n = 291
Work engagement4 Bhatnagar (2012)
r = –.41, p < .001, n = 102
Job engagement3 Saks (2006)
r = –.56, p < .001, n = 283
Employee engagement5
Shuck, Reio, et al. (2011)
r = –.34, p < .001, n = 209
Employee engagement1
Shuck et al. (2014)
a According to Cohen (1988), correlation coefficient effect size can be classified as (a) small, r = ± .10; (b) medium, r = ± .30; or (c) large, r = ± .50. b For the engagement construct used in the associated study, Table 2.3 provides additional specifics on the definition and measurement instruments: 1Employee engagement (Shuck & Reio, 2011) (study used the Job Engagement Scale (Rich et al., 2010) to measure employee engagement); 2Employee engagement (Saks, 2006); 3Job engagement (Saks, 2006) (study does not provide a definition of job engagement); 4Work engagement (Schaufeli et al., 2002); 5Employee engagement (May et al., 2004).
Defining and Conceptualizing Engagement for the Current Study
While the various definitions and conceptualizations of engagement may appear
similar, Shuck, Osam, et al. (2017) emphasized the distinctions among three common
conceptualizations of engagement often referenced in the literature—employee
engagement, job engagement, and work engagement—noting that each had a unique
58
definition, theoretical construct, and scale of measurement and they were not meant to be
used interchangeably. In the same article, Shuck, Osam, et al. (2017) cautioned
researchers to ensure clarity of the engagement construct used in a research design—i.e.,
the alignment of definition, theoretical framework, and measure. As such, the definition
of employee engagement offered by Shuck, Osam, et al. (2017) was used in this study,
where employee engagement is “a positive, active, work-related psychological state
operationalized by the maintenance, intensity, and direction of cognitive, emotional, and
behavioral energy” (p. 269). Aligned with this definition, the theoretical framework of
engagement that underpins this study is that proposed by Shuck and Reio (2011), who
conceptualized a framework for employee engagement consisting “of three separate
facets: cognitive engagement, emotional engagement, and behavioral engagement” (p.
421).
With a focus on the relationship between an employee and the organization,
specifically an employee’s state of engagement as aligned to “positive organizational
outcomes” (Shuck, Adelson, et al., 2017, p. 959) within the full experience of their work
(Shuck, Adelson, et al., 2017; Shuck, Osam, et al., 2017; Shuck & Wollard, 2010), this
definition and theoretical framework of employee engagement best supports the study’s
focus on the individual employees’ perception of their unique interaction with the
organization and the work environment that is a determinant in whether or not they may
develop a state of engagement (Kahn, 1990, 2010; Shuck, 2019; Shuck et al., 2014;
Shuck, Rocco, et al., 2011; Shuck & Rose, 2013; Wollard & Shuck, 2011). Having
discussed how the academic literature has conceptualized and defined engagement, this
59
section concludes with a brief review of what has been characterized as the “dark side,”
or the potential adverse outcomes, of engagement.
The “Dark Side” of Engagement
In attempting to more fully understand engagement, it is important to recognize
both positive and negative considerations. While much of the engagement literature has
focused on the positive aspects, some scholars have voiced concern over the potential
negative aspects, or the “dark side,” of engagement and its potential effect on employees
within an organization (Bakker et al., 2011; Halbesleben, 2011; Madden & Bailey, 2017).
Although not the focus of this study, it is worth noting that the literature on engagement’s
potential dark side has identified four main areas of concern: (a) a primary focus on the
organizational benefits of engagement, without consideration of potential negative
consequences for employees (J. M. George, 2011; Halbesleben, 2011; Madden & Bailey,
2017; Maslach, 2011); (b) effect on work-life balance (Bakker et al., 2011; J. M. George,
2011; Halbesleben, 2011; Halbesleben et al., 2009; Madden & Bailey, 2017); (c) an
assumption that all employees want to be engaged at work (Guest, 2014; Madden &
Bailey, 2017); and (d) a lack of focus on issues of power and social context (Madden &
Bailey, 2017).
While a focus on the organizational benefits of engaged employees is not
necessarily positive or negative, a concern voiced by some is the potential for the
manipulation of workers under the guise of engagement (J. M. George, 2011; Madden &
Bailey, 2017; Maslach, 2011). For example, if engaged employees accomplish more than
is expected or work additional hours towards organizational goals, is the organization the
sole beneficiary of the employee’s extra effort, or are engaged employees compensated
60
accordingly (J. M. George, 2011; Maslach, 2011)? As George (2011) observed, “If highly
engaged employees contribute more, shouldn’t they be paid more?” (p. 55).
A second area of concern is the potential for “negative consequences” (Bakker et
al., 2011, p. 18) resulting from engagement’s effect on employee work-life balance.
George (2011) suggested that “highly engaged employees are likely to have diminished
time and energy available for pursuits outside of work and may make real sacrifices in
other parts of their lives to sustain their high engagement over time” (p. 56).
Additionally, Halbesleben et al. (2009) examined the effect of work engagement on three
types of work interference with family: “time based (where time spent in one role takes
away from time in another role), strain based (where strain in one role either carries over
to the other role or makes it difficult to fulfill obligations in the other role), and behavior
based (where behaviors expected in one role make it difficult to fulfill obligations in the
other role)” (p. 1453). In one sample of 80 working adults, Halbesleben et al. (2009)
found a medium (J. Cohen, 1988) positive correlation between work engagement and the
three types of work interference with family: time based (r = .35, p < .01), strain based (r
= .25, p < .05), and behavior based (r = .29, p < .01).
A third area of concern is what Guest (2014) identified as an “implicit
assumption” (p. 150) that all employees want to be engaged. As an example, Guest
(2014) described employees who strictly view work as a “means to an end” (p. 150) and
who “will do a ‘fair day’s work for a fair day’s pay’ but they do not seek any further
involvement with the organization and feel no obligation to be engaged” (p. 150).
Additionally, Madden and Bailey (2017) noted that “a more balanced approach is needed
61
that ensures workers who are not engaged are not demonized due to the barriers to
engagement that arise from social differences” (p. 117).
Lastly, Madden and Bailey (2017) raised the concern that the engagement
literature has inadequately considered issues of power and the social context of engaging
employees in an organizational work context. As Madden and Bailey (2017) observed,
the engagement literature often reflects an “idealized perception of the engaged worker,
as someone who offers discretionary effort or is fully absorbed in role, [and] is assumed
to be ageless or gender neutral” (p. 115) and reflects a “lack of attention paid to the
cultural, social and historical realities of work and all the human struggles therein”
(p. 115). Overall, the concern is that the existing body of literature does not reflect a
complete or fully accurate depiction of engagement in the organizational context of work
(Madden & Bailey, 2017). Having discussed how engagement has been conceptualized
and defined in the scholarly literature, or the “what,” and considering the importance of
engagement to organizations, the “why,” the final section turns to the “how,” or
antecedents of engagement.
Antecedents of Engagement
Antecedents of engagement refer to the factors and conditions believed to provide
a necessary foundation from which engagement may develop (Rana et al., 2014; Saks,
2006; Wollard & Shuck, 2011). The engagement literature reflects that scholars have not
reached consensus on a definitive set of antecedents for engagement (Bailey et al., 2017;
Rana et al., 2014; Wollard & Shuck, 2011). For example, in a review of the employee
engagement literature, Wollard and Shuck (2011) identified 42 antecedents supported
with empirical evidence: 21 individual-level antecedents and 21 organizational-level
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antecedents. In narrowing the focus of this study, this section discusses (a) the influence
of managers in engaging employees and (b) the justification of the study variables.
Influence of Managers in Engaging Employees
Previous studies have established the benefits of engagement for both the
organization (Alagaraja & Shuck, 2015; Bakker, 2011; Bakker et al., 2011; Bakker &
Demerouti, 2008; Shuck, Reio, et al., 2011; Shuck & Reio, 2011) and the individual
employee (Bhatnagar, 2012; Biswas & Bhatnagar, 2013; Freeney & Fellenz, 2013; Saks,
2006; Shuck et al., 2014; Shuck, Reio, et al., 2011; Shuck & Reio, 2014). Yet, with
approximately one-third (U.S. Office of Personnel Management, 2018) to two-thirds
(Gallup, 2017) of the U.S. workforce disengaged, there are opportunities for managers to
improve employee engagement in organizations. With respect to engagement, Shuck,
Rocco, et al. (2011) commented that “a manager is one of the most, if not the most
influential individuals in an employee’s work-life” (p. 317) and that “consequently, his or
her ability to influence the development of engagement or disengagement is great” (p.
317). Further, in a Gallup report on a study of over 2,500 managers in the United States,
Harter and Rigoni (2015) identified that “managers account for at least 70% of the
variance in employee engagement scores across business units” (p. 8).
Justification of Study Variables
Scholars have identified a continuing need for research focused on antecedents of
engagement, specifically those organizational elements (Coyle-Shapiro & Shore, 2007),
or factors (Whittington et al., 2017; Whittington & Galpin, 2010) within the purview of
managers that can improve the engagement of employees and organizational
effectiveness (Alagaraja & Shuck, 2015; Coyle-Shapiro & Shore, 2007; Eldor & Vigoda-
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Gadot, 2017; Oswick, 2015; Whittington et al., 2017; Whittington & Galpin, 2010). Two
such factors identified in the literature as critical to creating conditions from which
employee engagement may arise are alignment (CEB Corporate Leadership Council,
2015b, 2015c; Harter & Rigoni, 2015; Rao, 2017; Ray et al., 2014; Stallard & Pankau,
2010) and perceived organizational support (Seijts & Crim, 2006; Shuck et al., 2014;
Shuck, Rocco, et al., 2011; Wollard & Shuck, 2011). Given the recognized importance of
alignment and perceived organizational support for the engagement of employees, and to
scope the focus of this research, this study intentionally limited its focus to these two
antecedent constructs.
Rather than approaching employee engagement as a phenomenon that could, and
possibly should, be directly managed, employee engagement must be encouraged,
enabled, and nurtured (Eldor, 2016; Oswick, 2015; D. Robinson et al., 2004). As
antecedents of engagement, alignment (Alagaraja & Shuck, 2015; Albrecht et al., 2018;
Biggs et al., 2014b; Stringer, 2007) and perceived organizational support (Biswas &
Bhatnagar, 2013; Mahon et al., 2014; Rich et al., 2010; Saks, 2006; Wang et al., 2017;
Wollard & Shuck, 2011; Zhong et al., 2016) provide a theoretical basis that may help to
further explain and better understand the engagement of employees within an
organizational context. Alignment and perceived organizational support are two key
factors within the purview of managers that could prove critical to creating the requisite
organizational environment in which engagement may thrive. Better understanding the
relation among alignment, perceived organizational support, and employee engagement
could assist managers with developing strategies to improve employee engagement,
which should contribute to achieving organizational goals, enhancing organizational
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competitiveness, and improving employee well-being. Having synthesized the relevant
academic literature on the engagement construct, the chapter continues with a synthesis
of the alignment construct.
Alignment of Employees
As discussed in the preceding section, research has found that engaged employees
are more likely than disengaged employees to be committed to organizational success and
to expend discretionary effort towards the accomplishment of organizational goals
(Alagaraja & Shuck, 2015; Bakker, 2011; Bakker et al., 2011; Bakker & Demerouti,
2008; Shuck, Reio, et al., 2011; Shuck & Reio, 2011). However, engagement, in and of
itself, is not necessarily sufficient for the realization of desired organizational outcomes.
Employees must also know where and how to focus this discretionary effort in order to
contribute to desired outcomes; that is, employees must also be aligned with the goals of
the organization (Alagaraja & Shuck, 2015; Ayers, 2013, 2015; Biggs et al., 2014b;
Boswell, 2000a, 2006; Boswell et al., 2006; Boswell & Boudreau, 2001; CEB Corporate
Leadership Council, 2015c; Herd et al., 2018; Powell, 1992; Semler, 1997; Wollard &
Shuck, 2011).
In synthesizing the relevant alignment literature, this section first discusses the
importance of alignment for managers in organizations. That is followed by a discussion
of how alignment has been conceptualized and defined in the academic literature. Third,
the section briefly addresses how alignment differs from the related constructs of person-
organization fit and person-job fit. Next, the discussion addresses the relation between the
constructs of employee alignment and employee engagement. This section concludes
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with a discussion of the relation between the constructs of employee alignment and
perceived organizational support.
The Importance of Alignment for Managers in Organizations
The alignment literature primarily discusses two types of alignment at the
organizational level, external and internal (Henderson & Venkatraman, 1991, 1993;
Ouakouak & Ouedraogo, 2013b). External alignment, often referred to as “fit” in the
management and strategy literature (Henderson & Venkatraman, 1991, 1993; R. E. Miles
& Snow, 1984; Venkatraman & Camillus, 1984), focuses on how managers align the
organization with its competitive environment as represented in their strategy (i.e.,
business strategy) (Henderson & Venkatraman, 1991, 1993; R. E. Miles & Snow, 1984).
Internal alignment refers to an organization’s administrative structure (e.g.,
organizational design and management processes) that supports the strategy (Henderson
& Venkatraman, 1991, 1993; R. E. Miles & Snow, 1984). While acknowledging the
importance of external alignment for organizational success, the focus of this inquiry is
on internal alignment in general and specifically on alignment as it relates to the extent to
which individual employees (i.e., their knowledge, skills, abilities, and effort) are aligned
with the goals of the organization. The focus on internal rather than external alignment is
in keeping with the objective of this study to explore the relation among employee
alignment, perceived organizational support, and employee engagement at the individual
(i.e., employee) level of analysis within an organizational context.
The premise of alignment theory (i.e., internal alignment) is that when there is
agreement, cooperation, or harmony among an organization’s strategy, structure,
processes, culture, and employees, there is a greater likelihood that the organization will
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successfully achieve its goals (Alagaraja & Shuck, 2015; Ayers, 2013, 2015; Biggs et al.,
2014b; Boswell, 2000a, 2006; Boswell et al., 2006; Boswell & Boudreau, 2001; CEB
Corporate Leadership Council, 2015c; Herd et al., 2018; Powell, 1992; Semler, 1997;
Wollard & Shuck, 2011). A well-aligned organization creates a clear linkage among the
goals of the strategy, processes, functional departments, workgroups, and individuals
(Alagaraja & Shuck, 2015; Powell, 1992; Semler, 1997). As Semler (1997) observed, this
agreement, or alignment, “creates an internal environment that facilitates achievement of
the organization’s strategic goals by removing internal barriers to cooperation and
performance that would otherwise reduce the efficiency and effectiveness of work toward
those goals” (p. 28).
At the individual employee level, an understanding of the organization’s goals can
be a critical determinant for achieving desired organizational outcomes (Alagaraja &
Shuck, 2015; Boswell, 2000a, 2006; Boswell et al., 2006; Boswell & Boudreau, 2001;
Gagnon & Michael, 2003; Kaplan & Norton, 2001; Stallard & Pankau, 2010; Stringer,
2007; Wollard & Shuck, 2011). In today’s competitive and uncertain environment,
managers increasingly rely on employees to be proactive and creative problem solvers
(Bakker, 2017; Boswell et al., 2006; Boudreau & Ramstad, 2005; Luthans & Youssef,
2004; Pfeffer, 2005; Simon, 1991; Stringer, 2007). These are employees, across all levels
of the organization, who (a) understand the goals of the organization; (b) understand how
their individual contributions and efforts contribute to achieving the goals; and (c) are
willing to expend discretionary effort towards achieving the organization’s goals—that is,
employees who are aligned and engaged (Alagaraja & Shuck, 2015; Boswell, 2000a,
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2006; Boswell et al., 2006; Boswell & Boudreau, 2001; Chalofsky & Krishna, 2009;
Kahn, 2010; Masson et al., 2008; Stringer, 2007).
However, while there is recognition of the value of aligned employees in
achieving organizational goals (Alagaraja & Shuck, 2015; Boswell, 2000a, 2006;
Boswell et al., 2006; Boswell & Boudreau, 2001; Gagnon & Michael, 2003; Kaplan &
Norton, 2001; Stallard & Pankau, 2010; Stringer, 2007; Wollard & Shuck, 2011), the
Corporate Executive Board Corporate Leadership Council (2015) has reported that
approximately two-thirds of employees do not “understand how corporate objectives
relate to their work” (p. 4). In addressing this issue, scholars have noted that it is the
responsibility of managers to help employees understand organizational goals, as well as
how their efforts contribute towards organizational goals (Boswell & Boudreau, 2001;
Harter et al., 2002; Masterson & Stamper, 2003; Stringer, 2007; Wollard & Shuck, 2011).
As Alagaraja and Shuck (2015) noted, managers must connect the “overarching goals at
the individual level, such that this individual connection generates emotion, drives
behavioral intention and resulting performance” (p. 29). Additionally, with respect to
alignment and employee effort, it may be appropriate for managers to reflect on the
question posed by Wollard and Shuck (2011): “If employees are not directing their
energies toward desired organizational outcomes then what are they directing their
energies toward?” (p. 439). Having considered why the alignment of employees would be
important for managers, the next section examines how alignment has been
conceptualized and defined in the academic literature.
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Conceptualizing and Defining Alignment
A review of the alignment literature reveals multiple labels, definitions, and
conceptualizations of the alignment construct: (a) alignment (Labovitz & Rosansky,
1997, 2012), (b) employee alignment (Ayers, 2013, 2015; Gagnon et al., 2008; Gagnon &
Michael, 2003), (c) employee strategic alignment (Gagnon et al., 2008; Gagnon &
Michael, 2003; Ouakouak & Ouedraogo, 2013a, 2013b), (d) goal alignment (Beehr et al.,
2009; De Graaf, 2012), (e) goal congruence (Ayers, 2013), (f) line of sight (Boswell,
2000a, 2006), (g) organizational alignment (Alagaraja et al., 2015; Alagaraja & Shuck,
2015; Powell, 1992; Semler, 1997), and (h) strategic alignment (Albrecht et al., 2018;
Biggs et al., 2014a, 2014b; Henderson & Venkatraman, 1991, 1993; Prieto & de
Carvalho, 2011; Stringer, 2007). Table 2.6 provides a summary of the various definitions
and labels found in the literature for alignment within an organization.
Table 2.6
Summary of Alignment Definitions
Definition of alignment Level Study reference Alignment: “The integration of key systems and processes and responses to changes in the external environment” (p. 5).
Organization Labovitz and Rosansky (1997, 2012)
Employee alignment: “The extent to which individual employees know how their work relates to the agency’s goals and priorities” (p. 498).
Employee Ayers (2013)
Employee alignment: “The extent to which individual employees know how their work relates to the agency’s goals and priorities” (p. 173).
Employee Ayers (2015)
Employee alignment: Employees are considered to be aligned when “they have knowledge of the organization’s strategic goals and purpose, which is coupled with the understanding of their job responsibilities and how they can contribute to the organization’s strategic goals” (p. 25).
Employee Gagnon and Michael (2003)
Employee strategic alignment: “The alignment of employees’ behaviours and objectives with the strategic orientation of the organization” (p. 150).
Employee Ouakouak and Ouedraogo (2013b)
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Definition of alignment Level Study reference
Goal alignment: “Individual employees are made aware of the organization’s goals so that they can align their work and behaviors to the greater strategic objectives of the organization” (p. 27).
Employee De Graaf (2012)
Goal congruence: “The extent to which individual employees know how their work relates to the agency’s goals and priorities” (p. 498).
Employee Ayers (2013)
Line of sight: “Employee understanding of the organization’s strategic objectives and how to contribute to those objectives” (p. 55).
Employee Boswell (2000a, 2006)
Line of sight: “An employee understanding the strategic objectives of an organization and how to contribute to those objectives” (p. 851).
Employee Boswell and Boudreau (2001)
Line of sight: “An employee’s understanding of the organization’s goals and what actions are necessary to contribute to those objectives” (p. 500).
Employee Boswell et al. (2006)
Organizational alignment: “An adaptive, dynamic resource capability achieved by developing a shared understanding of organizational goals and requirements by employees” (p. 20).
Organization Alagaraja et al. (2015)
Organizational alignment: “An adaptive, dynamic resource capability achieved by developing a shared understanding of interdependent systems, practices, and routines of the organization” (p. 21).
Organization Alagaraja and Shuck (2015)
Organizational alignment: “The extent to which the strategy, structure, and culture of the organization combine to create a synergistic whole that makes it possible to achieve the goals laid out in the organization’s strategy” (p. 27).
Organization Semler (1997)
Strategic alignment: “An organization’s ability to communicate strategic priorities that help employees understand how their daily job tasks and roles directly contribute to the success of strategic priorities” (p. 70).
Employee Albrecht et al. (2018)
Strategic alignment: “Employees’ perceived awareness and importance of the organization’s strategic priorities, in addition to their understanding of how their jobs align with these priorities” (p. 53).
Employee Biggs et al. (2014a)
Strategic alignment: “Relates to employee’s line of sight between their specific job tasks and the strategic priorities of the organization. . . . It encompasses an employee’s (i) awareness of the organization’s strategic priorities, (ii) perceived importance of those priorities, and (iii) understanding of how their daily job tasks and roles directly contribute to the organization’s capacity to achieve its priorities” (p. 301).
Employee Biggs et al. (2014b)
Strategic alignment: “Involves two dimensions: Strategic Fit and Functional Integration. Strategic Fit recognizes the need to make choices that both position the firm in an external market place as well as decide how to best structure internal arrangements of the firm to execute this market positioning. We refer to those choices that position the firm in a market as a Business Strategy, and those choices that determine internal structure of the firm as an Organizational Infrastructure & Processes” (p. 73).
Organization Henderson and Venkatraman (1991)
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Definition of alignment Level Study reference
Strategic alignment: “Based on two fundamental assumptions: One, economic performance is directly related to the ability of management to create a strategic fit between the position of an organization in the competitive product-market arena and the design of an appropriate administrative structure to support its execution. . . . Two, we contend that this strategic fit is inherently dynamic” (p. 473).
Organization Henderson and Venkatraman (1993)
Strategic alignment: “The integration of key systems and processes and responses to changes in the external environment” (p. 1407).
Organization Prieto and de Carvalho (2011)
Strategic alignment: “Occurs when employees have knowledge of the organization’s strategic goals and purpose, which is coupled with the understanding of their job responsibilities and how they can contribute to the organization’s strategic goals” (p. 21).
Employee Stringer (2007)
As can be inferred from the definitions of alignment in Table 2.6, the inconsistent
terminology (i.e., labels) applied to similar, if not equivalent, alignment constructs has
the potential to result in conceptual confusion for those exploring the concept. As an
example, there are similar definitions associated with the terms employee alignment
(Ayers, 2013, 2015; Gagnon & Michael, 2003), goal alignment (De Graaf, 2012), goal
congruence (Ayers, 2013), line of sight (Boswell, 2000a; Boswell & Boudreau, 2001),
and strategic alignment (Albrecht et al., 2018; Biggs et al., 2014a; Stringer, 2007).
For conceptual and definitional clarity, the term employee alignment was used in
this study to denote the alignment construct of interest. Based on Boswell's (2000a, 2006)
original conceptualization of employee line of sight and subsequent work on line of sight
by Boswell et al. (2006), as well as work on the alignment of employees by Ayers (2013,
2015), Gagnon and Michael (2003), and Stringer (2007), employee alignment is defined
in this study as the extent to which employees understand the organization’s goals and
understand how their work and job responsibilities contribute to achieving the
organization’s goals (Ayers, 2013, 2015; Boswell et al., 2006; Gagnon & Michael, 2003;
Stringer, 2007).
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Given this definition, employee alignment is conceptualized and operationalized
as a cognitive phenomenon measured at the individual level of analysis. Additionally,
although there is an implied behavioral component of this conceptualization of employee
alignment—i.e., an employee’s physical expression of acting as a result of an
understanding of how to contribute to achieving the organization’s goals—in alignment
with the conceptualizations depicted in Table 2.6, this study is focused solely on the
cognitive component of employee alignment. With a focus on the relationship between an
employee and the organization, specifically an employee’s alignment with the goals, this
conceptualization of employee alignment is believed to best align with the identified
purpose of this inquiry and hopefully avoids potential confusion that could arise from
using a label such as strategic alignment, which has been used in the literature to identify
both organizational-level and individual-level phenomena (see Table 2.6). Having
discussed how the academic literature has conceptualized and defined alignment, the next
sections briefly address how alignment differs from the related constructs of person-
organization fit/person-job fit, employee engagement, and perceived organizational
support.
Related Constructs: Person-Organization Fit and Person-Job Fit
With respect to aligning employees with an organization’s goals, Boswell (2000b,
2006) noted the importance of distinguishing line of sight (and as conceptualized in this
study, employee alignment) from the related, yet distinctively different, constructs of
person-organization fit and person-job fit. The person-organization fit literature focuses
primarily on the agreement between the individual and the culture, norms, and values of
an organization (Boon et al., 2011; Chatman, 1989; Kristof, 1996). Person-job fit focuses
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on the degree of match between an employee’s knowledge, skills, and abilities and the
job requirements or when the employee’s needs or desires are met by the job (Edwards,
1991). Rather than focusing on culture, norms, and values, or job skills and job
requirements, or the extent to which a job satisfies employee needs, the focus of
employee alignment is specifically on an employee’s understanding of the organization’s
goals and how his or her individual contributions and efforts contribute to achieving these
goals.
Relation Between Employee Alignment and Employee Engagement
As discussed, internal alignment is concerned with the extent to which there is
agreement, cooperation, or harmony among an organization’s strategy, structure,
processes, culture, and people (Alagaraja & Shuck, 2015; Powell, 1992; Semler, 1997)
and a clear linkage among the goals of the strategy, processes, functional departments,
workgroups, and individual employees (Alagaraja & Shuck, 2015; Powell, 1992; Semler,
1997). As an antecedent of engagement, it is important to note that, while alignment may
provide a supportive state from which engagement may develop, the alignment of
employees does not guarantee that employees will be engaged (Alagaraja & Shuck,
2015).
A key factor in the alignment-engagement dynamic is how an individual
employee perceives and interprets his or her unique alignment within the organizational
context (Alagaraja & Shuck, 2015). In moving from a sense of alignment to a state of
engagement (moving from cognitive engagement ® emotional engagement ® behavioral
engagement), Alagaraja and Shuck (2015) proposed that (a) cognitively engaged
employees share “a coupled purpose [i.e., goals] with their organization, they understand
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that purpose [i.e., goals], and they are willing to consider making a personal investment
of the resources they have influence over” (p. 23); (b) emotionally engaged employees
have moved beyond simply considering whether or not to invest of themselves and have
“made the personal decision to invest in productive, organizationally aligned behavior”
(p. 24); and (c) when employees become behaviorally engaged, they form the “intention
to act” (p. 24), where they “align their efforts toward identified organizational objectives
that move the organization in a positive direction” (p. 26).
A search of the empirical scholarly literature identified only three studies that
explicitly explored the relation between the alignment of individual employees and
engagement: those of Albrecht et al. (2018), Biggs et al. (2014b), and Stringer (2007).
These three studies examined the role of alignment as an antecedent to engagement, with
each showing a positive relation between alignment and engagement.
In the first study, Albrecht et al. (2018) examined the relation between strategic
alignment and engagement using data collected by an Australian consulting firm from
1,578 employees working across a range of occupations and industry sectors. Albrecht et
al. (2018) defined strategic alignment as “an organization’s ability to communicate
strategic priorities that help employees understand how their daily job tasks and roles
directly contribute to the success of strategic priorities” (p. 70) and used work
engagement (Schaufeli et al., 2002) to define and conceptualize engagement. Strategic
alignment was measured using four questions adapted from Biggs et al. (2014b), and
work engagement was measured using the 9-item version of the Utrecht Work
Engagement Scale (Schaufeli et al., 2006). The results showed a large (J. Cohen, 1988)
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positive correlation between strategic alignment and work engagement (r = .58, p < .01)
(Albrecht et al., 2018).
In the second study, Biggs et al. (2014b) examined the effect of employee
perceptions of strategic alignment on work engagement in the context of the Australian
State Police Service (n = 1,011). Data were collected at three points in time: September
2008 (Time 1), March 2010 (Time 2), and March 2011 (Time 3). Biggs et al. (2014b)
used work engagement (Schaufeli et al., 2002) as the definition and conceptualization of
engagement, measured using the 9-item version of the Utrecht Work Engagement Scale
(Schaufeli et al., 2006). The construct of strategic alignment utilized by Biggs et al.
(2014b) focused on employees’ “line of sight between their specific job tasks and the
strategic priorities of the organization” (p. 301), or how employees view their role and
work tasks in the organization as aligned to the overall strategic priorities and objectives
of the organization. Line of sight was defined as employees having “an accurate
understanding of the organization’s objectives and their role contributing to those
objectives” (Boswell & Boudreau, 2001, p. 854). Biggs et al. (2014b) developed a scale
of four questions to measure alignment: (a) “I have a clear understanding of [the
organization’s] strategic priorities”; (b) “I am aware of how my day-to-day work aligns
with [the organization’s] strategic priorities”; (c) “I have a clear understanding of how
my workgroup’s operational priorities help [the organization] achieve its strategic
objectives”; and (d) “It is important to me to help [the organization] achieve its strategic
objectives” (pp. 305-306). The results showed a medium (J. Cohen, 1988) positive
correlation between strategic alignment and work engagement at each of the three time
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periods: Time 1, r = .38, p < .001; Time 2, r = .40, p < .001; Time 3, r = .48, p < .001
(Biggs et al., 2014b).
Lastly, Stringer (2007) examined the relation between strategic alignment and
engagement using a convenience sample of 160 individuals across a range of occupations
and industry sectors. Unlike Albrecht et al. (2018) and Biggs et al. (2014b), who focused
on work engagement, Stringer (2007) used the definition, conceptualization, and measure
of engagement provided by May et al. (2004), which focused on engagement at work.
Stringer (2007) defined strategic alignment as occurring “when employees have
knowledge of the organization’s strategic goals and purpose, which is coupled with the
understanding of their job responsibilities and how they can contribute to the
organization’s strategic goals” (p. 21). To measure strategic alignment, Stringer (2007)
developed a scale of eight questions: (a) “I understand the purpose of my organization”;
(b) “I understand the goals of the organization”; (c) “I understand how the organization
will achieve its goals”; (d) “I understand what the organization aims to do for its
customers and stakeholders”; (e) “I understand my business unit’s goals”; (f) “I
understand how my business unit’s goals contribute to the organization’s goals”; (g) “I
understand what I need to do to help my business unit achieve its goals”; and (h) “I
understand how my job contributes to the organization’s ability to achieve its goals” (pp.
99-100). The results showed a medium (J. Cohen, 1988) positive correlation between
strategic alignment and work engagement (r = .38, p < .001) (Stringer, 2007). Table 2.7
summarizes the three studies that have explored the relation between alignment and
engagement.
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Table 2.7
Summary of Correlations Between Alignment and Engagement
Engagement construct a Correlation statistics b Study reference Work engagement1 r (1,576) = .58, p < .01 Albrecht et al. (2018)
Work engagement1 r (1,009) = .38, p < .001 Biggs et al. (2014b) r (1,009) = .40, p < .001 r (1,009) = .48, p < .001
Engagement at work 2 r (153) = .38, p < .001 Stringer (2007) a For the engagement construct used in the study, Table 2.3 provides details on the definition and measurement instruments: 1Work engagement (Schaufeli et al., 2002); 2Engagement at work (May et al., 2004). b According to Cohen (1988), correlation coefficient effect size can be classified as (a) small, r = ± .10; (b) medium, r = ± .30; or (c) large, r = ± .50.
The findings from these three studies (Albrecht et al., 2018; Biggs et al., 2014b;
Stringer, 2007) support a positive relation between alignment and employee engagement.
While a review of the literature did not reveal any research that examined the alignment-
engagement relation using the conceptual framework (Shuck et al., 2014; Shuck & Reio,
2011) and definition (Shuck, Osam, et al., 2017) of employee engagement used in this
inquiry, it is predicted that the previously identified positive relation between alignment
and engagement would remain valid. Based on the alignment and engagement literature,
the following hypotheses are proposed:
H1a: There is a statistically significant positive correlation between employee
alignment and employee engagement.
H1b: Employee alignment explains a statistically significant proportion of the
unique variance in employee engagement after controlling for perceived
organizational support.
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Relation Between Employee Alignment and Perceived Organizational Support
A review of the literature did not identify any previous research that explicitly
examined or discussed an actual or conceptual relation between employee alignment and
perceived organizational support. However, the literature did suggest a possible indirect
relation between job conditions, which have been identified as antecedents to perceived
organizational support, and employee alignment. Examples of these job conditions
include skill variety, autonomy, and role stressors (Kurtessis et al., 2017; Rhoades &
Eisenberger, 2002).
Of these job conditions, it is role ambiguity, as a type of role stressor (Kurtessis et
al., 2017; Rhoades & Eisenberger, 2002), that is proposed as the link between employee
alignment and perceived organizational support. Rhoades and Eisenberger (2002)
identified role ambiguity as an “absence of clear information about one’s job
responsibilities” (p. 700). Empirical research has shown a negative correlation between
role ambiguity and perceived organizational support (Kurtessis et al., 2017; Rhoades &
Eisenberger, 2002). For example, in a meta-analysis of over 70 studies, Rhoades and
Eisenberger (2002) found a small (J. Cohen, 1988) negative correlation (average
weighted correlation) (r = –.17, p < .001, n = 2,463) between role ambiguity and
perceived organizational support. Similarly, a later meta-analysis conducted by Kurtessis
et al. (2017) found a medium (J. Cohen, 1988) negative correlation (r = –.31, p < .05, n =
12,757) between role ambiguity and perceived organizational support. With respect to the
employee alignment-perceived organizational support relation, it would be expected that
higher levels of employee alignment, as defined in this study, would lead to a decrease in
role ambiguity and thus potentially increase perceived organizational support. Based on
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the alignment and perceived organizational support literature, the following hypothesis is
proposed:
H2: There is a statistically significant positive correlation between employee
alignment and perceived organizational support.
Having synthesized the relevant academic literature on the construct of alignment,
the discussion continues with a synthesis of the literature on the construct of perceived
organizational support.
Perceived Organizational Support
In synthesizing the relevant academic literature on perceived organizational
support, this section first discusses the importance of perceived organizational support for
managers in organizations. Next is a review of how perceived organizational support is
conceptualized and defined in the academic literature. That is followed by a discussion of
the relation between perceived organizational support and employee engagement
constructs. This section concludes with a discussion of perceived organizational support
as a moderating and/or mediating variable on the relation between employee alignment
and employee engagement in an organizational context.
The Importance of Perceived Organizational Support for Managers in
Organizations
Perceived organizational support is driven by the tendency of employees to
personify, or to impart humanlike characteristics to, an organization (Rhoades &
Eisenberger, 2002). It is through this personification that “employees view their favorable
or unfavorable treatment as an indication that the organization favors or disfavors them”
(Rhoades & Eisenberger, 2002, p. 698). Additionally, a study conducted by (Rhoades &
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Eisenberger, 2002) identified three general categories of treatment viewed as favorable
by employees that were positively related to perceived organizational support: fairness,
supervisor support, and rewards and job conditions. Expanding on these categories,
Rhoades and Eisenberger (2002) described fairness as the extent to which impartial and
objective decisions are made related to the allocation of resources among employees, to
include the transparency of those decisions; supervisor support as employee views
concerning the extent to “which supervisors value their contributions and care about their
well-being” (p. 700), and rewards and job conditions as human resource policies and
practices, along with characteristics of the work to include “recognition, pay, promotions,
job security, autonomy, role stressors, and training” (p. 700).
Furthermore, perceived organizational support is based on the concept of
reciprocity and employees’ effort-outcome expectations (Eisenberger et al., 1986;
Kurtessis et al., 2017; Rhoades & Eisenberger, 2002). As Kurtessis et al. (2017)
observed, perceived organizational support “should elicit the norm of reciprocity”
(p. 1856) and initiate “a social exchange process wherein employees feel obligated to
help the organization achieve its goals and expect that increased efforts on the
organization’s behalf will lead to greater rewards” (p. 1855).
Overall, perceived organizational support is expected to result in outcomes that
are favorable to both employees (e.g., recognition of efforts, increased job satisfaction,
heightened positive mood, and reduced job strains) and managers (e.g., increased
commitment, increased effort and performance towards achieving organizational goals,
reduced turnover, and engagement) (Eisenberger et al., 1986, 2016; Eisenberger &
Stinglhamber, 2011; Kurtessis et al., 2017; Rhoades & Eisenberger, 2002). Additionally,
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and of specific significance to this study, Eisenberger et al. (2016) observed that
“employees with high POS [perceived organizational support] are more inclined to care
about and further organizational goals” (pp. 4-5). Having considered why employee
perceptions of organizational support are important for managers, the next section
examines how the academic literature has conceptualized and defined perceived
organizational support.
Conceptualizing and Defining Perceived Organizational Support
Organizational support theory posits that employees enter into reciprocal social
exchange relationships with organizations based on the extent to which they perceive an
organization’s support and commitment to them (Eisenberger et al., 1986; Kurtessis et al.,
2017; Rhoades & Eisenberger, 2002). Eisenberger et al. (1986) defined perceived
organizational support as the degree to which “employees develop global beliefs concerning
the extent to which the organization values their contributions and cares about their well-
being” (p. 501). Perceived organizational support is thus a view of the employee-
organization relationship from an individual employee’s perspective (Kurtessis et al., 2017).
Relation Between Perceived Organizational Support and Employee Engagement
Studies have shown that perceived organizational support affects employee
perceptions of the organizational work environment and has a direct effect on
engagement (Biswas & Bhatnagar, 2013; Rana et al., 2014; Rich et al., 2010; Saks, 2006;
Shuck et al., 2014; Shuck, Rocco, et al., 2011; Zhong et al., 2016). As with employee
alignment, in considering perceived organizational support as an antecedent for employee
engagement, it is individual employees’ perception of their unique interaction with the
organization and the work environment that is a determinant in whether or not they may
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develop a state of engagement (Kahn, 1990, 2010; Shuck, 2019; Shuck et al., 2014;
Shuck, Rocco, et al., 2011; Shuck & Rose, 2013; Wollard & Shuck, 2011).
In moving from an employee’s perception of support to a state of engagement, it
is proposed that (a) cognitively engaged employees perceive that their contributions are
valued, they are “supported in their work . . . [and] their well-being was considered
fairly” (Shuck et al., 2014, p. 245); (b) arising from a positive cognitive assessment of
feelings of support from the organization (i.e., cognitive engagement), emotionally
engaged employees will feel “connected to and a part of the organization” (Shuck et al.,
2014, p. 246) and thus willing to contribute personal resources such as pride, knowledge,
skill, and ability (Shuck et al., 2014; Shuck & Reio, 2011) towards “productive,
organizationally aligned behavior” (Alagaraja & Shuck, 2015, p. 24); and (c) once
employees have made a positive cognitive assessment (cognitive engagement) and
decided to contribute personal resources (emotional engagement), behavioral engagement
indicates their willingness to “engage in discretionary effort” (Shuck & Reio, 2011,
p. 423) and their “intention to act” (Shuck & Reio, 2011, p. 423) in directing their
discretionary effort towards “identified organizational objectives that move the
organization in a positive direction” (Alagaraja & Shuck, 2015, p. 26).
In what is considered the first research to specifically relate perceived
organizational support to engagement, Saks (2006) found a medium (J. Cohen, 1988)
positive correlation between perceived organizational support and job engagement (r =
.44, p < .001, n = 102) and a large (J. Cohen, 1988) positive correlation between
perceived organizational support and organization engagement (r = .58, p < .001, n =
102). Similarly, in a study of full-time firefighters, Rich et al. (2010) found a medium (J.
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Cohen, 1988) positive correlation between perceived organizational support and job
engagement (r = .45, p < .05, n = 245). Table 2.8 summarizes a sample of studies that
have explored the relation between perceived organizational support and engagement.
Table 2.8
Summary of Correlations Between Perceived Organizational Support and Engagement
Engagement construct a Correlation statistics b Study reference Employee engagement1 r = .23, p < .01, n = 246 Biswas and Bhatnagar (2013) Organizational engagement2 r = .59, p < .01, n = 231 Mahon et al. (2014) Job engagement3 r = .45, p < .05, n = 245 Rich et al. (2010) Job engagement4 r = .44, p < .001, n = 102 Saks (2006) Organizational engagement2 r = .58, p < .001, n = 102 Saks (2006) Work engagement5 r = .56, p < .01, n = 264 Wang et al. (2017) Job engagement6 r = .45, p < .01, n = 605 Zhong et al. (2016) a For details on the engagement construct used in the associated study, see Table 2.3: 1Employee engagement (Saks, 2006); 2Organizational engagement (Saks, 2006) (study does not define organization engagement); 3Job engagement (Rich et al., 2010); 4Job engagement (Saks, 2006) (study does not define job engagement); 5Work engagement (Schaufeli et al., 2002); 6Job engagement (definition in the study is based on Kahn's [1990] definition of personal engagement but is measured using the Job Engagement Scale [Rich et al., 2010]). b According to Cohen (1988), correlation coefficient effect size can be classified as (a) small, r = ± .10; (b) medium, r = ± .30; or (c) large, r = ± .50.
While a review of the literature did not reveal any research that examined the
relation between perceived organizational support and engagement using the conceptual
framework (Shuck et al., 2014; Shuck & Reio, 2011) and definition (Shuck, Osam, et al.,
2017) of employee engagement used in this inquiry, it is hypothesized that the previously
identified positive relation between perceived organizational support and engagement
would remain valid. Based on the perceived organizational support, alignment, and
engagement literature, the following hypotheses are proposed:
H3a: There is a statistically significant positive correlation between perceived
organizational support and employee engagement.
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H3b: Perceived organizational support explains a statistically significant proportion
of the unique variance in employee engagement after controlling for employee
alignment.
Perceived Organizational Support as a Moderating and/or Mediating Variable
This study sought to examine how employee alignment and perceived
organizational support interact to contribute to employee engagement in an organizational
context. In exploring this interaction, the study examined perceived organizational
support as both a potential moderating and mediating variable.
Perceived Organizational Support as a Moderating Variable
With respect to this “interaction” among the variables, an area of particular
interest is whether or not perceived organizational support moderates the relation
between employee alignment and employee engagement. Analysis of moderation (i.e., an
interaction effect) is appropriate when a third variable is hypothesized to affect the
relation—i.e., when or under what conditions—between an explanatory and outcome
variable (Aguinis et al., 2017; Baron & Kenny, 1986; Frazier et al., 2004; Hayes, 2009,
2018; Hayes & Rockwood, 2017; Jose, 2019; Keith, 2015, 2019; Kelley & Maxwell,
2019). As defined by Baron and Kenny (1986), a moderating variable affects the
“direction and/or strength of the relation between an independent or predictor variable
and a dependent or criterion variable” (p. 1174). As Frazier et al. (2004) noted, “A
moderator effect is nothing more than an interaction whereby the effect of one variable
depends on the level of another” (p. 116).
An employee who experiences a higher level of perceived organizational support
is more likely to feel a greater personal connection to the organization and a greater
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willingness to expend effort to achieve organizational goals (Eisenberger et al., 2016;
Eisenberger & Stinglhamber, 2011; Simmons, 2013). Employee alignment provides a
focus for this individual effort in that it is through employee alignment that employees
experience the sense of awareness and understanding of both the organization’s goals and
of how their efforts contribute to achieving these goals (Alagaraja et al., 2015; Albrecht
et al., 2018; Ayers, 2013, 2015; Boswell, 2000a, 2006; Boswell et al., 2006; Boswell &
Boudreau, 2001; Gagnon & Michael, 2003; Stringer, 2007).
With perceived organizational support affecting the degree to which an employee
is willing to put forth effort to achieve organizational goals, an employee with a higher
level of perceived organizational support could be expected to have a greater
understanding of organizational goals and expend a higher level of effort towards those
goals than an employee experiencing a lower level of perceived organizational support
(Eisenberger et al., 2016; Rich et al., 2010; Shuck et al., 2014). Based on the perceived
organizational support, alignment, and engagement literature, the following hypothesis is
proposed:
H4: Perceived organizational support positively moderates the relation between
employee alignment and employee engagement in an organizational context.
Specifically, as perceived organizational support increases, the relation
between employee alignment and employee engagement becomes more
positive.
Figure 2.1 shows the hypothesized model of perceived organizational support
moderating the relation between employee alignment and employee engagement in an
organizational context.
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Figure 2.1
Hypothesized Moderation Model
Perceived Organizational Support as a Mediating Variable
In addition to the hypothesis that perceived organizational support moderates the
relation between employee alignment and employee engagement, this study also explored
the extent to which perceived organizational support mediates the relation between
employee alignment and employee engagement. Analysis of mediation, or an indirect
effect, is appropriate when a third variable is believed to indirectly affect the relation—
i.e., how or why an effect occurs—between a predictor (in this study, explanatory)
variable and the outcome variable (Baron & Kenny, 1986; Frazier et al., 2004; Hayes &
Rockwood, 2017; Keith, 2015). As Keith (2015) commented, “Mediation describes the
process by which one variable has an indirect effect on another variable through another
mediating variable” (p. 187). More specifically, Frazier et al. (2004) defined a mediator
as “a variable that explains the relation between a predictor and an outcome” (p. 116).
Given the previously discussed negative correlation between role ambiguity and
perceived organizational support (Kurtessis et al., 2017; Rhoades & Eisenberger, 2002),
it would be expected that an employee who had greater clarity with respect to work role
responsibilities could experience a greater feeling of being supported by the organization.
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As such, to the extent that employee alignment can reduce an employee’s role ambiguity,
it is suggested that higher levels of employee alignment would lead to an increase in
perceived organizational support, which in turn should lead to higher levels of employee
engagement. In other words, employee alignment may have an indirect effect on
employee engagement through perceived organizational support. Based on the alignment,
perceived organizational support, and engagement literature, a final hypothesis is
proposed as follows:
H5: Perceived organizational support mediates the effect of employee alignment
on employee engagement in an organizational context.
Figure 2.2 shows the hypothesized model of perceived organizational support
mediating the relation between employee alignment and employee engagement in an
organizational context.
Figure 2.2
Hypothesized Mediation Model
Inferences for the Current Study
A growing body of empirical research has shown that engaged employees can be
a critical enabler in achieving organizational (i.e., managerial) goals, improving
organizational effectiveness, and helping organizations remain competitive (Alagaraja &
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Shuck, 2015; Bakker, 2011, 2017; Bakker & Demerouti, 2008; Burke & Cooper, 2006;
Burke & Ng, 2006; Eldor, 2016; Frank et al., 2004; Harter et al., 2002; Saks, 2006; Saks
& Gruman, 2014; Shuck, Rocco, et al., 2011; Shuck & Reio, 2011; Shuck & Wollard,
2008). Research supports the view that engaged employees are more likely to perform
better, expend discretionary effort to help accomplish organizational goals, and be more
committed to the success of the organization than employees who are disengaged
(Alagaraja & Shuck, 2015; Bakker, 2011; Bakker et al., 2011; Bakker & Demerouti,
2008; Shuck, Reio, et al., 2011; Shuck & Reio, 2011). However, studies have also shown
that approximately one-third (U.S. Office of Personnel Management, 2018) to two-thirds
(Gallup, 2017) of the U.S. workforce remains disengaged, with an estimated impact on
the U.S. economy, due to lost productivity, between $483 billion and $605 billion per
year (Gallup, 2017, p. 19).
Scholars have identified a need for additional research focused on the factors within
the purview of managers that can improve the engagement of employees and organizational
effectiveness (Alagaraja & Shuck, 2015; Coyle-Shapiro & Shore, 2007; Eldor & Vigoda-
Gadot, 2017; Oswick, 2015; Whittington et al., 2017; Whittington & Galpin, 2010). Two
factors identified as critical to creating conditions from which employee engagement may
arise are alignment (CEB Corporate Leadership Council, 2015b, 2015c; Harter & Rigoni,
2015; Rao, 2017; Ray et al., 2014; Stallard & Pankau, 2010) and perceived organizational
support (Seijts & Crim, 2006; Shuck et al., 2014; Shuck, Rocco, et al., 2011; Wollard &
Shuck, 2011). However, while previous studies have examined both alignment and
perceived organizational support individually as antecedent variables of employee
engagement, there is a lack of empirical studies focused on exploring the relation among
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employee alignment, perceived organizational support, and employee engagement. As
such, how these variables—employee alignment and perceived organizational support—
interact to contribute to employee engagement remains relatively unexplored.
Additionally, while the literature recognizes the potential value of alignment to
improving the engagement of employees—work engagement (Albrecht et al., 2018;
Biggs et al., 2014b) and engagement at work (Stringer, 2007)—there is a lack of
empirical work specifically examining the alignment-engagement relation with respect to
the construct of employee engagement. Likewise, a review of the literature failed to
identify any previous research that examined the effect of perceived organizational
support, as it affects employee perceptions of the work environment, on the employee
alignment-engagement relation. This research sought to address the practical problem of
how managers can create conditions that may increase employee engagement in
organizations and the theoretical problem of better understanding the relation among
employee alignment, perceived organizational support, and employee engagement and
how employee alignment and perceived organizational support interact to contribute to
employee engagement among full-time nonsupervisory individuals in an organizational
context.
Informed by the literature review, two research questions (RQ) guided this
inquiry:
RQ1: To what extent is there a statistically significant relation among employee
alignment, perceived organizational support, and employee engagement in an
organizational context?
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RQ2: To what extent do employee alignment and perceived organizational support
explain a statistically significant proportion of the unique variance in
employee engagement?
Based on a review of the engagement, alignment, and perceived organizational
support literature, Figure 2.3 depicts the conceptual framework of the relation between
the constructs of employee alignment, perceived organizational support, and employee
engagement for the current study. The research design for the study is described in the
next chapter.
Figure 2.3
Conceptual Framework of the Hypothesized Relation Between Employee Alignment,
Perceived Organizational Support, and Employee Engagement
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Chapter Summary
To establish a theoretical, conceptual, and empirical foundation for this study, this
chapter reviewed and synthesized the theoretical and empirical literature associated with
the constructs of employee engagement, employee alignment, and perceived
organizational support. In exploring these three constructs, and the relation among them,
the discussion began with a review and synthesis of the engagement literature. In
synthesizing the engagement literature, the discussion addressed why engagement is, or
should be, important to managers in organizations, seminal conceptualizations and
definitions of the engagement construct, emerging views on the potential negative
aspects, or the dark side, of engagement, and antecedents of engagement. Next, the
review discussed the alignment literature, addressing the importance of alignment to
managers, the various conceptualizations and definitions of the alignment construct,
alignment as an antecedent of engagement, and the relation between alignment and
perceived organizational support. Third, was a discussion of the perceived organizational
support literature, addressing the importance of perceived organizational support to
managers, its conceptualization and definition, and perceived organizational support as an
antecedent of engagement. Lastly, this chapter discussed inferences for the current study.
The research design for the study is described in the next chapter.
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Chapter 3: Methods
This study was designed to enhance understanding of employee engagement in an
organizational context. The focus of the study was to (a) examine the relation among the
variables of employee alignment, perceived organizational support, and employee
engagement, (b) determine the extent to which employee alignment and perceived
organizational support explain a statistically significant proportion of the unique variance
in employee engagement, and (c) examine how employee alignment and perceived
organizational support interact to contribute to employee engagement.
As a framework for conducting research, a general research design model consists
of an interaction between five components: purpose, research questions, conceptual
framework, methods, and validity (L. Cohen et al., 2011; Dannels, 2019; Maxwell, 2013;
Robson & McCartan, 2016). These components are discussed in turn. The chapter begins
by presenting the study’s quantitative research design and addressing the first three
components: the purpose of the study, the research questions and hypotheses, and the
conceptual framework, research models, and analysis models. The chapter then turns to
methods. Robson and McCartan (2016) explained that methods are the “specific
techniques” (p. 72) used in a study. Methods applicable to this study include the
techniques associated with identifying the study population and sample, data collection,
preanalysis data handling, and data analysis (L. Cohen et al., 2011; Creswell, 2012, 2014;
Robson & McCartan, 2016). The chapter’s closing sections review threats to validity,
discuss human participants and ethics precautions, and present a chapter summary.
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Research Design
Creswell (2013) observed that a research design is the “plan for conducting the
study” (p. 49). As a plan, or framework, this study used a quantitative methodology,
specifically a nonexperimental, cross-sectional survey research design (Creswell, 2014;
Dannels, 2019; Robson & McCartan, 2016) using an internet-based survey questionnaire.
Creswell (2014) noted that a quantitative approach is appropriate for “examining the
relationship among variables” (p. 4) and when the research problem calls for “the
identification of factors that influence an outcome” (p. 20). This quantitative
methodology was situated in a realist ontology (Burrell & Morgan, 1992; Huff, 2009) and
postpositivist epistemology (Butin, 2010; Creswell, 2013, 2014; Robson & McCartan,
2016).
The current study was nonexperimental in that it did not seek to “determine if a
specific treatment influences an outcome” (Creswell, 2014, p. 13) and there was no
“active manipulation of the situation by the researcher” (Robson & McCartan, 2016, p.
103). Rather, this study sought to provide evidence of relations among the variables
(Dannels, 2019) of employee alignment, perceived organizational support, and employee
engagement. The current study was cross-sectional in that the data were collected at a
single point in time (Creswell, 2014; Robson & McCartan, 2016). With a survey research
design, a researcher seeks to generalize, or draw inferences, from a sample to the larger
population (Creswell, 2014). As Creswell (2014) stated, “Survey research provides a
quantitative or numeric description of trends, attitudes, or opinions of a population by
studying a sample of that population” (p. 13).
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Purpose of the Study
The purpose of this study was to explore the relation among employee alignment,
perceived organizational support, and employee engagement and how employee
alignment and perceived organizational support interact to contribute to employee
engagement among full-time nonsupervisory individuals in an organizational context
(i.e., those employed in organizations in the United States). A better understanding of this
relation could assist researchers, managers, and human resource (HR) professionals in
understanding, identifying, and developing strategies to improve employee engagement,
which should contribute to achieving organizational goals, enhancing organizational
competitiveness, and improving employee well-being.
Research Questions and Hypotheses
In support of the purpose, this study examined a hypothesized model of employee
engagement, exploring the relation among the two explanatory constructs (variables) of
employee alignment and perceived organizational support and the outcome construct
(variable) of employee engagement in an organizational context. Two research questions
(RQ) guided this inquiry:
RQ1: To what extent is there a statistically significant relation among employee
alignment, perceived organizational support, and employee engagement in an
organizational context?
RQ2: To what extent do employee alignment and perceived organizational support
explain a statistically significant proportion of the unique variance in
employee engagement?
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In answering the two research questions, seven hypotheses were tested:
H1a: There is a statistically significant positive correlation between employee
alignment and employee engagement.
H1b: Employee alignment explains a statistically significant proportion of the unique
variance in employee engagement after controlling for perceived organizational
support.
H2: There is a statistically significant positive correlation between employee
alignment and perceived organizational support.
H3a: There is a statistically significant positive correlation between perceived
organizational support and employee engagement.
H3b: Perceived organizational support explains a statistically significant proportion of
the unique variance in employee engagement after controlling for employee
alignment.
H4: Perceived organizational support positively moderates the relation between
employee alignment and employee engagement in an organizational context.
Specifically, as perceived organizational support increases, the relation between
employee alignment and employee engagement becomes more positive.
H5: Perceived organizational support mediates the relation between employee
alignment and employee engagement in an organizational context.
Conceptual Framework, Research Model, and Analysis Models
This study was based on the foundations provided by the engagement, alignment,
and organizational support literature. As antecedents of engagement, alignment (Albrecht
et al., 2018; Biggs et al., 2014b; Stringer, 2007) and perceived organizational support
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(Biswas & Bhatnagar, 2013; Mahon et al., 2014; Rich et al., 2010; Saks, 2006; Wang et
al., 2017; Wollard & Shuck, 2011; Zhong et al., 2016) provide a theoretical basis that
may help to further explain and better understand the engagement of employees within an
organizational context. Figure 3.1 depicts the conceptual model of the hypothesized
relation among the constructs of employee alignment, perceived organizational support,
and employee engagement support.
Figure 3.1
Simplified Conceptual Framework of the Hypothesized Relation Among Employee
Alignment, Perceived Organizational Support, and Employee Engagement
Research Model
Based on the conceptual framework, the conceptual research model, which
incorporates the research hypotheses, is shown in Figure 3.2.
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Figure 3.2
Conceptual Research Model Incorporating the Research Hypotheses
Analysis Models
In testing the research hypotheses, the statistical analysis explored four analysis
models: (a) Model 1 consisted of the control variables (i.e., the three demographic
variables of age, gender, and organizational tenure); (b) Model 2 consisted of Model 1
with the addition of the two explanatory variables of employee alignment and perceived
organizational support; (c) Model 3 consisted of Model 2 with the addition of the
interaction effect variable; and (d) Model 4 was a separate model testing the mediation
effect of perceived organizational support on the relation between employee alignment
and employee engagement. The four analysis models are depicted in Figure 3.3.
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Figure 3.3
Analysis Models
The conceptual framework, research models, and analysis models were expected
to provide the requisite structure to guide this inquiry to answer the stated research
questions and increase our understanding of the relation among the two explanatory
variables and their effect on employee engagement in organizations. The discussion now
turns to methods, beginning with the study population.
Population
When discussing empirical research, it is necessary to clearly differentiate
between a study’s population and sample of interest (L. Cohen et al., 2011; Creswell,
2012; Fraenkel et al., 2015; Litt, 2010; Lomax & Hahs-Vaughn, 2012; Robson &
McCartan, 2016). A study’s population is the “group of interest to the researcher, the
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group to whom the researcher would like to generalize the results of the study” (Fraenkel
et al., 2015, p. 93). As Lomax and Hahs-Vaughn (2012) noted, the key is that the
population “is well defined such that one could determine specifically who all of the
members of the group are and then information or data could be collected from all such
members” (p. 5). A sample is the segment of the population that is included in the
research study and the group from which information is collected (L. Cohen et al., 2011;
Creswell, 2012; Dillman et al., 2014; Fraenkel et al., 2015; Lomax & Hahs-Vaughn,
2012; Robson & McCartan, 2016). In some cases, the sample and the population may be
the same (L. Cohen et al., 2011; Creswell, 2012; Fraenkel et al., 2015; Fritz & Morgan,
2010; Robson & McCartan, 2016; Stapleton, 2019).
Authors often use differing terminology when discussing the concept of a
research population, with Lepkowski (2008) noting that the “definition of the term
population is not standardized” (p. 591). For clarity between a study’s population and
sample, Fritz and Morgan (2010) noted the differentiation between theoretical
population,6 accessible population,7 selected sample, and actual sample. As discussed by
Fritz and Morgan (2010), the theoretical population includes “all of the participants of
theoretical interest to the researcher” (p. 1304) and consists of “the individuals about
which the researcher is interested in making generalizations” (p. 1304). Recognizing that
it is often difficult, if not impossible, to study the entire theoretical population, Fritz and
6 The theoretical population discussed by Fritz and Morgan (2010) is sometimes referred to as the target population (Dillman et al., 2014; Fraenkel et al., 2015; Fritz & Morgan, 2010; Stapleton, 2019). 7 The accessible population (Fraenkel et al., 2015; Fritz & Morgan, 2010) is also referred to as the sampling frame (Creswell, 2012; Fowler, 2009; Fritz & Morgan, 2010; Robson & McCartan, 2016) or sampling frame population (Stapleton, 2019).
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Morgan (2010) identified an accessible population as those potential participants who are
a subset of the theoretical population which a researcher has access to. Fritz and Morgan
(2010) went on to define the selected sample as the “smaller group of individuals selected
from the accessible population” (p. 1304) and the individuals who “are asked by the
researcher to participate in the study” (p. 1304). Lastly, the actual sample consists of the
individuals from the selected sample “who agree to participate and whose data are
actually used in the analysis” (Fritz & Morgan, 2010, p. 1304). The representative
relationship among the theoretical population, accessible population, selected sample, and
actual sample is depicted in Figure 3.4.
Figure 3.4
Representative Relationship Among the Theoretical Population, Accessible Population,
Selected Sample, and Actual Sample
The process used to identify the sample for this study focused on purposefully
identifying a research site from which a sample could be selected to gather data on the
phenomena under study and the variables of interest (L. Cohen et al., 2011; Creswell,
2012; Fraenkel et al., 2015). In identifying an organization willing to participate in this
research and serve as the research site, an introduction and site access request (Appendix
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A) was emailed using a listserv to the students and alumni of the George Washington
University Executive Leadership Program. In response to the request, the research site for
the study was the HR department of a not-for-profit health care organization located in
the southern region (U.S. Census Bureau, n.d.) of the United States. The researcher
received permission and access to the site (see Appendix B).
The population—the accessible population (Fritz & Morgan, 2010)—for this
study consisted of all employees of the research site who met the following criteria: (a)
were full-time employees and (b) were nonsupervisory employees (i.e., employees who
do not directly supervise others). Organizationally, all 229 HR employees in the
accessible population fell under the corporate senior vice president of HR, but were
dispersed across the headquarters and multiple operating affiliate entities. In the
terminology of Fritz and Morgan (2010), the theoretical population of the current study
consisted of those full-time nonsupervisory individuals employed in organizations in the
United States, with the accessible population composed of the full-time nonsupervisory
individuals employed at the research site. As such, the study’s sampling frame—i.e., a list
of individuals in the accessible population used by the researcher to bound the sample
(Fowler, 2009; Robson & McCartan, 2016; Stapleton, 2019)—consisted of an email
distribution list of all employees within the HR department.
Sample
This section discusses the sample used in the current study. As a reference point
for sample selection, the discussion first addresses power analysis and the minimum
sample size required for statistical power. That is followed by a description of the actual
sample used in the study.
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Sample Size and Power Analysis
Researchers commonly rely on either rules of thumb (L. Cohen et al., 2011;
Keith, 2015; Kelley & Maxwell, 2019; Robson & McCartan, 2016) or a priori power
analysis (J. Cohen et al., 2003; Keith, 2015; Kelley & Maxwell, 2019; Lomax & Hahs-
Vaughn, 2012; Murphy, 2019; Robson & McCartan, 2016) in determining a minimum
sample size for a research study. For example, common rules of thumb include 10 to 20
(Keith, 2015) or 30 (L. Cohen et al., 2011; Robson & McCartan, 2016) observations, or
participants, for each independent, or explanatory, variable being studied.
However, while rules of thumb may be convenient and were often recommended
in the past, some discourage their use in determining and justifying sample size (Keith,
2015; Kelley & Maxwell, 2019). As Keith (2015) noted, rules of thumb “although
sometimes accurate, will produce low power in many real-world research problems” (p.
207). Rather than rely on rules of thumb, it has been recommended that researchers
determine and justify sample size based on an a priori power analysis (Keith, 2015;
Kelley & Maxwell, 2019; Lomax & Hahs-Vaughn, 2012). For example, Keith (2015) and
Lomax and Hahs-Vaughn (2012) recommended the use of power analysis software, such
as G*Power, in determining and justifying the minimum sample size for a research study.
The minimum sample size required to achieve statistical power for a research
study can be computed as a function of (a) level of significance, (b) level of statistical
power, and (c) effect size (J. Cohen et al., 2003; Hinkle et al., 2003; Keith, 2015). As a
reference point for identifying a sample for the current study, an a priori power analysis
was conducted using G*Power (Version 3.1.9.4) (Faul et al., 2007, 2009). Using the
G*Power Linear multiple regression: Fixed model, R2 deviation from zero statistical test
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(Faul et al., 2007, 2009; Keith, 2015), with a significance level of .05 (a = .05) (J. Cohen,
1988; J. Cohen et al., 2003; D. George & Mallery, 2020; Hinkle et al., 2003), a power (1
– b) of .80 (J. Cohen, 1988), an effect size of .15 (f 2 = .15) (Shuck, 2010), and three
predictor variables, the minimum sample size required for statistical power was 77
participants or returned responses (Appendix C). Details of the power analysis as follows.
Level of Significance
Lomax and Hahs-Vaughn (2012) defined the level of significance as the
“probability of a Type I error” (p. 127), or incorrectly rejecting a null hypothesis when it
is true (Hinkle et al., 2003; Lomax & Hahs-Vaughn, 2012). For behavioral science
studies, it has been suggested that Type I errors (false-positive claims) are considered
more serious than Type II errors (false-negative claims) (J. Cohen, 1988; Hinkle et al.,
2003). As such, the standard convention in the behavioral sciences is to use a = .05 (J.
Cohen, 1988; J. Cohen et al., 2003; Hinkle et al., 2003), and that was done for this study.
Level of Statistical Power
Cohen et al. (2003) identified statistical power as the “probability of rejecting the
null hypothesis when it is false” (p. 91). The concept of power is related to Type II error,
or the probability of failing to reject a false null hypothesis (Hinkle et al., 2003; Lomax &
Hahs-Vaughn, 2012). Statistical power is a function of (a) level of significance, (b) effect
size, and (c) sample size (J. Cohen et al., 2003; Hinkle et al., 2003; Keith, 2015). With
respect to selecting a value for statistical power, Cohen et al. (2003) identified that most
researchers select a value in a range between .70 and .90 (p. 180). Cohen (1988) further
narrowed the selection, proposing “as a convention that, when the investigator has no
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other basis for setting the desired power value, the value .80 be used” (p. 56). For this
study, a power value of .80 was used.
Effect Size
Effect size is often defined as the strength of the relation between the independent
and dependent variables (J. Cohen et al., 2003; Creswell, 2014; Morgan et al., 2013). In
addition to the strength of the variable relations, Cohen et al. (2003) stated that effect size
is also the degree to which the “total variation in the dependent variable is produced by or
associated with the independent variable” (p. 5). Additionally, Cohen (1988) noted that
effect size was the “degree to which the phenomenon is present in the population” (p. 9).
As a convention for behavioral science studies, Murphy (2019) suggested that “in the
absence of an acceptable estimate of the effect size expected in a particular study, it is
common practice to assume that the effect will be small,” further noting that “studies
designed to detect small effects will also have sufficient power for detecting larger
effects” (p. 383). As a frame of reference, Cohen et al. (2003) offered the following
values for estimations of population effect size: “‘small’ = .02, ‘medium’ = .15, and
‘large’ = .35” (p. 179). Building on Shuck's (2010) previous study of employee
engagement, a medium effect size (f 2 = .15) was used in this study.
Sampling and Study Sample
Fraenkel et al. (2015) commented that “one of the most important steps in the
research process is the selection of the sample of individuals who will participate”
(p. 92), with an expectation that the selected sample is representative of the larger
population to which results are hoped to generalize (Bornstein et al., 2013; Stapleton,
2019). Researchers use sampling, or a sampling plan, to select potential participants from
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the accessible population in order to identify a study’s sample (L. Cohen et al., 2011;
Creswell, 2014; Fraenkel et al., 2015; Fritz & Morgan, 2010; Robson & McCartan, 2016;
Stapleton, 2019). This study used a census sampling approach (L. Cohen et al., 2011;
Creswell, 2012; Fraenkel et al., 2015; Fritz & Morgan, 2010; Robson & McCartan, 2016;
Stapleton, 2019) in that all full-time nonsupervisory employees of the research site were
invited and given an equal opportunity to participate in the study.
With a census strategy, the selected sample consists of the same individuals as the
accessible population (or sampling frame) (Fritz & Morgan, 2010). In the current study,
the selected sample consisted of 229 employees.8 The actual sample (Fritz & Morgan,
2010) consisted of 109 individuals who agreed to participate and responded to the survey
questionnaire, and whose data was used in the analysis (Figure 3.5).
Figure 3.5
Relationship Among the Theoretical Population, Accessible Population, Selected Sample,
and Actual Sample
8 Of the 268 employees invited to participate, there were 150 initial responses (i.e., clicks on the survey link). Of the 150 initial responses, 39 records were excluded since the participants did not meet the inclusion criteria of being full-time and nonsupervisory or the participants’ eligibility for inclusion was uncertain, resulting in an accessible population/selected sample of 229 employees.
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Data Collection
This section provides a summary of the study’s data collection framework,
consisting of a discussion of the study’s level of analysis, survey research design, survey
instrumentation, pilot study, data collection procedures and survey administration, and
data storage.
Level of Analysis
As Hatch (2018) discussed, identifying the level of analysis for a research study
consists of how the researcher “differentiates phenomena to be analyzed on the basis of
their position in the nested hierarchy of systems” (p. 381). Hatch (2018) further
commented that in the social sciences, levels of analysis are typically “restricted to micro
(individual), meso (group or organizational), and macro (environmental) levels” (p. 381).
In this study, the level of analysis was at the micro (individual) level. Data were collected
from individual employees and total scores for each of the three constructs of interest—
employee alignment, perceived organizational support, and employee engagement—were
computed for each individual. Subsequent data analysis was performed using the
individual total scores.
Survey Research Design
As discussed previously, the current study utilized a cross-sectional survey
research design (Creswell, 2014; Robson & McCartan, 2016) and a self-completion, or
self-reported, internet-based survey questionnaire (Robson & McCartan, 2016). The
following provides an overview of the benefits and potential limitations of online surveys
and strategies that can minimize identified survey limitations.
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Benefits of Online Surveys
Several benefits have been identified with the use of online surveys to gather
research data (L. Cohen et al., 2011; Creswell, 2014; Robson & McCartan, 2016). For
example, an online survey can allow for a large amount of data to be collected
economically and efficiently from a large, and potentially geographically dispersed,
sample (L. Cohen et al., 2011; Creswell, 2014; Robson & McCartan, 2016). Another
significant benefit is the potential for a rapid response and thus a short data collection
period (L. Cohen et al., 2011; Creswell, 2014; Robson & McCartan, 2016). An online
survey provides a standardized and consistent means to collect data, in that the wording
and order of the questions are the same for all participants (Robson & McCartan, 2016).
Participants are provided the opportunity and the flexibility, within the data collection
period, to complete the survey at a time of their choosing (L. Cohen et al., 2011; Dillman
et al., 2014). Additionally, an online survey can provide respondents with anonymity,
which may encourage both participation and frankness in responses to the survey
questions (L. Cohen et al., 2011; Robson & McCartan, 2016). Lastly, the use of an online
survey platform—for example SurveyMonkey or Qualtrics—provides a secure means to
upload, format, and store survey questions and responses, as well as an automated
process to download and transfer survey data for subsequent data analysis without the
need for manual data entry (Halbgewachs, 2018).
Potential Limitations of Online Surveys
While benefits have been identified with using online surveys for data collection,
it is also important to note some of the limitations. A commonly cited limitation of online
surveys is recognition that not everyone has the internet access and technical skills with
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computer and mobile devices required to complete an online survey (L. Cohen et al.,
2011; Robson & McCartan, 2016). Responses to the survey are self-reported, which may
result in social desirability response bias, the tendency of participants to respond in a
manner that they believe is more socially acceptable or desirable (Nederhof, 1985;
Robson & McCartan, 2016). Additionally, as a self-report instrument, online surveys rely
on the clarity of the questions and participants interpreting and understanding the survey
questions in the manner intended; thus, misunderstandings of survey questions may go
undetected (Robson & McCartan, 2016). Respondents may be skeptical of the legitimacy
of the invitation to participate and may delete or ignore the invitation without
participating in the survey (L. Cohen et al., 2011). Lastly, online surveys have been
reported to have lower response rates than other types of surveys (L. Cohen et al., 2011;
Robson & McCartan, 2016).
Strategies for Minimizing Online Survey Limitations
To address potential limitations associated with online surveys, L. Cohen et al.
(2011) suggested that researchers consider the following: (a) provide clear details and
instructions for each section rather than placing all instructions at the beginning of the
survey; (b) keep surveys as low-tech as possible and avoid sophisticated computer
graphics; (c) provide a clear statement of the anonymity and confidentiality of all
participants and responses; (d) ensure the survey questionnaire is short, easy to
understand, and easy to complete; (e) clearly identify the researcher’s university
affiliation in all correspondence concerning the survey; and (f) send email reminders and
follow ups to those invited to participate in the survey. Each of these strategies was
implemented in the current study.
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Survey Instrumentation
This study used three established and validated self-report survey scales (i.e.,
instruments) to measure the variables of interest: (a) the Employee Engagement Scale
(EES) (Shuck, Adelson, et al., 2017), (b) the Stringer Strategic Alignment Scale
(Stringer, 2007), and (c) the Survey of Perceived Organizational Support (SPOS)
(Eisenberger et al., 1986). Limited demographic information (age, gender, and
organizational tenure) was also collected from the participants in this study. Two
additional questions were used to screen for the two inclusion criteria of being full-time
and not having supervisory responsibilities. In addition to a discussion of the instruments
and demographic questions used in this study, this section also addresses latent constructs
as related to the three variables of interest, the handling of data obtained from the use of a
Likert response format, instrument reliability and validity, and concludes with a summary
of the variables and instrument items.
Latent Constructs
Wagner et al. (2010) commented that “most constructs in research are latent
variables” (p. 697). Latent variables, or constructs, are those that cannot be directly
observed or measured (Bollen, 2002; Keith, 2015; Neiheisel, 2017; Wagner et al., 2010);
rather, latent variables are inferred from items (e.g., survey questions) that a researcher
can measure or observe (Bollen, 2002; Borsboom, 2008; Keith, 2015; Wagner et al.,
2010). Keith (2015) noted that while we cannot directly measure a latent variable, “we do
get indicators of it from many different behaviors” (p. 536). Additionally, Bollen (2002)
observed that “nearly all measurement in psychology and the other social sciences
assumes effect indicators” (p. 616), where effect indicators are “observed variables that
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are effects of latent variables” (p. 616). For example, using a scale, or instrument, to
measure a participant’s degree of agreement with statements about employee engagement
would be an effect indicator of the latent construct of employee engagement (Bollen,
2002).
With respect to measuring latent constructs, Bandalos and Finney (2019) noted
that “in practice composites of the observed variable scores are computed and used as
proxies for the constructs of interest” (p. 110). As reflected in prior research, the common
convention has been that the latent constructs of employee alignment, perceived
organizational support, and employee engagement have been operationalized—i.e.,
defining how the variable is to be measured (Butin, 2010)—through validated survey
instruments such as: the Stringer Strategic Alignment Scale (Stringer, 2007), the SPOS
(Eisenberger et al., 1986), and the EES (Shuck, Adelson, et al., 2017), respectively.
Likert Scale Data
There is an ongoing discussion within the research literature as to whether data
obtained from a questionnaire or survey instrument using a Likert “response format”9
(Carifio & Perla, 2007, p. 107), often referred to as a Likert scale, should be treated as
ordinal or interval for statistical analysis (Allen & Seaman, 2007; Carifio & Perla, 2007,
2008; Clason & Dormody, 1994; Creswell, 2012; Crocker & Algina, 1986; Jamieson,
2004; Knapp, 1990; Lomax & Hahs-Vaughn, 2012; Pell, 2005). Some maintain that such
data is unquestionably ordinal in nature and thus must be analyzed using nonparametric
9 Likert (1932) developed a method of quantitatively measuring an individual’s attitude—an individual’s “tendency toward a particular response in a particular situation” (p. 7)—that consisted of assigning values “from 1 to 5 to each of the five different positions on the five-point statements” (p. 25). For example, a five-point response scale consists of 1 (strongly disagree), 2 (disagree), 3 (neither agree nor disagree), 4 (agree), and 5 (strongly agree).
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statistical analysis methods (Barnette, 2010; Boone & Boone, 2012; L. Cohen et al.,
2011; Creswell, 2012; Jamieson, 2004; Norman, 2010). Others counter that the data can
be considered interval and thus the use of parametric statistical analysis methods is
appropriate (Allen & Seaman, 2007; Boone & Boone, 2012; Burns & Grove, 2009;
Carifio & Perla, 2007, 2008; Joshi et al., 2015; Norman, 2010; Pell, 2005). However, a
key distinction that is often missed, omitted, or possibly misunderstood in such
discussions is the level at which the data are being analyzed (Boone & Boone, 2012;
Carifio & Perla, 2007; Clason & Dormody, 1994). That is, is the researcher analyzing
responses to individual questions or statements (i.e., item level) or is the analysis
conducted on the aggregated individual-item responses (scale level) (Allen & Seaman,
2007; Barnette, 2010; Boone & Boone, 2012; Carifio & Perla, 2007, 2008; Clason &
Dormody, 1994; Joshi et al., 2015)?
For data in a Likert response format, the distinction between item-level and scale-
level analysis is of key importance in determining the appropriate statistical methods to
be used. There appears to be some consensus that while responses to individual items
using a Likert response format (item level) are ordinal data (Barnette, 2010; Burns &
Grove, 2009; Clason & Dormody, 1994; Joshi et al., 2015; Norman, 2010), items
summed across the individual items (scale level)—that is, creating an aggregate or
composite score—can be appropriately characterized as interval data (I. E. Allen &
Seaman, 2007; Boone & Boone, 2012; Burns & Grove, 2009; Carifio & Perla, 2007,
2008; Joshi et al., 2015; Norman, 2010). As Carifio and Perla (2008) noted,
[It is] perfectly appropriate to calculate Pearson correlation coefficients using the summative ratings from Likert scales and to use these correlations as the basis for various multivariate analytical techniques, such as multiple regression, factor
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analysis and meta-analysis, to obtain more powerful and nuanced analyses of the data and research hypotheses being investigated. (p. 1151)
In addition, Burns and Grove (2009) noted that “less random10 error and
systematic11 error exist[s] when using the total score of a scale” (p. 410). Based on the
preceding discussion, overall scores for each of the three variables of interest in this
study—employee alignment, perceived organizational support, and employee
engagement—were computed as the sum of the individual responses to each of the items
in the three survey instruments.
Instrument Reliability and Validity
Mueller and Knapp (2019) commented that “both reliability and validity are
essential parts of the psychometric properties of a measuring instrument” (p. 397) and are
key to providing readers of a research study with information required to determine the
“goodness” (p. 398) of the data collected and the findings derived from the data.
Reliability is concerned with the extent to which an instrument provides consistency in
the measurement of a given construct (Morgan et al., 2013; Mueller & Knapp, 2019;
Roberts & Hyatt, 2019; Robson & McCartan, 2016). As a measure of internal consistency
reliability, Mueller and Knapp (2019) noted that Cronbach’s alpha was the “most
commonly employed indicator of the reliability of a measuring instrument in the social
sciences” (p. 399). Cronbach’s alpha was used as the measure of reliability of the
instruments used in this study.
10 Random error is an error wherein the direction and magnitude of the error vary and are unpredictable (M. Allen, 2017; Burns & Grove, 2009). 11 Systematic error is not random, but rather impacts measurement in a consistent manner (M. Allen, 2017; Burns & Grove, 2009).
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Validity can best be described as the extent to which an instrument accurately
measures “what it is designed to measure” (Mueller & Knapp, 2019, p. 397) or what it
“purports to measure” (Roberts & Hyatt, 2019, p. 149). In the social and behavioral
sciences, researchers are typically concerned with three types of validity: (a) content
validity, the extent to which the items, the individual questions or statements of an
instrument, measure the content or domain intended (L. Cohen et al., 2011; Creswell,
2014; Mueller & Knapp, 2019), usually determined by expert judgment (Mueller &
Knapp, 2019); (b) construct validity, the extent to which the items of a particular
measurement instrument align to theoretical expectations and measure the intended
construct (L. Cohen et al., 2011; Creswell, 2014; Mueller & Knapp, 2019; Robson &
McCartan, 2016), usually determined by factor analyses (Mueller & Knapp, 2019); and
(c) criterion validity, the extent to which the instrument accurately predicts an outcome,
or criterion, measure (L. Cohen et al., 2011; Creswell, 2014; Robson & McCartan, 2016).
In assessing validity, the dimensionality of a measurement instrument is a key
factor of construct validity (Slocum-Gori & Zumbo, 2011). As Hattie (1985) observed,
“one of the most critical and basic assumptions of measurement theory is that a set of
items forming an instrument all measure just one thing in common” (p. 139). The extent
to which a set of items, i.e., a measurement instrument, measures “just one thing in
common” is referred to as unidimensionality (Falissard, 1999; Hattie, 1985; Hill et al.,
2016; Slocum-Gori & Zumbo, 2011). More formally, Hattie (1985) defined
unidimensionality as “the existence of one latent trait underlying the set of items”
(p. 152).
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Instrument reliability and validity, as reported in previous empirical research, are
discussed for each of the three survey scales used in this study to measure employee
engagement, employee alignment, and perceived organizational support.
Employee Engagement
Employee engagement, the outcome variable in this study, was measured using
the EES (Shuck, Adelson, et al., 2017). The EES is a 12-item scale, consisting of three
subscales (cognitive engagement, emotional engagement, and behavioral engagement) of
4 items each (Shuck, Adelson, et al., 2017). Sample items from the EES include “I care
about the future of <my company>” and “I am willing to put in extra effort without being
asked” (Shuck, Adelson, et al., 2017). All scale items are measured on a 5-point Likert
scale ranging from 1 (strongly disagree) to 5 (strongly agree) (Shuck, Adelson, et al.,
2017), where a higher numeric response indicates a higher level of engagement. Scores
for each of the three engagement subscales are computed as the sum of a participant’s
responses for the 4 items comprising each subscale (cognitive engagement, emotional
engagement, and behavioral engagement). The range of possible values for each subscale
is 4 to 20, with each subscale consisting of 4 items measured on a 5-point Likert scale.
An overall employee engagement score is computed as the sum of the three engagement
subscales. The range of possible values for the overall employee engagement score is 12
to 60.
In the original study, based on a sample of 1,067 employees working in financial
services, Shuck, Adelson, et al. (2017) found evidence for “strong internal consistency
reliability” (p. 968) for each of the three subscales of the EES, with Cronbach’s alphas of
.94 for the cognitive engagement scale, .88 for emotional engagement, and .91 for
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behavioral engagement. Subsequent studies have found similar empirical evidence
supporting the internal consistency reliability of the EES—total employee engagement
reliability—with Cronbach’s alphas of .88 (n = 259) (Osam et al., 2020) and .92 (n =
114) (Shuck, Alagaraja, et al., 2017).
In developing the EES, Shuck, Adelson, et al. (2017) used expert judgment to
establish content validity. To further assess validity, Shuck, Adelson, et al. (2017) found
evidence of convergent validity, reporting correlations of .77, .89, and .62 between the
EES and measures of similar constructs of job satisfaction, discretionary effort, and well-
being, respectively. Additionally, while the EES was positively correlated with measures
of similar constructs, Shuck, Adelson, et al. (2017) determined that employee
engagement was in fact a distinct construct from job satisfaction, discretionary effort, and
well-being.
Employee Alignment
Employee alignment, an explanatory variable in this study, was measured using
the 8-item Stringer Strategic Alignment Scale (Stringer, 2007). Sample items include “I
understand the goals of the organization” and “I understand how my job contributes to
the organization’s ability to achieve its goals” (Stringer, 2007). All scale items are
measured on a 5-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly
agree) (Stringer, 2007), where a higher numeric response indicates a higher level of
alignment. The overall employee alignment scale score is computed as the sum of a
participant’s responses to the 8 items of the Stringer Strategic Alignment Scale (Stringer,
2007) survey instrument. The range of possible values for the overall employee
alignment score is 8 to 40.
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In the original study, based on a sample of 160 employees, Stringer (2007)
established the internal consistency reliability of the scale, reporting a Cronbach’s alpha
of .95. In developing the Stringer Strategic Alignment Scale, Stringer (2007) used expert
judgment to establish content validity. Additionally, Stringer (2007) reported that
“construct and convergent validity was addressed by running interitem correlations for all
questions on the scale” (p. 71).
Perceived Organizational Support
Perceived organizational support, an explanatory variable in this study, was
measured using the SPOS (Eisenberger et al., 1986). The SPOS is a 36-item scale, with
8-item and 16-item versions also available. Sample items include “The organization
values my contribution to its well-being” and “The organization really cares about my
well-being” (Eisenberger et al., 1986). The 8-item version of the SPOS (Eisenberger et
al., 1986) was used in this study. All scale items are measured on a 7-point Likert scale
ranging from 0 (strongly disagree) to 6 (strongly agree) (Eisenberger et al., 1986), where
a higher numeric response indicates a higher level of perceived support. The overall POS
scale score is computed as the sum of a participant’s responses to the 8 items of the SPOS
survey instrument. The range of possible values for the overall perceived organizational
support score is 0 to 48.
In the original study, using the 36-item SPOS instrument and based on a sample
of 361 employees from nine diverse organizations, Eisenberger et al. (1986) reported an
internal consistency reliability (Cronbach’s alpha) of .97. In a subsequent meta-analysis
of 73 studies, Rhoades and Eisenberger (2002) found Cronbach’s alphas ranging from .67
to .98 (pp. 704-707), concluding that the SPOS has a “high internal reliability” (p. 703),
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with an average Cronbach’s alpha of .90. Similarly, in a study of 266 employees from a
community college located in the Midwestern United States, Worley et al. (2009)
reported a Cronbach’s alpha of .96.
Concerning the reliability of the shorter (i.e., 8-item) SPOS instrument, Rhoades
and Eisenberger (2002) observed that “because the original scale is unidimensional and
has high internal reliability, the use of shorter versions does not appear problematic”
(p. 699). Studies using the 8-item version of the SPOS survey instrument have found
Cronbach’s alphas of .93 (n = 266) (Worley et al., 2009, p. 115), .88 (n = 75) (Y. D.
Robinson, 2013), and .88 (n = 97) (Simmons, 2013).
In their original study, Eisenberger et al. (1986) used principal component and
factor analyses to determine that the 36 items of the SPOS instrument showed a strong
loading on the main factor of perceived support, accounting for 48.3% of the total
variance. Shore and Tetrick (1991) used confirmatory factor analysis to examine the
construct validity of the SPOS. In their study (n = 330), Shore and Tetrick (1991) found
evidence to support the unidimensionality of the SPOS as a measure of perceived
organizational support, as well as the distinctiveness of the perceived organizational
support construct from the similar constructs of affective and organizational commitment.
Similar to Shore and Tetrick (1991), Hutchison (1997), using a sample of 205 faculty and
staff from a state university in the Western United States, found support for the
unidimensionality of the SPOS as a measure of perceived organizational support, as well
as the distinctiveness of perceived organizational support from the similar constructs of
affective commitment, perceived supervisory support, and organizational dependability.
Worley et al. (2009), in the study of 266 community college employees, found further
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empirical evidence supporting the unidimensionality of the SPOS, as well as convergent
validity of the SPOS relative to the three correlates of affective commitment,
organizational communication, and organizational participation. Overall, studies have
found empirical support for the unidimensionality (Hutchison, 1997; Shore & Tetrick,
1991; Worley et al., 2009), discriminant validity (Hutchison, 1997; Shore & Tetrick,
1991), and convergent validity (Worley et al., 2009) of the SPOS as a measure of
perceived organizational support.
Interaction Effect Variable
In exploring the relation among employee alignment, perceived organizational
support, and employee engagement, an area of particular interest was whether or not
perceived organizational support moderated the relation between employee alignment
and employee engagement. Moderation, also commonly referred to as an interaction
effect (Baron & Kenny, 1986; Hayes, 2018; Hayes & Rockwood, 2017; Jose, 2019;
Keith, 2015, 2019; Kelley & Maxwell, 2019), affects the nature (i.e., magnitude and/or
direction) of the relation between an independent or explanatory variable and a dependent
or outcome variable (Aguinis et al., 2017; Baron & Kenny, 1986; Hayes, 2009, 2018;
Hayes & Rockwood, 2017).
To test for moderation, a cross-product, or interaction term, variable was created
and tested for statistical significance when entered into the regression equation (Baron &
Kenny, 1986; J. Cohen et al., 2003; Hayes, 2018; Hayes & Rockwood, 2017; Keith,
2015). The interaction effect variable was computed by multiplying the explanatory
variable (i.e., employee alignment) with the hypothesized moderating variable (i.e.,
perceived organizational support) (Baron & Kenny, 1986; J. Cohen et al., 2003; Hayes,
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2018; Hayes & Rockwood, 2017; Keith, 2015, 2019; Kelley & Maxwell, 2019; Lomax &
Hahs-Vaughn, 2012).
Prior to computing the interaction effect variable, the variables of employee
alignment and perceived organizational support were centered (Aiken & West, 1991; J.
Cohen et al., 2003). The centering consisted of subtracting the computed mean of each
variable from each observed value for employee alignment and perceived organizational
support (Aiken & West, 1991; J. Cohen et al., 2003; Hayes, 2018; Keith, 2015, 2019;
Kelley & Maxwell, 2019). The two variables were centered in order to increase
interpretability of the regression coefficients in the moderated multiple regression
equation by creating a meaningful zero-point within the range of the possible values for
the variables (J. Cohen et al., 2003; Dalal & Zickar, 2012; Echambadi & Hess, 2007;
Hayes, 2018; Keith, 2015; Kelley & Maxwell, 2019; McClelland et al., 2017).
Participant Demographic Questions
In addition to the data collected with the instruments to measure employee
alignment, perceived organizational support, and employee engagement, participant
demographic information was collected. Demographic information describes the personal
characteristics of the participants in the study sample (Lee & Schuele, 2010).
Demographic variables—such as age, gender, race, and ethnicity—are considered
independent variables in that they cannot be manipulated by the researcher (Creswell,
2014; Lee & Schuele, 2010). Additionally, for the purpose of generalization,
demographic information provides data that is necessary in determining whether or not
the study participants are a representative sample of the overall study population (Lee &
Schuele, 2010).
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Demographic variables are often treated as control variables in quantitative
studies (Creswell, 2014; Pole & Bondy, 2010). Control variables are variables whose
effect on the outcome variable the researcher wishes to control or eliminate in order to
better estimate the actual effect of the independent (or explanatory) variables of interest
on the outcome variable (Creswell, 2014; Pole & Bondy, 2010). In the present study, the
focus of interest was the effects of employee alignment and perceived organizational
support on employee engagement, independent of any influence of the demographic
characteristics of the participants.
In minimizing the amount of personally identifiable information collected from
participants (Lee & Schuele, 2010), the demographic information collected in this study
was limited to those key demographic characteristics that have been shown to influence
the outcome variable of engagement. While not all studies agree, several common
demographic characteristics have been repeatedly found to influence engagement, to
include age (Avery et al., 2007; Bhatnagar, 2012; Gomes et al., 2015; Toyama & Mauno,
2017), gender (Bae et al., 2013; Bhatnagar, 2012; Gomes et al., 2015; Mauno et al., 2005;
Toyama & Mauno, 2017), and organizational tenure (i.e., years employed by the current
organization) (Avery et al., 2007; Bae et al., 2013; Gomes et al., 2015).These three
variables were used in this study. Table 3.1 provides a summary of correlations between
demographic variables and engagement from a sample of empirical studies.
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Table 3.1
Summary of Correlations Between Demographic Variables and Engagement
Demographic variable Correlation statisticsa Study Reference Age r = –.12, p < .01, n = 901 Avery et al. (2007) r = .81, p < .01, n = 291 Bhatnagar (2012) r = .136, p < .05, n = 337 Gomes et al. (2015) r = .17, p < .001, n = 489 Toyama & Mauno (2017) Genderb r = .063, p = NRc, n = 304 Bae et al. (2013) r = .07, p < .05, n = 291 Bhatnagar (2012) r = .034, p = NRc, n = 337 Gomes et al. (2015) r = –.10, p < .01, n = 736 Mauno et al. (2005) r = –.08, p = NRc, n = 489 Toyama & Mauno (2017) Organizational tenure r = –.11, p < .01, n = 901 Avery et al. (2007) r = –.068, p = NRc, n = 304 Bae et al. (2013) r = .040, p = NRc, n = 337 Gomes et al. (2015) a According to Cohen (1988), correlation coefficient effect size can be classified as (a) small, r = ± .10; (b) medium, r = ± .30; or (c) large, r = ± .50. b Correlation coefficients were computed using dummy-coded variables (e.g., 0 = male, 1 = female). c NR = Not reported. Study did not report the p value associated with the correlation coefficient.
Lee and Schuele (2010) recommended that demographic variables be defined
“consistent with commonly used definitions or taxonomies (e.g., U.S. Census Bureau
categories)” (p. 347). As such, this study used the U.S. Census Bureau (2018)
demographic categorizations for the demographic variables of age and gender. Age is a
ratio variable (Lomax & Hahs-Vaughn, 2012) measured in whole years as of the date the
participant completed the survey. Gender is a nominal variable (Lomax & Hahs-Vaughn,
2012) with participants identifying as either male or female; responses were dummy-
coded as 0 = male, 1 = female, and 2 = not provided (i.e., the participant did not answer
this question). Organizational tenure is a ratio variable (Lomax & Hahs-Vaughn, 2012)
measured in whole years as of the date the participant completed the survey.
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Participant Screening Questions
To ensure that participants met the study’s inclusion criteria, screening questions
were asked to determine if the individual (1) directly managed or supervised other
employees and (2) was a full-time or part-time employee. Supervisory status is a nominal
variable (Lomax & Hahs-Vaughn, 2012) with participants identifying as either being in a
managerial or supervisory role or not. Participant responses were dummy-coded as 0 =
not currently in a managerial or supervisory role and 1 = currently in a managerial or
supervisory role. Employment status is a nominal variable (Lomax & Hahs-Vaughn,
2012), with participants identifying as either a full-time or part-time employee.
Participant responses were dummy-coded as 0 = full-time employee and 1 = part-time
employee.
Summary of Variables and Instrument Items
A summary of the variables and the associated measurement instruments utilized
in the current study is presented in Table 3.2. Permission to use the three instruments has
been granted (see Appendix D). Additionally, in assembling the final survey
questionnaire (Appendix E), the following best practices were followed: dividing the
questionnaire into groups of related questions—i.e., questions relating to the same
underlying construct; using headings to clearly identify sections; and providing clear
instructions for each section requiring a participant response—i.e., navigating the
questionnaire and responding to the 5-point and 7-point Likert scales (L. Cohen et al.,
2011; Dillman et al., 2014; Fanning, 2005).
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Table 3.2
Summary of Variables and Instruments Used in the Study
Variable Instrument Number of items
Employee engagement Employee Engagement Scale (Shuck, Adelson, et al., 2017)
12
Employee alignment Stringer Strategic Alignment Scale (Stringer, 2007) 8 Perceived organizational support
Survey of Perceived Organizational Support (Eisenberger et al., 1986)
8
Interaction effect N/Aa N/Aa Demographic information Researcher developed 3 Screening questions Researcher developed 2 a Interaction effect variable was computed and was not directly measured in this study.
Pilot Study
Although this study used three previously validated survey instruments to
measure the variables of interest—employee alignment, perceived organizational support,
and employee engagement—a pilot study was conducted to assess (a) clarity of
instructions, layout, ease of use, and completion time (L. Cohen et al., 2011; Creswell,
2014; Dillman et al., 2014; Roberts & Hyatt, 2019) and (b) internal reliability (i.e.,
Cronbach’s alpha) of the three survey instruments—the Stringer Strategic Alignment
Scale, the SPOS, and the EES—compared with the psychometric data obtained in
previous studies.
The pilot study was conducted from November 7 to 15, 2019, inviting a
convenience sample of 25 individuals from the researcher’s professional network. In
order to be representative of the main research study, pilot study participants had to be
full-time, nonsupervisory employees who were employees of an organization (i.e., not
self-employed). The survey questionnaire was administered online with the
SurveyMonkey platform. Potential participants were informed that their participation in
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the pilot study was completely voluntary and confidential, that all participant responses
would be completely anonymous, and that only group statistics would be prepared from
the survey results and feedback comments. Eighteen individuals agreed to participate in
the pilot study; one failed to complete any of the survey questions, resulting in 17 usable
participant responses, for a response rate of 68%. Seven individuals invited to participate
in the pilot study declined.
In addition to responding to the survey questions, participants were asked to
provide feedback on their experience with the survey questionnaire. They were asked six
questions: (1) How long did it take for you to complete the survey questionnaire? (2)
Were the survey questionnaire instructions clear to you? (3) Was it clear to you how
“company,” “organization,” and “business unit” were defined in the instructions for the
questions? (4) As you were answering the questions, how did you think of (or define) the
terms “company,” “organization,” and “business unit” (it is OK if you defined these three
terms the same)? (5) Were any of the questions unclear? and (6) Do you have any
recommendations on how the survey questionnaire might be improved?
Pilot Study Assessment
As reported in the feedback, the survey questionnaire took 2 to 10 minutes to
complete, with a mean completion time of 6 minutes and 30 seconds. As computed by
SurveyMonkey, the median time for all 17 participants to complete the survey was 5
minutes and 40 seconds. A significant majority of the pilot study participants—15
individuals, with two not providing feedback—felt that the survey questionnaire
instructions were clear. Additionally, 14 participants felt that the terms “company,”
“organization,” and “business unit” were clearly defined in the instructions for the
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questions; one participant viewed the “company” and “organization” to be the same, and
two participants did not respond to this question.
Given the speed at which participants in the pilot study completed the survey
questionnaire, there were concerns whether participant responses to the four reverse-
worded questions on the SPOS accurately reflected participant attitudes. Specifically,
there was concern of an increased potential for participant “misresponse” (Swain et al.,
2008, p. 116), where the participant “selects a response option that is opposite to his or
her beliefs [or attitudes]” (Swain et al., 2008, p. 116). Swain et al. (2008) observed that a
misresponse could arise when inattentive participants overlook reverse-worded items and
instead “rely on expectations regarding item content” (p. 117). As Swain et al. (2008)
suggested, these expectations are based on participants’ “experiences with statements in
everyday language” (p. 117) that lead participants to expect that survey questionnaire
items “are stated affirmatively” (p. 117). As a result, Swain et al. (2008) concluded that
“reversed items can be more confusing or difficult to process than nonreversed items and
thus result in greater misresponse” (p. 118). In addition to, or as a result of, issues of
misresponse, other concerns with the use of reverse-worded questions have been
identified to include difficulties with interpretation (van Sonderen et al., 2013), decreased
survey questionnaire validity (van Sonderen et al., 2013) and reliability (Swain et al.,
2008), and the potential for unexpected factor structures (Swain et al., 2008).
In addition to the concern over the reverse-worded questions, the feedback from
participants indicated some ambiguity concerning the terms “company” and
“organization.” For example, there was inconsistency among four individuals within the
same organization (a university) in interpreting “company” and “organization,” with two
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participants defining both “company” and “organization” as the overall university and
two participants defining the “organization” as the overall university and “company” as a
school within the university. Similarly, there appeared to be some ambiguity concerning
the term “business unit.” Specifically, seven participants defined “business unit” as the
entire department they work in, where the survey questionnaire instructions defined
department as the individual’s “immediate work group,” adding that “it is OK if your
business unit consists of only yourself.”
Changes to the Survey Questionnaire
The findings of the pilot study resulted in three changes to the survey
questionnaire (Appendix E) as follows:
• Recognizing the concerns over the use of reverse-worded questions, and given
that the other two survey instruments used in the study—the EES (Shuck,
Adelson, et al., 2017) and the Stringer Strategic Alignment Scale (Stringer,
2007)—did not use reverse-worded questions, the decision was made to reword
the four reverse-worded questions on the SPOS (i.e., Questions 22, 23, 25, and
27) to reflect an affirmative, rather than negative, orientation; rewording of these
four questions was based on Guillaume (2015).
• In response to the apparent ambiguity with the terms “company” and
“organization,” “company” and “organization” were replaced with “human
resources department” (i.e., Questions 5, 7, 8, 12, 13, 14, 15, 16, 18, 20, 21, 22,
23, 24, 25, 26, 27, and 28). This change also aligned the survey questionnaire with
the specific context of the main study’s research site.
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• In response to the apparent ambiguity with the term “business unit,” the term was
replaced with “team” (i.e., Questions 17, 18, and 19). This change also aligned the
survey questionnaire with the specific context and terminology of the main
study’s research site.
Pilot Study Questionnaire Internal Reliability
As a measure of internal reliability, a Cronbach’s alpha (L. Cohen et al., 2011;
Morgan et al., 2013) was computed for the variables measuring employee alignment,
perceived organizational support, and employee engagement. Statistical analysis was
performed using IBM SPSS Statistics (Version 26.0.0.1 for Mac) to assess the internal
reliability of the pilot study survey questionnaire. Cohen et al. (2011) offered the
following guidelines for interpreting Cronbach’s alpha coefficients: (a) >.90, very highly
reliable; (b) .80 to .90, highly reliable; (c) .70 to .79, reliable; (d) .60 to .69,
marginally/minimally reliable; and (e) <.60, unacceptably low reliability (p. 640). The
Cronbach’s alphas computed for the three variables were .93 for employee alignment,
.817 for perceived organizational support, and .899 for employee engagement. As
depicted in Table 3.3, these Cronbach’s alphas compare favorably to the results of
previous studies, which have found similar empirical evidence supporting the internal
reliability of the three instruments used to measure the variables of employee alignment,
perceived organizational support, and employee engagement.
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Table 3.3
Summary of Pilot Study Measures of Internal Reliability
Instrument Variable Cronbach’s alpha
Pilot study Previous studies Stringer Strategic Alignment Scale (Stringer, 2007)
Employee alignment
.930 .950 (Stringer, 2007)
Survey of Perceived Organizational Support (Eisenberger et al., 1986)
Perceived organizational support
.817 .930 (Worley et al., 2009) .880 (Y. D. Robinson, 2013) .880 (Simmons, 2013)
Employee Engagement Scale (Shuck, Adelson, et al., 2017)
Employee engagement
.899 .920 (Shuck, Alagaraja, et al., 2017) .880 (Osam et al., 2020)
Data Collection Procedures and Survey Administration
Once approved by the George Washington University Office of Human Research
Institutional Review Board (Appendix F), the data collection process occurred over a 4-
week period from January 27, 2020, to February 21, 2020. To administer the survey and
to collect and initially store participant responses, the researcher contracted with
SurveyMonkey to serve as the online survey platform.
Data Collection Procedures
All communication between the researcher and the study participants occurred
through an individual identified by the organization to serve as the survey sponsor and
coordinator. Following the procedures recommended by Dillman et al. (2014), the
researcher provided the organization coordinator four email communications (prenotice
announcement, invitation to participate, follow up #1, and follow up #2) (Appendix G) to
be sent to potential study participants on the researcher’s behalf. The first
communication, the prenotice announcement, was sent to potential participants to initiate
the 4-week data collection phase. This announcement introduced the online survey and
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the purpose of the study, requested volunteers to participate, and notified potential
participants that they would receive an email with the link to the survey within the next
week. In addition, the communication emphasized the confidentiality of the survey
process, stating that all “survey responses will be completely anonymous” and that the
researcher would not be able to identify who did or did not participate and would “only
report results from all of the employees who participate as one group.”
At the end of the first week, 4 days after the initial prenotice announcement was
sent, potential participants received a second email that formally invited their voluntary
participation to take the online survey (Appendix G). The invitation reiterated the
purpose of the study, the voluntary nature of participation, and the strict confidentiality of
participant responses. The invitation included the link to the online survey and a
requested completion date. In addition, the invitation email included a copy of the study’s
informed consent (Appendix H).
To increase the survey response rate (Dillman et al., 2014), two follow up emails
(Appendix G) were sent 1 and 2 weeks after the invitation. The follow up emails included
a thank you to those who had volunteered to participate and completed the survey, as well
as a request for participation to those who had not yet completed the survey. The follow
up emails included the link to the online survey and the requested completion date and
reiterated the purpose of the study, the voluntary nature of participation, and the
confidentiality of participant responses. A summary of the data collection timeline is
provided in Table 3.4.
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Table 3.4
Summary of the Data Collection Timeline
Date Action(s) Description Week 1 Pre-notice email
(Appendix G) • Introduction to the online survey • Discussion of the purpose of the research • Request for volunteers to participate • Discussion of the confidentiality of the survey process • Research study information sheet
Week 2 Invitation to participate email (Appendix G)
Data collection begins
• Invitation to potential participants • Reiteration of the purpose, voluntary nature of
participation, and confidentiality of the study • Link to the online survey and requested completion date • Informed consent for participation in a research study
Week 3 Follow up email #1 (Appendix G)
Data collection continues
• Thank you to participants • Request for voluntary participation • Reiteration of the purpose, voluntary nature of
participation, and confidentiality of the study • Link to the online survey and requested completion date
Week 4 Follow up email #2 (Appendix G)
Data collection continues
• Thank you to participants • Request for voluntary participation • Reiteration of the purpose, voluntary nature of
participation, and confidentiality of the study • Link to the online survey and requested completion date
End of data collection • No data collection actions Survey Administration
The online survey questionnaire used in this study was administered by
SurveyMonkey, a commercial provider of web-based survey solutions (Blight, 2014;
Halbgewachs, 2018; Y. D. Robinson, 2013; SurveyMonkey, n.d.-b). SurveyMonkey
served as the online survey platform for hosting the survey, gathering survey responses,
and storing participant survey responses until the survey closed. The SurveyMonkey
service allows surveys to be created and administered with only a web-browser and an
Internet connection (Blight, 2014; Halbgewachs, 2018; Y. D. Robinson, 2013;
SurveyMonkey, n.d.-b). Using SurveyMonkey, the actual study survey was hosted on a
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server in the company’s data center, with the survey published to study participants using
a survey link. With respect to security, the SurveyMonkey website uses the transport
layer security cryptographic protocol to encrypt data during transmission and while
stored on the server (SurveyMonkey, n.d.-a).
The EES (Shuck, Adelson, et al., 2017), Stringer Strategic Alignment Scale
(Stringer, 2007), SPOS (Eisenberger et al., 1986), and demographic and screening
questions were combined into a single survey instrument and uploaded to the
SurveyMonkey server. The final survey instrument (Appendix E) consisted of seven
sections: (a) an introduction providing an overview of the research study, the purpose,
survey procedures, potential risks to participants, confidentiality, potential benefits to
participants, compensation (no compensation was offered to participants), and researcher
contact information; (b) documentation of participant informed consent; (c) instructions
for completing the survey; (d) questions from the EES; (e) questions from the Stringer
Strategic Alignment Scale; (f) questions from the SPOS; and (g) demographic and
screening questions. The survey was constructed to request informed consent before
allowing the participant to proceed to the actual survey, with the statement, “By clicking
on the ‘I AGREE’ button below, I am providing my informed consent and voluntarily
agreeing to participate in the study.” After clicking the “I AGREE” button, participants
were able to proceed with the survey questionnaire. After completing the survey,
participants were asked to click on a “SUBMIT” button, which took them to a page that
thanked them for their participation in the study.
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Data Storage
All survey data were stored in a password-protected data file on the researcher’s
password-protected computer. Additionally, backup copies of the survey data were stored
as password-protected data files on the researcher’s university cloud account and an
external hard drive.
Preanalysis Data Handling
Prior to beginning data analysis, it is prudent for researchers to review the data to
identify errors and missing data and to check the assumptions of the statistical analysis
techniques used (Bandalos & Finney, 2019; Creswell, 2012; DeSimone et al., 2015; D.
George & Mallery, 2020; Morgan et al., 2013; Osborne, 2013; Van den Broeck et al.,
2005). Such reviews can minimize the impact of data problems on the study’s results,
enhance the rigor of the study, and increase confidence in the results of the research
(DeSimone et al., 2015; Osborne, 2013; Van den Broeck et al., 2005). This section
provides a summary of the study’s preanalysis data handling, consisting of a discussion
of the handling of data and checking of the assumptions of correlation and multiple
regression analysis.
Data Handling
Steps were taken to prepare the data for subsequent analysis. This process
included data entry, data screening and cleaning, handling of missing data, and data
transformation.
Data Entry
Once the survey closed, the data were downloaded from the online survey
platform to the researcher’s computer and saved as a password-protected data file. As
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identified by Halbgewachs (2018), one of the benefits of using an online survey platform
is the automated process to download and transfer survey data for subsequent data
analysis without the need for manual data entry.
Data Screening and Cleaning
The discussion of data screening and cleaning addresses four specific areas: (1)
data screening, (2) response rate, (3) data outliers, and (4) descriptive statistics of the data
set.
Data Screening. The first step in the review of the survey data was a visual
inspection to check for incomplete participant responses. The invitation to participate and
the two follow up emails (Appendix G) were sent to 268 employees, with 150 (55.97%)
responding by clicking on the survey link. Of the 150 participant responses, 39 records
were excluded from both the data set (i.e., the actual sample) and the accessible
population/selected sample due to either the participants explicitly not meeting the
inclusion criteria12 or the participants’ uncertain eligibility (American Association for
Public Opinion Research, 2016; Baruch & Holtom, 2008), resulting in 111 eligible
participant responses and an accessible population/selected sample of 229 employees.
The 39 excluded records included the following:
• Two participants did not provide consent and never entered the actual survey
questionnaire (eligibility indeterminant).
• Eight participants provided initial consent and entered the survey questionnaire
but did not answer any of the questions (eligibility indeterminant).
12 The study’s inclusion criteria required that participants were full-time and nonsupervisory employees.
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• Fifteen participants responded that they directly supervised other employees (not
eligible).
• One participant responded that he or she was a part-time employee (not eligible).
• Two participants responded that they were nonsupervisory employees but did not
respond as to whether or not they were full-time or part-time; due to the
anonymity of participants, it was not possible to verify whether or not these
respondents were full-time or part-time employees (eligibility indeterminant).
• Eleven participants did not respond as to whether or not they were supervisory or
nonsupervisory or if they were full-time or part-time employees (eligibility
indeterminant).
Of the 111 eligible participant responses, an additional two records were excluded
from the data set due to incomplete responses:
• One participant failed to answer 3 of the 12 employee engagement questions
(25.00% missing data) and 5 of the 8 perceived organizational support questions
(62.5% missing data); both exceeded the 15% missing data threshold identified by
George and Mallery (2020).13
• One participant failed to answer 2 of the 12 employee engagement questions
(16.67% missing data), which also exceeded the 15% missing data threshold
identified by George and Mallery (2020).13
13 George and Mallery (2020) noted that “an often-used rule of thumb suggests that it is acceptable to replace up to 15% of data by the mean of values for that variable (or equivalent procedures) with little damage to the resulting outcomes” (p. 63). George and Mallery (2020) further observed that “if a particular participant (or case) or a certain variable is missing more than 15% of the data, it is recommended that you drop that subject or variable from the analysis entirely” (p. 63).
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Response Rate. After screening the data as described above, 109 usable survey
responses were retained, resulting in a usable data record response rate14 of 47.6% of the
accessible population (N = 229) that the selected sample was drawn from.
While the importance of response rate to a study’s perceived quality and credibility
(e.g., with respect to the external validity of the findings) is well recognized (Anseel et al.,
2010; Couper, 2013; Draugalis et al., 2008; Evans & Mathur, 2018; Fan & Yan, 2010;
Rogelberg & Stanton, 2007; Stapleton, 2019), there is no established standard for a
minimum acceptable response rate (Baruch & Holtom, 2008; Draugalis et al., 2008;
Rogelberg & Stanton, 2007; Stapleton, 2019). To help contextualize the current study’s
47.6% response rate, meta-analyses of survey research (including email, phone, web, and
mail survey formats) in organizations conducted at the individual level have found
response rates between 52% (Werner et al., 2007) and 53% (Baruch & Holtom, 2008).
Similarly, meta-analyses of web-based survey research of individuals in organizations have
found response rates between 35% (Manfreda et al., 2008) and 48% (Archer, 2007, 2008).
Data Outliers. Outliers in a data set are simply those data values that differ from
(i.e., fall outside of) the overall distribution pattern of the rest of the data values (J. Cohen
et al., 2003; Hair et al., 2014; Hinkle et al., 2003; Keith, 2015; Kovach & Ke, 2016; Lomax
& Hahs-Vaughn, 2012). To identify outliers in the data set, the researcher used the Explore
procedure within the Descriptive Statistics analysis function of SPSS. Figure 3.6 shows the
boxplot analysis (D. George & Mallery, 2020; Hair et al., 2014; Hinkle et al., 2003; Lomax
& Hahs-Vaughn, 2012; Morgan et al., 2013) of the variables measuring the constructs of
14 Response rate was calculated as the number of usable participant responses (n = 109) divided by the number of eligible employees invited to participate in the study (N = 229) (American Association for Public Opinion Research, 2016; Draugalis et al., 2008; Fan & Yan, 2010).
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employee engagement, employee alignment, and perceived organizational support. Outlier
values are identified along with their corresponding case number (i.e., the “55” shown for
Q1 identifies that the data came from the data record for Participant 55). All outlier values
fell within the range of possible responses for the Likert scales used in the survey
questionnaire and were retained as valid data for subsequent analysis (J. Cohen et al., 2003;
Hair et al., 2014; Keith, 2015; Kovach & Ke, 2016; Lomax & Hahs-Vaughn, 2012).
Figure 3.6
Data Outliers
Descriptive Statistics of the Data Set. With the data set of usable survey
responses identified and a response rate calculated, the next step in the data review
process was to examine the distribution of the data to be analyzed (Bandalos & Finney,
2019; Creswell, 2012; D. George & Mallery, 2020; Morgan et al., 2013). SPSS was used
to compute minimum and maximum values, mean, standard deviation, skewness, and
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kurtosis for the 109 participant responses to the 28 questions associated with the scales
for employee engagement (questions 1–12), employee alignment (questions 13–20), and
perceived organizational support (questions 21 – 28) as well as the demographic
questions (D. George & Mallery, 2020; Morgan et al., 2013).
The descriptive statistics for the participant responses are presented in Table 3.5.
The values reported in the “n” column indicate the number of responses used in
computing the descriptive statistics; values less than 109 indicate missing data (as
discussed in the next section). Examining the values for skewness indicated that while the
data had an overall tendency for a negative skew—i.e., more of the data points were
toward the higher end of the distribution of the Likert scale (Lomax & Hahs-Vaughn,
2012)—the data fell within the range of ±2.0, providing acceptable evidence of a normal
distribution (D. George & Mallery, 2020; Lomax & Hahs-Vaughn, 2012; Osborne &
Waters, 2002). Lastly, the values for measuring kurtosis showed that six of the questions
(#1, 2, 3, 4, 7, and 19) had values that fell outside the ±2.0 range that would indicate a
relatively normal to a possible slightly nonnormal distribution of the data (D. George &
Mallery, 2020; Lomax & Hahs-Vaughn, 2012; Osborne & Waters, 2002).
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Table 3.5
Descriptive Statistics of Participant Data Set
Question n Min Max Mean Standard deviation
Skewness Kurtosis Statistic Std. Error Statistic Std. Error
Q1 109 1 5 4.31 .716 -1.467 .231 4.354 .459 Q2 109 1 5 4.35 .699 -1.431 .231 4.388 .459 Q3 109 1 5 4.50 .661 -1.973 .231 7.242 .459 Q4 109 1 5 4.42 .671 -1.679 .231 5.972 .459 Q5 108 1 5 4.13 .821 -.761 .233 .686 .461 Q6 108 1 5 4.09 .849 -.927 .233 1.030 .461 Q7 108 1 5 4.31 .742 -1.121 .233 2.321 .461 Q8 107 3 5 4.51 .620 -.902 .234 -.186 .463 Q9 109 3 5 4.56 .600 -1.020 .231 .056 .459 Q10 109 3 5 4.64 .536 -1.143 .231 .306 .459 Q11 109 3 5 4.53 .570 -.738 .231 -.454 .459 Q12 109 2 5 4.30 .739 -.692 .231 -.347 .459 Q13 109 2 5 4.41 .612 -.772 .231 .928 .459 Q14 109 2 5 4.22 .737 -.799 .231 .643 .459 Q15 108 2 5 3.87 .821 -.373 .233 -.318 .461 Q16 109 2 5 4.22 .658 -.465 .231 .185 .459 Q17 109 2 5 4.26 .725 -.881 .231 .935 .459 Q18 109 2 5 4.21 .708 -.646 .231 .374 .459 Q19 109 1 5 4.23 .765 -1.301 .231 3.004 .459 Q20 109 2 5 4.29 .685 -.804 .231 .888 .459 Q21 108 1 6 4.35 1.555 -.653 .233 -.614 .461 Q22 109 0 6 4.29 1.696 -.763 .231 -.404 .459 Q23 109 0 6 4.26 1.669 -.868 .231 -.037 .459 Q24 109 0 6 4.26 1.713 -.736 .231 -.422 .459 Q25 106 0 6 4.17 1.704 -.729 .235 -.286 .465 Q26 109 0 6 4.06 1.786 -.853 .231 -.141 .459 Q27 109 0 6 4.28 1.632 -.754 .231 -.196 .459 Q28 109 0 6 4.19 1.729 -.687 .231 -.531 .459 Age 95 25 74 44.72 11.316 .294 .247 -.519 .490 Gender 107 0 1 n/a n/a n/a n/a n/a n/a Tenure 94 1 30 7.73 7.316 1.154 .249 .275 .493 Valid n (listwise) = 83
Missing Data
An important issue when analyzing survey data is how to handle missing data
(Cox et al., 2014; Creswell, 2012; Kang, 2013; Kelley & Maxwell, 2019). In survey
research, missing data may result from participants inadvertently skipping questions or
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refusing to respond to a question they may find uncomfortable (J. Cohen et al., 2003; Cox
et al., 2014; Creswell, 2012). Missing data results in reduced statistical power (Kang,
2013) and can introduce bias in computing standard error that can threaten the quality of
the statistical analysis and effect the conclusions drawn from the data (Cox et al., 2014;
Enders & Gottschall, 2011; Kang, 2013; Lix & Keselman, 2019). For example, Cox et al.
(2014) noted that missing data will result in underestimation of standard errors,
“increasing the likelihood of making a Type-I error where one incorrectly finds that an
estimate is statistically significant” (p. 380). In addressing the missing data values, this
section discusses missing value analysis, replacing missing categorical data, and
replacing missing continuous data.
Missing Value Analysis. Prior to conducting the data analysis and testing the
study’s hypotheses, the data set created from the survey questionnaire responses was
analyzed using the SPSS Missing Values Analysis procedure (IBM, 2019). This
procedure helped identify the location of the missing values in the data set and the
extent of the missing data (IBM, 2019). A summary of the missing data analysis is
shown in Tables 3.6 and 3.7. Missing responses ranged from a low of 0.9% to a high
of 13.8%. Two of the demographic questions had the highest missing response rate:
age (12.8%) and tenure (13.8%). Of the 3,379 individual data elements—i.e., 109
participant responses of 31 questions each—in the data set, there were a total of 41
missing data values (i.e., the sum of the “Missing Count” column), for an overall
missing data rate of 1.21%. With respect to missing data, George and Mallery (2020)
observed that up to 15% of the missing data could be replaced without negatively
affecting the statistical findings. George and Mallery (2020) further commented that
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“if a particular participant (or case) or a certain variable is missing more than 15% of
the data, it is recommended that you drop that subject or variable from the analysis
entirely” (p. 63). As shown in Table 3.6, none of the data variables exceeded the 15%
threshold. Additionally, as shown in Table 3.7, none of the individual data records
(i.e., a data set of an individual participant’s responses) exceeded the 15% threshold.
Table 3.6
Missing Value Analysis: Summary by Data Variable
Data variable
n Missing count
Missing percent
Question
Q5 108 1 0.9% Working in the human resources department has a great deal of personal meaning to me.
Q6 108 1 0.9% I feel a strong sense of belonging to my job. Q7 108 1 0.9% I believe in the mission and purpose of the human
resources department. Q8 107 2 1.8% I care about the future of the human resources
department. Q15 108 1 0.9% I understand how the human resources department
will achieve its goals. Q21 108 1 0.9% The human resources department values my
contribution to its well-being. Q25 106 3 2.8% The human resources department notices when I do
a good job. Age 95 14 12.8% What is your current age (in whole years)?
Gender 107 2 1.8% What is your gender? Tenure 94 15 13.8% How long have you worked in the human resources
department (in whole years)? Valid n (listwise) = 83
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Table 3.7
Missing Value Analysis: Summary by Data Record
Data record Count Percent 3 1 3.3% 9 2 6.7%
11 1 3.3% 15 1 3.3% 28 1 3.3% 29 2 6.7% 32 2 6.7% 33 3 9.7% 37 1 3.3% 38 1 3.3% 43 2 6.7% 46 1 3.3% 50 1 3.3% 52 2 6.7% 58 3 9.7% 61 2 6.7% 63 2 6.7% 64 1 3.3% 68 2 6.7% 69 2 6.7% 74 2 6.7% 78 1 3.3% 98 1 3.3% 99 1 3.3% 102 1 3.3% 109 2 6.7%
Replacing Missing Categorical Data. As depicted in Table 3.6, the categorical
variable of gender had two instances of missing data. In responding to the survey
questionnaire, participants identified as either male or female, with responses dummy-
coded as 0 = male, 1 = female. To address the two missing data values, a third category
was added, with a dummy code of 2 signifying that the participant did not answer this
question. For categorical data, it is an acceptable practice to create an additional category,
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for example “not provided,” and substitute this value for the missing data (Creswell,
2012; D. George & Mallery, 2020; Robson & McCartan, 2016).
Replacing Missing Continuous Data. Methods for handling missing data of
continuous variables rely on assumptions concerning the mechanism, or the pattern, that
underlies the missingness of the data values—i.e., the pattern of the relationship between
the missing continuous data and the variables being analyzed in the analysis model
(Enders & Gottschall, 2011; Keith, 2015; Widaman, 2006). The three general categories
for the mechanisms of missing data are missing completely at random (MCAR), missing
at random (MAR), and missing not at random (MNAR) (Cox et al., 2014; De Ayala,
2019; Graham, 2009; Kang, 2013; Keith, 2015; Kelley & Maxwell, 2019; Lix &
Keselman, 2019). Data are considered to be MCAR when the reason for the missing data
is independent of (i.e., unrelated to) the values of the variable with the missing data and
also the other variables in the analysis model (Cox et al., 2014; De Ayala, 2019; Enders
& Gottschall, 2011; Graham, 2009; Keith, 2015; Kelley & Maxwell, 2019; Lix &
Keselman, 2019). Keith (2015) commented that MCAR was the “ideal missing data
scenario” (p. 526), with Graham (2009) further noting that “the good thing about MCAR
is that analyses yield unbiased parameter estimates (i.e., estimates that are close to
population values)” (p. 553). Data are characterized as MAR when the reason for the
missing data is dependent on (i.e., related to) the observed values of the other variables in
the analysis model but not to the values of the variable with the missing data itself (Cox
et al., 2014; Enders & Gottschall, 2011; Graham, 2009; Kelley & Maxwell, 2019; Lix &
Keselman, 2019). Lastly, MNAR data occur when the reason for the missing data is
dependent on (i.e., related to) either an outside variable not included in the analysis
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model or on the values of the variable with the missing data itself (Cox et al., 2014;
Enders & Gottschall, 2011; Keith, 2015; Kelley & Maxwell, 2019).
Little’s MCAR test (R. J. A. Little, 1988) was used to examine the mechanism
underlying the pattern of the missing data values in the sample data set. The null
hypothesis in Little’s MCAR test (R. J. A. Little, 1988) is that the data are missing
completely at random. The null hypothesis was tested using the SPSS Missing Values
Analysis procedure (IBM, 2019), which showed that Little’s MCAR test was not
significant (i.e., the null hypothesis could not be rejected), c2(341, N = 83) = 383.62, p =
.055, indicating that the missing data was MCAR.
Common approaches, or strategies, used to handle missing continuous data
include deletion, substitution, and imputation (Creswell, 2012; IBM, 2019; Widaman,
2006). When following a deletion strategy, data records with missing values are excluded
from analysis with either a listwise or pairwise approach (Creswell, 2012; Enders &
Gottschall, 2011; D. George & Mallery, 2020; Keith, 2015). With listwise deletion, all
data from a specific data record (i.e., the set of an individual participant’s responses) are
deleted and not used in any subsequent analysis if the record contains any missing values
(Enders & Gottschall, 2011; D. George & Mallery, 2020; Keith, 2015). With pairwise
deletion, the complete data record is not deleted; however, if a data value is missing for a
required calculation, the data record is excluded from that calculation only but is used for
other analysis involving the nonmissing data values (Enders & Gottschall, 2011; D.
George & Mallery, 2020; Keith, 2015). Although a common approach to handling
missing data, a deletion strategy is not usually recommended (Kelley & Maxwell, 2019;
Widaman, 2006). Deletion reduces the amount of data used for analysis (i.e., the sample
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size is reduced), which can affect standard error estimates and potentially weaken the
analysis and conclusions based on the results (Creswell, 2012; D. George & Mallery,
2020; Kelley & Maxwell, 2019).
With a substitution approach, missing data are replaced with a specific value
based on the nonmissing values for the variable; all missing data for a given variable are
replaced with the same substituted value (Cox et al., 2014; Creswell, 2012; Enders &
Gottschall, 2011; D. George & Mallery, 2020; Kang, 2013; Widaman, 2006). For
example, mean substitution is where all missing data for a particular variable are replaced
with the mean of all the nonmissing data for that variable (Cox et al., 2014; Enders &
Gottschall, 2011; D. George & Mallery, 2020; Kang, 2013; Widaman, 2006).
Substitution strategies can reduce the variability of the data (D. George & Mallery, 2020;
Lix & Keselman, 2019), introducing bias into subsequent analysis (Cox et al., 2014;
Enders & Gottschall, 2011; Kang, 2013), which can lead to an artificial decrease in the
standard deviation (Adelson et al., 2019), underestimation of standard error (Cox et al.,
2014; Kang, 2013), and overestimation of R squared (Cox et al., 2014). Although an
often used approach to dealing with missing data, substitution strategies, such as mean
substitution, are generally not recommended for replacing missing data values (Adelson
et al., 2019; Cox et al., 2014; Enders & Gottschall, 2011; Graham, 2009; Kang, 2013;
Kelley & Maxwell, 2019; Widaman, 2006).
With data imputation, the missing data point is replaced with a representative
estimate that is computed using the nonmissing values available in the data set for the
variable with missing values (Kang, 2013; Kelley & Maxwell, 2019; Widaman, 2006).
Unlike mean substitution where all missing data for a given variable are replaced with the
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same substituted value, with imputation a predicted value is estimated for each individual
missing data point (Bandalos & Finney, 2019; Cox et al., 2014; Enders & Gottschall,
2011; Graham, 2009; Kang, 2013; Kelley & Maxwell, 2019; Widaman, 2006). Kelley
and Maxwell (2019) observed that “at first, the idea of estimating data might seem
problematic, but it is often better to estimate what is usually a small amount of data than
to disregard valuable data with deletion [strategies]” (p. 322).
There are three primary methods for data imputation: regression (Cox et al., 2014;
Enders & Gottschall, 2011; Kang, 2013; Widaman, 2006), maximum likelihood
estimation (Bandalos & Finney, 2019; Cox et al., 2014; Enders & Gottschall, 2011;
Graham, 2009; Kang, 2013; Keith, 2015; Kelley & Maxwell, 2019), and multiple
imputation (Adelson et al., 2019; Bandalos & Finney, 2019; Lix & Keselman, 2019).
Regression imputation uses data from the other variables in the data set to
estimate a regression equation to compute a predicted value for each missing data point
(Cox et al., 2014; Enders & Gottschall, 2011; Kang, 2013; Widaman, 2006). However,
while an improvement over deletion and mean substitution strategies, regression
imputation is not usually recommended as an acceptable approach to handling missing
data (Cox et al., 2014; Graham, 2009), with preference given instead to the more
acceptable techniques of maximum likelihood estimation and multiple imputation
(Bandalos & Finney, 2019; Enders & Gottschall, 2011; Graham, 2009; Kang, 2013;
Keith, 2015; Kelley & Maxwell, 2019). As Enders and Gottschall (2011) commented,
“Maximum likelihood and multiple imputation are desirable because they yield unbiased
estimates under either an MCAR or MAR mechanism” (p. 361) and “because they
maximize statistical power” (p. 361).
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Maximum likelihood estimation techniques, such as expectation-maximization,
use an iterative estimation algorithm to predict the most probable (i.e., the most likely)
values for the missing data (Bandalos & Finney, 2019; Cox et al., 2014; Enders &
Gottschall, 2011; Graham, 2009; Kang, 2013; Keith, 2015). The expectation-
maximization algorithm—the maximum likelihood estimation technique available in
SPSS (IBM, 2019)—involves a two-step process consisting of an estimation step and a
maximization step (Cox et al., 2014; Enders & Gottschall, 2011; Kang, 2013). The
algorithm continues in a sequential and iterative process of the estimation and
maximization steps until the process converges on a solution set of values that best fits
the nonmissing data (Cox et al., 2014; Enders & Gottschall, 2011; Kang, 2013). While
estimates for missing values generated from the expectation-maximization estimation
technique generally result in unbiased correlation and regression coefficients (Cox et al.,
2014), researchers must recognize the potential for an underestimation of standard error
in estimates computed using expectation-maximization produced values and an
associated possibility of making Type I errors (Cox et al., 2014; Graham, 2009; Kang,
2013; Lix & Keselman, 2019).
Multiple imputation uses a regression-based approach to predict estimates of the
missing values (Adelson et al., 2019; Bandalos & Finney, 2019; IBM, 2019). The
multiple imputation approach computes multiple versions of the complete data set (i.e.,
multiple estimates of the data set are produced with different estimates for the missing
values) (Adelson et al., 2019; IBM, 2019). In subsequent statistical analysis, the multiple
data sets are pooled to provide a best fit estimate of the analysis results (IBM, 2019).
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Multiple imputation is often considered the preferred approach for addressing the issue of
missing data values (Adelson et al., 2019; IBM, 2019; Lix & Keselman, 2019).
In the current study, a comparative analysis was conducted using each of the three
missing value imputation techniques (i.e., regression, expectation-maximization
estimation, and multiple imputation) to provide estimates for the missing values for the
following data variables: Q5, Q6, Q7, Q8, Q15, Q21, Q25, Age, and Tenure. The
descriptive statistics (i.e., n, minimum and maximum values, mean, and standard
deviation) of the results are shown in Appendix I. Both regression imputation (Table I.1)
and multiple imputation (Table I.3) computed invalid (i.e., negative) replacement values
for the data variable tenure. As a result of the comparative analysis of the descriptive
statistics, the decision was made to use expectation-maximization imputation (Table I.2)
to compute the estimated values for the missing data in the data set.
Data Transformation
Total scores were calculated for the two explanatory variables—employee
alignment and perceived organizational support—by taking the sum of the individual
responses for each measurement. For employee engagement, scores for each of the three
subscales were computed for the four items comprising each subscale (i.e., cognitive
engagement, emotional engagement, and behavioral engagement), with an overall
employee engagement score computed as the sum of the three engagement subscales.
To test for moderation, a cross-product, or interaction, variable was created by
multiplying employee alignment (explanatory variable) by perceived organizational
support (the hypothesized moderator variable) (Aiken & West, 1991; J. Cohen et al.,
2003; Hayes, 2018; Keith, 2015, 2019; Kelley & Maxwell, 2019; Lomax & Hahs-
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Vaughn, 2012). Prior to computing the interaction variable, the variables employee
alignment and perceived organizational support were centered by subtracting the
computed mean of the variable from each observed value of the variable (Aiken & West,
1991; J. Cohen et al., 2003; Hayes, 2018; Keith, 2015, 2019; Kelley & Maxwell, 2019).
Once centered, the interaction variable was calculated by multiplying EA_Centered by
POS_Centered. The purpose of centering was to increase interpretability of the regression
coefficients in the moderated multiple regression model by creating a meaningful zero-
point within the range of the possible values for the employee alignment and perceived
organizational support variables (J. Cohen et al., 2003; Dalal & Zickar, 2012; Echambadi
& Hess, 2007; Hayes, 2018; Keith, 2015; Kelley & Maxwell, 2019; McClelland et al.,
2017).
Checking Assumptions
Statistical analysis techniques are tools that help researchers understand data and
the phenomena the data represent, and these tools are based on assumptions about the
data used in the analysis (L. Cohen et al., 2011; Hayes, 2018; Morgan et al., 2013;
Osborne & Waters, 2002). In order to have confidence in the results and interpretation
(i.e., making inferences to a larger population) of the correlation and regression analyses
used in this study, the assumptions underlying the analysis techniques must be examined
(L. Cohen et al., 2011; Hayes, 2018; Keith, 2015; Lomax & Hahs-Vaughn, 2012; Morgan
et al., 2013; Osborne & Waters, 2002). Violations of the underlying assumptions can
result in the estimates obtained from the statistical analysis—for example, correlations,
regression coefficients, R2, standard errors, statistical significance—being biased and not
an accurate reflection of the true population values, which can result in the researcher
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drawing inaccurate conclusions about the findings (J. Cohen et al., 2003; Hayes, 2018;
Keith, 2015; Morgan et al., 2013; Osborne & Waters, 2002).
Prior to testing the hypothesized model of employee engagement, the
underlying assumptions of bivariate (i.e., Pearson) correlation and multiple regression
analysis techniques were examined. Throughout the following discussion of the
assumptions underlying the statistical analyses, references to the “variables” and
“variables of interest” signify the variables of employee alignment, perceived
organizational support, and employee engagement. For the bivariate correlation
analyses, three assumptions were examined: (1) variables were continuous, (2)
variables were bivariately normally distributed, and (3) there is a linear relationship
between variables (Adelson et al., 2019; D. George & Mallery, 2020; Green & Salkind,
2011; Hinkle et al., 2003; Lomax & Hahs-Vaughn, 2012). For the multiple regression
analyses, five assumptions were examined: (1) a linear relationship between variables,
(2) normal distribution of residuals, (3) homoscedasticity, (4) independence of
residuals and (5) noncollinearity (J. Cohen et al., 2003; L. Cohen et al., 2011; Hayes,
2018; Keith, 2015; Lomax & Hahs-Vaughn, 2012; Morgan et al., 2013). Each of these
assumptions is discussed in turn.
Continuous Variables
One of the assumptions of bivariate correlations is that the variables are
continuous, that is, measured on an interval or ratio scale (Adelson et al., 2019). As
discussed earlier in this chapter under Instrumentation, each variable of interest was
computed as the sum of participant responses to the individual items (i.e., questions) of
the Stringer Strategic Alignment Scale (Stringer, 2007), SPOS (Eisenberger et al.,
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1986), and EES (Shuck, Adelson, et al., 2017). As variables computed as an aggregate
or composite score—that is, the individual items (i.e., questions) are summed—the
variables can be appropriately characterized as interval data and thus continuous
variables (I. E. Allen & Seaman, 2007; Boone & Boone, 2012; Burns & Grove, 2009;
Carifio & Perla, 2007, 2008; Joshi et al., 2015; Norman, 2010). As such, the
assumption that the variables are continuous was satisfied.
Bivariate Normal Distribution of Variables
The assumption of a normal distribution of variables assumes that the independent
(or explanatory) and dependent (or outcome) variables are normally distributed (Adelson
et al., 2019; D. George & Mallery, 2020; Green & Salkind, 2011; Hinkle et al., 2003;
Morgan et al., 2013). A common method of testing variables for the normality of their
distribution is to examine skewness and kurtosis, where values ≤ ±2.0 are considered as
acceptable evidence of normality (D. George & Mallery, 2020; Lomax & Hahs-Vaughn,
2012; Osborne & Waters, 2002). Additionally, the Shapiro-Wilk test provides a means to
test the extent that the distribution of the sample variables differs statistically from a
normal distribution (Lomax & Hahs-Vaughn, 2012). The skewness and kurtosis statistics
and the Shapiro-Wilk test were used to examine the assumption of a normal distribution
of the two explanatory variables and the outcome variable.
As shown in Table 3.8, the skewness and kurtosis values for all three variables
were < ±2.0, suggesting evidence of normality (D. George & Mallery, 2020; Lomax &
Hahs-Vaughn, 2012; Osborne & Waters, 2002). Interestingly, the results of the Shapiro-
Wilk test provided contradictory evidence, suggesting a nonnormal distribution of the
variables: employee engagement (W(109) = .940, p < .001), employee alignment
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(W(109) = .933, p < .001), and perceived organizational support (W(109) = .913, p <
.001). However, while the Shapiro-Wilk test indicated a possible nonnormal distribution
of the variables, the central limit theorem provides that for a sample greater than 30, it
can be assumed that the underlying distribution is normal and the data can be treated as
normally distributed (Hinkle et al., 2003; Lomax & Hahs-Vaughn, 2012). As such, there
was acceptable evidence that the assumption of a normal distribution of the variables was
satisfied.
Table 3.8
Normality Statistics for Explanatory and Outcome Variables
Variable n
Skewness Kurtosis Shapiro-Wilk
Statistic Std.
Error Statistic Std.
Error Statistic df Sig. Employee engagement 109 –.219 .231 –.912 .459 .940 109 <.001 Employee alignment 109 –.280 .231 –.111 .459 .933 109 <.001 Perceived organizational support
109 –.672 .231 –.456 .459 .913 109 <.001
Valid n (listwise) = 109 Linear Relationship Between Variables (Linearity)
The assumption of linearity posits that the relationship of the variables is linear
(Adelson et al., 2019; L. Cohen et al., 2011; Hayes, 2018; Keith, 2015; Lomax & Hahs-
Vaughn, 2012; Morgan et al., 2013; Osborne & Waters, 2002). With respect to the
bivariate correlations, the relationship is assumed to be linear between each pair of
variables, where the Pearson correlation is the measure of the linear relation between the
two variables—i.e., between the two explanatory variables and between each of the
explanatory variables and the outcome variable (Hinkle et al., 2003; Lomax & Hahs-
Vaughn, 2012). For bivariate correlations, violations of the assumption of linearity will
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result in underestimating the strength of the relationship between two variables (Hinkle et
al., 2003; Lomax & Hahs-Vaughn, 2012).
Keith (2015) observed that linearity was “the most important” assumption in
conducting multiple regression analyses (p. 188). For multiple regression, the
assumption of linearity is focused on the relationship between each of the explanatory
(independent) variables and the outcome (dependent) variable (L. Cohen et al., 2011;
Hayes, 2018; Keith, 2015; Lomax & Hahs-Vaughn, 2012; Morgan et al., 2013;
Osborne & Waters, 2002). When conducting multiple regression analyses, violations of
the linearity assumption may bias the estimates obtained from the regression—i.e.,
coefficient of determination, regression coefficients, standard errors, and statistical
significance (J. Cohen et al., 2003; Hayes, 2018; Keith, 2015; Lomax & Hahs-Vaughn,
2012).
The assumption of linearity was tested by visually examining the scatterplots of
the relationship of each of the explanatory (independent) variables and the outcome
(dependent) variable (L. Cohen et al., 2011; Keith, 2015; Lomax & Hahs-Vaughn,
2012). As shown in Figure 3.7, there appeared to be a general positive linear relation
between each of the explanatory variables (i.e., employee alignment and perceived
organizational support) and the outcome variable employee engagement, supporting the
assumption of linearity among the variables.
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Figure 3.7
Testing the Linear Relationship Between Variables
Normal Distribution of Residuals
The normality of the residuals assumption is that the residuals (i.e., the errors in
predicting values for the outcome variable, computed as the difference between the
observed and predicted value) are normally distributed (J. Cohen et al., 2003; Hayes,
2018; Keith, 2015; Lomax & Hahs-Vaughn, 2012; Morgan et al., 2013). While a
violation of the normality of the residuals assumption does not bias estimates of the
regression coefficients (J. Cohen et al., 2003; Keith, 2015), violations will bias
standard errors and thus statistical significance tests (Keith, 2015). However, while it is
prudent to test for violations of the normality of the residuals assumption, Keith (2015)
observed that regression analyses are “fairly robust to their violation” (p. 188) and that
a violation of this assumption “is only serious with small samples” (p. 188).
The assumption of the normality of the residuals was tested by a visual inspection
of the quantile-quantile plot (Figure 3.8) of the values of the observed versus predicted
(or expected) value of the unstandardized residuals (J. Cohen et al., 2003; Keith, 2015;
Kelley & Maxwell, 2019; Lomax & Hahs-Vaughn, 2012). A review of Figure 3.8 shows
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that most of the data points fell on or near the diagonal line, providing evidence of
normality (Keith, 2015; Lomax & Hahs-Vaughn, 2012).
Figure 3.8
Normal Quantile-Quantile Plot of the Unstandardized Residual
In addition to a review of the quantile-quantile plot, the skewness and kurtosis
statistics and the Shapiro-Wilk test were used to examine the assumption of the normality
of the residuals (Lomax & Hahs-Vaughn, 2012) (Table 3.9). The skewness and kurtosis
values for the unstandardized residuals were both less than ±2.0—skewness = –.377 (SE
= .231) and kurtosis = .327 (SE = .459)—suggesting evidence of normality (D. George &
Mallery, 2020; Lomax & Hahs-Vaughn, 2012; Osborne & Waters, 2002). As was found
when testing the assumption of a normal distribution of variables, the results of the
Shapiro-Wilk test provided contradictory evidence, suggesting a nonnormal distribution
of the unstandardized residuals with W(109) = .976, p = .046. Notwithstanding the results
of the Shapiro-Wilk test, given the quantile-quantile plot, the skewness and kurtosis
statistics, and the previously discussed implications of the central limit theorem, there
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appeared to be reasonable evidence that the assumption of a normal distribution of the
residuals was satisfied.
Table 3.9
Normality Statistics for the Unstandardized Residual
Variable n Skewness Kurtosis Shapiro-Wilk
Statistic Std. Error Statistic Std. Error Statistic df Sig. Unstandardized residual
109 –.377 .231 .327 .459 .976 109 .046
Valid n (listwise) = 109 Homoscedasticity
The assumption of homoscedasticity is that the variance of the residuals (i.e., the
errors in predicting values for the outcome variable, computed as the difference between
the observed and predicted value) around the regression line is constant (J. Cohen et al.,
2003; Hayes, 2018; Keith, 2015; Morgan et al., 2013; Osborne & Waters, 2002). While a
violation of the assumption of homoscedasticity does not bias estimates of the regression
coefficients (J. Cohen et al., 2003; Keith, 2015; Lomax & Hahs-Vaughn, 2012),
violations may bias estimates of the standard errors, overestimating them, and thus affect
the accuracy of statistical significance tests (J. Cohen et al., 2003; Keith, 2015; Lomax &
Hahs-Vaughn, 2012) and confidence intervals (J. Cohen et al., 2003).
The assumption of homoscedasticity was tested by a visual inspection of the
scatterplot of the unstandardized residuals versus each of the two explanatory
(independent) variables (employee alignment and perceived organizational support) (J.
Cohen et al., 2003; Keith, 2015; Lomax & Hahs-Vaughn, 2012). The unstandardized
residual scatterplot in Figure 3.9 showed a generally consistent variance of the residuals
around the regression line, indicating that the assumption of homoscedasticity held.
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Figure 3.9
Testing the Assumption of Homoscedasticity
Independence of Residuals
The assumption of the independence of residuals is that the errors in estimation
(i.e., residuals) for each data observation are random and independent (J. Cohen et al.,
2003; Hayes, 2018; Keith, 2015; Lomax & Hahs-Vaughn, 2012). While a violation of
this assumption does not bias estimates of the regression coefficients (J. Cohen et al.,
2003; Keith, 2015), violations will affect the estimation of standard errors and thus bias
the accuracy of statistical significance tests (J. Cohen et al., 2003; Hayes, 2018; Keith,
2015; Lomax & Hahs-Vaughn, 2012).
The assumption of the independence of residuals was tested by a visual inspection
of the scatterplot of studentized residuals versus unstandardized predicted values of the
outcome variable (Lomax & Hahs-Vaughn, 2012). Figure 3.10 shows a generally random
distribution of the plotted data points, with most falling within the band of ±2.0,
indicating independence of the residuals (Keith, 2015; Lomax & Hahs-Vaughn, 2012).
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Figure 3.10
Scatterplot of the Studentized Residuals vs. Unstandardized Predicted Values of the
Outcome Variable
In addition to the scatterplot (Figure 3.10), the Durbin-Watson test was also used
to test the independence of residuals, where values ≥ 1.0 and ≤ 3.0 indicate evidence of
the independence of residuals (Lomax & Hahs-Vaughn, 2012). The computed Durbin-
Watson value was 2.2 (see the simultaneous multiple regression output in Appendix Q),
providing additional evidence in support of the assumption of the independence of
residuals.
Noncollinearity
The assumption of noncollinearity is the absence of high intercorrelations among
two (collinearity) or more (multicollinearity) independent (i.e., explanatory) variables (J.
Cohen et al., 2003; Keith, 2015; Lomax & Hahs-Vaughn, 2012; Morgan et al., 2013).
Collinearity/multicollinearity occurs when explanatory variables are highly correlated
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with each other in the regression equation (J. Cohen et al., 2003; Keith, 2015). Highly
correlated variables have a very strong linear relationship (Lomax & Hahs-Vaughn,
2012) and indicate that the variables are measuring similar, possibly overlapping,
phenomena (Morgan et al., 2013). Violations of the assumption of noncollinearity can
increase the standard errors of the regression coefficients, result in instability of the
regression coefficients across different samples, and lead to a situation where there is a
statistically significant R2 without any of the explanatory variables being significant
(Lomax & Hahs-Vaughn, 2012). As a result, violations of the assumption of
noncollinearity can restrict the generalizability of the research model (i.e., external
validity) (Lomax & Hahs-Vaughn, 2012).
The two common statistics to examine in testing the assumption of
noncollinearity are tolerance and the variance inflation factor (J. Cohen et al., 2003;
Keith, 2015; Lomax & Hahs-Vaughn, 2012). Tolerance provides a measure of the
independence, or overlap, of an explanatory variable with other explanatory variables (J.
Cohen et al., 2003; Keith, 2015); tolerance values can range from 0 (no independence,
overlap exists) to 1 (independence, no overlap exists) (Keith, 2015). The rule of thumb
for tolerance is that a value greater than .10 indicates noncollinearity (J. Cohen et al.,
2003; Keith, 2015; Lomax & Hahs-Vaughn, 2012). The variance inflation factor is the
reciprocal of tolerance (J. Cohen et al., 2003; Keith, 2015), with a rule of thumb where a
value less than 10 indicates noncollinearity (J. Cohen et al., 2003; Keith, 2015; Lomax &
Hahs-Vaughn, 2012). Both tolerance and the variance inflation factor were examined to
test the assumption of noncollinearity.
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The collinearity statistics for the explanatory variables are shown in Table 3.10.
Both employee alignment and perceived organizational support had a tolerance value
greater than .10 and a variance inflation factor value less than 10. Based on the results of
the tolerance and variance inflation factor statistic, multicollinearity was not observed
and the assumption of noncollinearity among the two explanatory variables was satisfied.
Table 3.10
Collinearity Statistics for Explanatory Variables
Variable Collinearity statistics
Tolerance Variance inflation factor Employee alignment .624 1.602 Perceived organizational support .624 1.602
Data Analysis
Statistical analysis of the data utilized both bivariate correlation and multiple
regression techniques and was conducted using IBM SPSS Statistics (Version 26.0.0.1
for Mac). Descriptive statistics are reported on the collected data in chapter 4, including
mean and standard deviations for each variable (employee alignment, perceived
organizational support, and employee engagement) and relevant demographics of the
sample—age, gender, and number of years employed by the organization.
In addition to descriptive statistics, inferential analyses were conducted. This
study used the Pearson15 product moment correlation coefficient (r), which describes the
degree (Keith, 2015) or extent (Hinkle et al., 2003) of the relation between two variables
(J. Cohen et al., 2003; Hinkle et al., 2003; Keith, 2015; Lomax & Hahs-Vaughn, 2012).
15 The Pearson product moment correlation coefficient is regarded as the “standard measure of the linear relationship between two variables” (J. Cohen et al., 2003, p. 28).
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Pearson correlation coefficients have the following two key characteristics: (a) values can
range from –1 to +1, where the absolute value indicates the degree of the relation and (b)
the sign of the coefficient indicates the direction of the relation between variables (J.
Cohen et al., 2003; Hinkle et al., 2003; Keith, 2015; Lomax & Hahs-Vaughn, 2012). As
Hinkle et al. (2003) observed, “The sign of the coefficient indicates the direction of the
relationship; the absolute value of the coefficient indicates the magnitude of the
relationship” (p. 99).
Multiple regression was used to evaluate the hypothesized relations among the
variables and to determine the extent to which employee alignment and perceived
organizational support affect employee engagement. This approach was selected due to
its focus on the explanation of the contribution, and significance of the contribution, a
variable has on an outcome (Keith, 2015). Specifically, multiple regression analyses were
performed to determine how much variation in employee engagement could be explained
by employee alignment and perceived organizational support and to better understand the
unique contribution of each of these two explanatory variables. Multiple regression was
also used to test for the statistical significance of any interaction (i.e., moderation) effect
among variables (Hayes, 2018; Keith, 2015) and whether or not perceived organizational
support moderated the relation between employee alignment and employee engagement.
Lastly, multiple regression was also used to test for the statistical significance of an
indirect effect (Hayes, 2018; Keith, 2015) of employee alignment on employee
engagement and whether or not perceived organizational support mediated the relation
between employee alignment and employee engagement.
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A significance level of .05 (a = .05) was used in all hypothesis tests to determine
statistical significance (J. Cohen, 1988; J. Cohen et al., 2003; D. George & Mallery,
2020; Hinkle et al., 2003). Additionally, Cohen's (1988) benchmarks were used for
characterizing the magnitude of correlations and correlation effect size.
In answering the two research questions, seven hypotheses were tested. To test
Hypotheses 1a, 2, and 3a, Pearson product moment correlation coefficients were
computed to test the relation among employee alignment, perceived organizational
support, and employee engagement (J. Cohen et al., 2003; Hinkle et al., 2003; Keith,
2015; Lomax & Hahs-Vaughn, 2012). To test Hypotheses 1b, 3b, 4, and 5, multiple
regression analyses were performed to test the statistical significance and extent to which
employee alignment and perceived organizational support explain unique variance in
employee engagement, as well as to test the extent to which perceived organizational
support moderates and/or mediates the relation between employee alignment and
employee engagement. Table 3.11 shows the alignment of the hypotheses to the research
questions and summarizes the statistical analysis used to test the hypotheses.
Table 3.11
Alignment of Research Question, Hypotheses, Variables, Data, and Statistical Analysis
Research question Hypothesis Variablesa
Type of data Statistical analysis
RQ1 H1a EA and EE Continuous Pearson correlation coefficient H2 EA and POS Continuous Pearson correlation coefficient H3a POS and EE Continuous Pearson correlation coefficient H4 EA, POS, and EE Continuous Multiple regression H5 EA, POS, and EE Continuous Multiple regression
RQ2 H1b EA and EE Continuous Multiple regression H3b POS and EE Continuous Multiple regression
a EA, employee alignment; EE, employee engagement; POS, perceived organizational support.
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Threats to Validity
The concept of the validity of a research study concerns the accuracy, credibility,
and trustworthiness of the results and addresses how the study’s finding and conclusions
might be wrong (L. Cohen et al., 2011; Creswell, 2014; Maxwell, 2013; Robson &
McCartan, 2016). Threats to a study’s validity are categorized as either internal or
external (L. Cohen et al., 2011; Creswell, 2014; Robson & McCartan, 2016).
Internal Validity Threats
Internal threats to validity are related to the degree to which the researcher’s
findings and conclusions can be supported by the data (L. Cohen et al., 2011; Creswell,
2014; Robson & McCartan, 2016). As Creswell (2014) observed, internal validity threats
“threaten the researcher’s ability to draw correct inferences from the data about the
population” in a study (p. 174). In the current study, two threats to internal validity were
identified: (a) instrumentation and (b) Type I and Type II errors (L. Cohen et al., 2011).
Instrumentation Validity Threats
An instrumentation validity threat concerns the effect that unreliable instruments
can have in introducing error into a study (L. Cohen et al., 2011). Green and Salkind
(2011) observed that a “measure is reliable if it yields consistent scores across
administration” (p. 325). In other words, it is the degree to which an “instrument
consistently measures something” (Roberts & Hyatt, 2019, p. 149). To reestablish the
reliability of the survey instruments in the context of this study (Creswell, 2014), a
Cronbach’s alpha was computed for the sample data for the variables measuring
employee alignment, perceived organizational support, and employee engagement (L.
Cohen et al., 2011; D. George & Mallery, 2020; Morgan et al., 2013). The computed
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Cronbach’s alphas were .909 for employee alignment, .974 for perceived organizational
support, and .879 for employee engagement. These results are similar to those obtained
from the pilot study (Table 3.3) and are above the guidelines for interpreting Cronbach’s
alpha coefficients identified by Cohen et al. (2011) for an instrument being highly
reliable (i.e., .80 to .90) (p. 640). Chapter 4 further discusses internal reliability and
shows results of factor analysis conducted to reestablish the convergent and discriminant
validity (i.e., construct validity) of the three survey instruments in the context of the
current study (Creswell, 2014).
Type I and Type II Error Validity Threats
As discussed previously under the Sample Size and Power Analysis Section, a
Type I error occurs when a null hypothesis is incorrectly rejected when it is actually true
(Hinkle et al., 2003; Lomax & Hahs-Vaughn, 2012). The probability of a Type I error
occurring is represented by the level of significance (a) (Lomax & Hahs-Vaughn, 2012).
A researcher can reduce the occurrence of Type I errors by using a more rigorous level of
significance, for example a = .01 instead of a = .05 (L. Cohen et al., 2011). A Type II
error occurs when a null hypothesis is not rejected when it is actually false (Hinkle et al.,
2003; Lomax & Hahs-Vaughn, 2012). The probability of a Type II error (b) is inversely
related to the concept of statistical power (1 – b), in that as a researcher decreases the
probability of a Type II error, the statistical power increases. In addressing Type I and
Type II errors, a researcher is attempting to find an acceptable balance among level of
significance, statistical power, and sample size (J. Cohen et al., 2003; Hinkle et al., 2003;
Keith, 2015). This study used the standard behavioral sciences conventions of a = .05 (J.
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Cohen, 1988; J. Cohen et al., 2003; D. George & Mallery, 2020; Hinkle et al., 2003) and
a value for statistical power (1 – b) of .80 (J. Cohen, 1988).
External Validity Threats
External validity relates to the degree to which the researcher’s findings and
conclusions can be generalized to the larger population or different settings (i.e., a
different population than the one from which the study sample was drawn) (L. Cohen et
al., 2011; Creswell, 2014; Dannels, 2019; Fraenkel et al., 2015; Fritz & Morgan, 2010;
Landers & Behrend, 2015; Robson & McCartan, 2016; Zhu et al., 2015). Threats to
external validity can occur when a researcher makes erroneous generalizations of a
study’s findings beyond the sample and population studied (L. Cohen et al., 2011;
Creswell, 2012, 2014; Fraenkel et al., 2015). Threats to external validity were addressed
by recognizing that the specific results may not generalize beyond the current study.
Human Participants and Ethics Precautions
Prior to beginning any data collection for this study, the research proposal was
reviewed and approved by the George Washington University Office of Human Research
Institutional Review Board (Appendix F). This review ensured that the necessary
precautions had been identified to ensure that all participants were treated in accordance
with relevant policies. Although this study posed a minimal risk to participants (Office of
Human Research, n.d.-b), this section addresses potential risks and precautions taken to
mitigate them.
Potential risks to participants were identified in relation to disclosure, consent,
and anonymity (L. Cohen et al., 2011; Creswell, 2014). To address issues of disclosure, a
research study information overview sheet (Appendix J) was provided to all those invited
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to participate in the study. The study information sheet addressed (a) the purpose of the
study, (b) what participation involved, (c) risks of participating in the study, (d) benefits
of participating in the study, and (e) how participant confidentiality was protected.
With respect to consent, it was stressed in all communications with potential
participants that participation in the study was completely voluntary and that individuals
could discontinue their participation at any time. Additionally, the informed consent
document (Appendix H) was provided to all potential participants, attached to the initial
email introducing the study (Appendix G). Lastly, an informed consent acknowledgment
was built into the online survey questionnaire (Appendix E).
To ensure participant anonymity, all participant responses were kept strictly
confidential and used only for the purposes of this research. Specific precautions were
implemented to protect participant anonymity:
• All survey responses were gathered anonymously. The researcher did not have
any visibility on who did or did not participate in the study, or the responses for
those who did participate.
• No information was collected that would link an individual participant to his or
her responses (e.g., participant name or email address).
• All communication between the researcher and the study participants occurred
through an individual identified by the organization to serve as the coordinator.
The coordinator’s sole role was to serve as a conduit for the emails between the
researcher and potential participants; the coordinator was not able to see who did
or did not participate or view the responses for those who did participate.
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• All data were reported only in summary format, not a format attributable to a
specific individual.
• In any published articles or presentations, only aggregated data will be used. No
information will be included that would make it possible to identify individuals or
the organization as a participant in the research study.
• The survey questionnaire was administered using a secure online platform
(SurveyMonkey).
• All participant responses were stored on the researcher’s password-protected
home computer. Additionally, backup copies of the survey data were stored as
password-protected data files on the researcher’s university cloud account and an
external hard drive.
• All data analysis was conducted on a password-protected computer in the
researcher’s home.
In addition to the above steps, two additional actions were taken to help ensure
that all participants were treated in accordance with policies of the Office of Human
Research. First, the researcher and members of the dissertation committee who are
George Washington University faculty have completed the social/behavioral research
modules of CITI (Collaborative IRB Training Initiative) (Office of Human Research,
n.d.-a). Second, as recommended by the American Psychological Association (2010), the
researcher will retain all study data and materials for a period of 5 years.
Lastly, efforts were taken to ensure there were no issues with copyright violations
related to the survey instruments. As previously discussed, permission was obtained to
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use the EES (Shuck, Adelson, et al., 2017), the Stringer Strategic Alignment Scale
(Stringer, 2007), and the SPOS (Eisenberger et al., 1986) (Appendix D).
Chapter Summary
The purpose of this study was to explore the relation among employee alignment,
perceived organizational support, and employee engagement and how employee
alignment and perceived organizational support interact to contribute to employee
engagement in an organizational context. In support of the purpose, this study examined a
hypothesized model of employee engagement, exploring the relation among the two
explanatory constructs (variables) of employee alignment and perceived organizational
support and the outcome construct (variable) of employee engagement in an
organizational context.
This chapter described the research design used to conduct the study. A
nonexperimental, cross-sectional survey research design (Creswell, 2014; Dannels, 2019;
Robson & McCartan, 2016) was used with a self-completion Internet-based survey
questionnaire (Robson & McCartan, 2016). An a priori sample size analysis conducted
using G*Power showed that a minimum of 77 participants was required to achieve
statistical power for the study.
The research site for the study was the HR department of a not-for-profit health
care organization located in the southern region of the United States. The population—
the accessible population (Fritz & Morgan, 2010)—consisted of all employees of the
research site who were full-time, nonsupervisory employees. Among 268 employees
invited to participate, there were 150 initial responses, 39 of which were excluded for not
explicitly meeting the inclusion criteria, resulting in an accessible population of 229
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employees. The actual sample (Fritz & Morgan, 2010) consisted of 109 individuals who
agreed to participate in the study, responded to the survey questionnaire, and whose data
was used in the analysis.
This study used three self-report survey instruments: (a) the EES (Shuck,
Adelson, et al., 2017), (b) the Stringer Strategic Alignment Scale (Stringer, 2007), and (c)
the SPOS (Eisenberger et al., 1986). Additional demographic information was also
collected. In the statistical analysis of the data, correlation analysis was used to test H1a,
H2, and H3a, and multiple regression analysis was used to test H1b, H3b, H4, and H5.
The data analysis is discussed in the next chapter.
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Chapter 4: Results
This chapter presents the results of the statistical analysis of the data from the
study sample. The purpose of the study was to explore the relation among employee
alignment, perceived organizational support, and employee engagement and how
employee alignment and perceived organizational support interact to contribute to
employee engagement among full-time nonsupervisory individuals in an organizational
context. Two research questions guided the study:
RQ1. To what extent is there a statistically significant relation among employee
alignment, perceived organizational support, and employee engagement in an
organizational context?
RQ2. To what extent do employee alignment and perceived organizational support
explain a statistically significant proportion of the unique variance in employee
engagement?
In answering the two research questions, seven hypotheses were tested using
correlation and multiple regression analysis techniques. In presenting the results of the
data analysis, this chapter is divided into five sections: (a) participant demographics, (b)
survey questionnaire scale reliability and validity, (c) descriptive statistics of study
variables, (d) research questions and hypothesis testing, and (e) chapter summary.
Participant Demographics
As discussed in Chapter 3, the study’s research site was the human resources
department of a not-for-profit health care organization located in the Southern region
(U.S. Census Bureau, n.d.) of the United States. The accessible population (Fritz &
Morgan, 2010) consisted of all full-time nonsupervisory individuals employed at the
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research site. Of the 229 potential participants, 109 individuals (i.e., participants)—the
actual sample (Fritz & Morgan, 2010)—agreed to participate in the study and responded
to the survey questionnaire, for a response rate of 47.60%.
Level of Analysis
Employee alignment, perceived organizational support, and employee
engagement were conceptualized and operationalized at the individual level of analysis.
This study collected data from individual employees and computed total scores for each
of the three constructs of interest—employee alignment, perceived organizational
support, and employee engagement—for each of the participants. Data analysis was
performed using the participant total scores.
Participant Demographic Descriptive Statistics
To minimize the personally identifiable information collected from study
participants (Lee & Schuele, 2010), the demographic variables used in this study were
limited to three demographic characteristics that have been shown to affect engagement:
(1) age (Avery et al., 2007; Bhatnagar, 2012; Gomes et al., 2015; Toyama & Mauno,
2017), (2) gender (Bae et al., 2013; Bhatnagar, 2012; Gomes et al., 2015; Mauno et al.,
2005; Toyama & Mauno, 2017), and (3) organizational tenure (i.e., the number of years a
participant has been employed at the research site) (Avery et al., 2007; Bae et al., 2013;
Gomes et al., 2015). Participant descriptive statistics for the three demographic
characteristics of age, gender, and tenure are presented in Table 4.1. The age of the
participants ranged from 25 to 74 years old, with a mean of 44.72 years (SD = 11.32).
Most participants were female (83.49%), with males comprising 14.68% of the sample
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respondents and two participants (1.83%) not responding to the question. Participants’
tenure ranged from 1 to 30 years, with a mean of 7.73 years (SD = 7.32).
Table 4.1
Participant Demographic Descriptive Statistics
Data variable n Minimum Maximum Mean Standard deviation Percent
Agea 95 25 74 44.72 11.32 Gender 109
Male 16 14.68% Female 91 83.49% Not provided 2 1.83%
Tenureb 94 1 30 7.73 7.32 a Fourteen participants did not answer the question concerning their age (Question 29). b Fifteen participants did not answer the question concerning how long they have worked in the human resources department (Question 31).
Survey Questionnaire Scale Reliability and Validity
This study used three established survey scales to measure the variables of
interest: the Employee Engagement Scale (Shuck, Adelson, et al., 2017), the Stringer
Strategic Alignment Scale (Stringer, 2007), and the Survey of Perceived Organizational
Support (Eisenberger et al., 1986). As discussed in Chapter 3 and summarized in Table
4.2, previous empirical research has demonstrated the reliability and validity of these
three instruments.
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Table 4.2
Empirical Research Demonstrating Scale Reliability and Validity
Variable Scale Study
Reliability Validity Employee engagement
Employee Engagement Scale
Osam, Shuck, and Immekus (2019) Shuck, Adelson, et al. (2017) Shuck, Alagaraja, et al. (2017)
Shuck, Adelson, et al. (2017)
Employee alignment
Stringer Strategic Alignment Scale
Stringer (2007) Stringer (2007)
Perceived organizational support
Survey of Perceived Organizational Support
Eisenberger et al. (1986) Rhoades and Eisenberger (2002) Robinson (2013) Simmons (2013) Worley et al. (2009)
Eisenberger et al. (1986) Hutchison (1997) Shore and Tetrick (1991) Worley et al. (2009)
However, Creswell (2014) noted that “when one modifies an instrument or
combines instruments in a study, the original validity and reliability may not hold for the
new instrument, and it becomes important to reestablish validity and reliability during
data analysis” (p. 160). The reestablishment of instrument reliability and validity is
discussed in the following two sections.
Reestablishing Questionnaire Scale Reliability
As a measure of internal reliability for a specific data set (L. Cohen et al., 2011;
D. George & Mallery, 2020; Morgan et al., 2013), a Cronbach’s alpha was computed
from the actual sample data for the variables measuring employee alignment, perceived
organizational support, and employee engagement. The Cronbach’s alphas computed for
the three variables were .879 for employee engagement, .909 for employee alignment,
and .974 for perceived organizational support (Table 4.3). Cohen et al. (2011) offered the
following guidelines for interpreting Cronbach’s alpha coefficients: >.90, very highly
reliable; .80 to .90, highly reliable; .70 to .79, reliable; .60 to .69, marginally reliable; and
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< .60, unacceptably low reliability (p. 640). As depicted in Table 4.3, the computed
Cronbach’s alphas for the survey questionnaire scales fell in the very highly reliable to
highly reliable range and were similar to values obtained in previous studies.
Table 4.3
Summary of Measures of Internal Reliability
Instrument Variable
Cronbach’s alpha Survey
questionnaire Previous studies Employee Engagement Scale (Shuck, Adelson, et al., 2017)
Employee engagement
.879 .920 (Shuck, Alagaraja, et al., 2017) .880 (Osam et al., 2020)
Stringer Strategic Alignment Scale (Stringer, 2007)
Employee alignment
.909 .950 (Stringer, 2007)
Survey of Perceived Organizational Support (Eisenberger et al., 1986)
Perceived organizational support
.974 .930 (Worley et al., 2009) .880 (Y. D. Robinson, 2013) .880 (Simmons, 2013)
In addition to the analysis to reestablish internal reliability, the interitem
correlations of the 28 questions comprising the survey questionnaire scales were
examined for evidence of tautology or redundancy among scale items (Brewerton &
Millward, 2001; Clark & Watson, 1995; DeVon et al., 2007; Manikoth, 2013; Piedmont,
2014; Streiner, 2003). Some researchers have suggested that interitem correlation
coefficients > .90 suggest tautology of scale items, that is, the items are essentially
measuring the same thing (Brewerton & Millward, 2001; DeVon et al., 2007; Manikoth,
2013; Streiner, 2003). As reflected in the interitem correlation matrix shown in Appendix
K, none of the interitem correlation coefficients were > .90, suggesting that there were no
redundancies among the survey scale questions.
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Reestablishing Questionnaire Scale Validity
It is incumbent upon researchers to reestablish the validity of the survey
instruments used in a research study within the context of each unique application and
resulting data set. In the context of “reestablishing” validity, the focus is on validating the
construct validity of the instrument used to measure a construct of interest in a given
research study (L. Cohen et al., 2011; Creswell, 2014; Hair et al., 2014; Mueller &
Knapp, 2019; Robson & McCartan, 2016). As Hair et al. (2014) noted, “All constructs
must display adequate construct validity, whether they are new scales or scales taken
from previous research” (p. 606). Construct validity reflects the extent to which the items
of a particular measurement instrument align to, or reflect, theoretical expectations and
measure the latent construct that they were designed to measure (L. Cohen et al., 2011;
Creswell, 2014; Hair et al., 2014; Mueller & Knapp, 2019; Robson & McCartan, 2016).
While the importance of validating and reestablishing an instrument’s construct
validity is recognized (L. Cohen et al., 2011; Creswell, 2014; Hair et al., 2014; Mueller &
Knapp, 2019; Robson & McCartan, 2016), there is no single methodological test to
determine the construct validity of an instrument (Carlson & Herdman, 2010; Costello &
Osborne, 2005; Robson & McCartan, 2016). However, a common approach used by
researchers is to conduct a factor analysis to establish support for an instrument’s
construct validity (Keith, 2015; Mueller & Knapp, 2019; Robson & McCartan, 2016).
For the current study, exploratory factor analysis was conducted to reestablish the
construct validity of the three survey scales used in this study in the context of the study’s
actual sample and associated data set (Green & Salkind, 2011; Hadi et al., 2016; Kim et
al., 2016; Massey, 2019; Morgan et al., 2013; Reio & Shuck, 2015; Watkins, 2018). As
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noted by Bandalos and Finney (2019), exploratory factor analysis is appropriate for
“situations in which the variables to be analyzed are either newly developed or have not
been previously analyzed together” (p. 101).
Data Set Factorability
Prior to conducting the exploratory factor analysis, it was necessary to assess the
factorability of the data set (L. Cohen et al., 2011; D. George & Mallery, 2020; Morgan
et al., 2013; UCLA Statistical Consulting Group, 2020b; Watkins, 2018). This was done
through a Kaiser-Meyer-Olkin measure of sampling adequacy and Bartlett’s test of
sphericity (L. Cohen et al., 2011; D. George & Mallery, 2020; Morgan et al., 2013;
UCLA Statistical Consulting Group, 2020b; Watkins, 2018). The Kaiser-Meyer-Olkin
is a measure of the proportion of the variance in the variables (i.e., scale questions) that
are caused by the underlying factors (IBM, n.d.; Watkins, 2018). Values range from
0.00 to 1.00, with values > 0.70 desired and a value of 0.60 suggested as a minimum (L.
Cohen et al., 2011; D. George & Mallery, 2020; IBM, n.d.; Morgan et al., 2013; UCLA
Statistical Consulting Group, 2020b; Watkins, 2018). The Kaiser-Meyer-Olkin value for
the data set was 0.88 (Table 4.4), above the 0.60 recommended minimum value (L.
Cohen et al., 2011; D. George & Mallery, 2020; IBM, n.d.; Morgan et al., 2013; UCLA
Statistical Consulting Group, 2020b; Watkins, 2018), offering evidence of sampling
adequacy and suitability for exploratory factor analysis.
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Table 4.4
Kaiser-Meyer-Olkin and Bartlett’s Test
Test Value Kaiser-Meyer-Olkin measure of sampling adequacy .880 Bartlett’s test of sphericity Approx. chi-square 3162.780
df 378 Sig. <.001
Bartlett’s test of sphericity tests the hypothesis that the correlation matrix of
the data set variables (i.e., the 28 scale questions) is an identity matrix, which would
indicate that the variables were uncorrelated and thus unsuitable for factor analysis (L.
Cohen et al., 2011; D. George & Mallery, 2020; IBM, n.d.; Morgan et al., 2013;
UCLA Statistical Consulting Group, 2020b; Watkins, 2018). For the data to be
suitable for factor analysis, i.e., not an identity matrix, the null hypothesis needs to be
rejected (i.e., p < .05) (L. Cohen et al., 2011; D. George & Mallery, 2020; IBM, n.d.;
Morgan et al., 2013; UCLA Statistical Consulting Group, 2020b). Bartlett’s test of
sphericity was significant (Table 4.4) (c2(378) = 3162.780, p < .001), rejecting the null
hypothesis and thus indicating that the variables of the data set were correlated and
suitable for exploratory factor analysis.
Exploratory Factor Analysis
An exploratory factor analysis using principal axis factor extraction with
promax rotation and three factors was conducted to explore the convergent and
discriminant validity of the three survey scales: the Employee Engagement Scale, the
Stringer Strategic Alignment Scale, and the Survey of Perceived Organizational
Support. Principal axis factor and maximum likelihood are the two most commonly
used factor analysis extraction methods (Bandalos & Finney, 2019; Costello &
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Osborne, 2005; Robson & McCartan, 2016; Schmitt, 2011; Watkins, 2018). Principal
axis factoring was used due to the relatively small (in terms of factor analysis
standards) actual sample size (n = 109) being analyzed (Bandalos & Finney, 2019;
Watkins, 2018). Based on the theoretical nature of the three constructs of interest—
employee alignment, perceived organizational support, and employee engagement—it
was assumed that the factors would be correlated; therefore, an oblique (i.e., promax),
rather than orthogonal, rotation was used (Bandalos & Finney, 2019; L. Cohen et al.,
2011; Costello & Osborne, 2005; D. George & Mallery, 2020; Green & Salkind, 2011;
Reio & Shuck, 2015; UCLA Statistical Consulting Group, 2020a; Watkins, 2018).
Also based on a priori theory, which guided the conceptual framework explored in this
study, three factors were extracted to account for the three constructs of interest
(Green & Salkind, 2011; Reio & Shuck, 2015; UCLA Statistical Consulting Group,
2020b; Watkins, 2018). The interfactor correlations (structure matrix) and factor
loadings (pattern matrix) are shown in Table 4.5. Factor values less than the absolute
value of .30 (i.e., < |.30|) were considered insignificant and omitted from the table
(Bandalos & Finney, 2019; Brown, 2009; L. Cohen et al., 2011; Costello & Osborne,
2005; Morgan et al., 2013; UCLA Statistical Consulting Group, 2020b). Appendix L
provides a summary of all factor loadings (i.e., including loadings < |.30|).
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Table 4.5
Summary of Item-Factor Correlations and Factor Loadings for the 28 Scale Questions
Item Structure matrix Pattern matrixa Factor Factor
1 2 3 1 2 3 Factor 3: EE
Q1 .762 .779 Q2 .754 .787 Q3 .800 .854 Q4 .312 .868 .891 Q5 .483 .762 .346 .712 Q6 .605 .630 .423 .366 .330 Q7 .468 .827 .894 Q8 .407 .749 .829 Q9 .319 .629 .597 Q10 .317 .622 .590 Q11 .432 .701 .625 Q12 .528 .482 .441 .318
Factor 2: EA Q13 .331 .747 .411 .792 Q14 .426 .798 .348 .826 Q15 .549 .741 .642 Q16 .584 .811 .309 .725 Q17 .527 .523 .360 .341 Q18 .567 .692 .562 Q19 .533 .592 .340 .373 Q20 .510 .779 .752
Factor 1: POS Q21 .887 .622 .794 Q22 .897 .518 .904 Q23 .893 .558 .864 Q24 .894 .556 .865 Q25 .904 .461 .968 Q26 .924 .509 .953 Q27 .894 .476 .941 Q28 .940 .551 .939
Factor labels: EE = Employee engagement, EA = Employee alignment, POS = Perceived organizational support. Extraction Method: Principal Axis Factoring. Rotation Method: Promax with Kaiser Normalization. Note: Loadings < .30 are suppressed. Note: Significant pattern coefficients (> .55) are indicated in bold font. a Rotation converged in 5 iterations.
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Assessing Construct Validity
Both convergent16 and discriminant17 (sometimes referred to as divergent) validity
were examined to establish evidence for construct validity (L. Cohen et al., 2011; Hair et
al., 2014; Keith, 2015; Marnburg & Luo, 2014; Moutinho, 2011; Mueller & Knapp,
2019; Trochim, 2020). The assessment of the convergent and discriminant validity of the
three scales used in this study—the Employee Engagement Scale, the Stringer Strategic
Alignment Scale, and the Survey of Perceived Organizational Support—is discussed in
the following sections.
Assessing Convergent Validity. As previously discussed, convergent validity is
an indication of the extent to which a given grouping, or clustering, of items (i.e.,
questions of a scale) are measuring the same underlying construct (Carlson & Herdman,
2010; Hair et al., 2014; Keith, 2015; Prudon, 2015; Robson & McCartan, 2016). In this
study, convergent validity was assessed by examining item-factor correlations
(Moutinho, 2011), factor loadings (Bandalos & Finney, 2019; Costello & Osborne, 2005;
Hair et al., 2014; Marnburg & Luo, 2014; Morgan et al., 2013; UCLA Statistical
Consulting Group, 2020a; Watkins, 2018), average variance extracted (AVE) (Fornell &
Larcker, 1981; Hair et al., 2014), and composite reliability (CR) (Fornell & Larcker,
1981; Hair et al., 2014).
Item-Factor Correlations. The use of item-factor correlations as an indicator of
convergent validity posits that an item will correlate more strongly with the factor it is
16 Convergent validity is the “extent to which indicators [e.g., scale questions] of a specific construct converge or share a high proportion of variance in common” (Hair et al., 2014, p. 601). 17 Discriminant validity is the “extent to which a construct is truly distinct from other constructs both in terms of how much it correlates with other constructs and how distinctly measured variables [e.g., scale questions] represent only this single construct” (Hair et al., 2014, p. 601).
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intended to measure than with other factors (Moutinho, 2011). The structure
coefficients18 shown in the structure matrix of Table 4.5 reflect the correlation between
each item and its respective factor(s). Structure coefficient values > .50 are recommended
as a minimum for evidence of convergent validity, with values > .70 being desired
(Carlson & Herdman, 2010; Costello & Osborne, 2005; Hair et al., 2014).
As shown in Table 4.5, the eight structure coefficients for Q21 to Q28 (the
questions measuring perceived organizational support) correlated strongest with Factor 1
and ranged from .887 to .940. While these 8 items also correlated to Factor 2, with
structure coefficients ranging from .461 to .622, the items correlated strongest with
Factor 1, as expected based on theory, providing evidence of convergent validity for
Factor 1 (i.e., perceived organizational support). Similarly, the eight structure coefficients
for Q13 to Q20 (the questions measuring employee alignment) (Table 4.5) correlated
strongest with Factor 2, with the exception of Q17, with structure coefficients ranging
from .592 to .811; Q17 correlated with Factor 2 with .523 and with Factor 1 with .527.
While the 8 items also correlated to Factors 1 and 3, with structure coefficients ranging
from .331 to .584 and from .309 to .411, respectively, the items correlated strongest with
Factor 2, as expected based on theory, providing reasonable evidence of the convergent
validity of Factor 2 (i.e., employee alignment). Lastly, seven of 12 structure coefficients
for Q1 to Q12 (the questions measuring employee engagement) (Table 4.5) correlated
strongest with Factor 3, with structure coefficients ranging from .622 to .868. The
remaining five items (Q5, Q6, Q7, Q8, and Q12) had a stronger correlation to Factor 2
18 The structure coefficients reflect the bivariate correlations (i.e., the simple zero-order correlations) between each item and latent factor (Bandalos & Finney, 2019; Reio & Shuck, 2015; UCLA Statistical Consulting Group, 2020b, 2020a; Watkins, 2018).
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than to Factor 3: Q5 correlation of .346 to Factor 3 and .762 to Factor 2; Q6 correlation of
.423 for Factor 3 and .630 for Factor 2; Q7 no correlation to Factor 3 and correlation of
.827 to Factor 2; Q8 no correlation to Factor 3 and correlation of .749 to Factor 2; and
Q12 correlation of .482 to Factor 3 and .528 to Factor 2. While seven of the structure
coefficients all correlated strongest to Factor 3 with correlations > .50, the fact that five
of 12 items had a stronger correlation with Factor 2 rather than Factor 3 (which they were
designed to measure), contrary to expectations based on theory, suggests that there may
be some overlap between Factors 2 and 3 and indicated limited evidence of convergent
validity for Factor 3 (i.e., employee engagement).
Factor Loadings. The pattern coefficients19 (also often referred to as “loadings”)
shown in the pattern matrix column of Table 4.5 reflect the unique relation between each
item and its respective factor, controlling for the other factors (Bandalos & Finney, 2019;
UCLA Statistical Consulting Group, 2020a; Watkins, 2018). Pattern coefficient values >
.50 are recommended as a minimum for significance and evidence of convergent validity
(Costello & Osborne, 2005; Hair et al., 2014; Marnburg & Luo, 2014; Morgan et al.,
2013). Hair et al. (2014) suggested that the significance of pattern coefficients was
related to sample size. For example, a sample of 100 was required for pattern coefficients
to be considered significant at .55 and a sample of 120 was required for significance at
.50 (Hair et al., 2014, p. 115). With a sample size of 109, this study considered pattern
coefficients > .55 as significant (pattern coefficients > .55 are indicated in bold in Table
4.5). An additional guideline is that each factor should have significant pattern
19 The pattern coefficients are comparable to standardized regression coefficients of an item with a particular factor (Bandalos & Finney, 2019; UCLA Statistical Consulting Group, 2020a; Watkins, 2018).
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coefficients from a minimum of three or four items (L. Cohen et al., 2011; Costello &
Osborne, 2005; Green & Salkind, 2011; Hair et al., 2014; Reio & Shuck, 2015; Watkins,
2018).
Ideally, pattern coefficients would result in a simple structure, where a significant
pattern coefficient aligns (i.e., loads) with only a single factor (Bandalos & Finney, 2019;
D. George & Mallery, 2020; UCLA Statistical Consulting Group, 2020b; Watkins, 2018),
a situation that rarely occurs in social science research (D. George & Mallery, 2020).
Rather, an item’s pattern coefficient will most often cross-load—i.e., have a significant
value—with multiple factors (Bandalos & Finney, 2019; Costello & Osborne, 2005; Hair
et al., 2014; Morgan et al., 2013; Schmitt, 2011).
As shown in Table 4.5, the eight pattern coefficients for Q21 to Q28 (the
questions measuring perceived organizational support) were all significant, aligning
solely with Factor 1 and ranging from .794 to .968. The alignment and values for the
pattern coefficients for Q21 to Q28 provide evidence of convergent validity for Factor 1
(i.e., perceived organizational support). Similarly, the eight pattern coefficients for Q13
to Q20 (the questions measuring employee alignment) (Table 4.5) were all significant
and aligned to Factor 2, with the exception of Q17 and Q19, ranging from .562 to .826;
neither of the two exceptions had significant pattern coefficients. The value of the pattern
coefficient for Q17 was .341 and aligned with Factor 1, and the pattern coefficient for
Q19 aligned with Factor 2 with a value of .373. The alignment and values for the pattern
coefficients for Q13 to Q20 provided reasonable evidence of convergent validity for
Factor 2 (i.e., employee alignment). Lastly, seven of the 12 pattern coefficients for Q1 to
Q12 (the questions measuring employee engagement) (Table 4.5) were significant and
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aligned to Factor 3, ranging in value from .590 to .891. Of the remaining five items, three
(Q5, Q7, and Q8) had significant pattern coefficient values (.712, .894, and .829,
respectively), but were aligned to Factor 2. The pattern coefficients for the remaining two
items (Q6 and Q12) were not significant; the values of the pattern coefficients for Q6
were .366 and .330, aligned to Factors 1 and 2, respectively, and the values of the pattern
coefficients for Q12 were .441 and .318, aligned to Factors 2 and 3, respectively.
Contrary to expectations based on theory, only seven of the 12 items had significant
pattern coefficients aligned to Factor 3 (which they were designed to measure), and three
items had significant pattern coefficients aligned to Factor 2, indicating limited evidence
of convergent validity for Factor 3 (i.e., employee engagement).
Average Variance Extracted. Another means to assess convergent validity is by
examining the AVE for the pattern coefficients aligned to a given factor (Fornell &
Larcker, 1981; Hair et al., 2014). The AVE for a factor represents the amount of variance
that a factor explains in the observed item compared to the variance caused by
measurement error (Carter, 2016; Fornell & Larcker, 1981; Hair et al., 2014). AVE was
calculated as shown in Equation 1 (Fornell & Larcker, 1981; Moutinho, 2011):
𝐴𝑉𝐸 = ∑𝜆(
∑𝜆( + ∑(1– 𝜆()(1)
Where:
l (Lambda) = pattern coefficient for a given factor
1 – l2 = the error variance (measurement error)
A common guideline is that AVE values >.50 suggest acceptable evidence of
convergent validity (Fornell & Larcker, 1981; Hair et al., 2014). An AVE value > .50
indicates that more than 50% of the variance in an observed item is due to the
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hypothesized latent factor rather than to measurement error (Almén et al., 2018; Carter,
2016; Fornell & Larcker, 1981; Moutinho, 2011). Using the pattern coefficients (Massey,
2019; Moutinho, 2011), an AVE value was computed for each of the three factors. The
value was .82 for Factor 1 (perceived organizational support), .42 for Factor 2 (employee
alignment), and .33 for Factor 3 (employee engagement) (see Table M.1 in Appendix M
for details on the computations). These results provided support for the convergent
validity of Factor 1, but did not provide evidence for the convergent validity of Factor 2
and Factor 3.
Composite Reliability. The final indicator of convergent validity to be examined
was CR, sometimes referred to as construct reliability (Fornell & Larcker, 1981; Hair et
al., 2014). The CR provides an indication of the internal consistency and reliability of the
observed measurement items in representing the latent factor (Hair et al., 2014) and was
calculated as shown in Equation 2 (Fornell & Larcker, 1981; Hair et al., 2014):
𝐶𝑅 = (∑ 𝜆)(
(∑𝜆)( + ∑(1– 𝜆()(2)
Where:
l (Lambda) = pattern coefficient for a given factor
1 – l2 = the error variance (measurement error)
A common guideline is that CR values ≥ 0.70 suggest acceptable reliability and
evidence of convergent validity (Hair et al., 2014). Using the pattern coefficients
(Massey, 2019; Moutinho, 2011), a CR value was computed for each of the three factors
(see Table M.1 in Appendix M for details on the computations). The value was .97 for
Factor 1 (perceived organizational support), .84 for Factor 2 (employee alignment), and
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.79 for Factor 3 (employee engagement). All values were > 0.70, providing evidence to
support the convergent validity of the three factors.
It is worth noting that the limited support for the convergent validity of Factor 2
(employee alignment) and Factor 3 (employee engagement) from the preceding analysis
should be interpreted with caution given the actual sample size of the data set (n = 109).
As a general rule, factor analysis requires a large sample size (UCLA Statistical
Consulting Group, 2020b). While there is not consensus on the definition of large, some
have suggested the following guidelines: 50 is very poor, 100 is poor, 200 is fair, 300 is
good, 500 is very good, 1000 or more is excellent (UCLA Statistical Consulting Group,
2020b; Tabachnick & Fidellk, 2007, as cited in Cohen, Manion, and Morrison, 2011).
Similarly, others have advocated an approach for determining minimum required sample
size based on participant-to-item ratios, with a minimum ratio of 5:1 and a preferred ratio
of 10:1 (Hair et al., 2014; Reio & Shuck, 2015). Using an inadequate (i.e., too small)
sample size can result in inaccurate pattern and structure coefficient estimates (Bandalos
& Finney, 2019; Costello & Osborne, 2005), items loading on the wrong factors (i.e.,
misclassified) (Costello & Osborne, 2005), and understating the significance of factor
loadings (Hair et al., 2014). For example, applying the participant-to-item ratio approach
to the questionnaire used in this study, which consisted of 28 questions (i.e., items),
would result in a minimum required sample size for factor analysis of 140 (5:1 ratio),
with 280 (10:1 ratio) as the preferred sample size. With an adequate sample size, the
convergent validity of Factor 2 (employee alignment) and Factor 3 (employee
engagement) may very well have been reestablished as originally reported by Shuck,
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Adelson, et al. (2017) (the Employee Engagement Scale) and Stringer (2007) (the
Stringer Strategic Alignment Scale).
Assessing Discriminant Validity. Discriminant validity concerns the extent to
which a construct is unique and distinct from other constructs (Hair et al., 2014;
Moutinho, 2011), as well as the extent to which a given grouping, or clustering, of items
(i.e., questions of a scale) are measuring only this distinct underlying construct (Hair et
al., 2014; Keith, 2015). As Hair et al. (2014) observed, discriminant validity “provides
evidence that a construct is unique and captures some phenomena other measures do not”
(p. 619).
Discriminant validity was assessed by comparing each construct’s AVE with the
shared variance between two constructs (Fornell & Larcker, 1981; Hair et al., 2014;
Massey, 2019; Moutinho, 2011), where shared variance was equal to the square of the
correlation coefficient between the constructs (Fornell & Larcker, 1981). Evidence of
discriminant validity between two constructs was indicated if each construct’s AVE was
greater than their shared variance (Fornell & Larcker, 1981; Hair et al., 2014; Massey,
2019; Moutinho, 2011). Table 4.6 shows the exploratory factor analysis correlations
between the three constructs, Table 4.7 shows the shared variance for the constructs, and
Table 4.8 compares the construct AVE to the shared variance.
Table 4.6
Construct Correlation Matrix
Construct 1 2 3 POS – EA .587 – EE .221 .410 – Note: Constructs: POS = Perceived organizational support, EA = Employee alignment, EE = Employee engagement. Extraction method: principal axis factoring. Rotation method: Promax with Kaiser normalization.
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Table 4.7
Construct Shared Variance
Construct r r2 EE – EA .410 .168 EE – POS .221 .049 EA – POS .587 .345 Note: Correlations between constructs (r) are from Table 4.6. Constructs: EE = Employee engagement, EA = Employee alignment, POS = Perceived organizational support. Table 4.8
Comparison of Construct Average Variance Extracted to Shared Variance
Construct pairs Construct AVEa Shared variance (r2)b
EE – EA EE .335 .168 EA .416
EE – POS EE .335 .049 POS .819
EA – POS EA .416 .345 POS .819 a Average variance extracted (AVE) values are from Appendix M. b Shared variance values are from Table 4.7. Note: Construct pairs: EE = Employee engagement, EA = Employee alignment, POS = Perceived organizational support. To assess discriminant validity, the AVE value for each construct was compared
to the shared variance for a given pair of constructs (Fornell & Larcker, 1981; Hair et al.,
2014; Massey, 2019; Moutinho, 2011). As shown in Table 4.8, the AVE values were .335
and .416 for employee engagement and employee alignment, respectively, with their
shared variance equal to .168. With the AVE of both constructs greater than their shared
variance, the discriminant validity of the two constructs was supported. Similarly, the
AVE values for employee engagement and perceived organizational support were .335
and .819, respectively, with their shared variance equal to .049; the discriminant validity
of the two constructs was supported. Lastly, the AVE values for employee alignment and
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perceived organizational support were .416 and .819, respectively, and their shared
variance was .345; the discriminant validity of the two constructs was supported.
Overall, the exploratory factor analysis demonstrated evidence for the convergent
and discriminant validity, and thus the construct validity, of the three survey scales used
in this study. With the reliability and construct validity of the measurement scales used
for data collection established within the context of the current study and associated data
set, the analysis now turns to the descriptive statistics of the study variables.
Descriptive Statistics of Study Variables
Descriptive statistics for the three study variables—the two explanatory variables
of employee alignment and perceived organizational support and the outcome variable of
employee engagement—are presented in Table 4.9. Descriptive statistics for each
variable by individual survey question are presented in Appendix N.
Table 4.9
Descriptive Statistics of Study Explanatory and Outcome Variables
Variable n Mean Standard deviation Minimum Maximum
Employee engagement 109 52.65 5.43 39.00 60.00 Employee alignment 109 33.72 4.48 20.00 40.00 Perceived organizational support 109 33.83 12.40 1.00 48.00
Employee engagement was measured using the Employee Engagement Scale
(EES), a 12-item scale with three subscales (cognitive engagement, emotional
engagement, and behavioral engagement) of four items each (Shuck, Adelson, et al.,
2017). All scale items were measured on a 5-point Likert scale: 1 = strongly disagree, 2
= disagree, 3 = neither agree nor disagree, 4 = agree, and 5 = strongly agree (Shuck,
Adelson, et al., 2017); a higher numeric response indicated a higher level of engagement.
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Scores for each of the three engagement subscales were computed as the sum of a
participant’s responses to the four items comprising each subscale; the range of possible
values for each subscale was 4 to 20 (Shuck, Adelson, et al., 2017). An overall employee
engagement score was computed as the sum of the three engagement subscales, with
possible values ranging from 12 to 60 (Shuck, Adelson, et al., 2017). Employee
engagement scores ranged from 39.00 to 60.00, with a mean value of 52.65 (SD = 5.43).
Extrapolating from the 5-point Likert measure scale—i.e., individual item scores ranging
from 1 to 5—to a summed score with values ranging between 12 and 60, a mean value of
52.65 indicated that participants fell between agreeing (a summed score of 48) and
strongly agreeing (a summed score of 60) that they were engaged.
Employee alignment was measured using the Stringer Strategic Alignment Scale,
with 8 items measured on a 5-point Likert scale: 1 = strongly disagree, 2 = disagree, 3 =
neither agree nor disagree, 4 = agree, and 5 = strongly agree (Stringer, 2007); a higher
numeric response indicated a higher level of alignment. An overall employee alignment
scale score was computed as the sum of a participant’s responses to the 8 items, with a
range of possible values of 8 to 40. Employee alignment scores ranged from 20.00 to
40.00, with a mean value of 33.72 (SD = 4.48). Extrapolating from the 5-point Likert
measure scale to a summed score with values ranging between 8 and 40, a mean value of
33.72 indicated that participants fell slightly above agreeing (a summed score of 32) that
they understood the organization’s goals and how their work and job responsibilities
contributed to achieving those goals.
Perceived organizational support was measured using the 8-item version of the
Survey of Perceived Organizational Support (Eisenberger et al., 1986), with each item
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measured on a 7-point Likert scale: 0 = strongly disagree, 1 = moderately disagree, 2 =
slightly disagree, 3 = neither agree nor disagree, 4 = slightly agree, 5 = moderately
agree, and 6 = strongly agree (Eisenberger et al., 1986); a higher numeric response
indicated a higher level of perceived support. An overall scale score was computed as the
sum of a participant’s responses to the 8 items. The range of possible values for the
overall score was 0 to 48. Participants’ scores ranged from 1.00 to 48.00, with a mean
value of 33.83 (SD = 12.40). Extrapolating from the 7-point Likert measure scale—i.e.,
individual item scores ranging from 0 to 6—to a summed score with values ranging
between 0 and 48, a mean value of 33.83 indicated that participants fell slightly above a
perception of slightly agreeing (a summed score of 32) that the organization (i.e.,
research site) was committed to them, valued their contribution, and cared about their
well-being.
Research Questions and Hypothesis Testing
This study examined a hypothesized model of employee engagement, exploring
the relation among the two explanatory constructs (variables) of employee alignment and
perceived organizational support and the outcome construct (variable) of employee
engagement in an organizational context. This section reports the findings of the
statistical analyses used to test the hypotheses and answer the two research questions.
IBM SPSS Statistics (Version 26.0.0.1 for Mac) was used for the correlation and
regression analyses conducted in testing the hypotheses. A significance level of .05 was
used in the hypothesis tests to determine statistical significance (J. Cohen, 1988; J. Cohen
et al., 2003; Hinkle et al., 2003). Additionally, Cohen's (1988) benchmarks were used to
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characterize the magnitude of correlations and correlation effect size. Each of the
research questions and its associated hypotheses are addressed in turn.
Research Question 1
The first research question asked: To what extent is there a statistically significant
relation among employee alignment, perceived organizational support, and employee
engagement in an organizational context? In answering this question, Hypotheses 1a, 2,
3a, 4, and 5 were tested. Each of these hypotheses is discussed in turn. As discussed in
Chapter 3, the assumptions of continuous variables, normal distribution of variables,
linearity, normal distribution of residuals, homoscedasticity, independence of residuals,
and noncollinearity were met for the correlation and multiple regression analyses.
Correlation Matrix
Bivariate correlation coefficients describe the magnitude and direction of the
relation between two variables (J. Cohen et al., 2003; Hinkle et al., 2003; Keith, 2015;
Lomax & Hahs-Vaughn, 2012). To test Hypotheses 1a, 2, and 3a, bivariate correlations,
using the Pearson product moment correlation coefficient, were computed to examine the
relation among employee alignment, perceived organizational support, and employee
engagement (J. Cohen et al., 2003; Hinkle et al., 2003; Keith, 2015; Lomax & Hahs-
Vaughn, 2012). The computed Pearson correlation coefficients are shown in Table 4.10.
All correlations were significant at the p = 0.01 level (one-tailed).
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Table 4.10
Bivariate Correlation Matrix of Study Explanatory and Outcome Variables
Variable n EE EA POS Employee engagement (EE) 109 – Employee alignment (EA) 109 .650** – Perceived organizational support (POS)
109 .431** .613** –
** p < .01, correlation is significant at the 0.01 level. Based on the hypotheses tested, a one-tailed test was used (D. George & Mallery, 2020).
Hypothesis 1a
This hypothesis tested the relation between employee alignment and employee
engagement. The hypothesis stated: There is a statistically significant positive correlation
between employee alignment and employee engagement. In testing the hypothesis, a
Pearson product moment correlation coefficient was computed (Table 4.10). The result
indicated a strong (J. Cohen, 1988) and statistically significant positive correlation
between employee alignment and employee engagement (r (107) = .65, p < .01). Based
on the analysis, Hypothesis 1a was supported.
Hypothesis 2
This hypothesis tested the relation between employee alignment and perceived
organizational support. The hypothesis stated: There is a statistically significant positive
correlation between employee alignment and perceived organizational support. In testing
the hypothesis, a Pearson product moment correlation coefficient was computed (Table
4.10). The result indicated a strong (J. Cohen, 1988) and statistically significant positive
correlation between employee alignment and perceived organizational support (r (107) =
.61, p < .01). Based on the analysis, Hypothesis 2 was supported.
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Hypothesis 3a
This hypothesis tested the relation between perceived organizational support and
employee engagement. The hypothesis stated: There is a statistically significant positive
correlation between perceived organizational support and employee engagement. In
testing the hypothesis, a Pearson product moment correlation coefficient was computed
(Table 4.10). The result indicated a medium to strong (J. Cohen, 1988) and statistically
significant positive correlation between perceived organizational support and employee
engagement (r (107) = .43, p < .01). Based on the analysis, Hypothesis 3a was supported.
Hypothesis 4
This hypothesis tested for a moderation relation between employee alignment,
perceived organizational support, and employee engagement. The hypothesis stated:
Perceived organizational support positively moderates the relation between employee
alignment and employee engagement in an organizational context. Specifically, it was
hypothesized that as perceived organizational support increased, the relation between
employee alignment and employee engagement would become more positive.
Hierarchical multiple regression analysis was used to test the hypothesis (J. Cohen
et al., 2003; Hayes, 2018; Keith, 2015, 2019). Specifically, an interaction variable was
created as the cross-product of employee alignment and perceived organizational support
(i.e., the two explanatory variables hypothesized of interacting), with the interaction
variable added sequentially to the regression analysis (Baron & Kenny, 1986; J. Cohen et
al., 2003; Hayes, 2018; Hayes & Rockwood, 2017; Keith, 2015, 2019; Kelley &
Maxwell, 2019; Lomax & Hahs-Vaughn, 2012). Equation 3 represents the general form
of a two-predictor (i.e., explanatory) variable multiple regression equation for testing
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moderation (Aiken & West, 1991; J. Cohen et al., 2003; Hayes, 2018; Hayes &
Rockwood, 2017; Kelley & Maxwell, 2019):
𝑌 = 𝑏3 + 𝑏4𝑋4 + 𝑏(𝑋( + 𝑏6𝑋4𝑋( + 𝑒 (3)
Where Y is the outcome (or dependent) variable, b0 is the Y intercept, X1 is the predictor
(i.e., the independent or explanatory) variable, b1 is the unstandardized regression
coefficient for the X1 variable, X2 is the moderator variable, b2 is the unstandardized
regression coefficient for the X2 variable, b3 is the unstandardized regression coefficient
for the interaction term (i.e., the product of X1 and X2), and e represents the error term
(i.e., the error of estimation) (Aiken & West, 1991; J. Cohen et al., 2003; Dalal & Zickar,
2012; Hayes, 2018; Kelley & Maxwell, 2019; Kromrey & Foster-Johnson, 1998; Lomax
& Hahs-Vaughn, 2012).
Inserting the variables used in the current study (employee engagement, employee
alignment, and perceived organizational support), the general moderation Equation 3
transforms to Equation 4:
𝐸𝐸 = 𝑏3 + 𝑏4𝐸𝐴 + 𝑏(𝑃𝑂𝑆 + 𝑏6(𝐸𝐴 × 𝑃𝑂𝑆) + 𝑒 (4)
Moderation is supported if the addition of the interaction variable (i.e., EA ×
POS) results in a statistically significant increase in R2 (i.e., a statistically significant
R2change) (Baron & Kenny, 1986; Hayes, 2018; Keith, 2015, 2019). A significance level of
.05 was used to determine statistical significance (J. Cohen, 1988; J. Cohen et al., 2003;
D. George & Mallery, 2020; Hinkle et al., 2003). The hierarchical multiple regression
analysis was conducted as a three-step sequential process testing three nested analysis
models as shown in Figure 4.1.
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Figure 4.1
Hierarchical Multiple Regression Moderation Analysis Models
A model summary of the hierarchical multiple regression moderation analysis is
shown in Table 4.11, derived from Table O.1 and Table O.2 of Appendix O.
Table 4.11
Hierarchical Multiple Regression Moderation Analysis: Model Summary
Modela Model statistics Model change statistics
R2 dfreg dfres F p R2change Fchange df1 df2 p
1 .061 3 105 2.263 .085b .061 2.263 3 105 .085 2 .465 5 103 17.930 <.001c .405 38.975 2 103 <.001 3 .478 6 102 15.598 <.001d .013 2.569 1 102 .112
a Dependent Variable: EE b Predictors: (Constant), Tenure, Gender, Age c Predictors: (Constant), Tenure, Gender, Age, POS_Centered, EA_Centered d Predictors: (Constant), Tenure, Gender, Age, POS_Centered, EA_Centered, EA_Centered_x_POS_Centered Note: Analysis results summarized from Appendix O, Table O.1 and Table O.2. dfreg = degrees of freedom regression dfres = degrees of freedom residual
Hierarchical Multiple Regression Analysis – Step 1. In Step 1 (i.e., analysis
Model 1), the control variables of age, gender, and tenure (i.e., organizational tenure)
were entered into Block #1 of the regression model. In Step 1, employee engagement was
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regressed on the three control variables. As reflected in Table 4.11, Model 1 did not
explain a statistically significant amount of variance in employee engagement, F(3, 105)
= 2.263, p = .085, R2 = .061.
Hierarchical Multiple Regression Analysis – Step 2. In Step 2 (i.e., analysis
Model 2), the two centered explanatory variables of employee alignment and perceived
organizational support (i.e., the variables EA_Centered and POS_Centered) were entered
into Block #2 of the regression model. In Step 2, employee engagement was regressed on
the three control variables and the explanatory variables employee alignment and
perceived organizational support. As reflected in Table 4.11, Model 2 explained a
statistically significant amount of variance in employee engagement, F(5, 103) = 17.930,
p < .001, R2 = .465. Model 2 explained 46.5% of the variance in employee engagement,
40.5% more variance in employee engagement than Model 1, Fchange(2, 103) = 38.975, p
< .001, R2change = .405.
Hierarchical Multiple Regression Analysis – Step 3. Lastly, in Step 3 (i.e.,
analysis Model 3), the interaction variable (i.e., the product of the centered variables
employee alignment and perceived organizational support) was entered into Block #3 of
the regression model. In Step 3, employee engagement was regressed on the three control
variables, the explanatory variables employee alignment and perceived organizational
support, and the interaction variable. As reflected in Table 4.11, Model 3 explained a
statistically significant amount of variance in employee engagement, F(6, 102) = 15.598,
p < .001, R2 = .478. However, while Model 3 explained 47.8% of the variance in
employee engagement, 1.3% more variance in employee engagement than Model 2,
Fchange(1, 102) = 2.569, p = .112, R2change = .013, the change in explained variance in
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employee engagement due to the interaction variable (i.e., R2change = .013) was not
statistically significant (i.e., p = .112). Based on the analysis, Hypothesis 4 was not
supported.
Hypothesis 5
This hypothesis tested for a mediation relation between employee alignment,
perceived organizational support, and employee engagement. It was hypothesized that a
person with higher feelings of employee alignment would perceive higher levels of
support from the organization which, in turn, would produce higher levels of employee
engagement. In other words, employee alignment would have an indirect effect (Baron &
Kenny, 1986; Hayes, 2009, 2018; Jose, 2019; Keith, 2015, 2019; Preacher, 2015; Song &
Lim, 2015; Zhao et al., 2010) on employee engagement through perceived organizational
support. The hypothesis stated: Perceived organizational support mediates the relation
between employee alignment and employee engagement in an organizational context.
Equations 5 and 6 represent the general form of the regression equations for
mediation analysis, that is, for estimating the effect of X on M (Equation 5) and the effect
of X on Y through M (Equation 6) (Hayes, 2018; Preacher, 2015):
𝑀 = 𝑖@ + 𝑎𝑋 + 𝑒@ (5)
𝑌 = 𝑖C + 𝑐E𝑋 + 𝑏𝑀 + 𝑒C (6)
Where X is the explanatory (or independent) variable, M is the mediating variable, Y is
the outcome (or dependent) variable, iM and iY are regression constants, a, b, and c’, are
regression coefficients, and eM and eY represent the error terms (i.e., the error in the
estimation of M and Y) (Hayes, 2018).
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Inserting the variables used in the current study (employee engagement, employee
alignment, and perceived organizational support), Equations 5 and 6 transforms to
Equations 7 and 8:
𝑃𝑂𝑆 = 𝑖GHI + 𝑎𝐸𝐴 + 𝑒GHI (7)
𝐸𝐸 = 𝑖KK + 𝑐E𝐸𝐴 + 𝑏𝑃𝑂𝑆 + 𝑒KK (8)
A simple mediation model is shown in Figure 4.2 (Baron & Kenny, 1986; Hayes,
2009, 2018; Hayes & Rockwood, 2017; Jose, 2019; Keith, 2015, 2019; Preacher, 2015;
Song & Lim, 2015; Zhao et al., 2010). As reflected in the figure, mediation occurs when
a variable (X) affects the outcome variable (Y) through a mediating (or intermediate)
variable (M) (Baron & Kenny, 1986; Hayes, 2009, 2018; Hayes & Rockwood, 2017;
Jose, 2019; Keith, 2015, 2019; Preacher, 2015; Song & Lim, 2015; Zhao et al., 2010). In
other words, there is a causal sequence of effect where X affects M and M, in turn, affects
Y; this sequence can be depicted as X ® M ® Y (Baron & Kenny, 1986; Hayes, 2009,
2018; Hayes & Rockwood, 2017; Preacher, 2015; Zhao et al., 2010).
Figure 4.2
Simple Mediation Model
When examining mediation, it is important to note the distinction between the
effects among the variables involved: total effect, direct effect, and indirect effect (Baron
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& Kenny, 1986; Hayes, 2009, 2018; Hayes & Rockwood, 2017; Jose, 2019; Keith, 2015,
2019; Preacher, 2015; Song & Lim, 2015; Zhao et al., 2010).
Total Effect. The total effect of X on Y is depicted in Figure 4.3 as the c path and
reflects the basic relation between X and Y (Hayes, 2018; Jose, 2019; Zhao et al., 2010).
In other words, the total effect (c) can be computed with a simple regression of Y on X
(Hayes, 2018; Jose, 2019). Alternately, c (the total effect) can be calculated as the sum of
the direct and indirect effects (i.e., regression coefficients) of X on Y (Hayes, 2009, 2018;
Preacher, 2015; Song & Lim, 2015; Zhao et al., 2010):
total effect = direct effect + indirect effect
c = c’ + ab
where:
c denotes the total effect of X on Y
c’ denotes the direct effect of X on Y controlling for M (discussed below)
ab denotes the indirect effect of X on Y (discussed below)
Figure 4.3
Total Effect
Direct Effect. The direct effect of X on Y (i.e., X ® Y) is depicted in Figure 4.2 as
the c’ path and interpreted as the effect of X on Y after controlling for M (Baron &
Kenny, 1986; Hayes, 2009, 2018; Jose, 2019; Preacher, 2015; Zhao et al., 2010). From
the calculation of total effect discussed above, the direct effect (c’) can be calculated as
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(Baron & Kenny, 1986; Hayes, 2009, 2018; Jose, 2019; Preacher, 2015; Zhao et al.,
2010):
direct effect = total effect – indirect effect
c’ = c – ab
Indirect Effect. The indirect effect of X on Y through M reflects the estimated
difference in Y as a result of a one-unit change in X as a result of the casual sequence that
X affects M which in turn affects Y (Hayes, 2009, 2018; Hayes & Rockwood, 2017). The
indirect effect (i.e., X ® M ® Y) is represented in Figure 4.2 as the product of the
regression coefficients for the a path × the b path (Baron & Kenny, 1986; Hayes, 2009,
2018; Hayes & Rockwood, 2017; Keith, 2019; Preacher, 2015; Zhao et al., 2010). The
indirect effect (ab) is interpreted as the difference between the total effect of X on Y and
the direct effect of X on Y (i.e., the effect of X on Y controlling for M) (Hayes, 2018;
Hayes & Rockwood, 2017). The indirect effect (ab) can be calculated as follows (Baron
& Kenny, 1986; Hayes, 2009, 2018; Hayes & Rockwood, 2017; Keith, 2019; Preacher,
2015; Zhao et al., 2010):
indirect effect = total effect – direct effect
ab = c – c’
Incorporating the preceding discussion of total, direct, and indirect effects, the
mediation model tested in this study is shown in Figure 4.4.
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Figure 4.4
Mediation Model Tested
When testing for mediation, the primary focus is on estimating the direct and
indirect effects of the independent (i.e., explanatory) variable on the dependent (i.e.,
outcome) variable (Baron & Kenny, 1986; Hayes, 2009, 2018; Jose, 2019; Keith, 2015,
2019; Preacher, 2015; Song & Lim, 2015; Zhao et al., 2010). Evidence of mediation is
supported if the indirect effect of X on Y through M (i.e., X ® M ® Y) is statistically
significant (Hayes, 2009, 2018; Hayes & Rockwood, 2017; Keith, 2015, 2019; Preacher,
2015; Zhao et al., 2010). For Hypothesis 5, evidence of mediation would be supported if
the indirect effect of employee alignment on employee engagement through perceived
organizational support (i.e., EA ® POS ® EE) was statistically significant.
One of the most frequently used methods for examining mediation has been the
approach discussed by Baron and Kenny (1986), often referred to as the causal steps
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approach (Hayes, 2009, 2018; Hayes & Rockwood, 2017; Song & Lim, 2015), along
with the Sobel test to determine the statistical significance of the indirect effect (Hayes,
2009, 2018; Hayes & Rockwood, 2017; Jose, 2019; Keith, 2019; Song & Lim, 2015).
However, mediation researchers and methodologists are increasingly advising against the
use of the Baron and Kenny (1986) approach and Sobel test (Hayes, 2009, 2018; Hayes
& Rockwood, 2017; Jose, 2019; Keith, 2019; Song & Lim, 2015) and instead recommend
analyses that specifically focus on the indirect effect itself (i.e., a × b) (Hayes, 2009;
Hayes & Rockwood, 2017) and the use of bootstrap confidence intervals to test the
statistical significance of the indirect effect (Hayes, 2009, 2018; Hayes & Rockwood,
2017; Jose, 2019; Keith, 2019; Preacher, 2015; Song & Lim, 2015).
Multiple regression analysis was used to test for mediation (J. Cohen et al., 2003;
Hayes, 2009, 2018; Hayes & Rockwood, 2017; Keith, 2015, 2019; Preacher, 2015).
Specifically, the SPSS PROCESS macro (Version 3.5) (Hayes, 2018) was applied with
model–420 with 95th percentile bootstrap confidence intervals (Hayes, 2018). The
mediating effect of perceived organizational support on employee alignment and
employee engagement was tested by examining the paths depicted in Figure 4.4. The
output from the SPSS PROCESS Macro multiple regression mediation analysis is
provided in Appendix P.
a Path. Perceived organizational support was regressed on employee alignment to
estimate the coefficient for the a path (a). Both the regression model (F(4, 104) = 16.159,
20 The PROCESS macro model–4 estimates Equations 5 and 6 (Hayes, 2018). For this study, the PROCESS macro was used to estimate Equations 7 and 8.
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p < .001, R2 = .383) and the coefficient (a) (b = 1.707, t(104) = 7.939, p < .001) for the a
path were significant.
b Path and c’ Path. Employee engagement was regressed on employee
alignment and perceived organizational support to estimate the coefficients for the b path
(b) and c’ path (c’). The regression model (F(5, 103) = 17.930, p < .001, R2 = .465) was
significant and suggested that employee alignment and perceived organizational support
(along with the control variables of age, gender, and tenure) accounted for 46.5% of the
variance in employee engagement. As expected, the coefficient for the c’ path (c’) (b =
.751, t(103) = 6.724, p < .001) was also significant. However, contrary to expectations,
the coefficient for the b path (b) (b = .015, t(103) = .384, p = .701) was not statistically
significant.
c Path. Lastly, employee engagement was regressed on employee alignment to
estimate the coefficient for the c path (c). The regression model (F(4, 104) = 22.561, p <
.001, R2 = .465) was significant and suggested that employee alignment (along with the
control variables of age, gender, and tenure) accounted for 46.5% of the variance in
employee engagement. As expected, the coefficient for the c path (c) (b = .777, t(104) =
8.857, p < .001) was also significant.
ab Path. The coefficient for the indirect effect of employee alignment on
employee engagement (i.e., the ab path) was calculated as the product of the regression
coefficient estimates for the a and b paths (i.e., a × b), resulting in a coefficient value of
.026 (a = 1.707 × b = .015) (Baron & Kenny, 1986; Hayes, 2009, 2018; Hayes &
Rockwood, 2017; Keith, 2019; Preacher, 2015; Zhao et al., 2010). The statistical
significance of the indirect effect coefficient (ab) was tested through the use of a 95th
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percentile bootstrap confidence interval,21 using 5,000 bootstrap samples (Hayes, 2009,
2018; Hayes & Rockwood, 2017; Jose, 2019; Keith, 2019; Preacher, 2015; Song & Lim,
2015). A 95th percentile bootstrap confidence interval not including the value of zero
between the lower and upper limits would provide evidence that the indirect effect was
statistically significant and support a claim of mediation; that is, there is 95% confidence
that the value of the indirect effect is not zero (Hayes, 2009, 2018; Hayes & Rockwood,
2017; Keith, 2019).
Using the percentile bootstrap procedure, the indirect effect of employee
alignment on employee engagement through perceived organizational support (i.e., EA ®
POS ® EE) was found to be not statistically significant, ab = .026, bootstrap standard
error = .070, 95% CI = [–.106, .170] (Table 4.12). Based on the analysis, Hypothesis 5
was not supported. The results of the multiple regression mediation analysis are
summarized in Table 4.12 (the data in the table are derived from the SPSS PROCESS
Macro (Version 3.5) (Hayes, 2018) multiple regression mediation analysis output shown
in Appendix P.)
21 A bootstrap confidence interval was constructed by the PROCESS macro randomly resampling, with replacement, from the data from the original data set; the resampling process was repeated 5,000 times (Hayes, 2009, 2018; Hayes & Rockwood, 2017).
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Table 4.12
Total, Direct, and Indirect Effects – POS as a Mediating Variable
Estimated coefficientsa
Unstandardized coefficients
t Sig.
95% confidence interval for indirect effect (ab)b
b Std.
error Lower bound
Upper bound
a (EA ® POS) 1.707 .215 7.939 <.001 b (POS ® EE) .015 .040 .384 .701 c’ (EA ® EE) .751 .112 6.724 <.001 c (EA ® EE) .777 .088 8.857 <.001 ab (EA ® POS ®EE) .026 .070c –.106 .170 a Corresponding paths from Figure 4.4 are shown in parenthesis. b Percentile bootstrap confidence interval (Hayes, 2018). c Percentile bootstrap confidence standard error (Hayes, 2018). Note: Analysis results summarize the mediation analysis output shown in Appendix P.
Summary of Hypothesis Testing for Research Question 1
The first research question asked: To what extent is there a statistically significant
relation among employee alignment, perceived organizational support, and employee
engagement in an organizational context? In answering this question, Hypotheses 1a, 2, 3a,
4, and 5 were tested. The results provided support for medium to strong (J. Cohen, 1988)
and statistically significant positive correlations among the variables of employee
alignment, perceived organizational support, and employee engagement (Table 4.10). The
computed Pearson correlation coefficients (J. Cohen et al., 2003; Hinkle et al., 2003; Keith,
2015; Lomax & Hahs-Vaughn, 2012) were all significant at the .01 level (one-tailed).
Hypothesis 4 tested for a moderation relation between employee alignment,
perceived organizational support, and employee engagement. While the addition of the
moderating variable (i.e., perceived organizational support) explained an additional 1.3%
of the variance in employee engagement, the change in explained variance due to the
interaction variable was not statistically significant (Table 4.11); perceived organizational
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support did not moderate the relation between employee alignment and employee
engagement in an organizational context.
Hypothesis 5 tested for a mediation relation between employee alignment,
perceived organizational support, and employee engagement, where employee
alignment had an indirect effect (Baron & Kenny, 1986; Hayes, 2009, 2018; Jose, 2019;
Keith, 2015, 2019; Preacher, 2015; Song & Lim, 2015; Zhao et al., 2010) on employee
engagement through perceived organizational support. The indirect effect of employee
alignment on employee engagement through perceived organizational support was
found to be not statistically significant (Table 4.12); perceived organizational support
did not mediate the relation between employee alignment and employee engagement in
an organizational context. Next, the discussion examines the second research question.
Research Question 2
The second research question asked: To what extent do employee alignment and
perceived organizational support explain a statistically significant proportion of the
unique variance in employee engagement? In answering this question, Hypotheses 1b and
3b were tested. Simultaneous multiple regression analysis (J. Cohen et al., 2003; Keith,
2015; Lomax & Hahs-Vaughn, 2012) was used to test these two hypotheses, with
employee engagement regressed on the three control variables (i.e., age, gender, and
tenure) and the explanatory variables employee alignment and perceived organizational
support. As discussed in Chapter 3, the assumptions of linearity, normal distribution of
residuals, homoscedasticity, independence of residuals, and noncollinearity were met for
the multiple regression analysis.
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As reflected in Table 4.13, the simultaneous multiple regression model explained
a statistically significant amount of variance in employee engagement, F(5, 103) =
17.930, p < .001, R2 = .465. (Table 4.13 is derived from the simultaneous multiple
regression output shown in Appendix Q.) Overall, the model explained 46.5% of the
variance in employee engagement. Hypotheses 1b and 3b are discussed in turn, with the
results of the statistical analysis and hypothesis testing.
Table 4.13
Simultaneous Multiple Regression Analysis: Model Summary
Modela R R2 Standard
error dfreg dfres F p Model 1 .682b .465 4.068 5 103 17.930 <.001 a Dependent variable: Employee engagement. b Predictors: (Constant), Perceived organizational support, Gender, Tenure, Age, Employee alignment. Note: Analysis results summarized from the SPSS output in Appendix Q. dfreg = degrees of freedom regression; dfres = degrees of freedom residual.
Hypothesis 1b
This hypothesis further tested the relation between employee alignment and
employee engagement. The hypothesis stated: Employee alignment explains a
statistically significant proportion of the unique variance in employee engagement after
controlling for perceived organizational support. As shown in Table 4.14, employee
alignment explained a statistically significant amount of the unique variance in employee
engagement, b = .75, t(103) = 6.72, p < .001, sr2 = .234. Employee alignment explained
23.4% of the unique variance in employee engagement. Based on the analysis,
Hypothesis 1b was supported.
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Table 4.14
Simultaneous Multiple Regression Analysis: Model Coefficients
Modela
Unstandardized coefficients
Standardized coefficients
t Sig. sr2 B Std. error Beta 1 (Constant) 22.576 3.511 6.431 <.001
Age .113 .040 .230 2.804 .006 0.041 Gender -.324 1.018 -.023 -.319 .750 0.001 Tenure -.074 .065 -.093 -1.136 .259 0.007 EA .751 .112 .619 6.724 <.001 0.234 POS .015 .040 .035 .384 .701 0.001
a Dependent variable: Employee engagement. Note: Analysis results summarized from the SPSS output in Appendix Q. sr2 represents the square of the semipartial correlation. Hypothesis 3b
This hypothesis further tested the relation between perceived organizational
support and employee engagement. The hypothesis stated: Perceived organizational
support explains a statistically significant proportion of the unique variance in employee
engagement after controlling for employee alignment. As shown in Table 4.14, perceived
organizational support did not explain a statistically significant amount of the unique
variance in employee engagement, b = .02, t(103) =.38, p = .701, sr2 = .001. Based on the
analysis, Hypothesis 3b was not supported.
Summary of Hypothesis Testing for Research Question 2
The second research question asked: To what extent do employee alignment and
perceived organizational support explain a statistically significant proportion of the
unique variance in employee engagement? In answering this question, Hypotheses 1b and
3b were tested. The results indicated that employee alignment explained 23.4% of the
unique variance in employee engagement, providing support for Hypothesis 1b (Table
4.14). However, perceived organizational support did not explain a statistically
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significant proportion of the unique variance in employee engagement; Hypothesis 3b
was not supported (Table 4.14).
Chapter Summary
This research examined a hypothesized model of employee engagement,
exploring the relation among the two explanatory constructs (variables) of employee
alignment and perceived organizational support and the outcome construct (variable) of
employee engagement in an organizational context. Correlation and multiple regression
statistical analyses were conducted to answer two research questions:
RQ1. To what extent is there a statistically significant relation among employee
alignment, perceived organizational support, and employee engagement in an
organizational context?
RQ2. To what extent do employee alignment and perceived organizational support
explain a statistically significant proportion of the unique variance in employee
engagement?
This chapter presented the results of the statistical analysis of the data from the
study sample in four main sections. The first section discussed the participant
demographics. The second section assessed the reliability and validity of the survey
questionnaire scales within the context of the study’s sample. The third section discussed
the descriptive statistics of the study variables. The final section presented the results of
the statistical analyses of the seven study hypotheses.
The statistical results provided evidence of partial support for the researcher’s
hypotheses regarding the relation among employee alignment, perceived organizational
support, and employee engagement. Support was found for four of the seven tested
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hypotheses (Table 4.15). The results provided evidence for a positive relation among
employee alignment, perceived organizational support, and employee engagement, as
well as the statistically significant contribution of employee alignment in explaining
unique variance in employee engagement (i.e., 23.4%). Contrary to expectations, the
results did not provide evidence that perceived organizational support had a statistically
significant effect on employee engagement. Additionally, the results did not provide
statistically significant evidence of either a moderation or mediation effect of perceived
organizational support on the relation between employee alignment and employee
engagement.
Table 4.15
Summary of Hypothesis Testing
Hypothesis Description Result 1a There is a statistically significant positive correlation between
employee alignment and employee engagement. Supported
1b Employee alignment explains a statistically significant proportion of the unique variance in employee engagement after controlling for perceived organizational support.
Supported
2 There is a statistically significant positive correlation between employee alignment and perceived organizational support.
Supported
3a There is a statistically significant positive correlation between perceived organizational support and employee engagement.
Supported
3b Perceived organizational support explains a statistically significant proportion of the unique variance in employee engagement after controlling for employee alignment.
Not supported
4 Perceived organizational support positively moderates the relation between employee alignment and employee engagement in an organizational context.
Not supported
5 Perceived organizational support mediates the relation between employee alignment and employee engagement in an organizational context.
Not supported
The next chapter discusses the results presented in this chapter, to include
interpretations, limitations, conclusions, and recommendations.
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Chapter 5: Interpretations, Conclusions, and Recommendations
This study explored the relation among employee alignment, perceived
organizational support, and employee engagement and how employee alignment and
perceived organizational support contribute to employee engagement among full-time
nonsupervisory individuals in an organizational context. The research site for the study
was the human resources department of a not-for-profit health care organization located
in the southern region (U.S. Census Bureau, n.d.) of the United States. This chapter
discusses the results of the study presented in Chapter 4. In discussing the study results,
this chapter addresses (a) the research problem; (b) interpretation of the study findings;
(c) conclusions; (d) recommendations for theory, research, and practice; and (e)
researcher reflections on the study.
Research Problem
As discussed in Chapter 2, research has demonstrated the beneficial
organizational (i.e., managerial) and individual employee health and well-being outcomes
of engaged employees. However, approximately one-third (U.S. Office of Personnel
Management, 2018) to two-thirds (Gallup, 2017) of the U.S. workforce remains
disengaged—that is, “mentally ‘checked out’” (Seijts & Crim, 2006, p. 1)—with an
estimated impact on the U.S. economy, due to lost productivity, between $483 billion and
$605 billion per year (Gallup, 2017, p. 19).
In the ongoing pursuit of enhancing the engagement of employees, scholars have
identified a need for research focused on the organizational elements (Coyle-Shapiro &
Shore, 2007), or factors (Whittington et al., 2017; Whittington & Galpin, 2010), within
the purview of managers that can improve the engagement of employees (Alagaraja &
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Shuck, 2015; Coyle-Shapiro & Shore, 2007; Eldor & Vigoda-Gadot, 2017; Oswick,
2015; Whittington et al., 2017; Whittington & Galpin, 2010). Two such factors that have
been identified as critical to creating conditions for employee engagement are alignment
(CEB Corporate Leadership Council, 2015b, 2015c; Harter & Rigoni, 2015; Rao, 2017;
Ray et al., 2014; Stallard & Pankau, 2010) and perceived organizational support (Seijts &
Crim, 2006; Shuck et al., 2014; Shuck, Rocco, et al., 2011; Wollard & Shuck, 2011).
Using the employee engagement framework proposed by Shuck and Reio (2011), this
research addressed the (a) practical problem of how a manager can create conditions that
may increase employee engagement in an organization and (b) theoretical problem of the
need for a better understanding of the relation among employee alignment, perceived
organizational support, and employee engagement and how employee alignment and
perceived organizational support interact to contribute to employee engagement. The
statistical results provided evidence of partial support for the researcher’s hypotheses
regarding the relation among employee alignment, perceived organizational support, and
employee engagement. The interpretation of the study results follows.
Interpretation of the Study Findings
This study had three main findings: (1) there is a statistically significant positive
correlation among employee alignment, perceived organizational support, and employee
engagement; (2) employee alignment explained a statistically significant proportion of
the unique variance in employee engagement, but perceived organizational support did
not; and (3) perceived organizational support did not moderate or mediate the relation
between employee alignment and employee engagement. As discussed in Chapter 4, the
statistical analyses found support for four of the seven hypotheses tested in this study, as
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summarized in Table 4.15. These results, graphically depicted in Figure 5.1, serve as the
basis for the three study findings. Each of the three findings is discussed and interpreted
below within the context of current theory and research.22
Figure 5.1
Graphical Representation of the Results of the Tests of the Study Hypotheses
Finding 1: Correlations Between Employee Alignment, Perceived Organizational
Support, and Employee Engagement
The results from the statistical analysis of the bivariate correlations (also referred
to as zero-order correlations (J. Cohen et al., 2003)) provide evidence for statistically
significant medium to strong (J. Cohen, 1988) positive correlations among the variables
of employee alignment, perceived organizational support, and employee engagement.
The computed Pearson product moment correlation coefficients (J. Cohen et al., 2003;
Hinkle et al., 2003; Keith, 2015; Lomax & Hahs-Vaughn, 2012) were all significant at
22 Recognizing that different measures of engagement were used in the various studies, the discussion of previous research is limited to studies using the 8-item version of the Survey of Perceived Organizational Support (Eisenberger et al., 1986), the survey instrument used in the current study.
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the p = 0.01 level (one-tailed). Each of the three bivariate correlations are discussed as
follows.
Correlation Between Employee Alignment and Employee Engagement
This study found that employee perceptions of engagement were positively
associated with perceptions of alignment. Specifically, the analysis for Hypothesis 1a
found a strong (J. Cohen, 1988) and statistically significant positive correlation between
employee alignment and employee engagement (r (107) = .65, p < .01). Although this
study measured engagement using the Employee Engagement Scale (Shuck, Adelson, et
al., 2017), this result supports previous studies that indicate that the alignment of an
employee to the goals of the organization is positively correlated to an employee’s
engagement (Albrecht et al., 2018; Biggs et al., 2014b; Stringer, 2007).
Correlation Between Employee Alignment and Perceived Organizational Support
This study found that employee perceptions of being supported by the
organization were positively associated with perceptions of alignment. Specifically, the
analysis for Hypothesis 2 found a strong (J. Cohen, 1988) and statistically significant
positive correlation between employee alignment and perceived organizational support (r
(107) = .61, p < .01). Although the researcher did not identify any previous studies
explicitly addressing an actual or conceptual relation between employee alignment and
perceived organizational support, this result supports the hypothesized relation between
employee alignment and perceived organizational support discussed in Chapter 2.
Correlation Between Perceived Organizational Support and Employee Engagement
This study found that employee perceptions of engagement were positively
associated with perceptions of being supported by the organization. Specifically, the
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analysis for Hypothesis 3a found a medium to strong (J. Cohen, 1988) and statistically
significant positive correlation between perceived organizational support and employee
engagement (r (107) = .43, p < .01). Although this study measured engagement using the
Employee Engagement Scale (Shuck, Adelson, et al., 2017), this result supports previous
studies that indicated that an employee’s perception of being supported by the
organization is positively correlated to an employee’s engagement (Biswas & Bhatnagar,
2013; Mahon et al., 2014; Rich et al., 2010; Saks, 2006; Simmons, 2013; Wang et al.,
2017; Zhong et al., 2016).
Recognizing the different measures of engagement used in previous research
examining alignment and perceived organizational support as antecedent to engagement,
the results of statistically significant medium to strong (J. Cohen, 1988) positive
correlations among the variables of employee alignment, perceived organizational
support, and employee engagement were expected based on the literature review.
However, it is important to note that the results do not, in and of themselves, infer
causation in the relation among the explanatory variables (employee alignment and
perceived organizational support) and the outcome variable (employee engagement)
(Hinkle et al., 2003; Keith, 2015; Lomax & Hahs-Vaughn, 2012). Rather, the results
simply confirm a relation, in this case a positive relation, among the variables (J. Cohen
et al., 2003; Hinkle et al., 2003; Keith, 2015; Lomax & Hahs-Vaughn, 2012).
Finding 2: Accounting for Statistically Significant Unique Variance in Employee
Engagement
Having discussed the finding of statistically significant positive correlations
among the study variables, this finding addresses the extent to which variance in
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employee engagement was uniquely explained (i.e., controlling for the other antecedent
variable) by each of the two antecedent variables of employee alignment and perceived
organizational support. The results from the multiple regression analysis provided
evidence that employee alignment explained 23.4% of the unique variance in employee
engagement but perceived organizational support did not explain a statistically significant
proportion of the unique variance.
Employee Alignment
As discussed in Chapter 4, the analysis for Hypothesis 1b found that employee
alignment explained a statistically significant amount of the unique variance in employee
engagement, b = .75, t(103) = 6.72, p < .001, sr2 = .234 (Table 4.14). Employee
alignment explained 23.4% of the unique variance in employee engagement when
controlling for perceived organizational support and the control variables (age, gender,
and tenure). The result also reflects that for every one-unit increase in employee
alignment, employee engagement increased by .75 units (i.e., b = .75).
Perceived Organizational Support
While this study found evidence of a statistically significant relation between
perceived organizational support and employee engagement at the zero-order
correlational level (i.e., r (107) = .43, p < .01), the analysis for Hypothesis 3b found that
the relation became nonsignificant when entered into the regression model to test for
unique variance. That is, when controlling for employee alignment and the control
variables (age, gender, and tenure), perceived organizational support did not explain a
statistically significant amount of the unique variance in employee engagement, b = .012,
t(103) = .38, p = .701, sr2 = .001 (Table 4.14).
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The result that employee alignment explained 23.4% of the unique variance in
employee engagement was as expected based on the literature review and supports
previous research (Stringer, 2007) that indicated that the alignment of an employee to the
goals of the organization explains (or predicts) variance in an employee’s engagement.23
However, the result that perceived organizational support did not explain a statistically
significant proportion of the unique variance in employee engagement was unexpected
and diverges from previous studies that have found a significant relation between these
variables. This result of nonsignificance may be a result of shared variance (i.e., an
overlap of, or redundancy in, the variance) in employee engagement accounted for by
employee alignment and perceived organizational support (J. Cohen et al., 2003; Hinkle
et al., 2003; Keith, 2015).
Finding 3: Perceived Organizational Support as a Moderating/Mediating Variable
in the Relation Between Employee Alignment and Employee Engagement
Contrary to expectations, the results of the multiple regression moderation and
mediation analyses did not provide statistically significant evidence of either a
moderation or mediation effect of perceived organizational support on the relation
between employee alignment and employee engagement.
Perceived Organizational Support as a Moderating Variable
Based on current literature (Eisenberger et al., 2016; Rich et al., 2010; Shuck et
al., 2014), it was expected that perceived organizational support would moderate the
relation between employee alignment and employee engagement—that is, as perceived
23 Of the three aforementioned studies that examined a relation between alignment and engagement (i.e., Albrecht et al., 2018; Biggs et al., 2014; Stringer, 2007), only Stringer (2007) provided statistical results that can be compared to the results of the current study.
217
organizational support increased, the relation between employee alignment and employee
engagement would become more positive. However, the analysis for Hypothesis 4
showed that while the addition of the interaction effect (i.e., moderating) variable into the
regression equation explained an additional 1.3% of the variance in employee
engagement, the change in explained variance due to the interaction variable was not
statistically significant, Fchange(1, 102) = 2.57, p = .112, R2change = .013 (Table 4.11);
perceived organizational support did not moderate the relation between employee
alignment and employee engagement.
Perceived Organizational Support as a Mediating Variable
Based on current literature (Kurtessis et al., 2017; Rhoades & Eisenberger, 2002),
it was expected that higher levels of employee alignment would lead to an increase in
perceived organizational support which, in turn, should lead to higher levels of employee
engagement. In other words, employee alignment would have an indirect effect (Baron &
Kenny, 1986; Hayes, 2009, 2018; Jose, 2019; Keith, 2015, 2019; Preacher, 2015; Song &
Lim, 2015; Zhao et al., 2010) on employee engagement through perceived organizational
support, mediating the relation between employee alignment and employee engagement.
However, the multiple regression mediation analysis for Hypothesis 5 showed that the
indirect effect of employee alignment on employee engagement through perceived
organizational support was not statistically significant (Table 4.12); perceived
organizational support did not mediate the relation between employee alignment and
employee engagement.
The result that perceived organizational support did not moderate or mediate the
relation between employee alignment and employee engagement was unexpected and
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diverged from the hypothesized relation based on the literature review. However, given
the nature of moderation and mediation (i.e., as an interaction and indirect effect,
respectively), these results are not necessarily surprising given that perceived
organizational support was found to not explain a statistically significant amount of the
unique variance in employee engagement. That is, perceived organizational support did
not have a direct effect on employee engagement.
The three findings provide insights into answering the two research questions that
guided this inquiry: (1) To what extent is there a statistically significant relation among
employee alignment, perceived organizational support, and employee engagement in an
organizational context? (2) To what extent do employee alignment and perceived
organizational support explain a statistically significant proportion of the unique variance
in employee engagement?
Conclusions
Based on the research problem and the interpretations of the study findings, three
conclusions were drawn concerning the relation among employee alignment, perceived
organizational support, and employee engagement: (1) employee alignment is critical to
employee engagement, (2) perceived organizational support is affected by individual
employee perceptions of their unique work context, and (3) the study of employee
engagement requires a systems approach.
Conclusion 1: Employee Alignment is Critical to Employee Engagement
This study provided evidence that employee alignment is an organizational factor
within the sphere of influence of a manager that has a significant positive and practical
effect on employee engagement. The insights from this study are important given the lack
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of empirical research on employee alignment as an antecedent of employee engagement
and the calls for research on organizational factors within the purview of managers that
can improve the engagement of employees (Alagaraja & Shuck, 2015; Coyle-Shapiro &
Shore, 2007; Eldor & Vigoda-Gadot, 2017; Oswick, 2015; Whittington et al., 2017;
Whittington & Galpin, 2010). The importance of employee alignment is further
underscored given reports that one-third (U.S. Office of Personnel Management, 2018) to
two-thirds (Gallup, 2017) of the U.S. workforce is disengaged, with approximately two-
thirds of employees not understanding how their work relates to organizational goals
(CEB Corporate Leadership Council, 2015a).
Managers within an organization play a vital role in fostering employee
perceptions and understanding of alignment. In moving from a sense of alignment to
engagement, employees need to understand the goals of the organization and how their
efforts contribute to achieving these goals. As Alagaraja and Shuck (2015) noted,
managers must connect the “. . . overarching goals at the individual level, such that this
individual connection generates emotion, drives behavioral intention and resulting
performance” (p. 29).
Conclusion 2: Perceived Organizational Support is Affected by Individual Employee
Perceptions of Their Unique Work Context
The engagement literature suggests that it is the cognitive aspect of engagement
that starts the feeling of engagement within an employee (Shuck et al., 2014; Shuck &
Reio, 2011), with a key component of cognitive engagement being the extent that an
employee perceives that the “organization” values their contributions and cares about
their well-being (Shuck et al., 2014). The results of this study were unexpected and
220
diverged from previous research that has shown that perceived organizational support (a)
had a direct effect on and explained a statistically significant proportion of the unique
variance in engagement (Al-Omar et al., 2019; Meintjes & Hofmeyr, 2018; Simmons,
2013); (b) moderated the relation between engagement and an antecedent variable (Rai et
al., 2017); and (c) mediated the relation between engagement and an antecedent variable
(Pati & Kumar, 2010). Given these divergent results, it appears that there is more to be
understood concerning perceived organizational support as an antecedent to employee
engagement.
This study found wide variance in the data related to the construct of perceived
organizational support, suggesting that individual employee perceptions of their unique
work context greatly affects the extent to which they perceived support from the
organization. Unlike employee engagement and employee alignment, which focus on
how employees perceive and understand their own feelings of engagement and
alignment, perceived organizational support focuses on how employees feel about the
“organization” rather than themselves. As such, perceived organizational support depends
how an employee interprets who the “organization” is—that is, how an employee
personifies the organization—as well as individual employee expectations on how the
“organization” should act in order to show that employee contributions and well-being
matter (Eisenberger et al., 1986; Kurtessis et al., 2017; Rhoades & Eisenberger, 2002).
The significant role of individual employee perceptions of their unique work context is
conveyed by Shuck et al. (2014), who noted that “those who felt that their work mattered,
that they were supported in their work, and that their well-being was considered fairly
were likely to embrace and engage” (p. 245).
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Conclusion 3: The Study of Employee Engagement Requires a Systems Thinking
Approach
As discussed herein, employee engagement has been shown to be important to
both organizations and individual employees. Employee engagement is also a complex
organizational phenomenon, with numerous variables having been categorized as
antecedents. For example, in a review of the literature, Wollard and Shuck (2011)
identified 42 such antecedents of engagement (21 individual-level24 and 21
organizational-level25). As a complex phenomenon, the study of employee engagement
requires a systems thinking approach to fully understand the construct (Meadows, 2008;
Senge, 2006). That is, an analysis approach that acknowledges and examines the
interactions among the antecedent variables in the analysis model rather than simply
examining the individual effect of each antecedent on employee engagement in isolation.
A system can be defined as “an interconnected set of elements that is coherently
organized in a way that achieves something” (Meadows, 2008, p. 11) or as “a set of
interdependent parts that together make up a whole” (Cummings & Worley, 2015, p.
791). As applied to the study of organizational phenomena, the elements or
interdependent parts are simply the constructs being examined—as measured by the
study’s variables. With a systems thinking approach, the focus is on the interrelations
among the variables and how the variables interact and affect the outcome variable
24 Wollard and Shuck (2011) defined individual-level antecedents as “constructs, strategies, and conditions that were applied directly to or by individual employees and that were believed to be foundational to the development of employee engagement” (p. 433). 25 Wollard and Shuck (2011) defined organizational-level antecedents as “constructs, strategies, and conditions that were applied across an organization as foundational to the development of employee engagement and the structural or systematic level” (p. 433).
222
(Meadows, 2008; Reed, 2006; Senge, 2006; Wheatley, 2006); for example, conducting
analyses for moderation and/or mediation. A systems thinking approach is in contrast to
reductionist approach where the variables are studied individually and in isolation from
any interaction (Meadows, 2008; Reed, 2006; Wheatley, 2006). For example, simply
examining bivariate correlations and/or the unique variance accounted for by each
antecedent variable.
This is not to suggest that a researcher only examine the interactions among the
constructs of a research model. Rather, the intent is to highlight the importance of taking
a systems thinking approach and including an examination of the interactions as part of a
holistic analysis. As Meadows (2008) observed, “the behavior of a system cannot be
known just by knowing the elements of which the system is made” (p. 7).
Based on the study’s findings and conclusions, recommendations for theory,
research, and practice are discussed next.
Recommendations for Theory, Research, and Practice
This research study explored the relation among employee alignment, perceived
organizational support, and employee engagement in an organizational context. Better
understanding of this relation can assist researchers, managers, and human resources
professionals in identifying and developing strategies to improve employee engagement,
which in turn should contribute to achieving organizational goals, enhancing
organizational competitiveness, and improving employee well-being.
Recommendations for Theory
Maxwell (2013) defined theory as “a set of concepts and ideas and the proposed
relationships among these, a structure that is intended to capture or model something
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about the world” (p. 48). Further, Hatch (2018) noted that it was through the relationships
among concepts that theory can provide explanation and understanding. Although Kwon
and Park (2019) suggested that employee engagement theory “is in the initial stages of
becoming a formal theory” (p. 362), the results of this study support and extend the
current state of existing employee engagement theory. The conceptual significance of this
inquiry is an enhanced understanding of the relation among the constructs of employee
alignment, perceived organizational support, and employee engagement in an
organizational context. Based on the study findings, three recommendations are proposed
for extending existing employee engagement theory.
First, although the conceptual (Alagaraja & Shuck, 2015) and empirical (Albrecht
et al., 2018; Biggs et al., 2014b; Gorgi et al., 2019; Stringer, 2007) engagement literature
has examined the relation between alignment and engagement, most of the engagement
literature on antecedents of engagement (for example, Crawford et al., 2010; Kwon &
Park, 2019; Rana et al., 2014; Rich et al., 2010; Saks, 2006, 2019; Wollard & Shuck,
2011) has not explicitly identified alignment as an antecedent affecting the engagement
of employees. The study findings revealed that employee alignment accounted for 23.4%
of the unique variance in employee engagement. As such, existing theoretical
frameworks for employee engagement may benefit from a recognition of the significance
of employee alignment to employee engagement. The inclusion of employee alignment in
employee engagement frameworks may be especially important since employee
alignment is an organizational factor (Coyle-Shapiro & Shore, 2007; Whittington et al.,
2017; Whittington & Galpin, 2010) within the scope of influence of managers that has
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been shown to positively affect the engagement of employees (Albrecht et al., 2018;
Biggs et al., 2014b; Gorgi et al., 2019; Stringer, 2007).
This study appears to be the first empirical research to explore the relation
between employee alignment and perceived organizational support. Although the scope
of the findings is limited, this study makes a small contribution with its initial results in a
previously unreported area related to the understanding of how these variables interact
with one another and affect employee engagement in an organizational context. Thus, the
second proposed recommendation for theory is to extend existing employee engagement
theoretical frameworks to account for the relation between employee alignment and
perceived organizational support and their effect on employee engagement.
The third recommendation is to recognize the importance of a systems thinking
approach when studying antecedent variables of engagement. That is, to instill an
awareness of the importance of, and consideration for, the interactions among the
variables of a research framework as part of a holistic conceptualization of the constructs.
Recommendations for Research
In addition to the conceptual significance and recommendations for theory, this
inquiry makes methodological contributions to research. Scholars (Alagaraja & Shuck,
2015; Coyle-Shapiro & Shore, 2007; Eldor & Vigoda-Gadot, 2017; Oswick, 2015;
Whittington et al., 2017; Whittington & Galpin, 2010) have called for additional research
focused on the organizational elements (Coyle-Shapiro & Shore, 2007), or factors
(Whittington et al., 2017; Whittington & Galpin, 2010), within the purview of managers
that have been shown to improve the engagement of employees. This study partially
addressed this call for research by focusing on two such factors, employee alignment and
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perceived organizational support. Because this study explored a previously unexamined
combination of antecedents (employee alignment and perceived organizational support),
the findings should be considered preliminary, providing a basis for future research to
build upon. As such, eight recommendations for additional research are proposed.
The first recommendation is for a replication study. Given the limited
generalizability of the study results beyond the study participants (i.e., the actual sample
[Fritz & Morgan, 2010]), due to the use of a census sampling approach (L. Cohen et al.,
2011; Creswell, 2012; Fraenkel et al., 2015; Robson & McCartan, 2016; Stapleton,
2019), this study should be replicated with a different population. That is, with a sample
drawn from a population other than human resource professionals in the health care field.
A replication study could help validate the conceptual framework used and the results of
the relations among the constructs found in this study. Such a study may also provide
additional insights into the effect of perceived organizational support on employee
engagement as well as the role of perceived organizational support as either a moderator
or a mediator of the relation between employee alignment and employee engagement.
Lastly, a replication study could also provide additional evidence to support the
psychometric properties (e.g., reliability and construct validity) (Souza et al., 2017) of the
Stringer Strategic Alignment Scale (Stringer, 2007) and the Employee Engagement Scale
(Shuck, Adelson, et al., 2017).26
26 For context, the researcher was able to identify only two other studies that used the Stringer Strategic Alignment Scale to measure the alignment of employees: Stringer (2007) and Gorgi et al. (2019). Similarly, seven studies were identified that used the 12-item Employee Engagement Scale: Osam et al. (2020), Mishra and Kodwani (2019), Shuck et al. (2017), Shuck et al. (2019), Stenger, (2019), Zehr (2017), and Zhang et al. (2020). Two additional studies were identified that used a 6-item modified version of the Employee Engagement Scale: Ali et al. (2019) and Ghosh et al. (2019).
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A second recommendation would be an examination of the effect of demographic
variables, other than those used in this study (i.e., age, gender, and organizational tenure),
on each of the three study variables. For example, how might demographic variables such
as education, ethnicity, past work experience, race, and/or role (e.g., line versus staff
positions) affect employee perceptions of alignment and of feeling supported by the
organization? In turn, how do these demographic variables affect employee feelings of
engagement?
Third, would be an examination of if, and how, a manager’s feelings of their own
alignment, being supported by the organization, and being engaged affect their
employees’ perceptions of employee alignment, perceived organizational support, and
employee alignment. That is, do employees of managers with lower feelings of
alignment, support, and/or engagement have similar lower feelings of alignment, being
supported by the organization, and being engaged?
Next, in taking a systems thinking approach, would be a further examination of
the interactions among the study variables. For example, the lack of support for a direct
effect of perceived organizational support on employee engagement found in this study
suggests a possible indirect effect of perceived organizational support on employee
engagement. That is, that employee alignment may mediate the relation between
perceived organizational support and employee engagement. Such an examination would
focus on the interaction among employee alignment and perceived organizational support
and how the interaction—in this case a possible mediation effect of employee
alignment—affected employee engagement.
227
The fifth recommendation focusses on the development of survey questionnaires.
Specifically, a recommendation that researchers tailor the questions to the specific
research context to limit ambiguity in the question asked of participants. Rather than use
the term “organization,” it may be clearer to participants if questions specify the
organizational unit or person about which the researcher desires participants’ to respond.
For example, it may be clearer to specify “supervisor” (i.e., manager) support as opposed
to the more general, and possibly confusing, “organization” or “human resources
department” support when asking about employee perceptions of perceived
organizational support.
Based on the literature review, this study used a three factor model—i.e., the
constructs of employee alignment, perceived organizational support, and employee
engagement. However, the results of the exploratory factor analysis suggested that there
may be some overlap in how the three constructs are being measured. The sixth
recommendation is for future research that further examined the relation among these
constructs to see if the 3-factor model holds or if a different factor model best fits the
data.
The seventh recommendation is for a qualitative study to explore employee
perceptions related to if, how, and why employee alignment and perceived organizational
support affect feelings of being engaged. Creswell (2014) noted that a qualitative
research design is appropriate when a study seeks to explore and understand the meaning
of a phenomenon and experiences from the perspective of the participants (e.g.,
employees). This would differ from the current quantitative study in that the focus of the
proposed qualitative inquiry would be to explore, interpret, and understand the
228
mechanisms—i.e., the subjective experience and its meaning—of how employees’
perception of alignment and being supported by their organization affect their feelings of
being engaged (Burrell & Morgan, 1992; Creswell, 2013, 2014; Hatch, 2018; Maxwell,
2013; Ravitch & Carl, 2016; Robson & McCartan, 2016).
Lastly, although the identification of antecedent and outcome variables in this
study were based on the current engagement literature, a future study could explore other
antecedent–outcome variable relations. For example, an investigation of whether or not
there are situations where employee engagement acts as an antecedent for employee
alignment or perceived organizational support.
Recommendations for Practice
From a practice perspective, this study provides preliminary evidence of the
significance of employee understanding of the organization’s goals and how their work
and job responsibilities contribute to achieving those goals—that is, the importance of
employee alignment—in nurturing conditions that may increase employee engagement in
an organization. Additionally, this study also provides limited evidence that supports a
positive relation between perceived organizational support and employee engagement.
Organizations that want to further the achievement of organizational goals,
enhance organizational competitiveness, and improve employee well-being should focus
on nurturing practices conducive to developing employee engagement. For example,
scholars have noted the responsibility of managers to help employees understand
organizational goals and how their efforts support these goals (Alagaraja & Shuck, 2015;
Boswell & Boudreau, 2001; Harter et al., 2002; Masterson & Stamper, 2003; Stringer,
2007; Wollard & Shuck, 2011). In fulfilling this responsibility, employee alignment can
229
serve as a lever that managers can use to help nurture conditions for increased employee
engagement. Recognizing the critical role of an employee’s manager in influencing
feelings of engagement or disengagement (Harter & Rigoni, 2015; Shuck, Rocco, et al.,
2011), three recommendations are proposed for senior organizational leaders—e.g., the
chief executive officer, chief human resources officer, and/or a vice president for human
resources—and three for managers at all levels within an organization (to include senior
leaders who have direct reports).
Recommendations for Senior Organizational Leaders
The first recommendation suggests that it is not enough for senior organizational
leaders to simply state that employee engagement is important or to simply conduct
periodic employee engagement surveys. Rather, it is important that senior leaders take
steps to actually help managers, at all levels of the organization, understand: (a) what
employee engagement is, (b) why employee engagement is important to both the
organization and to employees, and (c) the critical role that they, as a manager, play in
either encouraging or discouraging feelings of engagement from their employees.
Second, in addition to promoting an understanding of the importance of employee
engagement, and how manager actions affect employee engagement, senior leaders also
need to take action to show that employee engagement is a priority within the
organization. For example, working with managers across an organization to identify the
resources necessary to help managers prioritize a focus of engaging their employees.
The final recommendation is more specific to the chief human resources officer or
a vice president for human resources. This recommendation focusses on seeking ongoing
feedback from employees through actions such as conducting: (a) exit interviews of
230
departing employees that focus on perceptions of employee alignment, perceived
organizational support, and employee engagement, and (b) focus groups with employees
to discuss perceptions of employee alignment, perceived organizational support, and
employee engagement and disengagement.
Recommendations for Managers
First, managers should take the time to help their employees clearly understand
the goals of the organization and how their work and job responsibilities contribute to
achieving the organization’s goals. This effort requires clear communication between the
employee and the manager and a mutual understanding of both the business strategy and
the tasks and responsibilities of the individual and how these align (Cummings &
Worley, 2015). A key insight is that it is not sufficient for managers to believe they have
communicated about goals—by, for example, distributing a strategic plan to employees
and noting linkages in an employee’s position description. That may not suffice to
achieve employee alignment. To fully impart the requisite understanding to employees of
the organization’s goals and how their work and job responsibilities contribute to
achieving the organization’s goals (Ayers, 2013, 2015; Boswell et al., 2006; Gagnon &
Michael, 2003; Stringer, 2007), what appears to matter is whether employees perceive an
alignment between their efforts and their contribution to achieving organizational goals.
Second, recognizing that the study findings identified a positive correlation
between perceived organizational support and employee engagement, it is recommended
that managers take the time to understand their employees’ perceptions of the support
they receive from the organization and to take actions necessary to improve employees’
feelings of being supported. While specific actions will differ, Eisenberger and
231
Stinglhamber (2011) identified four overarching actions that managers should consider to
enhance employee perceptions of organizational support: (1) establish and maintain open
communications with employees; (2) provide the necessary resources for employees to
effectively perform their job; (3) provide developmental and growth opportunities; and
(4) be consistent in speech and actions to demonstrate sincerity. As discussed concerning
employee alignment, it is not sufficient for managers to simply think they are supporting
their employees; what matters is whether employees perceive they are supported and that
their manager and/or the organization values their contributions and cares about their
well-being (Eisenberger et al., 1986; Rhoades & Eisenberger, 2002).
The third recommendation is that managers recognize that not all employees will
perceive alignment and organizational support in the same manner (Cummings &
Worley, 2015; Jin & McDonald, 2017). Addressing employee perceptions of alignment
and organizational support is not optimized with a “one-size-fits-all” approach. Rather, it
is important to recognize that it is the individual employees’ perception of their unique
interaction with the organization and the work environment that is a determinant in their
state of engagement (Kahn, 1990, 2010; Shuck, 2019; Shuck et al., 2014; Shuck, Rocco,
et al., 2011; Shuck & Rose, 2013; Wollard & Shuck, 2011). Thus, while there may be
some general overarching approaches, managers should focus and adapt efforts to
enhance employee feelings of alignment (Alagaraja & Shuck, 2015) and perceptions of
being supported (Eisenberger et al., 2016; Eisenberger & Stinglhamber, 2011) to each
individual employee.
232
Researcher Reflections on the Research Study
This study arose from observing employee responses to a variety of day-to-day
challenges and opportunities in a workplace that appears to be increasingly complex,
uncertain, and changing. Employee responses often ranged from enthusiasm, excitement,
passion, and obvious effort to cynicism, resentment, criticism, and disengagement.
Specifically, the desire was to better understand how actions taken, or not taken, by
managers might affect employees, especially with respect to employee efforts towards
achieving desired organizational goals.
As with most endeavors, there were challenges and opportunities in the process of
designing and conducting this study. In reflecting on the dissertation process, three
insights are worth mentioning. The first is the value of having and leveraging a network
to assist in identifying a possible research site. Second is the importance of pilot testing
survey instruments, even existing instruments, to ensure that the questions are understood
in the manner intended. Lastly, while there may be a desire to try to anticipate and
address all possible contingencies, questions, and “what-ifs” that may arise, the reality is
that there will never be enough time and resources to chase down every eventuality.
Completing a project such as this research study has been a journey requiring
critical thinking, imagination, humility, flexibility, and perseverance. In the end, my hope
is that managers will find the results of this study helpful in developing strategies to
improve the engagement of employees and, in turn, contribute to achieving
organizational goals, enhance organizational competitiveness, and improve employee
well-being.
233
Chapter Summary
As organizations struggle to become and remain competitive, the engagement of
employees may be a critical enabler in achieving organizational goals, enhancing
organizational competitiveness, and improving employee well-being. This research study
focused on employee alignment and perceived organizational support as antecedents of
employee engagement. This chapter discussed the results of the research study that were
presented in Chapter 4. It reviewed the research problem, interpreted the study findings,
presented conclusions, offered recommendations for theory, research, and practice, and
discussed researcher reflections.
Using the employee engagement framework proposed by Shuck and Reio (2011),
the study explored the relation among employee alignment, perceived organizational
support, and employee engagement and how employee alignment and perceived
organizational support interact to contribute to employee engagement among full-time
nonsupervisory individuals in an organizational context. In support of the study’s
purpose, bivariate correlation and multiple regression analyses were used to answer two
research questions: (1) To what extent is there a statistically significant relation among
employee alignment, perceived organizational support, and employee engagement in an
organizational context? (2) To what extent do employee alignment and perceived
organizational support explain a statistically significant proportion of the unique variance
in employee engagement?
The statistical results provided evidence for a positive relation (i.e., correlation)
among employee alignment, perceived organizational support, and employee
engagement, as well as the statistically significant contribution of employee alignment in
234
explaining unique variance in employee engagement (i.e., 23.4%). Contrary to
expectations, the results did not provide evidence that perceived organizational support
had a statistically significant effect on employee engagement. Additionally, the results
did not provide statistically significant evidence of either a moderation or mediation
effect of perceived organizational support on the relation between employee alignment
and employee engagement.
This study provides preliminary evidence that suggests that employee alignment,
and to a lesser extent perceived organizational support, are key factors within the purview
of managers that can be useful in creating the requisite organizational environment in
which engagement may thrive. As managers strive for organizational competitiveness
and survival in environments of complexity, uncertainty, and change, understanding the
relation among alignment, perceived organizational support, and employee engagement
should assist them in developing strategies to improve employee engagement, which
should contribute to achieving organizational goals, enhancing organizational
competitiveness, and improving employee well-being.
235
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Appendix A:
Introduction and Site Access Request Email
Subject: Seeking an Organization as an ELP Dissertation Research Site
My name is John Meier and I am a doctoral candidate in the Human and Organizational Learning, Executive Leadership Program at The George Washington University (cohort 29). I am reaching out for your assistance in finding an organization interested in being a research site for my dissertation, summarized below. Once the study is finished, the organization will receive an executive summary of the findings, with implications for organizational practice. Overview of the Research Study: This study explores the relation among employee alignment, perceived organizational support, and employee engagement in an organizational context. The purpose of this study is to examine how employee alignment and perceived organizational support contribute to employee engagement. Better understanding how employee alignment and perceived organizational support affect employee engagement could assist managers in identifying and developing strategies to improve employee engagement. Which, in turn, should contribute to achieving organizational goals, ultimately enhancing organizational competitiveness and employee well-being. This study will use a web-based survey consisting of 28 questions focused on employee perceptions of their state of engagement, feelings of being supported by the organization, and their understanding of the organization’s goals and how their efforts support these goals. Additionally, there will be three demographic questions on age, gender, and length of time with the organization. It is expected that it will take approximately 10 minutes to complete the survey. Responses will be completely anonymous and only group statistics will be prepared from the survey results. A minimum of 77 completed surveys are required for the study. I have attached a one-page summary of my proposed research study to help inform your decision. Please do not hesitate to let me know if I can answer any questions or provide additional information on the study. If your organization would like to participate, please email me at meierj@gwu.edu. I greatly appreciate your time and consideration, thank you. Very Best Regards, John John G. Meier III Doctoral Candidate Executive Leadership Doctoral Program Graduate School of Education and Human Development George Washington University Attachment: Research Study Overview
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Research Study Overview
Title: The Relation Among Employee Alignment, Perceived Organizational Support, and Employee Engagement Purpose of the Study: To explore the relation among employee alignment, perceived organizational support, and employee engagement and how employee alignment and perceived organizational support interact to contribute to employee engagement in an organizational context. Problem Addressed. There is a continuing need for research focused on organizational elements, or factors, within the purview of individual managers to improve employee engagement. Two such factors identified as critical to employee engagement are employee alignment and perceived organizational support. While studies have examined alignment and perceived organization support individually, the relation among employee alignment, perceived organizational support, and employee engagement remains relatively unexplored. Significance of the Study: To extend the understanding of employee engagement. Specifically, the nature to which employee alignment and perceived organizational support affect employee engagement. Better understanding this relation could assist managers in developing strategies to improve employee engagement, which should contribute to achieving organizational goals, enhancing organizational competitiveness, and improving employee well-being. Participants Sought for the Study: The desired sample for the study will consist of non-supervisory individuals employed in the United States. Ideally, participants will come from various areas within the organization. A minimum of 77 completed surveys are required. Data Collection: This study will use a web-based survey consisting of 28 questions focused on employee perceptions of their state of engagement, feelings of support by the organization, and their understanding of the organization’s goals and how their efforts support these goals. Additionally, there will also be three demographic questions on age, gender, and length of time with the organization. It should take approximately 10 minutes to complete the survey. Confidentiality: All participant responses will be kept strictly confidential and used only for the purposes of this research. Survey responses will be gathered anonymously and will only be reported in a summary format, not attributed to specific individuals or the organization; participants and their organizations will at all times remain anonymous. Data Analysis: Statistical analysis of the data will consist of both bivariate correlations and multiple regression analyses. About the Researcher: John Meier is a doctoral candidate in the Executive Leadership Doctoral Program in the Graduate School of Education and Human Development at The George Washington University in Washington, D.C.
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Appendix B:
Research Site Permission Letter
283
Appendix C:
A Priori Calculation off Minimum Sample Size for Statistical Power
284
Appendix D:
Permission to Use Instruments
Permission to Use the Employee Engagement Scale
Shuck, Adelson, et al. (2017) have granted permission to use the Employee Engagement
Scale as follows:
The employee engagement scale (EES) and cognitive work appraisal scale-11 (CWAS-11) are permitted for broad use in noncommercial settings, including but not limited to academically focused research to include dissertations and theses and original works of scholarship and grant activity within the limitations of the publication copyright, so long as this work is appropriately and correctly cited. (p. 974)
285
Permission to Use the Stringer Strategic Alignment Scale
John Meier <meierj@gwmail.gwu.edu>
Re: Requesting Permission to use the Stringer Strategic Alignment Scale1 message
Chelle Stringer <drchelle@therealizeenterprise.com> Fri, Aug 16, 2019 at 6:46 PMTo: John Meier <meierj@gwmail.gwu.edu>
John,
Thank you for your quick response. Yes, you have my permission to use the alignment scale.
My dissertation research transformed the way I approach my work. I hope your research furthers ourunderstanding!
Chelle
Sent from my iPhone
On Aug 16, 2019, at 5:14 PM, John Meier <meierj@gwmail.gwu.edu> wrote:
Hi Dr. Stringer,
I am requesting permission to use the alignment scale you developed for your dissertation. Specifically, the following eight questions (questions 11 - 18 of the survey instrument from yourdissertation, Appendix A, pp. 99-100):
I understand the purpose of my organization.I understand the goals of the organization.I understand how the organization will achieve its goals.I understand what the organization aims to do for its customers and stakeholders.I understand my business unit’s goals.I understand how my business unit’s goals contribute to the organization’s goals.I understand what I need to do to help my business unit achieve its goals.I understand how my job contributes to the organization’s ability to achieve its goals.
Thank you so much.
Very Best,John
John G. Meier IIIDoctoral CandidateExecutive Leadership Doctoral ProgramGraduate School of Education and Human DevelopmentGeorge Washington University
On Fri, Aug 16, 2019 at 4:32 PM Chelle Stringer <drchelle@therealizeenterprise.com> wrote:Hi John,
Can you please provide me with the content you request to use, so I can make sure Iunderstand your request.
Thank you!
John Meier <meierj@gwmail.gwu.edu>
Re: Requesting Permission to use the Stringer Strategic Alignment Scale1 message
Chelle Stringer <drchelle@therealizeenterprise.com> Fri, Aug 16, 2019 at 6:46 PMTo: John Meier <meierj@gwmail.gwu.edu>
John,
Thank you for your quick response. Yes, you have my permission to use the alignment scale.
My dissertation research transformed the way I approach my work. I hope your research furthers ourunderstanding!
Chelle
Sent from my iPhone
On Aug 16, 2019, at 5:14 PM, John Meier <meierj@gwmail.gwu.edu> wrote:
Hi Dr. Stringer,
I am requesting permission to use the alignment scale you developed for your dissertation. Specifically, the following eight questions (questions 11 - 18 of the survey instrument from yourdissertation, Appendix A, pp. 99-100):
I understand the purpose of my organization.I understand the goals of the organization.I understand how the organization will achieve its goals.I understand what the organization aims to do for its customers and stakeholders.I understand my business unit’s goals.I understand how my business unit’s goals contribute to the organization’s goals.I understand what I need to do to help my business unit achieve its goals.I understand how my job contributes to the organization’s ability to achieve its goals.
Thank you so much.
Very Best,John
John G. Meier IIIDoctoral CandidateExecutive Leadership Doctoral ProgramGraduate School of Education and Human DevelopmentGeorge Washington University
On Fri, Aug 16, 2019 at 4:32 PM Chelle Stringer <drchelle@therealizeenterprise.com> wrote:Hi John,
Can you please provide me with the content you request to use, so I can make sure Iunderstand your request.
Thank you!
286
287
Permission to Use the Survey of Perceived Organizational Support.
John Meier <meierj@gwmail.gwu.edu>
Re: Requesting Permission to use the Survey of Perceived OrganizationalSupport1 message
Eisenberger, Robert W <reisenbe@central.uh.edu> Fri, Aug 16, 2019 at 6:54 PMTo: John Meier <meierj@gwmail.gwu.edu>, "reisenberger2@uh.edu" <reisenberger2@uh.edu>
Hi John,Your project sounds interesting and I am happy to give permission to use the POS scale.Cordially,Bob
Robert EisenbergerProfessor of PsychologyCollege of Liberal Arts & Soc. SciencesProfessor of ManagementC. T. Bauer College of BusinessUniversity of Houston reisenberger2@uh.edu(302)353-8151
From: John Meier <meierj@gwmail.gwu.edu>Sent: Friday, August 16, 2019 11:58 AMTo: reisenberger2@uh.edu <reisenberger2@uh.edu>Cc: John Meier <meierj@gwmail.gwu.edu>Subject: Reques�ng Permission to use the Survey of Perceived Organiza�onal Support Dr. Eisenberger,
This email is to request permission to use the Survey of Perceived Organizational Support in my dissertationresearch. My name is John Meier and I am a doctoral candidate in the Human and Organizational Learning,Executive Leadership Program at The George Washington University.
The title of my dissertation is Exploring the Relation Between Employee Alignment, Perceived OrganizationalSupport, and Employee Engagement: A Hierarchical Multiple Regression Analysis. I am writing to request yourpermission to use the eight item short form of the Survey of Perceived Organizational Support in my research. My study examines the relation between employee engagement, employee alignment, and perceivedorganizational support in an organizational context. Additionally, as part of my study, I will explore the effect ofperceived organizational support as a moderating variable.
I would be pleased to share my findings with you at the completion of my study. Please do not hesitate to letme know if I can answer any questions or provide additional information. I greatly appreciate your considerationof this request, thank you.
Very Best Regards,John
John G. Meier IIIDoctoral CandidateExecutive Leadership Doctoral ProgramGraduate School of Education and Human Development
288
Appendix E:
Study Survey Questionnaire Instrument
Introduction and Informed Consent for Participating in a Research Study Introduction You are invited to take part in a research study examining employee engagement in organizations. This research is being conducted by John Meier, a doctoral candidate, under the direction of Dr. Ellen Goldman of the Department of Human and Organizational Learning, Graduate School of Education and Human Development at the George Washington University. Taking part in this research is entirely voluntary and, if you decide to participate, you may withdraw at any time. It should take approximately 10 minutes to complete the survey. The Study The purpose of the study is to explore the relation among employee alignment, perceived organizational support, and employee engagement in an organizational context. This study will use a web-based questionnaire consisting of 28 questions focused on employee engagement in the workplace. Potential Risks and Confidentiality You will not be exposed to any risks that exceed what you encounter in the daily conduct of your work, there are no physical risks associated with participating in this study. You are encouraged to answer all questions that you feel comfortable with, you are free to skip any questions or stop taking the survey at any point. All participant responses will be kept strictly confidential and used only for the purpose of this research. All survey responses will be gathered anonymously and only group statistics will be reported, not attributed to specific individuals or the organization. No information will be collected that will link an individual participant to their responses; the researcher will not have visibility on who did or did not participate in the study, or the responses for those who did participate. All data collected will be stored on a password-protected computer. Additionally in any published articles or presentations, no information will be included that would make it possible to identify individuals or the organization as a participant. Potential Benefits of Participation in the Study The intent of this study is to gain a better understanding of employee engagement. While you will most likely not benefit directly from participating in the study, you will be providing valuable insights that will add to our understanding of how to improve employee engagement in the workplace. Costs and Compensation There are no costs to you for participating in this study. You will not receive any compensation for participating. Questions If you have questions on the survey or research study, please contact the primary contact, John Meier at meierj@gwmail.gwu.edu or the principal investigator, Dr. Ellen Goldman at egoldman@gwu.edu. If you have questions regarding your rights as a research participant, please contact the GWU Office of Human Research at 202-994-2715 or ohrirb@gwu.edu.
289
Documentation of Informed Consent By clicking on the “I AGREE” button, you affirm that you have read the introduction and informed consent for participating in a research study (previous page), that the study has been explained to you, that your questions have been answered, and that you agree to participate in this study. You do not give up any legal rights by agreeing to participate in this study. If you do not wish to participate in the study, please click the “I DISAGREE (I do not wish to participate in the survey)” button; you will be exited from the study. Please respond to the following statement to continue with the survey and provide your informed consent:
By clicking on the “I AGREE” button below, you are providing your informed consent and voluntarily agreeing to participate in the study. ¨ I AGREE ¨ I DISAGREE (I do not wish to participate in the survey)
<Begin survey> Instructions for Completing the Survey Thank you for agreeing to participate in this study. The information collected in this survey is completely confidential, no identifying information will be used in the analysis or reporting of the survey data. You may skip any question or refrain from answering any question you do not feel comfortable answering. This survey should take approximately 10 minutes to complete. There is no opportunity to save your data so you should plan to complete the survey when you know you will have uninterrupted time, free from distractions. Please answer all questions to the best of your knowledge and ability. Your candid input is absolutely essential to this effort. Your answers should reflect what you experience from your perspective and not what you believe should be happening or how you perceive things should be. In order to progress through this survey, please use the following navigation links:
• Click the Next button to continue to the next page. • Click the Previous button to return to the previous page. • Click the Exit the Survey Early button if you want to exit the survey. • Click the Submit button to submit your survey responses after you have answered ALL
of the questions. Thank you for taking the time to participate in this study.
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Employee Engagement • Below are a series of 12 statements with which you may agree or disagree. Using the scale
provided, please indicate your degree of agreement or disagreement with each statement by selecting the appropriate response. Responses are indicated on a five-point scale ranging from Strongly Disagree to Strongly Agree.
• Please read each statement carefully and select the response that best represents your point of view of your individual experience in your human resources department.
• There are no right or wrong answers. Please respond to each statement as candidly as possible.
Strongly Disagree Disagree
Neither Agree Nor Disagree Agree
Strongly Agree
1. I am really focused when I am working. ¨ ¨ ¨ ¨ ¨
2. I concentrate on my job when I am at work. ¨ ¨ ¨ ¨ ¨
3. I give my job responsibility a lot of attention. ¨ ¨ ¨ ¨ ¨
4. At work, I am focused on my job. ¨ ¨ ¨ ¨ ¨
5. Working in the human resources department has a great deal of personal meaning to me.
¨ ¨ ¨ ¨ ¨
6. I feel a strong sense of belonging to my job. ¨ ¨ ¨ ¨ ¨
7. I believe in the mission and purpose of the human resources department.
¨ ¨ ¨ ¨ ¨
8. I care about the future of the human resources department. ¨ ¨ ¨ ¨ ¨
9. I really push myself to work beyond what is expected of me. ¨ ¨ ¨ ¨ ¨
10. I am willing to put in extra effort without being asked. ¨ ¨ ¨ ¨ ¨
11. I often go above what is expected of me to help my team be successful.
¨ ¨ ¨ ¨ ¨
12. I work harder than expected to help the human resources department be successful.
¨ ¨ ¨ ¨ ¨
291
Employee Alignment • Below are a series of eight statements with which you may agree or disagree. Using the scale
provided, please indicate your degree of agreement or disagreement with each statement by selecting the appropriate response. Responses are indicated on a five-point scale ranging from Strongly Disagree to Strongly Agree.
• Please read each statement carefully and select the response that best represents your point of view of your individual experience in your human resources department.
• There are no right or wrong answers. Please respond to each statement as candidly as possible.
Strongly Disagree Disagree
Neither Agree Nor
Disagree Agree Strongly Agree
13. I understand the purpose of the human resources department. ¨ ¨ ¨ ¨ ¨
14. I understand the goals of the human resources department. ¨ ¨ ¨ ¨ ¨
15. I understand how the human resources department will achieve its goals. ¨ ¨ ¨ ¨ ¨
16. I understand what the human resources department aims to do for its customers and stakeholders.
¨ ¨ ¨ ¨ ¨
17. I understand my team’s goals. ¨ ¨ ¨ ¨ ¨ 18. I understand how my team’s goals contribute to the human resources department’s goals. ¨ ¨ ¨ ¨ ¨
19. I understand what I need to do to help my team achieve its goals. ¨ ¨ ¨ ¨ ¨
20. I understand how my job contributes to the human resources department’s ability to achieve its goals.
¨ ¨ ¨ ¨ ¨
292
Perceived Organizational Support • Below are a series of eight statements with which you may agree or disagree. Using the scale
provided, please indicate your degree of agreement or disagreement with each statement by selecting the appropriate response. Responses are indicated on a seven-point scale ranging from Strongly Disagree to Strongly Agree.
• Please read each statement carefully and select the response that best represents your point of view of your individual experience in your human resources department.
• There are no right or wrong answers. Please respond to each statement as candidly as possible.
Strongly Disagree
Moderately Disagree
Slightly Disagree
Neither Agree
Nor Disagree
Slightly Agree
Moderately Agree
Strongly Agree
21. The human resources department values my contribution to its well-being.
¨ ¨ ¨ ¨ ¨ ¨ ¨
22. The human resources department appreciates any extra effort from me.
¨ ¨ ¨ ¨ ¨ ¨ ¨
23. The human resources department cares about my opinions.
¨ ¨ ¨ ¨ ¨ ¨ ¨
24. The human resources department really cares about my well-being.
¨ ¨ ¨ ¨ ¨ ¨ ¨
25. The human resources department notices when I do a good job.
¨ ¨ ¨ ¨ ¨ ¨ ¨
26. The human resources department cares about my general satisfaction at work.
¨ ¨ ¨ ¨ ¨ ¨ ¨
27. The human resources department shows concern for me.
¨ ¨ ¨ ¨ ¨ ¨ ¨
28. The human resources department takes pride in my accomplishments at work.
¨ ¨ ¨ ¨ ¨ ¨ ¨
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Demographic Information The following questions are for demographic data and will be used only for analytical purposes. These questions will not be used to identify any individual. 29. What is your current age (in whole years)? years 30. What is your gender?
¨ Male ¨ Female
31. How long have you worked in the human resources department (in whole years)? years 32. Do you directly manage or supervise other employees within the human resources department?
¨ No ¨ Yes
33. What is your current employment status? ¨ Full-time employee ¨ Part-time employee
You have completed the survey and your responses have been submitted. Thank you for participating in this study.
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Appendix F:
Institutional Review Board Approvals
Initial Submission – Exemption Determination
Date: October 29, 2019 To: Goldman, Ellen F., EdD From: The George Washington University Committee on Human Research, Institutional Review Board (IRB), FWA00005945 Subject: IRB# NCR191874 , “An Exploratory Study of the Relation Among Employee
Alignment, Perceived Organizational Support, and Employee Engagement” Exempt Determination Date: October 29, 2019
The request for an exemption determination for the above-referenced study has been completed. The study was determined to be research that is exempt from IRB review under DHHS regulatory Category 2. The project as described in the application may proceed without further oversight by the OHR.
The exemption determination applies only to the project described in your IRB Application. Any changes that may alter in any way the risks to participants, type of information to be accessed, addition of new populations, or change in PI may not be instituted without submission of a Modification within the iRIS system and further review by the OHR prior to implementation of the changes.
Questions or concerns regarding the exemption determination made for the study should be directed to the OHR staff at ohrirb@gwu.edu.
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Post–Pilot Study Modification Request Approval
Memorandum To: Goldman, Ellen F., Ed.D. From: The George Washington University Office of Human Research
Date: January 17, 2020
IRB#: NCR191874 Study Title: An Exploratory Study of the Relation Among Employee Alignment, Perceived Organizational Support, and Employee Engagement RE: Notification of Exempt Modification Approval The above reference study received approval for the modification request (reference# 008406) on 01/17/2020. There is no change in exempt determination. As a reminder, any changes that may alter in any way the risks to participants, type of information to be accessed, addition of new populations, or change in PI may not be instituted without submission of a Modification memo and further review by the OHR prior to implementation of the changes. Questions or concerns regarding the exemption determination made for the study should be directed to the OHR staff at ohrirb@gwu.edu.
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Appendix G:
Communications to Study Sample Participants
Communication 1: Prenotice Announcement Email To: [Insert email distribution list] Subject: Upcoming Employee Engagement Study Our organization has agreed to participate in a research study being conducted by John Meier, a doctoral student at The George Washington University, who is investigating employee engagement in the workplace. Within the next few days you will receive an email inviting your participation in a short, 10-minute, online survey. When you receive the email invitation, please take a few minutes to complete the survey and provide your feedback by the requested deadline. The survey is confidential and your survey responses will be completely anonymous; John will not be able to identify who you are, and he will only report results from all of the employees who participate as one group. Additionally, as the organization’s coordinator, I will not know who does or does not participate in the survey or what the responses are for those who do participate; your anonymity will be protected at all times. Participation is completely voluntary. In order to conduct a valid study, it is important to have adequate participation. Your input will be used to add to the understanding of employee engagement in the workplace. The attached information sheet provides additional details about the research. If you have any questions regarding the survey, please contact the researcher, John Meier, at meierj@gwmail.gwu.edu. If you have questions regarding your rights as a research participant, please contact The George Washington University Office of Human Research at 202-994-2715 or ohrirb@gwu.edu. Thank you for your consideration in being a part of this research.
297
Communication 2: Invitation to Participate Email To: [Insert email distribution list] Subject: Employee Engagement Study – Invitation to Participate Our organization has agreed to participate in a research study being conducted by John Meier, a doctoral student at The George Washington University, who is investigating employee engagement in the workplace. You are invited to participate in a short, 10-minute, online survey that will ask for your views on items that affect your engagement at work. Your participation is voluntary and confidential. Your survey responses will be completely anonymous; John will not be able to identify who you are, and he will only report results from all of the employees who participate as one group. Additionally, as the organization’s coordinator, I will not know who does or does not participate in the survey or what the responses are for those who do participate; your anonymity will be protected at all times. Attached is a copy of the “Informed Consent for Participation in a Research Study.” By completing the survey you are consenting to participate in the study. Please complete the survey as soon as possible, but no later than [insert date]. To begin the survey, click on the link below or copy and paste the URL into your browser and follow the instructions on the website:
[Insert Survey Link] Your input is very important. In order to conduct a valid study, it is important to have adequate participation. Your input will be used to add to the understanding of employee engagement in the workplace. If you have any questions regarding the survey, please contact the researcher, John Meier, at meierj@gwmail.gwu.edu. If you have questions regarding your rights as a research participant, please contact The George Washington University Office of Human Research at 202-994-2715 or ohrirb@gwu.edu. Thank you in advance for your time and consideration in being part of this research study.
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Communication 3: Follow-Up Email #1 To: [Insert email distribution list] Subject: Employee Engagement Study – Survey Reminder You were recently sent a request to participate in a short, 10-minute, online survey asking for your views on items that affect your engagement at work. The survey is for a research study being conducted by John Meier, a doctoral student at The George Washington University, who is investigating employee engagement in the workplace. If you have already completed the survey questionnaire, thank you for your participation. If you have not yet had the opportunity, please take 10 minutes to complete the survey at your earliest convenience, but no later than [insert date]. To begin the survey, click on the link below or copy and paste the URL into your browser and follow the instructions on the website:
[Insert Survey Link] Your input is very important and will be used to add to the understanding of employee engagement in the workplace. Your participation is voluntary and confidential. All survey responses will be completely anonymous; John will not be able to identify who you are, and he will only report results from all of the employees who participate as one group. Additionally, as the organization’s coordinator, I will not know who does or does not participate in the survey or what the responses are for those who do participate; your anonymity will be protected at all times. If you have any questions regarding the survey, please contact the researcher, John Meier, at meierj@gwmail.gwu.edu. If you have questions regarding your rights as a research participant, please contact The George Washington University Office of Human Research at 202-994-2715 or ohrirb@gwu.edu. Thank you for your assistance with this study.
299
Communication 4: Follow-Up Email #2 To: [Insert email distribution list] Subject: Employee Engagement Study – Final Survey Reminder You were recently sent a request to participate in a short, 10 minute, online survey asking for your views on items that affect your engagement at work. The survey is for a research study being conducted by John Meier, a doctoral student at The George Washington University, who is investigating employee engagement in the workplace. If you have already completed the survey questionnaire, thank you for your participation. If you have not yet had the opportunity, please take 10 minutes to complete the survey at your earliest convenience, but no later than [insert date]. To begin the survey, click on the link below or copy and paste the URL into your browser and follow the instructions on the website:
[Insert Survey Link] Your input is very important and will be used to add to the understanding of employee engagement in the workplace. Your participation is voluntary and confidential. All survey responses will be completely anonymous; John will not be able to identify who you are, and he will only report results from all of the employees who participate as one group. Additionally, as the organization’s coordinator, I will not know who does or does not participate in the survey or what the responses are for those who do participate; your anonymity will be protected at all times. If you have any questions regarding the survey, please contact the researcher, John Meier, at meierj@gwmail.gwu.edu. If you have questions regarding your rights as a research participant, please contact The George Washington University Office of Human Research at 202-994-2715 or ohrirb@gwu.edu. Thank you for your assistance with this study.
300
Appendix H:
Informed Consent for Participation in a Research Study
The Relation Among Employee Alignment, Perceived Organizational Support, and Employee Engagement
IRB # NCR191874 Principal Investigator: Ellen Goldman, Ed.D., egoldman@gwu.edu
Co-Investigator: John Meier, meierj@gwmail.gwu.edu Sponsor: The George Washington University,
Graduate School of Education and Human Development Introduction and Informed Consent for Participating in a Research Study Introduction You are invited to take part in a research study examining employee engagement in organizations. This research is being conducted by John Meier, a doctoral candidate, under the direction of Dr. Ellen Goldman of the Department of Human and Organizational Learning, Graduate School of Education and Human Development at the George Washington University. Taking part in this research is entirely voluntary and, if you decide to participate, you may withdraw at any time. It should take approximately 10 minutes to complete the survey. The Study The purpose of the study is to explore the relation among employee alignment, perceived organizational support, and employee engagement in an organizational context. This study will use a web-based questionnaire consisting of 28 questions focused on employee engagement in the workplace. Potential Risks and Confidentiality You will not be exposed to any risks that exceed what you encounter in the daily conduct of your work, there are no physical risks associated with participating in this study. You are encouraged to answer all questions that you feel comfortable with, you are free to skip any questions or stop taking the survey at any point. All participant responses will be kept strictly confidential and used only for the purpose of this research. All survey responses will be gathered anonymously and only group statistics will be reported, not attributed to specific individuals or the organization. No information will be collected that will link an individual participant to their responses; the researcher will not have visibility on who did or did not participate in the study, or the responses for those who did participate. All data collected will be stored on a password-protected computer. Additionally, in any published articles or presentations, no information will be included that would make it possible to identify individuals or the organization as a participant.
301
Potential Benefits of Participation in the Study The intent of this study is to gain a better understanding of employee engagement. While you will most likely not benefit directly from participating in the study, you will be providing valuable insights that will add to our understanding of how to improve employee engagement in the workplace. Costs and Compensation There are no costs to you for participating in this study. You will not receive any compensation for participating. Questions If you have questions on the survey or research study, please contact the primary contact, John Meier, at meierj@gwmail.gwu.edu or the principal investigator, Dr. Ellen Goldman, at egoldman@gwu.edu. If you have questions regarding your rights as a research participant, please contact the GWU Office of Human Research at 202-994-2715 or ohrirb@gwu.edu. Sincerely, John G. Meier III Doctoral Candidate Executive Leadership Doctoral Program Graduate School of Education and Human Development George Washington University Attachment: Research Study Overview
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Research Study Overview
Title: The Relation Among Employee Alignment, Perceived Organizational Support, and Employee Engagement Purpose of the Study: To explore the relation among employee alignment, perceived organizational support, and employee engagement and how employee alignment and perceived organizational support interact to contribute to employee engagement in an organizational context. Problem Addressed. There is a continuing need for research focused on organizational elements, or factors, within the purview of individual managers to improve employee engagement. Two such factors identified as critical to employee engagement are employee alignment and perceived organizational support. While studies have examined alignment and perceived organization support individually, the relation among employee alignment, perceived organizational support, and employee engagement remains relatively unexplored. Significance of the Study: To extend the understanding of employee engagement. Specifically, the nature to which employee alignment and perceived organizational support affect employee engagement. Better understanding this relation could assist managers in developing strategies to improve employee engagement, which should contribute to achieving organizational goals, enhancing organizational competitiveness, and improving employee well-being. Participants Sought for the Study: The desired sample for the study will consist of full-time, non-supervisory individuals employed in the United States. A minimum of 77 completed surveys are required. Data collection: This study will use a web-based survey consisting of 28 questions focused on employee perceptions of their state of engagement, feelings of support by the organization, and their understanding of the organization’s goals and how their efforts support these goals. Additionally, there will also be three demographic questions on age, gender, and length of time with the organization. It should take approximately 10 minutes to complete the survey. Confidentiality: All participant responses will be kept strictly confidential and used only for the purposes of this research. Survey responses will be gathered anonymously and will only be reported in a summary format, not attributed to specific individuals or the organization; participants and their organizations will at all times remain anonymous. Data Analysis: Statistical analysis of the data will consist of both bivariate correlations and multiple regression analyses. About the Researcher: John Meier is a doctoral candidate in the Executive Leadership Doctoral Program in the Graduate School of Education and Human Development at The George Washington University in Washington, D.C.
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Appendix I:
Comparative Analysis of Missing Value Imputation Techniques
Descriptive statistics are provided for missing value imputation techniques: regression
imputation (Table I.1), expectation-maximization imputation (Table I.2), and multiple
imputation (Table I.3).
Table I.1
Missing Value Imputation: Descriptive Statistics for Regression Imputation
Data Variable n Minimum Maximum Mean Standard Deviation Q5 109 1 5 4.12 .824 Q6 109 1 5 4.08 .849 Q7 109 1 5 4.29 .750 Q8 109 3 5 4.52 .615 Q15 109 2 5 3.88 .823 Q21 109 1 6 4.34 1.554 Q25 109 0 6 4.16 1.689 Age 109 25 74 45.20 11.427 Tenure 109 –1 30 7.76 7.302 Valid n (listwise) = 109
Table I.2
Missing Value Imputation: Descriptive Statistics for Expectation-Maximization
Imputation
Data Variable n Minimum Maximum Mean Standard Deviation Q5 109 1 5 4.12 .820 Q6 109 1 5 4.09 .847 Q7 109 1 5 4.30 .740 Q8 109 3 5 4.52 .616 Q15 109 2 5 3.87 .818 Q21 109 1 6 4.33 1.571 Q25 109 0 6 4.16 1.693 Age 109 25 74 44.71 10.998 Tenure 109 1 30 7.58 6.878 Valid n (listwise) = 109
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Table I.3
Missing Value Imputation: Descriptive Statistics for Multiple Imputation
Imputation Number
Data Variable n Minimum Maximum Mean
Standard Deviation
Original data Q5 108 1 5 4.13 .821 Q6 108 1 5 4.09 .849 Q7 108 1 5 4.31 .742 Q8 107 3 5 4.51 .620 Q15 108 2 5 3.87 .821 Q21 108 1 6 4.35 1.555 Q25 106 0 6 4.17 1.704 Age 95 25 74 44.72 11.316 Tenure 94 1 30 7.73 7.316 Valid n (listwise) = 83
1 Q5 109 1 5 4.12 .825 Q6 109 1 5 4.10 .848 Q7 109 1 5 4.30 .742 Q8 109 3 5 4.52 .618 Q15 109 2 5 3.88 .820 Q21 109 1 6 4.33 1.567 Q25 109 0 6 4.18 1.698 Age 109 18 74 44.61 11.408 Tenure 109 –3 30 8.02 7.372 Valid n (listwise) = 109
2 Q5 109 1 5 4.12 .819 Q6 109 1 5 4.09 .845 Q7 109 1 5 4.30 .746 Q8 109 3 5 4.51 .616 Q15 109 2 5 3.86 .820 Q21 109 1 6 4.34 1.552 Q25 109 0 6 4.18 1.692 Age 109 11 74 44.46 12.143 Tenure 109 –17 30 7.58 7.722 Valid n (listwise) = 109
3 Q5 109 1 5 4.12 .820 Q6 109 1 5 4.08 .850 Q7 109 1 5 4.30 .741 Q8 109 3 5 4.51 .616 Q15 109 2 5 3.87 .818 Q21 109 1 6 4.33 1.571 Q25 109 0 6 4.16 1.709 Age 109 19 74 44.70 11.299 Tenure 109 –5 30 7.69 7.650 Valid n (listwise) = 109
4 Q5 109 1 5 4.13 .818 Q6 109 1 5 4.09 .847 Q7 109 1 5 4.30 .746 Q8 109 3 5 4.52 .615
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Imputation Number
Data Variable n Minimum Maximum Mean
Standard Deviation
Q15 109 2 5 3.87 .818 Q21 109 1 6 4.32 1.580 Q25 109 0 6 4.15 1.704 Age 109 25 74 44.89 11.357 Tenure 109 –14 30 7.61 7.302 Valid n (listwise) = 109
5 Q5 109 1 5 4.12 .821 Q6 109 1 5 4.09 .845 Q7 109 1 5 4.30 .740 Q8 109 3 6 4.52 .627 Q15 109 2 5 3.86 .825 Q21 109 1 6 4.32 1.575 Q25 109 0 7 4.17 1.710 Age 109 21 74 44.24 11.190 Tenure 109 –3 30 7.87 7.281 Valid n (listwise) = 109
Pooled Q5 109 4.12 Q6 109 4.09 Q7 109 4.30 Q8 109 4.52 Q15 109 3.87 Q21 109 4.33 Q25 109 4.17 Age 109 44.58 Tenure 109 7.76 Valid n (listwise) = 109
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Appendix J:
Research Study Overview
Title: The Relation Among Employee Alignment, Perceived Organizational Support, and Employee Engagement Purpose of the Study: To explore the relation among employee alignment, perceived organizational support, and employee engagement and how employee alignment and perceived organizational support interact to contribute to employee engagement in an organizational context. Problem Addressed. There is a continuing need for research focused on organizational elements, or factors, within the purview of individual managers to improve employee engagement. Two such factors identified as critical to employee engagement are employee alignment and perceived organizational support. While studies have examined alignment and perceived organization support individually, the relation among employee alignment, perceived organizational support, and employee engagement remains relatively unexplored. Significance of the Study: To extend the theoretical and practical understanding of employee engagement. Specifically, the nature to which employee alignment and perceived organizational support affect employee engagement. Better understanding this relation could assist both researchers and managers in understanding, identifying, and developing strategies to improve employee engagement, which should contribute to achieving organizational goals, enhancing organizational competitiveness, and improving employee well-being. Participants Sought for the Study: The desired sample for the study will consist of full-time, non-supervisory individuals employed in the United States. A minimum of 77 completed surveys are required. Data collection: This study will use a web-based survey consisting of 28 questions focused on employee perceptions of their state of engagement, feelings of being supported by the organization, and their understanding of the organization’s goals and how their efforts support these goals. Additionally, there will be three demographic questions on age, gender, and length of time with the organization. It should take approximately 10 minutes to complete the survey. All participants will receive the same instrument. Confidentiality: All participant responses will be kept strictly confidential and used only for the purposes of this research. Survey responses will be gathered anonymously and will only be reported in a summary format, not attributed to specific individuals or the organization; participants and their organizations will at all times remain anonymous. Data Analysis: Statistical analysis of the data will consist of both bivariate correlations and multiple regression analyses. About the Researcher: John Meier is a doctoral candidate in the Executive Leadership Doctoral Program in the Graduate School of Education and Human Development at The George Washington University in Washington, D.C.
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Appendix K:
Inter-Item Correlation Matrix for Survey Questions
308
Appendix L:
Item-Factor Correlations and Factor Loadings for The 28 Scale Questions
Table L.1
Summary of Item-Factor Correlations and Factor Loadings for the 28 Scale Questions
Item
Structure Matrix Pattern Matrix a Factor Factor
1 2 3 1 2 3 Factor 3: EE
Q1 .166 .279 .762 .026 -.055 .779 Q2 .194 .247 .754 .098 -.133 .787 Q3 .167 .222 .800 .082 -.177 .854 Q4 .196 .312 .868 .047 -.082 .891 Q5 .483 .762 .346 .056 .712 .041 Q6 .605 .630 .423 .366 .330 .207 Q7 .468 .827 .240 -.031 .894 -.120 Q8 .407 .749 .208 -.053 .829 -.120 Q9 .113 .319 .629 -.095 .130 .597 Q10 .115 .317 .622 -.091 .128 .590 Q11 .125 .432 .701 -.178 .281 .625 Q12 .256 .528 .482 -.073 .441 .318
Factor 2: EA Q13 .331 .747 .411 -.162 .792 .122 Q14 .426 .798 .348 -.065 .826 .023 Q15 .549 .741 .294 .173 .642 -.008 Q16 .584 .811 .309 .164 .725 -.025 Q17 .527 .523 .360 .341 .247 .183 Q18 .567 .692 .252 .244 .562 -.033 Q19 .533 .592 .340 .286 .373 .124 Q20 .510 .779 .278 .079 .752 -.048
Factor 1: POS Q21 .887 .622 .169 .794 .190 -.085 Q22 .897 .518 .159 .904 .004 -.043 Q23 .893 .558 .235 .864 .039 .028 Q24 .894 .556 .169 .865 .068 -.050 Q25 .904 .461 .181 .968 -.113 .013 Q26 .924 .509 .171 .953 -.041 -.022 Q27 .894 .476 .250 .941 -.113 .088 Q28 .940 .551 .142 .939 .032 -.078
Note: Factor labels: EE = Employee engagement, EA = Employee alignment, POS = Perceived organizational support. Extraction method: Principal axis factoring. Rotation method: Promax with Kaiser normalization. a Rotation converged in 5 iterations.
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Appendix M:
Calculating Average Variance Extracted and Composite Reliability
Average variance extracted (AVE) is calculated using the following equation (Fornell &
Larcker, 1981; Moutinho, 2011):
𝐴𝑉𝐸 =∑𝜆(
∑ 𝜆( + ∑(1–𝜆()(1)
Where:
l (Lambda) = pattern coefficient for a given factor
1 – l2 = the error variance (measurement error)
Composite reliability (CR) is calculated using the following equation (Fornell & Larcker,
1981; Hair et al., 2014):
𝐶𝑅 = (∑ 𝜆)(
(∑𝜆)( + ∑(1– 𝜆()(2)
Where:
l (Lambda) = pattern coefficient for a given factor
1 – l2 = the error variance (measurement error)
Table M.1 shows the calculations of AVE and CR using Equations 1 and 2.
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Table M.1
Calculating Average Variance Extracted and Composite Reliability
Construct Item 𝜆 (𝜆 )2 (1 – 𝜆2) AVE CR EE Q1 0.7790 0.6068 0.3932 – –
Q2 0.7870 0.6194 0.3806 – – Q3 0.8540 0.7293 0.2707 – – Q4 0.8910 0.7939 0.2061 – – Q5 0.0410 0.0017 0.9983 – – Q6 0.2070 0.0428 0.9572 – – Q7 -0.1200 0.0144 0.9856 – – Q8 -0.1200 0.0144 0.9856 – – Q9 0.5970 0.3564 0.6436 – – Q10 0.5900 0.3481 0.6519 – – Q11 0.6250 0.3906 0.6094 – – Q12 0.3180 0.1011 0.8989 – –
Sum Q1–Q12 5.4490 4.0190 7.9810 0.3349 0.7881 EA Q13 0.7920 0.6273 0.3727 – –
Q14 0.8260 0.6823 0.3177 – – Q15 0.6420 0.4122 0.5878 – – Q16 0.7250 0.5256 0.4744 – – Q17 0.2470 0.0610 0.9390 – – Q18 0.5620 0.3158 0.6842 – – Q19 0.3730 0.1391 0.8609 – – Q20 0.7520 0.5655 0.4345 – –
Sum Q13–Q20 4.9190 3.3288 4.6712 0.4161 0.8382 POS Q21 0.7940 0.6304 0.3696 – –
Q22 0.9040 0.8172 0.1828 – – Q23 0.8640 0.7465 0.2535 – – Q24 0.8650 0.7482 0.2518 – – Q25 0.9680 0.9370 0.0630 – – Q26 0.9530 0.9082 0.0918 – – Q27 0.9410 0.8855 0.1145 – – Q28 0.9390 0.8817 0.1183 – –
Sum Q21–Q28 7.2280 6.5548 1.4452 0.8194 0.9731
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Appendix N:
Descriptive Statistics by Survey Questionnaire Question
Descriptive statistics for each of the three variables by individual survey questionnaire
question are presented in Table N.1.
Table N.1
Descriptive Statistics by Survey Questionnaire Question
Variable / Question n Mean Standard deviation Min Max
Employee Engagement 109 52.65 5.43 39.00 60.00 Cognitive Engagement 109 17.59 2.49 4.00 20.00
Q1. I am really focused when I am working. 109 4.31 .72 1.00 5.00 Q2. I concentrate on my job when I am at work. 109 4.35 .70 1.00 5.00 Q3. I give my job responsibility a lot of attention. 109 4.50 .66 1.00 5.00 Q4. At work, I am focused on my job. 109 4.42 .67 1.00 5.00
Emotional Engagement 109 17.03 2.55 6.00 20.00 Q5. Working in the human resources department has a great deal of personal meaning to me.
109 4.13 .82 1.00 5.00
Q6. I feel a strong sense of belonging to my job. 109 4.08 .85 1.00 5.00 Q7. I believe in the mission and purpose of the human resources department.
109 4.30 .74 1.00 5.00
Q8. I care about the future of the human resources department.
109 4.51 .62 3.00 5.00
Behavioral Engagement 109 18.04 2.05 12.00 20.00 Q9. I really push myself to work beyond what is expected of me.
109 4.56 .60 3.00 5.00
Q10. I am willing to put in extra effort without being asked.
109 4.64 .54 3.00 5.00
Q11. I often go above what is expected of me to help my team be successful.
109 4.53 .57 3.00 5.00
Q12. I work harder than expected to help the human resources department be successful.
109 4.30 .74 2.00 5.00
Employee Alignment 109 33.72 4.48 20.00 40.00 Q13. I understand the purpose of the human resources department.
109 4.41 .61 2.00 5.00
Q14. I understand the goals of the human resources department.
109 4.22 .74 2.00 5.00
Q15. I understand how the human resources department will achieve its goals.
109 3.87 .82 2.00 5.00
Q16. I understand what the human resources department aims to do for its customers and stakeholders.
109 4.22 .66 2.00 5.00
Q17. I understand my team’s goals. 109 4.26 .73 2.00 5.00 Q18. I understand how my team’s goals contribute to the human resources department’s goals.
109 4.21 .71 2.00 5.00
Q19. I understand what I need to do to help my team achieve its goals.
109 4.23 .77 1.00 5.00
312
Variable / Question n Mean Standard deviation Min Max
Q20. I understand how my job contributes to the human resources department’s ability to achieve its goals.
109 4.29 .69 2.00 5.00
Perceived Organizational Support 109 33.83 12.40 1.00 48.00 Q21. The human resources department values my contribution to its well-being.
109 4.33 1.56 1.00 6.00
Q22. The human resources department appreciates any extra effort from me.
109 4.29 1.70 0.00 6.00
Q23. The human resources department cares about my opinions.
109 4.26 1.70 0.00 6.00
Q24. The human resources department really cares about my well-being.
109 4.26 1.71 0.00 6.00
Q25. The human resources department notices when I do a good job.
109 4.17 1.70 0.00 6.00
Q26. The human resources department cares about my general satisfaction at work.
109 4.06 1.79 0.00 6.00
Q27. The human resources department shows concern for me.
109 4.28 1.63 0.00 6.00
Q28. The human resources department takes pride in my accomplishments at work.
109 4.19 1.73 0.00 6.00
Valid n (listwise) = 109
313
Appendix O:
SPSS Hierarchical Multiple Regression Moderation Analysis Output
Table O.1
Hierarchical Multiple Regression Analysis Results: Model Summary
Model R R2 Adjusted
R2 Std. Error of the Estimate
Change Statistics R2 Change F Change df1 df2 Sig. F Change
1 .246a .061 .034 5.341 .061 2.263 3 105 .085 2 .682b .465 .439 4.068 .405 38.975 2 103 <.001 3 .692c .478 .448 4.038 .013 2.569 1 102 .112
a. Predictors: (Constant), Tenure, Gender, Age b. Predictors: (Constant), Tenure, Gender, Age, POS_Centered, EA_Centered c. Predictors: (Constant), Tenure, Gender, Age, POS_Centered, EA_Centered, EA_Centered_x_POS_Centered
Table O.2
Hierarchical Multiple Regression Analysis Results: ANOVAa
Model Sum of Squares df Mean Square F Sig. 1 Regression 193.673 3 64.558 2.263 .085b
Residual 2995.079 105 28.525 Total 3188.752 108
2 Regression 1483.899 5 296.780 17.930 <.001c Residual 1704.853 103 16.552 Total 3188.752 108
3 Regression 1525.785 6 254.298 15.598 <.001d Residual 1662.967 102 16.304 Total 3188.752 108
a. Dependent Variable: EE b. Predictors: (Constant), Tenure, Gender, Age c. Predictors: (Constant), Tenure, Gender, Age, POS_Centered, EA_Centered d. Predictors: (Constant), Tenure, Gender, Age, POS_Centered, EA_Centered, EA_Centered_x_POS_Centered
314
Table O.3
Hierarchical Multiple Regression Moderation Analysis Results: Coefficientsa
Model
Unstandardized coefficients
Stand. coeff.
t Sig.
95% CI for b Correlations
b Std.
Error Beta Lower bound
Upper bound
Zero- order Partial Part
1 (Constant) 47.092 2.435 19.341 <.001 42.264 51.920 Age .128 .053 .260 2.425 .017 .023 .234 .245 .230 .229 Gender .015 1.329 .001 .012 .991 -2.620 2.651 .014 .001 .001 Tenure -.026 .085 -.033 -.306 .760 -.194 .142 .089 -.030 -.029
2 (Constant) 48.420 1.861 26.021 <.001 44.730 52.111 Age .113 .040 .230 2.804 .006 .033 .194 .245 .266 .202 Gender -.324 1.018 -.023 -.319 .750 -2.342 1.694 .014 -.031 -.023 Tenure -.074 .065 -.093 -1.136 .259 -.202 .055 .089 -.111 -.082 EA_Centered .751 .112 .619 6.724 <.001 .530 .973 .650 .552 .484 POS_Centered .015 .040 .035 .384 .701 -.064 .095 .431 .038 .028
3 (Constant) 48.128 1.856 25.934 <.001 44.447 51.808 Age .105 .041 .212 2.577 .011 .024 .185 .245 .247 .184 Gender -.142 1.016 -.010 -.140 .889 -2.158 1.873 .014 -.014 -.010 Tenure -.057 .065 -.072 -.870 .386 -.186 .073 .089 -.086 -.062 EA_Centered .758 .111 .625 6.836 <.001 .538 .979 .650 .561 .489 POS_Centered .018 .040 .042 .460 .647 -.061 .098 .431 .045 .033 EA_Centered_×_ POS_Centered
.012 .008 .118 1.603 .112 -.003 .027 .073 .157 .115
a. Dependent Variable: EE
315
Appendix P:
SPSS PROCESS Macro Multiple Regression Mediation Analysis Output
Run MATRIX procedure: ************* PROCESS Procedure for SPSS Version 3.5 ************* Written by Andrew F. Hayes, Ph.D. www.afhayes.com Documentation available in Hayes (2018). www.guilford.com/p/hayes3 ****************************************************************** Model : 4 Y : EE X : EA M : POS Covariates: Age Gender Tenure Sample Size: 109 ****************************************************************** OUTCOME VARIABLE: POS Model Summary R R-sq MSE F df1 df2 p .619 .383 98.539 16.159 4.000 104.000 .000 Model coeff se t p LLCI ULCI constant -23.928 8.238 -2.905 .004 -40.264 -7.591 EA 1.707 .215 7.939 .000 1.281 2.134 Age .064 .099 .652 .516 -.131 .260 Gender -2.263 2.473 -.915 .362 -7.167 2.641 Tenure -.091 .158 -.579 .564 -.405 .222 ****************************************************************** OUTCOME VARIABLE: EE Model Summary R R-sq MSE F df1 df2 p .682 .465 16.552 17.930 5.000 103.000 .000 Model coeff se t p LLCI ULCI constant 22.576 3.511 6.431 .000 15.614 29.539 EA .751 .112 6.724 .000 .530 .973 POS .015 .040 .384 .701 -.064 .095 Age .113 .040 2.804 .006 .033 .194 Gender -.324 1.018 -.319 .750 -2.342 1.694 Tenure -.074 .065 -1.136 .259 -.202 .055
316
************************** TOTAL EFFECT MODEL ******************** OUTCOME VARIABLE: EE Model Summary R R-sq MSE F df1 df2 p .682 .465 16.416 22.561 4.000 104.000 .000 Model coeff se t p LLCI ULCI constant 22.207 3.362 6.604 .000 15.539 28.875 EA .777 .088 8.857 .000 .603 .951 Age .114 .040 2.846 .005 .035 .194 Gender -.359 1.009 -.356 .722 -2.361 1.642 Tenure -.075 .064 -1.164 .247 -.203 .053 ********* TOTAL, DIRECT, AND INDIRECT EFFECTS OF X ON Y ********* Total effect of X on Y Effect se t p LLCI ULCI .777 .088 8.857 .000 .603 .951 Direct effect of X on Y Effect se t p LLCI ULCI .751 .112 6.724 .000 .530 .973 Indirect effect(s) of X on Y: Effect BootSE BootLLCI BootULCI POS .026 .070 -.106 .170 ****************** ANALYSIS NOTES AND ERRORS ******************* Level of confidence for all confidence intervals in output: 95.0000 Number of bootstrap samples for percentile bootstrap confidence intervals: 5000 ------ END MATRIX -----
317
Appendix Q:
SPSS Simultaneous Multiple Regression Analysis Output
Descriptive Statistics Mean Std. Deviation N
EE 52.651376 5.4337357 109 Age 44.70 10.998 109 Gender .87 .387 109 Tenure 7.58 6.872 109 EA 33.715596 4.4805996 109 POS 33.834862 12.4040867 109
Correlations
EE Age Gender Tenure EA POS Pearson Correlation EE 1.000 .245 .014 .089 .650 .431
Age .245 1.000 .047 .468 .092 .086 Gender .014 .047 1.000 -.010 .043 -.041 Tenure .089 .468 -.010 1.000 .117 .049 EA .650 .092 .043 .117 1.000 .613 POS .431 .086 -.041 .049 .613 1.000
Sig. (one-tailed) EE . .005 .444 .179 <.001 <.001 Age .005 . .313 <.001 .172 .186 Gender .444 .313 . .458 .329 .336 Tenure .179 <.001 .458 . .112 .306 EA <.001 .172 .329 .112 . <.001 POS <.001 .186 .336 .306 <.001 .
N EE 109 109 109 109 109 109 Age 109 109 109 109 109 109 Gender 109 109 109 109 109 109 Tenure 109 109 109 109 109 109 EA 109 109 109 109 109 109 POS 109 109 109 109 109 109
318
Variables Entered/Removeda
Model Variables Entered Variables Removed Method
1 POS, Gender, Tenure, Age, EAb
. Enter
a. Dependent Variable: EE b. All requested variables entered.
Model Summaryb
Model R R2 Adjusted
R2 Std. Error of the Estimate
Change Statistics Durbin- Watson
R Square Change F Change df1 df2
Sig. F Change
1 .682a .465 .439 4.0684119 .465 17.930 5 103 <.001 2.202 a. Predictors: (Constant), POS, Gender, Tenure, Age, EA b. Dependent Variable: EE
ANOVAa
Model Sum of Squares df Mean Square F Sig.
1 Regression 1483.899 5 296.780 17.930 <.001b Residual 1704.853 103 16.552
Total 3188.752 108
a. Dependent Variable: EE b. Predictors: (Constant), POS, Gender, Tenure, Age, EA
Coefficientsa
Model
Unstandardized Coefficients
Standardized Coefficients
t Sig. Correlations
B Std. Error Beta Zero-order Partial Part 1 (Constant) 22.576 3.511 6.431 <.001
Age .113 .040 .230 2.804 .006 .245 .266 .202 Gender -.324 1.018 -.023 -.319 .750 .014 -.031 -.023 Tenure -.074 .065 -.093 -1.136 .259 .089 -.111 -.082 EA .751 .112 .619 6.724 <.001 .650 .552 .484 POS .015 .040 .035 .384 .701 .431 .038 .028
a. Dependent Variable: EE
Residuals Statisticsa
Minimum Maximum Mean Std. Deviation N Predicted Value 42.878170 60.461052 52.651376 3.7067242 109 Residual -12.8154383 9.1218309 .0000000 3.9731197 109 Std. Predicted Value -2.637 2.107 .000 1.000 109 Std. Residual -3.150 2.242 .000 .977 109
a. Dependent Variable: EE
Recommended