The Relation Among Employee Alignment, Perceived ...

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

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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.


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

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© Copyright 2020 by John George Meier III All rights reserved

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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.

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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

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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!

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Abstract of Dissertation


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

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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.

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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Chapter 1: Introduction

Overview


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).

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(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).

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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


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,

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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

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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

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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).

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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).

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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 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


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.

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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

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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).

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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

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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

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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

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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)

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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

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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.

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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).

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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


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


Organizational commitment

r = .47, p < .05, n = 246 Employee engagement 4 Biswas and Bhatnagar (2013)

r = .53, p < .001, n = 102


Personal initiative

r = .51, p < .001, n = 186


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


r = –.56, p < .001, n = 283


r = –.34, p < .001, n = 209


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)

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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


Emotional exhaustion

r = –.30, p < .01, n = 216



Job satisfaction

r = .32, p < .05, n = 246


Biswas and Bhatnagar (2013)

r = .52, p < .001, n = 102

Job engagement3 Saks (2006)

Personal accomplishment

r = .48, p < .01, n = 216



Psychological well-being

r = .37, p < .01, n = 216



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


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. 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

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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

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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

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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

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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


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


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 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).


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:



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:



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



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


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:



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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:



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


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


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:




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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


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




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:





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:



H1b: Employee alignment explains a statistically significant proportion of the unique

variance in employee engagement after controlling for perceived organizational

support.





H3b: Perceived organizational support explains a statistically significant proportion of

the unique variance in employee engagement after controlling for employee

alignment.



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, 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


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)


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




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


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



RQ2. To what extent do employee alignment and perceived organizational support


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)


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)



.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:



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


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


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


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


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



RQ2. To what extent do employee alignment and perceived organizational support


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).


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.

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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


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


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

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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).

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(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


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.

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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


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

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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

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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

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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.

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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.

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Chapter Summary


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


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 [email protected]. 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.

282

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 <[email protected]>

Re: Requesting Permission to use the Stringer Strategic Alignment Scale1 message

Chelle Stringer <[email protected]> Fri, Aug 16, 2019 at 6:46 PMTo: John Meier <[email protected]>

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 <[email protected]> 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 <[email protected]> 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!


Re: Requesting Permission to use the Stringer Strategic Alignment Scale1 message

Chelle Stringer <[email protected]> Fri, Aug 16, 2019 at 6:46 PMTo: John Meier <[email protected]>

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 <[email protected]> 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 <[email protected]> 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!

287

Permission to Use the Survey of Perceived Organizational Support.


Re: Requesting Permission to use the Survey of Perceived OrganizationalSupport1 message

Eisenberger, Robert W <[email protected]> Fri, Aug 16, 2019 at 6:54 PMTo: John Meier <[email protected]>, "[email protected]" <[email protected]>

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 [email protected](302)353-8151

From: John Meier <[email protected]>Sent: Friday, August 16, 2019 11:58 AMTo: [email protected] <[email protected]>Cc: John Meier <[email protected]>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 [email protected] or the principal investigator, Dr. Ellen Goldman at [email protected]. If you have questions regarding your rights as a research participant, please contact the GWU Office of Human Research at 202-994-2715 or [email protected].

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.



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.



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.

294

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 [email protected].

295

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 [email protected].

296

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 [email protected]. 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 [email protected]. 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 [email protected]. 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 [email protected]. Thank you in advance for your time and consideration in being part of this research study.

298

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 [email protected]. 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 [email protected]. 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 [email protected]. 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 [email protected]. Thank you for your assistance with this study.

300

Appendix H:

Informed Consent for Participation in a Research Study


IRB # NCR191874 Principal Investigator: Ellen Goldman, Ed.D., [email protected]

Co-Investigator: John Meier, [email protected] 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 [email protected] or the principal investigator, Dr. Ellen Goldman, at [email protected]. If you have questions regarding your rights as a research participant, please contact the GWU Office of Human Research at 202-994-2715 or [email protected]. 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

302


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.

303

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

304

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



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

305

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


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

306

Appendix J:


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.

307

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.

309

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:



Composite reliability (CR) is calculated using the following equation (Fornell & Larcker,

1981; Hair et al., 2014):

𝐶𝑅 = (∑ 𝜆)(

(∑𝜆)( + ∑(1– 𝜆()(2)

Where:



Table M.1 shows the calculations of AVE and CR using Equations 1 and 2.

310

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

311

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


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


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


The Relation Among Employee Alignment, Perceived ...

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