The Relationship of Job Demands and Job Resources in Work Engagement of Sport Volunteers Thomas F

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COLLEGE OF EDUCATION

THE RELATIONSHIP OF JOB DEMANDS AND JOB RESOURCES IN WORK

ENGAGEMENT OF SPORT VOLUNTEERS

THOMAS F. MCMORROW JR.

A Dissertation submitted to the Department of Sport Management in partial fulfillment of the requirements for the degree of Doctor of Philosophy

Degree Awarded Spring Semester, 2014

The members of the supervisory committee were:

Jeffrey D. James

Professor Directing Dissertation

Gerald R. Ferris

University Representative

Pamela L. Perrewé

Committee Member

Ryan M. Rodenberg

Committee Member

The Graduate School has verified and approved the above-named committee members, and certifies that the dissertation has been approved in accordance with university requirements.

On a personal level I would like to dedicate this to my Parents, Thomas and Linda McMorrow,

and my partner, James Anderson. They all provided the love, understanding, and inspiration without which I could not have completed this project. On a professional level I dedicate this to

all of the volunteers who give of themselves and make this a better world.

iii

ACKNOWLEDGEMENTS

Work rarely occurs in a vacuum. There are many who had a hand in bringing this document to completion. A special acknowledgement is extended to my dissertation chairperson, Dr. Jeffrey James. He graciously took me on as a student though my academic interests were outside of his preferred content area and has unwaveringly supported me in my academic pursuits.

I am also indebted to the efforts of the other members of my committee: Dr. Gerald

Ferris, Dr. Pamela Perrewé, and Dr. Ryan Rodenberg. In addition to content for this document, they all serve as examples of the type of professional and person I want to be.

The faculty, staff, and students of the Department of Sport Management at Florida State

University must also be acknowledged for their part in my success. Drs. JoAnne Graf, Yu

Kyoum Kim, and Cecile Reynaud in particular have contributed to any success I can claim.

Very special thanks to Erika Bettilyon for all she has done. All of my colleagues in the program have contributed to my success. The following have had a special impact: Dr. Priscila Alfaro-

Barrantes, Mark DiDonato, Mark Howard, Dr. Tim Kellison, Young Do Kim, and Dr. Mar

Magnuson. Thanks to you all.

Dr. Judith Smith and HandsOn Jacksonville were instrumental with data collection by allowing me access to the volunteers of their organization.

Finally, I could not have done this without the love and support of my family and friends.

It is not possible to acknowledge all who have helped me but I would be remiss if I did not mention Gloria Sanders, Wayne Sutton, Karl Vierck, and Greg Young for the support you have given throughout the years. Of courses, special credit goes to my partner, James Anderson and

iv my parents, Thomas and Linda McMorrow. I could not have successfully completed my degree program without you.

TABLE OF CONTENTS

List of Tables ...... xi

List of Figures ...... xvi

Abstract ...... xviii

Chapter 1 ...... 1

Introduction ...... 1

Statement of Problem ...... 1

Positive Psychology ...... 2

Engagement...... 3

Significance of the Study ...... 7

Definition of Terms...... 8

Volunteer...... 8

Sport Related Volunteering...... 9

Non-Sport Related Volunteering ...... 9

Engagement...... 9

Vigor ...... 9

Dedication ...... 10

Absorption...... 10

Job Demands ...... 10

Job Resources...... 10

Volunteer Satisfaction ...... 11

Volunteer Commitment ...... 11

Volunteer Job Performance...... 12

Volunteer Retention ...... 12

Conceptual Model ...... 12

Structure of the Dissertation ...... 13

CHAPTER 2 ...... 14

Literature Review...... 14

Introduction ...... 14

Economic Impact of Sport Volunteers ...... 15

Goal ...... 16

Positive Psychology ...... 17

Engagement...... 19

Involvement ...... 23

Commitment ...... 24

Volunteers ...... 34

Satisfaction ...... 35

Theoretical Foundations and Conceptual Model ...... 37

Job Demands- Resources Model ...... 39

Self Determination Theory ...... 43

Research Hypotheses ...... 44

Relationship of Job Resources, Job Demands, and Engagement...... 44

Relationship of Engagement to Commitment, Satisfaction, and Volunteer Performance .... 45

The Relationship of Latent Variables to Volunteer Retention...... 48

Chapter 3 ...... 52

Methods...... 52

Research Design...... 53

Organization of the Research ...... 53

Initial Phase ...... 55

Main Study ...... 56

vii

Participants ...... 57

Sample Size ...... 57

Data Analysis Procedures ...... 59

Model Procedures ...... 60

Instrumentation ...... 65

Demographics ...... 79

Chapter Summary ...... 81

Chapter 4 ...... 82

Results ...... 82

Initial Phase Results ...... 82

Main Study Results ...... 89

Sampling...... 89

Description of Sample...... 91

Types of Volunteers Represented in Sample ...... 96

Evidence of Reliability & Validity ...... 97

Confirmatory Factor Analysis ...... 99

Sport vs. Non-Sport ...... 102

Differences in Generational Cohort Results for Job Resources ...... 104

Structural Equation Modeling (SEM) - The Measurement Model ...... 108

Results with the Measurement Model...... 108

Results of Multivariate Normality ...... 108

Results of Path Analysis ...... 114

Results for Research Questions ...... 117

Summary of Results ...... 121

Chapter 5 ...... 123

viii

Discussion and Conclusion ...... 123

Discussion of Findings ...... 124

Differences in Sport versus Non-Sport Volunteers ...... 125

Differences in Generational Cohort Results for Job Resources ...... 126

Job Resources and Job Demands ...... 127

The Relationship of Post Engagement Latent Constructs...... 130

Implications...... 131

Future Areas of Research ...... 139

Limitations ...... 143

Conclusions ...... 145

Appendix A ...... 148

IRB Approval Letter ...... 148

Appendix B ...... 149

HandsOn Jacksonville Letter of Agreement ...... 149

Appendix C ...... 150

HandsOn Jacksonville Newsletter 1 ...... 150

Appendix D ...... 156

HandsOn Jacksonville Newsletter 2 ...... 156

Appendix E ...... 161

Email Appeal for Participation ...... 161

Appendix F...... 162

Facebook Appeal for Participation - 1 ...... 162

Appendix G ...... 163

Facebook Appeal for Participation - 2 ...... 163

Appendix H ...... 165

Survey Consent Page ...... 165

Appendix I ...... 166

Variable Item Coding ...... 166

Appendix J ...... 169

Results of t-Tests...... 169

Appendix K ...... 172

Item-Total Statistics for All Items in Full Model ...... 172

Appendix L ...... 180

Item-Total Statistics for All Items Meeting Cut ...... 180

Appendix M ...... 188

Correlation Matrices and Mean Scores ...... 188

Appendix N ...... 193

Standardized Factor Loadings...... 193

Appendix O ...... 197

Normality Tests ...... 197

Appendix P...... 199

Path Analysis ...... 199

References ...... 208

Biographical Sketch ...... 225

LIST OF TABLES

3.1 Role Ambiguity Scale ...... 67

3.2 Role Conflict Scale ...... 68

3.3 Role Overload Scale ...... 69

3.4 Perceptions of Politics Scale ...... 69

3.5 Autonomy Scale ...... 71

3.6 Social Support Scale ...... 71

3.7 Supervisor Support Scale ...... 71

3.8 Feedback Scale ...... 72

3.9 Vigor Scale ...... 73

3.10 Dedication Scale ...... 73

3.11 Absorption Scale ...... 73

3.12 Leisure Satisfaction Measure – Psychology Scale ...... 74

3.13 Leisure Satisfaction Measure - Education/Intellectual Scale ...... 74

3.14 Leisure Satisfaction Measure – Social Scale ...... 75

3.15 Leisure Satisfaction Measure – Relaxation Scale ...... 75

3.16 Leisure Satisfaction Measure – Physiology Scale ...... 75

3.17 Leisure Satisfaction Measure - Aesthetic-Environmental Scale ...... 76

3.18 Affective Commitment ...... 77

3.19 Volunteer Performance Self-Report Scale ...... 77

3.20 Intention to Remain Scale ...... 78

4.1 Job Resources Factor Item Assignment ...... 83

4.2 Job Resources Items Deleted from Study ...... 84

4.3 Date Ranges for Generational Cohorts in the Literature ...... 91

4.4 Date Ranges for Generational Cohorts for Current Study ...... 91

4.5 Descriptive Statistics ...... 92

4.6 Distribution of Generational Cohort in Sample ...... 95

4.7 Distribution of Volunteers by Event Type ...... 96

4.8: Reliability Table based on Individual Items in Factors ...... 98

4.9: Items Deleted After Step One of CFA Iteration ...... 99

4.10: Items Deleted After Step Two of CFA Iteration...... 100

4.11: Item Deleted After Step Three of CFA Iteration ...... 100

4.12: Item Deleted After Step Four of CFA Iteration ...... 100

4.13: Reliability Table Based on Individual Items in Factors ...... 101

4.14: Reliability Table of Changed Items in Factors ...... 102

4.15: ANOVA Results for Generations Difference in Job Resources...... 105

4. 16: Scheffé Post Hoc Multiple Comparisons Results for Generations Difference in Job Resources...... 106

4.17: Measure of Skewness and Kurtosis on Factors ...... 113

4.18: Standardized Path Coefficients and p-values of Hypotheses...... 116

4.19: Factor Loading for Job Resources on Volunteer Engagement ...... 118

4.20: Factor Loading for Job Demands on Volunteer Engagement...... 118

4.21: Factor Loading for Job Resources and Job Demands on Volunteer Engagement ...... 119

4.22: Path Coefficients for Latent Constructs on Volunteer Retention ...... 121

5.1: Research Hypotheses ...... 124

I.1: Variable Item Coding ...... 166

J.1: 2 Groups 38 Cases T-Tests...... 169

xii

J.2: 2 Groups Uneven Samples T-Tests ...... 170

J.3: Mean Scores for Factors of Sport and Non-Sport Groups ...... 171

K.1: Job Demands Item-Total Statistics - All Items ...... 172

K.2: Role Ambiguity Item-Total Statistics - All Items ...... 173

K.3: Role Conflict Item-Total Statistics - All Items ...... 173

K.4: Role Overload Item-Total Statistics - All Items ...... 173

K.5: Role Perception of Politics -Total Statistics - All Items ...... 174

K.6: Job Resources Item-Total Statistics - All Items ...... 174

K.7: Autonomy Item-Total Statistics - All Items...... 175

K.8: Social Support Item-Total Statistics - All Items ...... 175

K.9: Supervisor Support Item-Total Statistics - All Items ...... 175

K.10: Feedback Item-Total Statistics - All Items ...... 175

K.11: Satisfaction Item-Total Statistics - All Items ...... 176

K.12: Satisfaction Psychological Item-Total Statistics - All Items ...... 176

K.13: Satisfaction Educational Item-Total Statistics - All Items ...... 177

K.14: Satisfaction Social Item-Total Statistics - All Items ...... 177

K.15: Satisfaction Relaxation Item-Total Statistics - All Items ...... 177

K.16: Satisfaction Physiological Item-Total Statistics - All Items ...... 177

K.17: Satisfaction Aesthetic Item-Total Statistics - All Items ...... 178

K.18: Engagement Item-Total Statistics - All Items ...... 178

K.19: Vigor Item-Total Statistics - All Items ...... 178

K.20: Dedication Item-Total Statistics - All Items ...... 178

K.21: Absorption Item-Total Statistics - All Items ...... 179

xiii

K.22: Affective Commitment Item-Total Statistics - All Items ...... 179

K.23: Volunteer Performance Item-Total Statistics - All Items ...... 179

K.24: Volunteer Retention Item-Total Statistics - All Items ...... 179

L.1: Job Demands Item-Total Statistics – Items Meeting Cut ...... 180

L.2: Role Ambiguity Item-Total Statistics – Items Meeting Cut ...... 180

L.3: Role Conflict Item-Total Statistics – Items Meeting Cut ...... 181

L.4: Role Overload Item-Total Statistics – Items Meeting Cut ...... 181

L.5: Perception of Politics -Total Statistics – Items Meeting Cut ...... 181

L.6: Job Resources Item-Total Statistics – Items Meeting Cut ...... 182

L.7: Autonomy Item-Total Statistics – Items Meeting Cut ...... 182

L.8: Social Support Item-Total Statistics – Items Meeting Cut ...... 182

L.9: Supervisor Support Item-Total Statistics – Items Meeting Cut ...... 183

L.10: Feedback Item-Total Statistics – Items Meeting Cut ...... 183

L.11: Satisfaction Item-Total Statistics – Items Meeting Cut...... 183

L.12: Satisfaction Psychological Item-Total Statistics – Items Meeting Cut ...... 184

L.13: Satisfaction Educational Item-Total Statistics – Items Meeting Cut ...... 184

L.14: Satisfaction Social Item-Total Statistics – Items Meeting Cut ...... 184

L.15: Satisfaction Relaxation Item-Total Statistics – Items Meeting Cut ...... 185

L.16: Satisfaction Physiological Item-Total Statistics – Items Meeting Cut ...... 185

L.17: Satisfaction Aesthetic Item-Total Statistics – Items Meeting Cut ...... 185

L.18: Engagement Item-Total Statistics – Items Meeting Cut ...... 185

L.19: Vigor Item-Total Statistics – Items Meeting Cut ...... 186

L.20: Dedication Item-Total Statistics – Items Meeting Cut ...... 186

xiv

L.21: Absorption Item-Total Statistics – Items Meeting Cut ...... 186

L.22: Affective Commitment Item-Total Statistics – Items Meeting Cut ...... 186

L.23: Volunteer Performance Item-Total Statistics – Items Meeting Cut ...... 186

L.24: Volunteer Retention Item-Total Statistics – Items Meeting Cut ...... 187

M.1: Correlation Matrix of Latent Variables ...... 188

M.2: Correlation Matrix of Factors for Engagement, Job Demands, and Job Resource ...... 189

M.3: Correlation Matrix for Sub-Factors of Satisfaction ...... 190

M.4: Mean Scores of Latent Variables ...... 191

M.5: Mean Scores for Factors for Engagement, Job Demands, and Job Resource ...... 191

M.6: Mean Scores for Sub-Factors of Satisfaction ...... 191

M.7: Mean Scores for Job Resources by Generational Cohort ...... 192

N.1: Standardize Factor Load for Factors During Iteration Process ...... 193

O.1: Normality Tests ...... 197

LIST OF FIGURES

1.1 The Role of Job Resources and Job Demands in Volunteer Engagement and Related Outcomes – Conceptual Model...... 12

2.1 Components of Organizational Commitment...... 27

2.2 Steers’ (1977) Hypothesized Antecedents and Outcomes of Organizational Commitment .29

2.3 The Role of Job Resources and Job Demands in Volunteer Engagement and Related Outcomes of Commitment, Satisfaction, Performance, and Retention – Conceptual Model...... 39

2.4 The Job Demands-Resources (JD-R) Model (Schaufeli & Bakker, 2004)...... 41

2.5 The Job Demands-Resources Model (Bakker & Demerouti, 2007)...... 42

2.6 The JD-R Model of Work Engagement (Bakker & Demerouti, 2008)...... 42

2.7 The Role of Job Resources and Job Demands in Volunteer Engagement and Related Outcomes – Measurement Model...... 51

3.1 The Role of Job Resources and Job Demands in Volunteer Engagement and Related Outcomes – Extended Measurement Model ...... 54

4.1 The Role of Job Resources and Job Demands in Volunteer Engagement and Related Outcomes – Revised Extended Measurement Model ...... 86

4.2 The Role of Job Resources and Job Demands in Volunteer Engagement and Related Outcomes – Revised Extended Exploded Measurement Model...... 87

4.3 The Role of Job Resources and Job Demands in Volunteer Engagement and Related Outcomes – Revised Extended Measurement Model Based on SEM Analysis...... 88

4.4 Educational Attainment of Participants ...... 94

4.5 Household Income of Participants...... 95

4.6 Distribution of Generations Cohort in Sample ...... 96

4.7 Event Type Report by Volunteer Participants in Sample ...... 97

4.8 Job Demands Histogram ...... 109

4.9 Job Resources Histogram...... 110

4.10 Volunteer Satisfaction Histogram...... 110 xvi

4.11 Volunteer Engagement Histogram...... 111

4.12 Affective Commitment Histogram...... 111

4.13 Volunteer Performance Histogram...... 112

4.14 Volunteer Retention Histogram...... 112

4.15 The Structural Model with Proposed Hypotheses ...... 115

4.16 The Path Analysis Model with Proposed Hypotheses ...... 115

4.17 The Path Analysis Model with Standardized Results ...... 116

xvii

ABSTRACT

Sport organizations rely on volunteers to accomplish their missions. Volunteers are a finite resource whose supply may not keep up with the ever increasing demand (Bussell &

Forbes, 2002; Cuskelly, 2004). The number of organizations dependent on volunteer workers is increasing, while the number of individuals willing to volunteer is decreasing. While recruiting volunteers is important to sport organizations, retention of volunteers may be more important in order to remain viable in the competitive arena of volunteer services. Retention of volunteers should be a priority for reasons of efficiency since organizations would save time, effort, and money they would otherwise spend on recruitment and training. To assist practitioner’s efforts to retain volunteers, researchers should investigate avenues to advance our understanding of volunteer retention. Toward this end the purpose of this study was three-fold: 1) to present a conceptual model illustrating the relationship between job demands and job resources as they relate to work engagement of sport volunteers leading to sport volunteer performance, commitment, satisfaction, and retention, 2) to investigate whether there were differences in volunteer engagement based on whether individuals were volunteering to work at sporting versus non-sporting events, and 3) exploring if there were differences based on a volunteer’s generational cohort.

A questionnaire was constructed incorporating measurements scales drawn from the literature which were modified to be applicable to the volunteer milieu, as well as items added by the researcher to assess content for which there were no items available in the existing literature.

The questionnaire was vetted by an expert panel with the final version incorporated into the study. Nine hypotheses about the relationships in the proposed model were posited. Evidence from the study was found to support eight of the nine hypotheses. Based on the evidence from

xviii the data analysis, the following conclusions were drawn: the job resource with the greatest impact on volunteer engagement was social support, followed closely by feedback and supervisor support; job demands had a negative impact on volunteer engagement. There was an inverse relationship between job demands and job resources in regard to engagement in the volunteer context; sporting event volunteers did not exhibit different levels of engagement than those volunteering with non-sporting events. Volunteers representing different generations did exhibit differing preferences in regard to job resources while volunteering.

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CHAPTER 1

INTRODUCTION

Statement of Problem

Many organizations in the public sector rely on volunteers in order to provide the services that allow the organization to operate. Some organizations are predominantly run by volunteers

(Cuskelly, 2004), and this is especially true of sport organizations. Many sporting events could not be successful without the support of volunteers. The Olympic Games, for example, could not be produced without volunteers. Hosting the Olympic Games is already a risky endeavor financially. Even Games that have concluded with reported profits have only done so because of the vast contribution of volunteers. It would be cost prohibitive if an organizing committee had to actually pay for all the staff members that work events such as the Olympic Games.

The reliance on volunteers may soon become problematic as several authors have reported a growing need for volunteers, while the number of people willing to volunteer is not increasing at a comparable rate (Bussell & Forbes, 2002; Cuskelly, 2004; Locke, Ellis, & Smith,

2003). In fact, Locke, Ellis, and Smith (2003) have posited volunteers are becoming a scarce commodity as the number of organizations dependent on volunteer workers increases, yet the number of individuals willing to volunteer is actually decreasing. It is incumbent for organizations to actively recruit volunteers and actively work to retain them.

Locke, Ellis and Smith (2003) have posited poor management as a potential cause for volunteers to leave organizations, while management that meets the volunteer’s needs by being explicit, developmental, supportive and appreciative may lead to retention of volunteers.

Another factor likely to exacerbate the problem concerns the generational distribution of the population. The largest segment of the population is the baby-boomers. Subsequent generations

1 have not grown at a similar rate. Definition of the exact dates that comprise the years of a generation vary but a rough estimate has baby boomers comprising 78 million individuals, while the Gen X-er cohort represents 45 million members (Smola & Sutton, 2002). The number of potential volunteers will continue to decrease in proportion to the number of available members in particular generational groups.

Baby-boomers as a group are either reaching retirement age or have already retired. This may free them to do more volunteering in the short term but as they age their physical capacity to volunteer will eventually decrease. Without going into attitudinal or behavioral characteristics of subsequent generations, and their willingness to become volunteers, event promoters must understand that the sheer number of potential volunteers is diminishing. This will have a dramatic impact on sport event production. Addressing the gap is essential. Two approaches include inducing more individuals to volunteer, and finding ways to retain those who already volunteer. In the past researchers would have investigated these approaches by looking at what prevented individuals from volunteering and what caused them to discontinue their volunteering.

Such a focus on the negative case is giving way to a more positivistic attitude of looking at what would induce volunteerism and what keeps people involved. This change is representative of the positive psychology paradigm.

Positive Psychology

In an effort to adhere to the current paradigm of positive psychology, the problem of retaining sport volunteers is approached from the perspective of looking for ways to improve the situation. According to Seligman and Csikszentmihalyi (2000) positive psychology is the scientific study of the strengths and optimal functioning of humans. Following this line of thought leads us to search for what will lead volunteers to want to stay.

Researchers first approached the question of how to attract and retain volunteers from investigations of commitment (Cuskelly, Harrington, & Stebbins, 2002; MacLean & Hamm,

2007; Mowday, Porter, & Steer, 1982). While this is an important construct which provides good information, it does not necessarily relate directly to participation. A construct that is being examined in the organizational behavior literature which has yet to be employed in the sport volunteer context is that of engagement. A better understanding of engagement may prove to be a better approach for retaining volunteers.

Engagement

An area of research in organizational behavior that is enjoying increasing popularity is work engagement. “Work engagement is a positive, fulfilling, affective-motivational state of work related well-being that can be seen as the antipode of job burnout” (Leiter & Bakker, 2010, p. 1). Ideas of attachment, involvement, commitment, attentiveness, passion, and enthusiasm are all common connotations of the term engagement. The Merriam-Webster dictionary supports the common understanding of the term noting engagement means “emotional involvement or commitment” and that it is “the state of being in gear.” With these definitions as well as the everyday connotations in mind, one can begin to see that work engagement is a state to be valued by management as engaged workers are more likely to be productive in their jobs than employees who are not. The benefits are not entirely one sided for the employer. Engaged workers report a wide range of benefits from working in situations they find engaging (Bakker &

Demerouti, 2008; Llorens, Schaufeli, Bakker, & Salanova, 2007).

One major aspect of engagement that differentiates it from related concepts such as involvement and commitment is that engagement is specifically focused on the work structure and experience. Managers who can successfully ascertain what engages their staff should be

3 able to retain them. So this brings us to the question of what is work engagement? Leiter and

Bakker (2010) write that it is a motivational concept. Engaged employees are characterized as feeling, “compelled to strive towards a challenging goal. They want to succeed” (p.2). Such employees pursue the challenges in a way that exhibits vigor, dedication and absorption in the task itself, exhibiting a personal commitment toward attainment of these goals. They tend to devote all of their energies enthusiastically and unreservedly with a strong focus on detail which can lead the employee to a level of absorption allowing the employee to experience a flow state.

Because of the focus and energy inherent in work engagement, engaged workers are able to bring their full potential to bear on their job (Leiter & Bakker, 2010). This brings us to the question of how is this motivational concept measured and studied?

Various instruments have been developed to measure engagement. These instruments vary based on the conceptualizations of engagement that have been posited. Some of the instruments designed to measure work engagement have been developed by private consultancy firms. The instruments are viewed as proprietary and the private firms do not publish data on the psychometric quality of the instruments. The one exception is Gallup’s Workplace Audit

(GWA) otherwise known as the . While the does have supporting data including information about its psychometric quality, it was designed explicitly from an “actionable standpoint” as a tool for managers to create change in the workplace (Schaufel & Bakker, 2010).

The while a valuable tool, was not designed to examine the construct from an academic perspective. The instrument designed for academic study of work engagement is the Utrecht

Work Engagement Scale which is discussed next.

Utrecht Work Engagement Scale. Schaufeli and Bakker (2004) posit “engagement as a positive, fulfilling, work-related state of mind that is characterized by vigor, dedication, and

4 absorption” (2004, p.295). Based on this definition the most appropriate instrument to study the construct will be the Utrecht Work Engagement Scale (UWES) by Schaufeli and Bakker as it is a three-dimensional questionnaire specifically developed to measure vigor, dedication, and absorption. The UWES is used as the measure of work engagement in the Job Demands-

Resources (JD-R) model. The UWES is an instrument that would be utilized as part of survey research, but there are other means by which to study work engagement. The work engagement construct has also been studied via quantitative diary studies (for example see: Bolger, Davis, &

Rafaeli, 2003; Sonnentag, 2003; Xanthopoulou, Bakker, Heuven, Demerouti, & Schaufeli, 2008;

Xanthopoulou, Bakker, Demerouti, & Schaufeli, 2008). Regardless of the research method the

JD-R model has been used by researchers as a tool to examine the effects of both job demands and job resources.

With a few exceptions, notably Bakker and Demerouti’s (2007) modified JD-R model of work engagement, the majority of studies using the JD-R model have associated job demands with burnout and job resources with engagement. This reflects a two-dimensional conceptualization of burnout forwarded in the Oldenburg Burnout Inventory which is the premise Demerouti et al. (2001) originally considered that the JD-R model would employ

(Demerouti & Cropanzano, 2010). As items were positively and negatively phrased, they could address both burnout as well as work engagement since work engagement was considered the antipode of burnout. Job resources were seen to boost work engagement when job demands were high in the employee context (Bakker A. B., Hakanen, Demerouti, & Xanthopoulou, 2007).

As such job demands essentially represent a negative effect to work engagement. This may not be the case in the volunteer milieu.

As is posited in self-determination theory (SDT), some individuals may be looking for outlets where they can exercise certain skills and abilities. In SDT, autonomy, competence and relatedness have been argued as fostering the most volitional and high quality form of motivation and serve as a source of engagement for activities (Deci & Ryan, 1985). In these cases what may be considered by some as job demands may be viewed as challenges by others. This could especially be the case in individuals who view volunteering as a form of serious leisure.

Stebbins (1996) asserts “many volunteer roles, because they offer their incumbents special careers and distinctive sets of rewards, can be understood as serious leisure” (p. 212). Thus, some individuals who volunteer may be looking for opportunities to support the experience of autonomy, competence, and/or relatedness through volunteering. As such job demands would actually facilitate engagement instead of operating negatively in the process. If this is the case, managers need to act accordingly to allow individuals to get the most out of their volunteer experience. Ultimately, properly managed, the construct of engagement may provide an avenue for retention in the sport volunteer context. Further study is warranted to assist practitioners to find what can be considered engaging for volunteers.

It is imperative to understand the parameters where engagement is most likely to occur.

What part do participant demographics or type of volunteering play in differentiating individual’s experiencing of engagement? What relationship is there between job demands and job resources in relation to engagement? Is the relationship between job demands and job resources unique to sport volunteering or applicable to volunteering in general?

Research Questions

The primary questions addressed in this study included:

 Which job resources have the greatest impact on volunteer engagement?

 Do job demands have a positive or negative impact on volunteer engagement?

 What is the relationship between job demands and job resources in regard to engagement

in the volunteer context?

 Do sporting event volunteers exhibit different levels of engagement than those

volunteering with non-sporting events?

 Do volunteers representing different generations exhibit differing preferences in regard to

job resources while volunteering?

Significance of the Study

Public, nonprofit, for-profit organizations, as well as individuals, benefit from volunteerism. Nearly 1 billion people worldwide volunteer their time (Salamon, Sokolowski, &

Haddock, 2011). The public sector in particular relies on volunteers to provide services, allowing organizations in many cases to exist. These organizations are predominantly run by the volunteers (Cuskelly, 2004). and this is especially true of sport organizations. Several researchers (Bussell & Forbes, 2002; Cuskelly, 2004; Locke, Ellis, & Smith, 2003) have posited reliance on volunteers may soon become problematic. These researchers have reported the need for volunteers is growing while the number of people willing to volunteer increases at a slower rate (Bussell & Forbes, 2002; Cuskelly, 2004; Locke, Ellis, & Smith, 2003). Facing an impending shortage of volunteers, organizations must look for methods to actively recruit and to retain volunteers. In addition to the primary need of volunteers for the sport industry to survive, one cannot overlook the impact the sport industry has on a nation’s economy. The economic impact of sport is prodigious (see; Salamon, Sokolowski, & Haddock, 2011; Nana, Sanderson, &

Goodchild, 2002; Rigg & Lewney, 1987).

Through this research, one goal was to add to existing theory and the study of engagement by looking beyond the work milieu and examining volunteers. This is not the first time volunteers have been the focus of a study on engagement (see: Tuckey, Bakker, & Dollard,

2012; Vecina, Chacon, Sueiro, & Barron, 2012), however, to my knowledge it was the first time sport volunteers have been the focus. Finding ways to stabilize the volunteer workforce would contribute to the continued viability of the sport industry. The impact volunteers have on the sport industry as well as a nation’s economy provide the impetus and rational for this study.

Before addressing the theoretical constructs, it is important to define the important terms related to the current proposal. Specifically the concepts of involvement, commitment, and engagement are similar and must be differentiated.

Definition of Terms

Volunteer

Some researchers will segment volunteers for functional reasons such as leaders versus non-leaders (Catano et al., 2001), non-profit versus for profit (Hobson, Rominger, Malec,

Hobson, & Evans, 1996), by demographics (Callow, 2004), or by type of organization (Grimm,

Dietz, Foster-Bey, Reingold, & Nesbit, 2006). As our target segment is sport event volunteers which can cross all of these segments, such functional difference are of less importance with this particular project but may prove to be an avenue of future research. The definition the researcher used for this study follows the definition for volunteerism posited by Callow (2004) where an individual participates in “an activity that is intended to benefit another person, group, or cause, is not done for monetary compensation or material gain and goes beyond one’s normal responsibilities” (p. 262).

Sport Related Volunteering

Sport related volunteering refers to volunteering that is done for any type of sport organization. Types of organization that fall under this category could include for profit professional sport organizations (i.e., the National Football League (NFL), Major League

Baseball (MLB), Minor League Baseball, National Basketball Association (NBA), etc.). In addition to professional sport, any community based sport organization such as little league,

YMCA, community recreation or sports, or university based sport opportunities that utilize volunteers would fall into this category.

Non-Sport Related Volunteering

Non-sport related volunteering refers to volunteering that is done for any type of organization that is not sport related. Organizations that are related to the arts, education, environment, religious, or other cause-related organizations would all fall into this category.

Engagement

As defined by Schaufeli and Bakker (2004, 2006, 2007) engagement is “a positive, fulfilling, work-related state of mind that is characterized by vigor, dedication, and absorption”

(2004, p.295).

Vigor

As the name implies vigor is characterized in workers by high levels of energy. The term as it is used in this context also engenders the characteristic of exhibiting a willingness to invest effort in one’s work, showing persistence in the difficulties as well as being associated with mental resilience while working (Schaufeli & Bakker, 2004).

Dedication

According to Schaufeli and Bakker (2004) “Dedication is characterized by a sense of significance, enthusiasm, inspiration, pride, and challenge” (p. 295).

Absorption

Absorption is the third dimension of engagement. This dimension is characterized as an individual “being fully concentrated and happily engrossed in one’s work, whereby time passes quickly and one has difficulties with detaching oneself from work” (Schaufeli & Bakker, 2004, p. 295).

Job Demands

Job demands refer to aspects of a job (i.e., physical, psychological, social, or organizational) that through sustained physical and/or psychological effort incur a degree of physiological and/or psychological costs. While not necessarily negative in nature, job demands can become job stressors when those demands require high effort from the employee to achieve.

Instances where high effort exacts a high cost on the employee may elicit negative responses including anxiety, depression and burnout (Schaufeli & Bakker, 2004). The following perceived stressors will be examined as job demands: role ambiguity, role conflict, role overload, and perceptions of politics.

Job Resources

Schaufeli and Bakker (2004) define job resources as “those physical, psychological, social, or organizational aspects of the job that either/or (1) reduce job demands, and the associated physiological and psychological costs; (2) are functional in achieving work goals; (3) stimulate personal growth, learning and development” (p. 295). Job resources encompass the following: autonomy, social support, supervisor support, supervisor coaching, and feedback

Volunteer Satisfaction

Volunteer satisfaction is being operationalized as an individual’s perception that certain personal needs are met. This conceptualization is derived from the field of leisure studies.

Needs can be broken into six components: psychological, intellectual, social, relaxation, physiological, and aesthetic (Beard & Ragheb, Leisure satidfaction measure, 2012). The psychological component encompasses such items as the need for challenge, accomplishment, self-expression, freedom, realizing abilities, and self-actualization. The intellectual component, also categorized as educational learning or intellectual, reflects an individual’s need for intellectual stimulation, learning, satisfying curiosity, and seeking novel experiences. The social component, reflects the needs social interaction in all forms. Relieving stress and strain from work as well as recuperation, restoration and resting are all items related to the relaxation component. The physiological component reflects the needs for the aspects of physical health and vitality. Finally, the aesthetic component reflects the need for the environment to be clean, interesting and pleasing (Beard & Ragheb, 2012).

Volunteer Commitment

Based on the works of Meyer and Allen (see, Allen & Meyer, 1990; Meyer & Allen,

1991, 1997), volunteer commitment refers to the link between an individual and an organization which decreases the likelihood the individual will leave the organization. The nature of these links fall into one of three categories: affective commitment where employees stay because they want to stay; continuance commitment, where employees feel they need to stay; and normative commitment, where employees feel they ought to stay.

Volunteer Job Performance

Contrary to research of for-pay employees, “systemic empirical research on the behaviors

of volunteers is less prevalent” (Farmer & Fedor, 2001, p. 192). According to Farmer and Fedor

(2001) research on volunteers is predominantly related to motivations to volunteer and volunteer

retention. Research on volunteer performance is further hampered because volunteer

performance when defined is generally consider to be multifaceted (Campbell, McCloy, Oppler,

& Sager, 1993), involving relevant behaviors reflecting the individual’s (volunteer’s) amount of

contribution toward the achievement of organizational goals (Farmer & Fedor, 2001).

Volunteer Retention

Volunteer retention refers to the intention of individuals to continue volunteering in the

future. A distinction will be made as to whether the individual plans to continue volunteering in

general as well as the context of their volunteering in regard to whether they plan to continue to

volunteer in sport, arts, education, or cause related endeavors.

Conceptual Model

Having defined the major variables included in the present study it is pertinent to provide

the proposed conceptual model showing the relationships of the variables (see Figure 1.1).

Volunteer Commitment Job Demands Volunteer Satisfaction Volunteer Volunteer Retention Engagement

Job Resources Volunteer

Performance

Figure 1.1 The Role of Job Resources and Job Demands in Volunteer Engagement and Related Outcomes – Conceptual Model. 12

The relationships among the variables will be discussed in length as part of the literature review in the next chapter.

Structure of the Dissertation

This dissertation will follow a typical format with the second chapter serving as a review of literature and research related to the subject of the dissertation. Chapter three addresses the study design and methods. This will include discussion of the study settings and participants.

Results and data analysis are the focus of chapter four. Chapter five serves as the discussion section which will include strengths and limitations of the study, practical implications of the study, and suggestions for areas of further research.

CHAPTER 2

LITERATURE REVIEW

Introduction

Nearly 1 billion people worldwide volunteer their time via public, nonprofit, for-profit organizations as well as directly for friends or neighbors (Salamon, Sokolowski, & Haddock,

2011). As previously stated many organizations in the public sector rely on volunteers to exist.

This reliance on volunteers may soon become problematic as the need for volunteers outpaces the number of people willing to volunteer (Bussell & Forbes, 2002; Cuskelly, 2004; Locke, Ellis,

& Smith, 2003). Organizations must actively recruit and actively work to retain volunteers.

Poor management has been posited as a potential cause for volunteers to leave organizations, while explicit, developmental, supportive and appreciative management that meets the volunteer’s may lead to retention of volunteers (Locke, Ellis, & Smith, 2003).

Sport organizations in particular rely on volunteers to accomplish their missions. As noted, volunteers are a finite resource whose supply cannot keep up with the ever increasing demand (Bussell & Forbes, 2002). Organizations need to discover ways to successfully recruit and more importantly retain volunteers in order to remain viable in the increasingly competitive arena of volunteer services. Retaining volunteers should be a priority for reasons of efficiency since organizations will save time, effort, and money they would spend on recruiting and training new volunteers.

A construct that is being examined in the organizational behavior literature which has yet to be employed in the sport volunteer context is that of engagement. There appears to be evidence that utilizing policies and procedures such as increasing job resources (i.e., physical, psychological, social, or organizational) has a positive impact on employee engagement (Bakker,

Hakanen, Demerouti, & Xanthopoulou, 2007; Mauno, Kinnunen, & Ruokolainen, 2007;

Schaufeli, Bakker, & Van Rhenen, 2009; Schaufeli & Bakker, 2004; Schaufeli, Taris, Le Blanc,

Peeters, Bakker, & De Jonge, 2001). At face value, one would assume that the practice of organizations utilizing employee engagement policies and procedures (i.e., increased job resources) resulting in increased employee engagement would hold true for volunteers. This assumption provides the impetus for this research. The importance of this line of study can easily be justified by looking at the contribution sport volunteers make to society.

Economic Impact of Sport Volunteers

The most cursory of investigations illustrates the importance of sport from an economic perspective. In the United States sport is projected to generate 414 billion dollars in revenue or

2.8% of the US GDP (Naterader, 2011). The importance of sport in the economy of other countries is also prodigious. Based on information reported in The Economic Value of Sport in

England, over the past two decades the United Kingdom has seen growth in the sport sector that has outstripped the English economy as a whole. Between 1985 and 2008, in the UK the economy’s annual contribution from sport reached £16.668 billion, an increase of 140%. In

2008, consumers in England spent £17.384 billion on sport.

In addition to the contribution to the economy from a spending standpoint, there is a significant contribution to the economy via job creation. The number of people with sport- related jobs has reached 1.8% of all employment in England or 441,000 jobs. Importantly from a governmental perspective, over three-quarters of these jobs are in the private sector (Sport

Industry Research Centre, 2010). When considering the importance of sport in the economy of most nations, the importance of volunteers in the equation of producing a successful event cannot be understated. Without volunteers sport could not operate - let alone be a force in the

15 economy of nations. Volunteers are a vital resource needed to deliver the sport product in the current paradigm. Davies (2004) has provided evidence that the size of the current sport voluntary sector in the UK is considerably smaller than was predicted based on national estimates. As would be expected, the estimates for the economic impact of American volunteers are significantly larger. Based on data from the private sector in 2000, Chelladurai (2006) indicates that American sport volunteers represent a value of $266 billion. If, as should be expected, this is true globally, volunteers are certainly a commodity worthy of research if sport is to continue to operate in its current incarnation.

Goal

The proposed study represents the beginning of an investigation regarding the role of job demands and job resources as the means to engage sport event volunteers. It is expected that engaging sport event volunteers would lead to their retention. A model of sport volunteer engagement with the antecedents of job demands and job resources leading to work engagement with the outcomes of volunteer commitment, satisfaction, performance and retention is proposed as a means to guide research in this area of study.

The proposed model has been developed in the spirit of the positive psychology paradigm. The problem of retaining sport volunteers is approached by looking for ways to improve the situation. Before moving to the discussion of the primary constructs, however, it is incumbent to begin by explicating the proposed model and differentiating key terms. Any coherent discussion of theoretical constructs should begin with some form of framework to explain the construct of discussion as well as defining important terms related to the current proposal. Especially of interest below, the concepts of engagement, involvement, and

16 commitment are similar and must be differentiated. This will be followed by an explanation of the Job Demands-Resources model (JD-R).

Positive Psychology

Research during the 1970s and 1980s focused on exploring the causes and mechanisms, in an effort to try to identify and if possible to cure burnout. The focus was on understanding what was wrong. An early researcher of burnout, Maslach, developed with colleagues (Maslach

& Jackson, 1981; Maslach, Jackson, & Leiter, 1996) the Maslach Burnout Inventory (MBI) to assess an individual’s level of burnout. It was designed to measure the three factors associated with burnout: exhaustion, detachment, and decreased professional efficacy. The focus of this stage of burnout research was finding those afflicted with burnout and seeking strategies to ameliorate the situation. Although researchers were striving to improve the overall working situation of employees, the focus was on the negative aspects of work. The focus on negativity continued into the 1990s.

In the 1990s two lines of study emerged that would expand research from strictly looking at what was wrong, to exploring mechanisms for positive development. Two important works were published that provided a platform from which research was launched that was more positivistic in nature. The first was Czikszentmihalyi’s (1990) work on flow which sought to understand what was necessary for individuals to become completely immersed in an activity so that they experienced what he terms a flow state. While in a flow state an individual experiences a heightened sense of awareness while at the same time the individual is so absorbed that time loses meaning. The second important publication was by Kahn (1990), in which he first conceptualized the construct of work engagement. Both of these works provided an alternative

17 platform for researchers interested in organizational behavior, and searching for positive aspects of work behavior.

Researchers who had previously been constrained by the norm of looking at what could be considered the negative field of burnout also began to investigate study engagement as well.

Initially, Maslach, Schaufeli, and Leiter (2001) posited that engagement was the antipode of burnout. Burnout has been characterized as having three dimensions: exhaustion, cynicism, and professional efficacy. Indications that an individual may be categorized as experiencing burnout include reporting high levels of exhaustion, high levels of indifference or at least a distancing attitude of work in general, and low levels of professional efficacy which can be manifested in both social and non-social aspects of occupational accomplishment. Engagement on the other hand is characterized by vigor, dedication and absorption (Schaufeli & Bakker, 2004).

Gonzalez-Roma, Schaufeli, Bakker, and Lloret (2006) found evidence to support specific dimensions of the burnout and engagement constructs which can be seen as opposites of two distinct bipolar dimensions. One dimension incorporated the burnout dimension of exhaustion and the engagement dimension of vigor as polar opposites on the continuum which the researchers termed energy. Another dimension the researchers dubbed identification consisted of the burnout dimension of cynicism and the engagement dimension of dedication. Other researchers (Schaufeli & Bakker, 2004; Bakker & Demerouti, 2006; Demerouti, Hakanen,

Bakker, & Xanthopoulou, 2007; Salanova, Bakker, & Schaufeli, 2008) have continued to investigate the relationship of burnout and engagement. From an academic standpoint, striving to understand this relationship of burnout and engagement is a worthy cause in itself.

Understanding these constructs also has practical implications and applications.

Managers are charged with getting the most out of their employees; ergo from a practical stand point the importance of studying both areas of employee burnout and engagement should be evident. Research on engagement should lead to a measure assessing when employees are in a position to operate at their maximum production capability. Arguably a more important product of the study of engagement is to illuminate the factors that are important to produce engagement in the employee. Armed with an understanding of the antecedents of engagement, employers should be better able to tailor policies and procedures to maximize the potential for employees to experience engagement.

Engagement

A discussion of engagement should actually begin with the work of Karasek (1979) who developed and tested the demands-control model (DCM) which examined stress-management.

The model was designed to predict mental strain resulting from an interaction between job demands and job decision latitude (the discretion the worker is allowed in deciding how to meet the demands). Karasek consistently found that the combination of heavy job demands and low decision latitude were associated with mental strain and job dissatisfaction. Prior to Karasek’s

(1979) work, researchers tended to either focus on job stressors or job decision latitude. Karasek introduced the need to address both elements of the work environment on employees to get a true picture of the mechanism being studied on the individual level. Later Karasek and Theorell

(1990) extended their research to work groups and found work teams which were characterized as exhibiting high social support and mutual trust tended to be more goal-directed and cohesive.

These teams also were characterized as having better morale and job-related well-being. While

Karasek’s work is similar to later research related to engagement it was limited by looking only at job decision latitude as the means of offsetting job demands. Later researchers of engagement

19 influenced by Hobfoll’s (1989) work conceptualizing stress and his conservation of resources

(COR) which is based on the assumption that well-being can be enhanced through salient factor from various resources (Hakanen & Roodt, 2011) began incorporating other job resources when investigating questions related to engagement. This more encompassing approach drew the researcher to examine the question of retaining volunteers from an engagement perspective.

Originally work engagement was thought of as the antipode of burnout and the two constructs were presented and studied as opposing poles of a continuum (Schaufeli & Bakker,

2004). The term engagement has become very popular after the turn of the century as evidenced by the sheer number of publications on the subject. Of the vast number of publications though, only a small minority have been produced in academia. While the research related to work engagement currently being examined has been mainly in the domain of organizational behavior, engagement is also extensively studied in the fields of psychology, sociology, and education

(Schaufeli & Bakker, 2010). This investigation, however, appears to be the first application of engagement in a sport event volunteer context.

As previously stated, Schaufeli and Bakker (2004) defined “engagement as a positive, fulfilling, work-related state of mind that is characterized by vigor, dedication, and absorption”

(2004, p.295). This is the definition used in the proposed study. Engagement as defined above is focused on the work structure and experience. Previously, no distinction between commitment and engagement was made. Callow (2004) found that volunteer staff at a senior citizen home reported commitment as being dependent on the structure of the volunteer jobs, a sense of good feeling about their work coupled with whether the socialization needs were being met. Callow’s findings describe what we term as engagement rather than the broader term commitment. Rather more than being a simple case of splitting hairs, there is an important functional distinction that

20 is addressed by the nomenclature. While a manager may not be able to greatly affect the commitment an individual has for an organization or activity, s/he may be able to affect an individual’s engagement. Engagement issues are related to the work structure and environment which fall within a manager’s purview.

The concept of engagement emerged in the late twentieth century where it first became a popular theme in business and consultancy. The first conceptualization of engagement in academia is attributed to Kahn (1990). There were many players in the engagement sandbox; unfortunately there was no agreement among practitioners or scholars on an exact conceptualization of engagement (Schaufeli & Bakker, 2010). The construct of engagement is defined as a work related state in which an individual experiences a sense of vigor, dedication, and absorption (Schaufeli & Bakker, 2004, 2007, 2008). Interestingly the roots of the engagement construct are based in the research of stress and burnout. To understand the current concept of engagement it is best to gain an historic perspective of how we have arrived to our current position of understanding. All research is an attempt to better understand the world. In the most fundamental expression of research we are trying to better understand humans and how they interact with their surroundings. Engagement can be traced back to the research of Cannon

(1932) who sought to understand how humans reacted to adverse conditions of the environment.

Conceptualizing human responses to extremes as being analogous to the way metals react to extreme conditions, Cannon termed human response to extremes as stress. Specifically,

Cannon sought to explore how humans dealt with stress resulting from extreme environmental conditions. This early work was exclusively concerned with the physical response of humans to the environment - specifically biological responses. Later Selye (1950, 1951-1956) moved beyond the purely biological response and looked at behavioral responses. To explain how

21 humans react to their environment, Selye developed what he termed the General Adaptive

Syndrome (GAS). This small step moved understanding forward from strictly addressing a biological response to examining behavior as well. Though it was a move forward, Selye’s GAS could not account for varying human responses to the same situation and thus was criticized as being too structured.

Greene’s 1961 popular press novel A Burn-Out Case provided the next step in our progression of understanding engagement. The novel concerns a disillusioned man’s experience of completely disassociating from society and retreating to the jungle to escape modern life.

This novel struck a nerve. It illustrated an extreme response to the angst many were feeling resulting from having to cope with modern life. It also provided a term for a particular experience that was becoming more prevalent in society. The academy was slow to take up the challenge posed by this popular conception. Fourteen years later the first academic research was published on the concept of burnout.

Freudenberg (1975) conducted research that centered on the reaction to the education he and his colleagues experienced. The next year Maslach and Jackson (1976) also began publishing work with burnout as the focus. Their work began the process that would eventually coalesce into the construct of burnout. Burnout, as it was formalized, encompasses the reaction where individuals report increased feelings of exhaustion, detachment from an organization or work environment, and decreased professional efficacy. Research of burnout was embedded in the academic approach which focused on the traditional four D’s of Disease, Damage, Disorder, and Disability which was the reigning paradigm prior to the turn of the century (Bakker & Leiter,

2010). This focus was mirrored by practitioners of the period as well. As burnout can have a direct impact on an employee’s ability to produce for the company, managers should be

22 concerned if strictly from a mercenary perspective. This did not change until the emergence of the Positive Psychology movement championed by Seligman & Csikszentmihalyi (2000).

The call for change was echoed by Luthans (2002) in the field of organizational psychology with his plea for “the study of positively oriented human resource strengths and psychological capabilities that can be measured, developed, and effectively managed for performance improvement in today’s workplace” (p. 698). The focus on the four D’s was virtually absolute prior to the turn of the century. Schaufeli and Bakker affirm “almost all scientific articles (related to engagement) appeared after the turn of the century” (Schaufeli &

Bakker, 2010, p. 11). They also posit the trend of studying engagement is related to the emergence of the Positive Psychology movement which seeks to examine strengths and optimal functioning of humans.

By definition when researching engagement one is examining a work related state that is characterized by vigor, dedication and absorption. It is important to distinguish engagement from other constructs investigated in organizational behavior. Previous research in the field related to work performance has centered on job involvement or commitment. Both of these constructs can have many causes, antecedents, and consequences some of which are latent in nature. It is pertinent to examine the concepts of involvement and commitment further to distinguish them from engagement.

Involvement

Catano et al. (2001) defined psychological involvement as “the importance of an activity, such as work, in a person’s life and the identification of the person with that work” (p. 257).

Catano et al. posited that individuals, volunteers in this case, with high levels of psychological involvement should be more committed to the organization and consequently participate more in

23 the organization. Catano et al. viewed psychological involvement as an antecedent to commitment. Involvement is a closely relate construct but can be discriminated from work engagement and organizational commitment (Hallberg & Schaufeli, 2006). While it may prove as another construct of interest for the study, an investigation of it is beyond the scope of the current study

Commitment

A single common definition for organizational commitment is elusive. The most widely cited definitions by organizational commitment researchers include those by Becker (1960);

Ritzer and Trice (1969); Porter, Steers, Mowday, and Boulian (1974); Buchanan’s (1974);

Reichers (1985); Brown (1996); and Le and Agnew (2003). With so many researchers positing definitions one would expect many differences to be present; however while the definitions may seem different at first glance, upon closer inspection a number of similarities become apparent.

The most notable similarities include: the belief of the existence of a persistent connection or bond between the employee and the organization; this connection/bond results from employees’ identification with or attachment to organizational goals and values; and employees willingly exert effort on behalf of the organization’s goals and values as a result of this connection,.

The early studies on commitment were by social scientists (Kelman, 1958; Becker, 1960) who approached it as behavior or as activity. Because of this approach to commitment Becker conceptualized it as lines of activity that are consistent and persist over time. Logically one could further posit that commitment to these activities necessarily leads to rejection of alternative activities.

Whereas Becker (1960) and Kelman’s (1958) approach was behavioral in nature, Etzioni

(1961, 1980) and Kanter (1968) conceptualized commitment and involvement from an attitudinal

24 approach. Kanter specifically argued that organizations sought to develop an individual’s psychological attachment to the collectivity by engaging in various activities for just this purpose. Organizations, he argued, rely on three mechanisms (continuance, cohesion, and control) of commitment to strengthen the attachment between the individual and the organization.

Mowday, Porter and Steers (1982) synthesize the two earlier approaches and forward the definition of commitment as reflecting the relative strength of an individual’s involvement

(activity /behavior) and identification (psychological attachment) with an organization or activity. Mowday et al.’s definition will be used in the current study as it is the most encompassing. While commitment may be a good construct to evaluate the overall feelings and behaviors an individual may hold for an organization or activity, it may be too broad a construct to be effectively operationalized for research that is specifically looking to predict future behavior. Further “identification” can be effected as a result of external factors to the work experience. As such these cannot be predicted or controlled by management.

Further, commitment and job involvement can become problematic especially when the goal is application of theory to practical situations. For instance, one may be committed to a company for many reasons including: family history (family members have worked at the same company for many years), location (the company is in their hometown), or emotional/nostalgic reasons (the company’s product saved the life of a family member or friend). Also an individual may be involved for ulterior motives that are not apparent. From a pragmatic perspective, a manager has virtually no control over many of these variables, therefore these are not issues a manager can address to improve how the employee feels about the company. Therefore another construct should be explored as a means of promoting employee or volunteer retention.

Considering engagement focuses on aspects of the work itself (worker autonomy, supervisor coaching, social network, and job feedback) a manager may be able to manipulate these variables so the employee can be more engaged to the work and company. While commitment and job involvement are important, a manager may not be able to impact them where work engagement is specifically oriented to aspects of the work itself. A manager has an opportunity to make a positive contribution to the job as well as the employee via procedures and policies designed to optimize instances of work engagement.

Ultimately, the major reason to pursue research about work engagement is because it focuses on positive dimensions of organizational behavior. There is an increasing trend for researchers to investigate from a positivistic point of view. Researchers including Seligman and

Czikszentmihalyi (2000), Luthans (2002), and Bakker and Schaufeli (2008), have extolled the need for research on the positive aspects of work. This only makes logical sense if the goal is to have a happy productive workforce; certainly one should see what the happy productive workers are doing and figure out why it works. Having happy workers may also foster feeling of commitment in those workers, as such commitment would appear to be an outcome of engagement. Therefore we should examine commitment further as we will assess it as on outcome of engagement.

The study of organizational commitment. Becker (1960) viewed commitment as a structural construct while Ritzer and Trice (1969) viewed it as a psychological construct. Later definitions tended to merge the two. Though definitions have tended to converge, there are still several contradictory perspectives. The biggest area of contention is whether organizational commitment is unidimensional or multidimensional. Kanter (1968) was the first to rationalize different types of organizational commitment: continuance commitment, cohesion commitment,

26 and control commitment. Continuance commitment is based on potential opportunity costs associated with leaving the organization. Cohesion commitment is a person’s mental and emotional attachment to social relationships at work. Control commitment is attachment to norms and abeyance to the authority of the group/organization. Based on this conceptualization,

Kanter considered organizational commitment a multidimensional construct. Porter et al. (1974) did not concur. Porter et al (1974) considered organizational commitment as unidimensional even though their definition of organizational commitment was composed of three different criteria. Providing more space for contention, O’Reilly and Chatman (1986) proposed a tridimensional model of organizational commitment where the three dimensions were compliance, identification, and internalization. Similarly Allen and Meyer (1990) and Meyer and Allen (1991), based on an extensive review of existing literature, proposed the three- component model (TCM) - a tridimensional model of organizational commitment. The three dimensions of the TCM are affective commitment, continuance commitment, and normative commitment (See Figure 2.1).

Organizational Commitment

Affective Continuance Normative (emotional attachment) (personal sacrifice, lack of (attachment to goal, values, Loyalty, affection, opportunities) mission) belongingness, fondness, Compliance, calculations Duty, obligation, calling

Figure 2.1. Components of Organizational Commitment. (Chelladurai, 1999).

The majority of articles written since the 1990s present organizational commitment as a multidimensional construct; however some researchers persist with a unidimensional approach to the construct. Brown (1996) argued that organizational commitment is a unidimensional construct that varies “according to differences in focus, terms, and time-specific evaluation” (p.

230). Further Brown said that by giving different names to the organizational commitment construct researchers ignored what he posited was the reality that people’s social behavior and psychological state change over time and/or after they evaluate their circumstances. Brown’s

(1996) argument is relevant as people’s behavior and attitudes can change over time. He points out a flaw in the research to date in that the majority of organizational commitment studies thus far have been cross-sectional in nature. This argument is supported by Bateman and Strasser

(1984) who admonished cross-sectional data should be used with caution as employee’s attitudes and behavior can be constantly changing. Bateman and Strasser (1984) and Brown (1996) among others have suggested that longitudinal studies rather than cross-sectional studies are needed to fully understand the concept of organizational commitment. A particular weakness of cross-sectional studies is the difficulty of establishing time order which is a necessary condition for establishing causality. Since longitudinal research is conducted over time, order can be fairly well established (Johnson & Christensen, 2008). The ability to establish time order facilitates identification of variables as antecedents and outcomes within a particular construct.

Antecedents and outcomes of organizational commitment. Researchers have demonstrated that several variables may function as antecedents and outcomes of an employee’s commitment to the organization. Social systems (e.g. family) and personality traits were found by Kanter (1968) to be important factors in an individual’s decisions leading to organization commitment. In addition, Buchanan (1974) found that positive socialization experiences at work

28 were related to organizational commitment. Steers (1977) seems to have written a most useful article and model (see Figure 2.2) related to antecedents and outcomes of organizational commitment. He divided antecedents into three subgroups: personal characteristics (i.e. variables which define an individual), job characteristics (e.g. opportunities for interaction, job challenge), and work experiences (i.e. socializing forces that influence the extent of psychological attachment to an organization). Outcomes, however, include job performance, desire and intent to remain with the organization, attendance, and employee retention. Steers’ model can be considered seminal as the majority of subsequent studies build from the model he proposed.

Antecedents Outcomes Personal characteristics (e.g. need for achievement, education) Desire to remain Intent to remain Job characteristics Organizational Attendance (e.g. social interaction, Commitment Employee feedback) retention Job performance Work experiences (organizational dependability, group attitudes)

Figure 2.2. Steers’ (1977) Hypothesized Antecedents and Outcomes of Organizational Commitment.

Several studies have provided support of a relationship between employee retention and organizational commitment. Angle and Perry (1981) found a negative relationship between organizational commitment and turnover intentions. In one of the few longitudinal studies,

Pierce and Dunham (1987) examined turnover and attendance in relation to organizational commitment among hospital employees. The study incorporated several stages of data collection. The initial stage involved the completion of a questionnaire by new employees on the first day of work and then after 3-month of employment. The researchers interpreted the results of that analysis as evidence of a negative relationship between organizational commitment, attendance, and turnover intentions which translated to higher levels of absenteeism for employees who demonstrated lower levels of organizational commitment than their counterparts.

The researchers then examined records of attendance and turnover at 6-months and one year.

After one year, employees who demonstrated the lowest levels of organizational commitment on the questionnaire administered at the outset and 3 month period showed high levels of absenteeism or were no longer employed by the hospital. Employees that demonstrated higher levels of commitment on the questionnaire were still employed and their attendance records did not show significant levels of absenteeism.

Meyer, Stanley, Herscovitch, and Topolnytsky (2002) examined the relationships among affective, continuance, and normative commitment, and turnover intentions and actual turnover and found evidence supporting the negative relationship. Snape and Redman (2003) further supported these findings and expanded on them by discerning that both affectively committed employees were more actively committed than normatively committed employees with continuance committed employees showing the least active commitment. Though turnover and retention have been well researched aspects of organizational commitment (e.g., Angle & Perry,

1981; Arnold & Davey, 1999; Cohen, 1993; Cohen & Hudecek, 1993; Hom, Katerberg, & Hulin,

1979; Koch & Steers, 1978; Porter, Steers, & Mowday, 1974; Randall, Cropanzano, Bormann, &

Birjulin, 1999; Riordan & Griffeth, 1995; Rosin & Korabik, 1995; Somers, 1995; in addition to

30 those already cited), they are by no means the only important outcomes that have been explored.

For researchers and practitioners alike an understanding of how organizational commitment can effect behavior is cogent, which takes us to another outcome of commitment that has been researched, namely job performance.

Organizational commitment and job performance. Mowday, Porter, and Dubin

(1974) found organizational commitment was directly related to job performance. Simply put,

Mowday et al. (1974) found highly committed employees perform better than less committed employees. As obvious as this finding may seem, these results were not supported by Steers

(1977) who found no relationship between organizational commitment and job performance.

Steers posited possible explanations but presented no empirical evidence to support them. The research above has been conducted mainly in the milieu of for profit organization. Commitment research in the non-profit sector is not as extensive. Studies specifically related to sport volunteers even more sparse.

Organizational commitment and non-profit organizations. A great deal of the research on commitment has taken place in the work environment. When researching public and private sectors’ employees, Mir, Mir, and Mosca (2002) found insignificant differences regarding organizational commitment. The same was not found when researching volunteers and employees for non-profit organizations. Pearce (1993) averred “volunteers will tend to have weaker behavioral commitment to their organizations than employees” (p.163). Pearce however attributed the apparent lack of behavioral indicator of organizational commitment to greater situational constraints that volunteers encounter than encountered by their employee counterpart.

Another point Pearce makes it that volunteers do not face some of the continuance commitment issues faced by employees such as having to sacrifice pay or benefits as a result of leaving the

31 organization. Pearce conclude that the circumstances surrounding volunteers can lead to a distorted view of their commitment levels especially in regard to behavioral indicators.

In a similar manner Goulet and Frank (2002) found employees of for-profit firms exhibited more commitment to their organizations than either workers in the public sector or employees of non-profit organizations. These results were contrary to what the authors had hypothesized. Goulet and Frank had expected non-profit employees to exhibit the highest commitment of the three groups. Their reasoning was more mercenary in nature. They concluded that the non-profit and public sector employees’ desire to help others or to be part of the government while important was outweighed by the extrinsic rewards such as higher salaries and more attractive benefit packages. The influence these extrinsic rewards had on employees’ organizational commitment was undeniable. This study, as well as the study by Pearce (1993), is an excellent example of a pitfall of commitment research as it relates to volunteers. The authors utilized the organizational commitment questionnaire (OCQ) developed by Mowday, Porter, and

Steers (1979). This questionnaire has been found to be mainly a measure of affective commitment (Allen & Meyer, 1990) yet is a composite score of affective, continuance, and normative commitment. In order to have a better representation of an employee’s organizational commitment level it is necessary to evaluate all three dimensions. Failing to examine their levels of normative and continuance commitment can present a distorted view of an entire class of workers. In matters of organizational commitment researchers need to consider the demographic make-up of the workers themselves in addition to dealing with various types of workers.

Organizational commitment and generational issues. Many researchers have forwarded the proposition that today’s workforce is the most diverse in history and that attention to generational differences of workers is necessary for proper management and leadership (e.g.,

Smola & Sutton, 2002; Arsenault, 2004; Glass, 2007; Gursoy, Maier, & Chi, 2008; Macky,

Gardner, & Forsyth, 2008; Twenge & Campbell, 2008; Cennamo & Gardner, 2008; Sullivan,

Forret, Carraher, & Mainiero, 2009). In one of the early works related to the current diversity in the workforce, Mir et al. (2002) examined organizational commitment among “new age employees” which they defined as workers who will attempt to enter the workforce within the next few years. Mir et al. state that these employees are “breaking glass ceilings on a variety of attributes, including age, gender, race, and immigration status” (2002, p. 197). Mir et al. further avers that emerging business practices such as downsizing, outsourcing, and telecommuting have a negative effect on organizational commitment; as such new age employees will be more likely to be committed to their profession and/or work role and less likely to become committed to an organization. Mir et al. admonished organizations that although new age employees may be highly committed to their career, they may never feel commitment or obligation toward a specific organization. Mir et al.’s new age workers have become known by other names such as

Generation X, Generation Y or millennials. These workers do not follow the norm of previous generations in regard to organizational commitment.

Researchers must constantly strive to understand organization behavior in the changing world of work. This point has been specifically applied to organizational commitment research when Patalano (2008) posited that organizational commitment literature should be constantly revised to account for new and future generations as they may develop feelings and attitudes toward commitment that differ from what is currently expected. Several of the researchers have noted the disparity between what Baby boomers preference for job stability and monetary rewards more as opposed to newer generations (e.g., Generation x, Generation Y - also known as millennials) who value other things more such as access to technology, a flexible schedule, and a

33 greater work/life balance (Glass, 2007; Patalano, 2008). The study of various generations as it related to organizational behavior should not be confined to the world of work. In the case of the baby boomers, many in that generation will reach or have already reached retirement age. The boomers will be transitioning from the work force to other pursuits to occupy their time and energy. In addition, researchers such as Glass (2007) and Patalano (2008) have found evidence that the millennials do not want to wait for retirement to enjoy life and seek to maximize a work/life balance their parents missed. Both groups are looking for opportunities to maximize their time away from work. On outlet for both generations to simultaneous engage in non-work activities is through volunteering.

Volunteers

Some researchers segment volunteers for functional reasons including non-profit versus for profit (Hobson, Rominger, Malec, Hobson, & Evans, 1996), leaders versus non-leaders

(Catano et al., 2001), demographics (Callow, 2004), and by type of organization (Grimm, Dietz,

Foster-Bey, Reingold, & Nesbit, 2006). As our specific target segment is sport event volunteers which can cross all of these segments, these functional differences are of less importance to this research, but may prove to be an avenue of future research. The definition for volunteer in the proposed study follows the definition for volunteerism posited by Callow (2004) where an individual participates in “an activity that is intended to benefit another person, group, or cause, is not done for monetary compensation or material gain and goes beyond one’s normal responsibilities” (p. 262).

A large part of the study of volunteers has dealt with motivation. The literature is resplendent with numerous motives associated with the act of volunteering. Cnaan and

Goldberg-Glenn compiled a list of 28 volunteer motives from a review of existing literature.

Researchers are divided with some taking the stance that motivation is unidimensional (Cnaan &

Goldberg-Glen; 1991) or multidimensional (Clary, et al., 1998; Okun, Barr, & Herzog, 1998;

Allison, Okun, & Dutridge, 2002; Gerstein, Anderson, & Wilkeson, 2004) with the majority falling on the side of multidimensionality. Understanding that there are many motives to volunteer is important. It is also valuable to know that the use of multiple motives when targeting communitcation, assigning tasks, and structuring volunteer experiences proved more effective for recruitment and retention of volunteers (Clary, et al., 1998; Clary, Snyder, Ridge,

Miene, & Haugen, 1994). This finding is especially pertinent for this study since the focus is to assess multiple job demands and resources relationship to volunteer engagement.

It is also import to note that motives have been found to change over time (Gidron, 1984) and that what initailly motivates may not sustain volunteerism (Finkelstein, 2008; Okun &

Schultz, 2003; Starnes & Wymer, Jr., 2001). In the practitioner’s world, volunteer coordinators need to understand what attracts volunteers so as to provide the benefits volunteers seek (Fairley,

Kellett, & Green, 2007; Green & Chalip, 2004). Simply understanding motives, however, is not the ultimate answer. Warner, Newland, and Green (2011) underscore this idea when they stated

“understanding volunteers’ motivations is an intuitively appealing way to build management systems to attract and retain volunteers. Yet, merely understanding and even catering to motives does not guarantee that volunteers will have a positive experience” (p. 393). To be assured that volunteers are having a positive experience practitioners and researchers began to focus on volunteers’ satisfaction (Warner, Newland, & Green, 2011).

Satisfaction

Since the middle of the twentieth century job satisfaction has been a topic of interest for organizational behavior researchers (Kemp, 2002). However, the focus of this study is on

35 volunteers who freely choose to participate. As participation is freely chosen by the individual and can be discontinued at any point without adverse ramification, we look at volunteering as a leisure activity. Based on this supposition it makes sense to pursue measurement of satisfaction from a leisure perspective. “Leisure satisfaction was defined as the positive perceptions or feelings that an individual forms, elicits, or gains as a result of engaging in leisure activities and choices” (Beard & Ragheb, Leisure Satisfaction Measure, 2012, p. 136). Originally the construct of satisfaction was “borrowed” from the marketing literature for application in leisure and recreation (Beard & Ragheb, 2012).

Scientific investigation of service and non-merchandizing satisfaction did not begin until the 1950’s (Ragheb, 2012). In these contexts the concepts of perceived service quality and value directly influence satisfaction (Cronin, Brady, & Hult, 2000). Additionally, research in the field of marketing (e.g., Cronin, Brady, & Hult, 2000; Murray & Howat, 2002) as it relates to customers has provided evidence that a key determinant of future behavioral intentions is satisfaction.

Smith (1981) posited that satisfaction and various forms of psychic benefits are expected by volunteers and should not be considered a by-product of the experience. Cnaan and

Goldberg-Glen (1991) found evidence in their research that as long as the volunteering experience was satisfying, people will continue to volunteer. Similarly, satisfaction with the volunteering experience has been posited as a driving factor leading to commitment of volunteers for an event organization. (Elstad, 1996; Farrell, Johnston, & Twynam, 1998). This idea was an extension of Iaffaldano and Muchinsky (1985) and Meyer and Allen (1997) from their worker’s job performance studies (Stebbins & Graham, 2004).

While satisfaction is one of the variables of interest in the proposed model there must be underlying theory to connect the variables with the model in a cohesive fashion. With this in mind we turn to an examination of the theoretical foundations and how they relate to our conceptual model.

Theoretical Foundations and Conceptual Model

Based on the current understanding of employee engagement, and incorporating the Job

Demand-Resource Model (Demerouti, Bakker, Nachreiner, & Schaufeli, 2001), a model illustrating the impact of job demands and job resources on engagement with the ultimate goal of retention is proposed (see Figure 2.3). The model will be investigated using current volunteers.

Unlike an employed worker who may be constrained to continue participation in a company or organization for any number of extenuating reasons, a volunteer is free to discontinue participation at any time. Indeed without some form of reinforcement associated with the organization or activity, it is questionable whether the volunteer will continue his/her association. One way to increase the likelihood of retaining volunteers is to make the act of volunteering rewarding and/or engaging. Toward that end, job resources can be employed to increase the likelihood of the volunteer experiencing engagement (i.e., vigor, dedication, and absorption).

As we are seeking means to increase this likelihood we should investigate variables managers can manipulate to produce the desired effect. With this in mind, the four particular types of job resources most cogent to a discussion of volunteer engagement would seem to be autonomy, performance feedback, supervisor coaching, and social support as outlined in the diagram. Hakanen and Roodt (2010) when commenting on studies in the workplace which used the Utrecht Work Engagement Scale when exploring motivational processes or testing the JD-R

37 model report most often included five types of job resources. The five included performance feedback/results, job control/autonomy, social support from colleagues, task variety/growth opportunities, supervisory support/coaching (Hakanen & Roodt, 2010). As task variety/growth opportunities were mainly related to career advancement in the working context, that dimension has not been included in the current study. With an understanding of the importance of the other dimensions, managers can tailor the volunteering experience to maximize the potential individuals may experience engagement.

Managers of volunteer workers generally decide the level of autonomy given to the individual workers. Understanding that some individuals may be seeking opportunities to exercise their duties autonomously allows managers to state the goals of an event as well as any parameters that exist then allow the volunteers to execute as they see fit. Managers, by definition, are directly responsible for providing performance feedback to volunteers. Knowing the potential value some volunteers may attribute to feedback represents to a viable tool for the manager for both maximizing the output of the volunteer as well as increasing the possibility of the volunteer experiencing engagement. Similarly, supervisor coaching represents another potential tool for the supervisor to achieve the previous two objectives. Supervisor coaching and performance feedback are closely related. From a simplistic standpoint, the difference between the two would be the proactive nature of coaching which seeks to improve future performance, whereas feedback acknowledges previous performance.

The final resource to be examined is social support. Again the reason to focus on this resource is that the volunteer manager can be a great resource to facilitate social support among volunteers. Strategies as simple as assigning friends to work in the same area may be enough to foster engagement in volunteers through strengthening bonds of social support. Ultimately each

38 of the resources listed can be manipulated by an astute volunteer manager to increase the potential for volunteers to experience engagement as a result of their volunteering activities.

Following the figure are the important theories and questions related to the discussion at hand.

Volunteer Commitment

Job Demands Volunteer Satisfaction

Volunteer Volunteer Engagement Retention

Job Resources

Volunteer Performance

Figure 2.3 The Role of Job Resources and Job Demands in Volunteer Engagement and Related Outcomes of Commitment, Satisfaction, Performance, and Retention – Conceptual Model.

Job Demands- Resources Model

According to Hakanen and Roodt, the Job Demands-Resources Model (JD-R) “can be traced back to several balance models of job stress, such as the demands-control model (DCM) of Karasek (1979)” (2011, p. 86), however, it is not merely an extension of Karasek’s work as it also incorporates motivational theories about using resources for positive motivational outcomes at work. Specifically the conservation of resources (COR) framework first proposed by Hobfoll

(1989) as a means to explain stress is also cited as a foundation of the JD-R (Hakanen & Roodt,

2011, p. 88). It is a resource-oriented model based on the assumption that people experience stress as a reaction to threats of the potential or actual loss of valued resources. As a result

39 people devote energy in an effort to build, retain, and protect resources (Hobfoll, 1989). To clarify what is meant by job resources in the proposed study, the operationalization by Bakker and Demerouti (2007) as well as Schaufeli and Bakker (2004) is used. “Job resources refer to those physical, social, or organizational aspects of the job that may: reduce job demands and the associated physiological and psychological costs; be functional in achieving work goals; and stimulate personal growth, learning, and development” (Bakker & Demerouti, 2008, p. 211).

A great deal of research has been conducted to examine the COR construct, many via the

Job Demands-Resources (JD-R) model proposed by Demerouti , Bakker, Nachreiner, and

Schaufeli (2001). Both the COR and JD-R models have been used to specifically examine the connection between job demands-resources in regard to burnout (Demerouti, Bakker,

Nachreiner, & Schaufeli, 2001; ), burnout and engagement (Schaufeli & Bakker, 2004; Hakanen,

Schaufeli, & Ahola, 2008; Korunka, Kubicek, Schaufeli, & Hoonakker, 2009), as well as engagement alone (Salanova, Agut, & Peiro, 2005; Mauno, Kinnunen, Ruokolainen, 2007;

Llorens, Schaufeli, Bakker, & Salanova, 2007; Bakker, Hakanen, Demerouti, & Xanthopoulou,

2007; Halbesleben, Harvey, & Bolino, 2009). The findings of these previous studies have consistently provided evidence supporting the idea that provision of job resources (i.e., social support from colleagues and supervisors, skill variety, performance feedback, learning opportunities, and autonomy) is positively related to work engagement with the employees studied. Evidence for the same relationship between job resources and engagement has also been found be true in the case of volunteers. What is unclear is the relationship between job demands and engagement of volunteers.

Examining the JD-R model (See Figure 2.4) proposed by Schaufeli and Bakker (2004) one should note job demands are strictly associated with burnout. Schaufeli and Bakker’s (2004)

40 research used Jones and Fletcher’s (1996) definition for demands as environmental stimuli peremptorily requiring attention and response. Quite simply demands are the “things that have to be done” (as cited in Schaufeli & Bakker, 2004, p. 296). In the work context of these studies, job demands impinge on job resources that are believed to foster work engagement which is supported to some extent by negative relationship between job demands and job resources found in these studies. The direct relationship with job demands and engagement is not addressed.

+ + Health Job Demands Burnout Problems

- - - + + Turnover Job + Engagement - Intentions Resources

Figure 2.4 The Job Demands-Resources (JD-R) Model (Schaufeli & Bakker, 2004).

Researchers in later studies related to burnout and engagement (e.g., Bakker & Demerouti, 2007;

Korunka, Kubicek, Schaufeli, & Hoonakker, 2009) examine the role of job demands as a moderator between job resources and engagement (motivation was the term used in place of engagement in the Bakker & Demerouti, 2007 study) in the JD-R model (see Figure 2.5).

Similarly, in Bakker and Demerouti’s (2007) study which was concerned with examining a model exclusively of work engagement, job demands was included as a moderator between job/ personal resources and work engagement (see Figure 2.6)

In the Job Demands-Resources model, job resources fulfill basic human needs for competence, autonomy, and relatedness (Bakker & Demerouti, 2007). All of these needs can be

41 met either through work or other activities such as volunteering. In this way the JD-R is directly related to the Self Determination Theory (SDT).

Mental

Emotional

Job Demands Strain Physical

Etc.

Organizational

Support Outcomes

Autonomy

Job Feedback Motivation Resources Etc.

Figure 2.5 The Job Demands-Resources Model (Bakker & Demerouti, 2007).

Job Demands - Work Pressure Job Resources - Emotional Demands - Mental Demands - Autonomy - Physical Demands - Performance Feedback - Etc. - Social Support - Supervisory Coaching - Etc. Work Engagement Performance - Vigor - In-role Performance Personal Resources - Dedication - Extra-role Performance - Absorption - Creativity - Optimism - Financial Turnover - Self-efficacy - Etc. - Resilience - Self-esteem - Etc.

Figure 2.6 The JD-R Model of Work Engagement (Bakker & Demerouti, 2008)

Self Determination Theory

Broadly speaking, Self Determination Theory (SDT) is a framework articulating a meta- theory that defines intrinsic and varied extrinsic sources of motivation for the study of human motivation and personality. Importantly, SDT research focuses on how social and cultural factors support or undermine individual’s sense of the quality of their performance, their autonomy, and well-being (Ryan & Deci, 2000). Specifically, researchers working with SDT posit motivation and engagement are maximized when conditions supporting the individual’s experience of autonomy, competence, and relatedness are optimized (Ryan & Deci, 2000).

Conversely, when autonomy, competence, and relatedness are not supported or even thwarted in a social context the impact will be detrimental and robust for that setting (Deci & Ryan, 1985,

2000; Ryan & Deci, 2000). In essence autonomy, competence, and relatedness represent the resources in JD-R directly linking the model to SDT.

So how does this all relate to engaging and ultimately retaining sport event volunteers?

Based on interviews in a study by Schaufeli, Taris, Le Blanc, Peeters, Bakker, and De Jonge,

(2001) there is evidence that engaged workers carry their energy and enthusiasm from the job to activities outside of work. Interviewees specifically cited this crossover effect in their pursuits of sports, creative hobbies, and volunteering work (Cited in Bakker & Demerouti, 2008). This, however, does not address the concerns of the volunteer manager. The volunteer manager cannot rely on other organizations to provide engaged workers to fill his/her needs. Ergo, the manager of volunteers must find ways to engage volunteers so the work becomes the motivation to continue participation. With this in mind, one must consider the unique problems with retaining any volunteer. What inducements could a manager use in the quest to obtain and retain a volunteer staff? This question is especially relevant when one considers there are few extrinsic

43 motivators that can be brought to bear when dealing with volunteers that may be instrumental in discussions of paid employees. The answer intuitively would seem to involve making the experience of volunteering itself gratifying for the volunteer.

Hobfoll et al., (2003) forwards that personal resources include positive self-evaluations which refer to the individual’s sense of their ability to control and/or have an impact on their environment. Further, positive self-evaluations are linked to the individual’s sense of resiliency.

Personal resources can be independent predictors of engagement however a manager can have limited if any substantial impact on an individual’s personal resources. A manager affecting an individual’s job resources is much more likely and pertinent to our current discussion.

Research Hypotheses

Relationship of Job Resources, Job Demands, and Engagement

Job resources – leading to engagement. The literature on engagement provides a great deal of evidence to support the claim that job resources have a positive relationship with engagement in a variety of fields such as health care providers (Mauno, Kinnunen, &

Ruokolainen, 2007), teachers (Basikin, 2007, Bakker, Hakanen, Demerouti, & Xanthopoulou,

2007), students (Llorens, Schaufeli, Bakker, & Salanova, 2007), police officers (Bakker, van

Emmerik, & Euwema, 2006), and midwives (Bakker & Demerouti, 2008) to name a few. In addition to various occupations studied, research has been conducted in several countries including: Germany (Demerouti E. , Bakker, Nachreiner, & Schaufeli, 2001), Indonesia (Basikin,

2007), Finland (Bakker, Hakanen, Demerouti, & Xanthopoulou, 2007; Mauno, Kinnunen, &

Ruokolainen, 2007) Spain (Llorens, Schaufeli, Bakker, & Salanova, 2007), and the Netherlands

(Bakker & Demerouti, 2008). This list is not exhaustive. Though the research to date has been varied based on occupation and location there has yet to be confirmation of the constructs

44 dealing with volunteers. As the job resources have proved to exhibit a positive relationship with paid employees it stands to reason that the same would be true for sport volunteers.

Hypothesis 1: Job resources will have a significant, positive impact on volunteer

engagement.

Job demands as they relate to engagement. One place where the current model deviates from previous versions of the JD-R model is the relationship of job demand with engagement. In the earlier versions of the JD-R model, job demands represented a negative relationship with engagement and a positive relationship with burnout. If the studies are based in

Hobfoll’s conservation of resources theory it is to be expected that job demands will only be examined in terms of burnout. When dealing with volunteers, job demands may in fact not mirror this relationship based on application of the SDT. As already stated, conditions that support an individual’s experiencing of autonomy, competence, and relatedness optimize motivation and engagement (Ryan & Deci, 2000). Looking at job demands from an SDT perspective they may be viewed as challenges where the volunteer can exercise their autonomy, demonstrate their competence, and experience relatedness on several levels. With this in mind job demands may have a negative impact on engagement but be a necessary element to facilitate engagement as opposed to being exclusively related to burnout.

Hypothesis 2: Job demands will have a significant, negative impact on volunteer

engagement.

Relationship of Engagement to Commitment, Satisfaction, and Volunteer Performance

Engagement’s relationship to commitment. Another line of possible research would be to examine how commitment is affected by engagement. Logically one would expect more engaged volunteers would have a greater commitment to the organization. Mechanisms that

45 increase commitment should be of interest to scholars as well as practitioners. This aspect of the model could provide a causal link that could explain commitment as facilitated via engagement.

Greater commitment could certainly lead to retention of the volunteer in some role with the organization, which is the ultimate goal of most sport event manager.

Hypothesis 3: Engagement will have a significant, positive impact on commitment.

The relationship of engagement and volunteer satisfaction. Satisfaction is often studied as an outcome of work engagement. A wide variety of employees have be the subjects of this research including: bank tellers (Koyuncu, Burke, & Fiksenbaum, 2006), nurses (Burke,

Koyuncu, & Fiksenbaum, 2010), managers in the hospitality sector (Burke, Koyuncu, &

Fiksenbaum, 2008, hotel managers (Fiksenbaum, Jeng, Koyuncu, & Burke, 2010). When looking at the relationship of engagement and satisfaction it is important to differentiate the terms. In the literature job satisfaction is generally based on the definition by Locke (1976) of “a pleasurable or positive emotional state resulting from the appraisal of one’s job” (p.1300). Based on this definition, “job satisfaction is concerned with affect about or toward work” (Schaufeli &

Bakker, 2010, p. 10). Conversely, Schaufeli and Bakker (2010) argue engagement relates directly to an employee’s mood or mental state at work. They posit that engagement connotes the activation state while satisfaction the satiation state. As engagement and satisfaction represent various stages or states of a related construct it stands to reason there should be a relationship between the constructs.

Hypothesis 4: Engagement will have a significant, positive impact on satisfaction.

The relationship of engagement and volunteer job performance. The relationship between engagement and job performance has been an active line of research for business

(practitioner) as well as academic researchers. The practitioner researcher literature is extensive.

Schaufeli and Bakker (2010) report an internet search yielded almost 650,000 hits related to engagement of which fewer than 2,000 could loosely be attributed to articles in scholarly publication. The vast major of the practitioner research purports to affirm claims of “conclusive and compelling evidence that work engagement increases profitability through higher productivity, sales, customer satisfaction, and employee retention” (Schaufeli & Bakker, 2010).

Unfortunately the majority of these pundits fail to provide scientific evidence to support their statements. Additionally, the majority of these reports are focused on employee productivity.

The practitioner researchers as well as the academic researchers have yet to thoroughly investigate volunteer’s engagement and job performance. This is certainly related to how engagement is operationalized in the JD-R specifically as it relates to job performance.

The JD-R model has been posited as a means to predict job performance (Bakker,

Demerouti, & Verbeke, 2004). These initial assertions to explain the process were based on the literature concerning mental fatigue which has been operationalized as a mind and body response to resource reduction because of mental task execution. Mental fatigue can serve as a warning sign indicating an increased risk of performance failure (Veldhuizen, Gaillard, & de Vries,

2003). This scenario is prevelant in situations where high levels of mental workload are experienced by individuals (workers) who are already fatigued and thus must exert extra mental effort as a compensatory measure to maintain task performance (Gaillard, 2001; Hockey, 1997;

Hockey, Coles, & Gaillard, 1986). This scenario can evolve into a downward spiral resulting in acute fatigue if the scenario persists without recovery periods. Further, without appropriate recovery periods, the effects of high workload demands can gradually accumulate over a period of time (Craig & Cooper, 1992; Frankenhaeuser, 1980; Frankenhaeuser & Johansson, 1986;

Gaillard, 2001). Unabated mental fatigue may lead to a chronic condition effecting an

47 individual’s health and well-being (Frankenhaeuser, 1979; Frankenhaeuser & Johansson, 1986).

The job demands had a greater effet on the outcome of job performance because of the their debilitating effects on the individual. This is based on the JD-R’s roots in COR which posits a strictly antagonistic relationship between job demands and job resources. By utilizing an SDT framework researchers can investigate the relationship as a synergistic relationship.

Hypothesis 5: Engagement will have a significant, positive impact on volunteer

performance.

The Relationship of Latent Variables to Volunteer Retention

Sport organizations are not unique in that volunteers are prone to leave the organization or at least their roles within a sports organization for many reasons. Many of the reasons volunteers leave are beyond the control of the organization or volunteer manager (Cuskelly &

Boag, 2001). Moving from the assumptions that volunteer retention is desirable as has been stated by several researchers (Barnes & Sharpe, 2009; Cnaan & Goldberg-Glen, 1991; Cuskelly,

2004; Cuskelly, Taylor, Hoye, & Darcy, 2006; Starnes & Wymer, Jr., 2001; Tett & Meyer,

1993;) there is value in understanding which and to what extent variable contribute to volunteer retention.

The relationship of commitment and volunteer retention. “Organizational commitment provides a basis for understanding the development of linkages between individuals and organizations” (Cuskelly & Boag, 2001, p. 67). Cuskelly and Boag (2001) posited a decline in commitment as reason for volunteer turnover.

Hypothesis 6: Commitment will have a significant, positive impact on volunteer

retention.

Volunteer satisfaction leading to volunteer retention. Since the 1980’s researchers

(Daily, 1986; Gidron, 1983; Pierucci & Noel, 1980) have hypothesized volunteers who report greater job satisfaction exhibit a higher likelihood to continue volunteering. Several researcher including Siason (1992), Omoto and Snyder (1995), and Wright, Larsen and Higgs (1995) have found evidence of a definite relationship between satisfaction and volunteer’s intention to quit.

Siason (1992) posited satisfaction as the sole basis volunteers used to determine their intention to leave. This is similar to the findings of Wright, Larsen, and Higgs (1995) who report volunteers who had fun (were satisfied) were less likely to leave volunteering. Omoto and Snyder (1995) affirmed that to increase a volunteer’s length of service one should strive to increase the volunteer’s satisfaction.

From the marketing literature Cronin, Brady, and Hult (2000) found evidence to support customers’ behavioral intentions and retention were influenced by satisfaction. Satisfaction and intentions to continue volunteering has also been reported by Mesch, Tschirhart, Perry, and Lee

(1998) while Silverberg, Marshall, and Ellis (2001) stated “job satisfaction is a key factor in the retention of volunteers” (p. 79). Similarly, Galindo-Kuhn and Guzley (2001) posited satisfaction as a means of increasing the probability of properly predicting turnover potential. Finally,

Vecina, Chacon, Sueiro, and Barron (2012) forwarded predictors of intent to remain vary depending on what stage in the process a volunteering may inhabit but “satisfaction is the variable most prevalently linked to intention to remain during the first stage(of volunteering)” (p.

133).

Hypothesis 7: Satisfaction will have a significant, positive impact on volunteer retention.

The relationship of job performance to volunteer retention. The competence dimension of STD can be equated with job performance. “The need for competence refers to the

49 need to experience that one is able to successfully carry out tasks and meet performance standards” (Boezeman & Ellemers, 2009, p. 898). Boezeman and Ellemers (2009) found that satisfaction of autonomy and relatedness needs were more relevant to feelings of overall satisfaction and influencing the volunteer’s intentions to remain than did satisfaction of competence needs however, their study was not based on sport volunteers. Indeed, the researchers acknowledged the minimal nature of work performance standards expected of most volunteers. Additionally, there was a lack of formal job descriptions with which to judge performance if in fact any performance evaluation was actuall performed. Sport by its nature provides many means of performance evaluation.

Hypothesis 8: Volunteer performance will have a significant, positive impact on

volunteer retention.

The relationship of volunteer engagement to volunteer retention. As has been stated previously, volunteers are becoming a scarce commodity. Organizations that utilize volunteers need to find ways to retain their volunteers. As engagement of employees has been found to be negatively related to intentions to quit (Saks, 2006), organizations should look to methods of engaging employees in order to retain them. This is especially cogent for sport organizations since most sport organizations rely heavily on the work of volunteers to operate.

Hypothesis 9: Sport volunteers who experience engagement are more likely to continue

volunteering.

The fully elaborated model is below (see Figure 2.7).

Role Ambiguity

Role Conflict Volunteer Job Commitment Demands Role Overload Volunteer Perception of Satisfaction Politics Volunteer Volunteer Engagement Retention Feedback Volunteer Social Performance Support Job

Supervisor Resources Support

Autonomy

Figure 2.7 The Role of Job Resources and Job Demands in Volunteer Engagement and Related Outcomes – Measurement Model

CHAPTER 3

METHODS

Chapter 1 served as an introduction to the study while Chapter 2 presented a literature review of the main themes included in the study. The current chapter will serve to explain the methods used to facilitate the study. In this capacity this chapter includes explanations of the type of research and design, addresses the sampling method, and introduces the scales as well as the data analysis used for the study.

The main purpose of the study is three fold. The first purpose was to compare the differences between levels of engagement between sport volunteers and other types of volunteers. In addition the relationships between job demands and job resources on engagement in the proposed model were investigated. Finally, the author proposed to investigate whether volunteers from various generational cohorts had differing preferences relating to job resources while volunteering.

Based on the literature review, it was determined that no empirical studies have been conducted through which researchers examined the relationship between job demands, job resources, engagement, satisfaction, commitment, job performance, and retention in the sport volunteer context. This quantitative, non-experimental study of the relationship between the independent variables of job demands and job resources, and the dependent variable engagement, and the outcome variables satisfaction, commitment, performance and retention of sport event volunteers, was undertaken to address this gap in the literature. The primary objectives of this dissertation included addressing the following questions:

 Which job resources have the greatest impact on volunteer engagement?

 Do job demands have a positive or negative impact on volunteer engagement?

 What is the relationship between job demands and job resources in regard to engagement

of sport event volunteers?

 Do sport event volunteers exhibit different levels of engagement than volunteers for other

types of events?

 Do volunteers representing different generations exhibit differing preferences in regard to

job resources?

Research Design

A non-experimental cross-sectional research design was used to compare differences between sport and non-sport volunteers. A targeted sampling method was employed. There was no random assignment or researcher manipulation of any independent variables. This placed the study clearly as a non-experimental research design (Johnson & Christensen, 2008).

Organization of the Research

This study employed a two phase approach. The initial phase focused on insuring the study instrument was adequate to address the research questions. In this initial phase a panel of experts was recruited to evaluate the proposed instrument. The main study had a three-fold purpose: (1) to compare the differences between levels of engagement between sport volunteers and other types of volunteers to ascertain whether sport volunteers differed from other types of volunteers. ; (2) to investigate whether volunteers from various generational cohorts had differing preferences relating to job resources while volunteering; and (3) examining the proposed structural equation model and investigating the relationships between job demands and job resources on engagement and other latent variables of volunteers within the proposed conceptual model (see Figure 3.1 below). Each phase will be examined in greater detail below.

RO1 Role | Overload RO3

RC1 Volunteer Role Job | Satisfaction Conflict Demands RC8 Demands / Stressors Volunteer Volunteer

Resources AC8

SO1 Social | Support Volunteer SO3 Performance

SU1 Supervisor

Job Resources | VP1 SU5 Support |

VP5 A1 | Autonomy A6

Figure 3.1 The Role of Job Resources and Job Demands in Volunteer Engagement and Related Outcomes – Extended Measurement Model.

Job Demands: Satisfaction: Engagement: RA – Role Ambiguity 6 Items SPY – Psychology 4 Items VI – Vigor 3 Items RC – Role Conflict 8 Items SE – Education/intellectual 4 Items DE – Dedication 3 Items RO – Role Overload 3 Items SS – Social 4 Items AB – Absorption 3 Items PP – Perception of Politics 12 Items SR – Relaxation 4 Items SPH – Physiology 4 Items Performance: Job Resources: SA – Aesthetic-Environmental 4Items VP – Volunteer Performance 5 Items F – Feedback 8 items SO – Social Support 3 items Commitment: Intent to Remain SU – Supervisor Support 5 items AC – Affective Commitment 8 items VR – Retention 3 Items A – Autonomy 6 items

Figure 3.1 – Continued

Initial Phase

The initial phase of the study was to craft a suitable instrument using a combination of existing scales to address the research questions. As most of the instruments originate from research in the field of organizational behavior they are associated with employees in a work environment. A panel of experts including professors and doctoral student in the field were recruited to assess the face validity of the items in the study, the prospective assignment of items to specific constructs (for instance: Autonomy, Feedback, social support, and supervisor support) as well as general suitability of items for the study. This part of the study was facilitated using

Qualtrics surveys where recruited experts were able to assign items to constructs they deem appropriate. They were also able to indicate any items or constructs they thought should be deleted to strengthen the instrument for the study.

Evidence of Reliability & Validity. Internal consistency was assessed using Cronbach's alpha coefficients and item-to-total correlations for the all factors (engagement, satisfaction, commitment, and retention). Confirmatory Factor Analysis was used to assess evidence of convergent and discriminant validity.

Main Study

An objective of the study was to assess a theoretical model of volunteer engagement (see

Figure 3.1). Specifically the researcher focused on examining the effects of job demands and job resources on engagement leading to the outcomes of satisfaction, commitment, performance, and retention of volunteers as detailed in the specified model.

In order to test the hypothesized model, SEM analysis was conducted. Prior to the model testing, model identification and data screening including the assessment of normality were conducted. For the model estimation, Maximum Likelihood Estimation (MLE) was used since the assumption of multivariate normality was met (if not, Satorra-Bentler chi-square statistic would have been used). Following suggested SEM best practices (Kline, 2005), model assessment was conducted in a two-step process: beginning by assessing the measurement model, then assessing the structural model.

The measurement model shows the relationships between the observed indicators and the latent factors as well as correlations between factors. The structural model is a representation of the relationship among exogenous variables and endogenous variables. The assessment of a measurement model follows the same procedure as that of Confirmatory Factor Analysis (CFA).

Once the fit of a measurement model with a reproduced model is ensured, a structural model is assessed. The model assessments were conducted with four fit indices: model chi square, Root

Mean Square Error of Approximation (RMSEA), Comparative Fit Index (CFI), and Standardized

Root Mean Square Residual (SRMR). According to Kline (2005), these represent the “minimum set of fit indexes that should be reported and interpreted when reporting the results of SEM analysis” (p. 134).

Participants

Participants for this study were volunteers for various organizations recruited via the assistance of volunteer coordinators in Jacksonville, Florida, a major Southeastern city in the

United States. The participants were recruited from the volunteer pools of the organizations participating in the study. Specifically, HandsOn Jacksonville, an organization that manages volunteer projects in the northeast Florida area provided access to a volunteer pool in the

Jacksonville area. It serves as a clearing house that connects volunteers with projects for member nonprofit organizations. Some of the organizations that utilize volunteers from

HandsOn Jacksonville include: sport organizations (Jacksonville Sports & Entertainment Board,

Jacksonville Sports Complex, ATP Tour, Tournament Players Championship , PGA TOUR, as well as organized community sport organizations such as the YMCA, and Special Olympics); arts and cultural organizations (Times Union Center for the Performing Arts, Jacksonville

Symphony); and other organizations ranging from medical, to political, environmental, and religious causes.

Sample Size

To determine sample size one must consider the level of power desired as well as the level of alpha, and effect size. Power represents the probability of correctly rejecting the null hypothesis when the alternative hypothesis is true (Kim, 2005). Tabachnick and Fidell (2007) explain effect size as reflecting the degree of relationship between the dependent variable (DV) and the independent variable (IV). As such effect size represents “the proportion of variance in the DV that is associated with levels of an IV…It assesses the amount of total variance of the DV that is predictable from knowledge of the levels of the IV” (Tabachnick & Fidell, 2007, p. 54).

Hair, et al.’s (2005) recommendation for an acceptable level of power is .80 or more. For the

57 proposed study, the power level of .80 and the alpha level of .05 and a minimum effect size of

0.5 were used. The researcher used a 0.5 effect size for two reasons: 1) according to Cohen

(1969, 1992) “a medium effect of .5 is visible to the naked eye of a careful observer” (p. 156), and 2) a 0.5 group difference effect size represents a 69.0 percentile standing (Carson, 2004) or put another way, an effect size larger than 0.5 represent a mean difference greater than one standard deviation. This should represent a significant practical as well a statistical difference between the groups being studied.

The current study incorporated 13 scales with 20 indicators measured with 95 items.

When considering SEM, conventional wisdom is a sample size should be greater than 200.

According Iacobucci (2010) the belief that as a rule of thumb SEM requires sample sizes greater than 200 is simplistic and can be conservative. She affirms “SEM models can perform well, even with small samples (e.g., 50 to 100)” (Iacobucci, 2010, p. 92). Her position is supported by

Anderson and Gerbing (1984, 1985) with the caveat of having more than two variables load on a factor. With only two variables on a factor parameter estimates will likely be biased, however with the condition that three or more indicators load on a factor any bias in parameter estimates nearly vanishes. Gerbing and Anderson (1985) when discussing bias reduction and model functioning found that by using “three or more indicators per factor, a sample size of 100 will usually be sufficient for convergence” while utilizing a sample size of 150 “will usually be sufficient for a convergent and proper solution” (Anderson & Gerbing, 1984, pp. 170-171). With this in mind and based on the online program called Statistics Calculators version 3.0 (Soper,

2012), a minimum of 116 participants was needed for the study. This number is based on the premise of attaining a desired power level of 0.8, with anticipated effect size of 0.5, a probability level of 0.05 (alpha), in a model incorporating 4 latent variables with 20 observed variables.

Although 116 participants is stated as the minimum need for the study the researcher planned to attain at least 200 participants. According to Iacobucci (2009), “bigger is always better when it comes to sample size” (p.91). From a practical perspective pursuing a larger than minimum sample served two purposes: 1) to insure an adequate sample for the two proposed groups (sport volunteers and non-sport volunteers) as well as acquiring enough participants representing the subgroups (baby boomers, Gen X-ers, and Gen Y-ers) in order to properly compute the ANOVA, and 2) as parameter estimates and chi-square tests of fit are sensitive to sample size (Tabachnick

& Fidell, 2007) larger samples tend to be more robust.

Data Analysis Procedures

All volunteers were recruited to participate in the research project via an email invitation sent by HandsOn Jacksonville in their organization newsletter (see Appendix C and D).

Approval from HandsOn Jacksonville for the project was obtained prior to the study invitation being included in the newsletter (see Appendix B). It was made clear in the introduction to the questionnaire that participation was voluntary. The participants were assured that the research would be conducted solely by the researcher who is external to the volunteer organization, participant confidentiality would be maintained, and no individual level responses would be shared with the volunteer organization. Only aggregate data was to be reported to the volunteer organization.

The newsletter included a link to the Qualtrics web survey. The link initially directed the participant to an informed consent statement describing the project, risks, and benefits. Those who agreed to participate were then directed to the questionnaire described later in this chapter.

Based on a review of literature, the researcher generated an item pool to measure the constructs of interest. A panel of experts was recruited to analyze the content of the items for

59 appropriateness of inclusion as measures of the proposed factors. Items from existing scales were used as components of the questionnaire for the study. The instrument was administered via an online survey constructed using the Qualtrics web-based survey software. Several factors were the basis for the decision to use an online survey including: low cost of administration, relative ease for participant recruitment and participation, as well as affording low environmental impact. Dillman, Smyth, and Christian’s (2008) web-survey construction principles served as a guided for instrument design.

Potential participants included all members of HandsOn Jacksonville’s volunteer base.

The organization’s newsletter with a link to the study instrument (questionnaire) was sent on

March 7, 2013 to all the potential participants inviting them to take part in the study. An additional reminder was sent on April 11, 2013, five weeks after the initial invitation was sent out. Assuming at least the minimum sample size was attained with sufficient numbers in each group (sport vs. non-sport volunteers) as well as in the subgroups (baby-boomers, Gen X-ers, and Gen Y-ers) the next step was to analyze the data received.

Model Procedures

Structural equation modeling (SEM). As the study incorporates several endogenous variables and several exogenous latent variables, Structural Equation Modeling (SEM) was used for data analysis. When using SEM, Anderson and Gerbing (1988) advise use of a two-step procedure. In the two-step process one first investigates the measurement model for suitability beginning with model identification. Hair et al (1988) explain that model identification is an assessment of “whether enough information exists to identify a solution for a set of structural equations” (p. 771). To determine a model’s identification status, one parameter is estimated by one variance and covariance of observed items. The number of variances and covariances must

60 equal or exceed the number of parameter estimates resulting in a “not identified,” “just identified,” or “over-identified” model respectively (Hair et al., 2005). Once the model has been established as either identified or over-identified the next step is to test the assumptions of multivariate normality.

Normality, the most fundamental assumption in the use of the maximum likelihood estimation (MLE), was evaluated using the histogram and the assessment of kurtosis and skewness. If the multivariate normality assumption was met, MLE would be used for model estimation. If the assumption of multivariate normality was not met, the Satorra-Bentler chi- square statistic and correction method would have been used as it has been found to outperform alternative test statistics and correction methods (Hu, Bentler, & Kano, 1992; Satorra & Bentler,

1994, 2001).

Goodness-of-fit indices were used to indicate how well the variance-covariance matrix of the actual data reproduced the variance-covariance matrix of the reproduced (hypothesized) model. For the proposed study, the following fit indices were utilized: chi-square, root means square error of approximation (RMSEA), comparative fit index (CFI), and standardized root mean residual (SRMR). For chi square, a comparison of the hypothesized model was compared with the just-identified model to determine if the hypothesized model still fit the data acceptably with the caveat that the more parsimonious model should receive preference (Kline, 2005).

When assessing the RMSEA, a value of 0.05 or less would represent a close fit; 0.08 or less would indicate a reasonable fit; while 0.10 or greater would be considered a poor fit (Browne &

Cudeck, 1992). In the case of CFI, values equal to or greater than 0.90 are considered acceptable

(Hair et al., 2005). Finally, SRMR values less than 1.0 are considered acceptable (Kline, 2005).

Improvement of the model can be accomplished using three diagnostic measurements: factor loadings, standardized residuals, and modification indices. When assessing whether a factor should be included in the model, Hair et al. (2005) recommend the cut-off value for factor loadings is 0.5, and that an ideal value is 0.7. Standardized residuals, the difference between the observed covariance and the hypothesized covariance, greater than 4.0 are unacceptable (Hair et al., 2005). Finally, for model improvement, modification indices which are a component of the chi-square test indicate paths that if modified (removed) would improve model fit. Caution should be exercised as suggested modifications should be conducted with a theoretical support.

Modifications should not be made using the modification index alone (Hair et al., 2005).

Evidence of validity was provided through two types of criterion-related assessment: convergent and discriminant validity. Convergent validity was based on the strength of the correlation between the measure of a conceptually and theoretically identical construct and the construct being investigated (Hair et al., 2005). Discriminant validity refers to the extent to which the investigated construct was distinct or different from other conceptually similar constructs (Hair et al., 2005). Average Variance Extracted (AVE) values were used to assess evidence of convergent and discriminant validity. According to Hair et al. (2005) “The AVE for each latent construct provides an estimate of the variance captured by the construct in relation to the amount of variance due to measurement error” (p. 413). AVE scores are obtained by dividing the standardized sum of squared factor loadings for items representing a construct by the standardized sum of squared factor loadings plus sum of error variance for items representing a construct (Fornell & Larcker, 1981).

For evidence of convergent validity the construct in question must have an AVE value greater that the cutoff value of 0.50 (Fornell & Larcker, 1981). For discriminant validity, the

AVE value of the construct in question must have an AVE value greater than the Average Shared

Squared Variance (ASV) and the Maximum Shared Squared Variance (MSV). Basically for evidence of discriminant validity AVE scores should be greater than the squared correlation of the constructs of interest (Fornell & Larcker, 1981). Once the measurement model has been analyzed verifying the relationships of the observed measurement items and latent factors has been found to be satisfactory, the second step of the two-step procedure was to assess the structural (hypothesized) model to examine the relationships among the latent constructs.

Additionally at this stage, following Anderson and Gerbing’s (1988) approach, the measurement model and the structural model were compared. The structural model should fit the data as well or better than the measurement model.

The findings of the SEM analysis were the basis for the next stage of the study. If the latent variables (engagement, satisfaction, commitment, and intent to remain) prove to be highly correlated as has been posited by some researchers (Vecina, Chacon, Sueiro, & Barron, 2012) then a situation of multicollinearity would exist. Kline (2005) states that in cases of multicollinearity “certain mathematical operations are either impossible or unstable because some denominators are close to zero” (p. 56). Intercorrelations greater than 0.85 would be an indicator of possible multicollinearity. Should this be the case, based on Moore and McCabe

(2003), since there are two factors / groups (sport and non-sport) with several levels for each group (babyboomers, Gen X-ers, and Gen Y-ers) two-way ANOVA would be best to assess differences when examining the research questions concerning the difference between sport and non-sport volunteer engagement as well as investigating the impact of various generational groups (boomer, Gen X-ers, and Gen Y-ers) in the workforce.

The researcher examined whether there was an overall difference based on type of volunteering (sport vs. non-sport) on engagement. This ANOVA was conducted with the independent variables (factors) being type of volunteering (sport versus Non-sporting) with the dependent variable being engagement. Although the volunteers may specify types of non-sport volunteering (e.g., arts/ cultural/ entertainment, medical cause related, environmental cause related, social change, political cause related, faith based, or other) these were all grouped in non-sport volunteering so that the sample size remained manageable.

Additionally the researcher examined whether there was a generational (Baby boomer,

Gen X-er, Millennial/Gen Y-er) difference in engagement based on type of volunteering (sport vs. non-sport). In this case the ANOVA was conducted with the independent variable (factor) being the type of volunteers (sport vs. non-sport) with various generations ( Baby boomers, Gen

X-ers, and millennials/ Gen Y-ers) the levels of the independent variable (factor) and the dependent variable being engagement. The generation was limited to these three since they represent the majority of individuals that are capable of volunteering at this time. Those born before the Baby boomer generation (i.e., born before 1940) are generally too old to be in the work force as well as being too old to volunteer (Smola & Sutton, 2002). While it may be enlightening to include them in the current study, the practical problem of finding a large enough sample of this generational cohort to be statistically sound would be daunting and was beyond the scope of the current study.

When conducting two-way ANOVA one must as a matter of course test the assumptions including: a simple random sample, normality, and constant variance must be checked.

Specifically the following assumptions are that observations between and within the factors were

64 independent, the response was normally distributed for each factor combination, and the variance of the response was the same for each factor combination.

Instrumentation

Measures for the following constructs were used: work engagement, job demands

(including role ambiguity, role conflict, role overload, and perceptions of politics), job resources

(including autonomy, feedback, social support, and supervisor support), leisure satisfaction, organizational commitment, and retention. The items for each of the scales for job demands, job resources, engagement, organizational commitment, and retention were originally structured for use with employees. Wording and phrasing of some items was modified to reflect the context of volunteering. For example the item “At my work, I feel bursting with energy” was modified to

“While volunteering, I feel bursting with energy” and “I get carried away when I am working” was modified to “I get carried away when I am volunteering.” Additionally the items on the

Leisure Satisfaction Measure (Beard & Ragheb, 1980) were modified to specifically reflect volunteering instead of leisure in general. For instance, “My leisure activities are interesting to me” was modified to “My volunteer activities are interesting to me.” Based on the collected data, evidence of the reliability and validity of the measures was assessed.

At the beginning of the process participants were instructed to indicate the last event where they volunteered and to base their responses to the items in the instrument on the experience of that event. At regular intervals participants were reminded to relate their answers to the event they indicated at the beginning of the survey with a sentence such as “Thinking about my volunteering experience with this event…”

The questionnaire contained seven sections corresponding to the major constructs (job demands, job resources, engagement, satisfaction, commitment, performance, and retention) as

65 well as a section for respondent demographic information and event specification. Several strategies were employed in order to control for method bias. One control method was random placement of items within the instrument. The Qualtics survey program allowed for this function. Additionally as part of the initial phase of the study items were examined to insure elimination of item ambiguity (Podsakoff, MacKenzie, & Podsakoff, 2012). Items used in the questionnaire were incorporated from previously validated instruments from various disciplines.

Because some items were modified to reflect the specific context of the study, a panel of experts was recruited to review the questionnaire to provide evidence of content validity. Each of the instruments are discussed below beginning with the instruments that measure the independent variables and proceeding through the model to the instruments that measure outcome variables

Job demands. Four job demands were assessed through an array of instruments. The items included in the questionnaire have been gleaned from instruments that measure role ambiguity, role conflict, role overload, and perceptions of organizational politics. Each of the item sections were introduced with a leading sentence such as “Thinking about my volunteering experience with this event…” as a prompt to relate their answers to the specific event. The instruments and items are presented in more depth in the following sections.

Role ambiguity. Role ambiguity is a “lack of the necessary information available to a given organizational position” (Rizzo, House, & Lirtzman, 1970, p. 151), which should increase the occurrences of dissatisfaction and anxiety, distort reality for the worker, and decrease performance for workers in an organizational setting. Item #2 was modified from the original “I have clear, planned goals and objectives for my job,” to “I had clear, planned goals and objectives for my volunteering duties,” to reflect the volunteering context of the study. The other items from this scale were changed to reflect the temporal change of retrospection in the

66 items. This construct was measured with six items developed by Rizzo, House, and Lirtzman

(1970). Responses were scored using a 7-point Likert scale.

TABLE 3.1: Role Ambiguity Scale Strongly Strongly Disagree Agree 1. I feel certain about how much authority I had. 1 2 3 4 5 6 7

2. I had clear, planned goals and objectives for my volunteering duties. 1 2 3 4 5 6 7

3. I know that I divided my time properly. 1 2 3 4 5 6 7

4. I knew what my responsibilities were. 1 2 3 4 5 6 7

5. I knew exactly what was expected of me. 1 2 3 4 5 6 7

6. I received a clear explanation of what had to be done. 1 2 3 4 5 6 7

Role conflict. Role conflict is viewed “in terms of the incompatibility of demands”

(Cook, Hepworth, Wall, & Warr, 1981, p. 199). The incompatibility can be manifested in several forms. The first form (measured by items 7, 8, and 9) is person-role conflict where there is conflict between the defined organizational role behavior expected of an employee, and the employee’s internal standards or values. The second form of conflict (measured by items 10, 12,

13, and 14) involves an employee’s inability to perform the defined role behavior as a result of problems related to time or other personal resource allocation imposed. Conflicting obligations to several other people (measured by item 11) represent the third form of role conflict. Role conflict was measured with eight items developed by Rizzo, House, and Lirtzman (1970). Again the items have been modified to accommodate retrospection. Responses were scored using a 7- point Likert scale.

TABLE 3.2: Role Conflict Scale Strongly Strongly Disagree Agree 7. I had to do things that I think should be done differently. 1 2 3 4 5 6 7

8. I worked under incompatible policies and guidelines. 1 2 3 4 5 6 7

9. I had to oppose a rule or policy in order to carry out an assignment. 1 2 3 4 5 6 7

10. I received assignments without the manpower to complete them. 1 2 3 4 5 6 7

11. I received incompatible requests from two or more people. 1 2 3 4 5 6 7

12. I had to work under vague directions or orders. 1 2 3 4 5 6 7 13. I received assignments without adequate resources and materials to 1 2 3 4 5 6 7 execute them 14. I worked on many unnecessary things 1 2 3 4 5 6 7

Role overload. Some researchers follow the thinking of Kahn, Wolfe, Quinn, and Snoek

(1964) and consider role overload a fourth kind of role conflict; however, other researchers

(Beehr, Walsh, & Taber, 1976; Seashore, Lawler, Mirvis, & Cammann, 1982) support the construct as distinct (Cook, Hepworth, Wall, & Warr, 1981). The items measuring this construct relate to an individual’s assessment of their personal capacity to execute their duties regardless of other considerations. Based on the pointed nature of the instrument the author agreed with the latter position and concluded role overload should be considered a distinct construct. The particular instrument used to assess role overload was developed by Seashore, Lawler, Mirvis and Cammann (1982), and was incorporated as part of the Michigan Organizational Assessment

Questionnaire (Cook, Hepworth, Wall, & Warr, 1981). Responses in this study for this assessment were gauged using a 7-point Likert scale anchored with strongly disagree and strongly agree. The items have been altered for retrospection.

TABLE 3.3: Role Overload Scale Strongly Strongly Disagree Agree 15. I had too much work to complete, to do everything well. 1 2 3 4 5 6 7

16. The amount of work I was asked to do was simply too much. 1 2 3 4 5 6 7

17. I did not seem to have enough time to get everything done. 1 2 3 4 5 6 7

Perceptions of organizational politics. Perceptions of organizational politics was measured with items from the Perceptions of Organizational Politics Scale (POPS) developed by

Kacmar and Ferris (1991). Responses for this assessment were measured using a 7-point Likert scale.

TABLE 3.4: Perceptions of Politics Scale Strongly Strongly Disagree Agree 18. There was a group of people at the event where I volunteered who always 1 2 3 4 5 6 7 got things their way because no one wanted to challenge them. 19. Since I have been a volunteer with this event, I have never seen the 1 2 3 4 5 6 7 operational policies applied politically. 20. Rewards came only to those who worked hard. 1 2 3 4 5 6 7 21. I have seen changes made in policies that only serve the purposes of a few 1 2 3 4 5 6 7 individuals, not the volunteers in general. 22. I can’t remember when a volunteer received recognition or a promotion 1 2 3 4 5 6 7 that was inconsistent with published policies. 23. People who volunteer for this event tend to build themselves up by tearing 1 2 3 4 5 6 7 others down. 24. Volunteers are encouraged to speak out frankly even when they are critical 1 2 3 4 5 6 7 of well-established ideas. 25. Favoritism rather than merit determines who gets ahead. 1 2 3 4 5 6 7 26. Volunteers usually don’t speak up for fear of retaliation by others. 1 2 3 4 5 6 7 27. There is no place for yes-men around here; good ideas are desired even 1 2 3 4 5 6 7 when it means disagreeing with superiors. 28. Choice assignments in this event generally go to top performers. 1 2 3 4 5 6 7 29. There has always been an influential group of volunteers in this event that 1 2 3 4 5 6 7 no one ever crosses.

Job resources. Items measuring job resources were gleaned from the 42-item Job

Demands-Resources Scale (JDRS) developed by Rothmann, Mostert and Strydom (2006). The items from this instrument related to job resources appropriate in the volunteer context were used. Items related to job resources such as “Can you live comfortably on your pay?”, “Do you think you are paid enough for the work that you do?” and “Does your job offer you the possibility to progress financially?” generally would not be a concern in the volunteer context and were eliminated. Additionally items such as “Do you need to be more secure that you will still be working in one year’s time” and “ Do you need to be more secure that next year you will keep the same function level as currently” relate to job security issues so they were eliminated.

The remaining items incorporated into the study represent areas a volunteer manager/supervisor would be able to manipulate to increase the possibility for volunteers to attain a state of engagement with their volunteer experience.

The items used have been grouped into factors that correspond with the hypothesized antecedents of job resources posited in the study. The items as grouped for the present study still fall within the factor analysis categories conducted by Rothmann et al. (2006) with the exception of one item “Can you participate in the decision about when a piece of work must be completed?” (labeled Autonomy item #33 below). The other 5 items grouped to assess autonomy loaded on factor two in Rothmann et al. In the original study, Rothmann et al. categorized autonomy item #33 as being attributed to factor one with a 0.54 factor loading. The other items labeled in this study as “Autonomy items” loaded on factor 2 in the original study.

Item # 33 had a factor loading of 0.48 on factor 2. Corroboration of correct placement of the items for this assessment was one of the goals for the initial phase of the study. In the initial study, items for this assessment were rated on a four-point scale ranging from 1 (never) to 4

(always). To conform to the rest of the current questionnaire as well as increase the potential variation of the items in the scale, these items were rated on a 7-point Likert scale ranging from 1

(never) to 7 (always).

TABLE 3.5: Autonomy Scale Never Always 30. Did your volunteering offer you the possibility of independent thought and 1 2 3 4 5 6 7 action? 31. Did you have freedom in carrying out your volunteering activities? 1 2 3 4 5 6 7

32. Did you have influence in the planning of your volunteering activities? 1 2 3 4 5 6 7 33. Could you have participated in the decision about when a piece of 1 2 3 4 5 6 7 volunteering work was completed? 34. Did you have a direct influence on your volunteer organization’s 1 2 3 4 5 6 7 decisions? 35. Could you have participated in decisions about the nature of your 1 2 3 4 5 6 7 volunteering?

TABLE 3.6: Social Support Scale Never Always 36. Could you have counted on your event colleagues when you came across 1 2 3 4 5 6 7 difficulties in your volunteering activities? 37. If necessary, could you have asked your event colleagues for help? 1 2 3 4 5 6 7

38. Did you get on well with your event colleagues? 1 2 3 4 5 6 7

TABLE 3.7: Supervisor Support Scale Never Always 39. Could you have counted on your event supervisor when you came across 1 2 3 4 5 6 7 difficulties in your volunteering duties? 40. Did you get on well with your event supervisor? 1 2 3 4 5 6 7 41. In your volunteering at this event, did you feel appreciated by your 1 2 3 4 5 6 7 supervisor? 42. Could you have discussed problems related to your event volunteer duties 1 2 3 4 5 6 7 with your direct supervisor? 43. Did your direct supervisor inform you about important issues related to 1 2 3 4 5 6 7 your volunteering?

TABLE 3.8: Feedback Scale Never Always 44. Did you know exactly what other people expected from your volunteer 1 2 3 4 5 6 7 work? 45. Did you know exactly what you were responsible for at the event? 1 2 3 4 5 6 7 46. Did you know exactly what your direct supervisor thought of your 1 2 3 4 5 6 7 performance? 47. Did you receive sufficient information on the purpose of your volunteer 1 2 3 4 5 6 7 duties? 48. Did you receive sufficient information on the results of your volunteer 1 2 3 4 5 6 7 activities? 49. Were you kept adequately up-to-date about important issues within your 1 2 3 4 5 6 7 event? 50. Was it clear to you to whom you should speak regarding specific problems 1 2 3 4 5 6 7 within the event? 51. Was the decision-making process by those in charge of the event clear to 1 2 3 4 5 6 7 you?

Work engagement. Work engagement was assessed utilizing a shortened version of the

Utrecht Work Engagement scale developed by Schaufeli, Bakker, and Salanova (2006). The researchers used an iterative process of analyzing data collected using the 17 item Utrecht Work

Engagement scale across 10 countries to determine the items with the highest β value and included those that contributed substantially to the variance explained. The process yielded three items for each of the three constructs composing work engagement: vigor, dedication, and absorption. Modification of the items facilitated use in the volunteering context of this study as well as accommodating the reflective tense of the questions. In most cases this entailed replacing the phrase “at my work” or “at my job” with the phrase “while volunteering” For example, the original item “At my work, I feel bursting with energy” was modified to “While volunteering, I felt bursting with energy.” An additional modification related to the response choices. The original instrument was anchored with points related to time intervals one would associate with long term continuous employment scenarios. The points included wording such

72 as “A few times a year”, “Once a month or less”, and “A few times a week”. For this study the responses were anchored only by “Never” and “Always” on a 7-point Likert scale.

Shortened Version of the Utrecht Work Engagement scale (with modifications).

TABLE 3.9: Vigor Scale Never Always 52. I felt bursting with energy while volunteering at this event. 1 2 3 4 5 6 7 53. While volunteering, I felt strong and vigorous. 1 2 3 4 5 6 7 54. When I got up the morning of the event, I felt like going to volunteer. 1 2 3 4 5 6 7

TABLE 3.10: Dedication Scale Never Always 55. I am enthusiastic about my volunteering. 1 2 3 4 5 6 7

56. My volunteering inspires me. 1 2 3 4 5 6 7

57. I am proud of the volunteering that I did. 1 2 3 4 5 6 7

TABLE 3.11: Absorption Scale Never Always 58. I felt happy when I was working intensely as a volunteer. 1 2 3 4 5 6 7

59. I was immersed in my volunteer work. 1 2 3 4 5 6 7

60. I got carried away when I was volunteering. 1 2 3 4 5 6 7

Leisure satisfaction measure. The Leisure Satisfaction Measure provided an assessment of six dimensions (Beard and Ragheb use the term “components”) of leisure satisfaction (Beard & Ragheb, 1980). Each of the six dimensions (psychology, educational/ intellectual, social, relaxation, physiological, aesthetic-environmental) were assessed with four items for a total of 24 items. The wording of the current version was modified to represent the volunteer context of the current study. For example, “My leisure activities are interesting to me” was modified to “My volunteer activities are interesting to me”. The original instrument utilized a five-point Likert-scale with individual anchor points ranging from “strongly disagree” to

“strongly agree”. Again, to conform to the rest of the current questionnaire as well as increase the potential variation of the items in the scale, these items were rated on a 7-point Likert scale ranging from 1 (Strongly disagree) to 7 (Strongly agree).

TABLE 3.12: Leisure Satisfaction Measure – Psychology Scale Strongly Strongly Disagree Agree 61. My volunteer activities are interesting to me. 1 2 3 4 5 6 7 62. My volunteer activities give me self-confidence. 1 2 3 4 5 6 7 63. My volunteer activities give me a sense of accomplishment. 1 2 3 4 5 6 7 64. I use many different skills and abilities in my volunteer activities. 1 2 3 4 5 6 7

TABLE 3.13: Leisure Satisfaction Measure - Education/Intellectual Scale Strongly Strongly Disagree Agree 65. My volunteer activities increase my knowledge about things around me. 1 2 3 4 5 6 7 66. Volunteering provides opportunities to try new things. 1 2 3 4 5 6 7

TABLE 3.13: -Continued Strongly Strongly Disagree Agree 67. My volunteer activities help me to learn about myself. 1 2 3 4 5 6 7 68. Volunteering helps me to learn about other people 1 2 3 4 5 6 7

TABLE 3.14: Leisure Satisfaction Measure – Social Scale Strongly Strongly Disagree Agree 69. I have social interaction with others through volunteer activities. 1 2 3 4 5 6 7 70. My volunteer activities have helped me to develop close relationships with 1 2 3 4 5 6 7 others. 71. The people I meet in my volunteer activities are friendly. 1 2 3 4 5 6 7 72. I associate with people in my free time who enjoy doing volunteer 1 2 3 4 5 6 7 activities.

TABLE 3.15: Leisure Satisfaction Measure – Relaxation Scale Strongly Strongly Disagree Agree 73. Volunteering helps me to relax. 1 2 3 4 5 6 7 74. My volunteer activities help relieve stress. 1 2 3 4 5 6 7 75. Volunteering contributes to my emotional well-being. 1 2 3 4 5 6 7 76. I engage in volunteer activities simply because I like doing them. 1 2 3 4 5 6 7

TABLE 3.16: Leisure Satisfaction Measure – Physiology Scale Strongly Strongly Disagree Agree 77. My volunteer activities are physically challenging. 1 2 3 4 5 6 7 78. I do volunteer activities which develop my physical fitness. 1 2 3 4 5 6 7

TABLE 3.16: - Continued Strongly Strongly Disagree Agree 79. I do volunteer activities which restore me physically. 1 2 3 4 5 6 7 80. Volunteering helps me to stay healthy. 1 2 3 4 5 6 7

TABLE 3.17: Leisure Satisfaction Measure - Aesthetic-Environmental Scale Strongly Strongly Disagree Agree 81. The areas or places where I engage in my volunteer activities are clean. 1 2 3 4 5 6 7 82. The areas or places where I engage in my volunteer activities are 1 2 3 4 5 6 7 interesting. 83. The areas or places where I engage in my volunteer activities are 1 2 3 4 5 6 7 beautiful. 84. The areas or places where I engage in my volunteer activities are well 1 2 3 4 5 6 7 designed.

Organizational commitment. Organizational commitment was measured using items from the instrument created by Allen and Meyer (1990). After considering use of the instrument in the volunteer context, only the items for affective commitment subscale were selected as appropriate for this study. The subscales for continuance commitment and normative commitment relate specifically to an employment context and thus were not included. Affective

Commitment Scale (ACS) was measured with 8 items. Responses are based on a 7-point Likert scale with responses anchored from “strongly disagree” to “strongly agree”. The individual items which have been modified to reflect the temporal context of the study are listed below.

TABLE 3.18: Affective Commitment – (items 89, 90, and 92 reversed scored) Strongly Strongly Disagree Agree 85. I would be very happy to spend the rest of my time as a volunteer with this 1 2 3 4 5 6 7 event. 86. I enjoyed discussing volunteering with people outside the event. 1 2 3 4 5 6 7 87. I really felt as if the problems of the event I volunteered for were my own. 1 2 3 4 5 6 7 88. The event where I volunteered had a great deal of personal meaning for 1 2 3 4 5 6 7 me. 89. I did not feel like ‘part of the family’ when I volunteered. 1 2 3 4 5 6 7 90. I did not feel ‘emotionally attached’ to the event I volunteered for. 1 2 3 4 5 6 7 91. The event I volunteered for had a great deal of personal meaning for me. 1 2 3 4 5 6 7 92. I did not feel a strong sense of belonging at the event when I volunteered. 1 2 3 4 5 6 7

Volunteer performance. Volunteer performance was assessed with the self-report measurement of performance from Kipnis and Schmidt (1988). One item, “Potential for promotion,” was removed from their scale as it is generally not applicable to the volunteer context of this study. Ideally the self-report measure would be paired with an evaluation from a supervisor or an objective observer, however, there was not a mechanism within this study to accommodate this type of cross reference. This is a limitation of the study as well as an avenue of future research. The items were introduced with the leading sentence reading “Think about your performance at the last event where you volunteered. Select the number that most closely reflects how you feel your event supervisor would have rated your performance”.

TABLE 3.19: Volunteer Performance Self-Report Scale Very poor Outstanding 93. Ability to work independently. 1 2 3 4 5 6 7

TABLE 3.19: - Continued Very poor Outstanding 94. Ability to work cooperatively. 1 2 3 4 5 6 7 95. Ability to solve problems. 1 2 3 4 5 6 7 96. Motivation to work hard. 1 2 3 4 5 6 7 97. Overall performance. 1 2 3 4 5 6 7

Volunteer retention. In the work literature, turnover or intention to quit is a construct used to measure an employee’s likelihood (behavioral intention) of leaving his or her job in an organization (Cook, Hepworth, Wall, & Warr, 1981). It is a retention related work variable. In many studies (Miller, Katerberg, & Hulin, 1979; Mobley, Griffeth, Hand, & Meglino, 1979;

Mobley, Horner, & Hollingsworth, 1978) the variable was called intent to quit because of its relationship to actual turnover (Miller, Powell, & Seltzer, 1990). Because intent to quit and intent to remain represent opposite ends of a continuum, three items from O’Reilly, Chatman, and Caldwell’s (1991) scale were adapted by the researcher as an “intention-to-remain” scale for volunteers. The adaptations of the items were to incorporate a volunteering context by substituting the word “job” with “volunteering”. The scale used a 7-point Likert response scale as illustrated below.

TABLE 3.20: Intention to Remain Scale Strongly Strongly Disagree Agree 98. I intend to remain as a volunteer with this event. 1 2 3 4 5 6 7 99. If I were to have my own way, I would be volunteering for this event 1 2 3 4 5 6 7 three years from now. 100. I have thought seriously about not continuing to volunteer for this event. 1 2 3 4 5 6 7

The third item was reverse coded (so that higher scores indicate higher intent to “remain”) and the three items were averaged to obtain an overall measure of intent to remain.

Demographics

A demographic section was included as part of the instrument in order to generate descriptive statistics regarding the sample population as well as provide a means of segmenting the sample into the various groups of interest posited in the study. It included information to differentiate the type of events people volunteered for (sport vs. non-sport). Items also addressed whether the individual exclusively volunteered for a specific type of event/organization as well as their tenure in the specific volunteer group which was the focus of their answers. The section included the socioeconomic information of the individual as well as the basic demographic information of age, gender, race and education. The specific items are listed below.

 The volunteer event you base your answers on can best be classified as which of the following:

a. Sport b. Arts, Cultural, or Entertainment c. Medical Cause Related d. Environmental Cause related e. Social change f. Politically Related g. Faith based h. Other, please specify______

 What was the name of the last event where you volunteered? ______

 The sport event where you last volunteered that you base your answers on can best be classified as:

a. Professional sport event b. Non-professional sport event c. Not sure.

 Please indicate the type(s) of volunteer events you have worked with in the last five years (select all that apply):

a. Sport b. Arts, Cultural, or Entertainment c. Medical Cause Related d. Environmental Cause related e. Social change f. Politically Related g. Faith based h. Other, please specify______

 How long have you been a volunteering? ______(years) _____(months)

 How long have you been a volunteering for this event? ______(years) _____(months)

 Within a year, how many events do you volunteer for?

 What is your annual household income before taxes?

a. Less than $25,000 b. $25,001 - $49,999 c. $50,000 - $74,999 d. $75,000 - $99,999 e. $100,000 - $124,999 f. $125,000 - $149,999 g. $150,000 or more h. Would rather not say

 Gender: ______Female ______Male

 In what year were you born? ______

 Marital Status: Married______Single ______Other______

 How would you classify yourself?

a. Asian/Pacific Islander b. Black/African American c. Caucasian/White d. Hispanic/Latino e. Multiracial f. Other ______

 What is the highest level of education you have attained?

a. High School b. Vocational degree c. Associates degree

d. Bachelor’s degree e. Master’s degree f. Doctorate degree g. Professional certifications h. Other

Chapter Summary

This chapter provided the methods used to conduct this research. It included an organization of the study and an overview of the research design. The research procedures, included information about reliability and validity evidence were discussed. Additionally, information about the sampling, data collection, and instrumentation were also discussed including the proposed items that were used to measure the four latent constructs. The study was composed of an initial phase to insure proper instrument development followed by a main study.

Once the appropriateness of the scales were assessed the main study was conducted.

CHAPTER 4

RESULTS

This chapter contains the results of the Initial Phase of the research as well as the results of the main study. The chapter will begin with the initial phase results then move to the main study which will include descriptive statistics, statistical analysis relating to the research questions, and the evaluation of the proposed SEM model.

Initial Phase Results

As explained in chapter 3, the initial phase of the study was to craft a suitable questionnaire using a combination of existing scales to address the research questions.

Instruments originating from research in the field of organizational behavior were associated with employees in a work environment. The researcher modified items to reflect the volunteer context of the present study. The modified items were then presented to an expert in the field for comment and corroboration of suitability for the study. A panel of experts including professors and a doctoral student in the field were recruited to assess the face validity of the modified job resource items in the study, the prospective assignment of items to specific constructs

(autonomy, feedback, social support, and supervisor support) as well as general suitability of job resource items for the study.

The review of items was facilitated using the Qualtrics survey software. Recruited experts were able to assign items to constructs they deemed appropriate. They were also able to indicate any items or constructs they felt should be deleted to strengthen the instrument for the study. Each item was presented individually and in random order. Each expert was then directed to place the item under the factor heading s/he felt was most appropriate. There was

82 also a category labeled “Unsure/ Item does not fit any of the above.” After the categorization portion of the survey the experts were further prompted with the following: “If you have any questions, comments, or concerns about the items and/or categories, please add them in the space below.” The results of the survey are presented in Table 4.1 below.

TABLE 4.1: Job Resources Factor Item Assignment

Social Supervisor Unsure / does # Item Feedback Support Support Autonomy not fit any 1 Did your volunteering offer you the possibility 0 0 0 5 0 of independent thought and action? 2 Did you have freedom in carrying out your 0 0 0 5 0 volunteering activities? 3 Did you have influence in the planning of your 0 0 0 5 0 volunteering activities? 4 Could you have participated in the decision about when a piece of volunteering work was 0 0 0 3 2 completed? 5 Did you have a direct influence on your 0 0 0 5 0 volunteer organization's decisions? 6 Could you have participated in decisions about 0 0 0 3 2 the nature of your volunteering? 7 Could you have counted on your event colleagues when you came across difficulties in 0 5 0 0 0 your volunteering activities? 8 If necessary, could you have asked your event 0 5 0 0 0 colleagues for help? 9 Could you have counted on your event supervisor when you came across difficulties in 0 0 5 0 0 your volunteering duties? 10 In your volunteering at this event, did you feel 0 0 5 0 0 appreciated by your event supervisor? 11 Did you get on well with your event colleagues? 0 5 0 0 0 12 Did you get on well with your event supervisor? 0 0 4 0 1 13 Could you have discussed problems related to your event volunteer duties with your direct 0 0 5 0 0 supervisor? 14 Did your direct supervisor inform you about 1 0 3 0 1 important issues related to your volunteering? 15 Did you know exactly what other people 3 1 0 0 1 expected from your volunteer work? 16 Did you know exactly what you were 4 0 0 0 1 responsible for at the event? 17 Did you know exactly what your direct 4 0 1 0 0 supervisor thought of your performance?

TABLE 4.1: - Continued

Social Supervisor Unsure / does # Item Feedback Support Support Autonomy not fit any 18 Did you receive sufficient information on the 4 0 0 0 1 purpose of your volunteer duties? 19 Did you receive sufficient information on the 5 0 0 0 0 results of your volunteer activities? 20 Were you kept adequately up-to-date about 4 0 0 0 1 important issues within your event? 21 Was it clear to you to whom you should speak 3 0 0 0 2 regarding specific problems within the event? 22 Was the decision-making process by those in 2 0 1 0 2 charge of the event clear to you?

Based on the results, six items were deleted because there was not consensus of at least 4 experts for their inclusion or common positioning. The deleted items are included below (see

Table 4.2).

TABLE 4.2: Job Resources Items Deleted from Study

Could you have participated in the decision about when a piece of volunteering work was completed?

Could you have participated in decisions about the nature of your volunteering?

Did your direct supervisor inform you about important issues related to your volunteering?

Did you know exactly what other people expected from your volunteer work?

Was it clear to you to whom you should speak regarding specific problems within the event?

Was the decision-making process by those in charge of the event clear to you?

There was unanimous agreement regarding the positioning of the majority of the remaining items. The exceptions were the item “Did you get on well with your event supervisor?” initially placed as part of the factor “supervisor support,” and the majority of items for the factor labeled “feedback.” One panel member stated “Most of the feedback items seem to 84 measure role clarity - not really feedback.” In aggregate when all original items were present the researcher agreed with this assertion. When the items “Was the decision-making process by those in charge of the event clear to you?”, “Was it clear to you to whom you should speak regarding specific problems within the event?”, and “Did you know exactly what other people expected from your volunteer work?” were deleted the remaining items were more in line with the concept of feedback and thus were included in the final instrument.

Other changes from the originally proposed instrument was that the item “The event I volunteered for had a great deal of personal meaning for me,” used to assess Affective

Commitment, was eliminated as it did not necessarily relate to the experience of the event but could be based on antecedents unrelated to the volunteer experience. A final change to the instrument was the addition of the item “When volunteering, was there someone who could help with your responsibilities if needed?” under the social support factor. The addition of the item was based on input from an expert to insure a minimum of at least three items per factor would be viable as a condition of the SEM analysis.

The initial proposed model Figure 4.1 (below) was expanded to better illustrate the complexity of the model while recognizing and remaining within the space limitations of the page format and is represented in Figure 4.2. As a result of the analysis to find the best indicators for the factors, the model was further revised to illustrate the items ultimately used to assess the factors from the original model as illustrated in Figure 4.2. Figure 4.3 (below) is the representation of the final model.

RO1 Role | Overload RO3

RC1 Volunteer Role Job | Satisfaction Conflict Demands RC8 Demands / Stressors Volunteer Volunteer

Resources AC8

SO1 Social | Support Volunteer SO4 Performance

SU1 Supervisor

Job Resources | VP1 SU4 Support |

VP5 A1 | Autonomy A4

Figure 4.1 The Role of Job Resources and Job Demands in Volunteer Engagement and Related Outcomes – Revised Extended Measurement Model.

SPY4 SE4 SS4 SR4 SPH4 SA4 RA1 Role | RA6 Ambiguity VI1 VI2 VI3 DE1 DE2 DE3

PP1 Perception of | Politics PP12 Volunteer Satisfaction RO1 Role Job | Overload Demands RO3 Volunteer

Demands / Stressors Volunteer Retention Engagement

RC1 Role Volunteer | Conflict Performance RC8 VP1 VP5

F1 VR1 VR2 VR3 Feedback AB1 AB2 AB3 VP2 VP4 | Job F5 Resources VP3

SO1 Social | Support Volunteer

SO4 Commitment

Supervisor SU1 Job Resources | Support

SU4

A1 AC6 AC7 | Autonomy AC1 AC2 AC3 AC4 AC5 A4

Figure 4.2 The Role of Job Resources and Job Demands in Volunteer Engagement and Related Outcomes – Revised Extended Exploded Measurement Model.

PP1 PP4 VI1 VI2 VI3 DE1 DE2 DE3 Perception of PP6 Politics PP8 PP9 PP12 Volunteer RO1 Role Satisfaction | Overload Job RO3 Demands

Demands / Stressors Volunteer Volunteer Retention RC1 Engagement

RC2 Role Volunteer RC4 Conflict Performance RC5 RC6 VP1 VP5 RC7 RC8

VR1 VR2 VR3 AB1 AB2 VP2 VP4 Job F1 VP3 | Feedback Resources F5

Volunteer SO1 Social | Commitment Support SO4

SU1 | Supervisor Job Resources SU4 Support

A1 AC6 AC7 | Autonomy AC5 A4

Figure 4.3 The Role of Job Resources and Job Demands in Volunteer Engagement and Related Outcomes – Revised Extended Measurement Model Based on SEM Analysis.

Main Study Results

Sampling

The researcher originally planned to coordinate data collection with a few select organizations whose mission was to coordinate volunteers for various organizations within a geographical region. Based on this plan, HandsOn Jacksonville was identified as a suitable organization that was willing to assist with the study. Due to concerns of oversaturating their volunteers with email, an invitation to participate in the study was included in the organization’s regular email newsletter (see Appendices B and C) with a link to the online survey. As the newsletter was distributed to over 7,000 of their volunteers a suitably sized sample was expected.

The overall response rate, however, was extremely low 0.02% (142 out of 7,000) of which only

87 were complete and useable for an actual response rate of 0.01%. Other efforts were needed to obtain a large enough sample for the study.

The researcher proceeded with a two prong snowball sampling approach which included a direct email appeal (see Appendix E) to the researcher’s personal and professional networks, as well as an online appeal (see Appendices F and G) to members of the researcher’s Facebook friends. The strategy is considered a snowball sampling method because the initial contacts are requested to recruit additional individual participants and organizations to collect a larger sample of appropriate subjects (Johnson & Christensen, 2008, p. 239). Another addition to the sampling method was the inclusion of a potential monetary incentive. Once a respondent had completed and submitted the survey, they were afforded the option of entering a drawing for a $50 gift card.

The gift card could be used by the winner of the drawing or was transferrable as a donation to the volunteer organization of their choice as a reward to an outstanding volunteer in their organization. The information provided for the drawing was not connected with the survey

89 responses. This second stage of sampling yielded an additional 243 cases of which 124 were complete and usable. When added to the 87 cases from the first stage of data collection the total aggregate sample consisted of 211 usable cases.

The researcher prompted participants to report their year of birth in the demographics section of the questionnaire as the means of determining their approximate age and generational cohort. For practical considerations relating to collecting data from minors, the scope of the study was limited to included anyone 18 years of age or older. Because of this restriction, participation was limited to those individuals born in or before 1995. The oldest participant reported being born in 1932. Participants were assigned to one of four generational cohorts based on their year of birth. The cohorts included were labeled “Traditionalists”, “Baby

Boomers”, “Gen X-ers”, or “Gen Y-ers”.

The literature relating to generational cohorts generally use the Kupperschmidt’s (2000) definition where a generation is defined as an “identifiable group that shares birth year, age location, and significant life events at critical developmental stages” (p. 66). As the definition is subjective, there is no set agreement on dates for when a cohort begins or ends. Indeed, Smola and Sutton (2002) when discussing cohorts lamented that “there is little agreement on the years encompassing them” (p. 364). Although the researchers in generational difference (Smola &

Sutton, 2002; Arsenault, 2004; Glass A. , 2007; Cennamo & Gardner, 2008; Gursoy, Maier, &

Chi, 2008; Twenge & Campbell, 2008; Sullivan, Forret, Carraher, & Mainiero, 2009) do not agree, they do have ranges for the cohorts which are relatively similar (see Table 4.3).

For the current study the researcher chose to use the dates posited by Smola and Sutton

(2002) as their research focused on the Baby boomer, Gen X-ers, and Gen Y-ers (Millennials) from an implications perspective. There is one adjustment to the dates posited by Smola and

Sutton to deal with the overlap between the Traditionalists and Baby Boomers. To deal with the overlap in the current study the Traditionalist cohort (which was not a cohort included this analysis) was set with an end point in 1939. Table 4.4 outlines the start and end dates for cohorts participants in the current study.

TABLE 4.3: Date Ranges for Generational Cohorts in the Literature

Traditionalist Baby Boomer Gen X Gen Y Start End Start End Start End Start End Smola & Sutton (2002) 1940 1940 1964 1965 1978 1979 1994 Arsenault (2004) 1922 1943 1944 1960 1961 1980 1981 2000 Glass (2007) 1925 1940 1941 1960 1961 1976 1977 1992 Gursoy, Maier, & Chi (2008) 1943 1960 1961 1980 1981 2000 Cennamo & Gardner (2008) 1946 1961 1962 1979 1980 Twenge & Campbell (2008) 1961 1981 1982 1999 Sullivan et. al (2009) 1922 1945 1946 1964 1965 1983 1984 2002

TABLE 4.4: Date Ranges for Generational Cohorts for Current Study

Traditionalist Baby Boomer Gen X Gen Y Start End Start End Start End Start End Current Study 1939 1940 1964 1965 1978 1979 1994

Description of Sample

Data collection began on March 6, 2013 when the online survey was activated and continued for approximately 14 weeks until it was closed on June 15, 2013. Prior to analysis questionnaires were examined for missing data and/or outliers which might skew the analysis.

There were 385 individuals that began the survey; a total of 174 were excluded prior to analysis due to missing information, yielding 211 usable surveys. The final sample is composed of

43.1% male respondents, 53.6% female respondents, 1.9% preferring not to identify their gender, and 1.4% failing to answer the question. Full demographic information of participants in the sample can be found in Table 4.5.

Based on the responses to the limited choices in regard to marital status, participants indicated that the composition of the sample most (60.4%) identified as married. Of the remaining participants, 30% identified as single while 9.7% indicated their marital status as

“other”. An even larger majority of one population was found in the race category.

When asked “how do you classify yourself?” the majority of respondents identified themselves as Caucasian. They represented 87.7% of the entire sample. All other potential response categories each represented less than 2.5% of the total sample. Three individuals selected “I prefer not to answer” and as with the responses for gender there were 4 individuals who did not respond at all.

TABLE 4.5: Descriptive Statistics Demographic Classification Frequency Percent Valid Information (n=211) Percent Gender Male 91 43.1 43.8 Female 113 53.6 54.3 Prefer not to answer 4 1.9 1.9 Missing 3 1.4 Age (by cohort) Traditionalist 6 2.8 3.0 Baby Boomers 85 40.3 42.7 Gen X 69 32.7 34.7 Gen-Y 39 18.5 19.6 Missing 12 5.7 Marital status Married 125 59.2 60.4 Single 62 29.4 30.0 Other 20 9.5 9.7 Missing 3 1.9 Race Asian/Pacific Islander 6 2.8 2.9 Black/African American 5 2.4 2.4 White/Caucasian 183 86.7 88.4 Hispanic/Latino/Latina 3 1.4 1.4 Multiracial 2 .9 1.0 Other 4 1.9 1.9 I prefer not to answer 4 1.9 1.9 Missing 4 1.9

TABLE 4.5: - Continued Demographic Classification Frequency Percent Valid Information (n=211) Percent Education Some schooling 1 .5 .5 High school graduate or GED 20 9.5 9.7 Vocational degree 7 3.3 3.4 Associates degree 20 9.5 9.7 Bachelor’s degree 69 32.7 33.3 Master’s degree 50 23.7 24.2 Doctorate degree 26 12.3 12.6 Professional certification 5 2.4 2.4 Other 9 4.3 4.3 Missing 4 1.9 Annual household Less than $25,000 13 6.2 6.3 income $25,001 – $49,999 18 8.5 8.7 $50,000 – $74,999 38 18.0 18.0 $75,000 - $99,999 37 17.5 17.9 $100,000 - $124,999 33 15.6 15.9 $125,000 - $149,999 16 7.6 7.7 $150,000 or more 37 17.9 17.9 Would rather not say 15 7.2 7.2 Missing 4 1.9

Unlike the distribution for race, the educational attainment of the respondents is more diverse. The pattern of responses approaches a normal curve shape when examining a histogram of responses (see Figure 4.4). Only one person (0.5% of the sample) reported not having graduated high school or obtaining a GED. High school graduates and participants who completed the GED represent 9.7% of the sample while those with vocational degree represent

3.4% and those with Associate degrees account for 9.7%. The largest group (33.3%) reported having attained a bachelor’s degree. Participants with Master’s degrees accounted for 24.2%,

Doctoral degrees accounted for 12.6% and professional certifications 2.4% of the sample.

Participants who indicated “other” account for 4.3%. There were four individuals who provided no information. The variation evidenced in education was also evident in participant income.

Figure 4.4 Educational Attainment of Participants.

Responses by participants regarding income are spread across the various response categories. The spike at the high end may be accounted for by the open-ended nature of that category, otherwise, there responses may have also have approached a relatively normal distribution found in the educational data. Figure 4.5 shows the graphical distribution of reported income. A final categorization of interest relates to generational cohort discussed next.

Figure 4.5 Household Income of Participants.

Based on the generational cohort scheme (see Table 4.5), the Baby Boomers accounted for 42.7% of the sample, Gen X-ers 34.7% and Gen Y-ers 19.6%. While not included in the generational analysis as part of the current study, 6 participants or 3% of the sample fell into the

Traditionalists cohort (see Table 4.6 and Figure 4.6). The type of events that volunteers chose to engage in is discussed in the next paragraph.

TABLE 4.6: Distribution of Generational Cohort in Sample Frequency Percent Valid Percent Traditionalist 6 2.8 3.0 Baby Boomer 85 40.3 42.7 Valid Gen X-ers 69 32.7 34.7 Gen Y-ers 39 18.5 19.6 Total 199 94.3 100.0 Missing System 12 5.7 Total 211 100.0

Figure 4.6 Distribution of Generations Cohort in Sample

Types of Volunteers Represented in Sample

Participants in the study represented volunteers engaged in a variety of event types.

Those reported as engaging in sport related events accounted for 18 percent of the sample. The breakdown of the other categories can be seen in Table 4.7 and Figure 4.7. It should be noted the

“Other” category was the largest with 30.8 percent of respondents opting to categorize their volunteering activity as fitting that category.

TABLE 4.7: Distribution of Volunteers by Event Type

N = 211 Frequency Percent Sport 38 18.0 Arts. Cultural, or Entertainment 23 10.9 Medical Cause Related 28 13.3 Environmental Cause Related 5 2.4 Valid Social Change 25 11.8 Politically Related 6 2.8 Faith based 21 10.0 Other 65 30.8 Total 211 100.0

Figure 4.7 Event Type Report by Volunteer Participants in Sample

Evidence of Reliability & Validity

Internal consistency was assessed using Cronbach's alpha coefficients (see Table 4.8 and

4.13) and item-to-total correlations (see Appendices K and L) for the all factors (i.e., engagement, satisfaction, commitment, and retention). Confirmatory Factor Analysis was used to assess evidence of convergent and discriminant validity.

A reliability analysis of the items composing the various constructs was conducted using the IBM SPSS Statistics Version 20. The first step was to assess the reliability of all items associate with the individual constructs. The values of the standardized Cronhbach’s alpha for constructs in this initial phase range from a low of 0.715 for the items measuring absorption in the engagement construct, to a high of 0.929 for both the aggregate constructs of job resources and satisfaction. The results of that initial analysis can be found in Table 4.8.

For this study the researcher followed the advice of Johnson and Christensen (2008) who afirm “a popular rule of thumb is that the size of coefficient alpha (Cronhbach’s Alpha) should

97 generally be, at minimum, greater than or equal to 0.70 for research purposes” (p. 149). All of the factors in the initial assessment have standardized Cronhbach Alpha scores (see Table 4.8) which meet or exceed this criteria. The seven constructs have an average Cronbach’s alpha score of approximately 0.86. Based on this evidence the researcher was relatively comfortable with the strength of the items as indicators of the construct of concern. The next step was to begin assessing whether the responses of sport volunteers differed from the responses of other types of volunteers. This distiction is necessary in determining whether to use the sample as an aggragate or if sport event volunteers needed to be analyzed separately. This would have ramification when assessing the structural equation model proposed as well as the Confirmatory Factor

Analysis (CFA) of the items in the specific context of the model.

TABLE 4.8: Reliability Table based on Individual Items in Factors N=211 All items included in factors individually measured

Latent Factor Item Factor Cronbach’s Cronbach’s Alpha All Items Alpha Standardized Job Demands 0.885 0.897 29 Role Ambiguity 0.842 0.844 6 Role Conflict 0.843 0.845 8 Role Overload 0.804 0.805 3 Perception of Politics 0.699 0.733 12 Job Resources 0.915 0.929 17 Autonomy 0.767 0.778 4 Social Support 0.857 0.864 4 Supervisor Support 0.864 0.866 4 Feedback 0.829 0.837 5 Satisfaction 0.923 0.929 24 Psychological 0.805 0.814 4 Educational 0.813 0.817 4 Social 0.724 0.720 4 Relaxation 0.836 0.840 4 Physical 0.801 0.799 4 Aesthetic 0.774 0.774 4 Volunteer Engagement 0.891 0.908 9 Vigor 0.828 0.832 3 Dedication 0. 792 0.806 3 Absorption 0.653 0.715 3 Affective Commitment 0.737 0.761 7 Volunteer Performance 0.891 0.894 5 Volunteer Retention 0.787 0.793 3

Confirmatory Factor Analysis

Confirmatory Factor Analysis (CFA) was conducted to find the items that would have the greatest predictive power in the most parsimonious model. An iterative approach was employed to account for fluctuation in the factor loading as items were removed. In the first iteration all items that fell below 0.30 were eliminated. This cut point was selected as it is a common practice in CFA to fix all relatively low factor loadings (i.e., those below 0.30) to zero (Kline,

2005, p. 205). As a result of this first iteration, six items (see Table 4.9) were eliminated. The analysis was run again after removing these initial items. All remaining items in this second iteration had factor loading above the original cut point of 0.30. A third iteration was completed in which the cut-point was raised to 0.55. This cut point was chosen to reflect that the item’s influence was greater than 50/50 or that of simple chance. As a result of raising the cut point to

0.55, 6 additional items were removed because they fell below the new criteria. The items that were deleted in step two are listed in Table 4.10. Again the analysis was run after removing the items from this second iteration. This process was repeated until all of the remaining items either met or were above the cut point. Table 4.11 and 4.12 correspond to the remaining items that were removed as not meeting the cut-point criteria.

TABLE 4.9: Items Deleted After Step One of CFA Iteration Code Item Since I have been a volunteer with this event, I have never seen the operational policies applied PP2 politically. PP3 Rewards came only to those who worked hard. I can’t remember when a volunteer received recognition or a promotion that was inconsistent PP5 with published policies. Volunteers are encouraged to speak out frankly even when they are critical of well-established PP7 ideas. There is no place for yes-men around here; good ideas are desired even when it means PP10 disagreeing with superiors. AC3 I really felt as if the problems of the event I volunteered for were my own.

TABLE 4.10: Items Deleted After Step Two of CFA Iteration Code Item RA3 I know that I divided my time properly. RC3 I had to oppose a rule or policy in order to carry out an assignment. PP11 Choice assignments in this event generally go to top performers. SS3 The people I meet in my volunteer activities are friendly. AB3 I got carried away when I was volunteering. AC1 I would be very happy to spend the rest of my time as a volunteer with this event.

TABLE 4.11: Item Deleted After Step Three of CFA Iteration Code Item AC2 I enjoyed discussing volunteering with people outside the event.

TABLE 4.12: Item Deleted After Step Four of CFA Iteration

Code Item AC3 I really felt as if the problems of the event I volunteered for were my own.

Having removed all items below the 0.55 cut point, it was necessary to reevaluate the reliability of the remaining items and constructs. By eliminating items that were not strong indicators one would expect the reliability of the items that measure the proposed latent constructs, as well as the overall (second level) latent constructs, would improve. As with the initial analysis the researcher followed the popular rule of thumb where the size of coefficient alpha (Cronhbach’s Alpha) should generally be, at minimum, greater than or equal to 0.70 for research purposes (Johnson & Christensen, 2008, p. 149). Again all of the factors in the assessment have standardized Cronhbach alpha scores (for all factors see Table 4.13) which meet and comfortablely exceed this criteria.

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TABLE 4.13: Reliability Table Based on Individual Items in Factors N=211 All items included in factors individually measured Only items above the 5.5 cut Latent Item Factor Cronbach’s Cronbach’s All Cronbach’s Cronbach’s Number Factor Alpha Alpha Alpha Alpha of Items Items Standardized Standardized Job 0.885 0.897 29 0.916 0.918 21 Demands Role 0.842 0.844 6 0.842 0.847 5 Ambiguity Role Conflict 0.843 0.845 8 0.836 0.839 7 Role 0.804 0.805 3 0.804 0.806 3 Overload Perception of 0.699 0.733 12 0.852 0.856 6 Politics Job 0.915 0.929 17 0.915 0.929 17 Resources Autonomy 0.767 0.778 4 0.767 0.778 4 Social 0.857 0.864 4 0.857 0.863 4 Support Supervisor 0.864 0.866 4 0.864 0.866 4 Support Feedback 0.829 0.837 5 0.828 0.837 5 Satisfaction 0.923 0.928 24 0.921 0.927 23 Psychological 0.805 0.814 4 0.805 0.814 4 Educational 0.813 0.817 4 0.813 0.817 4 Social 0.724 0.720 4 0.750 0.754 3 Relaxation 0.836 0.840 4 0.835 0.840 4 Physiological 0.801 0.799 4 0.801 0.799 4 Aesthetic 0.774 0.774 4 0.774 0.774 4 Volunteer 0.891 0.908 9 0.913 0.917 8 Engagement Vigor 0.828 0.832 3 0.828 0.832 3 Dedication 0. 792 0.806 3 0.792 0.806 3 Absorption 0.653 0.715 3 0.800 0.811 2 Affective 0.737 0.761 7 0.842 0.844 3 Commitment Volunteer 0.891 0.894 5 0.891 0.894 5 Performance Volunteer 0.787 0.793 3 0.787 0.793 3 Retention

The majority of the factor loadings were all above 0.70. On several of the re-assessed constructs there was a marked increase in the reliability coefficient. Three of the four constructs

(job demands, volunteer engagement, and affective commitment) showed improvement in scores for reliability (Cronbach’s alpha). The reliability score for the overall construct of satisfaction showed a very slight drop from 0.928 to 0.927 when re-assessed, yet each of the items either 101 remained constant or showed improvement (see Table 4.14 for factors that changed). This drop can be attributed to ‘noise’ in the analysis. Having examined the items for suitability the next step is to look at the the composition of the sample to see how and if it should be segmented when addressing the researche questions.

TABLE 4.14: Reliability Table of Changed Items in Factors N=211 All items included in factors individually measured Only items above the 5.5 cut Latent Item Factor Cronbach’s Cronbach’s All Cronbach’s Cronbach’s Number Factor Alpha Alpha Alpha Alpha of Items Items Standardized Standardized Job 0.885 0.897 29 0.916 0.918 21 Demands Role 0.842 0.844 6 0.842 0.847 5 Ambiguity Role Conflict 0.843 0.845 8 0.836 0.839 7 Perception of 0.699 0.733 12 0.852 0.856 6 Politics Satisfaction 0.923 0.928 24 0.921 0.927 23 Social 0.724 0.720 4 0.750 0.754 3 Volunteer 0.891 0.908 9 0.913 0.917 8 Engagement Absorption 0.653 0.715 3 0.800 0.811 2 Affective 0.737 0.761 7 0.842 0.844 3 Commitment

Sport vs. Non-Sport

In order to ascertain if the responses for sport volunteers differ from other types of volunteers a series of t-tests were conducted. In the first test the responses were divided into two categories, sport and non-sport, based on the response from the participants on the question,

“The volunteer event you base your answers on can best be classified as which of the following:” with the potential choices of “Sport; Arts, Cultural, or Entertainment; Medical Cause Related ;

Environmental Cause related; Social change; Politically Related; Faith based; or Other, please specify” Those who responded “sport” were placed in one group labeled “sport” while the rest

102 were grouped into the “non-sport” group. An Independent Samples T-Test was conducted using

IBM SPSS Statistics program version 20.

The initial test was of the entire sample which rendered an unequal group size where there were 38 cases in the sport category and 173 cases in the non-sport category. The results of this initial analysis were that there was not a statistically significant difference at the 0.05 level between the scores of participants based on event type for any of the factors assessed. Since the pooled variance estimate is equal to the mean of the two sample values when the n’s are equal

(Glass & Hopkins, 1996, p. 288), the analysis was conducted again with 38 cases in the sport group and 38 in the non-sport group so that the sample sizes for each group would be equal.

This was facilitated using all 38 cases of sport and a random sample of 38 of the 173 cases of non-sport responses from the study.

The findings of the first analysis were corroborated in the second analysis with the exception of the autonomy factor (see Appendix J). The scores for autonomy in the equal sample size analysis provided an indication of a statistically significant difference in the scores for autonomy based on whether a participant was in the sport group (mean = 5.1250) or the non- sport group (mean = 5.7697). Potential implications of this finding will be discussed further in chapter 5. The scores of the various t-tests (see Appendix J) provided evidence that overall the answers for sport volunteers did not statistically differ significantly from those of volunteers of other types of events. The one area, autonomy, that did indicate sport volunteers have answers that were significantly, statistically different than volunteers for other types of events was not corroborated in both analyses. While there was a significant difference with Autonomy, the mean scores for both groups were positive in the same direction. Since there is likely not much practical difference in the two mean scores for Autonomy and all of the other items indicated no

103 statistically significant differences in the responses of the sport and non-sport volunteers, the decision was made to treat the entire sample as one group. This was also done for logistical reasons as the splitting of the sample into smaller groups would result in two groups with sample sizes too small to utilize structural equation modeling as an appropriate analysis method. While there was little evidence of differentiation between the dichotomy of sport and non-sport in the sample of volunteers, there were group differences in responses based on generational cohort which will be addressed next.

Differences in Generational Cohort Results for Job Resources

Another goal associated with this study was to investigate whether volunteers representing different generations exhibit differing preferences in regard to job resources while volunteering. Since there was essentially no statistically significant difference between the answers for sport and non-sport volunteers in the previous analysis, and further segmenting of the sample by event types would result in a large disparity in sample group sizes because of the small sample size, the researcher elected to treat the sample as one group instead of two (i.e., a sport vs. non-sport). Based on the revised parameter of having to compare the means of three groups (Baby Boomers, Gen X-ers, and Gen Y-ers) in one classification of the population of interest (volunteers) one-way ANOVA was chosen as the appropriate analysis to assess whether there were appreciable differences in the responses given by individuals of the various generational cohorts regarding job resources while volunteering. To avoid the problems associated with unequal group sizes, the researcher followed the procedure outlined by

Tabachnick and Fidell (2007) where a random sample of the larger groups was selected to match the size of the smallest group. This resulted in three groups (Baby-boomers, Gen X-ers, and Gen

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Y-ers) of 38 cases in each group. The analysis was performed using IBM SPSS Statistics version 20. Results of the analysis are presented in Table 4.15.

The initial results from the analysis of the answers provided by members of the generational cohorts indicated statistically significant generational differences at the 0.05 alpha level for the individual factors of job resources (autonomy, social support, supervisor support, feedback) as well as the overall assessment of job resources. Post hoc analysis was needed to gain a better understanding of these differences. Results of the post hoc analysis are presented in

Table 4.16.

TABLE 4.15: ANOVA Results for Generations Difference in Job Resources. Sum of Squares df Mean Square F Sig. Autonomy Between Groups 749.128 2 374.564 350.515 .000* Within Groups 119.684 112 1.069 Total 868.812 114 Social Between Groups 1061.135 2 530.567 1132.835 .000* Support Within Groups 52.456 112 .468 Total 1113.590 114 Supervisor Between Groups 1096.858 2 548.429 3116.307 .000* Support Within Groups 19.711 112 .176 Total 1116.568 114 Feedback Between Groups 1035.320 2 517.660 2280.516 .000* Within Groups 25.423 112 .227 Total 1060.743 114 Job Resources – Between Groups 1010.944 2 505.472 3539.028 .000* Overall Within Groups 15.997 112 .143 Construct Total 1026.940 114

*. The mean difference is significant at the 0.05 level.

The test developed by Scheffé was selected to examine post hoc comparisons for the various groups because it is considered by Tabachnick and Fidell (2007) as “the most conservative and flexible of the popular methods” (p. 53). In the analysis the various cohorts were assigned numerical values. In the results below the values correspond to the cohorts as 105 follows: “2” represents Baby-boomers, “3” represents Gen X-ers, and “4” represents Gen Y-ers.

Data was also collected for members of the Traditionalist cohort and was assigned the numerical value of “1.” Because of the small sample size of that cohort and the fact that they were not a focus of the current study the traditionalist cohort was not included in the analysis.

TABLE 4.16: Scheffé Post Hoc Multiple Comparisons Results for Generations Difference in Job Resources. Dependent (I) (J) Mean Std. Sig. 95% Confidence Interval Variable Cohort Cohort Difference (I-J) Error Lower Bound Upper Bound Autonomy 2 3 .48684 .23716 .126 -.1015 1.0752 4 5.61842* .23563 .000* 5.0339 6.2030 3 2 -.48684 .23716 .126 -1.0752 .1015 4 5.13158* .23563 .000* 4.5470 5.7161 4 2 -5.61842* .23563 .000* -6.2030 -5.0339 3 -5.13158* .23563 .000* -5.7161 -4.5470 Social 2 3 .45395* .15700 .018* .0644 .8435 Support 4 6.63158* .15599 .000* 6.2446 7.0186 3 2 -.45395* .15700 .018* -.8435 -.0644 4 6.17763* .15599 .000* 5.7906 6.5646 4 2 -6.63158* .15599 .000* -7.0186 -6.2446 3 -6.17763* .15599 .000* -6.5646 -5.7906 Supervisor 2 3 6.56579* .09624 .000* 6.3270 6.8046 Support 4 6.56579* .09562 .000* 6.3286 6.8030 3 2 -6.56579* .09624 .000* -6.8046 -6.3270 4 .00000 .09562 1.000 -.2372 .2372 4 2 -6.56579* .09562 .000* -6.8030 -6.3286 3 .00000 .09562 1.000 -.2372 .2372 Feedback 2 3 6.37895* .10930 .000* 6.1078 6.6501 4 6.37895* .10860 .000* 6.1095 6.6484 3 2 -6.37895* .10930 .000* -6.6501 -6.1078 4 .00000 .10860 1.000 -.2694 .2694 4 2 -6.37895* .10860 .000* -6.6484 -6.1095 3 .00000 .10860 1.000 -.2694 .2694 Job Resources – 2 3 6.30341* .08670 .000* 6.0883 6.5185 Overall Construct 4 6.30341* .08614 .000* 6.0897 6.5171 3 2 -6.30341* .08670 .000* -6.5185 -6.0883 4 .00000 .08614 1.000 -.2137 .2137 4 2 -6.30341* .08614 .000* -6.5171 -6.0897 3 .00000 .08614 1.000 -.2137 .2137

*. The mean difference is significant at the 0.05 level.

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The results of the post hoc analysis (see Table 4.16 above) provided an indication that the members of the various cohorts had differing answers for the individual factors as well as the overall construct of job resources. There were a few exceptions to this trend. The first exception was that members of the Baby-boomer and Gen X-er cohorts with a mean difference of 0.48684 did not exhibit a statistically different response in regard to items related to the factor of autonomy. Another more extensive exception was in responses of the Gen X-ers and Gen Y-ers.

These two groups in the sample provided answers that were identical to one another for the factors relating to supervisor support and feedback as well as their aggregate scores for the overall composite score for job resources. Ramifications and other discussions of these results will be taken up in chapter 5.

When reviewing the results of the post hoc analysis, only four comparisons did not exhibit a statistically significant difference between the various groups. The first exception was that the difference between the answers given by the members of the Baby Boomer’s was not statistically different from the answers given by the Gen X-ers on the autonomy factor (mean difference of 0.48684 with a p-value of 0.126). The next three exceptions were related to the answers given by the members of the Gen X-er cohort and the Gen Y-er cohort. For the factors relating to supervisor support, feedback and the overall score of Job Demands there was no difference (mean difference of .000 with a p-value of 1.000) between their answers. This provides evidence that there is a marked difference between the way members of the Baby

Boomer cohort respond when compared to the Gen X-ers and Gen Y-ers, there may not be as big a difference between the Gen X-ers and the Gen Y-ers. Ramifications and other discussions of these results will be taken up in chapter 5. Results of the various aspect of structural equation modeling will be introduced next.

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Structural Equation Modeling (SEM) - The Measurement Model

The measurement model was specified to determine if it was identified. The final model

(see figure 4.3) consisted of seven latent constructs with three to twenty-three items measuring each latent construct. As the number of parameters was less than the sum of the number of variances and covariances of the latent factors, measurement errors, and estimated loading of the items on the latent factor, the model could be considered identified (Kline, 2005).

Once the model specification was determined and the model was deemed identified the

MPlus 7 statistical program was used to calculate the standardized factor loadings (see Appendix

N), Standard Errors, t-values, and P-values for the proposed model. SPSS was used to calculate the item-to-total correlations (see Appendices K and L) and construct reliabilities (CR). Average

Variance Extracted (AVE) scores were calculated in Excel using the data obtained from the previous analyses.

Results with the Measurement Model

The initial measurement model consisted of seven latent constructs measured by a total of

95 items. Utilizing an iterative process, the number of items was reduced to 80. One item (“The event I volunteered for had a great deal of personal meaning for me”) was removed prior to statistical analysis based on theoretical justification as this question relates to antecedents that may not have anything to do with the event or event management. Other items chosen for removal were based on statistical evidence such as standardized loadings below the predetermined cut point. The item reduction process is included earlier in this chapter.

Results of Multivariate Normality

An analysis of the multivariate normality of the factors in the model revealed that there was not a normal distribution of the responses. This could be due to having a small sample size.

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Because of the non-normal distribution combined with the small sample size the researcher opted to pursue path analysis as the method of analysis rather than structural regression. Because the researcher could not use structural regression as the analysis, use of the Satorra-Bentler chi- square statistic and correction method was not necessary. Use of path analysis afforded the opportunity to simply create histograms of the averaged scores for each of the factors. This change should be more informative and understandable to non-statisticians and lay readers as it provides a visual distribution of the averaged responses provided by participants of the study.

The histograms are included below followed by a table of the actual scores for skewness and kurtosis. The numerical results of tests of skewness and kurtosis are in Table 4.17 below.

Figure 4.8 Job Demands Histogram

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Figure 4.9 Job Resources Histogram

Figure 4.10 Volunteer Satisfaction Histogram

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Figure 4.11 Volunteer Engagement Histogram

Figure 4.12 Affective Commitment Histogram

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Figure 4.13 Volunteer Performance Histogram

Figure 4.14 Volunteer Retention Histogram

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TABLE 4.17: Measure of Skewness and Kurtosis on factors N=211 All items included in factors individually measured Latent Factor Item Factor Skewness Kurtosis

Job Demands 1.272 1.748 Role Ambiguity 1.075 0.357 Role Conflict 1.187 1.188 Role Overload 1.369 1.716 Perception of Politics 1.719 3.350 Job Resources -1.366 2.239 Autonomy -0.718 0.033 Social Support -1.995 6.150 Supervisor Support -2.371 7.048 Feedback -1.233 1.415 Satisfaction -0.666 0.703 Psychological -1.498 2.786 Educational -1.127 1.351 Social -1.082 0.785 Relaxation -0.859 0.722 Physiological 0.102 -0.614 Aesthetic -0.294 -0.635 Volunteer Engagement -1.292 2.401 Vigor -1.067 1.096 Dedication -1.535 2.851 Absorption -1.564 3.581 Affective Commitment -1.537 1.765 Volunteer Performance -1.490 2.144 Volunteer Retention -1.459 1.687

The only factor positively skewed was job demands. This is clearly visible in Figure 4.8.

The score for satisfaction while still negatively skewed (-0.666) came the closest to representing a normal distribution. The remaining factors (job resources, volunteer engagement, affective commitment, volunteer performance, and volunteer retention) are all clearly negatively skewed.

All of the factors except satisfaction (0.703) had scores greater than 0.79, the critical value for kurtosis, which indicated a leptokurtic distribution. Satisfaction fell within the range of a mesokurtic distribution. A possible reason for the scores for satisfaction being closest to a normal distribution could be related to the fact it had the largest number of items (23) averaged to make up its’ composite score. While the scores did not represent a normal distribution, they

113 do not depart from what would be expected based on theoretical postulation. Results of the path analysis are addressed in the next section.

Results of Path Analysis

While use of Structural Regression, what Kline (2005) states is “viewed as synthesis of path and measurement models” (p. 209) would be ideal in this situation, the model was too complex to be evaluated in toto based on the sample size. Kline (2005) warns more complex models may require larger samples sizes for an expectation of the results being reasonably stable because “complex models may require the estimation of more statistical effects” (p. 14). The researcher attempted to use structural regression but results of this analysis failed to yield stable results due to the complexity of the proposed model. As there were relationships between the latent variables the option of breaking the model into various segments for analysis was not viable because removing items or factors effected the entire model yielding results that could not be properly interpreted in the full model nor reintegrated into the model as a whole. Basically the factors in the model could not be accessed in piecemeal.

In order to address the hypotheses related to the structural model, path analysis was chosen by the researcher as the next most appropriate analysis. To facilitate the analysis, the scores for each factor were summed to obtain a single measure for each theoretical variable based on the a priori relationship posited among the variables. The MPlus 7 statistical program was used to calculate the path coefficients.

The proposed hypotheses for the structural model are illustrated in Figure 4.15. As already discussed, the small sample size precludes the use of structural modeling as the preferred analysis, therefore path analysis will be used to assess the hypotheses. The proposed hypotheses in the corresponding path model are represented in Figure 4.16.

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Volunteer Commitment

H6 Job H3 Demands Volunteer Satisfaction

H2 H4 H7

Volunteer Volunteer Engagement H9 Retention

H5 H1 H8

Job Volunteer Resources Performance

Figure 4.15 The Structural Model with Proposed Hypotheses

Volunteer Commitment H6 Job H3 Demands Volunteer Satisfaction

H2 H4 H7

Volunteer Volunteer Engagement H9 Retention

H5 H1 H8

Job Volunteer

Resources Performance

Figure 4.16 The Path Analysis Model with Proposed Hypotheses

The results of the path analysis have been represented in Figure 4.17. The data obtained through the analyses in this study provide evidence to support eight of the nine proposed

115 hypotheses. Hypothesis (H)7 was not supported. The standardized path coefficients and p- values for each hypothesis are presented in Table 4.18 below.

Volunteer

Commitment

0.265* Job 0.512* Demands Volunteer Satisfaction

-0.134 0.683* 0.061

Volunteer Volunteer Engagement 0.265* Retention

0.545* 0.505* 0.158*

Job Volunteer

Resources Performance

Figure 4.17 The Path Analysis Model with Standardized Results

TABLE 4.18: Standardized Path Coefficients and p-values of Hypotheses Label Hypothesis Path p-Value Coefficients H1 Job resources have a significant, positive impact on volunteer engagement. 0.505 * 0.000 H2 Job demands have a significant, negative impact on volunteer engagement. -0.134* 0.043 H3 Engagement has a significant, positive impact on commitment. 0.512 * 0.000 H4 Engagement has a significant, positive impact on satisfaction. 0.683 * 0.000 H5 Engagement has a significant, positive impact on volunteer performance. 0.545 * 0.000 H6 Commitment has a significant, positive impact on volunteer retention. 0.265* 0.000 H7 Satisfaction has a significant, positive impact on volunteer retention. 0.061 0.385 H8 Volunteer performance has a significant, positive impact on volunteer 0.158* 0.012 retention. H9 Sport volunteers who experience engagement are more likely to continue 0.364 * 0.000 volunteering. Note: * p< .05

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The path between Volunteer Satisfaction and Volunteer Retention, 0.061 and p-value of

0.385, was not statistically significant. This means Hypothesis 7, “Satisfaction has a significant, positive impact on volunteer retention,” was not supported. This finding will be discussed further in chapter 5. Results for the research questions and the other hypotheses are addressed in the following sections.

Results for Research Questions

When addressing the first research question “Which job resources have the greatest impact on volunteer engagement?” the researcher conducted an analysis of the measurement model in MPlus version 20 to assess the direct effect of the factors for job resources and job demands on volunteer engagement. The results of that analysis provide evidence that the job resources which have the most impact for volunteer engagement are social support, with a standardized factor loading of 0.846, followed closely by feedback and supervisor support with standardized factor loadings of 0.844 and 0.818 respectively. Autonomy had the smallest standardized factor loading at 0.689. All of the factors were statistically significant with p- values below the 0.001 level. These findings were corroborated by the results of the path analysis with a path coefficient of 0.534 which was significant at the 0.05 alpha level for H1 -

Job resources have a significant, positive impact on volunteer engagement. The results provide evidence to support research Hypothesis 1 (Job resources will have a significant, positive impact on volunteer engagement). The analysis output for factors of job resources (including the standardized estimate, standard error, critical ratio, and the two-tailed p-value of the critical ratio for each of the factors) on volunteer engagement is presented in Table 4.19.

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TABLE 4.19: Factor Loading for Job Resources on Volunteer Engagement Volunteer Engagement By Standardized Standard Est./S.E. Two-tailed P- Estimate Error Value Autonomy 0.689 0.052 13.314 0.000 Social Support 0.846 0.029 29.095 0.000 Supervisor Support 0.818 0.033 25.096 0.000 Feedback 0.844 0.032 25.978 0.000

The same approach used to evaluate the relationship between job resources and volunteer engagement was used when determining whether job demands have a positive or negative impact on volunteer engagement. The data collected and analyzed provide evidence that job demands have a negative impact on volunteer engagement. All of the job demand factors had negative standardized factor loadings with p-values smaller than 0.001. This provides evidence supporting Hypothesis 2 (Job demands will have a significant, negative impact on volunteer engagement). The job demand which had the greatest impact on volunteer engagement was role ambiguity with a standardized factor loading of -0.737. The other job demands followed role ambiguity at lessor levels in the following order: role conflict (-0.547); perception of politics (-

0.484); and role overload (-0.469). The analysis output for factors of job demands on volunteer engagement is presented in Table 4.20.

TABLE 4.20: Factor Loading for Job Demands on Volunteer Engagement Volunteer Engagement By Standardized Standard Est./S.E. Two-tailed P- Estimate Error Value Role Ambiguity -0.737 0.041 -18.020 0.000 Role Conflict -0.547 0.058 -9.378 0.000 Role Overload -0.469 0.065 -7.243 0.000 Perception of :Politics -0.484 0.0.62 -7.784 0.000

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As with the findings for job resources, the path analysis for job demands corroborated the findings above. The path coefficient was negative (-0.134) for H2 and statistically significant at the 0.05 alpha level. Although the magnitude of the path coefficients are not equal (0.505 for job resources versus -0.134 for job demands) there are several possibilities to explain this. One possible explanation can be attributed to the close relationship the items have for each factor.

There were indicators encountered during the analysis that some of the items in fact cross-loaded on the two factors. Specifically one of the suggested model improvements posited by the MPlus analysis was that the model would be improved by having Role Ambiguity load on the job resources factor instead of job demands. Further, the apparent inverse relationship the two factors exhibit may present a confounding influence in the analysis. The observed inverse relationship is discussed further in the next paragraph.

Based on the findings of the current study the question of the relationship between job demands and job resources in regard to volunteer engagement in the volunteer context indicated there is an inverse relationship between job resources and job demands. The standardized factor loadings for the factors associated with job resources are positive while the standardized factor loadings for job demands are negative. All of the factors have p-values less than 0.001, which is an indication the findings are statistically significant at the 0.05 level. The evidence provided by the factor loadings is corroborated by the path analysis. The analysis output for factors of both job resources and job demands on volunteer engagement is presented in Table 4.21.

TABLE 4.21: Factor Loading for Job Resources and Job Demands on Volunteer Engagement Volunteer Engagement By Standardized Standard Est./S.E. Two-tailed P- Estimate Error Value Autonomy 0.689 0.052 13.314 0.000 Social Support 0.846 0.029 29.095 0.000 Supervisor Support 0.818 0.033 25.096 0.000 Feedback 0.844 0.032 25.978 0.000

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TABLE 4.21: - Continued Volunteer Engagement By Standardized Standard Est./S.E. Two-tailed P- Estimate Error Value Role Ambiguity -0.737 0.041 -18.020 0.000 Role Conflict -0.547 0.058 -9.378 0.000 Role Overload -0.469 0.065 -7.243 0.000 Perception of Politics -0.484 0.0.62 -7.784 0.000

Having addressed the effects of job resources and job demands on volunteer engagement we now move on to examining the relationships among the other constructs in the proposed model.

The relationship of engagement with latent constructs. Hypotheses three through five relate to the impact volunteer engagement has on other latent variables (namely affective commitment, satisfaction, and volunteer performance) in the proposed model. The factor loading relating to Hypothesis 3 (Engagement will have a significant, positive impact on commitment) was 0.512. The factor loading for Hypothesis 4 (Engagement will have a significant, positive impact on satisfaction) was 0.683. The factor loading for Hypothesis 5

(Engagement will have a significant, positive impact on volunteer performance) was 0.545. The p-value for all the hypotheses was less than 0.001 indicating that the findings are statistically significant. The last four hypotheses explore the relationship of the latent factors to volunteer retention.

The relationship of latent variables to volunteer retention. As noted in chapter 2, volunteers, including sport volunteers, are prone to leave organizations or at least their roles within organizations for many reasons. Cuskelly and Boag (2001) forward the idea that many of the reasons volunteers leave are beyond the control of the organization or volunteer manager.

Following the assumptions that volunteer retention is desirable, as has been stated by several

120 researchers (Barnes & Sharpe, 2009; Cnaan & Goldberg-Glen, 1991; Cuskelly, 2004; Cuskelly,

Taylor, Hoye, & Darcy, 2006; Starnes & Wymer, Jr., 2001; Tett & Meyer, 1993;) the researcher has sought to understand the factors which contribute to volunteer retention. Hypotheses six through nine address the questions of whether affective commitment, satisfaction, volunteer performance, or volunteer engagement can be said to have a significant, positive impact on volunteer retention. See Table 4.22 for the results of hypotheses six through nine.

TABLE 4.22: Path Coefficients for Latent Constructs on Volunteer Retention Label Hypothesis Path Coefficients p-Value H6 Commitment has a significant, positive impact on volunteer retention. 0.265* 0.000

H7 Satisfaction has a significant, positive impact on volunteer retention. 0.061 0.385

H8 Volunteer performance has a significant, positive impact on volunteer 0.158* 0.012 retention. H9 Sport volunteers who experience engagement are more likely to 0.364* 0.000 continue volunteering. Note: * p< .05

The hypotheses relating to the factors: commitment, volunteer performance, and volunteer engagement (H6, H8, and H9 respectively) all provide evidence to support the assertion that they have a positive impact that is statistically significant at the 0.05 alpha level.

Hypothesis 7 (Satisfaction will have a significant, positive impact on volunteer retention) was the one hypothesis that was not supported by the data. With a path coefficient of 0.61 and a p-value of 0.385 the construct was not found to be significant. With results of the hypotheses addressed above, we can now move on to the results of the remaining research questions.

Summary of Results

The results of the current study provide evidence that there was not a significant difference between the responses of those who volunteered for sport events as opposed to other

121 types of events. There is evidence that volunteers did exhibit differences based on their generational cohort. The measurement model for the proposed construct was evaluated and modified to eliminate items that did not have significant predictive power leaving only the strongest items to measure each factor. This lead to the next step of evaluating the model itself via path analysis. After examining the results of the path analysis, statistical evidence was found in support of eight of the nine the proposed hypotheses. In the next chapter the significant findings of the study as well as practical implications, limitations, and future research are discussed.

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CHAPTER 5

DISCUSSION AND CONCLUSION

Research on employees and workers in regard to stress, job demands, job resources, engagement, and retention is prevalent in management and related literature. Few studies address these areas as they relate to volunteers. While the number of organizations, especially those related to sport, relying on volunteers increase there is currently a decline in the number of potential volunteers. A better understanding of these mechanisms has potential in many areas of volunteerism. For managers of volunteer organizations it may provide strategies to retain volunteers that are so vital to their operations while also providing for an optimal experience for their volunteers. For academicians it should extent the literature regarding volunteers.

Additionally by having the study focus on volunteers it should enhancing the literature on motivation, engagement, and stress-management because volunteers almost by definition are free to discontinue their activity without a great deal of adverse consequences. Most employees are not free to walk away from adverse conditions in the workplace. They are constrained to continue with employment where volunteers may not be. This provides for researchers to examine and gain greater understanding of these processes free from the normal constraints employees operate under.

As discussed in chapter three, the main purposes of the study were three fold: (1) to compare the differences between levels of engagement between sport volunteers and other types of volunteers; (2) investigate the relationships between job demands and job resources on engagement within the proposed conceptual model; and (3) to investigate whether volunteers from various generational cohorts had differing preferences relating to job resources while volunteering.

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Discussion of Findings

The research questions as outlined in chapter one and research hypotheses posited in chapter two will be discussed in tandem according to thematic relevance. To recap, the research questions posited in chapter one are:

 Do sport event volunteers exhibit different levels of engagement than volunteers for other

types of events?

 Do volunteers representing different generations exhibit differing preferences in regard to

job resources?

 Which job resources have the greatest impact on volunteer engagement?

 Do job demands have a positive or negative impact on volunteer engagement?

 What is the relationship between job demands and job resources in regard to engagement

of sport event volunteers?

Additionally, the research hypotheses posited in chapter two are presented in Table 5.1 below.

TABLE 5.1: Research Hypotheses Label Hypotheses H1 Job resources have a significant, positive impact on volunteer engagement. H2 Job demands have a significant, negative impact on volunteer engagement. H3 Engagement has a significant, positive impact on commitment. H4 Engagement has a significant, positive impact on satisfaction. H5 Engagement has a significant, positive impact on volunteer performance. H6 Commitment has a significant, positive impact on volunteer retention. H7 Satisfaction has a significant, positive impact on volunteer retention. H8 Volunteer performance has a significant, positive impact on volunteer retention. H9 Sport volunteers who experience engagement are more likely to continue volunteering.

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Following the presentation order of the research questions in chapter 4 the first topic will be a discussion of whether there are differences between the responses given by volunteers of sport events as opposed to volunteers of other types of event. This will be followed by discussion of differences between members of various generational cohorts represented in the study and discussion of the stated research hypothesis. Subsequent sections of this chapter will cover implications of the finding of this research, future areas of study, limitations of the study, and conclusions.

Differences in Sport versus Non-Sport Volunteers

There was only one difference in the responses given by sport event volunteers and other event type volunteers. The one exception was the responses for autonomy, one factor related to job resources. As reported in chapter 4, the sport group mean for the autonomy factor was lower than the mean rating for the non-sport group mean. Although there was a statistically significant difference, the difference does not appear to represent meaningful practical difference. There was less than half a point difference between the mean scores for autonomy. This difference could be attributed to well-established operations and familiarity sport volunteers have with sport events that are perceived to require less autonomy on the part of the volunteer. This supposition would need to be examined through qualitative methods to ascertain whether there is merit to the idea. Implications of a difference are discussed in the implication section. While there was little evidence of differentiation between the dichotomy of sport and non-sport in the sample of volunteers, there were group differences in responses based on generational cohort, which are addressed in the next section.

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Differences in Generational Cohort Results for Job Resources

Thinking about the results of the generational component of the study, there are two important points with volunteers to consider. First was the evidence of a lack of difference between the way members of the Baby Boomer cohort responded compared to the Gen X-ers in relation to need for autonomy. Differences between these two cohorts have been found by other researches. Smola and Sutton (2002) found responses for members of these cohorts differed on two of the three areas studied, desirability of work outcomes and moral importance of work.

Baby- Boomers reported a statistically significant higher preference for autonomy than members of the Gen Y-ers but not for Gen X-ers. The researcher posits this finding may be attributed to maturation of the Gen X-er cohort putting them more in line with the Baby Boomers in the lifecycle at least in this one area. This would be in line with the adage “with maturity comes responsibility.” This of course remains to be supported by research.

The second important bit of information gleaned from the results was that there was not as big a difference between the responses of Gen X-ers and the Gen Y-ers. In fact the responses for these two cohorts were identical for the measures of supervisor support, feedback, as well as the overall assessment of the job resources construct. The results are consistent with the findings of Cennamo and Gardner (2008) who reported some differences in work values but fewer than expected between the three cohorts. The results contradict Borges, Manuel, Elam, and Jones

(2006) who found that members of Generation X differed from Millenials (Gen Y-ers) on 10 of

16 personality factors. While the findings of similarity between the Gen X-ers and Gen Y-ers for these job resources may seem contradictory to the previous research and especially the relating to the evidence in the current study of Gen X-ers becoming more similar to Baby Boomer in regard to autonomy, the findings of similarity between Gen X-ers and Gen Y-ers relating to supervisor

126 support and feedback make sense as the younger cohorts may not have experience the older cohort possesses leading them to rely on these job resources. Taken in light of anecdotal evidence that Gen X-ers and Gen Y-ers are entering the workforce with little or no previous work experience or later in life than Baby Boomers this should be expected. Additionally a lack of previous work experience for the younger cohorts would also explain the majority of differences found among the cohorts for job resources in the current study. These suppositions remain to be verified by research. The two overall findings of the current study do not provide as much clarity as desired regarding how managers should deal with volunteers from the various generational cohorts, and that further research is warranted. The implications are discussed further in the implications section later in this chapter. The next section is a discussion of aspects of the proposed model.

Job Resources and Job Demands

The importance of the social aspect of volunteering was evident in the results. Social

Support had the highest factor loading of the variables examined. The obvious implication for volunteer organizations is to foster the social aspect of the experience with those who volunteer to strengthen the ties with the organization. There are myriad ways to foster the social aspect of event management that are limited only by the imagination of the administrators and time availability of the volunteers. This could include setting up a mentoring program with experienced volunteers who are paired with new volunteers. This mentoring can extend well beyond focusing on just the scope of duties for the job at hand and develop into a form of community “welcome wagon” that easies new members into the organization. An organization can also provide team building opportunities for volunteers outside of the work/event atmosphere where volunteers can improve skills needed for the jobs they perform while also meeting

127 coworkers in potentially less stressful situations. If the organization hosts various events the organizers should host preseason meetings to inform volunteers of upcoming events so they can become knowledgeable ambassadors who can disseminate information about the coming season.

This serves several purposes; it allows the volunteer to have contact with members of the organization as well as makes them an integral part of the organization with direct input to the success of the organization’s program(s). As importantly, it allows the volunteer to have an active role in the community both within the organization as well as to the community at large.

Feedback had the next highest factor loading, and the score was very close to Social

Support. The high factor loading for Feedback is likely an indication volunteers truly want to know how they are performing and that their efforts make a difference for the organization. The roll of the supervisor was also highlighted as Supervisor Support had a strong factor loading.

The importance of Supervisor Support combined with Feedback could indicate an area that volunteer organizations can actively develop to improve satisfaction experienced by volunteers, by recruiting and/or training supervisors to provide effective feedback to volunteers. The is supported in the literature as Bakker, Hakanen, Demerouti, and Xanthopoulou (2007), among others, definitely found encouraging feedback from a supervisor or one’s coworkers on a specific day increased work engagement for that day. Feedback from supervisors as well as coworkers will be discussed further in the implications section.

Autonomy had the lowest factor loading of the job resources. It was also the only factor where there was a statistically significant difference for the answers given by members of the various generational cohorts. Members of the Gen X-er cohort reported autonomy was significantly more important to them than to the Baby Boomer cohort. This indicates that a volunteer organization needs to be able to differentiate members of these cohorts as the Gen X-

128 ers may be looking for a more autonomous experience. While the results of the ANOVA provided evidence of a statistically significant difference for the various groups for Autonomy, the p-value for the results of a difference between the Baby Boomers and Gen Xers in Scheffé’s post hoc analysis were not statistically significant meaning that though there was a difference in the sample, this may not be the case in the general population. This finding could be due to the small sample size. Additional research in this area is indicated. Job demands, discussed next, proved even more problematic.

Unfortunately because of the small sample size the researcher was not able to drill down to ascertain the exact relationship between the individual factors of job demand or which job demands had the greatest impact in the model. Additionally, the function of role ambiguity as a job demand is unclear. Role ambiguity, while negatively related, seemed to fit better as a job resource. One of the model modifications suggested in the MPlus measurement model analysis was that the role ambiguity factor should actually load onto the latent factor of job resources.

This could be attributed to role ambiguity being seen as the antithesis of role clarity which is associated in the literature as a job resource. This duality was also raised by one of the experts in the pilot phase of instrument development. This confusion could be an explanation for why the factor loading for job demands was significant yet relatively low. The items for role ambiguity, role conflict, role overload, and perceptions of politics all had factor loadings which indicated a relationship with engagement but without the ability to utilize structural regression analysis the exact nature of the relationship cannot be revealed. To get some idea of the relative importance of the factors associated with job demands we can examine the average standardized factor loadings. Based on this approach role overload has the largest average standardized factor loading (0.759) followed by role ambiguity (0.723), perception of politics (0.705), and finally

129 role conflict (0.652). More research in this area is warranted. Limited implication will be discussed later in this chapter.

The Relationship of Post Engagement Latent Constructs

Satisfaction. Satisfaction as an outcome of work engagement has been studied with a wide variety of employees including: bank tellers, nurses, managers in the hospitality sector, and hotel managers (Koyuncu, Burke, & Fiksenbaum, 2006; Burke, Koyuncu, & Fiksenbaum, 2010;

Burke, Koyuncu, & Fiksenbaum, 2008; and Fiksenbaum, Jeng, Koyuncu, & Burke, 2010 respectively). As volunteers were the focus, the researcher sought to build upon previous work through assessment of engagement and satisfaction. Hypothesis 7 (Satisfaction will have a significant, positive impact on volunteer retention) was the one hypothesis that was not supported. Satisfaction did not have a significant positive impact on volunteer retention. A possible explanation for the lack of support for this particular hypothesis is that the composite factor of satisfaction is comprised of six sub-factors of satisfaction: psychological, intellectual, social, relaxation, physiological, and aesthetic (Beard & Ragheb, Leisure satidfaction measure,

2012). These sub-factors are measured by three or four items each for a total of 23 items that were summed to create an overall measure of satisfaction. With this many items and sub-factors there may be conflicting and/or confounding relationships. For instance, one individual may report great satisfaction with the psychological, social, and physiological components of the questionnaire but report low levels of satisfaction with the aesthetic, educational, and relaxation aspects while another individual reports the exact opposite so it is impossible to tell what aspects of satisfaction are being report as being important to the volunteer. An additional problem is that some of the sub-factors that have been summed cannot be controlled by organizations or managers. Aesthetic satisfaction is a prime example of this. The facility where volunteering

130 takes place may not be aesthetically pleasing to the volunteers so they experience low levels of satisfaction for that sub-factor but the facility may not belong to the organization and/or the organization cannot make alterations to the facility. The negative effect is still recorded in the volunteer’s response but this cannot be isolated from the other satisfaction sub-factors that organizations or managers may be able to adjust to raise the levels of overall satisfaction for the volunteers. In this case one sub-factor will negatively impact the overall measure.

Unfortunately, because of the small sample size, statistical methods (such as structural regression) which are equipped to deal with this type of issue were not an option. The question of the role of satisfaction in volunteer retention is still open to further investigation. Implications for the findings discussed to this point are presented in the Implications section.

The results for the analyses of the other hypotheses relating to the factors of commitment, volunteer performance, and volunteer engagement (H6, H8, and H9 respectively) all provide evidence to support the assertion that they have a positive impact on volunteer retention in the model that is statistically significant at the 0.05 alpha level. Implications of these findings will be discussed in the following section.

Implications

Sport versus non-sport. When addressing the question of whether sport event volunteers exhibit different levels of engagement than volunteers for other types of events, the conclusion drawn from the results of the present study is that there was not a difference between the various types of volunteers. Because there was no appreciable difference it may be inferred volunteers in the study did not need to be catered to solely on their preferred event type. Should this be found in future research to be generalized to the general population, opportunities for more cross-over of individuals to events they may not normally volunteer with could be realized.

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The same cannot be the case for volunteers of different generational cohorts. In essence, among the volunteers sampled, event type was not a particularly important issue. The respondents were inclined to volunteer regardless of the event type.

Generational cohort differences. As discussed in chapter 2, many researchers (e.g.,

Smola & Sutton, 2002; Arsenault, 2004; Glass, 2007; Gursoy, Maier, & Chi, 2008; Macky,

Gardner, & Forsyth, 2008; Twenge & Campbell, 2008; Cennamo & Gardner, 2008; Sullivan,

Forret, Carraher, & Mainiero, 2009) have expressed the belief that today’s workforce is the most diverse in history and that attention to generational differences of workers is necessary for proper management and leadership. The current study extends the previous research by investigating whether generational differences were present for volunteers. The question of whether volunteers representing different generational cohorts exhibit differing preferences in regard to job resources could have a great impact on how organizations who use volunteers in their operation need to operate in order to retain the volunteers. Knowing the preferences of various groups of volunteers enables the volunteer organizations to tailor the experience to meet the needs and desires of the volunteers. For example, members of the Baby Boomer cohort in the current study reported a preference for a higher level of autonomy than individuals in the Gen Y- er cohort. With this in mind a volunteer manager should recruit baby-boomers for jobs that require more autonomy as the work is likely to be more engaging for members of that cohort.

Conversely, group oriented activities would be more appealing and engaging for members of the

Gen Y-er cohort. Of course these are generalizations, but for a large organization this may be a means of delegating responsibilities to large numbers of volunteers until individual preferences are known by the volunteer managers. This may provide volunteer managers the opportunity to keep the volunteers long enough to learn the individual preferences of the volunteers. Retaining

132 volunteers by tapping into their generational cohort tendencies may enable volunteer organizers time to deploy job resources while managing job demands which may lead to volunteer engagement.

Job resources, job demands, and volunteer engagement. The findings reported by previous researchers (Salanova, Agut, & Peiro, 2005; Mauno, Kinnunen, Ruokolainen, 2007;

Llorens, Schaufeli, Bakker, & Salanova, 2007; Bakker, Hakanen, Demerouti, & Xanthopoulou,

2007; Halbesleben, Harvey, & Bolino, 2009) have consistently included evidence supporting the idea that job resources (i.e., social support, supervisors support, performance feedback, and autonomy) were positively related to work engagement with the employees studied. Through the current study, evidence has been reported which allows us to extend this research to include volunteers as well as employees. Specifically, based on the evidence reported, the job resource which had the greatest impact on volunteer engagement was social support. The implications for practitioners would be to take steps to engender or ensure volunteers receive an experience that strengthens feelings of social support. There are many potential avenues to accomplish this, including having volunteers work with people they are familiar with, or taking the steps to introduce volunteers to each other to begin to build feelings of community. Another strategy may be to rotate volunteers into different positions with different groups so they can increase the number of individuals with whom they have worked. This strategy could help broaden the volunteer’s circle of friends making them more comfortable volunteering, as they will know at least a few people each time they choose to volunteer. While some of the strategies to increase feelings of social support may prove cumbersome for supervisors as they must actively reposition the volunteers throughout an event, it will ultimately be beneficial as the volunteers

133 gain a greater sense of community and experience the social support that has been reported to be so important.

The next two job resources that were indicated as being important to volunteers were feedback and supervisor support. These are addressed together as they are thematically similar.

The importance of supervisory coaching and performance feedback has been found in two separate the research studies by Xanthopoulou, Bakker, Demerouti, and Schaufeli (2009a &

2009b). The first was a study of 163 employees from a corporation that examined job resources including supervisory coaching and performance feedback and found they did explain work engagement. Similarly in the second study of fast food workers where the job resources included coaching and team climate, Xanthopoulou et al. (2009b) found that an individual’s level of work engagement on a given day were effected by the previous day’s coaching. A study by

Xanthopoulou, Bakker, Heuven, Demerouti, & Schaufeli (2008) found colleague support among flight attendants predicted day-levels of job performance thorugh self-efficacy and work engagement. In the same manner, it would appear from the results of the current study that volunteers really want to know how they are doing when it comes to their contribution to a volunteer effort. For the supervisor this means that a concerted effort must be made to communicate with volunteers at all points of the volunteer experience. The manager must ensure that the volunteer has the information and tools necessary to do the job they are tasked with, check up with them throughout the event to make sure all is well or make adjustments as necessary, and probably most importantly, the supervisor must give feedback on the volunteer’s performance as well as what their contribution means to the supervisor and the organization in general. Again this is an added burden on the supervisor but without the volunteers they would not be there.

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Feedback does not have to be the sole domain of the supervisor or volunteer organization.

Feedback can be fostered among the volunteers themselves. As was found by Bakker A. B.,

Hakanen, Demerouti, and Xanthopoulou (2007) encouraging feedback and supportive comments from one’s coworkers or supervisor increased work engagement. With this in mind, the supervisor or organization can institute mechanisms where volunteers can inform the supervisors of superlative conduct of fellow volunteers as well as means to congratulate and praise each other for their actions. These strategies could have a synergistic effect to foster and improve engagement in the volunteer staff. One conclusion drawn from the results of the study is that job resources are a powerful positive influence for engagement of volunteers. They were in fact stronger that the negative impacts of job demands.

What was the relationship between job demands and job resources in regard to engagement of sport event volunteers? The original research related to job demands and job resources assumed a dual process where job demands related to symptoms of job burnout while job resources lead to engagement. The relationship evolved through a series of models where the two constructs were seen as moderators of one process or the other. In a meta-analysis of studies on work engagement Halbesleben (2010) found an inverse relationship between job demand, job resources and work engagement. Halbesleben also found “the relationship between demands and engagement was lower than the relationship between resources and engagement appears to be consistently reported. In nearly all cases, the estimated population correlations with resources are stronger than those with demands” (p. 107). The results of the current study corroborate

Halbesleben’s conclusion of an inverse relationship between job demands and job resources with work engagement. The current study also corroborates a stronger positive relationship between job resources and volunteer engagement than the negative relationship between job demands and

135 volunteer engagement. This would seem to indicate that organizations using volunteers should focus on providing the job resources needed in order to increase the engagement of their volunteers.

Job demands overall had a negative impact on volunteer engagement. Intuitively one could infer that volunteer organizations should make efforts to minimize any potential job demands their volunteers could encounter in order to foster engagement in their volunteers. As already discussed, the variable with the highest average standardized factor loadings among the dimensions of job demands was role overload. This could indicate role overload was a prime concern of volunteers and should be one of the things volunteer managers and organizations should seek to minimize in order to increase the potential for volunteer engagement. Next in order of concern based on averaged standardized factor loadings was role ambiguity. By clarifying the duties of volunteers, this issue should be addressed.

The factor related to perception of politics would seem to be the next area of concern for volunteers. By examining the items that were used to compose this factor namely (“There was a group of people at the event where I volunteered who always got things their way because no one wanted to challenge them,” “I have seen changes made in policies that only serve the purposes of a few individuals, not the volunteers in general,” “People who volunteer for this event tend to build themselves up by tearing others down,” “Favoritism rather than merit determines who gets ahead,” “Volunteers usually don’t speak up for fear of retaliation by others,” and “There has always been an influential group of volunteers in this event that no one ever crosses”) a strategy can be formulated to address this issue. Most items related to the feeling among volunteers that there was favoritism or related power structures involved in decision-making processes within the volunteer organization. Gilmore, Ferris, Dulebohn, and

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Harrell-Cook (1996) found that employees with a lower tenure of working for a supervisor had lower levels of attendance in situations associated with increased perceptions of politics. The finding by Gilmore etal., is particularly cogent in relation to the current study as volunteers could be classified in the lower tenure classification of workers. The factor loading for perception of politics was negative for engagment. Should this lead to volunteer non-retention, it would seem to support previous findings of employees absentism by Frost (1987) and Gilmore etal (1996).

From a practitioner’s standpoint efforts should be made to limit or control perceptions of politics in the organization’s operations if they exist. If this is not in fact how an organization operates it is in the best interests of the volunteer organization to educate the volunteers of the true basis of these perceptions. A certain amount of transparency is necessary to build trust and dispel these perceptions.

Role conflict while still a concern had the lowest average standardized factor loading of the job demand factors. By examining the individual factor loadings for the role conflict items we can make recommendations for improving engagement. The items with the highest factor loadings were “I received incompatible requests from two or more people” and “I had to work under vague directions or orders” indicate conflict is based on the way the volunteer is directed to do his/her job, which is a management and/or supervision issue. This is in contrast with the items which had the lowest factor loadings, “I had to do things that I think should be done differently” and “I worked under incompatible policies and guidelines,” which indicate there is a perceived incompatibility with the actual job to be done, which would be an operational issue.

The first issue is dealt with by improving the way the volunteer is managed while the second is related to the way the job is structured. Again this is an area that has just been touched on. More research is warranted.

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The researcher of the current study focused on looking at the direct effects of job demands and job resources on engagement of sport volunteers as an initial phase of a larger research agenda on the topic. For several reasons including expedience and concerns about sample size, the researcher did not look at potential interactions among the primary variables.

One obvious course would be to extend the research of Bakker and Demerouti (2008) in their study of the JD-R model of work engagement which investigated whether job demands moderated the relationship between job/ personal resources and engagement. This is an area of interest but is simply outside the scope of the current project.

Volunteer engagement, satisfaction, commitment, volunteer performance, and volunteer retention. The results of the analyses for commitment, volunteer performance, and volunteer engagement (H6, H8, and H9 respectively) provided evidence to support the assertion that each had a positive, statistically significant impact on volunteer retention in the proposed model. Because of the small sample size and the inability to use structural regression for analysis, there is little more that can be said in detail regarding these factors other than to say that volunteer organizations and managers should try to maximize the chances for volunteers in their organizations to feel engagement related to their time volunteering for the organization. The researcher has not found any studies relating volunteer performance to retention, however,

Bakker (2011) has posited there is a positive relationship between engagement and performance.

Further steps should be taken to bolster the volunteer’s sense of personal performance in their volunteering duties. Possible avenues that can be used to accomplish this could be proper instruction and training as well as constructive criticism and feedback all of which fall within the purview of providing job resources. And finally, volunteer organizations should strive to increase the affective commitment felt by their volunteers in order to improve volunteer

138 retention. All of these recommendations are by nature vague as more research is needed to illuminate the underlying mechanisms that drive these behaviors.

The majority of previous research on volunteer retention has focused on motivation and satisfaction. Silverberg, Marshall, and Ellis (2001) state, “because job satisfaction is a key factor in the retention of volunteers as well as in the ultimate success and stability of recreation programs, park and recreation managers should consider the usefullness of evaluating the satisfaction of their volunteers” (p. 79). Unfortunately this assumption is not supported by any data in their study. More research is needed in the area of satisfaction as it relates to volunteer satisfaction. Similarly, Dorsch, Riemer, Sluth, Paskevich, and Chelladurai (2002) indicate commitment is a key factor of volunteer retention, however they measure volunteer commitment, satisfaction with organizational performance, and organizational identity but do not have a measure for retention. As such this does not constitute a direct link between the construct in the literature. Though the factor for satisfaction had a positive standardized factor loading in the model in the current study, it was not found to be statistically significant in the study. This finding could be related to the factor being composed of six sub-factors which may well have confounded the results. Volunteer organizations and managers should not discount the role satisfaction has in volunteer retention. On the contrary, this should be an indication that further research is needed in this area to find which of the sub-factors do have a positive impact as these should be the focus of improvement for practitioners to improve volunteer retention. This leads to the next topic of what should be future areas of research.

Future Areas of Research

There are several major areas in particular that warrant further study as a result of the findings from the present study. One of the first areas the researcher suggests is to focus on

139 further instrument development. The current study highlighted some specific issues relating to using the items that were originally developed for employees in a setting of volunteers. A need for further definition of event type in the questionnaire was highlighted as well. In addition to the need for further instrument development, the study should be replicated to obtain a sufficient sample size to allow for more sophisticated analysis. Further, longitudinal studies could illuminate a potential life-cycle for volunteers that cannot be addressed by data collection at one point. The use of qualitative methods to gain greater understanding of the subject as well as clarify potential confusion due to shortcomings of the questionnaire format should also be addressed in future studies. And finally more research to develop the proposed model for use by both researchers and practitioners is warranted. These will be discussed further below.

Instrument development. As many of the items were drawn from instruments created for employees their conversion to a volunteer context may not have been as effective as had been hoped. For instance, the item “Since I have been a volunteer with this event, I have never seen the operational policies applied politically.” may not be applicable to a volunteer who has never volunteered before or for an event that has not happened before. It would be more applicable for a volunteer organization that for an event. One of the continuing points of concern was addressing the difference between targeting a particular event as opposed to volunteer organization. There may be room for improvement in this area.

Another potential improvement would relate to having fewer items. Depending on the answers an individual gives in the demographic sections there is a potential for over 100 items to respond to in the questionnaire. The length of the questionnaire may contribute to sampling fatigue experienced by participants. The extensiveness of the instrument may also be a reason for the relatively low completion rate in the study.

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Event definition. One area that definitely needs further investigation deals with events, event definition, and differentiation between event and charity/ focus of event. Specifically it seems there is a need to further explore the definition of sport in the volunteering context. A large number of respondents listed the event they participated in as “other” possibly due to confusion on where the event would actually fit in categorization. For instance, is a 5K or marathon considered a sport when associated with a cause such as the March of Dimes?

Additionally, some participants when specifying their event in the event description section of the questionnaire provided answers that clearly should have been in the sport category. For instance some respondents reported volunteering for “motorsport” events yet instead of reporting a sport event they selected “other” as the type of event.

Replication. After the instrument has been further developed and refined the original study should be replicated. One reason to replicate the study is to obtain a sufficient sample to allow for the use of structural regression as the means of analysis, which was the original intent with this study. This would provide additional information that was not captured about the relationships of the variables in the model. Another reason for replication is it would also allow for the sampling of volunteers representing various populations, locations, and events.

According to Johnson and Christensen (2008) replication of research should improve the confidence of findings as corroborating findings strengthen the evidence.

Longitudinal studies. The use of longitudinal studies would be especially beneficial for this area of study as one of the goals is to find ways to retain volunteers. Ideally the researcher should strive to obtain participants that are new to volunteering to obtain a baseline of the expectations of the volunteer prior to the volunteer experience. This could be followed by a

141 reassessment shortly after the event as well as after subsequent time intervals, to see if the people continue volunteering and if there are changes in the reasons for continued volunteering.

Another possible benefit of longitudinal research would be the potential to incorporate more of an experimental design in the research. This of course would have to be carefully crafted and need the full support of the volunteer organization. An experimental design could strengthen the research as experimental research is considered the “gold standard” of research.

Use of qualitative research strategies. Another area the researcher suggests as a fruitful pursuit to gain greater understanding of the topic would be to employ qualitative methods of research. Allowing volunteers to respond freely in a discussion of their volunteer experience should aid by gaining a greater depth to answers as well as allow the volunteer to clarify the meaning or intent of the questions posed. Use of qualitative research in conjunction with the other methods already discussed may lead to refinements and/or modifications to the proposed model.

Model development. The researcher feels it is too early to abandon the proposed model, yet there may be room for modification based on information that may be found in the additional areas of research discussed above. Until a sufficient sample is obtained that would allow for a full structural regression analysis it is difficult to offer potential model modification based on statistical evidence. The researcher believes there are theoretically based modifications to strengthen the model which may be fruitful depending on whether the research is primarily to be used for basic or applied research. As discussed earlier, the Aesthetic Satisfaction factor in many instances may not be appropriate for inclusion if the research is narrowly focused as applied research for practitioners especially if the volunteer organization cannot make changes in this area. This information would be valuable from a basic research perspective as it generates

142 fundamental knowledge and theoretical understanding of the volunteer so it seems too early to in the research process to eliminate it. This example brings to the forefront the use of the instrument. It may be very fruitful to develop a model for practitioner use as well as a more elaborate model for theory generation.

Another area of model development would be to investigate potential interactions discussed earlier such as the relationship of job resources serving to moderate the relationship between demands and engagement of volunteers. This is a modified approach from the one taken by Bakker and Demerouti (2008) as it looks at the potential impact of providing job resources to moderate job demands of volunteers. From a practitioner’s perspective it may be easier to provide resources than control demands.

Limitations

There are several limitations that need to be addresses in regard to this research. The limitations that are of greatest note include: instrumentation, sample size, the online format, broadcast format of volunteer recruitment, and the self-evaluation of volunteers without an external means of verification or corroboration. These will be explored further below.

Instrument. While the various instruments used to measure the latent constructs have been found to be reliable and valid for use it the employee context, this may not be the case for use with volunteers or specifically with volunteers on a one-time event basis. This is a major limitation and should be addressed by instrument development specifically for the context of volunteers.

Sample size. Although the sample exceeded the minimum required it was only by a small margin. This limited the number and types of analyses that could be performed. A larger sample would facilitate a more rigorous and extensive analysis of the topics in this initial study.

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The sample size also limited the examination of potential group differences. Of the factors studied, there was an indication that volunteers in sport vs. non-sport events may have had statistically different answers when examining the construct of autonomy. This may have been an aberration attributable to the small sample being analyzed or it may represent a true difference in these to populations. Further research is definitely warranted.

Online format. The online format of the survey proved not to be as profitable a format as anticipated. While this may be related to the broadcast format of recruitment, which will be discussed next, it may also be related to the lack of a human connection that can be afforded when a researcher or common organization make a personal appeal for participation. Both representatives of the volunteer organizations participating in the study affirmed the limiting nature of online surveys as being too easy to overlook or discontinue once begun. This assertion would seem to be corroborated by the large percentage of incomplete questionnaires.

Broadcast format of volunteer recruitment. A larger concern was the broadcast format of volunteer recruitment. The researcher was not able to directly address the potential participants. Participants received the invitation to participate via inclusion as one small part in the email newsletter of a volunteer organization. While there was an active link incorporated into the invitation, the researcher was limited in several ways in regard to placement of invitation and frequency of contact with the volunteers. Further, as the research was not tied to a specific event it was not possible to follow-up on potential participants for recruiting.

Self-evaluation of employees without external means of verification. An additional limitation of the study was the lack of an external measure for volunteer performance. The current study relied solely on the self-evaluation of the volunteer’s performance at the event.

The study would have been stronger if there was an outside indicator to serve as a form of

144 verification of the volunteer’s performance. This would require a much greater commitment on the part of the organization utilizing the volunteer. As such it may not be “cost-effective” for a one-time event or even an event that happens rarely. For events that occur regularly or for organizations that utilize volunteers for multiple events, a mechanism may be developed which would be beneficial to both to organization, volunteer, and researcher. The organization could benefit by being more familiar with the volunteer, thus deploying them to duties that best fit their nature and skills. The volunteer would benefit through feedback on their strengths and weaknesses. If this were combined with various forms of remediation, the volunteer could experience a positive spiraling of skills and attachment to volunteering as they feel more confident in their participation as a volunteer. The mechanism would also be beneficial to the researcher as a way to more closely track performance both form the volunteer’s perspective via self-report as well as from the organizations perspective. This may also lead to additional avenues of research based on any perceived disconnected between the two forms of evaluation.

Conclusions

Volunteers are a finite resource many organizations rely on to accomplish their missions.

This is especially true of sport organizations. The number of organizations (sport and non-sport) dependent on volunteer workers is increasing, yet there has not been a corresponding increase in number of individuals willing to volunteer (Bussell & Forbes, 2002; Cuskelly, 2004; Locke,

Ellis, & Smith, 2003). In fact there has been a decrease in volunteerism (Locke, Ellis, & Smith,

2003). Two strategies exist to stem this trend, recruiting new volunteers, and retaining existing volunteers. Of the two, retention of volunteers should be a priority for reasons of efficiency as organizations would save time, effort, and money retaining experienced volunteers. In order to retain existing volunteers it was necessary to explore the mechanisms leading to retention as well

145 as explore the makeup of the volunteer force to ascertain if there were differences between certain types of volunteer. As sport is so reliant on volunteers at all levels it was with special interest the researcher sought to address any specific differences related to their retention. With this in mind, this study set out with three main goals.

The first goal was to present and test a conceptual model illustrating the relationship between job demands and job resources as they relate to work engagement of sport volunteers leading to sport volunteer performance, commitment, satisfaction, and retention. Job demands and job resources were found to have an inverse relationship with volunteer engagement with job resources having a positive impact on the volunteers in the study. Volunteer engagement, was found to have a statistically significant positive impact on volunteer performance, affective commitment, and volunteer satisfaction. Volunteer engagement, volunteer performance, and affective commitment were all found to have a positive impact on volunteer retention for the sample. The role of volunteer satisfaction as it related to volunteer retention of those in the study was unclear. With the exception of the role of satisfaction, the current study provided evidence to support the proposed model.

The second goal was to investigate differences in volunteer engagement based on whether individuals were volunteering for sporting versus non-sporting events. In short, there was not a difference found between the responses individuals who volunteered for sport versus non-sport events.

The third goal was exploring if there were differences based on a volunteer’s generational cohort. There were statistically significant differences between the answers provided by members of the various generational cohorts. Specifically the members of the Baby Boomer cohort scored a higher for questions related to Autonomy indicating a preference for or need for

146 greater autonomy in their volunteering experience than members of the Gen X-er cohort. The results also provided evidence that members of the Gen X-er and Gen Y-er cohorts had very similar responses in the study, in several instances their answers were identical.

Although the study sample was too small to use the statistical analysis first planned, the current study provided evidence to support both the proposed model and the majority of the hypotheses. In order to better understand how to successfully engage volunteers and ultimately retain volunteers researchers need a tool that investigates the complexities inherent when dealing with volunteers. The current model provides a framework for future study relating to volunteer engagement and volunteer retention. The overall goal of this research project was to advance the understanding of volunteer engagement and retention in the hope of providing practitioner’s strategies to make the volunteer experience rewarding for the volunteers which would in turn allow the organization to retain their volunteers. This research should be seen as an initial foray to attain these goals.

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APPENDIX A

IRB APPROVAL LETTER

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APPENDIX B

HANDSON JACKSONVILLE LETTER OF AGREEMENT

149

APPENDIX C

HANDSON JACKSONVILLE NEWSLETTER 1

March 7, 2013 Vol 6, Issue 5

* BREAKING NEWS*

Visit Our Blog

______It's Time We Donate on

Save the date to Celebrate!GOOD In This Issue

April 18, 2013 Breaking News 6:00 p.m. Volunteers Engagement Study (doors open at 5:00) Times-Union Center for the Performing Arts Purpose Prize Grants Get more information & purchase tickets Youth Council Projects

Join us for an exciting evening as we honor the people who The Human Race are doing GOOD in our community through an awards Win $5,000! ceremony featuring inspiring videos that tell their stories. Upcoming Events The production is immediately followed by a lively reception to Quick Links meet the award recipients and sample cuisine from some of our area's finest restaurants - all while enjoying live music

150 and our spectacular silent auction! Opportunities to Volunteer Visit Our Website Introducing the 2013 Award Recipients A Visit From St. Nicholas Blueprint for Leadership Pinnacle Awards Celebrate!GOOD The Human Race Bernard V. Gregory Award Eleanor Ashby Become a Nonprofit Partner Teens & Youth Tillie Kidd Fowler Spirit of Service Award Visit our Blog Gator Bowl Association Shop at HOJ Boutique

Edward R. Hayes Unity in Action Award Bank of America

HandsOn Awards

HandsOn Business Synovus Bank-Jacksonville

HandsOn Community Jack Diamond

HandsOn Earth

Tim Armstrong

HandsOn Faith Seniors On A Mission

Hands On Health Pink Sisters & Friends Advocacy at Mayo

HandsOn Literacy City Year

HandsOn Mentoring Mayor's Mentoring Program

HandsOn Schools Florida Coastal School of Law

HandsOn Service in Uniform Patrol Squadron EIGHT

HandsOn Willing & Able JT Townsend

HandsOn Young at Heart John Sornberger

HandsOn Youth in Action Aaron Badida

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* VOLUNTEER ENGAGEMENT STUDY* Volunteers are the life blood of HandsOn Jacksonville. Many organizations could not operate without volunteers. A researcher at Florida State University is conducting a study to assess the factors that make volunteering an engaging activity. This information has the potential to enable us to improve what we do to make the volunteering experience even more positive.

Volunteers at all levels, please give us the benefit of your experience and knowledge by completing the 15-20 minute survey (click here to access). The findings will be provided to each participating organization so your organization will benefit directly from your participation. * OVER 60? CHECK OUT THE PURPOSE PRIZE GRANTS* Do you know of someone in your community - over 60 - doing some great work, who may have turned their passion into an extraordinary "encore career", someone who is making a difference, addressing a need?

The Purpose Prize http://www.encore.org/prize has been called "genius grants" for people over age 60, awarded every year to social entrepreneurs who devote their encore careers to making the world a better place. The application deadline for the 2013 prizes is coming up fast....April 4th. Five winners will each receive $100,000 to continue their enterprises. In the six years since Civic Ventures created the awards, the San Francisco nonprofit has given away more than $3 million to more than 30 winners.

* HELP OUR TEENS HELP OUR COMMUNITY*

Please support the AMAZING work of our Youth Leadership Council as they prepare for their Global Youth Service Day projects. They are addressing critical issues in our community from human trafficking to homelessness. Each project is listed on our site with a donation link.

Invest in teenagers who are positively shaping our community for the future!

Learn more.

*JOIN THE HUMAN RACE!*

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YOUR RACE. YOUR CAUSE.

5k Walk & Run 1 Mile Fun Run Saturday, April 6th 8:00 a.m. St. Johns Town Center

 50% of your registration will benefit a participating nonprofit of your choice!  Pre-race stretches by MBody Yoga  Refreshments provided by Papa John's, Mimi's Cafe, Arby's and Seasons 52  The Roar will sign autographs  Medallions given to all 5K finishers  Raffle drawings at Award Ceremony

LEARN MORE AND REGISTER

* YOU COULD WIN 5,000!*

Can you guess how many birdies will be made during THE PLAYERS 2013? If you guess correctly, you could win $5,000! AND if you win, HandsOn Jacksonville also wins $5,000! Click here to participate. Be sure to select HandsOn Jacksonville from the list of charities!

*UPCOMING EVENTS* Volunteer Opportunities

HandsOn Jacksonville and the Second Harvest Food Bank are working together to establish a garden space for ESE/SLA students at Atlantic Coast High School. We will include several activities such as seed starting, weeding and planting and garden art. Please join us in establishing joy for the ESE students! Even if you don't have a green thumb, we know you have a green heart! March 8th, March 15th, April 17th and May 29th. Register here.

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Get connected! Visit our opportunity calendar for all of the latest volunteer opportunities.

______Get Ur Hands on HOJ Tours - 2013 Schedule

These tours have been a great success! We have been delighted to renew old friendships and make new friends through tours of our office. Come join us for lunch and hear from our CEO, Dr. Judith Smith and our staff about all the ways HandsOn Jacksonville is working to do GOOD in our community! Each session begins at 12:00, includes lunch and lasts just one hour. Seating is limited to10, so email Mary Jury - [email protected] with the date you'd like to attend, your name, contact information and names of any guests you'd like to bring.

Next tour is 3/21.

Click here for the full 2013 tour schedule! ______BOARD LEADERSHIP TRAINING

Roles & Responsibilities of Nonprofit Boards

March 20 3:00 - 6:00 p.m.

In just 3 hours you'll

 increase understanding of the role of the nonprofit board of directors  identify qualities that make a board effective and barriers that limit board effectiveness  identify and explore the board's fiduciary, financial and programmatic responsibilities  identify and explore the corporate and individual duties of board members  learn the relationship between the board, the staff, and the executive

Cost is $90 for general registration - nonprofit agency partners receive a 50% discounted rate of $45. Special group rates for individualized group sessions may be negotiated. Learn More & Register. ______PROJECT LEADER TRAINING

Do you want to make positive community change through leadership? This free course will

154 prepare you to lead groups of volunteers in meaningful community service projects that we help develop with our nonprofit partner agencies. In three hours you'll learn

 about the nonprofit partners we work with  how to select and/or design projects to lead  how to recruit volunteers  how to manage projects using our technology

DATE: March 13 TIME: 4:00 - 5:30 p.m. PLACE: HandsOn Jacksonville COST: FREE!

We couldn't accomplish all of the amazing work we do without our Project Leaders. Once you've completed training, you'll commit an average of 3 - 6 hours per month on projects that last 2 - 3 hours plus an hour of administration. Are you ready to make a change? You can get started today! REGISTER HERE

sponsored in part by

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Forward email

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APPENDIX E

EMAIL APPEAL FOR PARTICIPATION

Howdy All,

I desperately need the help of my family and friends to graduate this year. The data collection for my dissertation has been much slower than expected. For me to graduate in August the deadline to submit my dissertation is July 1. Unfortunately I have less than half (72/200) the number of completed survey I need to be able to analyze the data.

There are two ways you can help. The first is to complete the survey. On average its takes approximately 15 minutes to complete. Anyone 18 years or older who has volunteered for any kind of event in the past year is welcome to participate. The reason for the one year limit is so that the event is fresh in your mind, however as long as the event is fresh in your mind you can use it for the survey. The directions used to access the survey are below.

The second way to is to pass the survey on to others that may be willing to complete it. I no longer have everyone's email address because of a computer crash so if you notice someone "in the family" that I have missed please send it to them as well. I plan to begin the initial examination of the data around May 24th but the survey will remain open until June 15th. I realize this is a great deal to ask but I really need your help!

*************************************************** Volunteers are the life blood of many organizations. Many organizations could not operate without volunteers. A researcher at Florida State University is conducting a study to assess the factors that make volunteering an engaging activity. This information has the potential to enable us to improve what we do to make the volunteering experience even more positive.

Volunteers at all levels, please give us the benefit of your experience and knowledge by completing the 15-20 minute survey (click here to access. If you have trouble accessing the survey with the link, please copy and paste the following in to your browser: https://fsu.qualtrics.com/SE/?SID=SV_1zD4cWHX1kfFdwV).

Once you have completed and submitted the survey, you will have the option of entering a drawing for a $50 gift card. The Gift card can be used by the winner of the drawing or is transferrable and can easily be donated to the volunteer organization of your choice as a reward to an outstanding volunteer in your organization. The information you provide for the drawing will not be connected with your survey responses.

*************************************************** If you have any questions or need clarification on anything, please do not hesitate to let me know! Thanks and take care,

Tom McMorrow

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APPENDIX F

FACEBOOK APPEAL FOR PARTICIPATION - 1

I desperately need the help of my Facebook family and friends to graduate this year. The data collection for my dissertation has been much slower than expected. For me to graduate in August the deadline to submit my dissertation is July 1. Unfortunately I have half the number of completed survey I need to be able to analyze the data. There are two ways you can help. The first is to complete the ...survey. On average its takes approximately 15 minutes to complete. Anyone 18 years or older who has volunteered for any kind of event in the past year is welcome to participate. The reason for the one year limit is so that the event is fresh in your mind, however as long as the event is fresh in your mind you can use it for the survey. The directions used to access the survey are below. The second way to is to pass the survey on to others that may be willing to complete it. I plan to begin the initial examination of the data around May 24th but the survey will remain open until June 15th. I realize this is a great deal to ask but I really need your help! *************************************************** Volunteers are the life blood of many organizations. Many organizations could not operate without volunteers. A researcher at Florida State University is conducting a study to assess the factors that make volunteering an engaging activity. This information has the potential to enable us to improve what we do to make the volunteering experience even more positive. Volunteers at all levels, please give us the benefit of your experience and knowledge by completing the 15-20 minute anonymous survey (click here to access. If you have trouble accessing the survey with the link, please copy and paste the following in to your browser: https://fsu.qualtrics.com/SE/?SID=SV_1zD4cWHX1kfFdwV). Once you have completed and submitted the survey, you will have the option of entering a drawing for a $50 gift card. The Gift card can be used by the winner of the drawing or is transferrable and can easily be donated to the volunteer organization of your choice as a reward to an outstanding volunteer in your organization. The information you provide for the drawing will not be connected with your survey responses. *************************************************** If you have any questions or need clarification on anything, please do not hesitate to let me know! Thanks and take care, Tom McMorrow

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APPENDIX G

FACEBOOK APPEAL FOR PARTICIPATION - 2

THANK YOU ALL!!! Although I still need the help of my Facebook family and friends, I am MUCH closer to being able to graduate this year. I only need a few more completed surveys to reach my goal. If you have not done so yet, please complete the survey. On average its takes approximately 10 - 15 minutes to complete. Anyone 18 years or older who has volunteered for any kind of event in the past year is welcome to participate. The reason for the one year limit is so that the event is fresh in your mind, however as long as the event is fresh in your mind you can use it for the survey. The directions used to access the survey are below. The second way to is to pass the survey on to others that may be willing to complete it. I have begun the initial examination of the data but the survey will remain open until June 15th. Again THANK YOU ALL FOR YOUR HELP!!! Word cannot express the gratitude for your help. *************************************************** Volunteers are the life blood of many organizations. Many organizations could not operate without volunteers. A researcher at Florida State University is conducting a study to assess the factors that make volunteering an engaging activity. This information has the potential to enable us to improve what we do to make the volunteering experience even more positive. Volunteers at all levels, please give us the benefit of your experience and knowledge by completing the 15-20 minute anonymous survey (click here to access. If you have trouble accessing the survey with the link, please copy and paste the following in to your browser: https://fsu.qualtrics.com/SE/?SID=SV_1zD4cWHX1kfFdwV). Once you have completed and submitted the survey, you will have the option of entering a drawing for a $50 gift card. The Gift card can be used by the winner of the drawing or is transferrable and can easily be donated to the volunteer organization of your choice as a reward to an outstanding volunteer in your organization. The information you provide for the drawing will not be connected with your survey responses. ***************************************************

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If you have any questions or need clarification on anything, please do not hesitate to let me know! Thanks and take care, Tom McMorrow

See more Survey | Qualtrics Survey Software fsu.qualtrics.com Survey Software, Enterprise Survey software for enterprise feedback management and CRM solutions. Enables high-quality data collection, panel management and results analysis. Perfect for market research or CRM solution (Customer Relationship Management) integration. Free trial and consultation.

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APPENDIX H

SURVEY CONSENT PAGE

165

APPENDIX I

VARIABLE ITEM CODING

TABLE I.1: Variable Item Coding Code Item RA1 I feel certain about how much authority I had. RA2 I had clear, planned goals and objectives for my volunteering duties. RA3 I know that I divided my time properly. RA4 I knew what my responsibilities were. RA5 I knew exactly what was expected of me. RA6 I received a clear explanation of what had to be done. RC1 I had to do things that I think should be done differently. RC2 I worked under incompatible policies and guidelines. RC3 I had to oppose a rule or policy in order to carry out an assignment. RC4 I received assignments without the manpower to complete them. RC5 I received incompatible requests from two or more people. RC6 I had to work under vague directions or orders. RC7 I received assignments without adequate resources and materials to execute them RC8 I worked on many unnecessary things RO1 I had too much work to complete, to do everything well. RO2 The amount of work I was asked to do was simply too much. RO3 I did not seem to have enough time to get everything done. There was a group of people at the event where I volunteered who always got things PP1 their way because no one wanted to challenge them. Since I have been a volunteer with this event, I have never seen the operational PP2 policies applied politically. PP3 Rewards came only to those who worked hard. I have seen changes made in policies that only serve the purposes of a few PP4 individuals, not the volunteers in general. I can’t remember when a volunteer received recognition or a promotion that was PP5 inconsistent with published policies. People who volunteer for this event tend to build themselves up by tearing others PP6 down. Volunteers are encouraged to speak out frankly even when they are critical of well- PP7 established ideas. PP8 Favoritism rather than merit determines who gets ahead. PP9 Volunteers usually don’t speak up for fear of retaliation by others. There is no place for yes-men around here; good ideas are desired even when it PP10 means disagreeing with superiors.

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TABLE I.1: - Continued Code Item PP11 Choice assignments in this event generally go to top performers. There has always been an influential group of volunteers in this event that no one ever PP12 crosses. A1 Did your volunteering offer you the possibility of independent thought and action? A2 Did you have freedom in carrying out your volunteering activities? A3 Did you have influence in the planning of your volunteering activities? A4 Did you have a direct influence on your volunteer organization’s decisions? SO1 Could you have counted on your event colleagues when you came across difficulties in your volunteering activities? SO2 If necessary, could you have asked your event colleagues for help? SO3 When volunteering, was there someone who could help with your responsibilities if needed?” SO4 Did you get on well with your event colleagues? SU1 Could you have counted on your event supervisor when you came across difficulties in your volunteering duties? SU2 Did you get on well with your event supervisor? SU3 In your volunteering at this event, did you feel appreciated by your supervisor? SU4 Could you have discussed problems related to your event volunteer duties with your direct supervisor? F1 Did you know exactly what you were responsible for at the event? F2 Did you know exactly what your direct supervisor thought of your performance? F3 Did you receive sufficient information on the purpose of your volunteer duties? F4 Were you kept adequately up-to-date about important issues within your event? F5 Did you receive sufficient information on the results of your volunteer activities? VI1 I felt bursting with energy while volunteering at this event. VI2 While volunteering, I felt strong and vigorous. VI3 When I got up the morning of the event, I felt like going to volunteer. DE1 I am enthusiastic about my volunteering. DE2 My volunteering inspires me. DE3 I am proud of the volunteering that I did. AB1 I felt happy when I was working intensely as a volunteer. AB2 I was immersed in my volunteer work. AB3 I got carried away when I was volunteering. SPY1 My volunteer activities are interesting to me. SPY2 My volunteer activities give me self-confidence. SPY3 My volunteer activities give me a sense of accomplishment. SPY4 I use many different skills and abilities in my volunteer activities.

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TABLE I.1: - Continued Code Item SE1 My volunteer activities increase my knowledge about things around me. SE2 Volunteering provides opportunities to try new things. SE3 My volunteer activities help me to learn about myself. SE4 Volunteering helps me to learn about other people. SS1 I have social interaction with others through volunteer activities. SS2 My volunteer activities have helped me to develop close relationships with others. SS3 The people I meet in my volunteer activities are friendly. SS4 I associate with people in my free time who enjoy doing volunteer activities. SR1 Volunteering helps me to relax. SR2 My volunteer activities help relieve stress. SR3 Volunteering contributes to my emotional well-being. SR4 I engage in volunteer activities simply because I like doing them. SPH1 My volunteer activities are physically challenging. SPH2 I do volunteer activities which develop my physical fitness. SPH3 I do volunteer activities which restore me physically. SPH4 Volunteering helps me to stay healthy. SA1 The areas or places where I engage in my volunteer activities are clean. SA2 The areas or places where I engage in my volunteer activities are interesting. SA3 The areas or places where I engage in my volunteer activities are beautiful. SA4 The areas or places where I engage in my volunteer activities are well designed. AC1 I would be very happy to spend the rest of my time as a volunteer with this event. AC2 I enjoyed discussing volunteering with people outside the event. AC3 I really felt as if the problems of the event I volunteered for were my own. AC4 The event where I volunteered had a great deal of personal meaning for me. AC5 I did not feel like ‘part of the family’ when I volunteered. AC6 I did not feel ‘emotionally attached’ to the event I volunteered for. AC7 I did not feel a strong sense of belonging at the event when I volunteered. VP1 Ability to work independently. VP2 Ability to work cooperatively. VP3 Ability to solve problems. VP4 Motivation to work hard. VP5 Overall performance. VR1 I intend to remain as a volunteer with this event. VR2 If I were to have my own way, I would be volunteering for this event three years from now. VR3 I have thought seriously about not continuing to volunteer for this event.

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APPENDIX J

RESULTS OF T-TESTS

TABLE J.1: 2 Groups 38 CASES T-Tests t-test Sig. (2-tailed) Levene's Test Equal Equal Equal for Equality of variance Variance not variance Equal Variance assumed assumed assumed Variance not Factors assumed RA 0.642 0.455 0.455 0.65 0.65 RC 0.053 1.269 1.269 0.208 0.208 RO 0.047 1.18 1.18 0.242 0.242 PP 0.164 1.837 1.837 0.07 0.07 JD 0.047 1.471 1.471 0.146 0.146

A 0.084 -2.195 -2.195 0.031* 0.032* SO 0.001 -1.972 -1.972 0.052 0.054 SU 0.537 -0.61 -0.61 0.544 0.544 F 0.867 -0.382 -0.382 0.704 0.704 JR 0.294 -1.415 -1.415 0.161 0.162

PSY 0.303 -0.884 -0.884 0.379 0.38 SE 0.869 -0.948 -0.948 0.346 0.346 SS 0.549 0 0 1 1 SR 0.362 0.113 0.113 0.91 0.91 SPH 0.866 0.515 0.515 0.608 0.608 SA 0.012 0.709 0.709 0.48 0.481 SAT 0.984 -0.044 -0.044 0.965 0.965

VI 0.998 0.098 0.098 0.922 0.922 DE 0.234 -1.484 -1.484 0.142 0.142 AB 0.939 0.138 0.138 0.891 0.891 ENG 0.608 -0.435 -0.435 0.665 0.665

AC 0.197 -1.01 -1.01 0.316 0.316 VP 0.033 -1.579 -1.579 0.119 0.119 VR 0.253 0.31 0.31 0.757 0.757

*. The mean difference is significant at the 0.05 level.

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TABLE J.2: 2 Groups Uneven Samples T-Tests t-test Sig. (2-tailed)

Levene's Test Equal Equal Equal Equal for Equality of variance Variance not variance Variance not Factors Variance assumed assumed assumed assumed RA 0.338 0.637 0.612 0.525 0.543 RC 0.021 1.361 1.123 0.175 0.267 RO 0.307 0.313 0.292 0.755 0.771 PP 0.006 2.472 1.998 0.014* 0.052 JD 0.009 1.674 1.375 0.096 0.176

A 0.336 -1.154 -1.043 0.25 0.302 SO 0.000 -2.157 -1.514 0.032* 0.137 SU 0.006 -1.836 -1.411 0.068 0.165 F 0.335 -0.121 -0.112 0.903 0.911 JR 0.017 -1.401 -1.107 0.163 0.274

PSY 0.402 -0.9 -0.783 0.369 0.437 SE 0.829 -0.489 -0.459 0.625 0.648 SS 0.852 -0.347 -0.328 0.729 0.744 SR 0.456 0.272 0.274 0.786 0.785 SPH 0.657 1.07 1.066 0.286 0.291 SA 0.107 1.286 1.185 0.2 0.242 SAT 0.877 0.317 0.298 0.751 0.767

VI 0.469 1.104 1.124 0.271 0.266 DE 0.248 -0.709 -0.596 0.479 0.554 AB 0.648 0.405 0.365 0.686 0.717 ENG 0.72 0.376 0.335 0.708 0.739

AC 0.064 -1.222 -1.077 0.223 0.287 VP 0.036 -1.872 -1.562 0.063 0.125 VR 0.073 -1.011 -0.914 0.313 0.365

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TABLE J.3: Mean Scores for Factors of Sport and Non-Sport Groups Factors Sport_vs_Non N Mean Std. Deviation Std. Error Mean Role Ambiguity Sport 38 2.1263 1.12218 .18204 Non-Sport 173 2.0046 1.05389 .08013 Role Conflict Sport 38 2.5038 1.48749 .24130 Non-Sport 173 2.2172 1.09629 .08335 Role Overload Sport 38 2.1228 1.37638 .22328 Non-Sport 173 2.0520 1.23598 .09397 Perception of Politics Sport 38 2.4430 1.48833 .24144 Non-Sport 173 1.9345 1.06106 .08067 Job Demands Sport 38 2.3421 1.18647 .19247 Non-Sport 173 2.0622 .86888 .06606 Autonomy Sport 38 5.1250 1.48614 .24108 Non-Sport 173 5.3960 1.26905 .09648 Social Support Sport 38 6.1316 1.17797 .19109 Non-Sport 173 6.4306 .65500 .04980 Supervisor Support Sport 38 6.0263 1.38133 .22408 Non-Sport 173 6.3569 .90387 .06872 Feedback Sport 38 5.9316 1.18301 .19191 Non-Sport 173 5.9549 1.04661 .07957 Job Resources Sport 38 5.8111 1.15983 .18815 Non-Sport 173 6.0299 .79630 .06054 Satisfaction Psychological Sport 38 5.9408 1.12771 .18294 Non-Sport 173 6.0939 .90633 .06891 Satisfaction Educational Sport 38 5.8750 1.09937 .17834 Non-Sport 173 5.9639 .99552 .07569 Satisfaction Social Sport 38 5.7105 1.27383 .20664 Non-Sport 173 5.7842 1.16403 .08850 Satisfaction Relaxation Sport 38 5.4803 1.21558 .19719 Non-Sport 173 5.4205 1.22704 .09329 Satisfaction Physiological Sport 38 4.3026 1.45792 .23651 Non-Sport 173 4.0246 1.44815 .11010 Satisfaction Aesthetic Sport 38 5.5987 1.19053 .19313 Non-Sport 173 5.3512 1.04750 .07964 Satisfaction Sport 38 5.4748 .95515 .15495 Non-Sport 173 5.4247 .86391 .06568 Vigor Sport 38 5.8947 1.15497 .18736 Non-Sport 173 5.6609 1.18751 .09028 Dedication Sport 38 6.1404 1.11132 .18028 Non-Sport 173 6.2543 .84499 .06424 Absorption Sport 38 6.0395 1.25408 .20344 Non-Sport 173 5.9595 1.06672 .08110 Engagement Sport 38 6.0230 1.11154 .18032 Non-Sport 173 5.9581 .93057 .07075 Commitment Sport 38 5.6930 1.64103 .26621 Non-Sport 173 6.0000 1.34587 .10232 Volunteer Performance Sport 38 6.3316 .80576 .13071 Non-Sport 173 6.5480 .60527 .04602 Volunteer Retention Sport 38 5.7281 1.55742 .25265 Non-Sport 173 5.9769 1.33168 .10125

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APPENDIX K

ITEM-TOTAL STATISTICS FOR ALL ITEMS IN FULL MODEL

TABLE K.1: Job Demands Item-Total Statistics - All Items Scale Mean if Scale Variance Corrected Item- Squared Multiple Cronbach's Alpha if Item Deleted if Item Deleted Total Correlation Correlation Item Deleted RA1R 69.57 509.844 .303 .436 .884 RA2R 69.79 509.051 .348 .449 .883 RA3R 69.79 515.386 .299 .319 .884 RA4R 70.07 509.258 .402 .534 .882 RA5R 70.00 502.393 .484 .647 .881 RA6R 69.88 501.979 .481 .685 .881 RC1 68.73 483.391 .542 .507 .879 RC2 69.95 498.764 .481 .453 .880 RC3 69.91 499.510 .444 .389 .881 RC4 69.69 491.121 .549 .529 .879 RC5 69.90 482.937 .693 .627 .876 RC6 69.40 476.793 .660 .553 .876 RC7 69.70 487.864 .544 .403 .879 RC8 69.96 489.945 .652 .572 .877 RO1 69.86 496.094 .520 .483 .880 RO2 69.91 490.666 .653 .626 .877 RO3 69.69 491.481 .559 .511 .879 PP1 69.80 486.459 .634 .615 .877 PP2 67.73 522.068 .053 .228 .893 PP3 68.86 502.653 .297 .289 .885 PP4 69.75 484.794 .650 .586 .877 PP5 68.23 506.541 .208 .218 .889 PP6 70.40 501.779 .560 .559 .880 PP7 67.05 529.321 .001 .286 .892 PP8 69.79 487.925 .579 .596 .878 PP9 70.07 498.035 .540 .515 .879 PP10 67.11 536.309 -.081 .327 .893 PP11 68.88 486.067 .507 .457 .879 PP12 69.35 484.969 .571 .498 .878

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TABLE K.2: Role Ambiguity Item-Total Statistics - All Items Scale Mean if Scale Variance Corrected Item- Squared Multiple Cronbach's Alpha if Item Deleted if Item Deleted Total Correlation Correlation Item Deleted RA1R 9.91 25.097 .549 .330 .833 RA2R 10.13 25.271 .607 .393 .819 RA3R 10.13 28.311 .475 .242 .842 RA4R 10.41 25.835 .675 .504 .807 RA5R 10.34 24.339 .736 .610 .793 RA6R 10.23 24.489 .703 .571 .799

TABLE K.3: Role Conflict Item-Total Statistics - All Items Scale Mean if Scale Variance Corrected Item- Squared Multiple Cronbach's Alpha if Item Deleted if Item Deleted Total Correlation Correlation Item Deleted RC1 14.69 61.490 .579 .389 .825 RC2 15.92 67.849 .514 .368 .832 RC3 15.88 67.926 .478 .297 .836 RC4 15.65 64.180 .615 .410 .819 RC5 15.86 62.722 .707 .533 .809 RC6 15.37 61.389 .624 .445 .818 RC7 15.67 65.033 .517 .293 .832 RC8 15.92 66.675 .602 .397 .822

TABLE K.4: Role Overload Item-Total Statistics - All Items Scale Mean if Scale Variance Corrected Item- Squared Multiple Cronbach's Alpha if Item Deleted if Item Deleted Total Correlation Correlation Item Deleted RO1 4.17 7.034 .632 .401 .751 RO2 4.22 7.305 .667 .446 .718 RO3 4.00 6.525 .657 .435 .727

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TABLE K.5: Role Perception of Politics -Total Statistics - All Items Scale Mean if Scale Variance Corrected Item- Squared Multiple Cronbach's Alpha if Item Deleted if Item Deleted Total Correlation Correlation Item Deleted PP1 33.53 91.762 .444 .477 .667 PP2 31.46 95.381 .157 .183 .715 PP3 32.59 92.383 .298 .182 .687 PP4 33.48 90.059 .497 .437 .659 PP5 31.95 91.887 .244 .160 .699 PP6 34.12 94.821 .494 .497 .668 PP7 30.77 99.533 .127 .208 .711 PP8 33.52 88.072 .543 .520 .652 PP9 33.80 93.582 .459 .416 .668 PP10 30.84 103.420 .025 .246 .724 PP11 32.61 86.863 .474 .319 .658 PP12 33.07 86.578 .540 .466 .650

TABLE K.6: Job Resources Item-Total Statistics - All Items Scale Mean if Scale Variance Corrected Item- Squared Multiple Cronbach's Alpha if Item Deleted if Item Deleted Total Correlation Correlation Item Deleted A1 96.14 194.370 .586 .451 .911 A2 95.78 198.726 .605 .456 .910 A3 96.86 188.789 .504 .497 .917 A4 97.19 189.992 .481 .464 .918 SO1 95.53 200.479 .648 .638 .910 SO2 95.36 204.040 .638 .582 .911 SO3 95.60 200.918 .630 .555 .910 SO4 95.37 206.615 .629 .590 .912 SU1 95.57 196.665 .703 .677 .908 SU2 95.41 200.834 .641 .587 .910 SU3 95.53 195.326 .691 .653 .908 SU4 95.65 191.419 .727 .641 .907 F1 95.61 198.610 .608 .497 .910 F2 96.34 186.386 .687 .555 .908 F3 96.07 192.386 .624 .569 .910 F4 95.91 194.749 .617 .588 .910 F5 95.52 199.851 .635 .498 .910

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TABLE K.7: Autonomy Item-Total Statistics - All Items Scale Mean if Scale Variance Corrected Item- Squared Multiple Cronbach's Alpha if Item Deleted if Item Deleted Total Correlation Correlation Item Deleted A1 15.69 18.005 .585 .379 .708 A2 15.33 20.621 .500 .304 .755 A3 16.41 13.728 .654 .441 .665 A4 16.74 14.336 .601 .397 .699

TABLE K.8: Social Support Item-Total Statistics - All Items Scale Mean if Scale Variance Corrected Item- Squared Multiple Cronbach's Alpha if Item Deleted if Item Deleted Total Correlation Correlation Item Deleted SO1 19.19 5.148 .745 .563 .801 SO2 19.02 5.938 .721 .528 .811 SO3 19.27 5.348 .686 .472 .829 SO4 19.04 6.541 .695 .484 .829

TABLE K.9: Supervisor Support Item-Total Statistics - All Items Scale Mean if Scale Variance Corrected Item- Squared Multiple Cronbach's Alpha if Item Deleted if Item Deleted Total Correlation Correlation Item Deleted

SU1 18.92 9.756 .734 .553 .818 SU2 18.76 10.829 .655 .445 .851 SU3 18.88 9.191 .754 .588 .809 SU4 19.00 8.667 .728 .547 .824

TABLE K.10: Feedback Item-Total Statistics - All Items Scale Mean if Scale Variance Corrected Item- Squared Multiple Cronbach's Alpha if Item Deleted if Item Deleted Total Correlation Correlation Item Deleted F1 23.53 20.441 .618 .417 .799 F2 24.25 17.503 .590 .370 .814 F3 23.98 17.866 .668 .481 .783 F4 23.82 18.498 .683 .493 .778 F5 23.43 21.113 .627 .424 .800

175

TABLE K.11: Satisfaction Item-Total Statistics - All Items Scale Mean if Scale Variance Corrected Item- Squared Multiple Cronbach's Alpha if Item Deleted if Item Deleted Total Correlation Correlation Item Deleted SPY1 124.92 402.674 .626 .607 .919 SPY2 125.51 388.872 .674 .636 .917 SPY3 124.94 400.378 .613 .598 .919 SPY4 125.41 397.845 .546 .466 .920 SE1 125.27 395.677 .607 .568 .919 SE2 125.26 396.028 .612 .479 .919 SE3 125.65 386.565 .697 .591 .917 SE4 125.08 399.254 .624 .575 .919 SS1 125.11 396.883 .567 .506 .919 SS2 125.61 389.165 .615 .566 .918 SS3 124.97 408.784 .445 .345 .921 SS4 125.76 395.178 .498 .452 .921 SR1 126.41 381.605 .635 .663 .918 SR2 126.34 381.714 .665 .671 .917 SR3 125.30 394.466 .645 .587 .918 SR4 125.27 396.268 .601 .607 .919 SPH1 127.26 399.932 .340 .401 .924 SPH2 127.76 391.762 .443 .649 .922 SPH3 127.45 383.817 .548 .701 .920 SPH4 126.28 380.028 .646 .565 .918 SA1 125.71 405.031 .370 .493 .923 SA2 125.37 395.478 .656 .592 .918 SA3 126.24 401.269 .418 .442 .922 SA4 126.15 393.663 .534 .590 .920

TABLE K.12: Satisfaction Psychological Item-Total Statistics - All Items Scale Mean if Scale Variance Corrected Item- Squared Multiple Cronbach's Alpha if Item Deleted if Item Deleted Total Correlation Correlation Item Deleted

SPY1 17.92 9.813 .606 .377 .769 SPY2 18.52 7.318 .683 .517 .727 SPY3 17.94 8.806 .698 .532 .723 SPY4 18.41 8.586 .543 .299 .797

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TABLE K.13: Satisfaction Educational Item-Total Statistics - All Items Scale Mean if Scale Variance Corrected Item- Squared Multiple Cronbach's Alpha if Item Deleted if Item Deleted Total Correlation Correlation Item Deleted SE1 17.80 9.611 .662 .447 .751 SE2 17.79 10.121 .599 .360 .780 SE3 18.18 8.967 .630 .398 .771 SE4 17.61 10.534 .656 .437 .759

TABLE K.14: Satisfaction Social Item-Total Statistics - All Items Scale Mean if Scale Variance Corrected Item- Squared Multiple Cronbach's Alpha if Item Deleted if Item Deleted Total Correlation Correlation Item Deleted SS1 17.45 9.411 .612 .396 .606 SS2 17.95 8.342 .618 .413 .595 SS3 17.31 12.557 .336 .121 .750 SS4 18.10 8.896 .516 .279 .667

TABLE K.15: Satisfaction Relaxation Item-Total Statistics - All Items Scale Mean if Scale Variance Corrected Item- Squared Multiple Cronbach's Alpha if Item Deleted if Item Deleted Total Correlation Correlation Item Deleted SR1 16.87 12.274 .707 .576 .779 SR2 16.80 12.227 .765 .619 .745 SR3 15.77 16.508 .582 .384 .828 SR4 15.73 15.805 .658 .453 .801

TABLE K.16: Satisfaction Physiological Item-Total Statistics - All Items Scale Mean if Scale Variance Corrected Item- Squared Multiple Cronbach's Alpha if Item Deleted if Item Deleted Total Correlation Correlation Item Deleted SPH1 12.29 21.878 .503 .289 .803 SPH2 12.80 18.430 .738 .619 .688 SPH3 12.48 17.915 .765 .642 .672 SPH4 11.31 22.566 .472 .261 .816

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TABLE K.17: Satisfaction Aesthetic Item-Total Statistics - All Items Scale Mean if Scale Variance Corrected Item- Squared Multiple Cronbach's Alpha if Item Deleted if Item Deleted Total Correlation Correlation Item Deleted SA1 16.03 11.228 .568 .410 .724 SA2 15.69 12.740 .516 .302 .751 SA3 16.56 10.981 .560 .335 .730 SA4 16.47 9.860 .676 .494 .663

TABLE K.18: Engagement Item-Total Statistics - All Items Scale Mean if Scale Variance Corrected Item- Squared Multiple Cronbach's Alpha if Item Deleted if Item Deleted Total Correlation Correlation Item Deleted VI1 46.07 58.586 .731 .585 .872 VI2 45.86 59.445 .757 .667 .870 VI3 45.72 58.695 .680 .516 .876 DE1 45.42 62.071 .745 .611 .874 DE2 45.62 60.425 .711 .557 .874 DE3 45.02 67.512 .578 .432 .887 AB1 45.41 61.831 .770 .663 .872 AB2 45.82 59.410 .721 .572 .873 AB3 47.75 59.308 .423 .250 .913

TABLE K.19: Vigor Item-Total Statistics - All Items Scale Mean if Scale Variance Corrected Item- Squared Multiple Cronbach's Alpha if Item Deleted if Item Deleted Total Correlation Correlation Item Deleted VI1 11.59 6.071 .676 .508 .773 VI2 11.38 6.131 .765 .592 .692 VI3 11.24 5.996 .629 .417 .825

TABLE K.20: Dedication Item-Total Statistics - All Items Scale Mean if Scale Variance Corrected Item- Squared Multiple Cronbach's Alpha if Item Deleted if Item Deleted Total Correlation Correlation Item Deleted

DE1 12.54 3.459 .650 .422 .699 DE2 12.73 2.796 .679 .462 .689 DE3 12.13 4.401 .635 .403 .749

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TABLE K.21: Absorption Item-Total Statistics - All Items Scale Mean if Scale Variance Corrected Item- Squared Multiple Cronbach's Alpha if Item Deleted if Item Deleted Total Correlation Correlation Item Deleted AB1 9.60 7.585 .546 .465 .522 AB2 10.02 6.277 .587 .494 .411 AB3 11.95 4.836 .379 .149 .798

TABLE K.22: Affective Commitment Item-Total Statistics - All Items Scale Mean if Scale Variance Corrected Item- Squared Multiple Cronbach's Alpha if Item Deleted if Item Deleted Total Correlation Correlation Item Deleted

AC1 32.54 42.311 .419 .223 .714 AC2 32.16 42.092 .508 .293 .694 AC3 34.52 49.270 .048 .030 .813 AC4 32.03 41.994 .526 .307 .690 AC5R 31.84 40.711 .592 .591 .675 AC6R 31.98 39.198 .608 .451 .668 AC7R 31.74 40.126 .641 .588 .665

TABLE K.23: Volunteer Performance Item-Total Statistics - All Items Scale Mean if Scale Variance Corrected Item- Squared Multiple Cronbach's Alpha if Item Deleted if Item Deleted Total Correlation Correlation Item Deleted VP1 25.99 6.924 .714 .512 .872 VP2 25.99 7.009 .783 .664 .857 VP3 26.07 7.133 .733 .610 .868 VP4 26.10 6.204 .753 .629 .867 VP5 26.03 7.361 .717 .575 .872

TABLE K.24: Volunteer Retention Item-Total Statistics - All Items Scale Mean if Scale Variance Corrected Item- Squared Multiple Cronbach's Alpha if Item Deleted if Item Deleted Total Correlation Correlation Item Deleted VR1 11.79 8.442 .721 .556 .622 VR2 12.00 8.010 .656 .515 .680 VR3R 11.80 8.627 .524 .285 .828

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APPENDIX L

ITEM-TOTAL STATISTICS FOR ALL ITEMS MEETING CUT

TABLE L.1: Job Demands Item-Total Statistics – Items Meeting Cut Scale Mean if Scale Variance Corrected Item- Squared Multiple Cronbach's Alpha if Item Deleted if Item Deleted Total Correlation Correlation Item Deleted RA1R 42.04 362.868 .383 .363 .916 RA2R 42.26 364.236 .396 .428 .916 RA4R 42.54 364.185 .462 .529 .915 RA5R 42.47 358.522 .537 .634 .913 RA6R 42.35 357.736 .542 .674 .913 RC1 41.20 346.158 .523 .429 .914 RC2 42.43 356.479 .509 .390 .914 RC4 42.16 352.700 .528 .503 .913 RC5 42.37 344.348 .698 .603 .910 RC6 41.88 338.055 .681 .535 .910 RC7 42.17 349.656 .529 .362 .914 RC8 42.43 348.945 .684 .542 .910 RO1 42.33 355.577 .522 .467 .913 RO2 42.39 352.780 .621 .612 .911 RO3 42.17 352.570 .545 .501 .913 PP1 42.27 344.516 .690 .581 .910 PP4 42.23 344.945 .672 .571 .910 PP6 42.87 359.346 .590 .547 .912 PP8 42.26 348.615 .582 .552 .912 PP9 42.54 356.428 .559 .475 .913 PP12 41.82 346.373 .569 .469 .913

TABLE L.2: Role Ambiguity Item-Total Statistics – Items Meeting Cut Scale Mean if Scale Variance Corrected Item- Squared Multiple Cronbach's Alpha if Item Deleted if Item Deleted Total Correlation Correlation Item Deleted

RA1R 7.82 18.961 .538 .314 .843 RA2R 8.03 19.030 .607 .390 .821 RA4R 8.31 19.393 .694 .504 .799 RA5R 8.24 18.292 .732 .604 .786 RA6R 8.13 18.506 .690 .558 .798

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TABLE L.3: Role Conflict Item-Total Statistics – Items Meeting Cut Scale Mean if Scale Variance Corrected Item- Squared Multiple Cronbach's Alpha if Item Deleted if Item Deleted Total Correlation Correlation Item Deleted RC1 12.72 49.126 .562 .361 .819 RC2 13.94 55.231 .477 .310 .829 RC4 13.68 51.157 .618 .409 .809 RC5 13.89 50.045 .701 .529 .796 RC6 13.39 48.224 .645 .442 .804 RC7 13.69 51.619 .530 .293 .823 RC8 13.95 53.488 .600 .391 .813

TABLE L.4: Role Overload Item-Total Statistics – Items Meeting Cut Scale Mean if Scale Variance Corrected Item- Squared Multiple Cronbach's Alpha if Item Deleted if Item Deleted Total Correlation Correlation Item Deleted RO1 4.17 7.034 .632 .401 .751 RO2 4.22 7.305 .667 .446 .718 RO3 4.00 6.525 .657 .435 .727

TABLE L.5: Perception of Politics -Total Statistics – Items Meeting Cut Scale Mean if Scale Variance Corrected Item- Squared Multiple Cronbach's Alpha if Item Deleted if Item Deleted Total Correlation Correlation Item Deleted PP1 10.07 34.306 .646 .453 .825 PP4 10.03 34.250 .637 .428 .827 PP6 10.67 37.695 .658 .490 .828 PP8 10.06 33.262 .666 .469 .822 PP9 10.34 36.754 .604 .402 .833 PP12 9.62 32.371 .649 .427 .827

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TABLE L.6: Job Resources Item-Total Statistics – Items Meeting Cut Scale Mean if Scale Variance Corrected Item- Squared Multiple Cronbach's Alpha if

Item Deleted if Item Deleted Total Correlation Correlation Item Deleted A1 96.14 194.370 .586 .451 .911 A2 95.78 198.726 .605 .456 .910 A3 96.86 188.789 .504 .497 .917 A4 97.19 189.992 .481 .464 .918 SO1 95.53 200.479 .648 .638 .910 SO2 95.36 204.040 .638 .582 .911 SO3 95.60 200.918 .630 .555 .910 SO4 95.37 206.615 .629 .590 .912 SU1 95.57 196.665 .703 .677 .908 SU2 95.41 200.834 .641 .587 .910 SU3 95.53 195.326 .691 .653 .908 SU4 95.65 191.419 .727 .641 .907 F1 95.61 198.610 .608 .497 .910 F2 96.34 186.386 .687 .555 .908 F3 96.07 192.386 .624 .569 .910 F4 95.91 194.749 .617 .588 .910 F5 95.52 199.851 .635 .498 .910

TABLE L.7: Autonomy Item-Total Statistics – Items Meeting Cut Scale Mean if Scale Variance Corrected Item- Squared Multiple Cronbach's Alpha if Item Deleted if Item Deleted Total Correlation Correlation Item Deleted A1 15.69 18.005 .585 .379 .708 A2 15.33 20.621 .500 .304 .755 A3 16.41 13.728 .654 .441 .665 A4 16.74 14.336 .601 .397 .699

TABLE L.8: Social Support Item-Total Statistics – Items Meeting Cut Scale Mean if Scale Variance Corrected Item- Squared Multiple Cronbach's Alpha if Item Deleted if Item Deleted Total Correlation Correlation Item Deleted SO1 19.19 5.148 .745 .563 .801 SO2 19.02 5.938 .721 .528 .811 SO3 19.27 5.348 .686 .472 .829 SO4 19.04 6.541 .695 .484 .829

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TABLE L.9: Supervisor Support Item-Total Statistics – Items Meeting Cut Scale Mean if Scale Variance Corrected Item- Squared Multiple Cronbach's Alpha if Item Deleted if Item Deleted Total Correlation Correlation Item Deleted SU1 18.92 9.756 .734 .553 .818 SU2 18.76 10.829 .655 .445 .851 SU3 18.88 9.191 .754 .588 .809 SU4 19.00 8.667 .728 .547 .824

TABLE L.10: Feedback Item-Total Statistics – Items Meeting Cut Scale Mean if Scale Variance Corrected Item- Squared Multiple Cronbach's Alpha if Item Deleted if Item Deleted Total Correlation Correlation Item Deleted F1 23.53 20.441 .618 .417 .799 F2 24.25 17.503 .590 .370 .814 F3 23.98 17.866 .668 .481 .783 F4 23.82 18.498 .683 .493 .778 F5 23.43 21.113 .627 .424 .800

TABLE L.11: Satisfaction Item-Total Statistics – Items Meeting Cut Scale Mean if Scale Variance Corrected Item- Squared Multiple Cronbach's Alpha if Item Deleted if Item Deleted Total Correlation Correlation Item Deleted

SPY1 118.64 384.252 .619 .605 .918 SPY2 119.23 370.443 .675 .630 .916 SPY3 118.65 381.904 .609 .596 .918 SPY4 119.12 379.194 .548 .463 .918 SE1 118.98 377.190 .606 .568 .917 SE2 118.97 377.523 .611 .479 .917 SE3 119.36 368.233 .698 .592 .915 SE4 118.80 380.973 .617 .563 .917 SS1 118.82 378.374 .566 .503 .918 SS2 119.32 370.762 .615 .567 .917 SS4 119.47 376.669 .498 .451 .919 SR1 120.12 363.490 .634 .661 .916 SR2 120.05 363.459 .666 .672 .916 SR3 119.02 376.133 .641 .585 .917 SR4 118.99 378.024 .595 .602 .917 SPH1 120.98 380.814 .347 .397 .923 SPH2 121.48 372.803 .450 .647 .921

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TABLE L.11: - Continued Scale Mean if Scale Variance if Corrected Item- Squared Multiple Cronbach's Alpha if Item Deleted Item Deleted Total Correlation Correlation Item Deleted

SPH3 121.16 364.974 .557 .699 .918 SPH4 119.99 361.962 .644 .563 .916 SA1 119.42 386.588 .364 .489 .921 SA2 119.08 377.284 .649 .585 .917 SA3 119.95 382.760 .416 .441 .921 SA4 119.87 375.420 .530 .589 .919

TABLE L.12: Satisfaction Psychological Item-Total Statistics – Items Meeting Cut Scale Mean if Scale Variance Corrected Item- Squared Multiple Cronbach's Alpha if Item Deleted if Item Deleted Total Correlation Correlation Item Deleted SPY1 17.92 9.813 .606 .377 .769 SPY2 18.52 7.318 .683 .517 .727 SPY3 17.94 8.806 .698 .532 .723 SPY4 18.41 8.586 .543 .299 .797

TABLE L.13: Satisfaction Educational Item-Total Statistics – Items Meeting Cut Scale Mean if Scale Variance Corrected Item- Squared Multiple Cronbach's Alpha if Item Deleted if Item Deleted Total Correlation Correlation Item Deleted SE1 17.80 9.611 .662 .447 .751 SE2 17.79 10.121 .599 .360 .780 SE3 18.18 8.967 .630 .398 .771 SE4 17.61 10.534 .656 .437 .759

TABLE L.14: Satisfaction Social Item-Total Statistics – Items Meeting Cut Scale Mean if Scale Variance Corrected Item- Squared Multiple Cronbach's Alpha if Item Deleted if Item Deleted Total Correlation Correlation Item Deleted SS1 11.16 6.850 .594 .374 .656 SS2 11.66 5.773 .627 .409 .606 SS4 11.80 6.215 .523 .275 .734

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TABLE L.15: Satisfaction Relaxation Item-Total Statistics – Items Meeting Cut Scale Mean if Scale Variance Corrected Item- Squared Multiple Cronbach's Alpha if Item Deleted if Item Deleted Total Correlation Correlation Item Deleted SR1 16.87 12.274 .705 .574 .779 SR2 16.80 12.198 .765 .620 .744 SR3 15.77 16.484 .582 .384 .828 SR4 15.73 15.786 .657 .452 .801

TABLE L.16: Satisfaction Physiological Item-Total Statistics – Items Meeting Cut Scale Mean if Scale Variance Corrected Item- Squared Multiple Cronbach's Alpha if Item Deleted if Item Deleted Total Correlation Correlation Item Deleted

SPH1 12.30 21.877 .502 .288 .803 SPH2 12.80 18.427 .737 .618 .688 SPH3 12.48 17.908 .765 .641 .672 SPH4 11.31 22.540 .473 .262 .815

TABLE L.17: Satisfaction Aesthetic Item-Total Statistics – Items Meeting Cut Scale Mean if Scale Variance Corrected Item- Squared Multiple Cronbach's Alpha if

Item Deleted if Item Deleted Total Correlation Correlation Item Deleted SA1 16.03 11.228 .568 .410 .724 SA2 15.69 12.740 .516 .302 .751 SA3 16.56 10.981 .560 .335 .730 SA4 16.47 9.860 .676 .494 .663

TABLE L.18: Engagement Item-Total Statistics – Items Meeting Cut Scale Mean if Scale Variance Corrected Item- Squared Multiple Cronbach's Alpha if Item Deleted if Item Deleted Total Correlation Correlation Item Deleted VI1 42.24 44.384 .710 .553 .903 VI2 42.03 44.470 .782 .663 .895 VI3 41.89 44.098 .683 .508 .906 DE1 41.59 46.805 .771 .611 .898 DE2 41.79 45.490 .724 .554 .901 DE3 41.19 51.554 .610 .427 .911 AB1 41.57 46.531 .802 .666 .896 AB2 42.00 44.814 .719 .560 .901

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TABLE L.19: Vigor Item-Total Statistics – Items Meeting Cut Scale Mean if Scale Variance Corrected Item- Squared Multiple Cronbach's Alpha if

Item Deleted if Item Deleted Total Correlation Correlation Item Deleted VI1 11.59 6.071 .676 .508 .773 VI2 11.38 6.131 .765 .592 .692 VI3 11.24 5.996 .629 .417 .825

TABLE L.20: Dedication Item-Total Statistics – Items Meeting Cut Scale Mean if Scale Variance Corrected Item- Squared Multiple Cronbach's Alpha if

Item Deleted if Item Deleted Total Correlation Correlation Item Deleted DE1 12.54 3.459 .650 .422 .699 DE2 12.73 2.796 .679 .462 .689 DE3 12.13 4.401 .635 .403 .749

TABLE L.21: Absorption Item-Total Statistics – Items Meeting Cut Scale Mean if Scale Variance Corrected Item- Squared Multiple Cronbach's Alpha if Item Deleted if Item Deleted Total Correlation Correlation Item Deleted AB1 5.76 1.763 .682 .465 . AB2 6.18 1.142 .682 .465 .

TABLE L.22: Affective Commitment Item-Total Statistics – Items Meeting Cut Scale Mean if Scale Variance Corrected Item- Squared Multiple Cronbach's Alpha if

Item Deleted if Item Deleted Total Correlation Correlation Item Deleted AC5R 11.88 8.432 .748 .582 .741 AC6R 12.01 8.433 .646 .418 .844 AC7R 11.78 8.669 .733 .566 .757

TABLE L.23: Volunteer Performance Item-Total Statistics – Items Meeting Cut Scale Mean if Scale Variance Corrected Item- Squared Multiple Cronbach's Alpha if Item Deleted if Item Deleted Total Correlation Correlation Item Deleted VP1 25.99 6.924 .714 .512 .872 VP2 25.99 7.009 .783 .664 .857 VP3 26.07 7.133 .733 .610 .868 VP4 26.10 6.204 .753 .629 .867 VP5 26.03 7.361 .717 .575 .872

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TABLE L.24: Volunteer Retention Item-Total Statistics – Items Meeting Cut Scale Mean if Scale Variance Corrected Item- Squared Multiple Cronbach's Alpha if Item Deleted if Item Deleted Total Correlation Correlation Item Deleted VR1 11.79 8.442 .721 .556 .622 VR2 12.00 8.010 .656 .515 .680 VR3R 11.80 8.627 .524 .285 .828

187

APPENDIX M

CORRELATION MATRICES AND MEAN SCORES

TABLE M.1: Correlation Matrix of Latent Variables Volunteer Volunteer Job Demands Job Resources Engagement Satisfaction Commitment Performance Retention Job Demands Pearson Correlation 1 Sig. (2-tailed) N 211 Job Resources Pearson Correlation -.546** 1 Sig. (2-tailed) .000 N 211 211 Engagement Pearson Correlation -.409** .578** 1 Sig. (2-tailed) .000 .000 N 211 211 211 Satisfaction Pearson Correlation -.260** .639** .683** 1 Sig. (2-tailed) .000 .000 .000 N 211 211 211 211 Commitment Pearson Correlation -.499** .546** .512** .396** 1 Sig. (2-tailed) .000 .000 .000 .000 N 211 211 211 211 211 Volunteer Pearson Correlation -.433** .636** .545** .503** .434** 1 Performance Sig. (2-tailed) .000 .000 .000 .000 .000 N 211 211 211 211 211 211 Volunteeer Pearson Correlation -.515** .590** .623** .490** .540** .498** 1 Retention Sig. (2-tailed) .000 .000 .000 .000 .000 .000 N 211 211 211 211 211 211 211 **. Correlation is significant at the 0.01 level (2-tailed).

188

TABLE M.2: Correlation Matrix of Factors for Engagement, Job Demands, and Job Resource Role Role Role Perception Social Supervisor Vigor Dedication Absorption Ambiguity Overload Conflict of Politics Autonomy Support Support Feedback Vigor Pearson Correlation 1 Sig. (2-tailed) N 211 Dedication Pearson Correlation .738** 1 Sig. (2-tailed) .000 N 211 211 Absorption Pearson Correlation .738** .768** 1 Sig. (2-tailed) .000 .000 N 211 211 211 Role Pearson Correlation -.425** -.507** -.446** 1 Ambiguity Sig. (2-tailed) .000 .000 .000 N 211 211 211 211 Role Pearson Correlation -.247** -.274** -.290** .288** 1 Overload Sig. (2-tailed) .000 .000 .000 .000 N 211 211 211 211 211 Role Pearson Correlation -.264** -.311** -.307** .415** .650** 1 Conflict Sig. (2-tailed) .000 .000 .000 .000 .000 N 211 211 211 211 211 211 Perception Pearson Correlation -.158* -.256** -.268** .442** .504** .697** 1 of Politics Sig. (2-tailed) .022 .000 .000 .000 .000 .000 N 211 211 211 211 211 211 211 Autonomy Pearson Correlation .373** .448** .252** -.387** -.084 -.128 -.173* 1 Sig. (2-tailed) .000 .000 .000 .000 .224 .064 .012 N 211 211 211 211 211 211 211 211 Social Pearson Correlation .445** .621** .520** -.533** -.381** -.422** -.294** .447** 1 Support Sig. (2-tailed) .000 .000 .000 .000 .000 .000 .000 .000 N 211 211 211 211 211 211 211 211 211 Supervisor Pearson Correlation .393** .519** .437** -.566** -.328** -.461** -.381** .506** .739** 1 Support Sig. (2-tailed) .000 .000 .000 .000 .000 .000 .000 .000 .000 N 211 211 211 211 211 211 211 211 211 211 Feedback Pearson Correlation .456** .517** .424** -.746** -.293** -.431** -.406** .557** .634** .723** 1 Sig. (2-tailed) .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 N 211 211 211 211 211 211 211 211 211 211 211 *. Correlation is significant at the 0.05 level (2-tailed). **. Correlation is significant at the 0.01 level (2-tailed).

189

TABLE M.3: Correlation Matrix for Sub-Factors of Satisfaction Satisfaction Sub-Factors Psychological Educational Social Relaxation Physiological Aesthetic Psychological Pearson Correlation 1 Sig. (2-tailed) N 211 Educational Pearson Correlation .735** 1 Sig. (2-tailed) .000 N 211 211 Social Pearson Correlation .672** .639** 1 Sig. (2-tailed) .000 .000 N 211 211 211 Relaxationl Pearson Correlation .622** .651** .520** 1 Sig. (2-tailed) .000 .000 .000 N 211 211 211 211 Physiological Pearson Correlation .351** .430** .378** .509** 1 Sig. (2-tailed) .000 .000 .000 .000 N 211 211 211 211 211 Aesthetic Pearson Correlation .499** .491** .366** .443** .398** 1 Sig. (2-tailed) .000 .000 .000 .000 .000 N 211 211 211 211 211 211 **. Correlation is significant at the 0.01 level (2-tailed).

190

TABLE M.4: Mean Scores of Latent Variables Mean Std. Deviation N Job Demands 44.3649 19.67727 211 Job Resources 101.8389 14.85398 211 Engagement 47.7583 7.70488 211 Satisfaction 124.9763 20.21396 211 Commitment 17.8341 4.21290 211 Volunteer Performance 32.5450 3.24575 211 Volunteer Retention 17.7962 4.12324 211

TABLE M.5: Mean Scores for Factors for Engagement, Job Demands, and Job Resource Mean Std. Deviation N Engagement Vigor 17.1090 3.54729 211 Dedication 18.7014 2.69054 211 Absorption 11.9479 2.20003 211 Job Demands Role Ambiguityl 10.1327 5.32393 211 Role Overload 6.1943 3.77778 211 Role Conflict 15.8815 8.24218 211 Perception of Politics 12.1564 6.97336 211 Job Resources Autonomy 21.3886 5.24456 211 Social Support 25.5071 3.12192 211 Supervisor Support 25.1896 4.04288 211 Feedback 29.7536 5.34753 211

TABLE M.6: Mean Scores for Sub-Factors of Satisfaction Satisfaction Mean Std. Deviation N Psychological 24.2654 3.79545 211 Educational 23.7915 4.05135 211 Social 17.3128 3.54519 211 Relaxation 21.7251 4.88928 211 Physiological 16.2986 5.80156 211 Aesthetic 21.5829 4.30354 211

191

TABLE M.7: Mean Scores for Job Resources by Generational Cohort N Mean Std. Std. Error 95% Confidence Interval for Mean Minimum Maximum Deviation Lower Bound Upper Bound Autonomy Traditionalist 6 5.4167 1.53840 .62805 3.8022 7.0311 2.50 6.75 Baby Boomers 86 5.5930 1.19438 .12879 5.3369 5.8491 1.75 7.00 Gen X-ers 69 5.1993 1.37772 .16586 4.8683 5.5302 1.00 7.00 Gen Y-ers 38 5.0000 1.41302 .22922 4.5356 5.4644 1.75 7.00 Total 199 5.3379 1.32447 .09389 5.1528 5.5231 1.00 7.00 Social Support Traditionalist 6 6.3333 .97040 .39616 5.3150 7.3517 4.50 7.00 Baby Boomers 86 6.5698 .56053 .06044 6.4496 6.6899 4.00 7.00 Gen X-ers 69 6.2971 .92482 .11133 6.0749 6.5193 1.75 7.00 Gen Y-ers 38 6.1513 .86708 .14066 5.8663 6.4363 3.75 7.00 Total 199 6.3882 .78801 .05586 6.2780 6.4983 1.75 7.00 Supervisor Traditionalist 6 6.6667 .58452 .23863 6.0532 7.2801 5.50 7.00 Support Baby Boomers 86 6.5058 .79887 .08614 6.3345 6.6771 2.75 7.00 Gen X-ers 69 6.1123 1.23188 .14830 5.8164 6.4082 1.00 7.00 Gen Y-ers 38 6.1908 1.01415 .16452 5.8574 6.5241 3.00 7.00 Total 199 6.3141 1.01627 .07204 6.1720 6.4561 1.00 7.00 Feedback Traditionalist 6 6.0333 1.17587 .48005 4.7993 7.2673 4.20 7.00 Baby Boomers 86 6.2837 .81887 .08830 6.1082 6.4593 2.80 7.00 Gen X-ers 69 5.7188 1.08102 .13014 5.4592 5.9785 3.00 7.00 Gen Y-ers 38 5.7947 1.29822 .21060 5.3680 6.2215 1.40 7.00 Total 199 5.9869 1.05334 .07467 5.8397 6.1342 1.40 7.00 Job Resources Traditionalist 6 6.1078 .79044 .32270 5.2783 6.9374 5.00 6.94 Baby Boomers 86 6.2408 .69657 .07511 6.0914 6.3901 3.35 7.00 Gen X-ers 69 5.8252 .94436 .11369 5.5984 6.0521 2.29 7.00 Gen Y-ers 38 5.7848 .99792 .16188 5.4568 6.1128 2.65 7.00 Total 199 6.0056 .87263 .06186 5.8836 6.1276 2.29 7.00 Note: The Traditionalist Cohort was not included in the overall analysis and is included for informational purposes only.

192

APPENDIX N

STANDARDIZED FACTOR LOADINGS

TABLE N.1: Standardize Factor Load for Factors During Iteration Process Item Standardized Factor Loading (Δ = items below cut point, * = deleted items) Label Full Model Loading > 0.3 Loadings ≥ 0.55 Loadings ≥ 0.55 Loadings ≥ 0.55 RA1 0.577 0.577 0.573 0.573 0.573 RA2 0.648 0.648 0.645 0.645 0.645 RA3 0.509 0.509 – Δ * * * RA4 0.737 0.736 0.744 0.744 0.744 RA5 0.847 0.847 0.849 0.849 0.849 RA6 0.808 0.808 0.803 0.803 0.803 RC1 0.596 0.596 0.588 0.588 0.588 RC2 0.585 0.586 0.573 0.573 0.573 RC3 0.519 0.518 – Δ * * RC4 0.614 0.615 0.617 0.617 0.617 RC5 0.772 0.772 0.766 0.766 0.766 RC6 0.727 0.728 0.742 0.742 0.742 RC7 0.565 0.565 0.574 0.573 0.573 RC8 0.705 0.704 0.705 0.706 0.705 RO1 0.707 0.708 0.709 0.709 0.709 RO2 0.827 0.827 0.824 0.824 0.824 RO3 0.743 0.743 0.744 0.744 0.745 PP1 0.717 0.721 0.735 0.734 0.734 PP2 0.013 – Δ * * * * PP3 0.298 – Δ * * * * PP4 0.723 0.724 0.732 0.732 0.732 PP5 0.137 – Δ * * * * PP6 0.694 0.699 0.698 0.699 0.699 PP7 -0.056 – Δ * * * *

193

TABLE N.1: - Continued PP8 0.722 0.723 0.714 0.714 0.714 PP9 0.659 0.662 0.661 0.661 0.661 PP10 -0.162 – Δ * * * * PP11 0.514 0.506 – Δ * * * PP12 0.697 0.692 0.687 0.687 0.687 A1 0.736 0.736 0.736 0.736 0.737 A2 0.699 0.698 0.699 0.699 0.699 A3 0.665 0.665 0.665 0.665 0.665 A4 0.617 0.617 0.617 0.617 0.617 SO1 0.811 0.811 0.811 0.811 0.810 SO2 0.793 0.793 0.793 0.793 0.793 SO3 0.761 0.761 0.762 0.762 0.762 SO4 0.770 0.770 0.770 0.770 0.770 SU1 0.814 0.814 0.814 0.814 0.814 SU2 0.728 0.728 0.727 0.727 0.728 SU3 0.814 0.814 0.814 0.814 0.814 SU4 0.795 0.795 0.796 0.796 0.796 F1 0.711 0.711 0.712 0.712 0.712 F2 0.692 0.692 0.693 0.693 0.693 F3 0.701 0.701 0.700 0.700 0.700 F4 0.756 0.756 0.755 0.755 0.755 F5 0.706 0.706 0.706 0.706 0.706 SPY1 0.767 0.767 0.767 0.767 0.767 SPY2 0.743 0.743 0.744 0.744 0.744 SPY3 0.759 0.758 0.757 0.756 0.756 SPY4 0.628 0.628 0.629 0.629 0.629 SE1 0.718 0.717 0.717 0.717 0.717 SE2 0.679 0.679 0.681 0.681 0.680 SE3 0.766 0.766 0.767 0.767 0.768

194

TABLE N.1: - Continued SE4 0.738 0.738 0.735 0.735 0.735 SS1 0.743 0.743 0.748 0.751 0.754 SS2 0.779 0.779 0.810 0.807 0.805 SS3 0.465 0.465 – Δ * * * SS4 0.565 0.564 0.569 0.568 0.568 SR1 0.783 0.783 0.782 0.783 0.783 SR2 0.834 0.834 0.832 0.832 0.832 SR3 0.680 0.680 0.681 0.681 0.682 SR4 0.736 0.736 0.738 0.737 0.737 SPH1 0.565 0.565 0.564 0.564 0.564 SPH2 0.839 0.839 0.839 0.839 0.839 SPH3 0.904 0.904 0.904 0.904 0.904 SPH4 0.578 0.578 0.578 0.578 0.578 SA1 0.651 0.651 0.650 0.648 0.647 SA2 0.674 0.674 0.675 0.678 0.678 SA3 0.617 0.617 0.619 0.621 0.621 SA4 0.783 0.783 0.780 0.778 0.777 VI1 0.733 0.733 0.726 0.726 0.726 VI2 0.798 0.798 0.797 0.797 0.797 VI3 0.709 0.709 0.707 0.707 0.708 DE1 0.811 0.811 0.813 0.814 0.814 DE2 0.787 0.787 0.786 0.786 0.785 DE3 0.665 0.665 0.668 0.667 0.667 AB1 0.839 0.839 0.841 0.841 0.841 AB2 0.764 0.764 0.762 0.762 0.762 AB3 0.411 0.411 – Δ * * * AC1 0.513 0.513 – Δ * * * AC2 0.562 0.561 0.523 – Δ * * AC3 0.026 – Δ * * * *

195

TABLE N.1: - Continued AC4 0.599 0.598 0.571 0.539 – Δ * AC5 0.783 0.783 0.818 0.841 0.860 AC6 0.709 0.709 0.721 0.719 0.713 AC7 0.793 0.793 0.822 0.839 0.842 VP1 0.742 0.742 0.742 0.742 0.742 VP2 0.843 0.843 0.844 0.844 0.844 VP3 0.761 0.761 0.761 0.761 0.762 VP4 0.845 0.845 0.845 0.845 0.844 VP5 0.759 0.759 0.759 0.759 0.759 VR1 0.844 0.844 0.846 0.846 0.847 VR2 0.812 0.812 0.812 0.812 0.811 VR3 0.630 0.630 0.627 0.627 0.627

196

APPENDIX O

NORMALITY TESTS

TABLE O.1: Normality Tests Label Test Statistic Value Prob Label Test Statistic Value Prob RA1 Shapiro-Wilk W 0.80 <.0001 SE1 Shapiro-Wilk W 0.76 <.0001 RA2 Shapiro-Wilk W 0.76 <.0001 SE2 Shapiro-Wilk W 0.77 <.0001 RA4 Shapiro-Wilk W 0.68 <.0001 SE3 Shapiro-Wilk W 0.84 <.0001 RA5 Shapiro-Wilk W 0.68 <.0001 SE4 Shapiro-Wilk W 0.73 <.0001 RA6 Shapiro-Wilk W 0.74 <.0001 SS1 Shapiro-Wilk W 0.68 <.0001 RC1 Shapiro-Wilk W 0.86 <.0001 SS2 Shapiro-Wilk W 0.81 <.0001 RC2 Shapiro-Wilk W 0.67 <.0001 SS4 Shapiro-Wilk W 0.83 <.0001 RC4 Shapiro-Wilk W 0.74 <.0001 SR1 Shapiro-Wilk W 0.89 <.0001 RC5 Shapiro-Wilk W 0.67 <.0001 SR2 Shapiro-Wilk W 0.89 <.0001 RC6 Shapiro-Wilk W 0.77 <.0001 SR3 Shapiro-Wilk W 0.78 <.0001 RC7 Shapiro-Wilk W 0.69 <.0001 SR4 Shapiro-Wilk W 0.77 <.0001 RC8 Shapiro-Wilk W 0.69 <.0001 SPH1 Shapiro-Wilk W 0.91 <.0001 RO1 Shapiro-Wilk W 0.71 <.0001 SPH2 Shapiro-Wilk W 0.89 <.0001 RO2 Shapiro-Wilk W 0.71 <.0001 SPH3 Shapiro-Wilk W 0.90 <.0001 RO3 Shapiro-Wilk W 0.75 <.0001 SPH4 Shapiro-Wilk W 0.87 <.0001 PP1 Shapiro-Wilk W 0.71 <.0001 SA1 Shapiro-Wilk W 0.85 <.0001 PP4 Shapiro-Wilk W 0.72 <.0001 SA2 Shapiro-Wilk W 0.82 <.0001 PP6 Shapiro-Wilk W 0.47 <.0001 SA3 Shapiro-Wilk W 0.90 <.0001 PP8 Shapiro-Wilk W 0.69 <.0001 SA4 Shapiro-Wilk W 0.89 <.0001 PP9 Shapiro-Wilk W 0.65 <.0001 VP1 Shapiro-Wilk W 0.61 <.0001 PP12 Shapiro-Wilk W 0.79 <.0001 VP2 Shapiro-Wilk W 0.64 <.0001 A1 Shapiro-Wilk W 0.80 <.0001 VP3 Shapiro-Wilk W 0.68 <.0001 A2 Shapiro-Wilk W 0.76 <.0001 VP4 Shapiro-Wilk W 0.64 <.0001 A3 Shapiro-Wilk W 0.83 <.0001 VP5 Shapiro-Wilk W 0.69 <.0001 A4 Shapiro-Wilk W 0.86 <.0001 AC5 Shapiro-Wilk W 0.67 <.0001 SO1 Shapiro-Wilk W 0.69 <.0001 AC6 Shapiro-Wilk W 0.70 <.0001 SO2 Shapiro-Wilk W 0.64 <.0001 AC7 Shapiro-Wilk W 0.65 <.0001 SO3 Shapiro-Wilk W 0.73 <.0001 VI1 Shapiro-Wilk W 0.86 <.0001 SO4 Shapiro-Wilk W 0.69 <.0001 VI2 Shapiro-Wilk W 0.84 <.0001 SU1 Shapiro-Wilk W 0.68 <.0001 VI3 Shapiro-Wilk W 0.76 <.0001 SU2 Shapiro-Wilk W 0.60 <.0001 DE1 Shapiro-Wilk W 0.75 <.0001 SU3 Shapiro-Wilk W 0.62 <.0001 DE2 Shapiro-Wilk W 0.76 <.0001 SU4 Shapiro-Wilk W 0.64 <.0001 DE3 Shapiro-Wilk W 0.59 <.0001 F1 Shapiro-Wilk W 0.68 <.0001 AB1 Shapiro-Wilk W 0.74 <.0001 F2 Shapiro-Wilk W 0.80 <.0001 AB2 Shapiro-Wilk W 0.82 <.0001 F3 Shapiro-Wilk W 0.77 <.0001 VR1 Shapiro-Wilk W 0.69 <.0001 F4 Shapiro-Wilk W 0.75 <.0001 VR2 Shapiro-Wilk W 0.72 <.0001 F5 Shapiro-Wilk W 0.65 <.0001 VR3 Shapiro-Wilk W 0.62 <.0001

197

TABLE O.1: - Continued SPY1 Shapiro-Wilk W 0.70 <.0001 SPY2 Shapiro-Wilk W 0.81 <.0001 SPY3 Shapiro-Wilk W 0.67 <.0001 SPY4 Shapiro-Wilk W 0.80 <.0001

System Label Test Statistic Value Prob Mardia Skewness Henze-Zirkler T 125E3 <.0001 Mardia Kurtosis Henze-Zirkler T 48.64 <.0001

198

APPENDIX P

PATH ANALYSIS

INPUT INSTRUCTIONS

TITLE: A Measurement Model DATA:File is 'Tom_Path.txt';

VARIABLE: NAMES ARE JobDem JobRes VolEng VolSat VolCom VolRet VolPer; USEVARIABLES ARE JobDem JobRes VolEng VolSat VolCom VolRet VolPer ;

ANALYSIS: TYPE IS GENERAL; ESTIMATOR IS ML; MODEL: VolSat on VolEng; VolCom on VolEng; VolPer on VolEng; VolRet on VolEng; VolRet on VolCom; VolRet on VolSat; VolRet on VolPer; VolEng on JobDem; VolEng on JobRes;

OUTPUT: RESIDUAL STDYX MODINDICES TECH4;

INPUT READING TERMINATED NORMALLY

A Measurement Model

SUMMARY OF ANALYSIS

Number of groups 1 Number of observations 211 Number of dependent variables 5 Number of independent variables 2 Number of continuous latent variables 0

Observed dependent variables

Continuous VOLENG VOLSAT VOLCOM VOLRET VOLPER

199

Observed independent variables JOBDEM JOBRES

Estimator ML Information matrix OBSERVED Maximum number of iterations 1000 Convergence criterion 0.500D-04 Maximum number of steepest descent iterations 20

Input data file(s) Tom_Path.txt

Input data format FREE

THE MODEL ESTIMATION TERMINATED NORMALLY

MODEL FIT INFORMATION

Number of Free Parameters 19

Loglikelihood

H0 Value -3154.603 H1 Value -3069.219

Information Criteria

Akaike (AIC) 6347.205 Bayesian (BIC) 6410.890 Sample-Size Adjusted BIC 6350.687 (n* = (n + 2) / 24)

Chi-Square Test of Model Fit

Value 170.767 Degrees of Freedom 11 P-Value 0.0000

RMSEA (Root Mean Square Error Of Approximation)

Estimate 0.262 90 Percent C.I. 0.228 0.298 Probability RMSEA <= .05 0.000

200

CFI/TLI

CFI 0.690 TLI 0.436

Chi-Square Test of Model Fit for the Baseline Model

Value 534.692 Degrees of Freedom 20 P-Value 0.0000

SRMR (Standardized Root Mean Square Residual)

Value 0.114

STANDARDIZED MODEL RESULTS

STDYX Standardization

Two-Tailed Estimate S.E. Est./S.E. P-Value

VOLSAT ON VOLENG 0.676 0.037 18.115 0.000

VOLCOM ON VOLENG -0.486 0.053 -9.233 0.000

VOLPER ON VOLENG 0.504 0.051 9.815 0.000

VOLRET ON VOLENG 0.262 0.088 2.968 0.003 VOLCOM 0.020 0.073 0.278 0.781 VOLSAT 0.222 0.086 2.570 0.010 VOLPER 0.009 0.077 0.120 0.904

VOLENG ON JOBDEM -0.005 0.059 -0.080 0.936 JOBRES 0.534 0.050 10.697 0.000

Intercepts VOLENG 2.284 0.601 3.801 0.000 VOLSAT 2.350 0.393 5.976 0.000 VOLCOM 4.348 0.292 14.903 0.000 VOLRET 2.427 0.819 2.965 0.003

201

VOLPER 7.061 0.629 11.229 0.000

Residual Variances VOLENG 0.714 0.053 13.572 0.000 VOLSAT 0.542 0.051 10.737 0.000 VOLCOM 0.764 0.051 14.954 0.000 VOLRET 0.808 0.049 16.497 0.000 VOLPER 0.746 0.052 14.410 0.000

R-SQUARE Observed Two-Tailed Variable Estimate S.E. Est./S.E. P-Value

VOLENG 0.286 0.053 5.446 0.000 VOLSAT 0.458 0.051 9.057 0.000 VOLCOM 0.236 0.051 4.616 0.000 VOLRET 0.192 0.049 3.928 0.000 VOLPER 0.254 0.052 4.907 0.000

QUALITY OF NUMERICAL RESULTS

Condition Number for the Information Matrix 0.272E-05 (ratio of smallest to largest eigenvalue)

RESIDUAL OUTPUT

ESTIMATED MODEL AND RESIDUALS (OBSERVED - ESTIMATED)

Model Estimated Means/Intercepts/Thresholds VOLENG VOLSAT VOLCOM VOLRET VOLPER ______1 51.592 131.270 6.166 13.806 32.545

Model Estimated Means/Intercepts/Thresholds JOBDEM JOBRES ______1 95.431 101.839

Residuals for Means/Intercepts/Thresholds VOLENG VOLSAT VOLCOM VOLRET VOLPER ______1 0.000 0.000 0.000 0.000 0.000

Residuals for Means/Intercepts/Thresholds JOBDEM JOBRES ______

202

1 0.000 0.000

Standardized Residuals (z-scores) for Means/Intercepts/Thresholds VOLENG VOLSAT VOLCOM VOLRET VOLPER ______1 0.000 0.000 0.000 0.000 0.000

Standardized Residuals (z-scores) for Means/Intercepts/Thresholds JOBDEM JOBRES ______1 0.000 0.000

Normalized Residuals for Means/Intercepts/Thresholds VOLENG VOLSAT VOLCOM VOLRET VOLPER ______1 0.000 0.000 0.000 0.000 0.000

Normalized Residuals for Means/Intercepts/Thresholds JOBDEM JOBRES ______1 0.000 0.000

Model Estimated Covariances/Correlations/Residual Correlations VOLENG VOLSAT VOLCOM VOLRET VOLPER ______VOLENG 75.635 VOLSAT 121.371 425.638 VOLCOM -17.752 -28.487 17.664 VOLRET 8.854 20.417 -1.915 6.270 VOLPER 14.194 22.777 -3.331 1.717 10.485 JOBDEM -14.042 -22.533 3.296 -1.644 -2.635 JOBRES 68.963 110.664 -16.186 8.073 12.942

Model Estimated Covariances/Correlations/Residual Correlations JOBDEM JOBRES ______JOBDEM 354.805 JOBRES -42.324 219.595

Residuals for Covariances/Correlations/Residual Correlations VOLENG VOLSAT VOLCOM VOLRET VOLPER ______VOLENG 0.000 VOLSAT 0.000 0.000 VOLCOM 0.000 -6.397 0.000 VOLRET 0.000 0.005 -0.190 0.000

203

VOLPER 0.000 11.560 -2.579 0.280 0.000 JOBDEM 0.000 27.246 20.116 1.931 -6.225 JOBRES 0.000 87.446 -17.844 2.620 17.587

Residuals for Covariances/Correlations/Residual Correlations JOBDEM JOBRES ______JOBDEM 0.000 JOBRES 0.000 0.000

Standardized Residuals (z-scores) for Covariances/Correlations/Residual Corr VOLENG VOLSAT VOLCOM VOLRET VOLPER ______VOLENG 0.000 VOLSAT 999.000 999.000 VOLCOM 0.000 -1.566 0.000 VOLRET 0.000 999.000 999.000 999.000 VOLPER 0.000 3.381 -3.293 999.000 0.000 JOBDEM 0.000 1.392 4.017 0.654 -1.701 JOBRES 0.004 5.053 -4.723 1.291 5.726

Standardized Residuals (z-scores) for Covariances/Correlations/Residual Corr JOBDEM JOBRES ______JOBDEM 0.000 JOBRES 0.000 0.000

Normalized Residuals for Covariances/Correlations/Residual Correlations VOLENG VOLSAT VOLCOM VOLRET VOLPER ______VOLENG 0.000 VOLSAT 0.000 0.000 VOLCOM 0.000 -0.994 0.000 VOLRET 0.000 0.001 -0.258 0.000 VOLPER 0.000 2.236 -2.525 0.486 0.000 JOBDEM 0.000 1.018 3.539 0.595 -1.467 JOBRES 0.000 3.487 -3.652 0.986 4.492

Normalized Residuals for Covariances/Correlations/Residual Correlations JOBDEM JOBRES ______JOBDEM 0.000 JOBRES 0.000 0.000

MODEL MODIFICATION INDICES

204

NOTE: Modification indices for direct effects of observed dependent variables regressed on covariates may not be included. To include these, request MODINDICES (ALL).

Minimum M.I. value for printing the modification index 10.000

M.I. E.P.C. Std E.P.C. StdYX E.P.C.

ON Statements

VOLENG ON VOLSAT 44.282 -0.414 -0.414 -0.982 VOLENG ON VOLCOM 32.170 1.459 1.459 0.705 VOLENG ON VOLPER 53.360 -2.469 -2.469 -0.919 VOLSAT ON VOLPER 15.614 1.478 1.478 0.232 VOLSAT ON JOBRES 44.594 0.558 0.558 0.401 VOLCOM ON VOLPER 13.293 -0.330 -0.330 -0.254 VOLCOM ON JOBDEM 17.960 0.057 0.057 0.256 VOLCOM ON JOBRES 31.761 -0.114 -0.114 -0.401 VOLPER ON VOLSAT 15.614 0.050 0.050 0.319 VOLPER ON VOLCOM 13.293 -0.191 -0.191 -0.248 VOLPER ON VOLRET 12.488 1.655 1.655 1.280 VOLPER ON JOBRES 53.244 0.112 0.112 0.514

WITH Statements

VOLSAT WITH VOLENG 44.282 -95.566 -95.566 -0.856 VOLCOM WITH VOLENG 32.170 19.695 19.695 0.730 VOLPER WITH VOLENG 53.360 -19.308 -19.308 -0.940 VOLPER WITH VOLSAT 15.614 11.560 11.560 0.272 VOLPER WITH VOLCOM 13.293 -2.579 -2.579 -0.251 JOBDEM WITH VOLCOM 12.542 16.677 16.677 0.241 JOBRES WITH VOLSAT 48.600 125.794 125.794 0.559 JOBRES WITH VOLCOM 24.105 -21.421 -21.421 -0.393 JOBRES WITH VOLPER 49.484 23.363 23.363 0.564

TECHNICAL 4 OUTPUT

ESTIMATES DERIVED FROM THE MODEL

ESTIMATED MEANS FOR THE LATENT VARIABLES VOLENG VOLSAT VOLCOM VOLRET VOLPER ______1 51.592 131.270 6.166 13.806 32.545

ESTIMATED MEANS FOR THE LATENT VARIABLES

205

JOBDEM JOBRES ______1 95.431 101.839

ESTIMATED COVARIANCE MATRIX FOR THE LATENT VARIABLES VOLENG VOLSAT VOLCOM VOLRET VOLPER ______VOLENG 75.635 VOLSAT 121.371 425.638 VOLCOM -17.752 -28.487 17.664 VOLRET 8.854 20.417 -1.915 6.270 VOLPER 14.194 22.777 -3.331 1.717 10.485 JOBDEM -14.042 -22.533 3.296 -1.644 -2.635 JOBRES 68.963 110.664 -16.186 8.073 12.942

ESTIMATED COVARIANCE MATRIX FOR THE LATENT VARIABLES JOBDEM JOBRES ______JOBDEM 354.805 JOBRES -42.324 219.595

ESTIMATED CORRELATION MATRIX FOR THE LATENT VARIABLES VOLENG VOLSAT VOLCOM VOLRET VOLPER ______VOLENG 1.000 VOLSAT 0.676 1.000 VOLCOM -0.486 -0.329 1.000 VOLRET 0.407 0.395 -0.182 1.000 VOLPER 0.504 0.341 -0.245 0.212 1.000 JOBDEM -0.086 -0.058 0.042 -0.035 -0.043 JOBRES 0.535 0.362 -0.260 0.218 0.270

ESTIMATED CORRELATION MATRIX FOR THE LATENT VARIABLES JOBDEM JOBRES ______JOBDEM 1.000 JOBRES -0.152 1.000

DIAGRAM INFORMATION

Use View Diagram under the Diagram menu in the Mplus Editor to view the diagram. If running Mplus from the Mplus Diagrammer, the diagram opens automatically.

Diagram output c:\users\ut08\desktop\sumtom_path.dgm

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BIOGRAPHICAL SKETCH

Thomas F McMorrow, Jr is the son of Thomas F. and Linda C. McMorrow. He had the great fortune of growing up a military brat which afforded him the opportunity to live and experience life in many states of America as well as other countries around the world.

He made my way through a Bachelor of Arts Degree in English Literature by working for the University of Florida Technical Services Department providing event services to various university departments and as an event coordinator for the Stephen C. O’Connell Center, the student center/arena located on the University of Florida campus. It was a great experience that set the course for his career. Tom graduated from the University of Florida in Gainesville,

Florida in 1984. His first job after graduation was teaching high school English and drama in south Florida. He returned to Gainesville in 1988 as a member of the management team of the

Stephen C. O’Connell Center where he was quickly promoted to Assistant Director. The job at the O’Connell Center afforded him the opportunity to continue to work a wide variety of events both in the Center as well as Ben Hill Griffin Stadium and other locations around campus.

During his time there he was afforded the opportunity to engage in several freelance employment opportunities including serving as the Assistant Venue Manager for volleyball competition at the

1996 Olympic Games held at the Omni Arena in Atlanta. During his time at the O’Connell

Center he also returned to the classroom to advance his education. In order to expand his experience and complete his degree he transferred from the O’Connell Center to become the

Auditorium Manager at the University Auditorium at UF in 2001. In 2005 Tom earned his

Master of Science in Recreation Studies with a focus in Recreation Administration. He earned the degree while working full time as the Auditorium Manager.

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From UF he moved to assume the position as the Event Manager for the Event Center at

San Jose State University in San Jose, California. Unfortunately the move was not a good fit which lead him to reexamine his career goals. The experience highlighted some of the deficiencies in the profession and the need for professional development for individuals in the industry. That is when he decided to pursue a doctorate and a career teaching facility and event management. This led him to Florida State University.

He is a member of the International Association of Venue Managers (IAVM) and the

North American Society for Sport Management (NASSM). His research interests include venue and event management, human resource management, employee engagement, and volunteer management.

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