ATHLETES CAUGHT in PERSONAL FAILING: DOES WINNING TAKE CARE of EVERYTHING? by ANNELIE LINA SOPHIE SCHMITTEL a DISSERTATION PRES

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ATHLETES CAUGHT IN PERSONAL FAILING: DOES WINNING TAKE CARE OF EVERYTHING?

ANNELIE LINA SOPHIE SCHMITTEL

A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY

UNIVERSITY OF FLORIDA

2015

To myself, as a reminder that anything is possible. And to my high school Latin teacher who told me I was never going to amount to anything: “Inveniam viam aut faciam” [I shall find a way or make one].

ACKNOWLEDGEMENTS

There is not a single more important section of this dissertation than the one I am about to write and the one you, the reader, are about the read.

Throughout my entire life I’ve been blessed to be surrounded by some of the kindest, most generous, hard-working, dedicated and smart people in the world. The first of which have been by my side since day one (literally): my dad and my mom,

Manfred and Heidrun Schmittel. Dad, it is safe to say that without you I would not be where I am today. From the subtle nudge to study abroad in the U.S. at the age of 16, to letting me move to Minnesota permanently before even turning 18, from allowing me to embark on the longest educational endeavor known to mankind (like you, I never thought it would end), to always being there when I needed you. You have always encouraged and supported my journey; in fact, it was you who started and fostered my love for sports. You’ve always believed in me and my dreams, and you never second- guessed the strange decisions I’ve made along the way; for that I am forever grateful and indebted to you (and yes, even financially this is probably true!). You’ve taught me the value of being ambitious and always working harder than the next person. The amount of sacrifices you have made to get me to this point are beyond anything I will ever comprehend, but know that my success is every bit your success. Mom, you have been my biggest cheerleader and there is not enough ink or pages in this world to express my appreciation for everything you have done. For as many doubts as I have had throughout this process and throughout life, you’ve always found the right words of encouragement and your unmatched love and belief in me has driven me to always go the extra mile.

To Erik Steine, bless your heart for sticking with me over the last eleven years and more particularly throughout my doctoral studies. You have been my biggest supporter and I can honestly say I could not have gotten through the last years of trials and tribulations without you. Thank you for loving me, challenging me to always aim higher, for listening to me and my endless talks about research ideas, the latest sports news, and god-knows-what-else. Thank you for always having a hug ready for me, for giving me space when I needed it, for never making me feel bad about being busy with school, for always offering advice when I needed it, and for encouraging me to always be a better version of myself. Most importantly, thank you for teaching me everything I know about football and for making me fall in love with the sport and the people who play it. I am so lucky to have you in my life and I cannot wait to see where life takes us now that we will both be doctors (yes, yes, I know, you are the ‘real’ doctor- medical degree and all).

There are so many other people who have supported and loved me throughout this process, and although I am sure the editorial office did not intend for me to write a whole dissertation worth of thank yous, I am so glad that there is a place in this document to acknowledge them all.

My deepest gratitude goes to my dissertation chair, Dr. Debbie Treise who patiently guided me through my doctoral career. Without you, Dr. Treise, I could not have completed this dissertation. Your kindness, dedication, smarts, and care kept me going on this long and very windy road. Thank you for never giving up on me, for always talking me off the ledge, for always leaving your door open and never turning me away when I had ‘just one more question’, for sending me Burberry sale emails and

encouraging my terrible retail therapy behavior (Dad, it’s all her fault!), and for allowing me to spend 8 wonderful weeks in Europe with some of the greatest students and professors in our college over the last two summers. You are truly one of the smartest, funniest, toughest, and most devoted women I have ever had the pleasure of meeting and I am beyond thankful for all you have done for me.

An enormous thank you to Professor Ted Spiker, who is not only the best teacher we have at the University of Florida, but also the greatest mentor any graduate student could ask for. You have had such a profound impact on my life and I will forever be grateful for you allowing me to be a part of the greatness that is #SportsMediaUF. You are the best role model in the classroom and I have learned an exponential amount from watching you teach and interact with students. If I can become half the educator you are, I will be just fine. Thank you for showing me what it is like to teach a class that you are so passionate about it never feels like work, for perfectly balancing fun and learning, for providing the most insightful practical non-researcher perspective, for creating some of my most memorable and funniest moments at UF (from Tweet-of-the-

Week to ‘I’m a man, I’m 40’) and for always checking in with me to make sure I’m ok.

Your support meant the world. Finally, thank you for all the rounds of boxing and

Saturdays filled with Ted Torture workouts- couldn’t have made it without them!

Thank you to Dr. Norm Lewis, who is the smartest man in Weimer. Since the day you handed me your 25-page syllabus for Perspectives you have challenged me to push my limits and to think beyond the obvious. Thank you for always having an open door and never being too busy, when really you are entirely too busy. There is not a single person other than my mom (and she has to do it) who I felt believed as much in

me as you have, Dr. Lewis, and I am forever grateful for all of your words of encouragement over the last three and a half years. You are a wonderful human being and I am fortunate to have been able to learn from you.

To Dr. Mike Sagas, you were the very first person I asked to be on my dissertation committee. Your humbleness, inquisitiveness, dedication and kindness are radiating and I knew immediately that I wanted to work with you. Thank you for making the Florida Gym and the Department of Sport Management as much of a home for me as the College of Journalism and Communications. Thank you for always treating me like one of your own students and offering me endless amounts of incredible opportunities, including PAADS, teaching in your Athlete Development program, working the Bayern Munich camp, and even offering me an assistantship within five seconds of learning I desperately needed one. You truly are one of the best department chairs at UF and everyone who gets to work with you is incredibly lucky. No amount of thank yous will be enough for you and the rest of my dissertation committee.

One of the most important thank yous goes to my research partner, friend, fellow pizza connoisseur, French fry lover, Blue Moon drinker, shoulder-to-cry-on-er, Kevin

Hull. I’ve often said that I would not have made it through without you and while that may or may not be true, I am 100% certain that this whole journey would not have been nearly as fun and/or successful without you. As the two most inexperienced researchers coming into this program, fellow sports nerds and secret (or not so secret) homebodies we found each other and made the most of the past three years. We shared research interests, an office, and a myriad of conference trips across the country, first

publications, countless pizzas and Chic-fil-A fries, infinite laughs and even some tears. I am honored to call you a friend. Thank you for everything!

Thank you to Jiyhe Kim for patiently teaching me statistics. You were an angel when I was in desperate need. I could not have completed this dissertation without your guidance. How you managed to get me to understand statistics is beyond me.

Thank you to Krissy Birnbrauer for the late night company studying for qualifying exams, for always being only a phone-call away, for letting me cry about statistics, for the many shared celebrations and laughs, and most importantly for being such a great friend and an inspiration.

Thank you to my partner-in-crime Nicki Karimipour who has traveled the world with me. There is no one I would have rather experienced study abroad with. We walked countless miles across a number of European cities together, visited more museums than either of us ever thought we would, ate an unhealthy amount of pizza, croissants, gelato, paninis, and even the occasional McDonalds, drank just the right amount of wine and aperitivos, took a million pictures, shared laughs and frustrations and made a million memories. I will never ever forget our adventures.

I thank Dr. Mike Weigold for not only taking me on several study abroad adventures, but also for his invaluable guidance in experimental designs and statistical analysis, as well as our deep discussions as to why sports and the athletes that play them matter and why fan behavior may or may not be completely irrational. Thank you for making me stop to critically think about sports fandom and my future in the sports industry.

To Dr. Brian Mills, thank you for the many impromptu statistics troubleshoot sessions in person and via email and for teaching me how to calculate double- depreciation. I am sure this will come in handy one day, Professor Moneybags.

Dr. Kiki Kaplanidou, you are the smartest sports educator there is (and I know you will deny it) and I thank you for making me fall in love with sports consumer research by teaching one of the most valuable courses I’ve taken in my entire educational career.

Thank you to Dr. Jimmy Sanderson at Clemson University for being the very first academic to mentor me, for teaching me the power of Twitter and always being willing to collaborate on a research study or bounce ideas off of.

To my former professors at my alma mater Winona State University, Dr. Tom

Grier, Dr. Ron Elcombe, Dr. George Morrow, and Dr. Teresa Waterbury thank you for encouraging me to go on this journey and supporting me throughout.

Huge thanks to my best friend since college, Jess Mallas. You were the first to fly me to Florida and drive me all the way to Gainesville to tour the campus with me. Thank you for making me a Gator, for supporting me, for cheering on my beloved Florida

Gators on any given weekend when I dragged you to a game, for always having my back, and for visiting when I desperately needed a night out.

I also thank the INC for being my DHQ (Dissertation Headquarters) and more importantly Matt Sheehan and Steve Johnson for not kicking me out of my unofficial office. Steve and Matt, you also deserve a big shout out for keeping me sane in my last year of the program. I could not have done it without you and the frequent coffee or Gin and Tonic dates.

To Andy Selepak, thank you for your friendship, innumerable amounts of candy, always having a spare umbrella ready for me to steal, um, I mean borrow, for forcing me to love Gainesville and all it has to offer, and for your constant support since Day 1.

A big thank you to the rest of my Weimer crew: Professor Tim Sorel and Bridget

Grogan, your Baggo Tournaments have been the highlight of my PhD career; Dr. John

Wright for getting the Sports Communication program started and allowing me to be a part of it; my ESPN 850 (now 95.3) family for being the best distraction anyone could ask for; and finally, Jodi, Kim and Sara for being the angels of Weimer- nothing would get done without you.

Finally, the biggest thank you to the University of Florida for being the greatest educational institution out there. I did not think I could fall in love with a campus and community as much as I have fallen in love with Gator Country and all it has to offer.

There is only one thing left to say: Gators.Always.

TABLE OF CONTENTS

page

ACKNOWLEDGEMENTS ...... 4

LIST OF TABLES ...... 15

LIST OF FIGURES ...... 18

LIST OF ABBREVIATIONS ...... 19

ABSTRACT ...... 21

CHAPTER

1 INTRODUCTION ...... 23

Purpose Statement ...... 27 Sports Industry vs. Corporate Industry...... 30 Athlete Celebrity Culture ...... 34 Contributions of the Study ...... 37 Study Overview ...... 39 Structure of the Dissertation ...... 39

2 REVIEW OF THE LITERATURE ...... 42

Image ...... 42 Athlete Image ...... 44 Q-Scores ...... 44 Other Measurements ...... 46 Image and Impression Management ...... 48 Athlete Scandal ...... 52 Impact of Transgressions on Sports Consumption ...... 54 Fan Identification ...... 57 BIRGing and CORFing ...... 59 The Black-sheep Effect ...... 62 Crisis and Crisis Repair Research ...... 64 Situational Crisis Communication Theory ...... 65 Image Restoration Theory ...... 68 Traditional Crisis and Crisis Repair Research in Sports ...... 71 Image Repair Communication in Sports ...... 75 Corporate Ability and Crisis ...... 80 Corporate Ability in Sports: Winning ...... 82 Theoretical Framework ...... 84 Violation Valence ...... 85 Predictive and Prescriptive Expectancies ...... 86 Expectancy Violation Theory in Crisis ...... 86

Violator Reward Valence ...... 88

3 METHODOLOGY ...... 92

Experimental Research Design ...... 92 Control ...... 93 Manipulation ...... 94 Participants ...... 95 Sports Selection ...... 97 Team and Scandal Selection ...... 99 Results of Transgression Study ...... 100 Winning and Losing ...... 101 Offensiveness ...... 102 Independent Variables ...... 107 Independent Variable #1: Expectancy ...... 107 Independent Variable #2: Fan Identification ...... 107 Independent Variable #3: Athlete Performance ...... 108 Independent Variable #4: Team Performance ...... 109 Dependent Variables ...... 111 Dependent Variable #1: Violation Valence ...... 111 Dependent Variable #2: Perceived Athlete Image ...... 111 Dependent Variable #3: CORFing Behavior ...... 112 Dependent Variable #4: Supportive Behavioral Intentions ...... 113 Dependent Variable #5: Athlete Advocacy ...... 113 Dependent Variable #6: Patronage Intentions ...... 114 Dependent Variable #7: Positive Word of Mouth (pWOM) ...... 114 Dependent Variable #8: Negative Word of Mouth (nWOM) ...... 115 Dependent Variable #9: Team Reputation ...... 116 Procedure ...... 116 Overview Summary ...... 118 Phase I ...... 119 Phase II ...... 121 Phase III ...... 122 Reliability and Validity ...... 124 Statistical Analyses ...... 126

4 RESULTS ...... 129

Study Results ...... 129 Participant Overview ...... 129 Participant Fandom ...... 132 Participant Fan Identification ...... 134 Treatment Group Overview ...... 135 Reliability ...... 137 Validity ...... 137 Attention and Manipulation Checks ...... 138 Role of Fan Identification ...... 141

Role of Image on Consumer Behavior ...... 141 Influence of Crisis ...... 142 Research Questions and Hypotheses...... 144 Hypothesis 1 ...... 144 Hypothesis 2 ...... 145 Research Question 1 ...... 147 Research Question 4 ...... 182

5 DISCUSSION ...... 188

Application of EVT in Sports Crisis ...... 190 Expectancies ...... 190 Violation Valence ...... 192 Positive Reward Valence...... 195 Image Repair through Performance ...... 196 Limitations and Future Research ...... 203 Conclusion ...... 209

APPENDIX

A INSTITUTIONAL REVIEW BOARD APPROVAL ...... 213

B MEASUREMENTS ...... 214

C CONSENT ...... 217

D ESPN ARTICLES ...... 219

Player Spotlight: Packers ...... 219 Player Spotlight: Buccaneers ...... 221 Transgression: Packers ...... 223 Transgression: Buccaneers ...... 224 Manipulation 1 Win/Positive ...... 225 Manipulation 2 Win/Negative ...... 227 Manipulation 3 Loss/Positive ...... 229 Manipulation 4 Loss/Negative ...... 231

E PRELIMINARY SURVEY ...... 233

F MAIN QUESTIONNAIRE ...... 245

Phase One ...... 245 Phase Two ...... 254 Phase Three ...... 257

G PRELIMINARY ANALYSIS ...... 261

Fan Identification and Athlete Image ...... 261

Fan Identification and Team Reputation ...... 261 Athlete Image and Consumer Behavior ...... 261 Team Reputation and Consumer Behavior ...... 263 Team Identification and Violation Valence ...... 264 Influence of fan ID on Consumer Behavior ...... 265 Crisis and Image ...... 269 Role of Fan ID on Consumer Behavior following Crisis ...... 273 Influence of Fan ID on Team Reputation following Crisis ...... 276

REFERENCES ...... 280

BIOGRAPHICAL SKETCH ...... 297

LIST OF TABLES

Table page

2-1 Scale of Athlete Brand Image (Arai, Ko & Kaplanidou, 2013)...... 49

2-2 Sports Spectator Identification Scale (Wann & Branscombe, 1993) ...... 59

2-3 Crisis Clusters according to SCCT (Coombs & Holladay, 2002) ...... 66

2-4 Strategies per Coombs (2007) Situational Crisis Communication Theory (p.155) ...... 69

2-5 Image Repair Strategies per Benoit’s (1995) Image Restoration Theory ...... 70

2-6 Sports Scandal Typology (Brown & Brown, 2013 in Brown, 2014) ...... 78

3-1 NFL Player Arrests Since 2000 (Rosenberg, 2015) ...... 98

3-2 Offensiveness of Transgression ...... 104

3-3 Descriptive Statistics of Transgressions ...... 105

3-4 Descriptive Statistics Likelihood to Forgive ...... 105

3-5 Descriptive Statistics of Transgressions ...... 106

3-6 Groups within the experiment ...... 110

4-1 Number of Participants in Each Cell ...... 130

4-2 Demographics of Participants Who Completed the Survey ...... 131

4-3 General Responses Regarding the NFL ...... 133

4-4 Mean Scores Fan vs. Non-Fan ...... 134

4-5 Attitudes toward NFL Football ...... 134

4-6 Attitudes toward NFL Football by Fan Identification ...... 134

4-7 Fan Identification by Groups ...... 135

4-8 Fan Identification by Groups ...... 136

4-9 Cronbach Alpha Results ...... 137

4-10 EFA Results...... 138

4-12 Means and SDs of Athlete Reputation by Treatment Conditions for Phase I-III 151

4-13 Means and SDs of Athlete Advocacy by Treatment Conditions for Phases I-III 153

4-14 Means and SDs of Athlete Supportive Behavioral Intentions by Treatment Conditions for Phases I-III ...... 156

4-15 Means and SDs of Athlete Positive Word-of-Mouth by Treatment Conditions for Phases I-III ...... 158

4-16 Means and SDs of Athlete Negative Word-of-Mouth by Treatment Conditions for Phases I-III ...... 162

4-17 Means and SDs of Cutting Off Reflected Failure (CORF) by Treatment Conditions for Phases II-III ...... 165

4-18 Means and SDs of Team Reputation by Treatment Conditions for Phases I- III ...... 168

4-19 Means and SDs of Team Patronage Intentions Repeat Purchase Domain by Treatment Conditions for Phases I-III ...... 174

4-20 Means and SDs of Team Patronage Intentions Word of Mouth Domain by Treatment Conditions for Phases I-III ...... 175

4-21 Means and SDs of Team Patronage Intentions Merchandise Consumption Domain by Treatment Conditions for Phases I-III ...... 175

4-22 Means and SDs of Team Patronage Intentions Media Consumption Domain by Treatment Conditions for Phases I-III ...... 175

4-23 Adjusted and Unadjusted Means and Variability for Post-Intervention Dependent Variables with Phase 1 and Phase 2 Scores as a Covariate ...... 178

4-24 Adjusted and Unadjusted Means and Variability for Post-Intervention Dependent Variables with Phase 1 and Phase 2 Scores as a Covariate ...... 181

4-25 Adjusted and Unadjusted Means and Variability for Post-Intervention Dependent Variables with Phase 1 and Phase 2 Scores as a Covariate ...... 185

4-26 Summary of Findings ...... 186

G-1 Perceived Athlete Image for Pre and Post Crisis Intervention ...... 270

G-2 Descriptive Statistics for Perceived Athlete Reputation for Pre and Post Crisis Intervention ...... 270

G-3 Descriptive Statistics for Athlete Advocacy for Pre and Post Crisis Intervention ...... 270

G-4 Descriptive Statistics for Athlete Supportive Behavioral Intentions for Pre and Post Crisis Intervention ...... 271

G-5 Descriptive Statistics for pWOM for Pre and Post Crisis Intervention ...... 271

G-6 Descriptive Statistics for nWOM for Pre and Post Crisis Intervention ...... 271

G-7 Descriptive Statistics for Perceived Team Reputation for Pre and Post Crisis Intervention ...... 272

G-8 Descriptive Statistics for Repeat Purchase Intentions for Pre and Post Crisis Intervention ...... 272

G-9 Descriptive Statistics for generated WOM (team) for Pre and Post Crisis Intervention ...... 272

G-10 Descriptive Statistics for Merchandise Consumption for Pre and Post Crisis Intervention ...... 273

G-11 Descriptive Statistics for Media Consumption for Pre and Post Crisis Intervention ...... 273

G-12 Perceived Athlete Image for High and Low Identification Fans Pre and Post Crisis ...... 274

G-13 Perceived Athlete Reputation for High and Low Identification Fans Pre and Post Crisis ...... 274

G-14 Athlete Advocacy for High and Low Identification Fans Pre and Post Crisis .... 275

G-15 Athlete Supportive Behavioral Intentions for High and Low Identification Fans Pre and Post Crisis ...... 275

G-16 pWOM for High and Low Identification Fans Pre and Post Crisis ...... 276

G-17 nWOM for High and Low Identification Fans Pre and Post Crisis ...... 276

G-18 Perceived Team Reputation for High and Low Identification Fans Pre and Post Crisis ...... 277

G-19 Repeat Purchase Intentions for High and Low Identification Fans Pre and Post Crisis ...... 278

G-21 Merchandise Consumption for High and Low Identification Fans Pre and Post Crisis ...... 279

G-22 Media Consumption for High and Low Identification Fans Pre and Post Crisis 279

LIST OF FIGURES

Figure page

4-1 Two-way interaction between Player Performance and Fan Identification on Perceived Athlete Image...... 150

4-2 Two-way interaction between Player Performance and Fan Identification on Perceived Athlete Reputation...... 154

4-3 Two-way interaction between Player Performance and Fan Identification on Athlete Advocacy...... 155

4-4 Two-way interaction between Player Performance and Fan Identification on Supportive Behavioral Intentions...... 157

4-5 Two-way interaction between Player Performance and Fan Identification on pWOM...... 160

4-6 Three-way Interaction between Team Performance, Player Performance and Fan ID on Negative Word of Mouth ...... 162

4-7 Two-way interaction between Player Performance and Fan Identification on nWOM...... 165

4-8 Two-way interaction between Player Performance and Fan Identification on CORF...... 167

4-9 Two-way interaction between team performance and player performance on Team Reputation...... 169

4-10 Two-way interaction between team performance and player performance on Merchandise Consumption...... 172

4-11 Two-way interaction between team performance and player performance on Media Consumption...... 173

G-1 Effect of Violation Valence and Fan Identification on Athlete Image ...... 266

G-2 Effect of Violation Valence and Fan Identification on Athlete Advocacy ...... 267

LIST OF ABBREVIATIONS

α Cronbach’s alpha; index of internal consistency

ANOVA Analysis of Variance statistical test

ANCOVA Analysis of Covariance statistical test

β Standardized Coefficient

BIRG Basking in Reflected Glory

BP British Petroleum

CA Corporate Ability

CI Confidence Interval

CORF Cutting off Reflected Failure

CSR Corporate Social Responsibility

DUI Driving Under the Influence

DWI Driving while Intoxicated

η2 Eta-squared; measure of effect size

EFA Exploratory Factor Analysis

EPO Erythropoietin- a glycoprotein hormone used for blood doping

ESPN Entertainment and Sports Programming Network

EVT Expectancy Violation Theory eWOM Electronic Word-of-Mouth

F F ratio; Computed value of ANOVA/ANCOVA

IRB Institutional Review Board

IRT Image Repair Theory

LoP Level of Processing Theory

M Arithmetic mean: sum of a number of measurements divided by the number of measures

MABI Model of Athlete Brand Image

MVP Most Valuable Player n Number

NBA National Basketball Association

NFL National Football League nWOM Negative Word-of-Mouth p Probability value

PED Performance Enhancing Drugs

PGA Professional Golf Association pWOM Positive Word-of-Mouth r Pearson’s r correlation

R2 Coefficient of determination

SABI Scale of Athlete Brand Image

SCCT Situational Crisis Communication Theory

SD Standard deviation

SPSS Statistical Package for the Social Sciences

SSIS Sports Spectator Identification Scale t t ratio

UF University of Florida

WOM Word-of-Mouth

WSU Winona State University

Q Score measurement of the familiarity and appeal of a brand, celebrity, company, or entertainment product

Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy

ATHLETES CAUGHT IN PERSONAL FAILING: DOES WINNING TAKE CARE OF EVERYTHING?

Annelie Lina Sophie Schmittel

December 2015

Chair: Debbie Treise Major: Mass Communication

This dissertation examines the effects of athletic performance on fan behavior in response to an athlete transgression. Using Burgoon’s (1989) expectancy violation theory (EVT) as a theoretical framework, this study sought to explore if within a setting that stresses success and failure as well as wins and losses, effective image restoration can be achieved or be determined by high athletic performance. Employing a 2 (fan identification [high vs. low]) × 2 (athlete performance [positive vs. negative) × 2 (team performance [win vs. loss]) factorial design, the process of sports consumer behaviors during an ongoing athlete transgression (pre-,post-crisis + follow-up) is examined. A number of dependent variables were assessed in the study to measure consumer attitude and supportive behavioral intentions toward both the athlete and his organization (team). Dependent variables included in this study are: perceived image of athlete and organization, cutting-off-reflected failure (CORF) behaviors, positive and negative word-of-mouth (WOM), and supportive behavioral intentions toward both the athlete and the team.

Results suggest a relationship between perceived image/reputation and behavior, meaning the more positive the image of an athlete the more stakeholders are

willing to support him and the less negative WOM is generated. A transgression, of course, negatively impacts perceived athlete image, which subsequently decreases fan support and leaves the athlete in need of successful image restoration procedures.

There is evidence that an athlete will have more success repairing his/her image when positively performing on the field and contributing to the success of the team; on the other hand, an athlete can further damage his/her image if he/she does not perform to the level expected by the fans. The influence of perceived image, transgression events, and subsequent consumer outcomes were further analyzed by fan identification levels and overall team performance.

In sum, this dissertation supports the applicability of expectancy violations theory to the sports setting. The findings suggest that expectancies are predictors that determine violation valence, which influence a variety of sports consumer outcomes. In addition, violation reward valence can be garnered through performance on the field, mitigating violation valance felt and positively influencing the subsequent fan behaviors.

Both theoretical and practical implications are provided.

CHAPTER 1 INTRODUCTION

In late September of 2010, just weeks into the football season, USA Today published an article entitled “Winning is the ultimate redeemer.” The piece, prompted perhaps by the flurry of recent sports scandals at the time (e.g., NFL quarterback Ben

Roethlisberger’s alleged rape case(s), pro golfer Tiger Woods’ extramarital affairs and

NFL receiver Braylon Edwards’ DWI) posited, “Nothing succeeds in the act of contrition like success” (Lopresti, 2010, para. 6). In the article, writer Mike Lopresti wondered if the recent successes of two sports figures, namely NFL quarterback Michael Vick1 and

NFL head coach Pete Carroll2, may, in fact, play a significant role in the efforts to polish their image after experiencing severe reputation threats. Lopresti speculated on the outcome of public forgiveness if Vick became the season’s Most Valuable Player (MVP) and Carroll were to be awarded Coach of the Year honors. He suggested athletic success would positively outweigh any other image repair efforts (e.g., public apology) developed by crisis managers, specifying, “No PR consultant on the planet could come up with a better strategy for legacy renovation” (para. 14). The writer concluded his piece with the following proposition: “Remorse is nice. But whether proper or not:

Winners are forgiven more quickly than losers.” Three years later, one company, which was no stranger to mitigating athlete crises, endorsed that view. The apparel company

1 Vick served twenty-one months in prison for his involvement in running a dogfighting ring (Macur, 2007). He returned to professional football in August 2009 when he signed with the Philadelphia Eagles (Van Grove, 2009).

2 Carroll’s image suffered in light of NCAA investigations into improper benefits given to student-athletes at the University of Southern California (USC), where he was a successful football head coach between 2001-2008. Carroll “abandoned the sinking ship” to take the head coaching job with the Seattle Seahawks. He denies knowledge of the incidents (Farrar, 2010).

Nike released an advertisement on its social media sites displaying golfer Tiger Woods overlaid with, “Winning takes care of everything” (Nike Golf, 2013). The advertisement premiered just one day after Woods regained the number one ranking by winning the esteemed 2013 Arnold Palmer Invitational golf tournament in Orlando, Florida (Zaldivar,

2013).

It was the first time Woods led the list of the sport’s most successful athletes since becoming the center of what some call one of the biggest scandals in sports

(Cohen, 2010; Levy, 2013). He previously held the prestigious spot for more than 280 consecutive weeks before tabloid magazines and other media outlets exposed his extramarital affairs in late 2009 and forced him into a personal and professional tailspin.

Woods went from being one of the most popular athletes and most sought-after product endorsers (Badenhausen, 2009) to a punch line fueled by his affairs with porn stars, divorce from his wife, parting with sponsors, and failure on the golf course. But a comeback followed, at least for a time for the superstar athlete who holds numerous records and is regarded as one of the best golfers ever. Between his return to tournament play after a leave of absence to work out personal issues with his family and the Arnold Palmer Invitational victory, Woods won six tournaments and landed new sponsorship deals with Rolex and Kowa, while continuing his multimillion-dollar partnerships with EA Sports and Nike. He entered into a new relationship with star athlete Lindsey Vonn, suggesting both his personal and professional lives were back on track.

However impressive Woods’ athletic and personal comeback at the time, the

Nike advertisement quickly came under criticism. Comments on Nike’s Facebook site

showcased an array of reviews with some users accusing the apparel titan of making a mockery out of Woods’ personal failings. One Facebook user commented,

“NO, winning does NOT take care of everything. It doesn't take care of the lies, of the betrayal, of the narcissistic behavior that uses people. The egocentric proposition that the statement makes belies the problem— the arrogance that the more success, fame and celebrity you have, the less the rules of moral discipline and integrity apply. False. Shame on you Nike for suggesting otherwise!” (Corey, 2013).

Another user stated, “Nice message Nike. Be a dirtbag, cheat on your wife with countless women, but hey, you win and it's all good baby. Way to target the youth with this message” (Davis, 2013). Nike quickly responded to the criticism voiced by its costumers, stating the ad merely referenced Woods’ well-known life motto and served as homage to his recent career achievements, “Tiger has always said he competes to win. When asked about his goals such as getting back to No. 1, he has said consistently winning's the way to get there. The statement references that sentiment and is a salute to his athletic performance” (Gast, 2013).

Nonetheless, the advertisement’s double meaning is difficult to ignore. The circumstances surrounding the personal failings of Tiger Woods and the countless scandals of other professional athletes beg the question of what successful image repair discourse looks like for these athletic competitors. Are traditional crisis repair strategies, as developed for organizational crisis situations (e.g., mortification, corrective action), effective to mitigate crises and image threats, or did the USA Today reporter,

Nike, and Woods have it right all along and athletic achievements of athletes are a better predictor of perceived image and stakeholder forgiveness following a transgression?

Research suggests that in order to mitigate the damages caused by a scandal, athletes (and other celebrities) must employ some form of crisis management and engage in image repair (Benoit, 1995; Coombs & Holladay, 1996; Moody, 2011). Often these transgressions lead to the involvement from public relations professionals, who use strategies such as offering accounts, excuses, or public apologies in hopes stakeholders will pardon the behavior. A number of researchers have devoted their time to conducting studies on the image repair efforts of athletes in crisis (see: Benoit &

Hanczor, 1994; Brazeal, 2008; Frederick, Burch, Sanderson & Hambrick, 2014; Glantz,

2010; Holdener & Kauffman, 2014; Meng & Pan, 2013; Walsh & McAllister-Spooner,

2011). However, most of these studies have taken a case-study approach centered on the sender (the athlete), and in what ways he/she used traditional crisis communication and image repair strategies to restore reputation. While these studies contribute to our understanding of athlete image repair, they only provide limited results through anecdotal evidence and cannot offer empirically tested recommendations. There is a relative dearth of sports crisis communication literature addressing how the public perceives an athlete’s image repair discourse, thus, lacking true practical and theoretical application.

Crisis experts have long called for more audience-centered image repair discourse research (Coombs, 2006). Although widely popular in the corporate and organizational environment, empirical research within the sports field has been limited.

Brown, Dickhaus and Long (2012) investigated the public’s perception of LeBron James subsequent to the July 8, 2010, controversial hour-long television show with ESPN in which he announced leaving the Cleveland Cavaliers to join the Miami Heat. The

authors employed experimental research methodologies to determine if the perceived image of James changed after presenting varying image repair communication strategies. The findings were in line with previous athlete image repair research studies that suggest mortification (saying “sorry”) is a better strategy than shifting the blame or bolstering. K. Brown (2014) expanded on this topic by examining the effects of the type of offense (criminal vs. non-criminal) and the athlete’s response to the offense

(mortification vs. attacking the accuser vs. bolstering) on the athlete’s perceived image and the amount of negative word-of-mouth generated by fans. Results indicated that athletes would achieve a more favorable image repair if they use the mortification strategy rather than the attacking the accuser or bolstering strategy, regardless of the type of transgression. Lee and Bang (2013) examined whether there is a difference between the type of transgression (competency-related vs. integrity-related) and image repair response type (apology vs. denial) in regard to regaining fans’ trust. They found apologies were more effective for both violations. The results showed that fans were less willing to forgive and restore trust in integrity-violating instances, regardless of which response was used.

While these research endeavors have shed light on how traditional crisis communication strategies such as apologies or justifications can influence image repair in the sports environment, they fail to take into account how other elements might influence consumer perceptions of athlete image repair.

Purpose Statement

The focus of this dissertation is to empirically investigate if other factors can impact the image repair discourse of professional athletes who have experienced a transgression or are involved in a personal and/or professional scandal. The study

investigates the role of athletic performance within a team setting during crisis. More specifically, the purpose of this research is to examine to what extent athletic success or athletic failure, that is, winning or losing, factor into the image repair discourse of an athlete. The study is informed by expectancy violation theory, and draws insights from interpersonal communication and organizational crisis research to explore factors affecting perceived athlete image, fan forgiveness and continued support. It extends research on the dimensions of corporate associations and crisis management into the sports setting to determine if athletic success or failure affects the athlete image repair discourse.

The dissertation is guided by and is, in part, an adaption of research on the impact of prior expectancies and relational satisfaction during corporate crisis by Kim

(2014), as well as a study examining corporate associations and crisis management

(Kim, 2013a). Kim (2014) investigated if relational satisfaction and expectancies predict stakeholders’ perception toward an organization experiencing a crisis event. Using expectancy violation theory as a theoretical framework and the 2010 British Petroleum

(BP) Gulf of Mexico oil spill as a sample crisis event, the researcher found relational satisfaction and expectancies predicted negative responses toward the organization in crisis (Kim, 2014). Moreover, the findings of the study revealed that expectancy violation theory is applicable to organizational crises and serves as a valid theoretical framework for predicting how stakeholders respond to organizational crisis events. However, a limitation within the study pointed toward a need to further explore how the theory could be applied to crisis events, as well as how relational satisfaction and expectancies influence stakeholders in non-corporate settings such as sports. Although Kim (2014)

used a real crisis (the BP oil spill) in her study participants were asked to evaluate their relational satisfaction and expectancies toward BP retrospectively; meaning, they were asked to think back and self-report how they felt about BP before the crisis, knowing the crisis had already transpired. This dissertation seeks to eliminate this limitation.

Furthermore, it aims to expand Kim’s findings into the realm of sports by illustrating how athletic performance (athletic ability) factors into negative responses toward a sports entity in crisis. The present study draws on research by Kim (2013a), which tested the dimensional consequences of positive prior corporate ability and corporate social responsibility, as well as negative corporate ability and corporate social responsibility during a product-harm crisis of a fictional corporation. The results of her investigation were that in times of product-harm crisis it is worse for companies to have negative prior corporate ability (CA) associations than to have negative corporate social responsibility

(CSR) associations, whereas positive prior CSR associations were more beneficial than positive CA associations (Kim, 2013a). To test her hypotheses, the study used s 2 × 2 ×

2 experiment and another 2 × 2 design. The first experiment tested consumer responses by exploring the interaction effects of the valence of corporate associations

(positive vs. negative), the dimension of corporate association (CA vs. CSR) and crisis type (victim crisis vs. preventable crisis). The second experiment studied how mixed corporate associations, that is, how positive corporate ability combined with negative corporate social responsibility or negative corporate ability combined with positive corporate social responsibility affect consumer reactions in time of corporate crisis (Kim,

2013a).

This study postulated that corporate associations that stakeholders make with a company, namely corporate ability (CA) associations and corporate social responsibility

(CSR) associations are also present in sports and apply to professional athletes. Here corporate ability associations do not reflect stakeholders’ opinions of a company’s ability to produce high-quality products or services (Biehal & Sheinin, 2007; Kim, 2011), but rather stakeholders’ (i.e., fans’) opinions regarding an athlete’s and/or team’s athletic performance. Similarly, corporate social responsibility associations could be used to reflect sports stakeholders’ associations related to the status of an athlete as a positive member of society, as opposed to stakeholders’ perception of a company’s good deeds

(Sen & Bhatacharya, 2001). While corporate social responsibility provides an interesting construct for understanding athlete image, it was not directly considered for this dissertation; instead, the focus was exclusively on athlete performance as one dimension of athlete association that may ultimately shape how the athlete is perceived, specifically in times of crisis following a transgression.

Sports Industry vs. Corporate Industry

Given a surfeit of published research investigating the impact of corporate associations one might question if there is a need to expand these inquiries into the sports setting. Arguably, without significant differences between the corporate and sports worlds, this type of research would provide limited meaning. Are associations, relational satisfactions, and expectations toward sports professionals distinctive or merely the same as corporate associations applied into a different industry? Smith and

Westerbeek (2007) opined that sports are distinctive in the sense that they are

“implicitly woven into society,” thus, positioning them to influence people at an easier rate than is business (p. 6). The importance of sports in society, and more specifically

the “power of sports as a communications medium” are most frequently credited as the fundamental difference between sports and corporations (Davies, 2002; Magnusen,

Hong & Mondello, 2011). Researchers have argued that sport has the potential to be exceedingly pervasive and influential (Smith & Westerbeek, 2007). Walker and Kent

(2009) determined the celebrity status of athletes, the level of loyalty displayed by sports fans, and the connections between sports organizations and their communities as distinctive characteristics that shape the perceptions of athletes and sports organizations, as well as reputations thereof held by sports consumers. Supporting these notions, Babiak and Wolfe (2009) identified four factors distinctive to sports: (a) passion, (b) economics, (c) transparency, and (d) stakeholder management. They posit that passion for and interest in sports related activities (games) or entities (athletes, teams) is higher than that for commercial businesses, citing sport fan specific phenomena such as basking in reflected glory3 and cutting off reflected failure4 (Cialdini,

Borden, Thorne, Walker, Freeman & Sloan, 1976) to support their conclusion. Likewise, the authors suggest that peculiar economic elements of the sports industry (e.g., monopoly power, governmental support, public funds for arena constructions), as well as transparency into most aspects of the industry (e.g., player salaries, performance in form of wins and loses, personal information) might lead to different stakeholder perceptions (Babiak & Wolfe, 2009).

3 Basking in reflected glory refers to the tendency of sports fans to emphasize their association with their favorite team (or athlete) following a win or success (Cialdini, et al., 1976)

4 Cutting off reflected failure describes the phenomenon of sports fans distancing themselves from their team (or athlete) after a loss or failure (Wann & Branscombe, 1990)

Identification toward an athlete or sports organization is unlike any other industry, because, “few commercial corporations or other organizations resonate so powerfully with their stakeholders as they are not so closely tied to a person’s own sense of identity” (Brown, 2014, p. 51). Furthermore, although consumers of products or stakeholders of other organizations might recognize their value, these consumers are less likely to display their associations as much as stakeholders of sports entities. For example, a University of Florida Gators fan is strongly inclined to show support by way of wearing merchandise, purchasing paraphernalia, and communicating with other people, whereas a BP customer might not be as inclined to do so with BP (Brown,

2014).

Although stakeholders play an important role in any industry, stakeholder management is vital in the sports environment where much of the success depends on

“a complex set of stakeholder relationships,” including internal and external relationships between organization, athletes, media, sponsors, fans, and even government entities (Babiak & Wolfe, 2009, p. 723). Furthermore, the prominent role of the primary employees in the sports environment, that is the athletes, as well as their athletic ability and performance, is arguably the most critical difference when compared to corporations. At its core the main reasons for media exposure, sponsorship opportunities, and connectivity to sport consumers are the athletes competing on the playing field (Foster, Greyser & Walsh, 2005). While previous research has confirmed the benefits of the sports and athlete factor, particularly in regards to corporate social responsibility (CSR) reputation (Agyemang & Singer, 2011; Babiak & Wolfe, 2009;

Giannoulakis & Drayer, 2009; Godfrey, 2009; Walker & Kent, 2009) and sports

consumer advocacy intentions (Magnusen, et al., 2011), it is critical to recognize that this may be a mixed blessing. The pivotal role athletes play leaves sports susceptible to disruption. Disruption can be caused by factors such as a shift in athletic success and athlete misdeeds (Lohneiss & Hill, 2014). Researchers have argued that there is great risk in the uncertainty of athletic performances, as well as athlete behavior and personal failings, because both may directly influence stakeholder perceptions and subsequent behaviors (see: Lohneiss & Hill, 2014; Prior, O’Reilly, Mazanov & Huybers, 2013; Smith

& Westerbeek, 2007.

33 Athlete Celebrity Culture

Sports and athletes have long been relevant for cultures across the globe.

Athletic competition may date back to prehistoric times, and its popularity has grown exponentially since (Smart, 2005). Today, athletes play an integral part in many societies and are looked upon as cultural phenomena, role models, and sought-after product endorsers (Andrews & Jackson, 2001). The athlete celebrity has become a popular target particularly in regards to product endorsements and other marketing endeavors. Companies within a wide array of industries have turned to the sports environment to promote their products and count on its popularity, and more importantly on the popularity of certain athletes, to reach their present and future consumers.

Athlete endorsers are able to create meaning and values in consumers, which in return transfer to the endorsed brand (Seno & Lukas, 2007; Halonen-Knight & Hurmerinta,

2010). In other words, marketers aim to produce positive brand associations in the consumer’s mind by using positive attributes of the athlete endorser (Erdogan & Baker,

2000; Lohneiss & Hill, 2014). Celebrity athletes are at the center of a complex marketing process, which allows for non-sports and sports industries alike to profit. As L’Etang

(2006) pointed out, the increased use of athlete endorsers by non-sports companies has mutual benefits for the sports industry: it can use the demand for athlete celebrities to attract a variety of sponsors, as well as create more media attention and bigger profits.

The popularity of professional athletes today is high, with society increasingly perceiving them as celebrity icons. Nowadays, the athlete brand— that is an athlete who has generated “awareness, reputation prominence, and so on in the market place”

(Keller, 2008, p. 2)— has transcended mere athletic performance. Athletes today have

the opportunity to extend their reach from the athletic playing field onto other platforms and endeavors, sometimes even after their athletic careers end. Researchers in the sports marketing field have pointed out the importance of a positive athlete brand, stating it can have positive effects on market value by building strong consumer (fan) relationships (Arai, Ko, & Kaplanidou, 2013; Gladden & Funk, 2002).

The status of athletes has increased over the past years, thanks in part to developments in mass media (Smart, 2005). The increase in sports coverage has only whetted fans’ appetite (Bruce & Tini, 2008). Fans not only expect coverage of athletic competitions and athlete accomplishments but also yearn for more information about the personal lives of sports celebrities (Lee & Bang, 2013). New media technologies such as social media networks have allowed fans to access even more information about sports and the athletes involved, and have also made direct contact with these competitors more attainable.

Platforms such as Twitter make it possible for fans to directly reach athletes and potentially form relationships with them. What used to be a parasocial relationship (an imagined relationship with a media figure, fostered by media consumption) between an athlete and fans (Horton & Wohl, 1956; Frederick, Lim, Clavio & Walsh, 2012; Kassing

& Sanderson, 2009) has become a circumsocial relationship (a combination of parasocial and social interaction) (Kassing & Sanderson, 2012). The difference between parasocial and circumsocial interaction lies in athletes and fans being able to interact with one another (Kassing & Sanderson, 2009), as opposed to a one-way relationship in which the fan is merely a spectator who believes to have a relationship with the athlete.

In the parasocial framework the athlete does not reciprocate the relationship, because

of a lack of awareness and/or direct contact. Kassing and Sanderson (2012) argue that social media allows for the variation between parasocial and social interaction, thus contributing to the system.

Although both traditional media coverage and social media usage increase fan- athlete relationships and promote increased fan identification, researchers have argued they may also place athletes at a greater risk of reputation threats (Lee & Bang, 2013).

In recent years many sports scandals have tarnished the athlete image often celebrated by society. Numerous athletes have generated headlines for their negative behavior inside and outside of the athletic arenas. Athlete scandals in just the past five years have included legal violations such as murder (e.g., Aaron Hernandez), alleged rape

(e.g., Ben Roethlisberger), spousal abuse (e.g., Chad Johnson), as well as moral failings, such as performance enhancing drug use (e.g., Alex Rodriguez) and infidelity

(e.g., Tiger Woods). And in 2013 alone, more than 54 players from the National Football

League (NFL) were arrested for various criminal offenses (“NFL Arrests,” 2014).

Although a main objective of sports is to win contests (Gilbert, 2011), personal failings of athletes are increasingly becoming of interest to fans and are therefore covered by the media. In fact, the media industry frequently capitalizes on these scandals and sometimes even relies on them for extended coverage. The Tiger Woods infidelity scandal, for example, caused him to have more consecutive cover appearances on the New York Post than any other person or event, even exceeding coverage of the September 11, 2001, terrorist attacks (Busbee, 2009). As a result, athletes today face more public scrutiny than ever before, making reputation

management, risk management, and crisis communication fundamental parts of the job for public relations personnel, sports agents, and the athletes themselves.

Researchers have long noted that an athlete’s market value strongly depends on image and reputation (Brazeal, 2008). The athlete-endorser marketing ploy only works as long as consumers are able to identify with the athlete and subsequently allow for the transfer of his/her image to the brand (Seno & Lukas, 2007). However, both a decline in performance as well as unpredictable and unmanageable transgressions of the athlete may jeopardize this effort, leaving consumers no longer willing to adopt (Lohneiss & Hill,

2014). Incidents similar to the aforementioned cases are shown to have had severe effects for those benefiting from the athlete brand and those closely aligned with athlete endorsers. Consequences from athlete transgressions may include a decline in sponsorship, event attendance, as well as reduced merchandise sales (Shilbury, Quick

& Westerbeek, 1998). The Tiger Woods scandal reveals just how damaging athlete transgressions can be. When media entities uncovered Tiger Woods’ myriad of extramarital affairs, several of Woods’ endorsers distanced themselves from the golfer, costing him an estimated $22 million in endorsements annually (Wei, 2010).

Furthermore, a look at Professional Golf Association (PGA) attendance figures subsequent to the scandal reveals not only declining tournament attendance and declining tournament sponsorship, but also declining television ratings (McCarthy,

2010).

Contributions of the Study

In light of the arguments differentiating the sports setting from the corporate setting, as well as calls for more audience-centric crisis research and particularly a lack of audience-centered sports crisis research, this dissertation can offer a number of

meaningful contributions. The research encompassed in this study provides practical and theoretical implications. As the literature review will show, few research studies within the sports setting have empirically tested factors of the image repair discourse of athletes involved in a scandal beyond mere crisis communication strategies. In the past, research studies on image repair in sports have largely focused on how athletes communicate during times of crises. Several studies have examined the official responses used by athletes after crisis (e.g., Brazeal, 2008; Fortunato, 2008; Glantz,

2010; Hambrick, Frederick, & Sanderson, 2013), but fewer studies have investigated just how these responses affect the athlete’s reputation or image from an audience perspective (Brown, Dickhaus & Long, 2012). Furthermore, there is a dearth of published research that examines athletic success and failure in relation to crisis discourse and image repair. This dissertation therefore expands the empirical evidence devoted to athlete image repair in the sports environment and will go beyond investigating communication strategies. Ultimately, this research fills an important gap in the academic literature and adds new insights to scholarship by examining another array of factors that could contribute to the complex topic of sports consumer (fan) forgiveness and support, and perceived athlete image following athlete transgression(s).

Furthermore, this dissertation was a first-known attempt to use expectancy violation theory within a sports crisis context. Consequently, the present study extends the theory into a new field and will aim to further advance its application.

Finally, the findings of this research benefit a variety of professionals in the sports realm who want to mitigate image threats of professional sport celebrities faced with crises. The dissertation will contribute to these tasks by providing practical

recommendations for public relations practitioners, sports managers, agents, sponsors, marketing professionals, and professional athletes seeking assistance in successful image repair discourse.

Study Overview

This dissertation experimentally tests the ability of athlete performance subsequent to a transgression to affect sports consumers’ evaluations of the transgressor and affiliated organization. To do so, the study investigates numerous elements such as perceived image of athlete and organization, cutting-off-reflected failure (CORF) behaviors, positive and negative word-of-mouth (WOM), and supportive behavioral intentions to understand the significance of athletic performances and game outcomes on the image restoration discourse of a professional athlete. This study uses a 2 × 2 × 2 factorial design to examine if different game outcomes, athlete performances, and degree of fan identification impact consumer attitudes and evaluation of the athlete transgressor’s image. This dissertation seeks to establish whether winning and losing can influence an athlete’s ability to salvage and restore his image following a transgression.

Structure of the Dissertation

Chapter 1 introduces the study by providing background of athlete culture in society as well as athlete scandals. The chapter includes a summary of a handful of recent athlete scandals and their impact on consumer behavior. Furthermore, the first chapter provides an overview of the current scholarship investigating scandals and crisis management in both the corporate and sports environments. It argues that there is a relative dearth of research in the sports crisis field, particularly as it relates to image repair through athlete performance. This suggested a gap in the literature and resulted

in the problem statement devised for this particular study. Moreover, chapter one includes a purpose statement, highlights the significance of study and provides a study overview.

Chapter 2 comprises a review of the literature. First, a general review of image and reputation is provided, followed by a more specific review of athlete image and image or impression management. Next, the impact of scandal on sports consumption is discussed along with a discussion of the role of fan identification and traditional identity management of consumers through sports. The second chapter also discusses traditional crisis and image repair scholarship in both the parent discipline of corporate crisis and within the sports environment. More particularly, an overview of crisis communication scholarship investigating the impact of corporate ability associations is provided and subsequently related to the sports setting. The role of winning and losing is also discussed. Finally, the chapter explains the theoretical framework of the study through application of expectancy violation theory.

Chapter 3 describes the experiment, including participation selection. The purpose of the experiment was to investigate the hypotheses and research questions related to the effect of athletic performance of both a team and the individual athlete on the perceived image and consumer behaviors subsequent to a scandalous transgression of an athlete. The chapter includes the research questions and hypotheses, methods of investigation, including manipulation task(s) and measurements of outcome variables (e.g., perceived image, supportive behavioral intentions, word-of-mouth, etc.).

Chapter 4 presents the results from the experiment. All hypotheses and research questions will be addressed and an explanation of how the results were obtained will be provided as well. A snapshot of study participants and demographic information is offered in this chapter.

Finally, Chapter 5 discusses how the results of this dissertation enhance the body of knowledge within the sports and crisis literature. The chapter discusses implications of the findings for academic scholarship and sports professionals, such as public relations managers, general managers of a professional sports team, as well as agents and the athletes themselves. Limitations of the present study and suggestions for future research will be discussed before providing final closing remarks.

41 CHAPTER 2 REVIEW OF THE LITERATURE

Image

The conceptualization and definition of the term image is inherently problematic.

Throughout the literature the term is applied loosely and often used interchangeably with the term reputation. Scholars from disciplines, such as communication, marketing, and business have used the terms to explain dissimilar conditions based on their disciplinary viewpoints (Fombrun & Rindova, 1996). Bromley (1993; 2000) pointed out that clear operational definitions for both image and reputation are missing from the literature, causing conceptual problems within research. He defined image as the way an entity (organization or person) presents itself to its stakeholders and reputation as the way stakeholders conceptualize the entity (Bromley, 2000). This view is supported by Brown and colleagues (2006) and Bernstein (1984), who also argued that image is a construction of public impressions generated to appeal to the audience. Berg (1985), on the other hand, defined image as the perception by the public or the impression of an entity framed by the actions of the entity. Berg’s definition is more audience-centered while Bernstein’s definition is focused on the entity. This simple illustration of just a handful of definitions found in the literature points to a confusing and interchangeable use of the terms image and reputation.

Caruana (1997) argues that the interchangeable use of both terms goes back to early researchers who did not see a need to differentiate between the two as both were identified as a total impression of the person or organization (e.g., Dowling, 1993;

Dichter 1985; Ind, 1997). However, it was public relations practitioners who moved away from using the term image, ironically due to image problems within the word

“image” itself (Bernstein, 1984). Several authors have noted the negative associations of the term image (see: Bernstein, 1984; Grunig, 1993), suggesting stakeholders had associated the word image with being opposite of reality, manipulation, and fabrication

(Grunig, 1993). As a result, both practitioners and academics alike began to use the term reputation, further contributing to the commonly interchangeable use of both terms.

While several researchers see no problem in the interchangeable use of reputation and image, arguing that as long as study participants understand the terminology there should be no confusion (e.g., Kennedy, 1977), others have called for more distinctively different definitions (Gotsi & Wilson, 2001; Bromley, 2000). Commonly these researchers define image as the overall perception of an entity and reputation as a collection of images over a period of time (Gotsi & Wilson, 2001). Gotsi and Wilson

(2001) posit that reputation is strongly tied to the everyday images formed by stakeholders while image can be influenced by stakeholders’ perceived reputation.

Stakeholders perceive reputation by how the entity performs while image is formed by strategic communication (Gotsi & Wilson, 2001). In that vein, a reputation is a long-term evaluation, whereas an image might be fleeting and susceptible to change brought on by circumstances (e.g., communication).

Both reputation and image can be used in the present investigation as both could be affected by an athlete transgression (Chun, 2005). However, for the purpose of this dissertation, image was used as the main construct of investigation and adopted arguments that it is “more theoretically accurate to measure an entity’s image,” because of “the ability to change the image in the public eye more quickly; accurately measuring reputation would require a longitudinal study” (Brown, 2014, p. 3). Accordingly, this

dissertation the definition by Benoit (1997) was adapted, by which image is “the perception of a person, group, or organization held by the audience, shaped by the words and the actions of that person, as well as by the discourse and behaviors of relevant actors” (Benoit, 1997, p. 251). This definition finds general support in the crisis communication literature and because it can be transferred to athlete image.

Athlete Image

Lohneiss and Bill (2014) state that celebrities, including athletes, cultivate their image by their overall behavior, as well as through media portrayals. Athletes are increasingly perceived as role models and looked upon as important agents of meaning extending beyond their own profession. Many believe that their image is tied directly to their marketability (Brazeal, 2008). This direct link between image and value sparks interest in examining not only what image is but also how it is evaluated. The following section will discuss how both academic researchers and industry professionals are assessing athlete image.

Q-Scores

The most widely known tool for determining an athlete’s image is Q Scores.

Determined by Marketing Evaluations Inc. of New York, a Q Score is a number assigned to a celebrity that provides insights into his or her popularity. One big reason why Q Scores are popular is because they are marketed to forecast athletes’ marketing versatility, or endorser success. Q Scores are determined by way of surveying a representative sample of U.S. households. Participants are asked to respond to questions regarding their awareness and opinion of an athlete (or other celebrity), and the combined answers determine a final Q Score. More particularly, participants are asked if they are familiar with the celebrity (athlete); and, if so, 2. how they would rate

the celebrity (athlete) on a 5-point scale ranging from poor to one of my favorites. The basic premise of the Q Score lies in answering the question as to how appealing a person is among those who know him or her. Roughly 500 sports personalities are inquired about on a yearly basis.

The Q Score is the percentage of the sample population who rate the athlete as

“one of my favorites” divided by the number of people who indicate they are familiar with the athlete- meaning those who indicate not to know or be aware of the athlete are excluded. The company also provides a negative Q Score, which is represented by the ratio of those rating the athlete as poor or fair to those who are familiar with the athlete.

The value of the Q Score is in measuring likability, which serves as a predictor of consumer involvement: “The higher the incidence of favorites, the stronger is the fan base or consumer franchise” (Marketing Evaluations, Inc., n.d.)

While Q Scores appear to be the leading evaluation tool for professionals, and have been since the 1960s, it has raised some concerns among academic scholars.

Kahle and Kahle (2006) critically evaluated the use of Q Scores to determine athlete image. They argue that the ratings have major flaws, particularly in regards to how scores are calculated and how constructs of popularity are measured. According to the researchers, the Q Score system fails to account for important factors that contribute to evaluation of athlete image and successfulness between the match-up of athlete and product (e.g., perceived trustworthiness, source credibility). Research from the sports management and marketing field has determined that various factors determine athlete and celebrity image in the consumer’s mind beyond those captured in the Q Scores

(Kahle & Kahle, 2006).

Other Measurements

From both a professional and academic perspective, celebrity image was first investigated in relation to its effects on advertising. As advertisers and marketers realized the potential of celebrity endorsers, more research was conducted inquiring what factors of the celebrity endorser contribute to the success of their use in advertisements. Early studies focused on celebrity credibility to explain the effectiveness of the brand-celebrity endorser relationship (McCracken, 1989). Ohanian

(1990) established the source credibility model (SCM) (Ohanian, 1990), which includes three dimensions that compose celebrity credibility: attractiveness, trustworthiness and expertise. Based on these elements, consumers evaluate the persuasive messages from the celebrity endorser and subsequently adjust their intent to purchase the endorsed product or service (Ohanian, 1990).

Arguing that credibility and image are dissimilar constructs, Choi & Rifon (2007) expanded on Ohanian’s research and reasoned genuineness, competence, excitement, and sociability are the dimensions that best reflect celebrity image and are independent of the credibility dimensions of prior studies. Genuineness refers to traits that are overwhelmingly agreed upon as positive (i.e., pleasant, humble, responsible); competence denotes characteristics such as power and confidence; excitement is signified by traits such as ruggedness and dominance; and sociability can be identified as an overall outgoing personality. Subsequently, a celebrity image scale was developed assessing the consumer perceptions of the celebrity endorser (Choi & Rifon,

2007).

Braunstein and Zhang (2005) investigated “star power,” the characteristics of an athlete that make him or her a celebrity. The researchers determined five dimensions

that comprise the scale of athletic star power (SASP): professional trustworthiness, likeable personality, athletic expertise, social attractiveness and characteristic style.

Overall the 40-item scale is made up of 11 items measuring professional trustworthiness (e.g., “he/she is responsible,” “he/she is ethical,” “I agree with his/her behavior”), 12 items measuring likeable personality (e.g., “he/she is personable,”

“he/she is a daredevil,” “he/she displays morality”), eight items measuring athletic expertise (e.g., “he/she is a team player,” “he/she is a hard worker”), six items measuring social attractiveness (e.g., “he/she is recognizable,” “he/she has a lot of media exposure”), and three items measuring characteristic style (e.g. “he/she is controversial,” “he/she is flamboyant”).

Extending this research, Arai, Ko and Kaplanidou (2013) developed the model of athlete brand image (MABI) and the scale of athlete brand image (SABI) to measure image dimensions of athletes. The MABI is a conceptual framework that pinpoints recommendations for athlete representatives who wish to build strong athlete images,

“by identifying strengths and weaknesses of the athlete brand” (p. 399). Inspired by

Keller’s (1993) brand knowledge schema, which posits that consumers form attributes toward a product or brand through one of two ways (product-related attributes and non- product-related) the MABI consists of 10 attributes within three dimensions: athletic performance, attractive appearance and marketable lifestyle.

Athletic performance (on-field attribute=product related attribute) is comprised of: athletic expertise, competition style, sportsmanship and rivalry. Arai and colleagues

(2013) define athletic expertise as an athlete’s accomplishments and skill as it relates to sports. Competition style is based on an athlete’s “philosophy of performance” (p. 387)

and is made up of specific traits of how an athlete performs during competition.

Sportsmanship regards the honorable features of the athlete, such as fair play or respect, whereas rivalry denotes the relationship with other competitors.

Attractive Appearance (off-field attribute=non-product related) factors in physical attractiveness, symbol and body fitness. Physical attractiveness is defined as the athlete’s physical and esthetically pleasing appearance in the mind of the consumer.

Symbol denotes the character or unique features of the athlete, which can be showcased through personal style (i.e., fashion sense). Body fitness refers to the attractiveness of the athlete’s body type (i.e., physical fitness).

Marketable Lifestyle (off-field attribute) encompasses life story, role model and relationship effort. Life story is defined as the athlete’s personal values shaped by his or her off-the-field life. Role model refers to the ethical behavior and the “active participation and contribution to the society, conformance to social norms and exhibition of virtuous behavior” (p. 389). Relationship effort is defined as the amount of interaction with sports consumers.

To complement the MABI, the researchers developed the scale of athlete brand image (SABI). The 30-item scale is illustrated in Table 2-1.

Image and Impression Management

Image or impression management is the process of controlling images about oneself in the minds of others (Leary, 1995; Schlenker, 1980). In that sense, people engage in impression management to attain a desired image. The presence of other people, both real and imagined, provokes a need to influence how they perceive the characteristics of the person ranging from attitudes about the person’s character or

Table 2-1. Scale of Athlete Brand Image (Arai, Ko & Kaplanidou, 2013). Athletic Expertise The athlete is a dominating player in his/her sport The athlete seems very knowledgeable in his/her sport The athlete has prominent athletic skills in his/her sport Competition Style The athlete’s competition style is distinctive from other players The athlete’s competition style is exciting to watch The athlete’s competition style is charismatic Sportsmanship The athlete shows sportsmanship in competition The athlete shows respect for his/her opponents and other players The athlete shows fair play Rivalry The rivalry match of this athlete is exciting The athlete does well against his/her major rival The rivalry match of this athlete is dramatic Physical Attractiveness The athlete is physically attractive The athlete is beautiful looking The athlete is sexy Symbol The athlete’s private fashion is attractive The athlete is stylish The athlete’s fashion is trendy Body Fitness The athlete is in good shape The athlete’s body fits to the sport The athlete’s body is well conditioned Life Story The athlete has dramatic episodes in his/her life The athlete has a dramatic personal life The athlete’s private lifestyle is newsy Role Model The athlete is socially responsible The athlete is good role model for others The athlete is a good leader in our community Relationship Effort The athlete shows appreciation for fans and spectators The athlete is responsive to fans The athlete tries to interact with fans personality to attitudes about their physical appearance (Schlenker, 1980). Although not always a conscious effort, people try to avoid behaviors that would create an undesired

image (Leary, 1995). For athletes in particular, impression management can be challenging as they are trying to convey multiple images. First is their own desired image, but athletes also have an image demand from agents, the team they compete for, and the sponsors for whom they work. Each entity attempts to create a fitting athlete image in the sports consumer’s mind and the athlete often must act upon and fulfill these demands. Jones and Pittman’s (1982) taxonomy of impression management identifies five groupings of impression management strategies frequently used by individuals: (1) self-promotion, (2) ingratiation, (3) exemplification, (4) intimidation, and

(5) supplication.

Individuals engage in self-promotion by proclaiming their successes and skills in hope they will be viewed as competent, or to create an image of competency. Jones and Pittman note that in some cases the impression management strategies backfire and instead of educing an anticipated response (i.e. desired image), the actor risks causing negative attributions instead. In the case of competence, for example, the individual seeking the desired image can be perceived as arrogant and vain. People employing the self-promotion strategy may also wish to link their attribution of competence to a particular source (i.e., hard work vs. innate talent); however, this can be difficult to convey. Furthermore, an individual who is self-promoting in order to be perceived as competent risks being proven inept (Jones & Pittman, 1982). To put this into a sports context, one can look at LeBron James. James has portrayed an image of competency since starting his career in the NBA; however, it wasn’t until his move to the

Miami Heat that he won a championship. For a long time he was not able to live up to the image he (and others) had created.

Another strategy is ingratiation, whereby individuals provoke an image of likability or attractiveness by engaging in kindnesses of any kind (e.g., conforming). The main danger for this strategy is that a person employing it may be perceived to be ingratiating or manipulating the audience. The balance between the two is defined as the

“ingratiator’s dilemma” (Jones & Pittman, 1982).

Appearing dedicated, committed or worthy is another impression management strategy. This strategy is known as exemplification, in which an individual self-sacrifices or goes above and beyond in order to be perceived as such. Exemplification frequently produces an identity of being moral or having integrity but may also create the image of feeling superior over others (Jones & Pittman, 1982).

Another type of image is created by intimidation, whereby an individual seeks to be thought of as dangerous and powerful. Making threats or exhibiting anger sometimes displays this, but can easily be perceived as domineering or socially undesirable. An example would be the 2014 Richard Sherman incident, in which the Seahawks’ cornerback gave an emotional post-game interview to Fox Sport’s Erin Andrews that evoked public and media outrage and created the image of Sherman being tough on the field, yet a “thug” off the field.

Another strategy is supplication, whereby a person showcases weaknesses (e.g., playing stupid) to produce an attribution of being humble. However, this strategy is risky because it can cause the audience to perceive the actor as lazy (Jones & Pittman,

1982). To sum, people use impression management strategies to achieve a desired (or undesired) image in the audience’s mind.

Although image or impression management is part of everyday activity (Benoit,

1995; Schlenker, 1980), it becomes especially critical when image is at risk. These image threats typically emerge after an unfavorable event occurs, or an unfavorable event is believed to have taken place, when a person is responsible or believed to be responsible for the event (Benoit, 1995). Several researchers over the years have argued that prosocial behavior subsequent to a transgression mitigates the crisis.

Prosocial behavior is, “Actions that provide benefits to another person and do not appear to be motivated by the benefactor’s desire to obtain immediate reinforcements for himself” (Tedeschi & Lindskold, 1976, p. 412). Performing well so that the team benefits from the athletic performance could be perceived as such. However, it should be noted that the behavior still is a way to restore one’s positive identity (Tedeschi &

Riordan, 1981).

Another way to mitigate crisis is through the use of verbal statements. Goffman

(1967) explains, “When a face has been threatened, face-work must be done” (p. 27).

This type of face-work is commonly a persuasive discourse through which people intend to weaken the image threats. These messages are most often in the form of accounts, excuses, or apologies and are generated to repair the damage or restore the image by offering justifications for the behavior (Benoit, 1995; Benoit, 2006).

Athlete Scandal

Prior, et al., (2014) define sports scandal as a “violation of a set of assumptions that stakeholders hold with reference to the expected behaviours of sports entities, including players, coaches and team support staff” (p. 191). Like traditional consumers, sports consumers have a set of beliefs or expectancies about what is classified as appropriate behavior (Hughes & Shank, 2005). A crisis occurs when the entity (e.g.,

athlete, organization) negatively violates stakeholders’ expectations and causes an unwanted or negative reaction. The key to the evolvement of an athlete scandal is that it is not merely the indiscretion by the sports entity that causes the scandal; rather it is the complex interactions among multiple actors that turns a transgression into a crisis.

Previous research by Hughes and Shank (2005) on scandals in sports suggests it is the sum of reactions and involvement from sports stakeholders that is, media, fans, organizations, or other players that propels an offense into a crisis. According to the researchers, these stakeholders evaluate player behavior and subscribe meaning. In times of an athlete transgression, the stakeholders not only evaluate whether the behavior violated norms, but furthermore determine to what extent the athlete experiences negative consequences. It is the complex interaction among stakeholders that ultimately shapes how much attention is given a particular offense. If, for example, an athlete misbehaves in a way that is determined to be highly offensive by various sports consumers (fans), then it is more likely that the event is covered by the media and subsequently receives even more scrutiny from the public. Similarly, if an event is deemed as minor misconduct, both media attention and negative fan reaction will be less (Hughes & Shank, 2005), “An action does not emerge as a scandal without its promotion by sports stakeholders” (Prior et al., 2014, p. 191).

Sports scandals can vary not only by transgression type but also by offense severity or degree. Early research classified two types of sports scandals: illegal and immoral. Illegal scandals are those that violate laws set forth by the government and/or regulations set forth by sports organizations or leagues. Illegal transgressions within sports that violate federal law are misconducts such as spousal abuse as committed by

NFL players Ray Rice or Chad Johnson, or use of illegal drugs, such as NHL’s Bob

Probert’s use and possession of cocaine. Illegal transgressions that violate regulations are most frequently the use of performance-enhancing drugs, such as the use of EPO

(doping) by former U.S. track star Marion Jones, cyclist Floyd Landis and baseball player Barry Bonds.

Immoral transgressions are violations that are not illegal but are not generally socially acceptable. The most prominent immoral offenses within professional sports are extramarital affairs, as experienced by Tiger Woods and Kobe Bryant. It should be noted that in some cases athlete transgressions could be considered as both illegal and immoral.

In sum, Prior and colleagues (2014) point out three characteristics of a sports scandal: first, the behavior of the athlete (or other sports representative) is conflicting to conventional standards; second, the behavior is recognized as having violated these norms by an authority (e.g., law enforcement, league); finally, consumers, other relevant stakeholders or media react. The following section will explore how sports consumers may react after being presented with negative information.

Impact of Transgressions on Sports Consumption

Consumer attitudes are often investigated by exploring behavioral outcomes. In the sports environment the most frequently used behavioral outcomes are purchase of game tickets, sports consumption in form of television viewing, merchandise purchases, and word-of-mouth communication (WOM) (e.g., Fink, Cunningham, & Kensicki 2004,

Funk & Pastore, 2000; Till & Shimp, 1998). A sports transgression “introduces a catalyst of potential change” (Sassenberg & Johnson Morgan, 2010, p. 4); upon learning about an athlete’s transgression, consumers may change their attitudes and/or

behavioral outcomes. Crisis communication scholarship frequently explores similar behavioral outcomes, namely, potential supportive behavior and word-of-mouth (see:

Coombs, 1999, 2004, 2007; Coombs & Holladay, 2007). Coombs (1999) found that image directly affects stakeholder behaviors. The intention to support or not to support an entity is related to how the stakeholder perceives the image of that entity. In some cases negative attitudes toward an endorser may transfer to negative attitudes toward the endorsed brand or product (Fink, Cunningham & Kensicki, 2004; Till & Shimp 1998).

That is why number of sponsors cut ties with Tiger Woods (and other athletes facing scandal) in light of his transgressions. They feared a decline in customers and anticipated a negative transfer of attitudes from Woods toward themselves or their products and services. Furthermore, the effect of the Woods crisis was also showcased by a decline in supportive behavioral intentions such as tournament attendance and television ratings. The loss of the Tiger effect therefore affected Tiger, the PGA, TV networks, and sponsors who clearly relied on Woods to drive their own business.

Networks achieve higher TV ratings and can charge more for advertising when Tiger plays, but when he doesn't compete they notice a decline in both ratings and advertising dollars (McCarthy, 2010).

Coombs (2007) further suggested that those with a negative image in the consumers’ mind are less likely to receive support and positive word-of-mouth, but are more likely to experience negative word-of-mouth communication. Word of mouth

(WOM) is defined as communication among stakeholders about an entity. Some scholars have argued that WOM is important to an entity’s success and can also contribute to its failure by shaping how consumers make decisions (Richins, 1984;

Coombs, 2011). There are two classifications within WOM: negative WOM and positive

WOM.

Positive word-of-mouth (pWOM) is communication by stakeholders whereby a product or service is recommended and others are encouraged to use the endorsed product (East, Hammond & Lomax, 2008). pWOM is one of the main motivations for brand decision making, because customers are inclined to adopt a product or service when it is advocated for by others whom we trust or whose opinion is valued. Negative word-of-mouth (nWOM), on the other hand, is defined as communication by stakeholders whereby a product, service or entity is being discussed unfavorably. In times of crisis, it is easy to see why negative word-of-mouth by stakeholders would occur. In crisis situations stakeholders are often dissatisfied and inclined to voice concerns (Coombs & Holladay, 2007). This is critical for the entity in crisis, because overall consumers attribute greater meaning to negative information than to positive information when making evaluations, suggesting negative WOM is more powerful than positive WOM (Coombs & Holladay, 2007; Skowronski & Carlston 1989). Coombs

(2004) suggests that stakeholders of an entity with a negative perceived image produce more nWOM than those of an entity with a positive perceived image. In that vein, an athlete who engages in a transgression would spark a wave of negative word-of-mouth.

Previous research has examined whether nWOM is mitigated by crisis communication strategies, but it is unclear how athletic success inspires positive word-of-mouth and reduces negative word-of-mouth. Provided that athletic success positively contributes to the overall image of an athlete, one could argue that image would also contribute to

WOM (Coombs, 2004; Brown, 2014).

Greater use of the Internet is contributing to more word-of-mouth and the ability to produce both negative and positive word-of-mouth in times of crisis (Coombs &

Holladay, 2007). An e-influencer is someone who shares attitudes about something or someone online, thus producing electronic word-of-mouth or eWOM (Hennig-Thurau,

Gwinner, Walsh & Gremler, 2004). Hennig-Thurau and colleagues (2004) found that people want to share their experiences or opinions because they are concerned about others or because they gain intrinsic or monetary benefits. Social further facilitate WOM and becoming an e-influencer. This can be beneficial or detrimental in times of crisis, making it important for researchers and practitioners to understand in which ways pWOM or nWOM are triggered. Consumer satisfaction, brand commitment, and identification contributes to WOM behaviors, indicating that highly-identified fans may be more inclined to engage in pWOM regardless of athlete transgression (Brown, Barry,

Dacin & Gunst, 2005).

Fan Identification

Social identity theory posits that individuals seek out membership in groups with which they can identify and which positively contribute to the person’s identity and self- esteem (Tajfel, 1972; Tajfel & Turner, 1979). The theory explains that people categorize themselves based on their profession, social status or social activities, among others, to help them find individuals with similar interests with whom they can connect and establish a bond. Many find this experience in the form of sports fandom. Associations with a certain team or athlete allow people to share a passion with other compatible individuals, and help form a social environment, or in-group (Abrams & Hogg, 1990).

For fans, sports are outlets that help build social identity and increase self-esteem through affiliation with a team or athlete and the success deriving from said team or

athlete (Lee, 1985; Madrigal, 2001). Researchers identified an ingroup and out-group bias in sports, which reflects the favoritism toward group associations toward a team or athlete and distain for other group memberships (Wann & Branscombe, 1993). The formation of social identity through sports is highly related to in-group success, stemming from the association with our team/athlete, as well as the failure of out- groups, who associate with rival teams or athletes (Festinger, 1954). Group membership and its intensity varies by person; some individuals derive more of their identity from sports than do others.

Fan identification, defined as “the extent to which a fan feels a psychological connection to a team and the team’s performances are viewed as self relevant” (Wann,

2006a, in Raney & Bryant, 2006, p. 332), is rooted in social identify theory and seeks to explain the varying levels of sports fandom. Wann and Branscombe (1993) developed a scale to identify the individual differences in identification of fans. Accordingly, the 7- item Sports Spectator Identification Scale (SSIS) inquires with fans how important team success is, to which degree they follow their team’s performance, how likely they are to wear team merchandise, and how much they dislike their team’s biggest rival, among other things. The scale then computes a fan identification score: low, moderate, or high

(Wann & Branscombe, 1993). Fans with a score of 18 or below have a low level of fan identification; fans within scores ranging from 19 to 34 have a moderate level of fan identification; and fans who score above 35 are considered to have high levels of identification (Wann, Melnick, Russell, & Pease, 2001). Table 2-2 shows all items of the

SSIS.

Table 2-2. Sports Spectator Identification Scale (Wann & Branscombe, 1993) 1. How important to you is it that the (team name) wins? 2. How strongly do you see yourself as a fan of the (team name)? 3. How strongly do your friends see you as a fan of the (team name)? 4. During the season, how closely do you follow the (team name) via any of the following: a) in person or television, b) radio, or c) television news or a newspaper? 5. How important is being a fan of the (team name) to you? 6. How much do you dislike the (team name’s) greatest rivals? 7. How often do you display the (team name’s) name or insignia at your place of work, where you live, or on your clothing?

The level of identification of fans forecasts how affected they will be by their team’s activities; more particularly, “highly identified fans feel like the team is a representation of themselves and they are, in turn, a representative of the team” (Potter

& Keene, 2012 p. 350). Much research has been conducted regarding fan identification and its impact and influence. Among these investigations are studies inquiring if and how fan identification plays into aggressive behavior (Wann, 1994), alcohol consumption (Wann, 1998), and, importantly for this study, attributions toward self and team in light of success or failure (Hirt, Zillmann, Erickson, & Kennedy, 1992).

BIRGing and CORFing

Due to the winning and losing nature of sports, associations with teams or athletes are not always positive, and a loss can hurt or lower self-esteem. While examining identity-management of sports fans, researchers discovered that fans increasingly engage in one of two ways: (1) They flaunt the success of the team or athlete they are affiliated with, referred to by researchers as basking in reflected glory

(BIRGing), in order to enhance their self-esteem; or (2) they distance themselves from the team or athlete they are associated with in light of failure, known as cutting off reflected failure (CORFing), as a response to threatened self-esteem (Cialdini, et al.,

1976; Wann & Branscomb, 1990).

The basking in reflected glory phenomenon refers to the tendency of sports fans to emphasize their association with their favorite sports entity following a win or success. The concept was first introduced through a study by Cialdini and colleagues

(1976), which examined the behavior of college students on the days following a win or loss of their football team. College students were more likely to wear team merchandise after a win as opposed to after ties or losses. Over the years BIRGing has been found in several different sports fan scenarios. Researchers discovered that fans not only wear team apparel after a victory (Cialdini et al., 1976), but they also increasingly view sports highlights and continue interaction with other sports fans after a win (Gantz & Wenner,

1995). Some studies have also identified frequent use of the Internet by fans after a victory as a way to publicly display their association with a successful sports entity

(Joinson, 2000; End, 2001).

Based on results from these studies, researchers reason that individuals may bask in reflected glory because they feel that by displaying their association to a successful team, person, or entity, others will perceive them to be successful as well

(End, 2001), despite not having directly contributed to the team’s (or athlete’s) success

(Hirt et al., 1992). These fans, therefore, are living vicariously through their team (or athlete) (Cialdini et al., 1976).

On the other hand, cutting off reflected failure (CORF) describes the phenomenon of how sports fans distance themselves from their team or favorite athlete after a loss or failure. In these cases, fans increasingly attempt to disassociate with the sports entity (Cialdini et al., 1976; Wann & Branscombe, 1990). Prior studies have found that fans are less likely to wear team merchandise and are more inclined to use the

word “they” when discussing losses, as opposed to the word “we” when discussing victories (Cialdini et al., 1976). They feel that by displaying their association to an unsuccessful team or athlete others will perceive them negatively (Cialdini & Richardson

1980; End, 2001). CORFing was evident in past research studies when fans were less socially engaged after a defeat (Gantz & Wenner, 1995) and were less likely to display team paraphernalia (Bizman & Yinon, 2002; Kimble & Cooper, 1992).

Taken together, BIRGing and CORFing are strategies employed by individuals to enhance (display or restore) a positive social identity. Cialdini (1976; 1984) suggests that humans use associations as a way to self-present, and nowhere is association behavior more observable than during winning and losing situations:

We purposefully manipulate the visibility of our connections with winners and losers in order to make ourselves look good to anyone who could view these connections. By showcasing the positive associations and burying the negative ones, we are trying to get observers to think more highly of us and to like us more (Cialdini, 1984, p. 195).

In times of negative events both on and off the field, and both performance and non-performance related, sports consumers might experience a threat to their self- esteem by mere association and identification levels; for some, especially those highly involved, the correlation between fandom and identity might not allow them to detach and prompt them to engage in an active effort to protect their threatened self-esteem

(Brown & Billings, 2013; Brown, 2014; Wann, 2006a). And while winning and losing is part of the game, other crises (i.e., athlete transgressions) are not inherently part of the fan experience. Thus, in addition to winning and losing, the personal failings of an athlete or sports organizations (or the personal achievements) would also stimulate a cognitive response by fans.

The Black-sheep Effect

Dietz-Uhler and colleagues (2002) investigated if the concepts of in-group bias or the black-sheep effect would explain fan reaction toward deceitful athletes. As mentioned previously, in-group bias insinuates that group members stay loyal to the group despite a crisis (Dietz-Uhler, 1999). Instead of disassociating, they look for ways to rationalize the behavior as a response to a threated identity. Fans might downplay the actions of the athlete or frame the action in a more positive light (Wann & Dolan,

1994) or question the trustworthiness of the accuser or source (Branscombe & Wann,

1994). Conversely, the black-sheep effect suggests that fans shun the athlete offender and label him or her as a black-sheep, or someone who is unlike the rest (Marques,

Yzerbyt, & Leyens, 1988 in Fink, et al., 2009). This coping strategy allows fans to uphold their positive associations despite being faced with negative information.

To investigate whether in-group bias or the black-sheep effect takes place in athlete transgressions, the researchers conducted a 2 (team membership: [own team vs. rival team]) × 2 (criminal behavior: [yes vs. no]) × 2 (status of the athlete: [star vs. average player]) between-subjects experiment5. Sports consumers were asked to evaluate a fictional athlete who had engaged in drunken driving. Results of the study indicated an in-group bias effect but did not find a black-sheep effect. Fans assessed the criminal athlete from their favorite team more favorably than they did the rival, even when the rival athlete was not involved in any criminal behavior (Dietz-Uhler, End,

Demakakos, Dickirson & Grantz, 2002). However, the study did not measure team

5 Because status of athlete did not appear to be a factor the researchers collapsed their experiment to a 2 X 2 for analysis (Dietz-Uhler, et al., 2009)

identification, so respondents could have been low-identified. As Fink et al. (2009) note,

“Certainly one can have a favorite team, yet not be highly identified” (p. 144).

Accounting for these limitations, Fink and colleagues (2009) conducted a follow- up experiment in which they accounted for identification level (high vs. low) and leadership response (strong vs. weak). The latter is defined as a response from team personnel (e.g., head coach, management). The researchers posited that a prompt and strong response from the team regarding an athlete’s transgression would help fans activate a black-sheep effect in which the player and his/her behavior is seen as an anomaly. This in return would make the fan feel good about his group membership and provide a sort of balance. On the other hand, if a team responded in a weak manner

(i.e. by overlooking the act; or providing a slap on the wrist) the response from fans would be different. They posited that lack of condemnation by leadership would reflect badly on the entire team, leaving fans unbalanced causing potential disassociation with the entire group or team (Fink, Parker, Brett & Higgins, 2009). For highly identified fans, there is greater need for a leadership statement and that highly identified fans experiencing athlete transgressions long for “something positive to maintain the balance of being connected to the group” (Fink, et al., 2009, p. 146). For these fans, a stance by team management against the athlete’s behavior might cause negative feelings toward the athletes while at the same time maintaining the positive feelings toward the team

(Fink, et al., 2009). The authors hypothesized that team identification would change among highly identified fans in light of leadership response. Results of the study indicated that team identification did indeed change when fans were faced with an

“unscrupulous act” (p. 149) by an athlete. Also, highly identified fans who were provided

with a weak leadership responses showed higher drops in team identification. The researchers explained this behavior by saying when team management does not condemn the actions of the athlete the team does not truly uphold its values, thus not contributing positively to the fans self-esteem and identity. While this research pinpoints a difference in response to sports crises based on identification levels, it did not take into account the various performance factors that make sports so interesting and different from other industries. This raises the question of whether fan identification might differ among those with high and low identification when faced with an athlete transgression and various different performance outcomes (e.g., winning vs. losing; positive vs. negative athlete performance).

Fan identification has frequently been used as an independent variable to predict behaviors, attitudes and purchase intentions. High levels of fan identification cause increased sport consumption in a variety of ways, including increased game attendance, sports media consumption, and heightened purchases by sports sponsors (Wann,

2006a). In addition, as social identity theory points out, high levels of fan identification also suggest that team or athlete associations are closely tied to self-esteem. In times of crisis then, this suggests a difference between crisis perceptions between fans of different identification levels. Little attention has compared differences in the evaluation of athlete scandals between fans that are highly identified with their favorite sports team and athletes versus those who are not. Particularly, there is a gap in the literature that takes into account fan identification and performance in times of crisis.

Crisis and Crisis Repair Research

Much research has been conducted on reputation or image threats and the best ways to mitigate crises. Marketing scholars have taken interest in crisis from the

standpoint of how negative (endorser) information affects consumer attitudes and subsequently the brand (Ahluwalia, Burnkrant & Unnava, 2000; Louie, Kulik &

Jacobson, 2001; Pullig, Netemeyer, Biswas, 2006; Till & Shimp, 1998). While research has examined how general celebrity endorser scandals affect brand evaluations, relatively few studies have investigated the effects of negative information on sports celebrity endorsers (e.g., Lohneiss & Hill, 2014; Murray & Price, 2012; White, Goddart &

Wilbur, 2009). A later section within this chapter will summarize the findings specific to sports-related crisis research.

Within the communications field, crisis events have promoted studies aimed at determining best practices for practitioners. Several theories have been used that aim to predict which communication strategies work best in light of certain crises and which communication strategies are less effective. The two most commonly used theories within crisis repair may be image restoration theory (Benoit, 1995) and situational srisis communication theory (Coombs & Holladay, 2002).

Situational Crisis Communication Theory

Situational Crisis Communication Theory (SCCT) is a post-crisis theory, which focuses on mitigating the reputational threat stemming from scandal events. As previously mentioned, a crisis event has the potential to threaten stakeholders’ expectations about an entity and can therefore negatively affect the organization’s image and ultimately its bottom line (Coombs, 2007). According to SCCT, an organization in crisis must respond to the events in order to mitigate these negative outcomes. That a corporation or a person involved in a transgression should respond to the allegations or the transgression in question might seem obvious; however, what is less clear is how a person or organization should respond. SCCT is unique in the sense

that it customizes image repair responses based on level of threat and crisis type. Crisis type allows for assessing the initial crisis responsibility ascribed to the crisis, which is important because it will determine the responsibility stakeholders attribute when a crisis event takes place (Coombs, 2007). Coombs and Holladay (2002) categorized crises type into three groups based on the level of initial crisis responsibility and the level of personal control. Table 2-3. provides an overview of the clusters proposed by

SCCT.

Table 2-3. Crisis Clusters according to SCCT (Coombs & Holladay, 2002) Victim Cluster – Natural Disasters: organization sustains damaged due to organization is weather or acts of nature (i.e. earthquakes, tornados, perceived as victim hurricanes) Workplace Violence: (former) Employee attacks other employee(s) in work environment Rumors: False or misleading information is maliciously circulated about an organization and/or its products and services. Malevolence: External actor or opponent causes damage(s) (i.e. product tampering)

Accidental Cluster – Challenge: stakeholders perceive the organization is organizational actions operating in an inappropriate manner leading to the crisis Technical-Error Accidents: Technology employed or are perceived as distributed causes an industrial accident. unintentional Technical-Error Product Harm: Technology employed or distributed fails and has to be recalled.

Preventable Cluster - Human-Error Accidents: Human error causes an accident. Organization is Human-Error Product Harm: Human error results in product perceived to have recall due to danger to stakeholders. knowingly placed Organizational misdeed with no injuries: Company misleads people at risk, took its stakeholders but no injury is caused. inappropriate actions Organizational misdeed management misconduct: or violated law(s) or Management violates laws and/or regulations. regulation(s). Organizational misdeed with injuries: Company misleads its stakeholders and injuries are sustained.

The first cluster according to STTC is the victim cluster, which includes crisis events such as natural disasters, workplace violence, malevolence, or rumors.

Typically, stakeholders attribute very low levels of responsibility because they perceive the organization as the victim of the crisis, rather than the offender. Next is the accidental cluster, which can include crises such as technical-error accidents. This type of crisis usually prompts moderate levels of attribution because these types are perceived as being unintentional or uncontrollable. The last cluster is the preventable cluster. Accidents or product harm that can be ascribed to a lack of care by the organization, as well as other organizational misdeeds fall within this category. This cluster provokes high levels of attribution because stakeholders perceive the organization knowingly engaged in behavior that caused the crisis event to take place

(Coombs & Holladay, 2002). Coombs (2004) argues that it is critical to determine in which cluster a particular crisis falls in order to successfully communicate crisis repair strategies; however, there are other elements that also need to be accounted for and factor into assessing image threats.

The performance history of the organization— that is both the crisis history and relationship history of the entity in question— is also an important factor to consider.

Whether or not an organization or a person has experienced similar or other crises prior plays a significant role in how he/she are evaluated by stakeholders. Stakeholders of entities that have experienced crises before are more likely to perceive a pattern of problems, which could negatively impact the long-term reputation. On the other hand, stakeholders of companies with no crisis history might evaluate a crisis differently, because they prescribe the incident to be a one-time occurrence (Coombs, 1999).

Another part of performance history is related to relationship history between the organization and its stakeholders. Whether stakeholders are treated well or poorly prior

to the crisis will ultimately determine how much crisis responsibility stakeholders will ascribe to the offender (Coombs, 2007). Overall, previous research has found that a positive image, strengthened by a positive crisis history and positive relationship history, can have a positive effect in time of crisis. That is, a positive reputation/image might mitigate the damage, because stakeholders are more likely to pardon or dismiss negative information. Similarly, a negative reputation/image can lead stakeholders to discount positive information. This is known as the “halo effect” (Coombs & Holladay,

2006). To summarize, both crisis history and relationship history can exaggerate or diminish the level of crisis responsibility ascribed to the perpetrator and subsequently contribute to image threats experienced.

After accounting for image threats, public relations professionals can finally employ one or more crisis response strategies as outlined by SCCT. Because the strategies used in SCCT were adapted from Benoit’s (1995) image restoration theory, they will be explained in more detail in the following section. Table 2-4 illustrates an overview of the crisis response strategies as set forth by SCCT.

Researchers have used SCCT to investigate crisis within the sport setting (e.g.,

Brown & Billings, 2013). However, it is most widely used to examine the impact of crisis situations on stakeholders’ attitudes toward organizations (i.e., sports organization), as opposed to individuals (i.e., athletes). A more common theoretical framework for scandals involving individuals is image restoration theory.

Image Restoration Theory

Image restoration theory (IRT) is built on the same premise as SCCT: a person or an organization experiencing a crisis must respond in order to mitigate image or reputation threats. IRT proposes that when a threat to image occurs, a person may rely

on communication strategies from within five categories. According to Benoit (1995;

1997), image repair discourse can be achieved through the use of denial, evading responsibility, reducing offensiveness, corrective action, and mortification. Table 2-5 provides an overview of the fourteen image repair strategies proposed by IRT.

Table 2-4. Strategies per Coombs (2007) Situational Crisis Communication Theory (p.155) Denial Attack the accuser confronting the person or group that proclaims the crisis Simple Denial ascertaining that there is no crisis Scapegoat blaming someone else for the crisis Diminish Excuse minimizing crisis responsibility (i.e. by claiming one had no control) Justification minimizing the crisis by claiming the damages were not extensive Rebuild Compensation providing money or other types of reimbursement to victims Apology stating that one fully takes responsibility for the event/crisis and asking for forgiveness Bolster Reminder stressing past good deeds Victimization explaining how the entity is also a victim Ingratiation praising stakeholders

Image repair strategies. First, someone seeking image restoration may employ one of two versions of denial, in the form of simple denial or scapegoating. Simple denial strategy is used to indicate that one did not commit the act and is therefore not responsible. Scapegoating strategy provides additional information by shifting the blame to someone else and offering up responsibility for the act. If denial is not an option, evading responsibility strategies may be used in one of four ways, claiming: (a) they were provoked by someone else, (b) the act was committed with good intentions, (c) what transpired was an accident, or (d) it happened given a lack of information or ability.

Six image repair strategies are encompassed within reducing the offensiveness of the

event: (a) stressing positive characteristics, referred to as bolstering, (b) indicating that the event has smaller impact than suggested originally, known as minimization, (c) separating the action from similar but worse ones denoted as differentiation, (d) claiming that the action served the greater good, or that there is a broader context to the act, branded as transcendence, (e) attacking the accusers, or (f) offering compensation.

Another way to combat image threats is by proclaiming corrective action. This strategy suggests a conscious and public effort to remedy the situation, while assuring the prevention of reoccurrence. Finally, mortification strategies may be applied in an effort to save face. Mortification strategy proposes taking responsibility, as well as asking for public forgiveness (Benoit, 1995; 1997).

Table 2-5. Image Repair Strategies per Benoit’s (1995) Image Restoration Theory Denial – person denies involvement in questionable act Simple Denial claiming one did not perform offensive act Scapegoating denying responsibility and shifting the blame to someone else Evade Responsibility – person tries to evade the responsibility of his/her actions Provocation asserting that the offensive act was in response was in response to someone else’s misconduct Defeasibility stating transgression was caused by lack of control or information, or misinformation Accident claiming the offense was an accident Good Intentions maintaining that the questionable act was performed with good intentions Reduce Offensiveness – person attempts to reduce the negative impact Bolstering mitigating negative perception by stressing positive previous behavior/acts Minimization suggesting that the transgression was not as bad as it seems Differentiation comparing transgression to more offensive acts in order to appear less offensive Transcendence placing the act in a more favorable context Attack the Accuser aiming to reduce the credibility of accusers Compensation repaying victims via monetary settlements or other service Corrective Action – person attempts to restores the situation to its pre-crisis state or promises to prevent reoccurrence Mortification – person takes responsibility, apologizes and/or asks for forgiveness

Although the 14 image repair strategies proposed by Benoit appear comprehensive, a number of researchers have added supplementary strategies to the framework. Smithson and Venette (2013) identified a stonewalling strategy in which the accused redirects the audience to insignificant details rather than to address the core of the crisis. They said, BP used stonewalling strategies subsequent to the oil spill in the

Gulf of Mexico. The scholars suggest that by using the stonewalling strategy, BP was able to postpone image threats, providing the company with additional time to configure crisis response. This crisis response strategy has since been found in sports related crisis communication research (e.g., Frederick, Burch, Sanderson & Hambrick, 2014;

Schmittel & Hull, 2015). Sanderson (2008) found a victimization (suffering) strategy while exploring the image repair discourse of former baseball player Roger Clemens, who was accused of doping. Victimization implies that those blamed for a crisis suggest they cannot adequately defend themselves, because others have already condemned their actions despite a lack of proof (Sanderson, 2008).

IRT has been a theoretical framework for many crisis related studies within a variety of settings ranging from politics (Benoit, 2006) to entertainment (Benoit, 1997) to business (Blaney, Benoit & Brazeal, 2002). A handful of studies have also analyzed the image repair within the sports context as the next section details.

Traditional Crisis and Crisis Repair Research in Sports

The majority of crisis-related research in the sports marketing environment has dealt with how negative information about athlete endorsers can affect brand attitudes.

White, Goddart and Wilbur (2009) investigated the impact of damaging information about a celebrity sports spokesperson on consumer perceptions of the advertised brand. To do so, they created a fictitious product (athletic shoes) and used active NFL

player Jamal Lewis as the athlete endorser. Lewis was chosen as the athlete for two distinct reasons: first, his positive image on the football field, and second, his crisis history— Lewis was convicted of assisting in a drug deal. Crisis history was used as the stimuli in the experiment. The researchers found that only 3.6 percent of a representative student sample in a pilot study recalled Lewis’ offense. This information confirmed the fit of Lewis for the purpose of this study because the previous offense was already forgotten by stakeholders and could therefore be used as a real and “as- live” crisis event. The study used experimental design (post-test only) and two distinct groups, plus a control group. This resulted in three groups exposed to three different conditions: (1) a control group that was provided with positive information about the product or athlete, (2) a group that was provided with negative information about the athlete, and (3) a group that was provided with negative information about the product.

The third group was of interest because a second part of the study aimed to explore how negative information about the brand influenced consumer perceptions of the athlete endorsers.

Findings showed that exposure to negative information about Lewis also had a negative impact on the endorsed product (White et al., 2009). The researchers found strong correlation between stakeholder perception of athlete image and the endorsed product. Students who were exposed to the negative information viewed the product significantly more negative after hearing about the athlete’s scandal, suggesting “a negative transference of the information toward the product” (p. 331). However, the same cannot be said for how stakeholders evaluate the endorser’s (athlete) image subsequent to receiving information about a scandal of a company or product with

which the athlete was associated. Lewis’ image remained positive even when he was associated with a bad company (White et al., 2009). Therefore when sports consumers find out about athlete transgressions, it can have a negative effect on the athlete and also have a negative effect on the product. Conversely, when a sports consumer is faced with an organizational transgression to which the athlete is only linked via endorsement association, they are less likely to transfer their negative opinion of the company toward the athlete.

Another study by Murray and Price (2012) examined a similar topic, but focused on how consumers’ levels of involvement in sports moderate the impact of athlete transgressions. In particular the study addressed differences between men and women as they relate to trustworthiness. The authors used two well-known athletes who had opposite images— Roger Federer, who had never experienced image threats, and

Tiger Woods, recently notorious for infidelity. Fictitious advertisements for two products

(sports and non-sports related) were created for each athlete. The study surveyed approximately 400 people and found less positive attitudes and purchase intentions toward the brand endorsed by Tiger Woods. Furthermore, results suggested that negative publicity had a greater main effect on attitudes and purchase intentions than sport involvement, but sport involvement significantly moderated the effect of athlete transgressions. Stakeholders with high involvement evaluated Roger Federer more positively, but were also more negative toward Tiger Woods. The study found no interaction between gender in regards to trustworthiness evaluation of the athlete endorser, but women had lower attitudes and lower purchase intentions. Sport

involvement did not factor in on attitudes and purchase intentions between genders

(Murray & Price, 2009).

In a recent study investigating consumer evaluation of athletes subsequent to crisis, Lohneiss and Hill (2014) investigated how processing of an athlete transgression influences brand image. The researchers used level of processing theory (LoP) as a theoretical framework and posited that stakeholders who “applied great meaning and elaboration to an athlete’s transgression and deemed it significant are likely to process that event deeply within their memory” (p. 176). They posited that after being exposed to the athlete (in a commercial) consumers are more likely to retrieve the transgression from their memory and subsequently might experience negative brand associations.

The reverse might be true as well, with stakeholders who apply low meaning. According to LoP theory, consumers evaluate athletes and their transgression, as well as the associated brand differently due to prescribing different meanings of and significance to the transgression. The researchers administered an online questionnaire and later used two 2 × 2 experiments to determine how one athlete’s transgression (Tiger Woods) influenced the brand evaluations (perceived brand image and purchasing intent) of

Nike, as well as a two 2 × 4 experiments to determine the level of processing of the transgression. Contrary to their hypotheses, negative information surrounding the athlete endorser did not decrease brand image of Nike. Although unanticipated, these findings are in line with research that suggests not all consumers transfer negative information about the athlete toward the brand (e.g., Ahluwalia, et al., 2000, Till &

Shimp, 1998). The study showed that cognitive processes factored into consumer evaluations of athlete endorsers but not necessarily due to transgressions. The results

of this study contrast the findings of Murray & Price (2009) mentioned before. Study participants reported continuous purchase intent despite Woods’ transgression, suggesting that sports consumers might discount athlete transgressions when making purchasing decisions.

Although the findings of marketing research provide interesting findings and relevant background knowledge regarding sports consumer evaluations, it did not address the image repair addressed in this dissertation. The following section will turn to communication, and more particularly public relations research to take a closer look at sports image repair discourse, and how certain communication strategies may or may not influence consumer evaluations of athlete image.

Image Repair Communication in Sports

In one of the first image repair studies within a sports environment, Benoit and

Hanczor (1994) investigated the image repair discourse of U.S. figure skater Tonya

Harding, who made headlines during the 1994 Olympic games. Harding was suspected of playing a central role in the physical attacks on fellow U.S. figure skater and rival

Nancy Kerrigan. Kerrigan was physically assaulted by a man hired by Harding’s husband, and reports quickly implied Harding’s involvement, suggesting she wanted to eliminate her competition in order to win the Olympic gold model. Using IRT as a basis, the study analyzed one of Harding’s TV interviews in which she addressed the allegations (Benoit & Hanczor, 1994). The researchers found that Harding mainly used denial, attack-the-accuser and bolstering strategies.

Several other sports scandals since then have provided a basis for more image repair research. The image repair of U.S. cyclist Floyd Landis, in response to performance-enhancing drug allegations and later confirmed doping charges,

investigation by researchers (Glantz, 2010; Sanderson, 2008). When media outlets published a picture of Olympic swimmer Michael Phelps smoking a marijuana bong,

Walsh & McAllister-Spooner (2011) looked into how he combated the negative press.

Another image repair case study involved NFL wide receiver Terrell Owens and his dispute with his employer, the Philadelphia Eagles (Brazeal, 2008). Studies have also looked into the image repair of amateur athletes such as Notre Dame football player

Manti Te’o, who was victimized by an online hoax (Frederick, et al., 2014).

Organizations in crises have also provided fruitful case studies. Len-Rios (2010) investigated the image repair of Duke University after several lacrosse student-athletes were accused of sexual assault and later exonerated; and DiSanza and colleagues

(2012) investigated the NHL’s image repair during the 2004-2005 lockout.

Researchers have shifted their attention away from image repair through traditional news channels to investigating the role of social media technologies.

Hambrick and colleagues (2013) explored the ways in which Lance Armstrong used

Twitter to enact crisis repair. Their findings suggested that, on Twitter, Armstrong predominantly employed attacking the accuser, bolstering, and stonewalling strategies.

However, when using traditional media as a vehicle for image repair, he used other strategies such as mortification, shifting blame, simple denial, provocation, and victimization. The study found that Armstrong used Twitter extensively during the investigation into his performance-enhancing drug use but failed to use the platform subsequent to his admission of doping in an interview with Oprah Winfrey. Instead of adding more information regarding his admission, or using Twitter to change negative media narratives, Armstrong continued to compose messages similar to his earlier

strategies. By evading further discussion, Armstrong “missed an opportunity to cultivate and enhance support from fans” (Hambrick et al., 2013, p.19). Similarly, Schmittel and

Hull (2015) investigated the image repair discourse of Richie Incognito during the Miami

Dolphins’ bullying scandal, and how he employed both traditional outlets as well as

Twitter. The study found that Incognito used contrasting image repair strategies on each outlet. With growing use of social media platforms by sports consumers, this posits the question of which platform is more successful in aiding an athlete in image repairs? This question points toward a need to investigate image repair beyond descriptive research and toward a more common framework.

Following the same method as Coombs and Holladay (2002) in establishing the

SCCT, Brown and Brown (2013) established a sports scandal typology. They organized

12 sports-related crises based on perceived crisis responsibility of each crisis type.

However, their typology is largely aimed at sports organizations rather than individual athletes. Table 2-6 illustrates the typology. The authors conceptualized three crisis clusters: environmental/individual crises, rules and norms violations, and organizational mismanagement, with ascending crisis responsibility attributed to the organization.

As the previously mentioned literature review shows, there appears to be a trend toward more sports-specific crisis-related research, and despite a plethora of studies investigating what types of image repair strategies athletes, organizations or even stakeholders use, few published articles have gone beyond mere description. There is a lack of research that uses the above mentioned cases to investigate how the image repair discourse and strategies employed by athletes are perceived by sports consumers and whether they are successful.

Table 2-6. Sports Scandal Typology (Brown & Brown, 2013 in Brown, 2014) Environmental/Individual Crises (Low Crisis Responsibility) “Act of God” Event: actions that affect an athlete or a sports organization that were outside of his/her/its control. For example, delay or postponement of games due to catastrophic weather conditions.

Controversial Statement/Action: statements or actions by an athlete that are viewed as inappropriate causing controversy, but did not lead directly to an arrest and/or conviction, and did not address some aspect of the team. For example, NFL wide receiver Roddy White, who faced criticism after controversial statements following the George Zimmerman verdict on Twitter in July 2013.

Personal Lifestyle Transgression: actions involving a sports figure that affect his/her personal life, but do not lead to an arrest and/or conviction. This action is seen as being more morally wrong than criminally wrong. For example, Bobby Petrino’s extramarital affair in April 2012.

External Criminal Transgression: actions involving an athlete that lead to an arrest, legal action and/or conviction. The actions that led to the legal action did not happen during the course of competition. For example, NFL wide receiver Chad Johnson, who was arrested on counts of domestic battery.

Internal Criminal Transgression: actions involving a sports figure that lead directly to an arrest, legal action and/or conviction that happened during the course of competition. For example, NHL player Marty McSorley who was charged with assault with a weapon after hitting Donald Brashear with a hockey stick during a game.

Rules and Norms Violations (Moderate Crisis Responsibility) Fan Involvement Issue: actions or statements made by sports fans that result in negative consequences for a sports organization or athlete. This also includes actions by collegiate boosters that could result in NCAA sanctions. For example, the violation of rules and a $5000 fine for the university after Ole Miss fans rushed the field after the football team beat Mississippi State University.

Amateurism Transgression: issues that affect the amateur status of a sports figure. For example, Shabazz Muhammad who was declared ineligible to compete as a member of the UCLA basketball team by the NCAA for violating amateurism rules.

Competition Transgression: actions involving an athlete or sports organization that directly compromise the fair nature of competition. For example, the New England Patriots’ Deflategate scandal.

Organizational Mismanagement (High Crisis Responsibility) League/Conference Management Issue: issues surrounding a team affiliation or league operations that do not directly affect the course of competition. For example, University of Maryland’s move from the ACC to the Big Ten conference.

Logistical/Operational Issue: issues that affect the viewing of a sporting event that were not caused by an act of God. For example, the power outage during the 2013 Super Bowl.

Player/Coach Management Issue: issues surrounding a sports figure that would directly affect the team’s active roster or coaching staff. For example, Monty Williams’ firing by the New Orleans Pelicans despite his contract, with management citing the organization wanted to go in a different direction.

Misleading Internal Information: statements made by an athlete about internal operations that cause some controversy or compromise his/her position with the team. For example, criticism of Tim Tebow as a quarterback voiced by his teammates both during his time with the Denver Broncos and New York Jets.

Only a few studies in sports used experimental designs to investigate the more pressing question of how image repair is actually affecting those to whom it is aimed toward. To expand on information from the previous chapter, Brown, Dickhaus and

Long (2012) explored how sports consumers perceived LeBron James after his move to

Miami. Through experimental research methodologies they sought to investigate if the perceived image of James by fans could be influenced by image repair communication strategies. Results suggested that James’ perceived image improved in light of an apology, as opposed to shifting the blame or bolstering himself. These findings confirmed early crisis repair scholarship, which suggests that many times mortification is the best strategy. Lee and Bang (2013) examined whether there was a difference between the type of transgression (competency-related vs. integrity-related) and image repair response type (apology vs. denial) to regaining fans’ trust. The findings pointed out that regardless of the type of transgression, perceived image improved when mortification was employed rather than the attacking the accuser or bolstering strategies. K. Brown (2014) investigated the effects of response on athletes’ perceived image after criminal or noncriminal scandals. Findings showed that attacking the accuser had equal perceptions in consumers as mortification strategy in criminal transgressions, but less in noncriminal transgressions and bolstering was the least effective strategy regardless of crisis type (K. Brown, 2014).

Although still in its infancy, there appears to be a shift toward a more audience- centered sports crisis response and athlete image repair research. However, unlike in its parent discipline, organizational crisis management research, sports crisis research

focuses solely on communication strategies and often fails to take into account other factors that could influence consumer evaluation in times of crisis or scandal.

Corporate Ability and Crisis

People and/or organizations aim to create a positive image in the consumer’s mind in order to maximize their impact (e.g., by transferring image to brand). Prior research suggests that corporate associations positively influence consumer attitudes and subsequently influence purchase intentions (Biehal & Sheinin, 2007). Corporate associations are defined as “memory-based psychological associations and evaluations,” which make up how we evaluate an entity’s reputation or image (Kim,

2013a, p. 241). Two distinct associations of an entity are perceived or recognized by others. First are corporate ability (CA) associations, whereby a consumer evaluates an entity’s skills as they relate to producing quality products or services. Second are corporate social responsibility (CSR) associations, whereby a consumer evaluates the status of an entity as it relates to social involvement (Brown & Dacin 1997). Although

CSR was not a focus of the present study, it will be discussed to provide a better understanding of corporate associations.

As the public relations literature points out, companies frequently use or rely on communication strategies to stress either corporate ability or CSR associations in order to reinforce an image in the consumers mind. Prior studies have determined that in everyday evaluations consumers are more likely to respond to corporate ability strategies than they are to CSR strategies (Brown & Dacin, 1997). In addition, these associations were found to have an impact on consumer evaluations in times of crisis.

In fact, these corporate associations (both CA and CSR) mitigate the negative effects of a scandal (Coombs & Holladay, 2006; Klein & Dawar, 2004). Grunwald and

Hempelmann (2011) found that consumers of companies who elicited positive corporate ability associations, or as they termed it, pre-reputation for quality, attribute less crisis responsibility than those with negative corporate associations. Kim (2013a, 2013b,

2014) investigated the topic of corporate ability and CSR associations in times of crisis further and wondered if they could operate as an “insurance policy” in times of crisis.

Kim (2013a) discovered that in times of product-harm crisis, it is worse for companies to have negative prior corporate ability (CA) associations than to have negative corporate social responsibility (CSR) associations, while at the same time positive prior CSR associations appear to be more beneficial than positive CA associations. This can easily be explained by the fact that in the case of a product crisis for stakeholders who have strong CSR associations, the crisis did not violate associations as much as it did for those with strong CA association and higher expectations toward product performance. Kim suggested that the disadvantages of having negative CA associations would be great in times of product-harm crises as well as in non-crisis situations simply because consumers act on the basic premise that the products are good (economic basis expectations). This is different than CSR associations, which are not always part of the basic expectations of stakeholders (Kim,

2013a).

Kim (2013b) extended her research by including various types of crisis (i.e., preventable and victim) as well as varying crisis severity as experienced by a fictional fast-food company. The author employed a 4 (corporate associations [Positive CA,

Negative CA, Positive CSR, Negative CSR]) × 2 (Crisis Type [victim, preventable)] × 2

(crisis severity [high and low]) between-subjects experimental design and studied the

influence of corporate associations on locus, stability and control perceived by stakeholders experiencing a corporate crisis event and how they may influence company evaluation, product evaluation, supportive behavioral intentions and purchase intentions. The author confirmed her previous findings of positive CSR being more effective at mitigating crisis than prior CA associations. Furthermore, results indicate that stakeholders with positive corporate associations (both CA and CSR) have more positive evaluations in their attribution despite the level of crisis responsibility; meaning,

“Postcrisis company evaluations are not necessarily mediated by their attributions of crisis responsibility when considering previous reputations” (p. 253). Kim reasoned that although people use realistic crisis responsibility judgments, they are not likely to change their minds or attitudes about the company and are not likely to form negative attitudes (Kim, 2013b). Provided the strong connections sports consumers have with their teams and the athletes, it would be interesting to see if this finding applies to them, as well. Arguably, factors such as high fan identification with the team could mitigate the formation of negative attitudes in fans, despite being aware of the crisis and attributing high crisis responsibility.

Corporate Ability in Sports: Winning

While in an organizational setting corporate associations represent either consumer associations toward a company’s abilities as they relate to its products (CA) or its status as a good member of society (CSR), this dissertation argues that corporate associations, particularly corporate ability, can be adopted into the sports setting. This study conceptualizes athlete ability associations (AA) as fans’ associations with an athlete in terms of the athlete’s ability to perform well on the playing field. In the sports setting, then, winning (or a great individual performance) would be representative of

positive (corporate) ability associations, and losing (or a bad individual performance) would result in negative (corporate) ability associations.

Success can be one of the most important factors in creating brand equity over time (Gladden, Milne & Sutton, 1998). The importance of winning in sports is exemplified by the quotation wrongly attributed to Vince Lombardi who said, “winning isn’t everything; it’s the only thing”. Generally speaking winning means to outscore the opponent. However, winning or success in sports does not necessarily always have to do with defeat; instead, winning could also be characterized by “playing with courage and grace,” or simply doing better than before, or better than anticipated (Kruse, 1981, p. 273). Research examining how game outcomes impact fan loyalty found when a team loses regularly, or a fan is presented with better entertainment options, he or she may decide to no longer follow or support the team or athlete. In fact, winning is frequently credited to affect cognitive processes by shifting movement from the awareness stage to the attachment stage (Funk & James, 2001). Furthermore, fans distance themselves from a team after a loss (Bizman & Yinon, 2002), especially when they are low-identified (Grieve et al., 2009). High-identified fans continue to support their team regardless of team performance (Bizman & Yinon, 2002). This is thought to be true, because highly identified fans are not able to disassociate with a team or player even when he or she is performing poorly (Grieve et al., 2009). This may also be true in times of crises or scandalous events. Kruse’s (1981) determined that traditional crisis repair efforts are only somewhat important for athletes facing crisis; he argued that traditionally fans are more interested in the success or failure of the team as opposed to the image or character issues of an individual athlete.

Theoretical Framework

Expectancy violation theory informs this study by predicting how fans evaluate athlete conduct and further respond to athlete transgressions. In its infancy, expectancy violation theory (EVT) was used to explain how individuals interpret violations of their personal space during interpersonal communication (Burgoon & Jones, 1976; Burgoon,

1978). Since then the theory has been applied to explain other behaviors in interpersonal communication, and online communication (Kalman & Rafaeli, 2011), and has been extended into crisis communication (Kim, 2013b; 2014). Pioneered by Burgoon and Jones (1976) EVT, holds that people evaluate their surroundings and and other people based on their own expectations. Accordingly, people use expectancies to process information, frame interactions, and subsequently behave based on their perceptions of the encounter (Burgoon, 1993; Burgoon & Hale, 1988). By definition, expectancy is an enduring pattern of predictable behavior that is most often a product of group norms (Burgoon, 1993). Because expectations are frequently formed by societal norms, expectancy violations occur when an act is interpreted as deviating from a behavior that is normal or predictable (Afifi & Metts, 1998).

When an expectancy violation occurs, people evaluate both the violation and the person who committed the violation (the violator) in order to make sense of the event or encounter. Upon expectancy violation resulting from verbal or non-verbal interaction with another individual, people follow a series of cognitive steps. First, attention is directed to the interaction; then the interaction is processed and attributed as an expectancy violation; second, is the evaluation stage in which the person consciously or subconsciously copes with the violations and determines how to proceed (Bachman &

Guerrero, 2006).

Through the application of the theory into a broader context, researchers determined that expectancy violations do not strictly have to be of negative nature, but can instead be positive, therefore suggesting there are two kinds of expectancy violations. Negative expectancy violations occur when a behavior is interpreted to be unexpected and of negative nature. On the other hand, positive expectancy violations are encounters that are perceived to exceed expectations (Afifi & Burgoon, 2000). In each instance the outcome is either a positive or negative evaluation that has been triggered by either a positive or negative event.

Violation Valence

Violation valence, or how negatively or positively an event is perceived in comparison to the expectancy, predicts how people respond to the expectation violation and proceed. EVT suggests that people who violate expectations are perceived more negatively than those who either meet the expectations or the ones who do not violate them (Jackson, Sullivan & Hodge, 1993). Transgressions or athlete scandals typically cause negative violation valence; however, level of violation valence differs depending on context, relationship and even violator characteristics (Burgoon, Newton, Walther, &

Baeseler, 1989). The same behavior performed by two athletes could cause differing expectancy valence in the same person depending on relationship and/or context. For example, a fan might perceive high negative valence toward athlete A, but lower negative valence toward athlete B. Moreover, one behavior may be liked in one context, but not in another (Afifi & Metts, 1998). To provide a sports example, consider physical violence. Physical violence might be expected during a mixed martial arts (MMA) fight, thus, two fighters viciously attacking one another would produce positive violation valence. However, if physical violence occurred between two athletes competing in a

non-violent sport (e.g., golf, tennis), an individual may very well experience negative violation valence. Nonetheless, negative violations tend to cause negative communication (Burgoon & Hale, 1988), and less forgiveness and forgiveness communication (Guerrero & Bachman, 2010).

Predictive and Prescriptive Expectancies

Burgoon (1993) suggested that there are two distinctive kinds of expectancies: predictive and prescriptive. A predictive expectancy is described as an anticipated behavior for a specific individual. This type of anticipated behavior is grounded in and related to the person’s past behavior. For example, an individual might observe and evaluate an athlete’s behavior to be uncharacteristically more aggressive one day, resulting in a violation of the predictive expectancy because the athlete’s past behavior was contrary (non-aggressive). In contrast to predictive expectancies, which once more are evaluations particular for a specific individual, a prescriptive expectancy is an anticipated behavior that is grounded in societal norms and holds true for society at large. For example, based on social norms a person is to abide by the law and should not commit a crime. For the purpose of this study, predictive expectancies are those resulting from the athlete’s past behaviors that point toward a commitment of excellence both on and off the playing field (e.g., athlete social responsibility). Prescriptive expectancies are based on broader societal norms related to an athlete’s conduct.

Expectancy Violation Theory in Crisis

Kim (2013a) argued that in instances of organizational crises (i.e., following a transgression or violation of rules) it is the crisis itself that prompts stakeholders’ interpretations and evaluations of the crisis event, as well as those responsible for it

(e.g., organization, management). Applying expectancy violation theory (EVT), athlete

scandals can be considered violations of fan expectancy toward the athlete, or even the organization for which he or she competes. For professional athletes who are often viewed as role models and have to live up to high societal expectations, this means that crises cause negative expectancy violations, which in return trigger negative views and evaluations about the athlete and perhaps even his associates (e.g., team, sponsors).

In times of an athlete crisis, sports fans may experience arousal from expectancy violation, which is influenced by previously held beliefs in the form of prescriptive and predictive expectancies toward the offender. Fans expect athletes to be socially responsible based on societal rules (i.e., high prescriptive expectancy) and to consistently behave with integrity (i.e., high predictive expectancy); the fans would reveal more negative reactions toward the athlete after a scandal due to expectancy violations (i.e., negative violation valence). For example, Lance Armstrong was once coined an American hero for his accomplishments in cycling. He was most often celebrated for making an astonishing comeback subsequent to facing testicular cancer, winning the Tour de France seven consecutive times. He was also celebrated for his philanthropic efforts of his multi-million dollar cancer foundation Livestrong. However, he also faced doping allegations for much of his career. These allegations were confirmed in 2012, years after his retirement from the sport. Armstrong’s confession resulted in the retrospective disqualification of his titles and a life-time ban from competitive cycling, sparked disappointment among fans, and morphed into one of the biggest sports scandals of all time. Using EVT, one could argue that fans experienced high negative violation valence in light of his confession, provided they had high prescriptive expectancies (e.g., doping as illegal and immoral act within sports), and predictive

expectancies stemming from Armstrong’s pre-scandal characteristic (e.g., winner, survivor, philanthropist).

Violator Reward Valence

Whether violation valence is deemed positive or negative is largely determined by violator reward valence, which is defined as an assessment of the violator in form of previous relationships or characteristics. Reward valence in turn predicts people’s reactions to unexpected behaviors (Burgoon, Stern, & Dillman, 1995). The seminal research pieces using EVT as a theoretical framework for violations during interpersonal communication found that individuals are able to reward others by offering supplemental verbal or non-verbal cues or characteristics during conversations (e.g., smile, eye- contact, credibility). These rewards then allow for mitigation when a person experiences negative valence from another individual. EVT predicts that when someone who is physically attractive evades our personal space we are more likely to experience negative valence and excuse the evasion than when someone we find physically repulsive evades our personal space. In fact, we may even rationalize personal space violation for the benefits of interacting with a physically individual (Kim, 2014). In that vein, highly rewarding individuals or entities have more room before violating expectations (Le Poire & Burgoon, 1996). To apply this to a sports setting, one could argue that fans of the Miami Dolphins might rationalize the unsportsmanlike conduct of players like defensive tackle Ndamukong Suh, because Suh rewards the team by contributing positively to the team’s success.

Kim (2014) argued that violator reward and negative valence significantly influence stakeholder perceptions in times of organizational crisis. Using a BP oil spill,

Kim tested whether high/positive expectancies, both predictive and prescriptive, also

result in more negative violation valence toward a violator. The study supported this notion and determined individuals who perceive stronger negative violation valence perceive a more negative image of the culprit. Furthermore, the author suggested that how people react to the crisis (i.e., BP oil spill) depends on past relational satisfaction with the company, that is “the extent of rewards experienced from the relationship based on a social exchange perspective” (p. 141). Stakeholders perceive highly rewarding violators less negatively in expectancy violation, whereas low rewarding violators are perceived more negatively (Kim, 2014). EVT postulates that positive reward valence can mitigate negative violation valence when expectancies are negatively violated. In the BP case this translates to stakeholders who experienced high satisfaction from their association with BP view the events surrounding the oil spill as less negative and, furthermore, identify a better corporate image. Although the results confirm previous findings supporting EVT, the findings are important in the sense they extended EVT into a crisis communication setting. Moreover, the study provides reason to believe that in organizational crises, expectancies and relational satisfaction function contrarily:

Expectancies reflect stakeholders’ anticipation of the organization’s behaviors, not necessarily including a positive evaluation of the quality of relationships. This indicates that relational satisfaction is based on stakeholders’ evaluations of the quality of organization—public relationships or the extent of rewards experienced from the relationships rather than an organization’s behavioral patterns or socially desirable behaviors. (Kim, 2014, p. 150)

Perceived rewards have the potential to mitigate violation valence and can predict people’s reactions to unanticipated events. Extending this idea into the current investigation, a violator reward for fans could come in from of positive athletic performance of the transgressor. A winning (i.e., high rewarding) athlete and/or a team

may experience fewer negative consequences in negative expectancy violation situations than low rewarding (i.e., losing) athletes and/or teams. This would hold true especially for highly involved fans, who seek out membership with sports teams and athletes in order to enhance their self-esteem. Highly involved fans are more likely to experience a self-esteem threat when an athlete they are associated with is involved in a scandal, which may then result in CORFing behaviors (Cialdini, et al., 1976). At the same time, as Kim’s (2014) research showed, highly involved consumers who have strong attitudinal ties to an entity might be less likely to cut these ties in times of crisis and change their attitudes toward the entity due to past relationships and existing attitudes. In and of itself these are conflicted findings, which suggest a need for further investigation, particularly as it relates to sports consumers. Particularly investigations into the effects (benefits and drawbacks) of rewards (winning) or lack thereof (losing) during a sports crisis and image repair discourse of an athlete are needed, as winning and losing are fundamental elements of sports.

All together, it was the premise of the researcher that negative violation valence will result in a more negative evaluation of the athlete’s image subsequent to athlete scandal or transgression. Furthermore, this dissertation seeks to investigate if a strong and positive athlete performance and/or team performance will benefit the athlete during times of crisis. In that sense, this study seeks to explore the role of winning and losing on the impact of public perception of an athlete facing a transgression and the subsequent behavioral outcomes of sports consumers. Based on this review of the aforementioned literature the following research questions and hypotheses are postulated:

H1: Fans with higher athlete expectancies will report more negative violation valence toward the athlete experiencing the scandal.

6 Correlations for H2a-e are predicted to be negative, while H2f+g are predicted to be positive

CHAPTER 3 METHODOLOGY

The purpose of this dissertation is to investigate the effect of athlete and team performance, which was broadly classified as winning or losing, on the perceptions of sports consumers toward an athlete and the team with which he is affiliated with subsequent to an athlete transgression or scandal. The research seeks to explore: first, to what extent prior athlete expectancies influence perceived violation valence; second, to what extent positive athlete performances or team performances affect the athlete’s image, as well as behavioral intentions of sports consumers after crisis; and, third, to what extent fan identification factors into these evaluations by fans.

Experimental Research Design

To investigate the hypotheses and research questions, an experimental methods design was used. More specifically, a 2 (Team identification [high vs. low]) × 2 (Team

Performance [win vs. loss]) × 2 (Athlete Performance [positive vs. negative]) factorial experiment produced eight distinct conditions. Experiments or quasi-experiments are frequently used within the crisis management field, and a few sports crisis communication researchers who stress the importance of audience-centered research have used factorial experiment studies for their inquiries (K. Brown, 2014; N. Brown,

2014; Brown, et al.., 2012). Most frequently within these types of sports studies fans are assigned to groups and exposed to manipulations (typically image repair communication strategies) inserted into mock sports news articles presented within an online survey.

Experiments are being used more often in sports crisis research because they can explore causality and they are easier to replicate. Causality requires showing that

(1) change in one variable causes change in an other variable; (2) the effect follows the cause; and (3) another variable is not influencing the relationship (Wimmer & Dominick,

2011). Experiments have two types of designs: factorial and repeated measures.

Factorial designs allow for the analysis of a minimum of two independent variables whereby each level of each independent variable can be tested in combination with other variables or levels (Wimmer & Dominick, 2011). The current study used a factorial design.

Control

A key requirement of experiments is that study participants are randomly assigned to one of the treatment groups. Random assignment of participants helps assure that the treatment is the cause of the outcome, rather than pre-existing conditions. Frequently experiments are conducted in a lab setting, where the researcher can ensure that each participant is exposed to the same environment and can attempt to control for exogenous variables, though no experiment can eliminate all extraneous variables (Shadish, Cook & Campbell, 2002).

Some experiments are now being done online, by web-based questionnaires, because they offer access to more study participants, which enhances the recruitment of study participants because they do not have to leave their environment (Reips, 2000).

Participants can still be assigned to conditions randomly (Shadish, et al., 2002); however, researchers can no longer control the research setting, which introduces potential threats to validity. Some researchers have argued that the uncontrolled setting interferes with true experimental research by not guaranteeing full control over a variety of aspects. However, others argue that this is not a problem, because it eliminates the

frequently mentioned limitation of experiments, which is that lab settings are not a true representation of reality and can therefore contaminate the results.

Manipulation

Manipulation is used to create the levels of the independent variables. Stimuli are created in order to manipulate these variables; at a later time participants are recruited and assigned to one of the stimuli groups. For this study about athlete transgressions and performance, the researcher manipulated the reporting of athlete transgressions, as well as athlete and team performance using online news articles. The researcher designed the news articles to resemble articles extracted from ESPN.com. ESPN was not only chosen because it is the self-proclaimed ‘worldwide leader in sports,’ but also because the organization is viewed as the top destination for sporting news and entertainment (Thompson, 2013). Furthermore, its website espn.com was the most visited sports news website every month throughout the past and present year (Fisher,

2014).

Online news articles were chosen because of the increasing consumption of sports news via web sources. The recently published Know the fan: The global sports media consumption report 2014 found that more than half of online consumers read sports news on the Internet. The same report found that even though television remains the leader in terms of sports consumption, online consumption, including mobile and social media platforms, has the highest growth rates (Sporting News Media, 2014). For those in the 18-24 age range, online news consumption is rising so fast that it now is the largest demographic group for online news consumption. This was of particular importance to the present study, as the participant pool was made up of college

students who predominantly are 18 to 24. Thus, students are an appropriate population for a study on sports.

Participants

The researcher sought a minimum of 25 participants for each of 8 cells (Cohen,

1988), resulting in a minimum requirement of 200 total participants for this study. The researcher inflated the sample size by at least 10% for each phase within the experiment to account for participation attrition and to ensure a sufficient number of participants in each cell of the factorial design (Dunn, 2012). Participants were recruited from two public universities in different regions: the University of Florida and Winona

State University.

While many researchers caution against the use of college students as research participants because they fear external validity is lost for the sake of easy access

(Winer, 1999; Wells, 1993), the use of such samples has also been defended. Lynch

(1999) argued that student samples are not any more problematic than the use of a homogeneous group of members of a different organization (e.g., church members). He maintained that researchers must specify and make a case for why the construct(s) within the study might interact with the experimental treatment manipulations within a certain group of people, regardless of whether they are students or not. The use of college students within this study was justified due to the nature of the research. Many sports studies have utilized college student samples. For example, Wann and

Branscombe (1992) used college students to determine sports fans’ emotional responses. A few years later, Wann (1995) used another set of college students to test his Sport Fan Motivation Scale. In studies investigating sports fan consumer behaviors, college samples are also quite common (e.g., Pham, 1992). Sports crisis

communication researchers have also turned to college student participants for their experimental studies (e.g., K. Brown, 2014; N. Brown, 2014).

Furthermore, as mentioned before, men and women between the ages of 18 to

24 are the biggest group of sports consumers (Sporting News Media, 2014). College students are also among the highest consumers of online sports media, which adds validity to this study and its manipulations through a web-based ESPN article.

Approval from the Institutional Review Board at the University of Florida was acquired in order to recruit participants for the study. Once authorization from IRB was granted (see Appendix A) the pre-survey and pilot test were conducted in the first weeks of the Spring 2015 semester. Upon incorporating the changes and suggestions from both the survey and pilot test, the researcher recruited participants from several courses taught in the Spring 2015 semester in both the College of Journalism and

Communications and the College of Health and Human Performance at the University of Florida and the College of Liberal Arts at Winona State University for the main study.

Varying courses were chosen in order to avoid cherry-picking participants and to enhance reliability.

Participation was voluntary, although extra credit in exchange for participation was available at the discretion of each of the course instructors. Also, students completing all three phases of the study were entered in a drawing for two iPad minis.

This procedure was embedded in the data collection process in order to provide an incentive for continued participation throughout all three phases of the study and in an effort to combat attrition. Participants were recruited through either personal class visits or emails that included a brief description of the study. After agreeing to participate in

the study, students were provided with a link to the online questionnaire. After the participants completed the final stage of the study (phase three) they were debriefed.

Debriefing took place in the form of a written portion at the end of the questionnaire that described the full intent of the study, as well as a section which explicitly stated that the player, scandal, and athletic performance presented within the study were fictional.

Sports Selection

The present study sought to explore how the participants react to the transgression(s) of an athlete and how their evaluations about the player and his or her team changes with winning and losing. American professional football was chosen because it has been the most followed sports in the U.S. for several years (Sporting

News Media, 2014; Nielsen, 2014). One in two adults in the U.S. follows football, compared to only 31% of people who follow the second most popular sport, baseball.

This means that the NFL has approximately 117 million fans; of those, 56%, or 65.5 million, are engaged fans, defined as those who have a high degree of emotional attachment (Sporting News Media, 2014).

Also, the NFL was selected because the occurrence of an athlete transgression is not uncommon. In 2014 alone, 40 arrests were made involving active NFL players; in

2013, 57 arrested and in the previous year 47 (“NFL Player Arrests,” 2014). These arrests vary in offense type and degree, including crimes such as domestic violence, drugs, burglaries, and drunken driving. A closer look at the NFL player arrests from

2000 to 2015 found that 30.3% of arrests, or 264, were made for alcohol-related crimes,

13.3% of arrests, or 116, were made for drug offenses, and 11.1% of all arrests, or 97, were made for domestic violence.

Table 3-1. NFL Player Arrests Since 2000 (Rosenberg, 2015) Crime # of arrests % of all arrests DUI/Alcohol-related 264 30.3% Drugs 116 13.3% Domestic Violence 97 11.1% Assault 78 8.9% Guns 65 7.5% Disorderly Conduct 50 5.7% Resist/Evade Arrest 24 2.8% Theft/Burglary 24 2.8% Battery 20 2.3% Reckless Driving 19 2.2% License Issue 19 2.2% Sex/Indecent Exposure 12 1.4% All other 84 9.6% *Analysis of USA Today database; includes multiple charges (872) for 801 players arrested

Within the first few weeks of the 2014 season alone, the NFL, and several of its teams had to battle public outrage stemming from multiple player transgressions:

Baltimore Ravens’ Ray Rice, who viciously beat his then-fiancé in a casino elevator,

Minnesota Vikings’ Adrian Peterson, who disciplined his 4-year-old son with a tree branch, and Dallas Cowboys’ Greg Hardy, who assaulted his girlfriend and threatened to kill her, had created intense media coverage. These cases and resulting coverage suggest a transgression-prone environment within the NFL, and point to strong interest in these athlete behaviors by both fans and media representatives. In that vein, creating, reporting, and exposing participants to a player misbehavior for the purpose of the present study, was more likely to be seen as realistic than if the player was from a different sporting environment such as tennis or golf. That is not to say that players of other sports do not engage in questionable behavior. Although such widespread deviance could invoke the inoculation effect (McGuire, 1961), the reactions of participants — for example 7,000 fans returned their Ray Rice jerseys (Meoli, 2014)

following his domestic abuse incident — suggests such behavior is still seen as unacceptable.

Team and Scandal Selection

In order to choose a transgression for this study, a preliminary screening of levels of offensiveness of varying athlete wrongdoings was conducted. This method was modified from a procedure by Brown (2012), in which the researcher sought to determine which professional athletes evoked mostly neutral perceptions in fans’ minds despite forms of athlete misconduct. Brown (2012) asked 30 participants to rate their perception of several NFL players involved in transgressions using a 7-point Likert scale item (“How would you rate your overall perception of Athlete X?”). Based on these responses, four athletes and scandal cases were chosen for Brown’s experiment, “the researcher chose cases where the mean perception score was close to four (neutral on a 7-point scale), and a standard deviation was close to zero” (Brown, 2012, p. 58).

The present study used a convenience sample of students to determine which type of transgression would be implemented. Using an online Qualtrics survey, participants were asked to respond to questions regarding athlete transgressions. The athlete offenses were compiled by conducting an environmental scan in which the researcher compiled prominent athlete transgressions of the past 10 years.

Study participants were asked to read summaries of different athlete crimes and were then asked to rate their perceived offensiveness on a Likert scale of 1 to 7, with 7 being highly offensive and 1 representing not offensive at all. Participants were also asked how likely they were to support the athlete who committed the act, as well as how likely they were to forgive the transgression; offered were answer options of “I would be likely to forgive this transgression if… ” a) the athlete apologized, b) the athlete

performed well on the field, c) the athlete compensated the victims, d) the athlete underwent therapy, e) I would never forgive this transgression, no matter the corrective actions undertaken by the athlete. Appendix E provides an overview of the questionnaire. Contrary to the intent of Brown (2014), the focus here was on the transgression, not the athlete. Yet the criteria were similar: the offense chosen was a neutral one based on the mean scores and standard deviation, because each transgression should evoke a moderately negative response. Low mean scores on the first question would indicated fans don’t find the act offensive enough and there might not be a need for image repair. Extremely high mean scores, on the other hand, might suggest that the act was unforgivable, in which case no type of image repair discourse would be successful. Overall, the intent of the researcher was to determine a scandal or type of transgression grouped closer to neutral, or the center, than the extremes (very negative or very positive).

Results of Transgression Study

The survey aimed at determining levels of offensiveness of athlete transgressions, as well as willingness to forgive these transgressions, was administered prior to the main study in January 2015. A link to the online survey was sent electronically to 264 undergraduate students enrolled in a mass communications course at the University of Florida during the 2014 fall semester. The number of participants included was 135. Males made up 58.5% (n = 79) of the sample and the rest were female (n = 56). All but three (n =132, 97.8%) fell within the 18 to 24 age range.

Participants represented different colleges within the University of Florida with two-thirds coming from the Colleges of Journalism and Communications and Health and Human

Performance.

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Participants were first asked to answer a number of general questions to identify their favorite NFL teams and their perceptions of successful seasons. Of the participants 21.5% (n = 29) identified the Miami Dolphins as their favorite team, whereas

11.9% (n = 16) named the Tampa Bay Buccaneers and 9.6% (n = 13) the New England

Patriots as their favorite team. Twenty-five other NFL teams were mentioned in small numbers, and 6.7% of participants in the sample (n = 9) reported not having a favorite

NFL team.

Winning and Losing

Because the main study was interested in determining whether winning and losing might influence the success of image repair, participants were asked how they determined the success of their favorite team. Most (93%; n = 127) indicated that the win/loss record was a good indication of a team’s success. Participants were asked about specific win/loss records in order to explicate what constitutes a winning or a losing season. Sixty-three percent of all respondents stating a 10-6 or better record was successful. Roughly 11% of respondents stated that as long as the team makes it to the playoffs, the actual regular season win/loss record was irrelevant. Similarly, the same number of respondents indicated a losing season was characterized by a failure to advance to the playoffs. Furthermore, most participants perceived a losing season to be anytime the team has more losses than wins, with close to 77% believing a record of 8-

8 or worse would be considered a losing and unsuccessful season for their team.

Finally, this part of the survey inquired about the perceptions of performance of three potential teams for the main study: The Green Bay Packers (12-4 regular season), the

Miami Dolphins (8-8), and the Tampa Bay Buccaneers (2-14). Participants were provided with a short summary of the teams’ records and accomplishments (e.g., playoff

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appearance) and were then prompted to indicate how successful they believed the teams’ seasons to have been on a 7-point Likert scale with 1 = very successful and 7 = very unsuccessful. Out of all participants 78.5% (n = 106) believed the Packers’ season was successful, with 20.7% (n = 28) believing their run to the NFC championship was very successful (M = 2.10; SD = .93). Participants evaluated the Dolphins’ season as less favorable (M = 4.77, SD = 1.26), with 65.9% (n = 89) reporting the team’s season was somewhat unsuccessful or worse, and only 26.7% indicating the season was somewhat successful. Just more than 7% indicating they weren’t sure. For the

Buccaneers’ season; the majority of respondents (82.2%, n = 111) stated Tampa Bay had a very unsuccessful season (M = 6.70; SD = .81), with another 15.5% believing the team had an unsuccessful or somewhat unsuccessful season.

These findings confirm the suitability of the use of winning and losing records for the main study. The results showed that the Green Bay Packers were an appropriate example of a winning team in the 2014 NFL season whereas the Tampa Bay

Buccaneers were considered a losing team.

Offensiveness

The second part of the preliminary screening was concerned with determining levels of offensiveness of athlete transgressions as well as willingness to forgive these transgressions. In order to do so, participants were presented with a short player profile

(see Appendix E) of a professional football player created for the study. Participants were told that the athlete is, indeed, a current player in the NFL, but that his name was changed for the purpose of the research study in order to taint the data with predetermined perceptions about the player not relevant to the research. The player profile mentioned player statistics and accomplishments, charitable work, and a brief

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synopsis addressing personal background. Participants were then asked to evaluate the athlete and indicate how favorable their own perceptions toward the player were on a 1 to 7 Likert scale with 1 = very favorable and 7 = very unfavorable. Overall, as intended by the researcher, the athlete was perceived favorably (M = 1.85, SD = .720). An independent-sample t-test was conducted to compare the scores for males and females. There was no significant difference in scores for males (M = 1.82; SD = .694) and females (M = 1.89, SD = .762; t (132) = -.537; p = .592 two-tailed).

Next, participants were asked how likely they were to support the athlete on a

Likert scale from 1 to 7 with 1 = very unlikely and 7 = very likely. Overall, the participants were likely to support the athlete (M = 5.01; SD = 1.349). The mean scores were good considering participants did not know the team, and much of a fan’s decision to support an athlete has to do with what team he or she represents. There was no significant difference in scores for males (M = 5.18; SD = 1.269) and females (M = 4.78,

SD = .1.436; t (132) = 1.681; p =.095 two-tailed).

To determine which type of transgression would be used in the main experiment, the level of offensiveness of a variety of athlete transgression was investigated.

Participants were exposed to and asked to evaluate 15 athlete transgressions ranging from moral failings (e.g., an affair) to illegal offenses (e.g., domestic abuse). The transgressions were presented in the form of ESPN news stories and were all committed by the previously mentioned athlete created for the study. Overall 15 total transgressions7 were investigated. The transgressions represented offenses that have occurred or may be likely to occur in the professional sports setting, and were selected

7 Point-shaving was excluded from the study because of its direct relationship to player performance.

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based on the environmental scan mentioned in the previous chapter of this dissertation.

Although 135 students participated in the research, each student was only exposed to five transgressions in order to avoid fatigue and achieve better results. Qualtrics randomly selected five transgressions and each case was presented to a minimum of

40 study participants (M = 45), who were asked to indicate the offensiveness of the transgression of a 1 to 5 point Likert scale with 1 = not offensive and 5 = severely offensive. Table 3-2 has the descriptive statistics for the transgressions inquired about.

Table 3-2. Offensiveness of Transgression Transgression Mean Standard Deviation Possession of Marijuana 2.56 1.097 Accusations of selling of pain medication 2.85 1.095 Illegal possession of firearm 3.00 1.121 Performing Enhancing Drugs 3.11 1.243 Solicitation of a prostitute 3.35 1.269 Failure to pay child support 3.38 1.230 Violence toward fan 3.42 1.252 Anti-gay comments on Twitter 3.67 1.148 Affair with teammates wife 3.80 .885 Bounty-Gate 3.98 1.196 Hit and Run resulting in injury 4.20 .919 Sexual harassment of a team employee 4.36 .857 Domestic Abuse 4.39 .930 Alleged Rape 4.55 .653

Independent-sample t-tests were conducted to compare the scores for each transgression for males and females. Of the 14 transgressions only three cases showed a significant difference between genders. Women perceived possession of marijuana, sexual harassment, and bounty-gate offenses to be significantly more offensive than men. There were no significant differences in mean scores for males and females for the remaining 11 transgressions per Table 3-3.

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Table 3-3. Descriptive Statistics of Transgressions Transgression Males Females t P M SD M SD 2-tailed Possession of Marijuana 2.17 .816 3.12 1.219 (40) = -2.99 .005 ** Accusations of selling of pain 2.87 1.167 2.81 .981 (44) = .158 .875 medication Illegal possession of firearm 2.96 .999 3.05 1.276 (42) = -.267 .791 Performing Enhancing Drugs 3.09 1.345 3.14 1.153 (42) = -.147 .884 Solicitation of a prostitute 3.12 1.336 3.65 1.137 (44) = -1.43 .159 Failure to pay child support 3.35 1.268 3.40 1.225 (43) = -.134 .894 Violence toward fan 3.57 1.357 3.13 .990 (37) = 1.22 .231 Anti-gay comments on Twitter 3.58 1.216 3.73 1.116 (43) = -.434 .666 Affair with teammates wife 3.79 .861 3.82 .951 (44) = -.111 .912 Bounty-Gate 3.52 1.312 4.67 .485 (35.7) =-4.1 .000 *** Hit and Run resulting in injury 4.25 .844 4.12 1.054 (43) = .464 .645 Sexual harassment of a team 4.13 .922 4.86 .363 (43) = -3.79 .000 *** employee Domestic Abuse 4.46 .948 4.30 .923 (44) = .579 .565 Alleged Rape 4.59 .568 4.50 .786 (45) = .436 .665 * = p < .05, ** = p < .001, *** = p < .001

Forgiveness: Furthermore, participants were asked how likely they were to forgive the athlete’s transgression, via a Likert scale question anchored at 1 = very unlikely to forgive and 7 = very likely to forgive. The mean scores can be seen in Table

3-4 below.

Table 3-4. Descriptive Statistics Likelihood to Forgive Transgression Mean Standard Deviation Possession of Marijuana 5.10 1.707 Illegal possession of firearm 4.23 1.710 Accusations of selling of pain medication 4.15 1.751 Violence toward fan 4.13 1.984 Failure to pay child support 3.91 1.756 Anti-gay comments on Twitter 3.91 2.098 Solicitation of a prostitute 3.89 1.900 Performing Enhancing Drugs 3.73 2.016 Affair with teammates wife 3.57 1.669 Bounty-Gate 2.93 1.876 Sexual harassment of a team employee 2.89 1.849 Domestic Abuse 2.59 1.627 Hit and Run resulting in injury 2.58 1.500 Alleged Rape 2.26 1.567

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Again, independent-sample t-tests were conducted to compare the scores for males and females. There were no significant differences in mean scores in likelihood to forgive certain transgressions for males and females, per Table 3-5.

Table 3-5. Descriptive Statistics of Transgressions Transgression Males Females t P M SD M SD 2-tailed Possession of Marijuana 5.43 1.376 4.65 2.029 (26.5) =1.38 .178 Illegal possession of firearm 4.21 1.693 4.25 1.773 (42) = -.080 .937 Accusations of selling of pain 4.27 1.837 3.94 1.611 (44) = .603 .550 medication Violence toward fan 3.97 2.109 4.47 1.727 (43) = -.794 .432 Failure to pay child support 3.95 2.212 3.88 1.333 (30) = .125 .902 Anti-gay comments on Twitter 4.21 2.097 3.69 2.112 (43) = .815 .419 Solicitation of a prostitute 4.31 1.828 3.35 1.899 (44) = 1.732 .090 Performing Enhancing Drugs 3.65 2.308 3.81 1.692 (40) = -.259 .797 Affair with teammates wife 3.79 1.634 3.18 1.704 (44) = 1.216 .230 Bounty-Gate 3.33 1.961 2.33 1.609 (43) = 1.796 .080 Sexual harassment of a team 3.03 1.835 2.57 1.910 (43) = .770 .445 employee Domestic Abuse 2.65 1.599 2.50 1.701 (44) = .315 .754 Hit and Run resulting in injury 2.32 1.219 3.00 1.837 (25) = -1.35 .188 Alleged Rape 2.28 1.601 2.22 1.555 (45) = .113 .911 * = p < .05, ** = p < .001, *** = p < .001

The objective of the survey was to determine which transgression to use for the main study. The aim was to find transgressions that would fall close to 3.5 on the 1 to 5 offensiveness scale, as this score would indicate that the transgression is perceived as slightly more than neutral and, thus, would have a chance of being forgiven.

Transgressions which fell below the middle and did not evoke significant negative feelings of offensiveness were likely to be forgiven quickly (e.g., smoking marijuana), while those high on the scale and that evoked severe negative feelings of offensiveness

(e.g., domestic abuse) were unlikely to be forgiven; both were eliminated from consideration for the main experiment. Based on the results, violence toward a fan was chosen for the main study. This offense had a mean score of 3.42 and was, closest to the 3.5 standard The willingness-to-forgive mean score was 4.13 on a 7-point Likert

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scale, which suggests that, provided the right image repair, fans could be willing to forgive the transgression.

Independent Variables

Independent Variable #1: Expectancy

Expectancies were defined as enduring patterns of anticipated behavior, grounded in societal (or group) norms (Burgoon, 1993). For the present study expectancies were operationally defined as anticipated expectancies of athlete behavior. Expectancies were measured using modified scales by Kim (2014) and

Burgoon (1993). Kim’s scale showed good reliability (Cronbach’s α = .88). Kim’s scale was used for corporations, so the items were modified to capture expectancies specific to sports. The 3-item, seven point Likert scale (1 = strongly disagree; 7 = strongly agree) was: 1) Athletes should live up to responsibility to society, 2) Athletes should not harm their teams in any way (reverse-scored), and, 3) Athletes should engage in behavior(s) that reflect social norms. Expectancies were measured in the first phase of the experiment; questions regarding expectancies of professional athletes were asked and measured at the beginning of the questionnaire and before exposure to the athlete created for the study, the athlete scandal, and any performance manipulations.

Independent Variable #2: Fan Identification

Fan identification with a team has been used as an independent variable to predict attitudes toward sports organizations, merchandise purchases, and game attendance (Wann, 2006). High-identified fans engage in increased sports consumption when compared to low-identified fans. Because research indicated that identification also plays a role in how fans perceive and react to crises in sports (Brown & Billings,

2013), fan identification was included as a variable in this study. Fan identification is the

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emotional connection that a fan feels toward his or her team of choice and the extent to which that team’s performance is important to the individual. The present study seeks to determine how fan identification level affects crisis evaluations. To do so, this study presented two sports organizations (the Green Bay Packers and the Tampa Bay

Buccaneers) to participants. For participants these teams either reflected their team of choice, meaning they were fans of the team (high identification), or they perceived the team as a rival or neutral (low identification), which theoretically meant they were not a fan.

This study used Wann and Branscombe’s (1993) sport spectator identification scale (SSIS). Based on the recommendations of prior research by Wann and colleagues (2001), participants who scored below 35 were classified as low-identified fans while fans who scored above 35 were classified as high-identified fans. The scale is made up of seven Likert scale questions: 1) How important to you is it that the (team name) win? 2) How strongly do you see yourself as a fan of the (team name)? 3) How strongly do your friends see you as a fan of the (team name)? 4) During the season, how closely do you follow the (team name) via any of the following: a) in person or television b) radio, or c) television news or a newspaper? 5) How important is being a fan of the (team name) to you? 6) How much do you dislike the (team name’s) greatest rivals? and 7) How often do you display the (team name’s) name or insignia at your place of work, where you live, or on your clothing?

Independent Variable #3: Athlete Performance

One of the central tenets of this study was to examine whether an athlete’s performance influences the image repair discourse, that is the efforts undertaken by the athlete to repair his/her image, following a transgression. To do so properly, ESPN

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online news articles were manipulated to include varying game statistics and performance analyses of the athlete, reflecting either positive or negative athlete performance(s). Positive athlete performance was operationally defined as the clear contribution of skills shown by the athlete during a game, whereby the overall team performance is enhanced (e.g., scoring touchdowns, catching passes). Negative athlete performance was defined by the researcher as the clear failure of skills shown by the athlete during a game whereby the overall team performance is worsened (e.g., dropped passes).

Although participants were exposed to mock-news articles, the articles were written by a professional writer who has written for national publications and online platforms in order to assure face validity of the manipulation(s). The manipulated news articles ranged from stressing the athlete’s positive performances throughout the season, which in return positively contributed to the overall team performance (e.g., several winning touchdown catches), to stressing the athlete’s negative performances throughout the season (e.g., several dropped passes), which in return negatively contributed to the overall team performance (i.e., causing the team to lose), and a combination thereof.

Information about the athlete’s performance was presented to the study participants in phase three of the study, at least one week after being exposed to the athlete transgression.

Independent Variable #4: Team Performance

Because football is a team sport fans cheer for the successes of the team rather than just one individual athlete team performance needs to be considered in fan evaluation of the athlete following a indiscretion. In that vein, it was investigated whether

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winning or losing contributes to fan evaluations of and assessments about an athlete’s image subsequent to a transgression. Team performance was broadly defined as both winning and losing.

A winning team performance was operationalized as the team’s overall positive game record, in which wins positively outweighed losses, and the team, therefore, advanced to the playoffs. Furthermore, the team’s performance was evaluated in comparison to overall expectancies by fans and experts. A team that was predicted to go undefeated, yet ended up losing 5 games while still advancing to playoffs, was not chosen for having had a stellar and winning season. A losing team performance was operationally defined as the team’s overall negative game record, in which losses outweighed wins, and the team, therefore, did not advance to the playoff stage. Similar to the previous statement, this losing team was carefully evaluated in the previously mention preliminary survey in order to ensure that its season was truly perceived as such. Information about the team’s performance was presented to the study participants in phase three of the study, at least one week after being exposed to the athlete transgression.

Overall, participants, both high and low-identified, were exposed to one of four

‘boxes,’ in which athlete and team performance were manipulated; Table 3-2 illustrates these groupings.

Table 3-6. Groups within the experiment Group Manipulation 1 Team win + positive individual performance 2 Team win + negative individual performance 3 Team loss + positive individual performance 4 Team loss + negative individual performance

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Dependent Variables

Dependent Variable #1: Violation Valence

Violation valence or how negatively or positively an event is perceived in comparison to the expectancy (Burgoon, 1993), was operationally defined as the perceived negative value of a breach of expectations. Violation valence was measured using a modified scale by Kim (2014) and Afifi and Metts (1998). Kim’s scale was validated and showed adequate reliability scores (Cronbach’s α = . 89). Due to the nature of sport crises in contrast to general corporate crises (as studied by Kim), the items were modified to capture violation valence specific for an athlete crisis. The 4-item seven point Likert scale (1 = strongly disagree; 7 = strongly agree) included the following items: 1) the athlete’s transgression made me feel bad about the athlete, 2) the athlete’s transgression made me feel that the athlete does not care about others, 3) the athlete’s transgression made me feel negative about the athlete, and 4) the athlete disappointed me in a great deal. Violation valence was measured twice during the experiment, after study participants were exposed to the scandal (phase two) and again after the manipulations (phase three).

Dependent Variable #2: Perceived Athlete Image

Based on Benoit (1995), this study defines athlete image as the perception of an athlete held by the audience, shaped by the athlete’s behavior (including transgressions), and the athlete’s performance on the field (including performance subsequent to the transgressions). Athlete image was measured using a 4-item Likert scale used by K. Brown (2014), which was modified from Choi and Rifon’s (2007) scale used to measure celebrity athlete genuineness. The items of the scale included: 1) I believe this athlete is wise after reading this article, 2) I believe this athlete is pleasant

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after reading this article, 3) I believe I could be comfortable around this athlete after reading this article; and 4) I believe this athlete is sophisticated after reading this article.

The researcher also incorporated a modified scale of athlete reputation used by Walker and Kent (2009) adapted from Gaines-Ross (1998). The three-item scale includes: 1)

(Athlete) sets an example of how a professional athlete should be, 2) I would believe in

(Athlete) if he were under media attack, and 3) I have admiration and respect for the

(athlete). In the present study, perceived athlete image was measured multiple times and in all three phases, first, after the player vignette was introduced; second, after study participants were exposed to the scandal; and third, after the manipulation of player and team performance subsequent to the transgression.

Dependent Variable #3: CORFing Behavior

CORFing behavior is the “severing of associations with others who have failed, in the interest of avoiding negative evaluations by others” (Snyder, Lessegaard, & Ford,

1986, p.383). CORFing was operationalized by an existing scale developed by Arai

(2014). This scale was selected because it was used within a study that measured

CORFing behaviors in sports consumers following an athlete scandal. Arai’s (2014) scale was a modification of scales from sports marketing and communication literature

(Kwon, Trail & Lee, 2008; Spinda, 2011; Trail, Fink, & Anderson, 2003). These original scales were used as a basis because they were previously tested for reliability and validity (Trail et al.,2003; Kwon, Trail, & Lee, 2008; Trail, Anderson & Fink, 2000;

Spinda; 2011) Furthermore, the original scales were created to measure CORFing behaviors toward sports teams, whereas Arai’s scale was tailored to fit the individual athlete context. This resulted in a three-item scale: 1) I do not want to be associated with the athlete, 2) I will not wear clothing or jerseys that are associated with the athlete,

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and 3) I would like to disconnect myself from the athlete. In the present study CORFing behavior was measured twice: after study participants were exposed to the scandal and second, after the manipulation of player and team performance subsequent to the transgression.

Dependent Variable #4: Supportive Behavioral Intentions

Supportive behavioral intentions are intentions of sports consumers to support the athlete or team. This support can come in a variety of forms, including purchasing of merchandise, composing of positive messages online (via social networks) and game attendance, among others. Supportive behavioral intentions were measured using K.

Brown’s (2014) potential supportive behavior scale: 1) after reading this article, I would watch this athlete’s game on television, 2) after reading this article, I would discuss this athlete in a positive light, 3) after reading this article, I would consume sports news that discussed this athlete, 4) after reading this article, I would attend this athlete’s games, and 5) after reading this article, I would buy this athlete’s paraphernalia (jerseys, T- shirts, etc.).

Dependent Variable #5: Athlete Advocacy

The present study adapted Arai’s (2014) athlete advocacy scale because it accounts for additional advocating behavior, such as composing social media messages on behalf of the athlete. This scale is a three-item 7-point Likert type scale: 1)

I will maintain my support for the athlete, 2) I would post messages online to show my support for the athlete, and 3) I am willing to defend the athlete publicly, even if it causes controversy.

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Dependent Variable #6: Patronage Intentions

Walker and Kent’s (2009) patronage intentions scale was used to measure consumer behavior intentions toward the team. The 13-item scale has four questions regarding repeat purchases: 1) I will attend another game being played by this team in the near future, 2) I will attend more games being played by this team in the next few years, 3) I will attend a game being played by this team in the next home series/game, and 4) I will attend another game being played by this team this season. Three questions regarding word-of-mouth: 1) I will speak favorably of this organization to others, 2) I will encourage others to attend this team’s games, and 3) I will encourage others to support this organization. Three questions were geared toward merchandise consumption: 1) I will buy this organization’s clothing (T-shirts, caps, etc.) in the future,

2) I will buy this organization’s merchandise, and 3) I will purchase this organization’s souvenirs. Three questions were in the domain of media consumption included: 1) I will read about this organization on traditional and online platforms, 2) I will visit this organization’s website for information on the team, and 3) I will watch sports broadcasts on the local TV news for information about the organization. Supportive behavioral intentions were measured three times, and in each phase of the experiment: first, after study participants were exposed to the player vignette, next, after exposure of the scandal, and last, after the manipulation of player and team performance subsequent to the transgression.

Dependent Variable #7: Positive Word of Mouth (pWOM)

pWOM is communication directed at other sports fans promoting the athlete. This variable was measured by a six-item, 7-point Likert scale modified by K. Brown (2014) and adapted from prior pWOM scales (Brown et al., 2005; Coombs & Holladay, 2007):

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1) I would publicly say and/or post messages on my social media websites encouraging people to support this athlete, 2) I would publicly say and/or post messages on social media websites saying positive things about this athlete to other people, 3) I would publicly say and/or post messages on social media websites encouraging others to cheer for this athlete during games, 4) I would publicly say and/or post messages on social media websites to make sure that others know I support this team, 5) I would say and/or post messages on social media websites to support this athlete, and 6) I would publicly say and/or post on social media websites the positive aspects of this athlete to those who criticize him.

pWOM was measured three times, in each phase: first, after the player vignette was introduced; second, after study participants were exposed to the scandal; and, third, after the manipulation of player and team performance subsequent to the transgression.

Dependent Variable #8: Negative Word of Mouth (nWOM)

Negative word-of-mouth, or nWOM, is “communication among the audience about an athlete that is detrimental to his/her success in repairing his/her image”

(Brown, 2014). nWOM was measured using an adaptation of a three-item, 7-point Likert scale used in prior research (Coombs & Holladay, 2007). The items included: 1) I would encourage people not to support this athlete, 2) I would say negative things about this athlete to other people, and 3) I would not recommend someone to cheer for this athlete during games. In the present study, nWOM was measured multiple times and in all phases of the experiment: first, after player vignette was introduced; second, after study participants were exposed to the scandal; and, third, after the manipulation of player and team performance subsequent to the transgression.

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Dependent Variable #9: Team Reputation

Based on K. Brown (2014, team reputation was defined as the perception of a team held by the audience, shaped by the team’s performance, athlete’s transgression, and the performance of said player. Team reputation was measured using the corporate reputation scale developed by Walker and Kent (2009), who also measured team reputation. Their scale was an adaption of early corporate reputation scales. Walker and

Kent (2009) compiled a three-item scale fit for evaluating “team-level corporate reputation” modified by an existing scale from Gaines-Ross (1998, p.752). The items included: 1) (Team) sets an example of how an NFL organization should be run, 2) I would believe in the (team) if it were under media attack, and 3) I have admiration and respect for the (team). In the present study, team reputation was measured multiple times and in all phases of the experiment: first, before the player vignette was introduced; second, after study participants were exposed to the scandal; and, third, after the manipulation of player and team performance subsequent to the transgression.

A summary of all scales used in the proposed dissertation can be found in Appendix B:

Measurements.

Procedure

First, a survey, was conducted to determine which athlete transgressions were perceived as offensive yet forgivable. Violence toward a fan was chosen as the sample transgression for this dissertation. This was an important part of the dissertation as the findings were not only used to help select an appropriate transgression for the main study, but also shed light on how the various types of sports offenses were perceived by sports consumers. In that vein, this survey helped draw conclusions as to what athlete transgressions call for image repair and if there were any offenses, which were either

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too minor to seriously threaten the image of an athlete or too major to benefit from any active image repair efforts.

This survey also included a section on winning and losing records and how sports fans perceived the season records of their favorite teams. This section of the survey included several fill-in questions: 1) Please select your favorite NFL team 2) I believe my favorite NFL team had a successful and satisfying season if the overall record is: (fill in the blank), 3) I believe my favorite team had a unsuccessful and unsatisfying season if the overall record is: (fill in the blank), and 4) I believe the win/loss record of my team is a good representation of how well my team did this season (true or false). These particular questions were aimed at ensuring a selection of

NFL teams with appropriate records that stimulated participants to either think of them as either losing or winning teams. One more question regarding athlete performance was asked, how do you determine if a player had a good or bad season? (Short answer).

After analysis of the survey data, mock news articles were created. These included: 1) two player vignette/spotlight articles in which the athlete belongs to a team with a winning record (the Green Bay Packers) or a team with a losing record (the

Tampa Bay Buccaneers); 2) a news article outlining the athlete transgression (two articles total for Packers and Buccaneers player), 3) four news articles highlighting player and team performance (manipulation stimuli).

The main study featured a 2 (Team identification[high vs. low]) × 2 (Team

Performance [win vs. loss]) × 2 (Athlete Performance [positive vs. negative]) factorial design in which participants were randomly assigned into one of the treatment groups.

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Each study participant was exposed to a cover letter explaining the purpose of the study and an informed consent form (see Appendix C) with the option to participate in the study and the three phases within the experiment. After agreeing to participate in the survey, student participants were prompted to complete phase one of the study. Phase one and phase two were identical for all participants, with the exception of the team affiliation of the athlete introduced. Participants were introduced to the athlete by way of reading a player spotlight (Appendix D-1, D-2); in the player spotlight mock story, the athlete belonged to the team with either the winning or losing record, but all other parts of the spotlight were identical to one another. Phase three introduced the manipulations.

Overview Summary

Participants of this study were:

1. Informed of the study purpose, duration, research contact (in person or email) 2. Asked to read a brief introduction outlining IRB required information 3. Asked to consent 4. Prompted to complete Phase One: a. Provided demographic information b. Provided contact information (email for phase two) c. Completed general athlete expectation questions d. Completed the fan identification questions e. Completed team reputation questions f. Completed patronage intentions questions g. Read player vignette h. Completed athlete image and athlete reputation questions i. Completed potential supportive behavior and advocacy questions j. Completed WOM questions 5. Prompted to complete Phase Two, two-three days after completing the first phase 6. Exposed to scandal stimuli in form of mock ESPN web article

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7. Asked to answer violation valence questions 8. Asked to reevaluate the player: a. Completed athlete image and reputation questions b. Completed CORFing questions c. Completed potential supportive behavior and advocacy questions d. Completed WOM questions e. Completed team reputation questions f. Completed patronage intentions questions g. Provided contact information (email for phase three) 9. Prompted to complete Phase Three, 2 to 4 weeks after completing the second phase 10. Exposed to win/loss and performance stimuli in form of mock ESPN web article 11. Exposed to manipulation check questions 12. Asked to re-evaluate the player: a. Completed athlete image and reputation questions b. Completed CORFing questions c. Completed potential supportive behavior and advocacy questions d. Completed WOM questions e. Completed team reputation questions f. Completed patronage intentions questions 13. Thanked for their participation in the study 14. Debriefed and entered into drawing for iPad mini Phase I

In phase one, participants were first asked to provide demographic information about age, gender, and field of study, as well as primary contact information for subsequent communication. Students were also asked questions about their general

NFL football fandom, including what role NFL football plays in their lives, their favorite

NFL team, how strongly they see themselves as fans of their favorite team, as well as the Tampa Bay Buccaneers and the Green Bay Packers. Participants were then asked

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to provide answers to their general expectancies of professional athletes. Three questions modified from Kim (2014) and Burgoon (1993) were asked to capture expectancies specific to athletes and their behavior. See Appendix B for all measures.

Next, study participants were asked to answer several questions intended to measure their fan identification for both their favorite NFL team, and for either the

Tampa Bay Buccaneers or the Green Bay Packers. Fan identification was measured using Wann and Branscombe’s (1993) 7-question sports spectator identification scale.

Those who scored below 35 were classified as low-identified fans while fans who scored above 35 were classified as high-identified fans (Wann, et al., 2001). Each of the seven questions was answered on a scale of 1 through 8; the identification score was then determined by adding the numbers of each response, for a minimum total of 7 and a maximum total of 56. Participants were then asked three questions developed by

Walker and Kent (2009) to evaluate the reputation of the NFL team they were assigned to for the purpose of this research (the Green Bay Packers or Tampa Bay Buccaneers).

Assignment into these groups/team was conducted one of two ways: if participants indicated to be a fan of either the Packers or the Buccaneers they were assigned to that team; if the participants indicated to be a fan of another team, they were randomly assigned to one of the two via the randomization tool through Qualtrics.

Next, participants were asked to read a feature story/player vignette of a fictional player (Appendix D-1, D-2). Participants were informed that the information provided was indeed based on a real player but that due to privacy issues the name of the player had to be redacted. Following the exposure to the player vignette, participants were asked seven questions aimed at understanding how the athlete was perceived by sports

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fans. Four questions were adopted by Brown (2014), and modified from research by

Choi and Rifon’s (2007). Three more questions about the perceived athlete reputation were incorporated from Walker and Kent (2009).

Next, participants were asked a number of questions regarding their potential supportive behaviors toward the athlete and the team. Supportive behavioral intentions toward the athlete were measured using two scales from prior research. The first set of questions, five questions total, had been adapted from K. Brown’s (2014) potential supportive behavior scale. Another three questions were from Arai’s (2014) athlete advocacy scale. Supportive behavioral intentions toward the team were measured by

Walker & Kent’s (2009) patronage intentions scale, with 13 questions in the domains of: repeat purchase, word-of-mouth, merchandise consumption, and media consumption.

Next, positive and negative word-of-mouth about the athlete were measured. pWOM was measured with six questions modified by K. Brown (2014), whereas nWOM was measured using three questions adapted from Coombs and Holladay (2007).

Last, participants were informed that there were two more phases within this study and that after completing all phases of the experiment they would be entered into a drawing for a number of prizes, including two Apple iPad minis.

All questions can be viewed in detail in Appendix F-1.

Phase II

Two to three days after completing the first phase, participants were prompted to complete the second phase. An email was sent to all participant email addresses provided in the previous survey. Two additional reminders were sent via the Qualtrics reminder feature to those who did not immediately complete the survey.

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In this second survey, participants were first asked to indicate for which team the player in the previous survey played; this served as a memory check for the study.

Participants were then exposed to one of two ESPN web news article (varied only by team membership) (see Appendix D-3, D-4), which reported and summarized the transgression of the previously introduced athlete. Following the news article, participants were asked to rate their violation valence by answering four questions from

Kim’s (2014) and Afifi and Metts’ (1998) violation valence scale. They were also promoted to re-evaluate both the athlete and his team by answering the same questions from phase one regarding: 1) athlete image, 2) athlete reputation, 3) potential supportive behavior, 4) athlete advocacy, 5) WOM, 6) team reputation and 7) patronage intentions toward the team. Furthermore, participants were asked questions about their

CORFing behaviors. CORFing was measured by an existing scale developed by Arai

(2014), which involved three questions to be answered in response to the transgression.

Again, participants were asked for their contact information and reminded that they would be contacted once more for the final survey. All questions can be viewed in detail in Appendix F-2.

Phase III

After completing the second phase, study participants were prompted via email to complete the final phase (survey 3) of the experiment. Again, several reminders were sent to those who did not immediately complete the survey through the Qualtrics reminder tool.

Because the scales used in phase two and phase three were identical, there was a prolonged time gap (2 to 4 weeks) to lessen the chances that students would not merely fill in the same answers. Phase three was the first time in which participants

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were exposed to distinct materials within the groups, aside from varying athlete affiliations with the team. Each group received one sports article in which the performance of a team and the athlete involved in the scandal was discussed (Appendix

D). The article recapped the season of the team, highlighting its accomplishments or failures, as well as the successes or failures in regard to the performance of the athlete.

Participants were randomly assigned to a treatment group based on the team affiliation established in the first survey.

The first group received the stimuli in form of a news article from ESPN.com (see

Appendix D-5), which highlights the winning record of the Green Bay Packers, as well as the positive performances by the fictional athlete. This article positively portrayed not only the team’s winning season and accomplishments, but also showcased how the athlete had contributed to the team’s success.

The second group received a stimulus in the form of a news article from

ESPN.com (See Appendix D-6); however, while this article also highlighted the winning record of the Green Bay Packers, it addressed the negative performance by the fictional athlete. This article positively portrayed the team’s winning season and accomplishments, but furthermore pointed to the disappointing performance and failure to perform on the field by the athlete.

The third group received a stimulus in the form of a news article from ESPN.com

(See Appendix D-7), which highlighted the losing record of the Tampa Bay Buccaneers, as well as the positive performance by the fictional athlete. This article negatively portrayed the team’s losing season, but also pointed out the superior performance(s) and success of the athlete on the field.

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The fourth group received the stimuli in the form of a news article from

ESPN.com (See Appendix D-8), which, too, highlighted the losing record of the Tampa

Bay Buccaneers, but furthermore also discussed the negative athletic performances by the fictional athlete. This article negatively portrayed not only the team’s losing season and its failures, but also reviewed how the athlete contributed to the team’s failure by not performing well.

After reading the articles, each participant in each group was asked to revaluate both team and athlete, answering the same questions as in the previous phases, the participants were asked to complete: 1) athlete image and athlete reputation questions,

2) CORFing questions, 3) potential supportive behavior and athlete advocacy questions,

4) WOM questions, 5) team reputation and patronage intentions questions and 6) violation valence questions.

After answering all the questions, participants read the debriefing section (see

Appendix F-3). This section of the questionnaire provided a brief explanation of the purpose and an overview of the experiment. Furthermore, the debriefing explained that the player transgression was fictional and that as far as we know, no active athlete on any NFL team committed the offense. Finally, participants were thanked for their participation in the dissertation research, asked to provide their email one last time to be entered in the drawing and asked not to discuss the research study with any of their classmates or friends while data were being collected. The questionnaire can be viewed in Appendix F-3.

Reliability and Validity

Reliability is defined as the extent to which an instrument will generate similar results on repeated trials (Babbie, 2012). Reliability is important because it can

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determine whether scales, tests, or experiments in a study produce consistent results, which in return is helpful for making conclusions about the results. Reliability in this study was determined using Cronbach’s alpha. Based on prior research, Cronbach’s alpha of .70 through .80 are regarded as acceptable, between .80 and .90 are considered respectable, and Cronbach’s alpha scores of .90 and above are regarded as excellent (Wimmer & Dominick, 2011).

Validity is defined as the degree to which a research instrument measures what it is supposed to measure (Babbie, 2012) and include: face validity, predictive validity, construct validity, and content validity. Face validity is the quality of an instrument that seems like a reasonable measure of a variable. Face validity was achieved by ensuring that all parts of the experiment were perceived as authentic, which was determined via the pre-test. This included making sure that the mock player profile and scandal selection appeared realistic and the manipulated ESPN.com articles appeared to be realistic in both wording and appearance.

Predictive validity is defined as the degree to which a measure relates to some external criterion (Babbie, 2012). In this study sports consumer behaviors such as game attendance, purchase of team or athlete merchandise or simply proclaiming association with a team or player could be criteria for determining validity of fandom. However, the better way to determine validity for this study would be to look at the constructs’ validity.

Construct validity is “the degree to which a measure relates to other variables as expected within a system of theoretical relationships” (Babbie, 2012, p. 152). In other words, are the instruments used within the study related to comparable measures and dissimilar to instruments with which they should not be interrelated? Construct validity

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provides the capability to theoretically generalize from the instrument (i.e. scale) used to the constructs outlined in the research. Mirroring comparable studies, the present investigation determined construct validity through conducting an exploratory factor analysis (EFA) (Brown, 2014).

Statistical Analyses

Following the completion of all surveys, the results were exported from Qualtrics.

Responses from participants who completed all three phases were matched up and compiled into spreadsheet. The cleaned and combined spreadsheet was then imported into SPSS, a statistical analysis software, where the following statistical tests were run to examine the research questions and hypotheses:

H1: Fans with higher athlete expectancies will report more negative violation valence toward the athlete experiencing the scandal. Independent Variable Dependent Variable Statistical Analysis Expectancy Violation Valence Regression

H2: There will be a correlation between violation valence and a) perceived athlete image, b) perceived athlete reputation, c) athlete advocacy, d) supportive behavioral intentions, e) positive word-of-mouth, f) negative word-of- mouth, and g) CORFing8 Independent Variable Dependent Variables Statistical Analysis Violation Valence a) Perceived Athlete Correlation Image, b) Perceived Athlete Reputation, c) Athlete Advocacy, d) Supportive Behavioral Intentions, e) Positive Word-of-Mouth, f) Negative Word-of- Mouth, and g) CORFing

8 Correlations for H2a-e are predicted to be negative, while H2f+g are predicted to be positive

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RQ1: Are there interactions among team performance, player performance and fan identification on a) perceived athlete image, b) perceived athlete reputation, c) athlete advocacy, d) athlete supportive behavioral intentions, e) pWOM, f) nWOM, g) team reputation, and h) patronage intentions toward the team during an athlete crisis? Independent Variables Dependent Variables Statistical Analysis Athlete performance; Perceived athlete 3-Way & 2-Way Team performance; Image; perceived athlete ANCOVAs Fan Identification reputation; athlete advocacy; athlete supportive behavioral intentions; pWOM; nWOM; team reputation; patronage intentions

RQ2: Does player performance influence a) perceived athlete image, b) perceived athlete reputation, c) athlete advocacy, d) athlete supportive behavioral intentions, e) pWOM, f) nWOM, g) team reputation, and h) patronage intentions toward the team during an athlete crisis? Independent Variable Dependent Variables Statistical Analysis Player performance Perceived athlete One-Way ANCOVA Image; perceived athlete reputation; athlete advocacy; athlete supportive behavioral intentions; pWOM; nWOM; team reputation; patronage intentions

RQ3: Does team performance influence a) perceived athlete image, b) perceived athlete reputation, c) athlete advocacy, d) athlete supportive behavioral intentions, e) pWOM, f) nWOM, g) team reputation, and h) patronage intentions toward the team during an athlete crisis? Independent Variable Dependent Variables Statistical Analysis Team performance Perceived athlete One-Way ANCOVA Image; perceived athlete reputation; athlete advocacy; athlete supportive behavioral intentions; pWOM; nWOM; team reputation; patronage intentions

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RQ4: Does fan identification influence a) perceived athlete image, b) perceived athlete reputation, c) athlete advocacy, d) athlete supportive behavioral intentions, e) pWOM, f) nWOM, g) team reputation, and h) patronage intentions toward the team during an athlete crisis? Independent Variable Dependent Variable Statistical Analysis Fan Identification Perceived athlete One-Way ANCOVA Image; perceived athlete reputation; athlete advocacy; athlete supportive behavioral intentions; pWOM; nWOM; team reputation; patronage intentions

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CHAPTER 4 RESULTS

Study Results

Data for the main study were collected between April and June 2015. Data were analyzed starting in July 2015 using IBM SPSS Version 23.0 with a confidence interval

(CI) of 95% and a threshold of p < .05 for statistical significance.

Results Section Roadmap

Before presenting the results for each of the research questions and hypotheses of this dissertation, this chapter features a number of key sections aimed at providing more detail and context. More particularly, early sections highlight participant demographics and fan characteristics, as well as offer specifics regarding reliability, validity, attention, and manipulation checks. Most importantly, provided the nature of this study and the multi-dimensional nature of an athlete transgression that goes beyond mere image repair, including pre-crisis perceptions and post-crisis evaluations by fans, this chapter offers supplementary results and interpretation of relationships directly related to the image repair efforts that are not specifically addressed in the research questions and hypotheses. The purpose for this is offering the readers, both academics and practitioners a solid foundation about the roles of image and reputation, fan identification, and transgressions on fan perceptions and behaviors. This knowledge is fundamental and vital for understanding the image repair discourse, particularly as it relates to image repair through performance addressed in the present study.

Participant Overview

In total, 222 students participated in the full experiment. As anticipated, attrition was high provided the nature of the experiment, in which study participants were asked

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to complete three surveys over the course of roughly three to four weeks. 560 students clicked on the survey link provided in an email to start the survey for phase one. Of those, 417 completed the survey and met requirements for participation, for a completion rate of 74%. The second survey was sent to all 417 participants who completed the first phase. Of those, 308 started and 299 completed the second survey for a 97% completion rate. The link to the final survey was sent to the 299 participants via email; 235 opened the link, and 222 finished for a 94.5% completion rate. The 222 participants made up 53% of the original sample. Attrition was relatively consistent across all eight cells, so that each cell had at least 25 participants, as required to achieve sufficient power to discern differences. Table 4-1 breaks down the participants by cell.

Table 4-1. Number of Participants in Each Cell Winning Team & Winning Team & Losing Team & Losing Team & Positive Athlete Negative Athlete Positive Athlete Negative Athlete Performance Performance Performance Performance High Team ID 25 25 26 26 Low Team ID 32 29 29 30

Table 4-2 contains a profile of all research participants in this study. The mean age was 21.22 years old (range 18-54, SD = 3.144); the mean age for females (M =

20.78, SD = 1.53) was slightly lower than that of males (M = 21.62, SD = 4.11). The sample was fairly evenly split between genders, with males making up 50.8% (n = 113) of the total sample and females making up 48.1% (n = 107); two participants did not select a gender or identified otherwise. A majority of participants (87.4%, n = 194) identified themselves as white, while the others identified as Black or African American

(4.5%, n = 10), mixed race (2.3%, n = 5), Korean (1.4%, n = 3), Filipino (.9%, n = 2),

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Table 4-2. Demographics of Participants Who Completed the Survey Demographics Total (n = 222) Percentage (%) Gender Male 113 50.9 Female 107 48.1 Other 1 .5 No answer 1 .5 Ethnicity Not of Hispanic, Latino or Spanish Origin 183 82.4 Cuban 10 4.5 Puerto Rican 8 3.8 Mexican, Mexican American, Chicano 2 .9 Another Hispanic, Latino or Spanish Origin 17 7.7 No answer 2 .9 Race White 194 87.4 Black or African American 10 4.5 Other 5 2.3 Mixed 5 2.3 Korean 3 1.4 Filipino 2 0.9 American Indian/Alaska Native 1 0.4 Asian Indian 1 0.4 No answer 1 0.4 Age 18 11 5.0 19 37 16.7 20 52 23.4 21 40 18.0 22 45 20.3 23 14 6.3 24 6 2.3 25 6 2.7 26 1 0.5 27 3 1.4 28 1 0.5 29 2 0.9 35 2 0.9 No answer 3 1.4

College University of Florida 171 77.0 Winona State University 47 21.2 Other 4 1.8

Major Advertising 39 17.6 Telecommunications 42 18.9 Sports Management 31 14.0 Journalism 23 10.4 Business 22 9.9 Public Relations 18 8.1 Health/Exercise Science 4 1.8 Tourism 2 0.9 Other 41 18.5

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American Indian/Alaska Native (.4%, n = 1), Asian Indian (.4%, n = 1) and other (2.3%, n = 5). 37 (16.7%) participants indicated they were of Hispanic, Latino or Spanish origin.

Data were primarily collected at two campuses, the University of Florida and

Winona State University. A majority (77%) of participants attended UF (n = 171), while

47 participants attended WSU (21.2%); and four participants indicated attending neither or another institution. This number can be explained by participants who have recently graduated and were still on an email list/class roster used by the researcher for recruitment. Student majors were varied, though a large number of students came from a media, communications, or liberal arts background.

Participant Fandom

Out of the 222 participants 80% (n = 178) said they were a fan of the NFL.

Participants ranged in team association; out of the 32 NFL teams 24 were indicated by one or more participants as their favorite team. The Green Bay Packers were selected as the favorite team by almost 20% of participants, followed by 35 people or roughly

16% of the total sample who indicated the Tampa Bay Buccaneers were their favorite

NFL team and 33 participants who named the Miami Dolphins. Table 4-3 has a breakdown of participants’ general responses toward football and the NFL.

Independent-sample t-tests on a number of key variables (athlete image, expectancy, violation valence, pWOM, CORF) confirmed no statistically significant differences between the participants who indicated to be NFL fans and those who self- reported not to be NFL fans, with all p-levels at .05 or above (Table 4-4). These findings warranted inclusion of responses from self-reported non-fans in the analysis. Overall, the sample in this dissertation stated they have a strong liking of NFL football, as denoted by a 4.47 mean score with a .722 standard deviation (Table 4-5).

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Table 4-3. General Responses Regarding the NFL General Football Questions Total (n = 222) Percentage NFL Fan Yes, I am a NFL fan 178 80.2 No, I am not a NFL fan 29 13.1 Undecided 15 6.8 Favorite NFL Team Green Bay Packers 43 19.4 Tampa Bay Buccaneers 35 15.8 Miami Dolphins 33 14.9 Minnesota Vikings 13 5.9 New York Giants 13 5.9 New England Patriots 9 4.1 Jacksonville Jaguars 8 3.6 Philadelphia Eagles 7 3.2 New York Jets 6 2.7 Seattle Seahawks 5 2.3 Pittsburgh Steelers 4 1.8 Carolina Panthers 4 1.8 Detroit Lions 3 1.4 Atlanta Falcons 2 .9 Chicago Bears 2 .9 Indianapolis Colts 2 .9 San Francisco 49ers 2 .9 Buffalo Bills 1 .5 Dallas Cowboys 1 .5 New Orleans Saints 1 .5 Oakland Raiders 1 .5 Saint Louis Rams 1 .5 San Diego Chargers 1 .5 Washington Redskins 1 .5 I like football, but don’t have a favorite team 18 8.1 I don’t care about the NFL, so I don’t have a favorite team 4 1.8 No answer 1 .5 Importance of NFL Football Very Important 90 40.5 Important 81 36.5 Neither important nor unimportant 38 17.1 Unimportant 7 3.2 Not at all important 6 2.7 Likeability of NFL Football Like very much 128 57.7 Like 76 34.2 Neither like nor dislike 15 6.8 Dislike 1 .5 Dislike very much 2 .9 Fan of Green Bay Packers No, I am not a fan of the Packers 134 60.4 Yes, I am a fan of the Packers 73 32.9 Undecided 15 6.8 Fan of Tampa Bay Buccaneers No, I am not a fan of the Buccaneers 156 70.3 Yes, I am a fan of the Buccaneers 57 25.7 Undecided 9 4.1

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Table 4-4. Mean Scores Fan vs. Non-Fan Variable Fan Non-Fan t P M SD M SD 2-tailed Expectancy 4.27 .600 4.09 .678 (205) = 1.45 .148 Violation Valence 3.78 .627 3.61 .790 (205) = 1.25 .213 Athlete Image 5.18 .987 4.81 1.37 (205) = 1.80 .162 pWOM 3.86 1.58 3.57 1.58 (205) = .922 .358 CORF 4.86 1.29 5.08 1.24 (205) =-.867 .387

Table 4-5. Attitudes toward NFL Football Category Mean (n = 222) SD Importance of NFL Football 4.09 .971 Likeability of NFL Football 4.47 .722 Responses based on a scale of 1 to 5, where 1 represents “not important at all/Strongly dislike” and 5 represents “very important/strongly like”

Participant Fan Identification

Participants were asked questions to determine how strongly they identified with a certain team. Students were first asked to respond to the seven-question sports spectator identification scale (SSIS) developed by Wann and Branscombe (1993) based on their favorite NFL team. Survey participants were later asked the same questions based on the manipulation group they were assigned to, identifying their fan identification with either the Green Bay Packers of the Tampa Bay Buccaneers. The fan identification (Fan ID) of all participants toward their favorite NFL team was 35.74 (SD =

13.492, Range 7-56), indicating high identification as determined by Wann and

Branscombe (1993). Based on the SSIS, 73 participants identified as low involved

(score of 7-30), whereas 149 identified themselves as high involved (score of 31-56).

Those with higher fan identification, as outlined by Wann and Branscombe (1993), also reported higher likeability and importance of football (Table 4-6).

Table 4-6. Attitudes toward NFL Football by Fan Identification Category Mean SD Low ID High ID Low ID High ID Importance of NFL Football 3.62 4.32 1.022 .856 Likeability of NFL Football 4.08 4.66 .795 .600 Responses based on a scale of 1 to 5, where 1 represents “not important at all/Strongly dislike” and 5 represents “very important/strongly like”

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Because the survey inquired about two specific NFL teams the Green Bay

Packers and the Tampa Bay Buccaneers, fan identification with these specific teams was also measured. Fan identification of the survey participants who selected the Green

Bay Packers as their favorite team (n = 43; 19 male, 24 female) was high (M = 41.12,

SD = 11.780, Range 15-56). While slightly lower when directly compared to that of the

Packers, fans of the Tampa Bay Buccaneers (n = 35; 28 male, 6 female) still presented high fan identification (M = 36.09, SD = 11.046, Range 15-56).

Fan identification for the assigned teams dropped to 22.55 (SD = 16.376, Range

7-56, n = 222). Fan identification broken down by groupings, that is whether participants were exposed to Packers or Buccaneers manipulation material9, is displayed in Table 4-

7. One-way ANOVA was conducted to determine if there were any differences in fan identification among the groups; results suggest there was no statistically significant difference, F(3, 218) = 1.347, p = .267.

Table 4-7. Fan Identification by Groups Team Groups Favorite SD Assigned SD Team ID Team ID Green Bay Packers Fan (n = 50) 39.18 13.39 39.66 14.30 Tampa Bay Buccaneers Fan (n = 51) 33.59 11.31 34.14 11.79 Non-Green Bay Packers Fan (n = 58) 35.76 13.75 11.34 5.43 Non-Tampa Bay Buccaneers Fan (n = 63) 36.30 16.75 9.90 5.35

Treatment Group Overview

25 participants or more were assigned to each manipulation group (cell) based on team affiliation and fan identification. Table 4-8 illustrates the breakdown by fan

9 Fans were not only asked who their favorite NFL team was, but also whether they were a fan of the Packers (Group 1) and the Buccaneers (Group 2). Based on these responses they were assigned to manipulation groups. If neither the Packers nor the Buccaneers were selected, participants were randomly assigned to either one of them (Group 3 Non-Fan Packers; Group 4 Non-Fan Buccaneers).

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identification, as well as gender per cell. As anticipated fan identification in the low ID groups, those who were not fans of the team were much lower than that in the high ID groups, those who indicated to be fans of the team.

Table 4-8. Fan Identification by Groups # Manipulation Group Fan ID SD 1 Winning Team/Positive Performance High ID (n = 25) 36.36 12.551 2 Winning Team/Negative Performance High ID (n = 25) 42.96 12.371 3 Losing Team/Positive Performance High ID (n = 26) 35.42 12.140 4 Losing Team/Negative Performance High ID (n = 25) 31.81 12.352 5 Winning Team/Positive Performance Low ID (n = 32) 11.19 5.391 6 Winning Team/Negative Performance Low ID (n = 29) 10.76 4.954 7 Losing Team/Positive Performance Low ID (n = 29) 10.97 5.840 8 Losing Team/Negative Performance Low ID (n = 30) 9.57 5.618

Because of the high attrition rate and the need to fill each cell with 25 participants, the researcher moved 4 participants into manipulation group 2 as high-identified despite their fan ID score below 35; these participants were self-reported Packers fans and a independent-sample t-test was conducted to assure no statistically significant differences between those participants on a number of key variables (expectancy t (23)

= 1.33, p = .197; violation valence t (23) = -.407, p = .688; team reputation t (23) = -1.32, p = .249; athlete image t (23) = .225, p = .824). The results suggested that although these four participants reported lower fan identification scores, their answers regarding the previously mentioned dependent variables were not different from those with identification scores of 35 or higher. The same was true for 5 participants in manipulation group 4; these participants were self-reported Tampa Bay fans, but did not meet the high fan ID score of 35. Again, t-test results suggest no statistically significant differences with all p-levels above the .05 threshold, suggesting these fans did not respond differently and could therefore be treated as high-identified fans (expectancy t

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(24) = .732, p = .471; violation valence t (24) = -.392, p = .698; team reputation t (24) = -

1.43, p = .167; athlete image t (24) = 1.42, p = .162)

Reliability

Cronbach’s alpha tests were used to test reliability of the scales used to measure the variables in the experiment. Table 4-9. provides an overview of Cronbach’s alpha scores for all scales used in this dissertation across all three phases.

Table 4-9. Cronbach Alpha Results Scale Cronbach Alpha (α) α Phase I α Phase II α Phase III Expectancy .76 n/a n/a Violation Valence n/a .74 .93 Athlete Reputation .88 .79 .93 Athlete Image .91 .87 .94 CORF n/a .94 .93 Athlete Advocacy .89 .92 .93 Supportive Behavioral Intentions .92 .82 .90 Positive Word-of-Mouth .98 .96 .98 Negative Word-of-Mouth .95 .92 .95 Team Reputation .90 .94 .94 Patronage Intentions: Repeat Purchase Domain .96 .98 .97 Word-of-Mouth Domain .94 .96 .97 Merchandise Consumption Domain .99 .99 .99 Media Consumption Domain .94 .94 .94 Fan Identification Favorite Team .95 n/a n/a Assigned Team .98 n/a n/a

Validity

Validity of the dependent variables was determined through exploratory factor analysis (EFA) (Lu, 2006). Following procedures from prior sports crisis research by N.

Brown (2013) and K. Brown (2014) no rotation was used, since the items were projected to (and did, indeed) load on just one factor. Construct validity of all scales was confirmed by EFA. Factors had acceptable loadings above the recommended 0.60

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Kaiser-Meyer-Olkin (KMO) level. Table 4.10 showcases both the variance and eigenvalues for all dependent measures used in this dissertation.

Table 4-10. EFA Results Scale Variance (%) Eigenvalues Athlete Reputation 81.23% 2.437 Athlete Image 79.44% 3.178 Athlete Supportive Behavioral Intentions 79.33% 3.816 Athlete Advocacy 82.04% 2.461 pWOM 91.52% 5.491 nWOM 91.33% 2.740 CORF 88.98% 2.669 Violation Valence 69.30% 2.372 Team Reputation 83.98% 2.519 Patronage Intentions Repeat Purchase Domain 92.77% 2.783 WOM Domain 89.89% 2.697 Merchandise Consumption Domain 97.57% 2.927 Media Consumption Domain 89.39% 2.682

Attention and Manipulation Checks

One attention and two manipulation checks were conducted. The first was an attention check in phase two (i.e., second survey). Respondents were asked to identify for which team the athlete they read about in survey one (player vignette) played for. All

222 experiment participants passed the attention check and identified the correct team of the player they were assigned to in phase one.

The manipulation checks were conducted in phase three of the experiment after treatment was administered to the groups. Participants were asked to rate the success of both the team and the athlete they read about in the treatment article, the mock

ESPN news article highlighting team and athlete performance. The manipulations worked. Participants exposed to the Packers (M = 2.12, SD = 1.01, n = 111) reported the team to be much more successful than those exposed to the Buccaneers (M = 6.16.

SD = 1.45, n = 111, t (203.8) = 23.54, p < .001, two-tailed) and participants exposed to

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the positive athlete performance (M = 2.71, SD = 1.15, n = 112) reported the athlete to be much more successful than those exposed to the negative athlete performance (M =

5.16. SD = 1.59, n = 110, t (198.1) = 13.12, p < .001, two-tailed). Taking into account level of identification, high-identified Packers fans (M = 1.66, SD = .658, n = 50) perceived the team to be significantly more successful than the low-identified Packers fans (M = 2.49, SD = 1.22, n = 60, t (95.45) = -4.576, p < .001). High-identified

Buccaneers fans (M = 6.42, SD = 1.14, n = 52) perceived the team to be slightly more unsuccessful than the low-identified Buccaneers fans (M = 5.93, SD = 1.65, n = 59, t(109) = 1.80, p = .075), but the difference here was not significant. More importantly however, there was a significant difference between both high and low-identified fans suggesting that the team performance manipulations worked (Packers low ID: M = 2.49,

SD = 1.22 and Buccaneers low ID: M = 5.93, SD = 1.65, t (118) = -13.023, p < .001;

Packers high ID: M = 1.66, SD = .658 and Buccaneers high ID: M = 6.42, SD = .1.14, t

(100) = -25.648, p < .001).

Taking into account level of identification for the athlete performance manipulation, high-identified fans exposed to the positive performance (M = 2.61, SD =

1.00, n = 51) perceived the athlete to be slightly more successful than the low-identified fans exposed to the positive performance (M = 2.80, SD = 1.26, n = 61, t (110) = -.895, p = .373), but the difference was not significantly different. High-identified fans exposed to the negative athlete performance (M = 5.82, SD = 1.29, n = 51) perceived the athlete to be significantly more unsuccessful than the low-identified fans exposed to the negative performance (M = 4.59, SD = 1.62, n = 59, t (107.335) = 4.356 p < .001). More importantly however, there was a significant difference between both high and low-

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identified fans exposed to either the negative or the positive performance suggesting that the athlete performance manipulations worked (Low ID positive performance: M =

2.80, SD = 1.26 and low ID negative performance: M = 4.59, SD = 1.62, t (109.60) = -

6.736, p < .001; high ID positive performance: M = 2.61, SD = 1.00 and high ID negative performance: M = 5.82, SD = 1.29, t (100) = -14.050, p < .001).

Foundational Background Knowledge

Before addressing the main research questions and hypotheses of this dissertation concerning image repair through performance, the researcher examined a number of relationships in order to gain and provide a comprehensive understanding of how both the independent and dependent variables behave before and during an athlete crisis. This was done in order to provide building blocks and to lay a foundation for the main inquiries of this study. It was the premise of the researcher to investigate all aspects of an athlete crisis, not merely the crisis response. Since research on athlete transgressions is limited, particularly as it relates to fan perceptions and subsequent consumer intentions, this effort was believed to strengthen the analysis of the research questions and hypotheses and to eliminated any concerns over participant perceptions and behaviors prior to the image repair discourse. To do so, various statistical tests were performed on data predominately gathered in phase one and phase two of the experiment. More particularly the following topics were investigated: the role of fan identification on athlete image and team reputation, the relationship between image and reputation on consumer behavior intentions, the influence of a transgression on athlete image and consumer intentions, and the role of fan identification, particularly as it

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relates to the relationship between expectancies and violation valence after athlete transgression.

The following sections will highlight the main findings and why they relate to the overarching topic of this study; in-depth results can be found in Appendix G.

Role of Fan Identification

Analysis of data from phase one demonstrated that fan identification plays a meaningful role not only in the evaluation of a team, but also in the evaluations of the players who make up the team. As anticipated, fans with higher fan identification reported a more positively perceived team reputation; additionally, they also reported a more positively perceived athlete image than low-identified fans. There was a significant difference in perceived image between the two identification groups in phase one. This finding provided evidence that group association evokes a more positive athlete image in the fan’s mind, given that all other information presented to the participants was identical. Fan identification has previously been credited with predicting a number of outcomes and is said to influence behaviors, attitudes, and purchase intentions; present analysis suggests that perceived image and reputation are also outcomes influenced by fan identification. This particular finding supported the need for inclusion of fan identification as a variable to consider in the evaluation of the athlete image repair discourse following a transgression.

Role of Image on Consumer Behavior

Furthermore, preliminary analysis supports the notion that perceived image is at the center of a number of consumer behaviors. Fans who perceived a more favorable athlete image also indicated higher degrees of support and advocacy. Specifically, these fans were more willing to generate positive word-of-mouth, such as posting

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positive messages, about the athlete on social media sites or proclaiming their association in conversations with others. Not surprisingly, these fans were also less likely to generate negative word-of-mouth. Analysis also supported the notion that those who perceived a high team reputation have higher patronage intentions. Fans indicated stronger repeat purchase intentions such as attending games, as well as merchandise consumptions such as the purchase of game apparel and souvenirs; they were more inclined to speak favorably of the organization, as well as encourage others to support the team. Furthermore, they were more likely to consume media, both on traditional and social platforms that discussed the team in any way. Based on this it became evident that a transgression that causes a shift in perceived image or reputation would subsequently also shift attitudes and behavioral outcomes. These findings were important, because they allowed the researcher to establishing the relationship between perceived image and subsequent consumer behavior intentions and, furthermore, provided a baseline for how a transgression would influence fan perceptions and subsequent behavioral intentions.

Influence of Crisis

Data showed that the athlete transgression significantly decreased potential supportive behaviors geared toward the athlete. Pre and post test data from before and after the crisis clearly showed a drop in the outcome variables. More specifically, after learning about the transgression fans had a more negative perceived athlete image and reputation, they were less likely to engage in athlete advocacy behaviors, and had much lower intentions to support the athlete, including engaging in positive word-of-mouth in favor of the athlete. Furthermore, the analysis supported Coombs’ (2007) findings, fans were considerably more inclined to generate negative word-of-mouth. While these

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findings are not groundbreaking or even surprising, they set the stage for the examination of successful image repair discourse for an athlete transgressor through athletic performance. In addition, the results show that overall the athlete crisis did not affect outcomes toward the team. There were no statistically significant changes in perceived team reputation, nor were there substantial changes in patronage intentions.

Influence of crisis based on fan identification: High-identified fans perceived the athlete image better in phase one (pre-crisis) than low-identified fans, there was no difference for perceived athlete image following the crisis, with both high and low- identified fans reporting low perceived image scores. Furthermore, the drop in perceived image was significantly higher among high-identified fans, suggesting the crisis effected their perception of the athlete more than low-identified fans. The same pattern was found for the dependent variables of supportive behavioral intentions and pWOM. Along similar lines, negative word-of-mouth was not different between the high and low- identified fans in the pre-crisis phase; however, following the transgression manipulation, high-identified fans were much more likely to generate nWOM than did low-identified fans. Again, this suggests that high-identified fans were more affected and subsequently engaged in more drastic negative outcomes than did low-identified fans.

Interestingly, athlete reputation and athlete advocacy was higher among high-identified fans in the pre-crisis stage, but following the crisis these scores were actually higher for the low-identified fans. This suggests that low-identified fans not only perceived the athlete more favorably, but also were more likely than high-identified fans to advocate on his behalf.

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When looking at team-related outcomes, the results indicated that while there were differences between high and low-identified fans’ perceived team reputation, with high-identified fans perceiving the team more favorably, there was no difference in pre and post scores among either one of the groups. However, looking at patronage intentions, it was interesting to see that in each domain high-identified fans’ scores dropped, while low-identified fans’ scores remained the same or, strangely, even increased.

As mentioned before, these inquiries and findings were important for the main study because by taking into account their perceptions prior to the transgression, and immediately following the transgression, the researcher was able to see how the participants’ evaluations changed over time, as well as how different these changes were depending on fan identification level. Investigating and discussing the influence of a specific image repair strategy without accounting for the pre- and post-crisis factors would certainly limit the study’s findings and provide only a one-dimensional view.

Overall, it is hoped that the main study is better framed by showcasing and providing this additional analysis.

Research Questions and Hypotheses

Hypothesis 1

H1 projected that fans with higher athlete expectancies would report more negative violation valence toward the athlete experiencing the scandal. The relationship between athlete expectancies (as measured by the Expectancy Scale) and negative violation valence (as measured by the Violation Valence Scale) was investigated using

Pearson product-moment correlation coefficient. Preliminary analyses were performed to ensure no violation of the assumptions of normality, linearity and homoscedasticity.

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There was a weak, positive correlation between the two variables, r = .270, n = 222, p <

.001, with high levels of expectancies associated with higher levels of negative violation valence. Therefore, H1 was supported.

Hypothesis 2

H2 was concerned with how violation valence would impact the various dependent variables concerning fan evaluations of the athlete within the present study.

Therefore, hypothesis seven had seven sub-hypotheses. The relationship between violation valence (as measured by the Negative Violation Valence Scale) and the various dependent variables was investigated using Pearson product-moment correlation. Analyses were performed to ensure no violation of assumptions of normality, linearity and homoscedasticity for all correlation tests for each dependent variable. Correlation between the two variables was calculated twice; for phase two

(after the transgression was introduced) and phase three (after performance treatments were provided).

H2a postulated that there would be a negative correlation between violation valence (as measured by the Negative Violation Valence Scale) and perceived athlete image (as measured by Athlete Image Scale). In phase two of the experiment there was a strong negative correlation between the two variables r = -.51, n = 222, p < .001, with high levels of perceived violation valence associated with lower levels of perceived athlete image. In phase three of the experiment there was an even stronger negative correlation between violation valence and perceived athlete image r = -.77, n = 221, p <

.001. Hypothesis 2a was supported.

H2b hypothesized that there would be a negative correlation between violation valence (as measured by the Negative Violation Valence Scale) and perceived athlete

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reputation (as measured by Athlete Reputation Scale). Data showed a medium strength negative correlation between the two variables r = -.48, n = 222, p < .001, with high levels of perceived violation valence associated with lower levels of perceived athlete reputation in phase two of the experiment, and a strong negative correlation between violation valence and perceived athlete reputation r = -.80, n = 221, p < .001 in phase three. Hypothesis 2b was supported.

H2c posited there would be a negative correlation between violation valence (as measured by the Negative Violation Valence Scale) and athlete advocacy (as measured by Athlete Advocacy Scale). The study found a medium strength negative correlation between the two variables r = -.38, n = 222, p < .001, with high levels of perceived violation valence associated with lower levels of athlete advocacy in phase two of the experiment, and a strong negative correlation between violation valence and athlete advocacy r = -.63, n = 221, p < .001 in phase three. Hypothesis 2c was supported.

H2d suggested a negative correlation between violation valence (as measured by the Negative Violation Valence Scale) and supportive behavioral intentions toward the athlete. In phase two of the experiment there was a negative correlation of medium strength between the two variables r = -.31, n = 222, p < .001, with high levels of perceived violation valence associated with lower levels of supportive behavioral intentions. In phase three of the experiment there was a strong negative correlation between violation valence and supportive behavioral intentions r = -.57, n = 221, p <

.001. Hypothesis 2d was supported.

H2e hypothesized that there would be a negative correlation between violation valence (as measured by the Negative Violation Valence Scale) and positive word-of-

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mouth (pWOM) about the athlete (as measured by Positive Word-of-Mouth scale). The findings showed a medium strength negative correlation between the two variables r = -

.33, n = 222, p < .001, with high levels of perceived violation valence associated with lower levels of positive word-of-mouth in phase two of the experiment, and a strong negative correlation between violation valence and positive word-of-mouth r = -.58, n =

221, p < .001 in phase three. Therefore, hypothesis 2e was supported.

H2f suggested a positive correlation between violation valence (as measured by the Negative Violation Valence Scale) and negative word-of-mouth (nWOM) generated about the athlete. The study found a positive correlation of medium strength between the two variables r = .49, n = 222, p < .001, with high levels of perceived violation valence associated with higher levels of negative word-of-mouth intentions. In phase three of the experiment there was a strong positive correlation between violation valence and nWOM r = .68, n = 221, p < .001. Hypothesis 2f was supported.

H2g posited there would be a positive correlation between violation valence (as measured by the Negative Violation Valence Scale) and cutting off reflected failure

(CORF)-behavior (as measured by the CORF Scale). The study first found a medium strength positive correlation between the two variables r = .45, n = 222, p < .001, and a strong positive correlation between violation valence and CORF r = .74, n = 221, p <

.001 in phase three, with high levels of perceived violation valence associated with higher levels of CORFing. Therefore, hypothesis 2g was also supported.

Research Question 1

To examine any difference in the dependent variables across the conditions a number of three-way ANCOVAS with team performance (winning, losing), athlete performance (positive, negative) and fan identification (high, low) as between-subject

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factors and the dependent variable scores from phase 1 and phase 2 as covariates were performed. Dimitrov and Rumrill (2003) argued that using pretest scores as covariates in ANCOVA with pretest-posttest design eliminates systematic bias and reduces the error in variance stating, “The main purpose of ANCOVA is to adjust the posttest means for differences among groups on the pretest” (p. 161). Preliminary checks were conducted to assure all assumptions were met. Descriptive statistics for each dependent variable by condition or treatment group can be found below each research question. When three-way interaction could not be detected, two-way interactions were assessed next. First, it was investigated if the effect of player performance on various dependent variables depended on fan identification. Two-way

ANCOVAS with athlete performance (positive, negative) and fan identification (high, low) as between-subject factors and the dependent variable scores from phase 1 and phase 2 as covariates were performed. Next two-way interactions assessing the effect of player performance on various dependent variables depended on team performance/outcome were investigated. To do so, two-way ANCOVAS with athlete performance (positive, negative) and team performance (winning, losing) as between- subject factors and the dependent variable scores from phase 1 and phase 2 as covariates were performed. Last, interaction effects between team performance and fan identification were analyzed. Two-way ANCOVAS with team performance (winning, losing) and fan identification (high, low) as between-subject factors and the dependent variable scores from phase 1 and phase 2 as covariates were performed.

RQ1a: There was no three-way interaction between team performance, athlete performance and fan identification on perceived athlete image F (1, 212) = .404, p =

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.525, η2 = .002, indicating that perceived athlete image following a crisis and image repair through performance was not dependent on all three independent variables.

Table 4-11. Means and SDs of Athlete Image by Treatment Conditions for Phases I-III Condition Phase I Phase II Phase III M Diff M Diff M Diff Team Perform. ID I - III II - III I - III Win Positive High 5.83 (1.03) 2.47 (1.03) 4.64 (1.23) -3.36 2.17 -1.19 Win Negative High 5.71 (0.98) 2.36 (0.68) 2.54 (1.26) -3.35 0.18 -3.17 Loss Positive High 4.99 (1.22) 2.45 (1.05) 4.30 (0.71) -2.54 1.85 -0.69 Loss Negative High 5.06 (0.79) 2.07 (0.72) 2.89 (0.89) -2.99 0.82 -2.18 Win Positive Low 4.49 (1.05) 2.95 (0.84) 4.11 (1.13) -1.54 1.16 -0.38 Win Negative Low 4.96 (0.82) 2.72 (0.97) 3.84 (1.42) -2.24 1.12 -1.12 Loss Positive Low 5.13 (1.00) 2.22 (0.77) 3.75 (1.17) -2.91 1.53 -1.39 Loss Negative Low 5.03 (0.90) 2.34 (0.96) 3.74 (1.24) -2.69 1.40 -1.26

There was a statistically significant interaction between player performance and level of identification for perceived athlete image score, F (1, 216) = 25.355, p < .001, partial η2 = .105. An analysis of simple main effects was performed with statistical significance receiving a Bonferroni adjustment and being accepted at the p < .03 level.

There was a statistically significant difference in mean perceived image score between high and low-identified fans in both the positive and the negative player performance group, F (1, 216) = 4.936, p = .027, partial η2 = .022 and F (1, 216) = 22.954, p < .001, partial η2 = .096, respectively. For high and low-identified fans exposed to the positive athlete performance, mean perceived athlete image score was .497 (95% CI, .056 to

.938) points higher for high than low-identified fans (adjusted MhighID = 4.440, adjusted

MlowID = 3.943). But for high and low-identified fans exposed to the negative athlete performance, mean perceived athlete image score was -1.071 (95% CI, -1.512 to -.631) points lower for high than low-identified fans (adjusted MhighID = 2.728, adjusted MlowID =

3.799). This suggests that high-identified fans do perceive athlete image more positively following positive athlete performance, but also perceive athlete image a lot more negatively than low-identified fans provided a negative athlete performance. There was

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a statistically significant difference in mean perceived athlete image scores between high-identified fans exposed to both positive and negative athlete performance level, F

(1, 216) = 55.787, p < .001, partial η2 = .205. High-identified fans exposed to the positive athlete performance reported a mean perceived athlete image 1.713 (95% CI,

1.261 to 2.165) points higher than high-identified fans exposed to the negative athlete performance, a statistically significant difference, p < .001 (adjusted Mpositive = 4.440, adjusted Mnegative = 2.728). However, athlete performance (either positive or negative) did not have a statistically significant effect on mean perceived athlete image score for low-identified fans, F (1, 216) = .469, p = .494, partial η2 = .002. Low-identified fans exposed to the positive athlete performance reported a mean perceived athlete image only .145 (95% CI, -.271 to .561) points higher than low-identified fans exposed to the negative athlete performance, a statistically insignificant difference, p = .494 (adjusted

Mpositive = 3.943, adjusted Mnegative = 3.799).

Figure 4-1. Two-way interaction between Player Performance and Fan Identification on Perceived Athlete Image.

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There was no statistically significant interaction between player performance and team performance for perceived athlete image, F (1, 216) = 1.987, p = .161, partial η2 =

.009. Neither was statistically significant interaction between team performance and fan identification for perceived athlete image, F (1, 216) = .586, p = .445, partial η2 = .003.

RQ1b: There was no three-way interaction between team performance, athlete performance and fan identification on perceived athlete reputation F (1, 212) = .497, p =

.482, η2 = .002, indicating that perceived athlete reputation following a crisis and image repair through performance was not dependent on all three independent variables.

Table 4-12. Means and SDs of Athlete Reputation by Treatment Conditions for Phase I- III Condition Phase I Phase II Phase III M Diff M Diff M Diff Team Perform. ID II - III II - III I - III Win Positive High 5.92 (1.13) 2.45 (1.06) 4.69 (1.31) -3.47 2.24 -1.23 Win Negative High 5.96 (1.04) 1.95 (0.57) 2.33 (1.37) -4.01 0.38 -3.73 Loss Positive High 5.46 (1.25) 2.74 (1.13) 4.37 (1.01) -2.72 1.63 -1.09 Loss Negative High 5.23 (0.82) 2.05 (0.76) 2.53 (0.90) -3.18 0.48 -2.71 Win Positive Low 4.77 (1.21) 2.91 (0.91) 4.23 (1.30) -1.86 1.32 -0.54 Win Negative Low 5.04 (0.81) 2.63 (0.98) 3.85 (1.44) -2.41 1.22 -1.18 Loss Positive Low 5.32 (0.98) 2.39 (0.95) 3.94 (1.28) -2.93 1.55 -1.38 Loss Negative Low 5.30 (0.89) 2.32 (0.99) 3.75 (1.45) -2.98 1.43 -1.54 There was a statistically significant interaction between player performance and level of identification for perceived athlete reputation score, F (1, 216) = 28.394, p <

.001, partial η2 = .116. This was followed with an analysis of simple main effects.

There was a statistically significant difference in mean perceived reputation score between high and low-identified fans in the negative player performance group, F (1,

216) = 32.981, p < .001, partial η2 = .132. For high and low-identified fans exposed to the negative athlete performance, mean perceived athlete reputation score was -1.385

(95% CI, -1.860 to -.910) points lower for high than low-identified fans (adjusted MhighID

= 2.426, adjusted MlowID = 3.881). However, there was no statistically significant difference in mean perceived reputation score between high and low-identified fans in

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the positive player performance group, F (1, 216) = 2.787, p = .096, partial η2 = .013.

For high and low-identified fans exposed to the positive athlete performance, mean perceived athlete reputation score was a mere .400 (95% CI, -.072 to .872) points higher for high than low-identified fans (adjusted MhighID = 4.485, adjusted MlowID =

4.085).

There was a statistically significant difference in mean perceived athlete reputation scores between high-identified fans exposed to both positive and negative athlete performance level, F (1, 216) = 67.723, p < .001, partial η2 = .239. High- identified fans exposed to the positive athlete performance reported a mean perceived athlete reputation 2.060 (95% CI, 1.566 to 2.553) points higher than high-identified fans exposed to the negative athlete performance, a statistically significant difference, p <

.001 (adjusted Mpositive = 4.448, adjusted Mnegative = 2.426). However, athlete performance (either positive or negative) did not have a statistically significant effect on mean perceived athlete reputation score for low-identified fans, F (1, 216) = 1.481, p =

.225, partial η2 = .007. Low-identified fans exposed to the positive athlete performance reported a mean perceived athlete reputation only .275 (95% CI, 0.81 to 9.20) points higher than low-identified fans exposed to the negative athlete performance, a statistically insignificant difference, p = .225 (adjusted Mpositive = 4.085, adjusted Mnegative

= 3.811).

No statistically significant interaction could be detected between player performance and team performance for perceived athlete reputation, F (1, 216) = 1.121, p = .291, partial η2 = .005, nor between team performance and fan identification for perceived athlete reputation, F (1, 216) = .082, p = .774, partial η2 = .000.

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RQ1c: There was no three-way interaction between team performance, athlete performance and fan identification on athlete advocacy F (1, 212) = .341, p = .560, η2 =

.002, indicating that athlete advocacy following a crisis and image repair through performance was not dependent on all three independent variables.

Table 4-13. Means and SDs of Athlete Advocacy by Treatment Conditions for Phases I- III Condition Phase I Phase II Phase III M Diff M Diff M Diff Team Perform. ID I - III II - III I - III Win Positive High 4.92 (1.52) 2.37 (1.24) 4.41 (1.59) -2.55 2.04 -0.51 Win Negative High 5.27 (1.28) 1.96 (0.92) 1.81 (1.47) -3.31 -0.15 -3.45 Loss Positive High 4.71 (1.26) 2.74 (1.12) 3.99 (1.23) -1.97 1.25 -0.72 Loss Negative High 4.53 (1.16) 2.12 (0.82) 2.11 (1.07) -2.41 -0.01 -2.41 Win Positive Low 3.47 (1.21) 2.93 (1.08) 3.45 (1.40) -0.54 0.52 -0.01 Win Negative Low 3.51 (1.53) 2.64 (1.14) 2.79 (1.51) -0.87 0.15 -0.72 Loss Positive Low 3.63 (1.35) 2.36 (1.12) 2.93 (1.28) -1.27 0.57 -0.70 Loss Negative Low 3.32 (1.06) 2.38 (0.98) 2.81 (1.56) -0.94 0.43 -0.51

There was a statistically significant interaction between player performance and level of identification for athlete advocacy, F (1, 216) = 26.928, p < .001, partial η2 =

.111. An analysis of simple main effects was performed with statistical significance receiving a Bonferroni adjustment and being accepted at the p < .025 level. There was a statistically significant difference in mean perceived image score between high and low- identified fans in both the positive and the negative player performance group, F (1,

216) = 6.279, p = .013, partial η2 = .028 and F (1, 216) = 18.792, p < .001, partial η2 =

.080, respectively. For high and low-identified fans exposed to the positive athlete performance, mean athlete advocacy score was .636 (95% CI, .136 to 1.136) points higher for high than low-identified fans (adjusted MhighID = 3.950, adjusted MlowID =

3.314). But for high and low-identified fans exposed to the negative athlete performance, mean athlete advocacy score was -1.139 (95% CI, -1.657 to -.621) points lower for high than low-identified fans (adjusted MhighID = 1.859, adjusted MlowID = 2.998).

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This suggests that high-identified fans do report higher athlete advocacy following positive athlete performance, but also indicate less athlete advocacy than low-identified fans provided a negative athlete performance.

Figure 4-2. Two-way interaction between Player Performance and Fan Identification on Perceived Athlete Reputation.

There was a statistically significant difference in mean athlete advocacy scores between high-identified fans exposed to both positive and negative athlete performance level, F (1, 216) = 67.804, p < .001, partial η2 = .239. High-identified fans exposed to the positive athlete performance reported a mean perceived athlete image 2.091 (95% CI,

1.590 to 2.591) points higher than high-identified fans exposed to the negative athlete performance, a statistically significant difference, p < .001 (adjusted Mpositive = 3.950, adjusted Mnegative = 1.859). However, athlete performance (either positive or negative) did not have a statistically significant effect on mean athlete advocacy score

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Figure 4-3. Two-way interaction between Player Performance and Fan Identification on Athlete Advocacy. for low-identified fans, F (1, 216) = 1.860, p = .174, partial η2 = .009. Low-identified fans exposed to the positive athlete performance reported a mean perceived athlete image only .316 (95% CI, -.140 to .772) points higher than low-identified fans exposed to the negative athlete performance, a statistically insignificant difference, p = .174 (adjusted

Mpositive = 3.314, adjusted Mnegative = 2.998).

Results suggested no statistically significant interaction between player performance and team performance for player advocacy, F (1, 216) = 3.755, p = .055, partial η2 = .017, as well as no statistically significant interaction between team performance and fan identification for athlete advocacy, F (1, 216) = .022, p = .882, partial η2 = .000.

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RQ1d: There was no three-way interaction between team performance, athlete performance and fan identification on supportive behavioral intentions F (1, 212) = .411, p = .522, η2 = .002, indicating that supportive behavioral intentions following a crisis and image repair through performance was not dependent on all three independent variables.

Table 4-14. Means and SDs of Athlete Supportive Behavioral Intentions by Treatment Conditions for Phases I-III Condition Phase I Phase II Phase III M Diff M Diff M Diff Team Perform. ID I - II II - III I - III Win Positive High 5.65 (1.43) 3.61 (0.95) 5.07 (1.33) -2.04 1.46 -0.58 Win Negative High 5.66 (1.09) 3.19 (1.04) 3.03 (1.23) -2.47 -0.16 -2.63 Loss Positive High 5.23 (1.17) 3.59 (0.94) 4.88 (0.99) -1.64 1.29 -0.35 Loss Negative High 5.18 (0.87) 3.09 (1.01) 3.27 (0.97) -2.09 0.18 -1.91 Win Positive Low 3.86 (1.13) 3.58 (1.00) 3.99 (1.24) -0.28 0.41 0.13 Win Negative Low 3.97 (1.38) 3.52 (1.22) 3.63 (1.50) -0.45 0.11 -0.34 Loss Positive Low 4.01 (1.11) 3.18 (0.84) 3.55 (1.12) -0.83 0.37 -0.46 Loss Negative Low 3.83 (0.99) 3.09 (0.89) 3.19 (1.02) -0.74 0.10 -0.64 There was a statistically significant interaction between player performance and level of identification for athlete supportive behavioral intentions, F (1, 216) = 20.761, p

< .001, partial η2 = .088. An analysis of simple main effects followed. There was a statistically significant difference in mean athlete supportive intentions score between high and low-identified fans in both the positive and the negative player performance group, F (1, 216) = 8.084 p = .005, partial η2 = .036 and F (1, 216) = 9.080, p = .003, partial η2 = .040, respectively. For high and low-identified fans exposed to the positive athlete performance, mean athlete supportive behavioral intentions score was .641

(95% CI, .197 to 1.085) points higher for high than low-identified fans (adjusted MhighID =

4.629, adjusted MlowID = 3.988). But for high and low-identified fans exposed to the negative athlete performance, mean athlete supportive behavioral intention score was -

.688 (95% CI, -1.137 to -.238) points lower for high than low-identified fans (adjusted

MhighID = 2.968, adjusted MlowID = 3.655). This suggests that high-identified fans do

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report higher athlete supportive behavioral intentions following positive athlete performance, but also have less athlete supportive behavioral intentions than low- identified fans provided a negative athlete performance.

There was a statistically significant difference in mean athlete supportive behavioral intention scores between high-identified fans exposed to both positive and negative athlete performance level, F (1, 216) = 59.146, p < .001, partial η2 = .215.

Figure 4-4. Two-way interaction between Player Performance and Fan Identification on Supportive Behavioral Intentions.

High-identified fans exposed to the positive athlete performance reported mean athlete supportive intentions 1.662 (95% CI, 1.236 to 2.088) points higher than high- identified fans exposed to the negative athlete performance, a statistically significant difference, p < .001 (adjusted Mpositive = 4.629, adjusted Mnegative = 2.968). However,

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athlete performance (either positive or negative) did not have a statistically significant effect on mean athlete supportive behavioral intentions for low-identified fans, F (1, 216)

= 2.864, p = .092, partial η2 = .013. Low-identified fans exposed to the positive athlete performance reported a mean perceived athlete image only .333 (95% CI, -.055 to .721) points higher than low-identified fans exposed to the negative athlete performance, a statistically insignificant difference, p = .174 (adjusted Mpositive = 3.988, adjusted Mnegative

= 3.655).

Again, there was no statistically significant interaction between player performance and team performance for supportive behavioral intentions, F (1, 216) =

.725, p = .396, partial η2 = .003, nor was there statistically significant interaction between team performance and fan identification for supportive behavioral intentions, F

(1, 216) = 2.068, p = .152, partial η2 = .009.

RQ1e: There was no three-way interaction between team performance, athlete performance and fan identification on pWOM F (1, 212) = .602, p = .439, η2 = .003, indicating that pWOM following a crisis and image repair through performance was not dependent on all three independent variables.

Table 4-15. Means and SDs of Athlete Positive Word-of-Mouth by Treatment Conditions for Phases I-III Condition Phase I Phase II Phase III M Diff M Diff M Diff Team Perform. ID I - II II - III I - III Win Positive High 4.60 (1.82) 2.23 (1.32) 3.75 (1.56) -2.37 1.52 -0.85 Win Negative High 4.95 (1.47) 2.07 (0.94) 1.92 (1.26) -2.88 -0.15 -3.03 Loss Positive High 4.20 (1.43) 2.38 (1.05) 3.77 (1.44) -1.82 1.39 -0.43 Loss Negative High 4.20 (1.25) 2.08 (0.77) 1.93 (0.82) -2.12 -0.15 -2.27 Win Positive Low 3.17 (1.28) 2.54 (1.12) 3.31 (1.34) -0.63 0.77 0.14 Win Negative Low 3.16 (1.53) 2.45 (0.99) 2.56 (1.43) -0.71 0.11 -0.60 Loss Positive Low 3.31 (1.44) 2.09 (0.95) 2.84 (1.57) -1.22 0.75 -0.47 Loss Negative Low 3.24 (1.21) 2.26 (0.91) 2.83 (1.17) -0.98 0.57 -0.41

There was a statistically significant interaction between player performance and level of identification for pWOM generated, F (1, 216) = 19.240, p < .001, partial η2 =

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.082. This was followed with an analysis of simple main effects. There was a statistically significant difference in mean pWOM generated between high and low-identified fans in the negative player performance group, F (1, 216) = 20.745, p < .001, partial η2 = .088.

For high and low-identified fans exposed to the negative athlete performance, mean pWOM score was -1.103 (95% CI, -1.581 to -.626) points lower for high than low- identified fans (adjusted MhighID = 1.754, adjusted MlowID = 2.858). However, there was no statistically significant difference in mean pWOM score between high and low- identified fans in the positive player performance group, F (1, 216) = 1.717, p = .191, partial η2 = .008. For high and low-identified fans exposed to the positive athlete performance, mean perceived athlete reputation score was a mere .309 (95% CI, -.155 to .773) points higher for high than low-identified fans (adjusted MhighID = 3.557, adjusted

MlowID = 3.249).

There was a statistically significant difference in mean pWOM scores between high-identified fans exposed to both positive and negative athlete performance level, F

(1, 216) = 57.893 p < .001, partial η2 = .211. High-identified fans exposed to the positive athlete performance reported a mean perceived athlete reputation 1.803 (95% CI, 1.336 to 2.270) points higher than high-identified fans exposed to the negative athlete performance, a statistically significant difference, p < .001 (adjusted Mpositive = 3.557, adjusted Mnegative = 1.754). However, athlete performance (either positive or negative) did not have a statistically significant effect on mean pWOM score for low-identified fans, F (1, 216) = 3.223, p = .074, partial η2 = .015. Low-identified fans exposed to the positive athlete performance reported a mean perceived athlete reputation only .391

(95% CI, -.038 to .819) points higher than low-identified fans exposed to the negative

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athlete performance, a statistically insignificant difference, p = .074 (adjusted Mpositive =

3.249, adjusted Mnegative = 2.858).

Figure 4-5. Two-way interaction between Player Performance and Fan Identification on pWOM.

Interaction between player performance and team performance for pWOM, F (1,

216) = 1.468, p = .227, partial η2 = .007 were non significant; as was the interaction between team performance and fan identification for pWOM generated, F (1, 216) =

.202, p = .654, partial η2 = .001.

RQ1f: There was a three-way interaction of team performance, athlete performance and fan identification on nWOM F (1, 212) = 4.217, p = .041, η2 = .020.

Since there was a significant 3-way interaction, a simple two-way interaction for team

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performance and fan identification among those exposed to the positive and negative athlete performance was conducted. There was no simple two-way interaction for team and fan identification among those exposed to the positive athlete performance (F (1,

212) = .004, p = .947, η2 = .000). But there was a significant simple two-way interaction of fan identification and team performance for those in the negative performance group

(F (1, 212) = 6.759, p = .010, η2 = .031). Next, a simple two-way interaction for high and low-identified fans was performed. There was no simple two-way interaction for team and player performance among high-identified fans (F (1, 212) = 2.762, p = .098, η2 =

.013). There was no simple two-way interaction for team and player performance among low-identified fans (F (1, 212) = 2.425, p = .121, η2 = .011).

A simple two-way interaction for player performance and identification among those exposed to the winning and losing team was conducted. There was a significant simple two-way interaction of fan identification and player performance for those in the winning team (F (1, 212) = 25.975, p < .001, η2 = .109), as well as for those in the losing team (F

(1, 212) = 4.571, p = .034, η2 = .021).

This was followed up with simple simple tests. In the condition of positive performance, for the winning team there was no significant difference between high ID and low ID (Mhigh = 2.68, SD = 1.17; Mlow = 3.00, SD = 1.32, F (1, 53) = 2.054, p = .158).

For the losing team there was also no significant difference between high and low ID

(Mhigh = 2.47, SD = 1.15; Mlow = 3.06, SD = 1.31, F (1, 51) = 2.468, p = .122).

In the condition of negative performance, for the winning team there was significant difference between high ID and low ID (Mhigh = 5.39, SD = 1.40; Mlow = 2.98,

SD = 1.53, F (1, 50) = 16.891, p < .001). For the losing team there was no significant

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difference between high and low ID (Mhigh = 4.29, SD = 1.68; Mlow = 3.48, SD = 1.15, F

(1, 52) = 1.771, p = .189).

Table 4-16. Means and SDs of Athlete Negative Word-of-Mouth by Treatment Conditions for Phases I-III Condition Phase I Phase II Phase III M Diff M Diff M Diff Team Perform. ID I - II II - III I - III Win Positive High 1.89 (1.18) 4.33 (1.39) 2.68 (1.17) 2.44 -1.65 0.79 Win Negative High 2.35 (1.50) 5.09 (1.22) 5.39 (1.40) 2.74 0.30 3.04 Loss Positive High 2.15 (1.39) 3.82 (1.32) 2.47 (1.15) 1.67 -1.35 0.32 Loss Negative High 2.75 (1.22) 4.47 (1.38) 4.30 (1.68) 1.72 -0.17 1.54 Win Positive Low 1.89 (0.81) 3.58 (1.40) 3.00 (1.32) 1.69 -0.58 1.11 Win Negative Low 2.72 (1.50) 3.49 (1.45) 2.98 (1.53) 0.77 -0.51 0.25 Loss Positive Low 2.26 (1.08) 4.05 (1.22) 3.06 (1.31) 1.79 -0.99 0.79 Loss Negative Low 2.19 (0.82) 3.99 (1.33) 3.48 (1.15) 1.80 -0.51 1.29

A) Positive performance.

Figure 4-6. Three-way Interaction between Team Performance, Player Performance and Fan ID on Negative Word of Mouth

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B) Negative Performance

Figure 4-6. Continued

Although a significant three-way interaction was detected, the effect was small; therefore two-way interactions were assessed and are reported below.

There was a statistically significant interaction between player performance and level of identification for nWOM generated, F (1, 216) = 25.890, p < .001, partial η2 = .107. An analysis of simple main effects was performed with statistical significance receiving a

Bonferroni adjustment and being accepted at the p < .025 level. There was a statistically significant difference in mean nWOM between high and low-identified fans in both the positive and the negative player performance group, F (1, 216) = 4.600, p = .033, partial

η2 = .021 and F (1, 216) = 24.316, p < .001, partial η2 = .101, respectively. For high and low-identified fans exposed to the positive athlete performance, mean nWOM score was

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-.531 (95% CI, 1.019 to -.043) points lower for high than low-identified fans (adjusted

MhighID = 2.600, adjusted MlowID = 3.131). But for high and low-identified fans exposed to the negative athlete performance, mean nWOM was 1.270 (95% CI, .763 to 1.778) points higher for high than low-identified fans (adjusted MhighID = 4.585, adjusted MlowID =

3.314). This suggests that high-identified fans generate less nWOM following positive athlete performance, but also generate a lot more nWOM than low-identified fans provided a negative athlete performance.

There was a statistically significant difference in mean nWOM between high- identified fans exposed to both positive and negative athlete performance level, F (1,

216) = 56.539, p < .001, partial η2 = .207. High-identified fans exposed to the positive athlete performance reported mean nWOM -1.984 (95% CI, -2.504 to 1.464) points lower than high-identified fans exposed to the negative athlete performance, a statistically significant difference, p < .001 (adjusted Mpositive = 2.600, adjusted Mnegative =

3.131). However, athlete performance (either positive or negative) did not have a statistically significant effect on mean nWOM for low-identified fans, F (1, 216) = .585, p

= .445, partial η2 = .003. Low-identified fans exposed to the positive athlete performance reported nWOM only -.183 (95% CI, -.655 to .289) points lower than low-identified fans exposed to the negative athlete performance, a statistically insignificant difference, p =

..445 (adjusted Mpositive = 3.131, adjusted Mnegative = 3.314).

There was no statistically significant interaction between player performance and team performance for nWOM, F (1, 216) = .065, p = .799, partial η2 = .000; nor was there statistically significant interaction between team performance and fan identification for nWOM generated, F (1, 216) = 2.485, p = .116, partial η2 = .011.

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Figure 4-7. Two-way interaction between Player Performance and Fan Identification on nWOM.

RQ1g: There was no three-way interaction between team performance, athlete performance and fan identification on CORFing behavior F (1, 212) = .1.503, p = .222,

η2 = .007, indicating that CORF following a crisis and image repair through performance was not dependent on all three independent variables.

Table 4-17. Means and SDs of Cutting Off Reflected Failure (CORF) by Treatment Conditions for Phases II-III Condition Phase II Phase III M Diff Team Perform. ID II - III Win Positive High 5.16 (1.46) 2.96 (1.36) -2.20 Win Negative High 5.59 (0.90) 5.73 (1.26) 0.14 Loss Positive High 4.51 (1.14) 3.17 (1.29) -1.34 Loss Negative High 5.17 (0.87) 5.04 (1.23) -0.13 Win Positive Low 4.10 (1.25) 3.78 (1.33) -0.32 Win Negative Low 4.71 (1.37) 4.41 (1.44) -0.30 Loss Positive Low 5.24 (0.94) 4.05 (1.26) -1.19 Loss Negative Low 4.82 (1.38) 4.23 (1.21) -0.59

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There was a statistically significant interaction between player performance and level of identification for CORFing behavior, F (1, 217) = 27.336, p < .001, partial η2 =

.112. Therefore, an analysis of simple main effects was performed. There was a statistically significant difference in mean CORFing behavior between high and low- identified fans in both the positive and the negative player performance group, F (1,

217) = 14.129, p < .001, partial η2 = .061 and F (1, 217) = 12.984, p < .001, partial η2 =

.056, respectively. For high and low-identified fans exposed to the positive athlete performance, mean CORF was -.897 (95% CI, -1.367 to -.427) points lower for high than low-identified fans (adjusted MhighID = 3.082, adjusted MlowID = 3.979). But for high and low-identified fans exposed to the negative athlete performance, mean CORF was

.878 (95% CI, .398 to 1.358) points higher for high than low-identified fans (adjusted

MhighID = 5.235, adjusted MlowID = 4.357). This suggests that high-identified fans less

CORFing behavior following positive athlete performance, but also generate a lot more

CORFing behavior than low-identified fans provided a negative athlete performance.

There was a statistically significant difference in mean CORF between high- identified fans exposed to both positive and negative athlete performance level, F (1,

217) = 73.324, p < .001, partial η2 = .253. High-identified fans exposed to the positive athlete performance reported mean CORF -2.153 (95% CI, -2.648 to 1.657) points lower than high-identified fans exposed to the negative athlete performance, a statistically significant difference, p < .001 (adjusted Mpositive = 3.082, adjusted Mnegative =

5.235). However, athlete performance (either positive or negative) did not have a statistically significant effect on mean CORF for low-identified fans, F (1, 217) = .2.719, p = .101, partial η2 = .012. Low-identified fans exposed to the positive athlete

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performance reported CORF -.378 (95% CI, -.830 to .074) points lower than low- identified fans exposed to the negative athlete performance, a statistically insignificant difference, p = .101 (adjusted Mpositive = 3.979, adjusted Mnegative = 4.357).

Figure 4-8. Two-way interaction between Player Performance and Fan Identification on CORF.

Interaction between player performance and team performance for CORF, F (1,

216) = 1.794, p = .182, partial η2 = .008 was non statistically significant. And there was no statistically significant interaction between team performance and fan identification for CORFing behavior, F (1, 216) = .160, p = .689, partial η2 = .001.

RQ1h: There was no three-way interaction between team performance, athlete performance and fan identification on team reputation F (1, 212) = .025, p = .875, η2 =

.000, indicating that team reputation following an athlete crisis and his image repair through performance was not dependent on all three independent variables.

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Table 4-18. Means and SDs of Team Reputation by Treatment Conditions for Phases I-III Condition Phase I Phase II Phase III M Diff M Diff M Diff Team Perform. ID I - II II - III I - III Win Positive High 6.12 (0.90) 4.99 (1.61) 6.20 (0.70) -1.13 1.21 0.08 Win Negative High 6.41 (0.78) 6.30 (0.82) 6.65 (0.66) -0.11 0.35 0.24 Loss Positive High 4.94 (1.18) 4.73 (1.37) 4.69 (1.27) -0.21 -0.04 -0.24 Loss Negative High 4.58 (1.11) 5.00 (1.32) 5.19 (1.23) 0.42 0.19 0.61 Win Positive Low 4.38 (1.28) 4.37 (1.27) 4.74 (1.20) -0.01 0.37 0.36 Win Negative Low 4.45 (1.24) 4.64 (1.60) 4.40 (1.53) 0.19 -0.24 -0.46 Loss Positive Low 3.91 (1.34) 4.20 (1.14) 3.52 (1.46) 0.29 -0.68 -0.39 Loss Negative Low 4.14 (0.78) 3.86 (1.24) 3.77 (1.04) -0.28 -0.09 -0.38 Result suggested no statistically significant interaction between player performance and fan identification for team reputation, F (1, 216) = 3.493, p = .063, partial η2 = .016.

However, there was a statistically significant interaction between player performance and team performance for team reputation, F (1,216) = 4.391, p = .037, partial η2 = .020. Therefore, an analysis of simple main effects for team performance was performed with statistical significance receiving a Bonferroni adjustment and being accepted at the p < .025 level.

There was a statistically significant difference in mean team reputation score between those in the winning and losing team who were exposed to the positive athlete performance, F (1, 216) = 15.135, p < .001, partial η2 = .065.

For the participants in the winning and the losing team exposed to positive athlete performance, mean team reputation score was .821 (95% CI, .401 to 1.224) points higher for those in the winning than the losing team group (adjusted Mwinning = 5.191, adjusted Mlosing = 4.379)

There was no statistically significant difference in mean team reputation score between those in the winning and losing team who were exposed to the negative athlete performance, F (1, 216) = .827, p = .364, partial η2 = .004. For the participants in the

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winning and the losing team exposed to negative athlete performance, mean team reputation score was only .197 (95% CI, -.230 to .624) points higher for those in the winning than the losing team group (adjusted Mwinning = 4.972, adjusted Mlosing = 4.775).

There were no statistical differences in mean team reputation score between either participants in the winning team nor losing team exposed to positive and negative athlete performance, F (1, 216) = 1.099, p = ..296, partial η2 = .005 and F (1, 216) =

3.717, p = .055, partial η2 = .017 respectively. Those exposed to the positive performance within the winning team evaluated the team only .219 (95% CI -.193 to

.632) points higher than those exposed to the negative athlete performance (adjusted

Mpositive = 5.191, adjusted Mnegative = 4.972). Those exposed to the positive performance within the losing team actually evaluated the team -.396 (95% CI, -.810 to .009) lower, but it was non-significant (adjusted Mpositive= 4.379, adjusted Mnegative = 4.775).

Figure 4-9. Two-way interaction between team performance and player performance on Team Reputation.

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There was no statistically significant interaction between team performance and fan identification for team reputation, F (1, 216) = .015, p = .904, partial η2 = .000.

RQ1i: There was no three-way interaction between team performance, athlete performance and fan identification on repeat purchase intentions F (1, 212) = .698, p =

.404, η2 = .003, nor on team WOM F (1, 212) = .044, p = .833, η2 = .000, nor merchandise consumption F (1, 212) = .1.217, p = .271, η2 = .006, nor for media consumption F (1, 212) = .763 p = .383, η2 = .004. For the full patronage intention scale there was also no 3-way interaction F (1, 212) = .491 p = .484, η2 = .002 indicating that patronage intentions following a crisis and image repair through performance was not dependent on all three independent variables.

For the two-way analysis, there was no statistically significant interaction between player performance and fan identification for patronage intentions in the repeat purchase intentions domain, F (1, 216) = 0.18, p = .892, partial η2 = .000. Nor were there statistically significant interaction between player performance and fan identification in the WOM domain F (1, 216) = 0.576, p = .449, partial η2 = .003, the merchandise consumption domain F (1, 216) = 2.750, p = .099, partial η2 = .013, and the media consumption domain F (1, 216) = 1.680, p = .196, partial η2 = .008.

There was no statistically significant interaction between player performance and team performance for patronage intentions toward the team in the repeat purchase domain, F (1, 216) = 1.983, p = .160, partial η2 = .009; nor was there statistically significant interaction between player performance and team performance patronage intentions toward the team in the WOM domain F (1, 216) = .282, p = .596, partial η2 =

.001. However, in both the merchandise consumption domain and the media

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consumption domain statistically significant interaction between player performance and team performance were detected with F (1, 216) = 10.582, p = .001, partial η2 = .047 for merchandise consumption and F (1, 216) = 4.309, p = .039, partial η2 = .020 for media consumption. Therefore, simple main effects were performed.

Merchandise Consumption. There was a statistically significant difference in mean merchandise consumption score between those in the winning and losing team who were exposed to the positive athlete performance, F (1, 216) = 14.139, p < .001, partial η2 = .061. For the participants in the winning and the losing team exposed to positive athlete performance, mean merchandise consumption score was .875 (95% CI,

.416 to 1.333) points higher for those in the winning than the losing team group

(adjusted Mwinning = 4.306, adjusted Mlosing = 3.431). There was no statistically significant difference in mean merchandise consumption score between those in the winning and losing team who were exposed to the negative athlete performance, F (1, 216) = .759, p

= .385, partial η2 = .004. For the participants in the winning and the losing team exposed to negative athlete performance, mean merchandise consumption score was -.207

(95% CI, -.676 to .262) points lower for those in the winning than the losing team group

(adjusted Mwinning = 3.766, adjusted Mlosing = 3.973).

There were statistical differences in mean merchandise consumption score between both the participants in the winning team and those in the losing team groups exposed to positive and negative athlete performance, F (1, 216) = 5.160, p = .024, partial η2 = .023 and F (1, 216) = 5.371, p = .021, partial η2 = .024 respectively. Those exposed to the positive performance within the winning team indicated merchandise consumption .540 (95% CI .071 to 1.009) points higher than those exposed to the

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negative athlete performance (adjusted Mpositive = 4.306, adjusted Mnegative = 3.766).

Those exposed to the positive performance within the losing team actually indicated merchandise consumption -.542 (95% CI, -.810 to .009) points lower than those in the negative athlete performance group (adjusted Mpositive= 3.431, adjusted Mnegative =

3.973).

Figure 4-10. Two-way interaction between team performance and player performance on Merchandise Consumption.

Media Consumption. There were no statistically significant differences in mean media consumption scores between those in the winning and losing team who were exposed to the positive athlete performance nor those who were exposed to the negative athlete performance, F (1, 216) = 2.906, p = .090, partial η2 = .013 and F (1,

216) = 1.527, p = .218, partial η2 = .007 respectively. For the participants in the winning and the losing team exposed to positive athlete performance, the mean media

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consumption score was only .381 (95% CI, -.060 to .823) points higher for those in the winning than the losing team group (adjusted Mwinning = 4.870, adjusted Mlosing = 4.489).

For the participants in the winning and the losing team exposed to negative athlete performance, the mean media consumption score was only -.283 (95% CI, -.735 to

.168) points lower for those in the winning than the losing team group (adjusted Mwinning

= 4.385, adjusted Mlosing = 4.668).

Figure 4-11. Two-way interaction between team performance and player performance on Media Consumption.

There was statistically significant difference in mean media consumption scores between the participants in the winning team exposed to positive and negative athlete performance, F (1, 216) = 4.581, p = .033, partial η2 = .021. Those exposed to the

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positive performance within the winning team indicated media consumption to be .486

(95% CI, .038 to .933) points higher than those exposed to the negative athlete performance (adjusted Mpositive = 4.870, adjusted Mnegative = 4.385). There was no statistically significant difference in mean media consumption between the participants in the losing team exposed to the positive and negative athlete performance F (1, 216) =

.631, p = .428, partial η2 = .003. Those exposed to the positive performance within the losing team actually indicated media consumption to be -.179 (95% CI, -.622 to .265) points lower than those in the negative athlete performance group (adjusted Mpositive=

4.489, adjusted Mnegative = 4.668).

Lastly, there were no statistically significant interactions between team performance and fan identification for patronage intentions. For the repeat purchase domain the study found no interaction, F (1, 216) = 1.817, p = .179, partial η2 = .008; for the WOM domain similar results showed no interaction, F (1, 216) = .057, p = .811, partial η2 = .000; for the merchandise consumption domain F (1, 216) = .047, p = .829, partial η2 = .000 and finally for media consumption, F (1, 216) = .302, p = .583, partial η2

= .001.

Table 4-19. Means and SDs of Team Patronage Intentions Repeat Purchase Domain by Treatment Conditions for Phases I-III Condition Phase I Phase II Phase III M Diff M Diff M Diff Team Perform. ID I - II II - III I - III Win Positive High 5.59 (1.88) 4.96 (1.86) 5.68 (1.54) -0.63 0.72 0.93 Win Negative High 6.32 (0.94) 5.99 (0.89) 6.21 (0.66) -0.33 0.22 -0.11 Loss Positive High 5.58 (1.40) 4.91 (1.40) 5.27 (1.54) -0.67 0.36 -0.31 Loss Negative High 5.88 (1.31) 5.03 (1.44) 5.49 (0.84) -0.85 0.46 -0.39 Win Positive Low 3.43 (1.68) 3.91 (1.67) 4.06 (1.48) 0.48 0.15 0.64 Win Negative Low 3.43 (1.60) 4.47 (1.84) 3.87 (1.70) 1.04 -0.60 0.44 Loss Positive Low 3.93 (1.67) 3.95 (1.34) 3.23 (1.77) 0.02 -0.72 -0.70 Loss Negative Low 3.87 (1.49) 3.54 (1.47) 3.39 (1.60) -0.33 -0.15 -0.49

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Table 4-20. Means and SDs of Team Patronage Intentions Word of Mouth Domain by Treatment Conditions for Phases I-III Condition Phase I Phase II Phase III M Diff M Diff M Diff Team Perform. ID I - II II - III I - III Win Positive High 6.03 (1.21) 4.99 (1.82) 6.01 (0.95) -1.04 1.02 -0.01 Win Negative High 6.27 (0.78) 6.00 (0.89) 6.19 (0.66) -0.27 0.19 -0.08 Loss Positive High 5.36 (1.30) 4.86 (1.39) 5.22 (1.52) -0.50 0.36 -0.14 Loss Negative High 5.15 (1.18) 4.76 (1.29) 5.18 (1.26) -0.39 0.42 0.21 Win Positive Low 3.58 (1.33) 3.68 (1.43) 3.98 (1.41) 0.10 0.30 0.40 Win Negative Low 3.26 (1.34) 4.24 (1.82) 3.66 (1.58) 0.98 -0.58 0.39 Loss Positive Low 3.51 (1.73) 3.84 (1.36) 3.65 (1.46 0.33 -0.19 0.15 Loss Negative Low 3.56 (0.97) 3.73 (1.12) 3.44 (1.32) 0.17 -0.29 -0.11

Table 4-21. Means and SDs of Team Patronage Intentions Merchandise Consumption Domain by Treatment Conditions for Phases I-III Condition Phase I Phase II Phase III M Diff M Diff M Diff Team Perform. ID I - II II - III I - III Win Positive High 5.47 (2.09) 4.61 (2.04) 5.44 (1.64) -0.86 0.83 -0.27 Win Negative High 6.24 (0.90) 5.68 (1.34) 6.05 (0.85) -0.56 0.37 -0.19 Loss Positive High 4.81 (1.63) 4.17 (1.76) 4.31 (1.83) -0.64 0.14 -0.51 Loss Negative High 4.74 (1.56) 4.55 (1.39) 5.02 (1.15) -0.19 0.47 0.26 Win Positive Low 2.04 (1.34) 2.84 (1.45) 3.18 (1.65) 0.80 0.34 1.14 Win Negative Low 2.33 (1.22) 3.62 (1.89) 2.63 (1.35) 1.29 0.01 0.30 Loss Positive Low 2.31 (1.79) 3.08 (1.64) 2.32 (1.49) 0.77 -0.76 0.12 Loss Negative Low 2.42 (1.35) 3.03 (1.31) 2.83 (1.39) 0.61 -0.20 0.41

Table 4-22. Means and SDs of Team Patronage Intentions Media Consumption Domain by Treatment Conditions for Phases I-III Condition Phase I Phase II Phase III M Diff M Diff M Diff Team Perform. ID I - II II - III I - III Win Positive High 5.79 (1.53) 5.18 (1.56) 5.71 (1.37) -0.61 0.53 -0.08 Win Negative High 6.11 (1.04) 5.95 (0.79) 5.99 (0.81) -0.16 0.04 -0.12 Loss Positive High 5.71 (1.30) 5.08 (1.55) 5.50 (1.57) -0.63 0.42 -0.21 Loss Negative High 5.40 (1.48) 4.97 (1.44) 5.55 (0.89) -0.70 0.58 0.15 Win Positive Low 3.23 (1.52) 4.02 (1.52) 4.07 (1.66) 0.79 0.05 0.84 Win Negative Low 3.39 (1.30) 4.48 (1.81) 3.53 (1.67) 1.09 -0.95 0.14 Loss Positive Low 3.51 (1.69) 4.01 (1.33) 3.61 (1.60) 0.50 -0.40 0.10 Loss Negative Low 3.18 (1.48) 3.87 (1.29) 3.52 (1.49) 0.69 -0.35 0.34

Research Question 2

To test main effects, one-way, between groups analysis of covariance was conducted to compare the effectiveness of different player performances in improving perceived athlete image and supportive behavioral intentions following a crisis. The

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independent variable was the type of athlete performance (positive, negative) and the dependent variable consisted of the various dependent variables tested after the intervention was completed. Participants’ scores on the dependent variables in phase 1 and phase 2 were used as the covariates in this analysis. Preliminary analyses were conduced to ensure that there was no violation of assumptions. Descriptive statistics can be found in table 4-23.

Results suggest that post-intervention perceived image and consumer behavior intentions toward the athlete were significantly greater in the positive performance versus the negative performance intervention, but athlete performance had no significant effects on team reputation or subsequent consumer behaviors toward the team.

RQ2a: There was a statistically significant difference in post-intervention perceived image between those exposed to the positive and those exposed to the negative performance groups, F (1, 218) = 27.345, p < .001, partial η2 = .111. Post- intervention perceived athlete image was significantly greater in the positive performance versus the negative performance intervention.

RQ2b: Post-intervention perceived athlete reputation between those exposed to the positive and those exposed to the negative performance groups was statistically significant, F (1, 218) = 34.525 p < .001, partial η2 = .137. Post-intervention perceived athlete reputation was significantly greater in the positive performance versus the negative performance intervention.

RQ2c: Post-intervention athlete advocacy between those exposed to the positive and those exposed to the negative performance groups was, F (1, 218) = 37.951 p <

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.001, partial η2 = .148. Post-intervention athlete advocacy was significantly greater in the positive performance versus the negative performance intervention.

RQ2d:, Post-intervention athlete supportive behavioral intentions were significantly greater in the positive performance versus the negative performance intervention F (1, 218) = 37.859 p < .001, partial η2 = .148. (p < .001).

RQ2e: Post-intervention pWOM was significantly greater in the positive performance versus the negative performance intervention F (1, 218) = 37.966 p < .001, partial η2 = .148.

RQ2f: Post-intervention nWOM between those exposed to the positive and those exposed to the negative performance groups was significantly different, F (1, 218) =

27.377 p < .001, partial η2 = .112. Post-intervention nWOM was significantly greater in the negative performance versus the positive performance intervention.

RQ2g: Post-intervention CORF between participants exposed to the positive and those exposed to the negative performance groups was significantly greater in the negative performance versus the positive performance intervention, F (1, 219) = 43.544 p < .001, partial η2 = .166.

RQ2h: There was no statistically significant difference in post-intervention team reputation between the those exposed to the positive and those exposed to the negative performance groups, F (1, 218) = .253 p = .616, partial η2 = .001.

RQ2i: There was no statistically significant difference in post-intervention repeat purchase between the those exposed to the positive and those exposed to the negative performance groups, F (1, 218) = .162 p = .688, partial η2 = .001, nor was there statistically significant difference in post-intervention WOM toward team F (1, 218) =

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1.027p = .312, partial η2 = .005, merchandise consumption F (1, 218) = .000, p = .990, partial η2 = .000, and media consumption between those exposed to the positive and those exposed to the negative performance groups, F (1, 218) = .902, p = .343, partial

η2 = .004.

Table 4-23. Adjusted and Unadjusted Means and Variability for Post-Intervention Dependent Variables with Phase 1 and Phase 2 Scores as a Covariate Unadjusted Adjusted N M SD M SE Perceived Image Positive 112 4.18 1.12 4.17 .116 Negative 102 3.30 1.33 3.31 .117 Perceived Reputation Positive 112 4.29 1.25 4.25 .127 Negative 102 3.14 1.42 3.19 .128 Athlete Advocacy Positive 112 3.66 1.47 3.60 .127 Negative 102 2.42 1.39 2.477 .128 Athlete Supportive Behavioral Intentions Positive 112 4.33 1.32 4.28 .107 Negative 102 3.29 1.20 3.34 .108 pWOM Positive 112 3.39 1.52 3.39 .118 Negative 102 2.34 1.25 2.35 .119 nWOM Positive 112 2.82 1.25 2.90 .132 Negative 102 3.97 1.69 3.89 .133 CORF Positive 112 3.52 1.25 3.58 .126 Negative 102 4.81 1.69 4.76 .127 Team Reputation Positive 112 4.74 1.51 4.79 .106 Negative 102 4.92 1.58 4.87 .107 Repeat Purchase Positive 112 4.49 1.84 4.60 .115 Negative 102 4.66 1.74 4.54 .116 Team WOM Positive 112 4.64 1.64 4.66 .109 Negative 102 4.53 1.68 4.51 .110 Merchandise Cons. Positive 112 3.72 2.00 3.88 .120 Negative 102 4.03 1.88 3.87 .121 Media Cons. Positive 112 4.65 1.78 4.68 .113 Negative 102 4.56 1.71 4.53 .114

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Research Question 3

One-way, between groups analyses of covariance were conducted to compare the effectiveness of different team performances in improving perceived athlete image and supportive behavioral intentions following a crisis. The independent variable was the type of team performance (winning, losing) and the dependent variables tested after the intervention was completed. Participants’ scores on the dependent variables in phase 1 and phase 2 were used as the covariates in this analysis. Preliminary analyses were conduced to ensure that there was no violation of assumptions. Descriptive statistics can be found in table 4-24.

Results suggest that post-intervention perceived image and consumer behavior intentions toward the athlete were not significantly greater when the athlete belonged to a winning team, versus a losing team, but team performance showed significant main effects on team reputation or subsequent consumer behaviors toward the team.

RQ3a: After adjusting for the phase 1 and phase 2 scores, there was no statistically significant difference in post-intervention athlete image between those exposed to the winning and those exposed to the losing team, F (1, 218) = .047, p =

.828, partial η2 = .000.

RQ3b: There was no statistically significant difference in post-intervention athlete reputation between those exposed to the winning and those exposed to the losing team,

F (1, 218) = .161, p = .688, partial η2 = .001.

RQ3c: Post-intervention athlete advocacy between those exposed to the winning and those exposed to the losing team, F (1, 218) = .135, p = .713 partial η2 = .001, was non-statistically significant.

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RQ3d: Results show no statistically significant difference in post-intervention supportive behavioral intentions between those exposed to the winning and those exposed to the losing team, F (1, 218) = .125, p = .724 partial η2 = .001.

RQ3e: Post-intervention pWOM between those exposed to the winning and those exposed to the losing team was not statistically significant, F (1, 218) = .075, p =

.785 partial η2 = .000.

RQ3f: There was no statistically significant difference in post-intervention nWOM between those exposed to the winning and those exposed to the losing team, F (1, 218)

= .670, p = .414 partial η2 = .003.

RQ3g: Post-intervention CORF between those exposed to the winning and those exposed to the losing team was also not statistically significant, F (1, 218) = .372, p =

.543 partial η2 = .002.

RQ3h: After adjusting for the phase 1 and phase 2 scores, there was a statistically significant difference in post-intervention team reputation between those exposed to the winning and those exposed to the losing team groups, F (1, 218) =

10.98, p < .001, partial η2 = .048. Post-intervention team reputation was significantly greater in the winning team versus the losing team intervention (p = .001).

RQ3i: There was also statistically significant difference in post-intervention repeat purchase, F (1, 218) = 11.479, p < .001, partial η2 = .050 and merchandise consumption between those exposed to the winning and those exposed to the losing team groups, F (1, 218) = 4.156 p = .043, partial η2 = .019. Post-intervention repeat purchase and merchandise consumption was significantly greater in the winning team versus the losing team intervention (p = .001). However, there were no statistically

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Table 4-24. Adjusted and Unadjusted Means and Variability for Post-Intervention Dependent Variables with Phase 1 and Phase 2 Scores as a Covariate Unadjusted Adjusted N M SD M SE Perceived Image Winning 111 3.80 1.45 3.76 .125 Losing 111 3.68 1.14 3.72 .125 Perceived Reputation Winning 111 3.79 1.60 3.76 .136 Losing 111 3.66 1.28 3.68 .136 Athlete Advocacy Winning 111 3.12 1.73 3.08 .138 Losing 111 2.96 1.36 3.01 .138 Athlete Supportive Behavioral Intentions Winning 111 3.92 1.49 3.94 .116 Losing 111 3.70 1.22 3.78 .116 pWOM Winning 111 2.90 1.55 2.85 .129 Losing 111 2.84 1.42 2.90 .129 nWOM Winning 111 3.46 1.71 3.47 .139 Losing 111 3.32 1.47 3.31 .139 CORF Winning 111 4.20 1.65 4.22 .138 Losing 111 4.12 1.39 4.10 .138 Team Reputation Winning 111 5.41 1.45 5.08 .106 Losing 111 4.25 1.41 4.58 .106 Repeat Purchase Winning 111 4.86 1.73 4.85 .114 Losing 111 4.28 1.81 4.30 .114 Team WOM Winning 111 4.85 1.68 4.72 .109 Losing 111 4.32 1.60 4.45 .109 Merchandise Cons. Winning 111 4.86 1.73 4.85 .114 Losing 111 4.28 1.81 4.30 .114 Media Cons. Winning 111 4.73 1.77 4.64 .114 Losing 111 4.48 1.71 4.58 .114 significant differences in post-intervention team WOM F (1, 218) = 2.890, p = .091 partial η2 = .013 and post-intervention media consumption between those exposed to

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the winning and those exposed to the losing team, F (1, 218) = .145, p = .703. partial η2

= .001.

Research Question 4

One-way, between groups analysis of covariance was conducted to compare the difference in perceived athlete image and supportive behavioral intentions following a crisis based on fan identification level. The independent variable was fan identification

(high, low) and the dependent variable consisted of the various dependent variables tested after the intervention was completed. Participants’ scores on the dependent variables in phase 1 and phase 2 were used as the covariates in this analysis.

Preliminary analyses were conduced to ensure that there was no violation of assumptions. Descriptive statistics can be found in table 4-25.

Results suggest that post-intervention perceived image and consumer behavior intentions toward the athlete were not significantly greater among high and low- identified fans, but fan identification had significant effects on team reputation or subsequent consumer behaviors toward the team.

RQ4a: After adjusting for the phase 1 and phase 2 scores, there was no statistically significant difference in post-intervention athlete image between high and low-identified fans, F (1, 218) = 2.402, p = .123, partial η2 = .011.

RQ4b: There was however statistically significant difference in post-intervention athlete reputation between high and low-identified fans, F (1, 218) = 5.642, p = .018, partial η2 = .025. Post-intervention perceived athlete reputation was significantly greater among low-identified versus high-identified fans, but the effect size was small.

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RQ4c: There was no statistically significant difference in post-intervention athlete advocacy between high and low-identified fans, F (1, 218) = .921, p = .338, partial η2 =

.004.

RQ4d: Post-intervention athlete supportive behavioral intentions between high and low-identified fans was not statistically significant, F (1, 218) = .012, p = .912, partial

η2 = .000.

RQ4e: Neither were there statistically significant differences in post-intervention pWOM, F (1, 218) = 3.430, p = .065, partial η2 = .015; and RQ4f: post-intervention nWOM between high and low-identified fans, F (1, 218) = 2.501, p = .115, partial η2 =

.011.

RQ4g: There was also no statistically significant difference in post-intervention

CORF between high and low-identified fans, F (1, 218) = .046 p = .830, partial η2 =

.000.

RQ4h: Nonetheless, there was statistically significant difference in post- intervention team reputation between high and low-identified fans, F (1, 218) = 15.939, p < .005, partial η2 = .068. Post-intervention perceived team reputation was significantly greater among high-identified versus low-identified fans.

RQ4i: Furthermore, there was statistically significant difference in post- intervention repeat purchase intentions toward the team between high and low-identified fans, F (1, 218) = 13.686, p < .005, partial η2 = .059. Post-intervention repeat purchase was significantly greater among high-identified versus low-identified fans. The same can be said for team WOM; there was a statistically significant difference in post-intervention team WOM between high and low-identified fans, F (1, 218) = 10.478, p = .001, partial

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η2 = .046. There was also a statistically significant difference in post-intervention team

Merchandise Consumption between high and low-identified fans, F (1, 218) = 9.333, p =

.003, partial η2 = .041. Finally this study found a statistically significant difference in post-intervention team Media Consumption between high and low-identified fans, F (1,

218) = 9.616, p = .002, partial η2 = .042. Post-intervention media consumption was significantly greater among high-identified versus low-identified fans.

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Table 4-25. Adjusted and Unadjusted Means and Variability for Post-Intervention Dependent Variables with Phase 1 and Phase 2 Scores as a Covariate Unadjusted Adjusted N M SD M SE Perceived Image High ID 102 3.59 1.37 3.59 .130 Low ID 120 3.87 1.24 3.87 .119 Perceived Reputation High ID 102 3.46 1.58 3.59 .130 Low ID 120 3.95 1.30 3.94 .131 Athlete Advocacy High ID 102 3.08 1.75 2.93 .154 Low ID 120 3.01 1.38 3.14 .141 Athlete Supportive Behavioral Intentions High ID 102 4.07 1.45 3.80 .134 Low ID 120 3.60 1.25 3.82 .122 pWOM High ID 102 2.84 1.58 2.67 .140 Low ID 120 2.90 1.40 3.04 .128 nWOM High ID 102 3.70 1.80 3.56 .146 Low ID 120 3.13 1.33 3.25 .135 CORF High ID 102 4.22 1.74 4.14 .145 Low ID 120 4.11 1.32 4.18 .133 Team Reputation High ID 102 5.67 1.27 5.19 .115 Low ID 120 4.12 1.39 4.53 .105 Repeat Purchase High ID 102 5.66 1.25 4.97 .133 Low ID 120 3.65 1.65 4.24 .120 Team WOM High ID 102 5.64 1.22 4.94 .132 Low ID 120 3.69 1.44 4.29 .119 Merchandise Cons. High ID 102 5.19 1.54 4.26 .153 Low ID 120 2.75 1.49 3.54 .137 Media Cons. High ID 102 5.68 1.20 4.95 .137 Low ID 120 3.69 1.60 4.31 .123

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Table 4-26. Summary of Findings Hypothesis Finding H1 Fans with higher athlete expectancies Supported will report more negative violation valence toward the athlete experiencing the scandal.

H2 There will be a correlation between Supported violation valence and a) perceived athlete image, b) perceived athlete reputation, c) athlete advocacy, d) supportive behavioral intentions, e) positive word-of-mouth, f) negative word-of-mouth, and g) CORFing10

Research Questions Finding RQ1 Are there interactions among team Only one statistically significant performance, player performance and 3-way interaction for nWOM. fan identification on a) perceived athlete image, b) perceived athlete For 2-way interaction between reputation, c) athlete advocacy, d) player performance and fan athlete supportive behavioral identification, significant intentions, e) pWOM, f) nWOM, g) interactions for all athlete team reputation, and h) patronage dependent variables. No intentions toward the team during an interaction effects for team athlete crisis? dependent variables.

For 2-way interaction between player performance and team performance, this study found significant interaction effects for team reputation, as well as team merchandise and team media consumption. But no significant interactions for all athlete dependent variables.

No significant interaction effects for 2-way interactions between team performance and fan identification

10 Correlations for H7a-e were predicted to be negative, while H7f+g were predicted to be positive

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Table 4-26. Continued Research Questions Finding RQ2 Does player performance influence a) Significant differences for all perceived athlete image, b) perceived athlete depend variables. athlete reputation, c) athlete advocacy, Positive performance had a d) athlete supportive behavioral more positive effect on DVs a-f. intentions, e) pWOM, f) nWOM, g) No significant differences for team reputation, and h) patronage team reputation and patronage intentions toward the team during an intentions. athlete crisis?

RQ3 Does team performance influence a) Significant differences for team perceived athlete image, b) perceived reputation, repeat purchase and athlete reputation, c) athlete advocacy, merchandise consumption, but d) athlete supportive behavioral no significant differences for intentions, e) pWOM, f) nWOM, g) WOM and media consumption. team reputation, and h) patronage No significant differences on intentions toward the team during an athlete dependent variables. athlete crisis?

RQ4 Does fan identification influence a) Significant differences for perceived athlete image, b) perceived athlete reputation, team athlete reputation, c) athlete advocacy, reputation, and patronage d) athlete supportive behavioral intentions. intentions, e) pWOM, f) nWOM, g) team reputation, and h) patronage intentions toward the team during an athlete crisis?

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CHAPTER FIVE DISCUSSION

Few athletes experience a shift in perceived image as extreme as LeBron James has during his 12-year career in the NBA. It started as a Cinderella story in 2003 when a young man from Akron, Ohio, was drafted out of high school as the first overall pick by his home-state team, the Cleveland Cavaliers. James became a hometown hero, whom fans believed would bring the city its first NBA championship. But after seven seasons with the Cavaliers and no championship rings on his fingers, LeBron James announced on a live television program that he would take his “talents to South Beach” (Abbott,

2010). While players electing to play for different teams was nothing new, the way

James proclaimed his decision certainly was contrary to the usual press release or news conference. James declared he would sign with the Miami Heat in an hour-long

ESPN special entitled The Decision. Despite the fact that the event raised $2.5 million for the Boys and Girls Clubs of America, James’ appearance came under rapid criticism and the once-celebrated athlete became known as a villain, a traitor, and a narcissist only interested in his own success. A look at James’ Q scores before and after the announcement illustrates just how damaging The Decision truly was. In January of

2010, just months before the ESPN event, 24 percent of people thought of James in a positive light, compared to 22 percent who held a negative opinion. Polling the population again after the move to Miami, only 14 percent saw James in a positive light, a 41.6 percent drop in perceived image, while 39 percent view him in a negative light, a

77 percent increase (Rovell, 2010). In fact, the drop was the biggest the Q Scores company had ever seen after a non-criminal event (Davis, 2013).

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James’ image continued to suffer in the 2010-2011 season, particularly after performing sub-par during the 2011 NBA Championship finals and losing to the Dallas

Mavericks. Media and sports fans alike continued the villain narrative and cited a lack of positive performance on the court as a chief reason for their dislike of James. ESPN personality Stephen A. Smith echoed what many may have felt during a segment on

ESPN First Take, stating, “It’s like he wants to be crowned [the King], with no rings!”

(Smith, 2011).

A year later, James made good on his promise and brought an NBA championship back to Florida. James not only helped the Heat win the trophy in two consecutive years (2012 and 2013), but was also named a two-time NBA finals most valuable player. His success on the court translated well to his image among the general population. Shortly after winning the second title, James' positive Q Score increased to 25 among sports fans, while his negative score went back to 21, a score close to his pre-decision image (Davis, 2013). Thanks to his recent successes and, in part, his return to home to the Cleveland Cavaliers, James’ latest Q score rating is at an impressive 29 (positive Q Score) (McMenamin, 2015). One has to wonder if his return alone caused his rehabilitated image, or if James’ performances on the court throughout the last few years contributed significantly to his image recovery.

The purpose of this dissertation was to examine how fans evaluate and react to athlete transgressions or personal failings and, more importantly, whether or not athlete performance could influence (positively or negatively) the image restoration discourse.

Rather than using anecdotal evidence such as the LeBron James case or the Tiger

Woods infidelity scandal, this study sought to empirically test the role of athlete

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performance following a transgression. The findings provide an interesting glimpse into sports consumer behaviors and open an avenue for future research in the area of crisis and sports.

Application of EVT in Sports Crisis

One of the principal objectives of this study was to assess if Burgoon’s (1989) expectancy violations theory (EVT) could be extended to sports transgressions.

Previously, Kim (2014) tested EVT’s application within the traditional organizational crisis setting and found the theory applied. However, the nature of the sports industry when compared to other industries is distinctive, thus suggesting closer examination.

Expectancies

One of the main tenets of EVT is that prior expectancies toward a violator tend to affect violation valence, or, the negative value assigned to a violation of expectancies.

Expectancies are patterns of predicted behavior that are grounded in previous experience. Fans in this study were asked about their general expectancies of professional athletes; provided the nature of the study, as well as the use of a fictional athlete, questions were largely directed at the prescriptive expectancies grounded in social norms rather than predictive expectancies, which are based on individual interactions. The findings showed that fans had high expectancies; they reported a mean score of 4.25 on a 5-point Likert scale indicating they believed athletes share responsibility to society, should engage in behaviors that reflect social norms, and should not harm their organization. This is not entirely surprising, for athletes are held to higher standards than are actors, singers or TV hosts (Cohen, 2010).

More importantly for the purpose of extending the theory, the results of the dissertation suggested that fans with higher expectancies experienced more violation

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valence, demonstrating expectancies are a significant predictor of fan evaluation and response following an athlete transgression. While the relationship was not as strong as anticipated, it does support previous scholarship and EVT. The findings show support for the influence of expectancies on violation valence (VV). Increased expectancies prior to the transgression will indeed contribute to the violation valence felt. Sports personalities and their management representatives should therefore be careful with how they present and promote the athlete; Kim (2014) stipulated that fostering

“unrealistic expectations” would backfire when a crisis occurs (p. 152). Admittedly, not fostering such expectations in the public’s eyes is no easy task. NBA Hall of Famer

Charles Barkley once famously said, “I’m not a role model […] just because I dunk a basketball doesn’t mean I should raise your kids” (Barkley, 1994). However, by and large society looks at professional athlete as just that: role models; and by default, expectancies of these role models are high. Nevertheless, some athletes have made the mistake of overselling their image, or promoting a false persona, resulting in extremely high expectancies by fans and even higher violation valence felt subsequent to the crisis event. Tiger Woods and his staff, for example, worked tirelessly throughout his entire career to promote the image of the devoted family man, a disciplined athlete who is always in complete control over every facet of his life, both on and off the course.

This, of course, proved to be untrue. In a profile on Woods, golf reporter Phil Taylor wrote about the public perception Woods and Co. promoted:

Woods encouraged the tableau of the ultimate family man, the beautiful wife there to embrace him and hand him the beautiful baby after winning another major. Woods seemed charmed he wasn't just the greatest golfer on the planet, he was classy, dignified, admirable. Even if we're not totally shocked that he isn't perfect, we thought his biggest flaws were along the

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lines of throwing the occasional club or cursing in range of the microphones. (Taylor, 2009)

This illustrates just how damaging the unrealistic expectancies raised in people’s minds can be after learning about an athlete’s transgressions. Taylor (2009) summarized this by writing, “Other celebrity athletes may have committed worse offenses, but no one in recent memory has acted in a manner so at odds with his public persona, and thus fallen so far” (para. 8).

In all fairness, the creation and promotion of self-image and the subsequent expectancy formation might be a double-edged sword. On one hand, doing so can raise the athlete’s fan following and increase marketability. On the other hand, as EVT suggests, it may contribute to increasing negative violation felt in the event of a crisis.

While no one person is perfect, this indicates a greater need for athlete education and development efforts, in order to eliminate or lessen the frequency and degree of transgressions in sports. However, this is a topic beyond the scope of the present study.

Violation Valence

While expectancies are one important facet of EVT, there is another component that is of equal importance. Violation valence has been found to predict a number of outcomes or reactions toward both the offender and the offense itself.

The study found a relationship between levels of violation valence and scores of the dependent variables investigated in this dissertation. For example, the results suggest that as violation valence increases, perceived athlete image decreases, meaning those who are more offended by the transgression also perceive the athlete to be worse. Similarly, perceived reputation, athlete advocacy, supportive behavioral intentions and pWOM scores all mirrored the same correlation. Additionally, both

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generated nWOM and CORFing behavior increase when violation valence increases.

These findings pertaining to violation valence are important because they further support the recommendation of extending EVT into the sports crisis realm. Furthermore, these findings stress that practitioners will need to find ways combat feelings of negative violation valence, in order to mitigate the damage of transgressions.

Violation Valence and Fan Identification: The present study did not find an ingroup bias; and this was also mirrored when investigating violation valence. One might expect that those who have higher fan identification would report less violation valence. This could be explained by ingroup favoritism (Wann & Dolan, 1994) or by

Rusbult’s (1980) investment model, which posited that individuals’ responses are dependent on their investment in the relationship and whether alternatives are present.

Applying the investment model, high-identified fans should experience less violation valence because they are more invested in the relationships, and it would be harder for them to change their prior perceptions about the athlete when compared to the low- identified fans. Cohen (2010) explained this the best when she stated, “The more that a relationship is voluntary, easily replaceable, and disconnected from external pressures to continue, the more vulnerable it is to expectancy violation damage” (p.99). Using this rationale and findings from previous research on crisis (Kim, 2013b), as well as studies on BIRGing and CORFing (Wann & Branscombe, 1990), high-identified fans should have a harder time processing the negative information and subsequently rationalize the negative behavior more, causing lower negative violation valence scores.

Surprisingly, this was not the case in this dissertation. In fact, high-identified fans reported more violation valence than low-identified fans; in other words, they were more

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offended by the athlete transgression than were low-identified fans. These findings are in line with those of Murray and Price (2009), but are opposed to the findings by Dietz-

Uhler and colleagues (2002). While Dietz-Uhler’s (2002) study found an ingroup bias, the present study found what appears to be a black-sheep effect. One possible explanation for this finding could be the nature of the transgression. Because the sample offense included a player punching one of his own fans, high ID participants may have felt that the event could have happened to them, therefore increasing their level of offendedness. Another explanation might be that high-identified fans are emotionally more affected by the behaviors of a player than low-identified fans, since it is a member of the ingroup, rather than the out-group. Perhaps low-identified fans genuinely do not care about the behaviors of an athlete on an opposing team, thus their level of offendedness could be lower.

This study also investigated if fan identification influenced the relationship between VV and athlete perceptions as well as supportive behaviors among fans. Fan

ID moderated the relationship for only two of the dependent variables in this study: perceived athlete image and athlete advocacy. The combined influence of both terms in the model only explained a small number of additional variance (2.8% and 1.8%). Fan identification did not moderate the relationship between violation valence and reputation; nor did it moderate the relationships between VV and supportive behavioral intentions, pWOM, nWOM or CORFing behavior. This means that VV’s relationship with most dependent variable did not change under the different condition of fan identification, and, furthermore, changed very little in the other two dependent variables.

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Positive Reward Valence

EVT posits that people who are perceived to be highly rewarding have more freedom to breach expectancies (Le Poire & Burgoon, 1996). That is to say, when stakeholders perceive an offender to be highly rewarding they perceive the reward as reason to compensate the negative feelings toward him or her and restore their evaluations to the previous (pre-crisis) state (Burgoon et al., 1995). Similarly, if the offender is not perceived as highly rewarding, stakeholders are more likely to alter their mind and continue to feel negative violation valence. This dissertation proposed winning would be viewed as a positive reward by fans and that, therefore, winning could mitigate violation valence. The results support this and found positive reward valence to be at work when an athlete performed well and contributed to the success of his team.

Similarly, the findings suggest that when there is no positive reward, fans perceived the offender even more negatively. This implies, just as expectancy violation theory suggests, that winning is indeed viewed as a positive reward that lessens negative violation valence.

The results support Kim’s (2014) notion that much like Situational Crisis

Communication Theory (SCCT), EVT can be used as a theory for evaluating crisis response. Furthermore, the findings suggest that even though EVT is grounded in interpersonal communication, it can be extended to sports and athlete transgressions.

Additionally, results indicate that athletic performance can function similarly as crisis communications techniques employed by public relations practitioners who aim to mitigate the effect of a transgression.

In conclusion, the findings support that EVT can be a useful theoretical framework for studying athlete crisis, therefore, contributing new understanding to

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existing literature on crisis, image repair, and sports consumer behavior. In that sense, the findings provide meaningful information for academic researchers, as well as practitioners and open the door for future exploration into this complex topic.

Image Repair through Performance

Goffman (1967) famously said, “When a face has been threatened, face-work must be done” (p. 27), referring not to the need for Botox, but rather to the need for a person or entity to engage in a form of image repair in light of a transgression. The present study posited that among the differences is the unique ability for athletes to potentially repair their image following a transgression through their athletic performance in the playing arenas. The central research questions addressed were: does athletic performance influence stakeholder perceptions and consumer behaviors following a transgression? And, furthermore, do team performance and fan identification influence the relationship between athlete performance and stakeholder perceptions or consumer behaviors following a transgression?

The study found that performance influenced the attitudes and supportive behavioral intentions of fans toward the athlete transgressor. Results suggest that all dependent variables in this study aimed at the outcomes toward the athlete (image, reputation, advocacy, supportive behavioral intentions, pWOM, nWOM, CORF) were positively affected when the athlete performed well. After fans learned about the athlete’s positive contributions on the field, their perceived athlete image scores increased considerably. Fans were more likely to advocate for the athlete, had higher supportive behavioral intentions, were more inclined to generate positive word-of- mouth, less likely to generate negative word-of-mouth, and less likely to engage in cutting-off-reflected-failure (CORF) behaviors. This was not the case for the fans

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exposed to information highlighting negative athlete performance. As discussed in the previous section of this chapter, one explanation for this could be violation reward valence (Burgoon, 1978). Fans might excuse the off-field transgression of a successful athlete because he contributes to the accomplishments of the team he plays for and in that sense “does his job” well; after all, the primary goal of any athlete is to win. On the other hand, an athlete who does not perform well on the field hurts his team even further and does not offer the fans sufficient reason to justify their support. These findings might suggest that in a team sport, as investigated in this dissertation, it is not necessarily the winning that propels the image repair discourse of an individual athlete, but rather the contribution of said athlete to his team. Because football is a team sport, and consequently a football player’s performance seldom stands on its own, two types of team performance were considered as potentially affecting the relationship between player performance and fan evaluations. Differences between the effects of player performance were consistent for participants exposed to the successful (winning season) team (Green Bay Packers) and the unsuccessful (losing season) team (Tampa

Bay Buccaneers), thus supporting the null hypothesis. This suggests that while winning might be a predictor of successful image restoration, it is not enough for an athlete to be part of a winning team, rather he or she has to contribute to the team’s success in order to profit from performance as a image discourse strategy. In other words, the reward valence from the overall team performance is not enough to make fans perceive the athlete more favorably following a crisis.

These findings should be viewed with caution provided their oversimplified conceptualization of the sports consumer (fan). Fan identification is a key feature for

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sports consumer behaviors; consequently, this study sought to investigate if and how fan identification affects the relationship between player performance and fan evaluations following a transgression. Not surprisingly, the results of the experiment suggest that the differences between the effects of player performance on fan perceptions are not the same for fans of varying identification level and in some cases that any systematic differences between high and low-identified fans are not the same for each form of player performance, therefore rejecting the null hypothesis. In particular, high-identified fans are more susceptible to change their perceived image depending on athlete performance, whereas it appears for low-identified fans the performance does not really matter at all. The mean scores for perceived athlete image, athlete advocacy, supportive behavioral intentions, nWOM, and CORF all illustrated that positive performance significantly and positively influenced highly identified fans, but did not significantly influence low-identified fans. As expected, low-identified fans in the positive performance manipulation reported lower scores for the previously mentioned depend variables. This was expected based on prior scholarship that suggests higher fan identification would result in more favorable perceptions and consumer behaviors

(Murray & Price, 2009). Interestingly, however, low-identified fans reported significantly higher perceptions and behavioral intentions than did high-identified fans in the negative performance group. This suggests that while high-identified fans do perceive athlete image more positively and would engage in more favorable behaviors following positive athlete performance, they (high ID fans) also perceive athlete image a lot more negatively and would engage in less athlete favorable behaviors than low-identified fans provided a negative athlete performance. So while positive performance can do

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wonders for the athlete image, negative performance appears to be hurting the athlete image repair discourse even more. This finding should be of particular interest to practitioners, because it would suggest that athletes who are not performing at their expected level – which certainly could happen for a number of reasons (e.g., injury, going through a slump) (Lohnheiss & Hill, 2014; Prior et al., 2013) – need to engage in other forms of image repair in order to rehabilitate their reputation and garner back the support of their fans. Similar results were found, for pWOM intentions and perceived reputation; however, there were no differences between high and low-identified fans exposed to the positive athlete performance manipulation.

Results of this dissertation yielded an interaction among player performance, team performance and fan identification. The level of moderation by which player performance differentially affects high and low-identified fans’ nWOM varies depending on team performance. High-identified participants exposed to negative performance of an athlete on a winning team were significantly more likely to engage in negative word- of-mouth than low-identified participants exposed to the same set of independent variables; however, there was no difference in nWOM intentions of high and low- identified fans exposed to the negative performance of an athlete on the losing team.

There were also no differences in nWOM intentions of high and low-identified fans exposed to the positive performance of an athlete on either a winning or losing team.

While there is no prior research that could explain this finding, one reason could be the expectation to win among Packers fans. The Packers are an organization rich in traditions and much success. Green Bay has accumulated 13 NFL Championships, which are the most all-time among all NFL teams, including four Super Bowl wins, 31

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playoffs appearances, and 18 divisional championships since their establishment in

1921. Add to that, the Super Bowl trophy is named after their famous coach Vince

Lombardi and you get a glimpse into just what kind of football dynasty the Green Bay

Packers really are. This might allude to the fact that fans of the Packers have high expectations for the team and its players, and anyone that hurts the team’s potential success (such as the fictitious athlete in this study) will not be tolerated.

The Tampa Bay Buccaneers on the other hand have a much less glamorous résumé that includes only one Super Bowl win, six divisional championships and ten playoff appearances over the course of their 39-year existence, as well as numerous disappointing seasons. Over the course of the last 25 years, the Bucs have had a winning percentage of .500 in only nine seasons. Perhaps due to its history, fans of the

Tampa Bay Buccaneers may have lower expectations of their team and its players; thus, a negative performance by an athlete does not evoke as negative of a reaction as it would for fans of the Green Bay Packers. Of course this analysis is merely a proposition.

Aside from the effects of player transgressions on the athlete image, this dissertation also sought to investigate if and how one player’s transgression might influence the reputation of his team as well as the various patronage intentions of fans toward it, and whether player performance could mitigate any reputation threats.

Results showed that all-in-all athlete crisis did not affect outcomes perceptions and behavioral intentions toward the team. Therefore, it is no surprise that results from phase three of the study indicated that overall player performance did not influence reputation and patronage intentions. This study found no main effect for performance.

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Fan identification was considered as potentially affecting the relationship between player performance and fan evaluations. Findings for the effects of player performance were consistent for participants at different identification levels, thus supporting the null hypothesis. As anticipated there was a main effect for identification, with high-identified fans reporting both a more positive team reputation and higher patronage intentions.

These findings support prior research (Wann & Branscombe, 1994; Murray & Price,

2013) and were true across all three phases (pre-crisis, post-crisis, follow-up). These results, again, could be an indication of the black-sheep effect being at play (Fink, et al.,

2009), with stakeholders rejecting the transgressor’s behavior, shunning him from the rest of the team in an effort to justify their continued support of the organization.

All in all the findings of this dissertation propose that positive player performance following a crisis can mitigate reputation threats and restore public perception of the athlete. While this study is the first of its kind exploring what role athletic performance plays in the image repair discourse of a professional athlete, research findings by crisis researchers investigating non-sports entities’ crises may provide a helpful framework.

The previously published scholarship suggests that corporate associations play a key role in consumer attitudes and purchase intentions (Biehal & Sheinin, 2007). These corporate associations are commonly defined as “memory-based psychological associations and evaluations, ”that form perceived reputation or image in the consumer’s mind (Kim, 2013a, p. 241). One of these associations is known as corporate ability (CA) associations, a memory in the consumer’s mind that assesses an entity’s skills as they relate to producing quality products or services. Brown & Dacin (1997) determined consumers are more likely to respond to corporate ability strategies than

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they are to other strategies when evaluating an entity. In other words, the main influencer of perceived reputation is how well a product or service is perceived. Not surprisingly, these associations also have an impact on consumer evaluations following transgressions; in fact, several researchers have argued that corporate associations mitigate the negative effects of a scandal (Coombs & Holladay, 2006; Klein & Dawar,

2004). Kim (2013a) further advised that negative CA associations are a great disadvantage in crisis because consumers act on the basic premise that the products are good.

While in an organizational setting consumer associations relate to its products and services, this dissertation argued that corporate associations are also present in the sports setting. Rather than being about a physical product or service, however, these associations are grounded in an athlete’s ability to perform in his/her respective sport or position. In the sports world winning or a positive individual performance might be representative of positive (corporate) ability associations, while losing or a bad individual performance might result in negative (corporate) ability associations. Looking at the findings of the present study and comparing them to previous research on CA in crisis, these findings appear to align, thus supporting the researcher’s proposition that

CA associations are present in sports by way of wins and losses or positive and negative performances.

Kruse (1981) posited that traditional crisis repair efforts are only somewhat important for athletes facing crisis because fans are more interested in success and failure rather than the image or character issues of an individual athlete. This sentiment is in line with a famous saying often attributed wrongly to former Packers head coach

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Vince Lombardi, “Winning isn’t everything; it’s the only thing.” While it is beyond the scope of the present study to support these two claims, the findings of the research do lay the foundation for future inquiry of what successful image repair discourse looks like for athlete competitors and whether it can be achieved through anything other than mere crisis communication strategies.

Limitations and Future Research

It was the goal of the researcher to dabble into new terrain within sports and crisis research, and to explore the topic of image repair of athletes from a new perspective. Much research has been conducted on the effects of transgressions on perceived image and supportive behavior, as well as how crises can be mitigated through the use of traditional crisis communication strategies. This dissertation, however, inquired about perhaps the most intuitive and noticeable form of image repair discourse for an athlete: performance. Although this dissertation offers valuable insight into the role and effectiveness of athletic performance following a transgression, it is important to address the limitations of the study. It is hoped that by acknowledging the limitations, the findings will help shape and drive future inquiries and propel research on how other factors aside from traditional crisis communication efforts may influence crisis management practices.

First, the utilization of a convenience sample of college students should be evaluated with caution. Although there were a number of reasons justifying the use of a college sample for this dissertation, such as the fact that those between 18-24 years of age are the biggest group of sports consumers and online sports media (Sporting News

Media, 2014), as well as the common use of college students in previous sports research (e.g., Brown, 2014), many researchers have advised against the use of

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college students citing concern over external validity issues (Winer, 1999; Wells, 1993).

Certainly, this research could be conducted with a more generalizable and representative sample that includes participants from a wide variety of demographics backgrounds and ages. Luckily sports consumers are a broad and diverse group, which lends itself well for research. It would be interesting to learn if more mature study participants would evaluate athlete transgressions differently than college students who frequently operate under a “who am I to judge” mentality. Along similar lines, it should be noted that the sample in this research featured a predominately Caucasian pool of participants; in fact, more than 87% of participants identified themselves as white.

Again, future research should broaden their population to determine if there may be a difference between evaluations of transgressions, image repair efforts, and forgiveness among race.

Second, the present study tests the effects of winning and losing after only one type of transgression. Additional research should examine how effective positive athlete performance or how detrimental negative athlete performance is provided other circumstances. Prior studies in crisis communication research have investigated the differences between illegal and immoral transgressions (Brown, 2014), competency- related versus integrity-related (Lee & Bang, 2013) as well as on and off-field crimes

(Lee, Kwan & Moore, 2015). Future research could explore similar avenues. In addition, it would be advisable to replicate the present study to account for varying degrees of offenses, as well as differences in crisis history (i.e., first offense vs. repeated offense).

For the present study a transgression that evoked a negative fan response but was not extreme enough to be unforgiveable was used; future studies could take a look at the

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transgressions that fell closer to the lower and higher ends of the offensiveness and forgiveness spectrum (e.g., drugs and rape) to see how fans evaluate image repair through performance in those cases.

Future research should also investigate if positive athlete ability associations prior to a transgression could operate as an insurance policy in times of crisis or if positive associations pre-crisis evoke even more negative violation valence.

Considering the findings of this study and anecdotal evidence (e.g., Tiger Woods), positive (corporate) ability associations may directly influence or raise expectancies and could then in return affect negative violation valence felt. More empirical research is necessary to support these claims.

The current investigation only looked at one athlete playing American football.

This begs the question if similar results would be produced had the athlete played a different sport or even played a different position. Upcoming studies could consider the differences between team versus individual sport athletes, athletes who play what most consider masculine sports (e.g., football, MMA) versus athletes who play what are considered gentlemen sports (e.g., golf, tennis), or even the difference between an athlete in a prominent role (e.g., quarterback) versus an athlete who plays a less prominent role (e.g., long snapper) within a team. Additionally, factors such as prior crisis history and prior performance history, as well as corporate social responsibility reputation, could also be studied. Altogether, adjusting the research to incorporate some of these suggestions would provide a more complete outlook on the effects of athletic performance in the image repaid discourse.

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As mentioned previously, there is a stark difference in history and success between the two teams used in this study. Perhaps due to these factors, fans of the

Tampa Bay Buccaneers may have different or lower expectations of their team and its players than do fans of the Green Bay Packers, which are often in contention for playoffs and championship. Further investigation will need to be conducted to support this claim and to show if the finding of a three-way interaction between player performance, team performance and fan identification can be replicated.

Furthermore, there are some methodological limitations that stem from using experimental designs as a method for inquiry. The main weakness credited to experimental studies is the inherently artificiality (Wimmer & Dominick, 2011). While the present study used two real teams in order to preserve a form of reality, it also used a fictitious player in order to eliminate some of the underlying and preexisting biases (e.g., race, prior perceptions). However, by using a fictional athlete some elements of reality are lost. When participants are asked to evaluate a player and report their supportive behaviors toward said player – whom, they do not know – much of how they would truly react is uncertain. While for research purposes, specifically experimental research, it is advisable to eliminate some of the potential confounding variables, doing so does not necessarily explain the whole picture and process. Athletes are frequently credited as celebrities, role models, or ideal spokespersons and they are able to do so because they can evoke a social or parasocial relationship with their fans, which ultimately strengthen the bond between consumer and athlete. By providing a fictional athlete, or simply not stating the name of the player, some of these elements are lost, which then might trigger a different response in the research participants. This certainly is a

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limitation of this research and its subsequent findings and one that should be addressed in following studies. But despite these limitations, experimental studies will likely continue to be the preferred method for future empirical examinations of image repair discourse. However, scholars should make every effort to assure scenarios and manipulation stimuli are as representative and valid as possible. Future research should address these limitations by potentially using a real athlete. Doing so could also further provide additional insight into whether other factors play a role in the evaluation of athletes in crisis, such as the athlete’s race, attractiveness, or status, just to name a few.

Moreover, experimental studies can be troublesome because participants, particularly college students who are frequently asked to participant in research, might be aware of the research setting and, thus, might be more cognizant of its artificial nature. This dissertation presented participants manipulated ESPN articles first detailing an athlete’s transgression and later recapping athletic performance as part of the stimuli. As Brown (2013) pointed out, experimental research studies involving a crisis event and subsequent image repair management, have the inherent issue of being one- dimensional, “it is relatively hard to expect a person’s preconceived ideas about an organization to be damaged by a crisis situation and then be repaired by a crisis response in such a relatively short period of time during the course of the experiment”

(p.110). Rather than offering one crisis management strategy (whether a communication strategy as outlined by SCCT or IRT, or athletic performance), public relations professional likely employ multiple over the course of several days, weeks and even months. Consequently, this imposes a limitation for research, where it is

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challenging to gage how fans authentically react to a transgression “with the singular

“snapshot” that experimental research provides” (Brown, 2013, p. 110).

In addition, future research could examine some supplementary confounding variables and how they affect the process of image restoration and evaluation of the athlete image following transgressions. For example, a study could incorporate

Schlenker, Weigold and Schlenker’s (2008) study on the impact of integrity on admiration and interpersonal judgment and control for integrity versus result-driven personality, as well as moral judgment, or evaluation of crisis responsibility, among others.

In regards to EVT, results demonstrated expectancies are a significant predictor of fan evaluation and response following an athlete transgression though the relationship was not as strong as anticipated. It could be that the relationship would be stronger had participants been exposed to a ‘real’ athlete and their identification with athlete himself had been higher. Furthermore, it should be noted that the findings are limited due to the homogeneity of the sample. Only 44 of the 222 (19.8%) participants in the sample indicated expectancy scores of less than 4 (agree) on the Likert scale; therefore, it is difficult to provide an all-inclusive view. A follow-up study should aim to diversify the sample to include a broader range in participants that have low expectancies; furthermore, it would be wise to consider testing both prescriptive and predictive expectancies, and to evaluate if there are differences between participants exposed to a real athlete versus a fictitious or unidentified one

Finally, while the investigation into image restoration through athletic performance is an important endeavor and should be examined closer in the future, it is

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important to point out that image repair is seldom one dimensional. That being said, future studies should evaluate which combinations of image repair are the most successful. Perhaps a future study could investigate if athletic performance is superior to other forms of image restoration such as traditional crisis communication strategies.

Another investigation could focus on which crisis communication strategy is most successful and which are least successful when coupled with athletic performance.

Regardless of the study’s limitations, the findings do provide some empirical evidence to support previous anecdotal analysis and it is safe to say that the future of crisis research within the sports setting is bright. Because this is a first-known attempt to examine the role of athletic performance in image repair there are limitless opportunities for future research in this realm.

Conclusion

As the sports world continues to grow and awareness of athlete transgressions continues to increase, future research studies will play an important role in extending our understanding and finding ways to combat the damage caused by these personal failings. Until now, the role of athlete performance on image rehabilitation has only been explained anecdotally. This study provided scientific evidence to support the notion that image repair can be affected by the performance on the field or within the playing arena.

In this vein, the present research extended both expectancy violation theory and the crisis literature which had not previously conceptualized athletic performance as a valid mitigation for athlete transgressions. More often than not crisis research solely focuses on the traditional crisis communication strategies that can be employed provided a certain type of transgression. These findings most often suggest that apology is the prime tactic for forgiveness. But in recent years with more and more transgressions of

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actors, politicians, athletes, and other public figures coming to light, these press conferences or statements by the offenders began to sound like a broken record. An article published in the New York Times entitled Seeking Redemption, Sometimes With a Familiar Ring candidly illustrated this phenomena by way of one apology speech effortlessly pieced together from a number of previous statements made in the aftermath of transgressions (Marsh, Parlapiano & Andrews, 2014). This begs the question: are verbal statements, such as apologies, truly the key image repair method or can other factors contribute more to stakeholder forgiveness? Whether performance is a better predictor of fan forgiveness than situational crisis communication strategies is beyond the scope of this study, but it certainly appears that future research should consider performance as another important facet of the image repair discourse of professional athletes.

Furthermore, the results indicate that identification does play a role in how stakeholders evaluate image repair efforts. This finding could be of particular interest to sports consumer researchers who are predominately concerned with understanding how potential crises influence sponsor perceptions and consumer behaviors toward the sponsor. Often sponsors aim to attract a broad range of consumers and are not merely interested in the subgroups – that is, those that are either high or low involved with a team. For example, when Reebok signed Houston Texans defensive end J.J. Watt it was not merely aiming its products at high-identified Houston fans or even high- identified J.J. Watt fans, but rather a broad range of potential costumers. It would be of interest to potential sponsors, sports organizations on the team and league level, as well as the athletes, just how stakeholders within each group react to transgressions in

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order to get a more comprehensive view of the impact of crises on the affiliated entities and the offenders themselves.

As mentioned in the introduction, sports crises are distinctive for a number of reasons, including the distinctive differences between sports organizations compared to non-sports organizations, such as the inherently different levels of attachment of stakeholders, and certainly the unusual standards of its employees. Unlike in other industries, the chief employees of the sports industry are most often celebrated role models, frequently placed on a pedestal by society. They are loved and celebrated, held to tremendous standards both on and off the field, and their every action is under a microscope. Their fall from grace after a transgression is often one of the hardest and one that attracts the most media attention. This provides ample opportunities for future scholarship.

While the study’s findings open a new door for crisis research by pointing out the importance of studying athlete crises, and the effects of player performance on fan perceptions and consumer outcomes, its results may not be generalized. There are millions of sports fans around the globe all from very different backgrounds and with different expectancies and attitudes toward athletes, as well as evaluations of transgressions, and what it would take for subsequent forgiveness. Future research will unquestionably need to investigate these topics and others mentioned in the previous section in more detail. Furthermore, while experimental studies will continue to be a worthy methodology to shed light on these topics, forthcoming scholarship should account for some of the limitations of the present study.

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In conclusion, the findings of this dissertation suggest that there is an opportunity for athlete offenders to repair their image by what they have trained to do their entire lives: win, or at least contribute to the overall success of the team they play for. And while the world may have been outraged by the 2013 Tiger Woods Nike ad declaring,

“Winning takes care of everything,” the results of the present study show that it cannot simply be put in the “false advertising” category.

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APPENDIX A INSTITUTIONAL REVIEW BOARD APPROVAL

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APPENDIX B MEASUREMENTS

Sports Spectator Identification Scale by Wann & Branscombe, 1993 7-item eight point Likert scale (1 = low identification; 8 = high identification) 1. How important to you is it that the (team name) wins? 2. How strongly do you see yourself as a fan of the (team name)? 3. How strongly do your friends see you as a fan of the (team name)? 4. During the season, how closely do you follow the (team name) via any of the following: a) in person or television, b) radio, or c) television news or a newspaper? 5. How important is being a fan of the (team name) to you? 6. How much do you dislike the (team name’s) greatest rivals? 7. How often do you display the (team name’s) name or insignia at your place of work, where you live, or on your clothing?

Expectancy Scale modified from Burgoon (1993) & Kim (2014) 3-item seven point Likert scale (1 = strongly disagree; 5 = strongly agree) 1. Athletes should live up to responsibility to society 2. Athletes should not harm their teams in any way (reverse coded) 3. Athletes should engage in behavior(s) that reflect social norms

Violation Valence Scale modified from Afifi and Metts (1998) & Kim (2014) 4-item seven point Likert scale (1 = strongly disagree; 5 = strongly agree) 1. The athlete’s transgression made me feel bad about the athlete 2. The athlete’s transgression made me feel that the athlete does not care about others 3. The athlete’s transgression made me feel negative about the athlete 4. The athlete disappointed me in a great deal

Athlete Image Scale modified from Choi and Rifon’s (2007) and Brown (2014) 4-item seven point Likert scale (1 = strongly disagree; 7 = strongly agree) 1. I believe this athlete is wise after reading this article 2. I believe this athlete is pleasant after reading this article 3. I believe I could be comfortable around this athlete after reading this article 4. I believe this athlete is sophisticated after reading this article.

Athlete Image/Reputation Scale modified from Kent & Walker (2009) 3-item seven point Likert scale (1 = strongly disagree; 7 = strongly agree) 1. (Athlete) sets an example of how a professional athlete should be 2. I Would believe in (athlete) if he were under media attack 3. I have admiration and respect for (athlete)

CORFing Behavior Scale by Arai (2014) 3-item seven point Likert scale (1 = strongly disagree; 7 = strongly agree) 1. I do not want to be associated with the athlete 2. I will not wear clothing or jerseys that are associated with the athlete 3. I would like to disconnect myself from the athlete

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Supportive Behavioral Intentions Scale by Brown (2012) 5-item seven point Likert scale (1 = strongly disagree; 7 = strongly agree) 1. After reading this article, I would watch this athlete’s game on television 2. After reading this article, I would discuss this athlete in a positive light 3. After reading this article, I would consume sports news that discussed this athlete 4. After reading this article, I would attend this athlete’s games 5. After reading this article, I would buy this athlete’s paraphernalia (jerseys, T- shirts, etc.)

Athlete Advocacy Scale by Arai (2014) 3-item seven point Likert scale (1 = strongly disagree; 7 = strongly agree) 1. I will maintain my support for the athlete 2. I would post messages online to show my support for the athlete 3. I am willing to defend the athlete publicly, even if it causes controversy.

Positive Word of Mouth (pWOM) by Brown (2014) 6-item seven point Likert scale (1 = strongly disagree; 7 = strongly agree) 1. I would publically say and/or post messages on my social media websites encouraging people to support this athlete 2. I would publically say and/or post messages on social media websites saying positive things about this athlete to other people 3. I would publically say and/or post messages on social media websites encouraging others to cheer for this athlete during games. 4. I would publically say and/or post messages on social media websites to make sure that others know I support this team 5. I would say and/or post messages on social media websites to support this athlete 6. I would publically say and/or post on social media websites the positive aspects of this athlete to those who criticize him.

Negative Word of Mouth (nWOM) by Coombs & Holladay (2007) 3-item seven point Likert scale (1 = strongly disagree; 7 = strongly agree) 1. I would encourage people not to support this athlete 2. I would say negative things about this athlete to other people 3. I would not recommend someone to cheer for this athlete during games.

Team Reputation Scale by Walker & Kent (2009) 3-item seven point Likert scale (1 = strongly disagree; 7 = strongly agree) 1. (Team) sets an example of how an NFL organization should be run. 2. I would believe in the (team) if it were under media attack 3. I have admiration and respect for the (team).

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Patronage Intentions Scale by Walker & Kent (2009) 13-item seven point Likert scale (1 = strongly disagree; 7 = strongly agree) Domain – Repeat Purchase 1. I will attend another game being played by this team in the near future 2. I will attend more games being played by this team in the next few years 3. I will attend a game being played by this team in the next home series/game 4. I will attend another game being played by this team this season Domain – Word of Mouth 1. I will speak favorably of this organization to others 2. I will encourage others to attend this team’s games 3. I will encourage others to support this organization Domain – Merchandise Consumption 1. I will buy this organization’s clothing (T-shirts, caps, etc.) in the future 2. I will buy this organizations merchandise 3. I will purchase this organization’s souvenirs Domain – Media Consumption 1. I will read about this organization on traditional or online platforms 2. I will visit this organization’s website for information on the team 3. I will watch sports broadcasts on the local TV news for information about the organization

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APPENDIX C CONSENT

Participant Consent Form

This study is being conducted in an effort to understand how sports fans evaluate NFL players and their respective NFL teams.

Your participation is completely voluntary. If you desire to withdraw from the survey once you start, please close your Internet browser and notify the research investigator via email: [email protected]. You do not have to answer any questions that make you uncomfortable.

Procedures This dissertation entails three phases consisting of multiple questionnaires, which will be administered over the course of the next couple of weeks. Each questionnaire will take approximately 20 minutes or less to complete. You are asked to please take part and complete all three phases.

Risks and Benefits No risks are anticipated from your participation in this study. There are no direct benefits for participants. However, it is hoped that through your participation, the principal researcher will learn more about your expectancies of professional athletes. Your personal information will not be shared with any third parties.

Confidentiality Your responses will be kept confidential and will not be released in any individually identifiable form, unless otherwise required by law. As a technology, Internet communications may be insecure and there is a limit to the confidentiality that can be guaranteed. However, once the researcher receives the materials, standard confidentiality procedures will be employed. All questionnaires will be concealed, and no one other than the primary investigators and course instructor listed below will have access to them. The data collected will be stored in the HIPPA-compliant, Qualtrics- secure database.

Questions about the Research This research has been approved by the University of Florida’s Institutional Review Board 02 (Protocol # 2015-U-0147).

If you have any further questions now or during the course of this study, please feel free to contact the researcher: Annelie Schmittel, Department of Graduate Studies, Weimer Hall 2040, University of Florida, College of Journalism and Communications, Gainesville, FL, 32611; email address: [email protected].

Supervising Researcher: Dr. Debbie Treise, Department of Graduate Studies, College of Journalism and Communications, 2012 Weimer Hall, University of Florida, Gainesville, FL, 32611; Phone: (352) 392-5059; email address: [email protected]

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Questions or concerns about the research participants' rights can be directed to the IRB02 office, PO Box 112250, University of Florida, Gainesville, FL 32611-2250; phone (352) 392-0433.

If you agree to participate, please check the "yes" button below and follow the instructions. By selecting "yes" below, you are providing your consent. Please print a copy of this page for your records.

I have read, understood, and printed a copy of, the above consent form and desire of my own free will to participate in this study.

 Yes

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APPENDIX D ESPN ARTICLES

Player Spotlight: Packers

NFL Player Spotlight: Packers’ Tight End [Player Name]

ESPN.com Staff 24. September 2014 The position may be one of the last ones drafted for fantasy owners, but if ______has his way, a lot more people are going to place a high value on tight ends.

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After a modest few years in the NFL, the Green Bay Packers’ starting tight end is emerging as one of the top tight ends in the league. In four weeks this season, he already has 234 receiving yards and one touchdown (last year, he had 586 yards and no touchdowns).

“I’ve just put in a lot of work in the off-season, really trying to developing not only my pass-catching and blocking, but really my route-running,” he said. “And Aaron [Rodgers] is just doing a really nice job distributing the ball to all the guys—receivers, backs, and the tight end. But bottom line is if I’m not open, he’s not going to throw the ball, so that’s why I really worked on crisp routes and finding space.”

______has steadily improved since joining the Packers in 2012 after being a fourth-round pick in 2011. He’s always been considered a solid blocker and situational tight end.

“______hasn’t been a guy that you think of being in the ranks of the elite tight ends we’ve seen, like Tony Gonzalez or anything,” said ESPN’s NFL expert Chris Mortensen. “But he’s always been a really good guy, works hard, puts in time, well-liked by his teammates.”

In fact, ______has been a very visible part of the Green Bay community, working with a number of charities, such as the NFL’s Play60 campaign and others. But it’s what’s happening on the field that people have started really noticing.

Packers’ head coach Mike McCarthy said: “This is one of the keys to our offense clicking right now. The fact that we know that we can get the ball downfield to ______, whether it’s eight yards to get a first down or even stretch the field a bit is a big plus.”

______’s speed and size makes him a difficult matchup, in that he can move better than most linebackers but is typically bigger and stronger than defensive backs.

“Everybody talks about Gronk,” said former NFL tight end and CBS analyst Shannon Sharpe, “and they should because Gronk really stands out as a tight end. But if ______keeps progressing the way he is, I think he’s going to be talked about in the same conversations as some of the elite tight ends in the league.”

______set the tone right from the start of the season with a 7-catch, 103-yard, 1-touchdown performance in the opening week. That, he says, was important for building confidence and setting up a good year.

“We all just want to win,” he said. “All the guys here. So I just want to make sure I’m someone that Aaron and the whole team can trust. That means catching, blocking, even trying to draw some attention off the other guys so they get more balls. You know how it is with tight ends, you don’t hear about us much, and that’s ok. I want to get in the end zone, sure, but if I can help any of our guys get there, that’s what matters.”

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Player Spotlight: Buccaneers

NFL Player Spotlight: Buccaneers’ Tight End [Player Name]

ESPN.com Staff 24. September 2014 The position may be one of the last ones drafted for fantasy owners, but if ______has his way, a lot more people are going to place a high value on tight ends.

After a modest few years in the NFL, the Tampa Bay Buccaneers’ starting tight end is emerging as one of the top tight ends in the league. In four weeks this season, he already has 234 receiving yards and one touchdown (last year, he had 586 yards and no touchdowns).

“I’ve just put in a lot of work in the off-season, really trying to developing not only my pass-catching and blocking, but really my route-running,” he said. “And Josh [McCown] is just doing a really nice job distributing the ball to all the guys—receivers, backs, and the tight end. But bottom line is if I’m not open, he’s not going to throw the ball, so that’s why I really worked on crisp routes and finding space.”

______has steadily improved since joining the Buccaneers in 2012 after being a fourth-round pick in 2011. He’s always been considered a solid blocker and situational tight end.

In fact, ______has been a very visible part of the Tampa Bay community, working with a number of charities, such as the

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NFL’s Play60 campaign and others. But it’s what’s happening on the field that people have started really noticing.

Buccaneers’ head coach Lovie Smith said: “This is one of the keys to our offense clicking right now. The fact that we know that we can get the ball downfield to ______, whether it’s eight yards to get a first down or even stretch the field a bit is a big plus.”

______’s speed and size makes him a difficult matchup, in that he can move better than most linebackers but is typically bigger and stronger than defensive backs.

“Everybody talks about Gronk,” said former NFL tight end and CBS analyst Shannon Sharpe, “and they should because Gronk really stands out as a tight end. But if _____ keeps progressing the way he is, I think he’s going to be talked about in the same conversations as some of the elite tight ends in the league.”

“We all just want to win,” he said. “All the guys here. So I just want to make sure I’m someone that Josh and the whole team can trust. That means catching, blocking, even trying to draw some attention off the other guys so they get more balls. You know how it is with tight ends, you don’t hear about us much, and that’s ok. I want to get in the end zone, sure, but if I can help any of our guys get there, that’s what matters.”

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Transgression: Packers

Packers Fan Punched by Packers’ Player; No Arrest Yet

Starting Green Bay Packers’ tight end ______was involved in a fight after Sunday’s 27-24 win against Miami, according to police. Police have not yet arrested or charged ______with any crimes, pending further investigation, but police confirmed that he was involved in an incident that took place in the Sun Life stadium parking lot.

Witnesses told ESPN.com that the fan who was struck had visible injuries to the head. They said that they did not see any provocation by the fan in the incident, such as taunting or yelling, but they did say that ______appeared agitated by the crowd formed in the stadium lot.

Police did not comment on the incident, but did confirm that “an incident took place in the stadium parking lot involving a Packers player and a fan wearing a Packers jersey.” And police confirmed that the injuries of a fan (who was a man appearing to be in his mid-30s) appeared to be non-serious, though he was taken to the hospital for further information.

A Packers’ spokesman said, “We are looking into an alleged incident that happened approximately one hour after Sunday’s game and we are fully cooperating with the police.”

______had 1 catch for 13 yards in Sunday’s game.

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Transgression: Buccaneers

Bucs Fan Punched by Bucs’ Player; No Arrest Yet

Starting Tampa Bay Buccaneers’ tight end ______was involved in a fight after Sunday’s 37- 31 loss to New Orleans, according to police. Police have not yet arrested or charged ______with any crimes, pending further investigation, but police confirmed that he was involved in an incident that took place in the Mercedes-Benz Superdome parking lot.

Police did not comment on the incident, but did confirm that “an incident took place in the stadium parking lot involving a Buccaneers player and a fan wearing a Buccaneers’ jersey.” And police confirmed that the injuries of a fan (who was a man appearing to be in his mid-30s) appeared to be non-serious, though he was taken to the hospital for further information.

A Buccaneers’ spokesman said, “We are looking into an alleged incident that happened approximately one hour after Sunday’s game and we are fully cooperating with the police.”

______had 1 catch for 13 yards in Sunday’s game.

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Manipulation 1 Win/Positive

Packers Season Recap

ESPN.com Staff 22. January 2015 As Aaron Rodgers cleaned out his locker yesterday, he packed the last of his belongings into a green duffel bag and looked up at reporters. “Of course, there’s only one way we all want to finish a season,” he said. “And that’s with a ring and a trophy, not a tee time for tomorrow.”

The Green Bay Packers (12-4) were beat by the Seattle Seahawks (15-1) Sunday in a thrilling overtime NFC Championship Game that featured six lead changes, and while most players pointed to the disappointment of not making it to the Super Bowl, they also made sure to echo the sentiment that the season wasn’t all bad.

“We certainly did a lot of really good things—the kinds of things that Packers’ fans are used to seeing. Lots of points, lots of wins, lots of really good football,” Rodgers said. Rodgers finished as the league MVP, leading an offense that scored 486 points (30.4 per game), which led the league, and finished third in point differential, allowing 21.8 points per game.

After losing two of their first three games, the Packers bounced back with strong team and individual performances. “The Packers played some of their best football this season,” said ESPN NFL expert Chris Mortensen. “It’s a shame we won’t see them in the Super Bowl because they are undoubtedly one of the most talented teams in the league on both sides of the ball.” Besides Rodgers’ top-tier year, the Packers had six other players named to the Pro Bowl—WR Randall Cobb, FB John Kuhn, LB Clay Matthews, WR Jordy Nelson, CB Sam Shields and G Josh Sitton. Rodgers will sit out the Pro Bowl with a calf injury.

“We would have liked to have led the league in defense,” Matthews said, “but the bottom line is that we won a lot of games, and many times we did so decisively. This just makes us hungrier to come back and take care of business next year.”

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In addition, the Packers coaching staff seemed pleased with the emergence of tight end ______as a key component to the offense. ______finished with an impressive 682 receiving yards and three touchdowns, but really established himself as a strong blocker to open up the running game.

“I said it in the beginning of the season and I’ll say it again, ______has the talent to be among the elite tight ends,” said former NFL tight end and CBS analyst Shannon Sharpe. “His numbers and performances this season clearly confirm that.”

“______was able to bounce back really nicely after a distraction early in the season to open up the offense,” head coach Mike McCarthy said of ______, who was involved in a now-resolved off-the- field incident in which he was accused of punching a fan. “As we think about next year, there’s no doubt in my mind that he’ll be a big part of the offense.”

The Packers’ biggest holes next year will be on defense with a several players eligible for free agency.

“It’s no surprise that we really want to toughen up the middle of our defense and continue to get pressure on opposing quarterbacks,” McCarthy said. “When you can do that, you win a lot of ballgames.”

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Manipulation 2 Win/Negative

Packers Season Recap

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In addition, the Packers coaching staff indicated it had hoped for more production from the tight end position. ______finished with only 321 receiving yards and 1 touchdown, after starting the season with a lot of promise.

“We can’t drop passes and miss blocking assignments if we want to win important ballgames,” said head coach Mike McCarthy, likely alluding to ______, who was involved in a now-resolved off-the-field incident in which he was accused of punching a fan. “We have to clean up those mistakes if want to win championships.”

“To me ______is one of the biggest disappointments of the year,” said former NFL tight end and CBS analyst Shannon Sharpe. “His numbers this season are lower than they should be for a guy like him. He has the talent, but he’s just not using it.”

The Packers’ biggest holes next year will be on defense with a several players eligible for free agency.

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Manipulation 3 Loss/Positive

Tampa Bay Season Recap

ESPN.com Staff 30. December, 2014 As Josh McCown cleaned out his locker yesterday, he packed the last of his belongings into a red duffel bag and looked up at reporters. “There’s not much to say after a year like this,” he said. “Obviously, we expected more. The fans expected more. We wanted to win—and win a lot.”

Coming off a regular-season loss to finish the regular season 2-14, the Buccaneers finished near last in total points (they scored 277 or 17.3 per game, which ranked 29th our of 32 teams). Plus, they allowed 410 points, which also ranked near the bottom of all teams. The only wins came against the Steelers (27- 24) and the Redskins (27-7).

“I don’t think you can blame any one factor,” McCown said. “The offense didn’t perform well. The defense didn’t perform well. Heck, we even had a couple of bad long snaps. Nobody is happy, and we have to spend the off-season trying to figure out how we can get better—not just in terms of strategy and execution, but also in terms of personnel.”

DT Gerald McCoy was the only Tampa Bay player to make the Pro Bowl.

“We have to stop the ball. That’s it. Have to stop the ball. You can’t give up all those yards and expect to win. I hope we find a way to play with more energy and more smarts so we can give our offense a chance,” McCoy said.

One player who the coaching staff seemed pleased with was tight end ______, who emerged as one of the bright spots of the offense. ______finished with an impressive 682 receiving yards and three touchdowns, but really established himself as a strong blocker to open up the running game.

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“______was able to bounce back really nicely after a distraction early in the season to open up the offense,” head coach Lovie Smith said of ______, who was involved in a now-resolved off-the-field incident in which he was accused of punching a fan. “As we think about next year, there’s no doubt in my mind that he’ll be a big part of the offense.”

The Buccaneers’ biggest holes next year will be on defense with a several players eligible for free agency.

“It’s no surprise that we really want to toughen up the middle of our defense and continue to get pressure on opposing quarterbacks,” Smith said. “When you can do that, you win a lot of ballgames.”

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Manipulation 4 Loss/Negative

Tampa Bay Season Recap

ESPN.com Staff 30. December, 2014 As Josh McCown cleaned out his locker yesterday, he took the last of his belongings into a red duffel bag and looked up at reporters. “There’s not much to say after a year like this,” he said. “Obviously, we expected more. The fans expected more. We wanted to win—and win a lot.”

Coming off a regular-season loss to finish the regular season 2-14, the Buccaneers finished near last in total points (they scored 277 or 17.3 per game, which ranked 29th out of 32 teams). Plus, they allowed 410 points, which also ranked near the bottom of all teams. The only wins came against the Steelers (27- 24) and the Redskins (27-7).

DT Gerald McCoy was the only Tampa Bay player to make the Pro Bowl.

In addition, the Buccaneers’ coaching staff indicated it had hoped for more production from the tight end position. ______finished with only 321 receiving yards and 1 touchdown, after starting the season with a lot of promise.

“We can’t drop passes and miss blocking assignments if we want to win important ballgames,” said head coach Lovie Smith, likely alluding to ______, who was involved in a now-resolved off-the-field

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incident in which he was accused of punching a fan. “We have to clean up those mistakes if want to win championships.”

The Buccaneers’ biggest holes next year will be on defense with a several players eligible for free agency.

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APPENDIX E PRELIMINARY SURVEY

1. What is your gender?  Male (1)  Female (2)

2. How old are you?  Under 18 (1)  18-24 (2)  25-34 (3)  35-44 (4)  45 or older (5)

3. What college are you in?  College of Agricultural and Life Sciences (1)  College of the Arts (2)  Warrington College of Business Administration (3)  College of Dentistry (4)  College of Design, Construction and Planning (5)  College of Education (6)  College of Engineering (7)  College of Health and Human Performance (8)  College of Journalism and Communications (9)  Levin College of Law (10)  College of Liberal Arts and Sciences (11)  College of Medicine (12)  College of Nursing (13)  College of Pharmacy (14)  College of Public Health and Health Professions (15)  College of Veterinary Medicine (16)

You will be asked to answer a number of questions regarding your perceptions of and reactions toward NFL teams and their performances this season. Please answer each of the following questions truthfully and to the best of your ability.

4. Which is your favorite NFL team?  Arizona Cardinals (1)  Cleveland Browns (8)  Atlanta Falcons (2)  Dallas Cowboys (9)  Baltimore Ravens (3)  Denver Broncos (10)  Buffalo Bills (4)  Detroit Lions (11)  Carolina Panthers (5)  Green Bay Packers (12)  Chicago Bears (6)  Houston Texans (13)  Cincinnati Bengals (7)  Indianapolis Colts (14)

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 Jacksonville Jaguars (15)  St. Louis Rams (26)  Kansas City Chiefs (16)  San Diego Chargers (27)  Miami Dolphins (17)  San Francisco 49ers (28)  Minnesota Vikings (18)  Seattle Seahawks (29)  New England Patriots (19)  Tampa Bay Buccaneers  New Orleans Saints (20) (30)  New York Giants (21)  Tennessee Titans (31)  New York Jets (22)  Washington Redskins (32)  Oakland Raiders (23)  I don't have a favorite NFL  Philadelphia Eagles (24) team (33)  Pittsburgh Steelers (25)

5. I believe the win/loss record of my favorite team is a good indication of how well my team did this season  Agree (1)  Somewhat agree (2)  Neither agree nor disagree (3)  Somewhat disagree (4)  Disagree (5)

6. I believe my favorite NFL team had a successful, satisfying and/or winning season if the overall regular season record is at least ______(i.e. 15 wins - 1 loss)  16-0 (1)  15-1 (2)  14-2 (3)  13-3 (4)  12-4 (5)  11-5 (6)  10-6 (7)  9-7 (8)  8-8 (9)  7-9 (10)  6-10 (11)  5-11 (12)  4-12 (13)  3-13 (14)  2-14 (15)  1-15 (16)  0-16 (17)  I don't care about the record as long as we at least make it to playoffs (18)  I expect my team to at least make it to the divisional championship (19)  It's not a successful season unless we win the Super Bowl (20)

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7. I believe my favorite NFL team had an unsuccessful, unsatisfying and/or losing season if the overall regular season record is ____ or worse. (i.e. 2 wins - 14 losses)  1-15 (1)  2-14 (2)  3-13 (3)  4-12 (4)  5-11 (5)  6-10 (6)  7-9 (7)  8-8 (8)  9-7 (9)  10-6 (10)  11-5 (11)  12-4 (12)  13-3 (13)  14-2 (14)  15-1 (15)  16-0 (16)  If we don't make it to the playoffs it is an unsuccessful season (17)  If we don't make it to the divisional championship I consider it an unsatisfying season (18)  It's an unsuccessful season unless we win the Super Bowl (19)

8. The Green Bay Packers finished their regular season with a 12-4 regular season record, winning the NFC North. They went on to play the Seattle Seahawks in the NFC Championship and barely missed their chance at a Super Bowl appearance losing in overtime 28-22. How successful would you say the Packers' season was?  Very successful (1)  Successful (2)  Somewhat successful (3)  Undecided (4)  Somewhat unsuccessful (5)  Unsuccessful (6)  Very unsuccessful (7)

9. The Miami Dolphins finished their regular season with an 8-8 regular season record, finishing 3rd in the AFC East and missing the playoffs. How successful would you say the Dolphins’ season was?  Very successful (1)  Successful (2)  Somewhat successful (3)  Undecided (4)  Somewhat unsuccessful (5)

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 Unsuccessful (6)  Very unsuccessful (7)

10. The Tampa Bay Buccaneers ended their regular season with an 2-14 regular season record, finishing with the worst record in the league. How successful would you say the Buccaneers’ season was?  Very successful (1)  Successful (2)  Somewhat successful (3)  Undecided (4)  Somewhat unsuccessful (5)  Unsuccessful (6)  Very unsuccessful (7)

11. How do you evaluate the performance of an offensive player on your team? In other words, how do you determine if he had a good or a bad season? Particularly as it relates to the tight end position?

I am interested in determining the perceptions of athlete transgressions from the fan perspective. You will now read a brief NFL player profile, followed by a small number of news excerpts detailing a handful of transgressions by the player. Subsequently you will be prompted to answer a series of questions relating to what you read and your personal evaluations of the athlete transgressions. Please answer honestly and to the best of your abilities.

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Player Spotlight: Tight End Collin Smith* NFL.com Staff 30. December, 2014

(*Name changed for the purpose of this study)

Name: Collin J. Smith Date of Birth: January 17, 1989 Drafted: 4th round, 2012 Draft Position: Tight End Team: [redacted for the purpose of this study]

Notable statistics: Top 20 tight end with 436 receiving yards in his rookie year. This season, he upped his receiving yards to 644 and five touchdowns in the regular season, landing him in the Top 15 among tight ends in both receiving and scoring.

Charitable Work: · Founder of The CS Foundation – which partners with various organizations to provide opportunities for character and leadership development, giving youths the tools to positively impact their family, school and community. · Active member and advocate of the NFL Play 60 movement · Very involved in community initiatives including Boys & Girls Club visits, Make-A-Wish visits, Drive for Life and Hometown Huddle.

Personal: Smith married his girlfriend Kelsey last summer. His younger brother James is a redshirt freshman fullback at [Division I program-redacted for confidentiality purposes].

What other say about him: “One of the most talented young tight ends in the league.“ --Tony Gonzalez, former tight end Atlanta Falcons

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“With players like Rob Gronkowski and Jason Witten we are seeing a new NFL trend. More and more tight ends are being called upon to be effective and touchdown scoring receivers while at the same time protecting their quarterback. Smith is one of the few tight ends that can do both” – ESPN analyst Cris Carter

“Smith has established himself as a close to every-down tight end which means increased responsibilities. He’s been really effective for us on and off the field.” – Head Coach of the [team name redacted]

12. Q17 How favorable is your opinion about the athlete you just read about?  Very favorable (1)  Favorable (2)  Somewhat favorable (3)  Undecided (4)  Somewhat unfavorable (5)  Unfavorable (6)  Very unfavorable (7)

13. How likely are you to support this athlete (i.e. verbal support, purchase of jersey) provided the above information?  Very Unlikely (1)  Unlikely (2)  Somewhat Unlikely (3)  Undecided (4)  Somewhat Likely (5)  Likely (6)  Very Likely (7)

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SMITH CHARGED WITH RAPE

ESPN.com Staff

Prosecutors filed charges of rape and battery resulting in bodily injury against NFL tight end Collin Smith on Friday.

According to the police report the incident occurred on December 20, after the victim and Collins met in a downtown bar. The woman said Collins, her, and a group of friends went to a few different establishments before all ending up at an "unknown apartment". She alleges that it was there that Smith began to make advances toward her that eventually lead to a physical altercation between the two before he sexually assaulted her. According to the police statement, the woman left the apartment and called the police.

Smith denied the charges in a statement released by his lawyer and plead not guilty yesterday.

14. Please indicate how you feel about the behavior of the athlete  Right (1)  Somewhat right (2)  Neutral (3)  Somewhat wrong (4)  Wrong (5)

15. Please indicate how you feel about the athlete’s transgression you read about in the previous article:  Not offensive (1)  Slightly offensive (2)  Moderately offensive (3)  Very offensive (4)  Severely offensive (5)

16. How ethical do you think the behavior of the athlete was?

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 Ethical (1)  Somewhat ethical (2)  Moderate (3)  Somewhat unethical (4)  Unethical (5)

17. How likely are you to support this athlete (i.e. verbal support, purchase of jersey) provided the previous information?  Very Unlikely (1)  Unlikely (2)  Somewhat Unlikely (3)  Undecided (4)  Somewhat Likely (5)  Likely (6)  Very Likely (7)

18. How likely are you to forgive the athlete mentioned in the previous article?  Very Unlikely I will forgive (1)  Unlikely I will forgive (2)  Somewhat Unlikely I will forgive (3)  Undecided (4)  Somewhat Likely I will forgive (5)  Likely I will forgive (6)  Very Likely I will forgive (7)

19. Please indicate all that apply: “I would be most likely to forgive the athlete if... “  "... the athlete apologized" (1)  "... the athlete performed well on the field" (2)  "... the athlete compensated the victims" (if applicable) (3)  "... the athlete sought help (i.e. therapy, spiritual guidance...)" (4)  "... the athlete was properly punished (by league or law enforcement)" (5)  "I would never forgive the athlete, no matter the corrective actions undertaken by him" (6)

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11ESPN.com Staff SMITH CHARGED WITH POSSESSION Possession of marijuana charges were filed against Collin Smith on Thursday, after he was pulled over by police. A traffic officer said he spotted Smith’s black Range Rover after smelling marijuana. According to the police complaint, a medium-sized bag with about 20g of marijuana was recovered in the SUV. Smith also faces potential DUI charges for driving under the influence of marijuana.

ESPN.com Staff SMITH UNDER INVESTIGATION FOR SELLING PAIN MEDICATION NFL tight end Collin Smith is under investigation for allegedly selling prescription pain killers to teammates. Details of the investigation into Smith’s case are unclear at this point, but Federal drug agents have been conducting surprise inspections of NFL teams as part of an ongoing investigation into prescription drug abuse in the league. The inspections, which typically entail bag searches and questioning of players and team doctors by Drug Enforcement Administration agents, are based on the suspicion that NFL teams and players dispense drugs illegally to keep players on the field in violation of the Controlled Substances Act, according to a senior law enforcement official with knowledge of the investigation. Smith could face charges and a suspension by the league.

ESPN.com Staff SMITH ACCUSED OF AFFAIR Tight end Collin Smith has been accused of having an affair with a teammate's wife. Reports of the affair have surfaced on several website and multiple sources close to the team confirm having witnessed a recent argument between Smith and a teammate. Smith himself has been married for 9 months. He could not be reached for comment.

ESPN.com Staff SMITH PUNCHES FAN A brawl during Tuesday’s practice featured a brief altercation between a fan and tight end Collin Smith. The unidentified fan was reportedly struck in the face by the player. Spectators witnessing the brawl say that the fan had taunted Smith as he headed to the locker room during practice; Smith reacted by confronting the fan and eventually struck him several times before team staff and bystanders broke up the altercation. Medical staff immediately tended to the fan, his injuries appeared non-life threatening. No charges had been filed as of this morning. Smith could not be reached for comment.

11 After each transgression Questions 14 through 19 were asked. They were eliminated from this appendix for formatting purposes. Formatting of the articles was the same as seen previously in the “Smith Charged with Rape” article

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ESPN.com Staff TIGHT END NOT PAYING CHILD SUPPORT NFL tight end Collin Smith has not paid child support for five months, according to his ex-girlfriend and mother of 16-months old daughter Emma. Brittany Landon, the mother of the child said she has been faced with financial hardship because Smith refuses to pay child support. Landon’s lawyer delivered a letter to the NFL on Wednesday asking the league to investigate if Smith has violated its Personal Conduct Policy by failing to pay child support. Smith recently signed a 3-year $6 million dollar contract.

ESPN.com Staff SMITH UNDER INVESTIGATION FOR CHEATING Tight end Collin Smith is under investigation after accusations of illegal sports gambling have been brought to the teams and federal authorities’ attention. Smith allegedly placed several bets on the point spread on a number of his own games, a violation of Title 18, United States Code, Section 371. Police are investigating whether Smith was involved in a larger scheme to fix the point spread for a number of games this season. If this is the case, he could face up to five years in prison and a $250,000 fine. Smith has been suspended with pay pending the investigation.

ESPN.com Staff TIGHT END CHARED WITH POSSESSION OF WEAPONS Tight end Collin Smith is charged with two felony counts of illegal possession of an assault weapon, prosecutors said in a release Wednesday. The charges come six months after officers entered Smith’s house after receiving a noise violation from neighbors following a house party. Police located two illegal assault weapons when entering the premises. Smith is expected to surrender himself to authorities later this month. His agent could not be reached for comment.

ESPN.com Staff SMITH UNDER CRITICISM FOR ANTI-GAY COMMENTS Tight end Collin Smith has come under fire following an incident on Saturday where he sent out a tweet critical of gay athletes. Smith sent out several tweet critical of gay athletes in the NFL after seeing Michael Sam, the first openly gay player to enter the NFL draft was selected with the 249th overall pick in the seventh and final round of the NFL Draft, was caught on camera kissing his boyfriend in celebration. Just after the kiss happened, Smith tweeted, 'Horrible', 'not in my locker room' and 'we can't let this enter the league'.

ESPN.com Staff SEXUAL HARRASSEMENT SUIT FILED AGAINST TIGHT END A former NFL public relations staff member filed a sexual harassment suit Monday against Collin Smith, charging she sexually harassed and had to leave her job after she spurned the tight ends' crude advancements via text messages. In papers filed with the court, the woman says Smith made his move last summer during training camp. Allegedly the player sent text messages prompting the woman to get together for drinks

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and "a sleepover". After repeatedly asserting she did not want to meet, Smith allegedly called the woman a "slut no-one wants". The woman says she quit her job one-month later to avoid further harassment.

ESPN.com Staff TIGHT END ARRESTED FOR SOLICITATION Tight end Collin Smith has been jailed and accused of soliciting two prostitutes. According to the arrest report, Smith approached two female undercover officers during a sting and offered $200 for sex. He reportedly said to the officer, “You got condoms? I need to get some.” The police report further reads that Smith admitted to having paid for sex in the past. Smith was released on a $5000 dollar bond. He could not be reached for comment.

ESPN.com Staff ANOTHER BOUNTY-GATE? Tight end Collin Smith is under investigation by the league for allegedly deliberately trying to hurt his opponents. Sources within the NFL say they have received information that Smith had bragged to family and friends about his scheme to cause “career ending injuries.” The investigation comes at the tail of a one-game suspension following an unsportsmanlike conduct penalty in last week’s game. The league is exploring the claims and investigates if others are involved in what could be another “bounty-gate”- activity.

ESPN.com Staff TIGHT END INVOLVED IN HIT AND RUN Per multiple reports, Collin Smith will likely face charges in a hit-and-run accident earlier this month in Los Angeles. According to a police report, a black BMW struck a 39-year- old man crossing the roadway on a bicycle, before fleeing the scene. Police later located the damaged car and determined it was rented by Smith. The victim sustained several broken bones and was taken to Los Angeles Memorial Hospital for overnight monitoring. Police continue to investigate what caused the accident and whether the bicyclist was crossing illegally. In the meantime, Smith could face jail time for fleeing the scene of an accident regardless.

ESPN.com Staff SMITH ARRESTED FOR DOMESTIC ABUSE Collin Smith was arrested in connection with domestic abuse allegation, police said Wednesday night. The announcement from the investigating police department said two incidents allegedly occurred on consecutive days in late September between Smith and his wife. They were reported last week. Detectives interviewed Smith and he “admitted to the incidents, however, denied any physical assault” the police statement said. Smith was booked on one count of aggravated assault causing a fracture and two counts of criminal damage. He has since posted bail and been released.

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ESPN.com Staff SMITH SUSPENDED FOR PED USE Collin Smith has been suspended without pay for violating the NFL’s policy on performance-enhancing substances, the NFL announced Thursday. An NFL official said Smith tested positive for banned substances in a test administered two weeks ago at the team’s practice facility. Smith asked for a second test, which confirmed the results. Under its agreement with the union, the NFL does not identify the substance when a player is punished. Smith has not responded to a request for comment.

Thank you for your participation. As you likely know, all transgressions and even the athlete were purely hypothetical in nature. Nonetheless, your help in completing this questionnaire will be helpful for sports management practitioners to determine how fans perceive varying athlete transgressions, such as the ones mentioned in this survey. If you enter your email address in the field below, your name will be entered into a drawing for a gift card. If you have any questions, please feel free to contact me via email: [email protected]

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APPENDIX F MAIN QUESTIONNAIRE

Phase One

[Insert Participant Consent: see Appendix B]

1. What is your gender?  Male (1)  Female (2)  Other (3) ______

2. Are you of Hispanic, Latino, or Spanish origin?  No, not of Hispanic, Latino, or Spanish origin (1)  Yes, Mexican, Mexican Am., Chicano (2)  Yes, Puerto Rican (3)  Yes, Cuban (4)  Yes, another Hispanic, Latino, or Spanish origin (please specify) (5) ______

3. Which race/ethnicity best describes you (mark one or more boxes):  White (1)  Black, African American, or Negro (2)  American Indian or Alaska Native (3) ______ Asian Indian (4)  Chinese (5)  Filipino (6)  Other Asian (i.e., Thai, Hmong) (7)  Japanese (8)  Korean (9)  Vietnamese (10)  Native Hawaiian (11)  Guamanian or Chamorro (12)  Samoan (13)  Other Pacific Islander (i.e., Fijian) (14) ______ Other (15) ______

4. Please indicate your current age:  17 (1)  18 (2)  19 (3)  20 (4)  21 (5)  22 (6)  23 (7)

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 24 (8)  25 (9)  26 (10)  27 (11)  28 (12)  29 (13)  30 (14)  31 (15)  32 (16)  33 (17)  34 (18)  35 or older (19)  If 17 Is Selected, Then Skip To End of Survey

5. What is your major? (please select one)  Advertising (1)  Business (2)  Journalism (3)  Photo Journalism (4)  Public Relations (5)  Telecommunication/Broadcasting (6)  Sport Management (7)  Tourism (8)  Health/Exercise Science (9)  Other (please specify) (10) ______

6. Which University do you attend?  University of Florida (1)  Winona State University (2)  Other (please specify) (3) ______

7. Please provide your contact information so you can be informed about winning a prize in exchange for your participation. Please assure that your information, especially your email is accurate, as you will be receiving an additional email upon completion of the research study. (IMPORTANT: Your information will NOT be shared with any third parties and are stored in a secure database).  First Name (1)  Last Name (2)  Primary Email (3)  Re-enter you Email (4)

8. How important is (American) football to you?  Not at all Important (1)  Unimportant (2)  Neither Important nor Unimportant (3)

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 Important (4)  Very Important (5)

9. How much do you like (American) football?  Dislike Very Much (1)  Dislike (2)  Neither Like nor Dislike (3)  Like (4)  Like Very Much (5)

10. Do you consider yourself a NFL fan?  Yes (1)  No (2)  Undecided (3)

11. Please select your favorite NFL team?  Arizona Cardinals (1)  New Orleans Saints (20)  Atlanta Falcons (2)  New York Giants (21)  Baltimore Ravens (3)  New York Jets (22)  Buffalo Bills (4)  Oakland Raiders (23)  Carolina Panthers (5)  Philadelphia Eagles (24)  Chicago Bears (6)  Pittsburgh Steelers (25)  Cincinnati Bengals (7)  Saint Louis Rams (26)  Cleveland Browns (8)  San Diego Chargers (27)  Dallas Cowboys (9)  San Francisco 49ers (28)  Denver Broncos (10)  Seattle Seahawks (29)  Detroit Lions (11)  Tampa Bay Buccaneers  Green Bay Packers (12) (30)  Houston Texans (13)  Tennessee Titans (31)  Indianapolis Colts (14)  Washington Redskins (32)  Jacksonville Jaguars (15)  I like football, but don’t  Kansas City Chiefs (16) have a favorite NFL team  Miami Dolphins (17) (33)  Minnesota Vikings (18)  I don’t care about football,  New England Patriots (19) so I don’t have a favorite team (34)

12. Would you consider yourself a fan of the Green Bay Packers?  Yes (1)  No (2)  Undecided (3)

13. Would you consider yourself a fan of the Tampa Bay Buccaneers?  Yes (1)  No (2)

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 Undecided (3)

14. Do you play fantasy football?  Yes (1)  No (2)  I have played Fantasy Football in the past, but I don't anymore (3)

15. How important is fantasy football to you?  Not at all Important (1)  Unimportant (2)  Neither Important nor Unimportant (3)  Important (4)  Very Important (5)

16. How important to you is it that your favorite NFL team wins?  1-not important at all (1)  2 (2)  3 (3)  4 (4)  5 (5)  6 (6)  7 (7)  8-very important (8)

17. How strongly do you see yourself as a fan of your favorite NFL team? a. 1-not at all a fan (1)  2 (2)  3 (3)  4 (4)  5 (5)  6 (6)  7 (7)  8-very strong fan (8)

18. How strongly do your friends see you as a fan of your favorite NFL team?  1- not at all a fan (1)  2 (2)  3 (3)  4 (4)  5 (5)  6 (6)  7 (7)  8-very strong fan (8)

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19. During the season, how closely do you follow your favorite NFL team via ANY of the following: a) in person or television, b) radio, or c) television news or a newspaper? d) social media platforms like Twitter  1-never (1)  2 (2)  3 (3)  4 (4)  5 (5)  6 (6)  7 (7)  8-very closely (8)

20. How important is being a fan of your favorite NFL team to you?  1-not important at all (1)  2 (2)  3 (3)  4 (4)  5 (5)  6 (6)  7 (7)  8-very important (8)

21. How much do you dislike your favorite NFL team's greatest rivals?  1-I don't dislike the other teams (1)  2 (2)  3 (3)  4 (4)  5 (5)  6 (6)  7 (7)  8-I very much dislike the other teams (8)

22. How often do you display your favorite NFL team's name or insignia at your place of work, where you live, or on your clothing?  1-Never (1)  2 (2)  3 (3)  4 (4)  5 (5)  6 (6)  7 (7)  8-very often (8)

23. Please read the following statements and rate your agreement with each statement (1 Strongly Disagree-5 Strongly Agree):

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 NFL athletes should live up to their responsibility to society  NFL athletes should not harm their teams in any way  NFL athletes should engage in behavior(s) that reflect social norms

24. How important to you is it that the ______12 win?  1-not important at all (1)  2 (2)  3 (3)  4 (4)  5 (5)  6 (6)  7 (7)  8-very important (8)

25. How strongly do you see yourself as a fan of ______?  1-not at all a fan (1)  2 (2)  3 (3)  4 (4)  5 (5)  6 (6)  7 (7)  8-very strong fan (8)

26. How strongly do your friends see you as a fan of ______?  1- not at all a fan (1)  2 (2)  3 (3)  4 (4)  5 (5)  6 (6)  7 (7)  8-very strong fan (8)

27. During the season, how closely do you follow ______via ANY of the following: a) in person or television, b) radio, or c) television news or a newspaper? d) social media platforms like Twitter  1-never (1)  2 (2)  3 (3)

12 For questions 24-30 blanks were inserted with either “the Green Bay Packers” or “the Tampa Bay Buccaneers” depending on previous answer of favorite team. If neither the Packers nor the Buccaneers were selected, participants were randomly placed in either one of the groups.

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 4 (4)  5 (5)  6 (6)  7 (7)  8-very closely (8)

28. How important is being a fan of ______to you?  1-not important at all (1)  2 (2)  3 (3)  4 (4)  5 (5)  6 (6)  7 (7)  8-very important (8)

29. How much do you dislike ______’s greatest rivals?  1-I don't dislike the other teams (1)  2 (2)  3 (3)  4 (4)  5 (5)  6 (6)  7 (7)  8-I very much dislike the other teams (8)

30. How often do you display ______’s name or insignia at your place of work, where you live, or on your clothing?  1-Never (1)  2 (2)  3 (3)  4 (4)  5 (5)  6 (6)  7 (7)  8-very often (8)

31. Please read the following statements and rate your agreement with each statement (1 Strongly Disagree – 7 Strongly Agree)  The [TEAM NAME] set an example of how a national football league organization should be run  I would believe in the [TEAM NAME] organization if it were under media attack  I have admiration and respect for the [TEAM NAME].

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32. Please read the following statements about the (team name) and rate your agreement (1=strongly disagree; 7=strongly agree)  I would attend a game being played by this team in the near future  I would attend games being played by this team in the next few years  I would attend a game being played by this team in the upcoming season  I would speak favorably of this organization to others  I would encourage others to attend this team’s games  I would encourage others to support this organization  I would buy this organization’s clothing (T-shirts, caps, etc.) in the future  I would buy this organization’s merchandise  I would purchase this organization’s souvenirs  I would read about this organization on online media platforms (i.e. ESPN; Twitter)  I would visit this organization’s website for information on the team  I would watch sports broadcasts on TV news for information about the organization

As mentioned before, the purpose of this research is to investigate how fans evaluate professional athletes. You will now be asked to read a feature article on a professional NFL player. Please read the article carefully. Following the article, you will be asked a number of questions about your perceptions of the athlete you read about. Due to privacy restrictions the name of the player is redacted. Please evaluate the athlete based on the information provided to you in the news article.

[Insert Player Spotlight: see Appendix D-1 or D2 for sample]

33. Based on the previous feature, please rate your perceptions of the athlete (1=strongly disagree; 7=strongly agree)  He sets an example of how a professional athlete should behave  I would believe in him if he were under media attack  I have admiration and respect for the athlete  I believe this athlete is wise after reading this article  I believe this athlete is pleasant after reading this article  I believe I could be comfortable around him after reading this article  I believe this athlete is sophisticated after reading this article.

34. Based on the previous feature, please rate your support of the athlete (1=strongly disagree; 7=strongly agree)  After reading this article, I would watch this athlete’s game on television  After reading this article, I would discuss this athlete in a positive light  After reading this article, I would consume sports news that discussed this athlete  After reading this article, I would attend this athlete’s games  After reading this article, I would buy this athlete’s paraphernalia (jerseys, T-shirts, etc.)

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 I would support and maintain my support for the athlete  I would post messages online to show my support for the athlete  I am willing to defend the athlete publicly

35. Based on the previous feature, please rate your verbal/written support of the athlete (1=strongly disagree; 7=strongly agree)  I would publically say and/or post messages on my social media accounts encouraging people to support this athlete (1)  I would publically say and/or post messages on social media sites saying positive things about this athlete (2)  I would publically say and/or post messages on social media sites encouraging others to cheer for this athlete during games. (3)  I would publically say and/or post messages on social media sites to make sure that others know I support this athlete (4)  I would say and/or post messages on social media sites to support this athlete (5)  I would publically say and/or post on social media sites the positive aspects of this athlete to those who criticize him. (6)  I would encourage people NOT to support this athlete (7)  I would say negative things about this athlete (8)  I would NOT recommend someone to cheer for this athlete (9)

36. UF Students - If your professor offered you extra credit for your participation, please enter your name and UFID.  LAST NAME (1)  UFID (2)

Thank you for completing the first part of this research study. Please submit your survey your survey by clicking the "submit" button below. Please do not discuss any parts of this survey with your classmates or friends until completion of this research study later this month. Your help in answering these questions is greatly appreciated and will contribute to the inquiries of the researcher. Please be aware that you will be sent another link with a second short survey in the next few days. The second survey will be shorter and not take up too much of your time-I appreciate your continued contributions.

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Phase Two

Thank you for participating in the second phase of this study. Please read the article and all questions carefully and answer to the best of your abilities. The player’s name has been redacted for the purpose of this study and in order to maintain privacy of the athlete. Please evaluate this player as you would knowing his name. Please provide your contact information once more. Please use the same information/email address as in the previous survey (This information will not be shared, it is only used for the prize giveaway and for the purpose of the research study).  First Name (1)  Last Name (2)  Primary Email (3)  Re-enter your Email (4)

1. Before we begin the second survey, please verify your participation by answering the following question: In the previous survey you read a player profile feature story of a professional football player; please indicate which team the player belongs to:  Green Bay Packers (1)  Tampa Bay Buccaneers (2)  New England Patriots (3)  Seattle Seahawks (4)  Other NFL Team

[INSERT TRANSGRESSION ARTICLE – SEE APPENDIX D-3 or D-4]

2. Based on the information presented to you in the previous news article please rate the following statements about the athlete (1=strongly disagree; 5=strongly agree)  The athlete’s behavior makes me feel negative about the athlete  The athlete disappoints me in a great deal  The athlete’s behavior makes me feel bad about the athlete  The athlete’s behavior makes me feel that the athlete does not care about others

37. Based on the previous feature, please rate your perceptions of the athlete (1=strongly disagree; 7=strongly agree)  The athlete sets an example of how a professional athlete should behave  I would believe the athlete if he were under media attack  I have admiration and respect for the athlete  I believe this athlete is wise after reading this article  I believe this athlete is pleasant after reading this article  I believe I could be comfortable around him after reading this article  I believe this athlete is sophisticated after reading this article.

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3. Based on the information presented to you in the previous news article please rate the following statements about your response to the athlete’s transgression (1=strongly disagree; 7=strongly agree)  I do not want to be associated with the athlete  I will not wear clothing or jerseys that are associated with the athlete  I would like to disconnect myself from the athlete

4. Based on the previous feature, please rate your support of the athlete (1=strongly disagree; 7=strongly agree)  After reading this article, I would watch this athlete’s game on television  After reading this article, I would discuss this athlete in a positive light  After reading this article, I would consume sports news that discussed this athlete  After reading this article, I would attend this athlete’s games  After reading this article, I would buy this athlete’s paraphernalia (jerseys, T-shirts, etc.)  I would support and maintain my support for the athlete  I would post messages online to show my support for the athlete  I am willing to defend the athlete publicly

5. Based on the previous feature, please rate your verbal/written support of the athlete (1=strongly disagree; 7=strongly agree)  I would publically say and/or post messages on my social media accounts encouraging people to support this athlete (1)  I would publically say and/or post messages on social media sites saying positive things about this athlete (2)  I would publically say and/or post messages on social media sites encouraging others to cheer for this athlete during games. (3)  I would publically say and/or post messages on social media sites to make sure that others know I support this athlete (4)  I would say and/or post messages on social media sites to support this athlete (5)  I would publically say and/or post on social media sites the positive aspects of this athlete to those who criticize him. (6)  I would encourage people NOT to support this athlete (7)  I would say negative things about this athlete (8)  I would NOT recommend someone to cheer for this athlete (9)

6. Please read the following statements and rate your agreement with each statement (1=strongly disagree; 7=strongly agree)  The [TEAM NAME] set an example of how a national football league organization should be run  I would believe in the [TEAM NAME] organization if it were under media attack  I have admiration and respect for the [TEAM NAME].

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7. Please read the following statements about the (team name) and rate your agreement (1=strongly disagree; 7=strongly agree)  I would attend a game being played by this team in the near future  I would attend games being played by this team in the next few years  I would attend a game being played by this team in the upcoming season  I would speak favorably of this organization to others  I would encourage others to attend this team’s games  I would encourage others to support this organization  I would buy this organization’s clothing (T-shirts, caps, etc.) in the future  I would buy this organization’s merchandise  I would purchase this organization’s souvenirs  I would read about this organization on online media platforms (i.e. ESPN; Twitter)  I would visit this organization’s website for information on the team  I would watch sports broadcasts on TV news for information about the organization

Thank you for completing the second part of this research study. Please submit your survey by clicking the button below. Your help in answering these questions is greatly appreciated and will contribute to the inquiries of the researcher. Please do not discuss any parts of this survey with your classmates or friends until completion of this research study.

Your name will be entered into the drawing to win prizes such as an iPad mini once more. Please be aware that you will be sent another link with the final survey in the upcoming days. I appreciate your continued contributions.

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Phase Three

Thank you for participating in the third and final phase of this dissertation research study investigating sports fans evaluations about NFL players and NFL teams. Please read the article carefully and answer the questions. Once again, the player’s name has been redacted for the purpose of this study and in order to maintain privacy of the athlete. Please evaluate the player and team based on the information presented to you.

Please provide your contact information once more (this will be used to match up your responses and verify your continued participation, as well as enter you in the grand prize drawing). Please make sure that you enter the same information/email address as you have in the previous two surveys.  First Name (1)  Last Name (2)  Primary Email (3)

[INSERT TREATMENT (TEAM PERFORMANCE+ATHLETE PERFORMANCE) SEE APPENDIX D-4 THROUGH D-8]

1. Based on the previous information, how successful do you believe the Packers were in the 2014-2015 season?  Very successful (1)  Successful (2)  Somewhat successful (3)  Undecided (4)  Somewhat unsuccessful (5)  Unsuccessful (6)  Very unsuccessful (7)

2. Based on the previous information, how successful do you believe the athlete (tight end) was in the 2014-2015 season?  Very successful (1)  Successful (2)  Somewhat successful (3)  Undecided (4)  Somewhat unsuccessful (5)  Unsuccessful (6)  Very Unsuccessful (7)  3. Based on the previous article, please rate your perceptions of the athlete (1=strongly disagree; 7=strongly agree)  The athlete sets an example of how a professional athlete should behave

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 I would believe the athlete if he were under media attack  I have admiration and respect for the athlete  I believe this athlete is wise after reading this article  I believe this athlete is pleasant after reading this article  I believe I could be comfortable around him after reading this article  I believe this athlete is sophisticated after reading this article.

6. Based on the information presented to you in the previous news article please rate the following statements about your response to the athlete’s transgression (1=strongly disagree; 7=strongly agree)  I do not want to be associated with the athlete  I will not wear clothing or jerseys that are associated with the athlete  I would like to disconnect myself from the athlete

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7. Based on the information presented to you in the previous news article please rate the following statements about the athlete (1=strongly disagree; 5=strongly agree)  The athlete’s behavior makes me feel negative about the athlete  The athlete disappoints me in a great deal  The athlete’s behavior makes me feel bad about the athlete  The athlete’s behavior makes me feel that the athlete does not care about others

8. Please read the following statements and rate your agreement with each statement (1=strongly disagree; 7=strongly agree)  The [TEAM NAME] set an example of how a national football league organization should be run  I would believe in the [TEAM NAME] organization if it were under media attack  I have admiration and respect for the [TEAM NAME].

9. Please read the following statements about the (team name) and rate your agreement (1=strongly disagree; 7=strongly agree)  I would attend a game being played by this team in the near future  I would attend games being played by this team in the next few years  I would attend a game being played by this team in the upcoming season  I would speak favorably of this organization to others  I would encourage others to attend this team’s games  I would encourage others to support this organization  I would buy this organization’s clothing (T-shirts, caps, etc.) in the future  I would buy this organization’s merchandise  I would purchase this organization’s souvenirs  I would read about this organization on online media platforms (i.e. ESPN; Twitter)  I would visit this organization’s website for information on the team  I would watch sports broadcasts on TV news for information about the organization

10. Since the name of the player was redacted, did you spend any time researching who the player might be? (i.e. through Internet searches, conversations with others, etc.) a. Yes (1) b. No (2)

11. Do you have any idea who the player might be? (If yes, please indicate who you believe it might be) a. Yes (1) ______b. No (2)

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Answer If Do you have any idea who the player might be? (If yes, please indicate who you believe it might be) Yes Is Selected 12. Why do you believe the athlete in this research is the one you indicated in the previous question?

Thank you for completing this questionnaire and participating in this study. Your contributions and help are greatly appreciated and will deeply contribute to the inquiries of the researcher. It is important for you to know that this study was an experiment to help academic researchers and sports practitioners learn how you respond to athlete transgressions. To do so, the researcher created a fictional athlete experiencing a fictional scandal. Please understand that no professional NFL player is currently involved in this scandal and that all stories you read were created for the sole purpose of this research study. Please do not discuss any parts of this survey with your classmates or friends until completion of this research study. Your name will be entered into the drawing to win a number of prizes once more. All winners will be notified via email within the next two weeks.

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APPENDIX G PRELIMINARY ANALYSIS

Fan Identification and Athlete Image

The researcher posited that there would be a positive correlation between fan identification and perceived athlete image. A Pearson product-moment correlation was used to explore the relationship between fan identification and perceived athlete image

(phase 1). There was a moderate significant, positive correlation between participant identification and perceived athlete image r = .360, n = 222, p < .001. Fan identification explained about 13% of the variability in perceived athlete image as determined by the r2.

Fan Identification and Team Reputation

The researcher posited that there would be a positive correlation between fan identification and perceived team reputation. A Pearson product-moment correlation was used to explore the relationship fan identification and perceived team reputation. As projected there was a significant, positive correlation between participant identification and perceived team image r = .604, n = 222, p < .001. Fan identification explained

36.5% of the variability in perceived team reputation as determined by r2.

Athlete Image and Consumer Behavior

Correlations between perceived athlete image and a number of dependent variables were evaluated.

Athlete Advocacy: It was projected that fans with higher perceived athlete image would report more athlete advocacy. The relationship between athlete image (as measured by the Athlete Reputation Scale) and athlete advocacy (as measured by the

Athlete Advocacy Scale) was investigated using a Pearson product-moment correlation.

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Correlations were calculated for all three phases of the present study. Analyses were performed to ensure no violation of the assumptions of normality, linearity and homoscedasticity. There was a strong, positive correlation between the two variables in all three phases (r = .558, n = 222, p < .001; r = .652, n = 222, p < .001; r = .775, n =

222, p < .001) with high scores of athlete advocacy associated with higher perceived athlete image.

Supportive Behavioral Intentions: It was hypothesized that fans with higher perceived athlete image would report more supportive behavioral intentions. The relationship between athlete image (as measured by the Athlete Reputation Scale) and supportive behavioral intentions (as measured by the Supportive Behavioral Intentions

Scale) was investigated using Pearson product-moment correlation for all three phases.

There was a strong, positive correlation between the two variables in all three phases (r

= .619, n = 222, p < .001; r = .584, n = 222, p < .001; r = .707, n = 222, p < .001) with high scores of supportive behavioral intentions toward the athlete associated with higher perceived athlete image. Therefore, hypothesis 3b was supported.

pWOM: It was projected that fans with higher perceived athlete image would generate more positive WOM (pWOM). The relationship between athlete image (as measured by the Athlete Reputation Scale) and pWOM (as measured by the Positive

Word-of-Mouth Scale) was investigated using Pearson product-moment correlation for all three phases. Based on the analysis, there were significant positive correlations between perceived image and generated positive WOM before the transgression (r =

.495, n = 222, p < .001) after the transgression (r = .577, n = 222, p < .001) and after the treatment (r = .707, n = 222, p < .001).

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nWOM: It was postulated that there would be a negative correlation between perceived athlete image and generated nWOM. There was a weak negative correlation between the two variables in phase 1 (r = -.190, n = 222, p = .004), and stronger negative correlation in phase 2 (r = -.320, n = 222, p < .001), and phase 3 (r = -.575, n =

222, p < .001).

CORF: It hypothesized that there would be a negative correlation between perceived athlete image and CORFing behavior. Correlations were calculated twice, after the transgression and after the performance treatment. Based on the analysis, there were significant negative correlations between perceived image and CORF before exposure to the performance manipulation (r = -.499, n = 222, p < .001) and after the manipulations (r = -.790, n = 222, p < .001).

Team Reputation and Consumer Behavior

The researcher projected that fans with higher perceived team reputation would report higher patronage intentions. The relationship between team reputation (as measured by the Team Reputation Scale) and patronage intentions (as measured by the Patronage Intention Scale) was investigated using Pearson product-moment correlation. Correlations were calculated for all three phases of the present study.

Analyses were performed to ensure no violation of the assumptions of normality, linearity and homoscedasticity. There was a strong, positive correlation between the two variables in all three phases (r = .688, n = 222, p < .001; r = .848, n = 222, p < .001; r =

.795, n = 222, p < .001) with high scores of patronage intentions associated with higher perceived team image. Because the Patronage Intention Scale consists of four domains, correlation analyses were also performed for all the domains separately. For the repeat-purchase domain the study found significant positive correlations between

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perceived team image and repeat purchases intentions throughout phase 1-3 respectively (r = .621, n = 222, p < .001; r = .795, n = 222, p < .001; r = .705, n = 222, p

< .001). Within the WOM domain there were also significant positive correlations in all phases (r = .733, n = 222, p < .001; r = .852, n = 222, p < .001; r = .818, n = 222, p <

.001). For the merchandise-consumption domain the study found significant positive correlations (r = .577, n = 222, p < .001; r = .736, n = 222, p < .001; r = .740, n = 222, p

< .001). And finally, for the media-consumption domain, results were similar, with strong positive correlations found between perceived team image and media consumption intentions (r = .604, n = 222, p < .001; r = .769, n = 222, p < .001; r = .705, n = 222, p <

.001).

Team Identification and Violation Valence

It was assumed that high team identification will result in less negative violation valence toward the athlete than will low team identification. An independent-sample t- test was conducted to compare the violation valence scores for high and low-identified fans. There was a significant difference in scores for low-identified (M = 3.67, SD = .63) and high-identified (M = 3.87, SD = .64) t (220) = 2.31; p = .02, two-tailed. The magnitude of the differences in the means (mean difference =.20, 95% CI: .03 to .36) was small (eta squared = .01). Contrary to what was predicted, high-identified fans reported higher negative violation valence than did low-identified fans. Furthermore, the relationship between team identification (as measured by the Sports Spectator Scale), athlete expectancies (as measured by the Expectancy Scale) and negative violation valence (as measured by the Violation Valence Scale) was investigated using Pearson product-moment correlation. There was a weak, positive correlation between the two variables for both high-identified (r = .258, n = 102, p < .001) and low-identified fans (r =

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.290, n = 120, p < .001), with high levels of expectancies associated with higher levels of negative violation valence. zobs values (= .24) were calculated to determine if there is a statistically significant difference in the strength of the correlation between expectancies and violation valence for those with high identification and those with low identification. The zobs value of .24 falls within the specified -1.96 and 1.96 and, therefore, there is not a statistically significant difference in the strength of the correlation (Pallant, 2010).

Influence of fan ID on Consumer Behavior

The researcher wondered if fan identification would influence the relationship between violation valence and fan athlete evaluations and fan consumer behavior toward the athlete. Data from phase two of this study, before the performance manipulations, were analyzed using hierarchical multiple regression. To create interaction terms between the independent variables and relationship length, the researcher mean-centered the variables before multiplying them. This procedure leaves each variable’s standard deviation unchanged. It also reduces multicollinearity problems

(Levin, Whitener & Cross, 2006).

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Figure G-1. Effect of Violation Valence and Fan Identification on Athlete Image

Athlete Image: Violation Valence and fan identification were entered at step 1, explaining 26.0% of the variance in athlete image. After entry of the interaction variable

(violation valence * fan identification) at step 2 the total variance explained by the model as a whole was 28.8%, F (3, 218) = 29.358, p < .001. The interaction variable explains an additional 2.8% of the variance of the image, after controlling for violation valence and fan identification, R square change =.028, F change (1, 218) = 8.635, p < .005. Fan identification moderates the relationship between violation valence and perceived athlete image, β = .169, t = 2.939, p = .004.

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Athlete Reputation: Violation Valence and fan identification were entered at step

1, explaining 23.6% of the variance in athlete reputation. After entry of the interaction variable (violation valence * fan identification) at step 2 the total variance explained by the model as a whole was 24.0%, F (3, 218) = 23.008, p < .001. The interaction variable explains an additional 0.5% of the variance of the reputation, after controlling for violation valence and fan identification, R square change = .005, F change (1, 218) =

1.404, p = .237. Fan identification does not moderate the relationship between violation valence and perceived athlete reputation, β = .070, t = 1.185, p = .237.

Figure G-2. Effect of Violation Valence and Fan Identification on Athlete Advocacy

Athlete Advocacy: Violation Valence and fan identification were entered at step 1, explaining 15.3% of the variance in athlete advocacy. After entry of the interaction variable (violation valence * fan identification) at step 2 the total variance explained by

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the model as a whole was 17.0%, F (3, 218) = 14.904, p < .001. The interaction variable explains an additional 1.8% of the variance of the advocacy, after controlling for violation valence and fan identification, R square change =.018, F change (1, 218) =

4.616, p =. 033. Fan identification moderates the relationship between violation valence and athlete advocacy, β = .133, t = 2.149, p = .033.

Supportive Behavioral Intentions: Violation Valence and fan identification were entered at step 1, explaining 9.8% of the variance in supportive behavioral intentions.

After entry of the interaction variable (violation valence * fan identification) at step 2 the total variance explained by the model as a whole was 11.3%, F (3, 218) = 9.266, p <

.001. The interaction variable explains an additional 1.5% of the variance of the supportive behavioral intentions, after controlling for violation valence and fan identification, R square change = .015, F change (1, 218) = 3.616, p = .060. Fan identification does not moderate the relationship between violation valence and supportive behavioral intentions, β = .122, t = 1.902, p = .060.

pWOM: Violation Valence and fan identification were entered at step 1, explaining 10.7% of the variance in generated pWOM. After entry of the interaction variable (violation valence * fan identification) at step 2 the total variance explained by the model as a whole was 11.7%, F (3, 218) = 9.659, p < .001. The interaction variable explains an additional 1% of the variance of the pWOM generated, after controlling for violation valence and fan identification, R square change = .010, F change (1, 218) =

2.582, p = .110. Fan identification does not moderate the relationship between violation valence and generated pWOM, β = .103, t = 1.607, p = .110.

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nWOM: Violation Valence and fan identification were entered at step 1, explaining 25.7% of the variance in generated nWOM. After entry of the interaction variable (violation valence * fan identification) at step 2 the total variance explained by the model as a whole was 25.8%, F (3, 218) = 25.232, p < .001. The interaction variable explains only an additional 0.1% of the variance of nWOM generated, after controlling for violation valence and fan identification, R square change < .001, F change (1, 218) =

.094, p = .759. Fan identification does not moderate the relationship between violation valence and nWOM generated, β = -.018, t = -.307, p = .759.

CORF: Violation Valence and fan identification were entered at step 1, explaining

20.9% of the variance in CORF behavior. After entry of the interaction variable (violation valence * fan identification) at step 2 the total variance explained by the model as a whole was 21.6%, F (3, 218) = 20.027, p < .001. The interaction variable explains an additional 0.7% of the variance of CORF, after controlling for violation valence and fan identification, R square change = .007, F change (1, 218) = 2.010, p = .158. Fan identification does not moderate the relationship between violation valence and CORF generated, β = .086, t = 1.418, p = .158.

Crisis and Image

One of the fundamental questions asked prior to this study was if athlete crisis influences perceived athlete image. A one-way repeated measures ANOVA was conducted to compare athlete reputation scores before (prior to transgression intervention in Phase I) and after the athlete crisis (following the crisis intervention in

Phase II). Means and standard deviations are presented in Table 4-11. There was significant effect of crisis on athlete image, Wilks’ Lambda = .239, F (1, 221) = 703.168,

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p < .001, multivariate eta-squared = .761. This suggests athlete crisis had significant effect on athlete image score.

Table G-1. Perceived Athlete Image for Pre and Post Crisis Intervention Time Period n Mean Standard Deviation Phase 1 (Pre-Intervention) 222 5.121 1.047 Phase 2 (Post-Intervention) 222 2.460 0.914

The same statistical procedure was performed for all dependent variables in this study. There was significant effect of crisis on athlete reputation, Wilks’ Lambda = .222,

F (1, 221) = 773.744, p < .001, multivariate eta-squared = .778. This suggests athlete crisis had significant effect on athlete reputation score.

Table G-2. Descriptive Statistics for Perceived Athlete Reputation for Pre and Post Crisis Intervention Time Period n Mean Standard Deviation Phase 1 (Pre-Intervention) 222 5.347 1.083 Phase 2 (Post-Intervention) 222 2.446 0.972

There was significant effect of crisis on athlete advocacy, Wilks’ Lambda = .566 F

(1, 221) = 169.281, p < .001, multivariate eta-squared = .434. Suggesting athlete crisis had significant effect on athlete advocacy score.

Table G-3. Descriptive Statistics for Athlete Advocacy for Pre and Post Crisis Intervention Time Period n Mean Standard Deviation Phase 1 (Pre-Intervention) 222 4.110 1.472 Phase 2 (Post-Intervention) 222 2.456 1.086

There was significant effect of crisis on athlete supportive behavioral intentions,

Wilks’ Lambda = .628 F (1, 221) = 130.680, p < .001, multivariate eta-squared = .372.

This suggests athlete crisis had significant effect on athlete supportive behavioral intention score.

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Table G-4. Descriptive Statistics for Athlete Supportive Behavioral Intentions for Pre and Post Crisis Intervention Time Period n Mean Standard Deviation Phase 1 (Pre-Intervention) 222 4.610 1.377 Phase 2 (Post-Intervention) 222 3.358 1.003

There was significant effect of crisis on generated pWOM, Wilks’ Lambda = .583

F (1, 221) = 158.001, p < .001, multivariate eta-squared = .417. Athlete crisis had significant effect on pWOM score.

Table G-5. Descriptive Statistics for pWOM for Pre and Post Crisis Intervention Time Period n Mean Standard Deviation Phase 1 (Pre-Intervention) 222 3.800 1.556 Phase 2 (Post-Intervention) 222 2.273 1.017

There was significant effect of crisis on generated nWOM, Wilks’ Lambda = .483

F (1, 221) = 236.338, p < .001, multivariate eta-squared = .517. This suggests athlete crisis had significant effect on nWOM score.

Table G-6. Descriptive Statistics for nWOM for Pre and Post Crisis Intervention Time Period n Mean Standard Deviation Phase 1 (Pre-Intervention) 222 2.172 1.221 Phase 2 (Post-Intervention) 222 4.074 1.409

Furthermore, it was asked if athlete crisis influences perceived team reputation and team related outcomes. A one-way repeated measures ANOVA was conducted to compare team reputation scores before (prior to transgression intervention in Phase I) and after the athlete crisis (following the crisis intervention in Phase II). Means and standard deviations are presented in Table 4-17. There was no significant effect of crisis on team reputation, Wilks’ Lambda = .996, F (1, 221) = .827, p = .364, multivariate eta- squared = .004. This suggests athlete crisis had no significant effect on team reputation score.

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Table G-7. Descriptive Statistics for Perceived Team Reputation for Pre and Post Crisis Intervention Time Period n Mean Standard Deviation Phase 1 (Pre-Intervention) 222 4.807 1.375 Phase 2 (Post-Intervention) 222 4.716 1.463

Similarly, a one-way repeated measures ANOVA was conducted to compare patronage intention scores before (prior to transgression intervention in Phase I) and after the athlete crisis (following the crisis intervention in Phase II). Means and standard deviations are presented in Table 4-18. There was no significant effect of crisis on repeat purchase intentions, Wilks’ Lambda = .996, F (1, 221) = .963, p = .328, multivariate eta-squared = .004. This suggests athlete crisis had no significant effect on patronage intentions in the repeat purchase domain.

Table G-8. Descriptive Statistics for Repeat Purchase Intentions for Pre and Post Crisis Intervention Time Period n Mean (M) Standard Deviation Phase 1 (Pre-Intervention) 222 4.662 1.877 Phase 2 (Post-Intervention) 222 4.539 1.671

There was also no significant effect of crisis on repeat purchase intentions, Wilks’

Lambda = .999, F (1, 221) = .143, p = .706, multivariate eta-squared = .001. This suggests athlete crisis had no significant effect on patronage intentions in the WOM domain. Means and standard deviations are presented in Table 4-19.

Table G-9. Descriptive Statistics for generated WOM (team) for Pre and Post Crisis Intervention Time Period n Mean (M) Standard Deviation Phase 1 (Pre-Intervention) 222 4.496 1.697 Phase 2 (Post-Intervention) 222 4.554 1.584

Results indicated no significant effect of crisis on merchandise consumption,

Wilks’ Lambda = .990, F (1, 221) = 2.321, p = .129, multivariate eta-squared = .010.

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This suggests athlete crisis had no significant effect on patronage intentions in the merchandise consumption domain. Means and standard deviations are presented in

Table 4-20.

Table G-10. Descriptive Statistics for Merchandise Consumption for Pre and Post Crisis Intervention Time Period n Mean (M) Standard Deviation Phase 1 (Pre-Intervention) 222 3.666 2.168 Phase 2 (Post-Intervention) 222 3.875 1.835

Finally, there was no significant effect of crisis on media consumption, Wilks’

Lambda = .987, F (1, 221) = 3.019, p = .084, multivariate eta-squared = .013. This suggests athlete crisis had no significant effect on patronage intentions in the media consumption domain. Means and standard deviations are presented in Table 4-21.

Table G-11. Descriptive Statistics for Media Consumption for Pre and Post Crisis Intervention Time Period n Mean (M) Standard Deviation Phase 1 (Pre-Intervention) 222 4.435 1.872 Phase 2 (Post-Intervention) 222 4.642 1.573

Role of Fan ID on Consumer Behavior following Crisis

It was also investigated if fan identification influences perceived image and consumer behaviors toward the athlete following athlete crisis. A mixed between-within subjects ANOVA was conducted to assess the impact of fan identification (high, low) on patronage intentions, across two time periods (pre and post crisis). There was a significant interaction between fan identification and pre/post-test score on perceived athlete image, Wilks’ Lambda = .941, F (1, 220) = 13.843, p < .001, partial eta squared

= .059. An independent sample t-test was conducted to compare pre and post crisis perceived athlete image for high and low-identified fans. Before the crisis there was a

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significant difference between high and low-identified fans (MHigh = 5.39, SD = 1.07; Mlow

= 4.89, SD = .97, t (220) = 3.631, p < .001). After the crisis there was no significant difference between the groups (MHigh = 2.34, SD = .89; Mlow = 2.56, SD = .93, t (220) = -

1.869, p = .063.

Table G-12. Perceived Athlete Image for High and Low Identification Fans Pre and Post Crisis Time Period High ID Low ID n M SD n M SD Phase 1 (Pre) 102 5.391 1.075 120 4.891 0.969 Phase 2 (Post) 102 2.336 0.890 120 2.565 0.926

The same statistical procedure was performed for all the other dependent variables tested in this dissertation. There was a significant interaction between fan identification and pre/post-test score on perceived athlete reputation, Wilks’ Lambda =

.933, F (1, 220) = 15.832, p < .001, partial eta squared = .067. This was followed up by an independent sample t-test to compare pre and post crisis perceived athlete reputation for high and low-identified fans. Before the crisis there was a significant difference between high and low-identified fans, perceived reputation of high ID fans is higher than the low ID fans (MHigh = 5.64, SD = 1.10; Mlow = 5.10, SD = 1.01, t(220) =

3.80, p < .001). After the crisis there was significant difference between the groups, perceived reputation of low ID fans is higher than high ID fans (MHigh = 2.30, SD = .35;

Mlow = 2.57, SD = .97, t(220) = -2.07, p = .040).

Table G-13. Perceived Athlete Reputation for High and Low Identification Fans Pre and Post Crisis Time Period High ID Low ID n M SD n M SD Phase 1 (Pre) 102 5.637 1.101 120 5.101 1.007 Phase 2 (Post) 102 2.300 0.955 120 2.569 0.973 There was also a significant interaction between fan identification and pre/post- test score on athlete advocacy, Wilks’ Lambda = .809, F (1, 220) = 52.009, p < .001,

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partial eta squared = .191. This was followed up by an independent sample t-test to compare pre and post crisis athlete advocacy for high and low-identified fans. Before the crisis there was a significant difference between high and low-identified fans; athlete advocacy of high ID fans is higher than the low ID fans (MHigh = 4.85, SD = 1.33; Mlow =

3.48, SD = 1.28, t (220) = 7.78, p < .001). After the crisis there was significant difference between the groups, perceived reputation of low ID is higher than high ID (MHigh = 2.30,

SD = .1.06; Mlow = 2.59, SD = 1.09, t (220) = -1.944, p = .049.

Table G-14. Athlete Advocacy for High and Low Identification Fans Pre and Post Crisis Time Period High ID Low ID n M SD n M SD Phase 1 (Pre) 102 4.850 1.333 120 3.481 1.283 Phase 2 (Post) 102 2.301 1.063 120 2.589 1.092 Similarly, there was a significant interaction between fan identification and pre/post-test score on supportive behavioral intentions, Wilks’ Lambda = .792, F (1,

220) = 57.649, p < .001, partial eta squared = .208. An independent sample t-test was conducted to compare pre and post crisis athlete supportive behavioral intentions for high and low-identified fans. Before the crisis there was a significant difference between high and low-identified fans (MHigh = 5.43, SD = 1.16; Mlow = 3.91, SD = 1.14, t (220) =

9.726, p < .001). After the crisis there was no significant difference between the groups

(MHigh = 3.37, SD = 1.00; Mlow = 2.35, SD = 1.00, t (220) = .177, p = .860.

Table G-15. Athlete Supportive Behavioral Intentions for High and Low Identification Fans Pre and Post Crisis Time Period High ID Low ID n M SD n M SD Phase 1 (Pre) 102 5.427 1.161 120 3.915 1.149 Phase 2 (Post) 102 3.371 1.001 120 3.347 1.009 Furthermore, there was a significant interaction between fan identification and pre/post-test score on pWOM, Wilks’ Lambda = .849, F (1, 220) = 39.195, p < .001, partial eta squared = .151. An independent sample t-test was conducted to compare pre

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and post crisis pWOM for high and low-identified fans. Before the crisis there was a significant difference between high and low-identified fans (MHigh = 4.48, SD = 1.51; Mlow

= 3.22, SD = 1.35, t (220) = 6.560, p < .001). After the crisis there was no significant difference between the groups (MHigh = 2.19, SD = 1.03; Mlow = 2.34, SD = 1.01, t (220)

= -1.08, p = .283.

Table G-16. pWOM for High and Low Identification Fans Pre and Post Crisis Time Period High ID Low ID n M SD n M SD Phase 1 (Pre) 102 5.427 1.161 120 3.915 1.149 Phase 2 (Post) 102 3.371 1.001 120 3.347 1.009 Finally, there was a significant interaction between fan identification and pre/post- test score on nWOM, Wilks’ Lambda = .969, F (1, 220) = 7.014, p = .009, partial eta squared = .031. An independent sample t-test was conducted to compare pre and post crisis nWOM for high and low-identified fans. Before the crisis there was no significant difference between high and low-identified fans (MHigh = 2.29, SD = 1.35; Mlow = 3.25,

SD = 1.11, t (220) = .214, p =.831). After the crisis there was a significant difference between the groups (MHigh = 4.42, SD = 1.39; Mlow = 3.78, SD = 1.36, t (220) = 3.512, p

= .001. High-identified fans were more likely to generate nWOM than low-identified fans.

Table G-17. nWOM for High and Low Identification Fans Pre and Post Crisis Time Period High ID Low ID n M SD n M SD Phase 1 (Pre) 102 2.291 1.345 120 2.256 1.111 Phase 2 (Post) 102 4.425 1.361 120 3.775 1.361

Influence of Fan ID on Team Reputation following Crisis

A mixed between-within subjects ANOVA was conducted to assess the impact of fan identification (high, low) on team reputation, across two time periods (pre and post crisis). There was no significant interaction between fan identification and time period

(crisis), Wilks’ Lambda = .991, F (1, 220) = 2.059, p = .153, partial eta squared = .009.

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There was no substantial main effect for time period = .995, F (1, 220) = 1.051, p =

.306, partial eta squared = .005. This confirms the previous finding that the crisis had no effect on perceived team reputation score. The main effect of fan identification was significant, F (1, 220) = 62.389, p < .001, partial eta squared = .221, suggesting significant differences of perceived team reputation score depending on the level of fan identification. Means and standard deviations are presented in Table 4-28.

Table G-18. Perceived Team Reputation for High and Low Identification Fans Pre and Post Crisis Time Period High ID Low ID n M SD n M SD Phase 1 (Pre) 102 5.498 1.262 120 4.222 1.185 Phase 2 (Post) 102 5.248 1.434 120 4.264 1.334

Patronage Intentions: The research investigated if fan identification influences patronage intentions following athlete crisis. A mixed between-within subjects ANOVA was conducted to assess the impact of fan identification (high, low) on patronage intentions, across two time periods (pre and post crisis). There was a significant interaction between fan identification and time period (crisis) in the repeat purchase domain, Wilks’ Lambda = .939, F (1, 220) = 14.375, p < .001, partial eta squared = .061.

A paired-sample t-test was conducted to compare pre and post crisis scores for high and low-identified fans. For the high-identified fans there was a significant difference in repeat purchase intentions pre and post crisis (MPre = 5.84, SD = 1.44; Mpost = 5.22, SD

= 1.48, t (101) = 3.688, p < .001). For the low-identified fans there was no significant difference in pre/post crisis purchase intention score MPre = 3.66, SD = 1.61; Mpost =

3.96, SD = 1.61, t (119) = -1.745, p = .084.

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Table G-19. Repeat Purchase Intentions for High and Low Identification Fans Pre and Post Crisis Time Period High ID Low ID n M SD n M SD Phase 1 (Pre) 102 5.840 1.436 120 3.611 1.611 Phase 2 (Post) 102 5.216 1.488 120 3.964 1.606

There was a significant interaction between fan identification and time period in the WOM domain, Wilks’ Lambda = .923, F (1, 220) = 18.352, p < .001, partial eta squared = .077. A paired-sample t-test was conducted to compare pre and post crisis scores for high and low-identified fans. For the high-identified fans there was a significant difference in generated WOM pre and post crisis (MPre = 5.69, SD = 1.21;

Mpost = 5.14, SD = 1.46, t (101) = 3.442, p = .001). For the low-identified fans there was also significant difference in pre/post crisis WOM score, although the post crisis WOM score in this case was higher than the pre crisis score MPre = 3.48, SD = 1.35; Mpost =

3.87, SD = 1.45, t (119) = -2.598, p = .011.

Table G-20. Generated WOM for High and Low Identification Fans Pre and Post Crisis Time Period High ID Low ID n M SD n M SD Phase 1 (Pre) 102 5.693 1.210 120 3.479 1.355 Phase 2 (Post) 102 5.144 1.455 120 3.867 1.452

There was a significant interaction between fan identification and time period in the merchandise consumption domain, Wilks’ Lambda = .879, F (1, 220) = 30.178, p <

.001, partial eta squared = .121. A paired-sample t-test was conducted to compare pre and post crisis scores for high and low-identified fans. For the high-identified fans there was a significant difference in merchandise consumption pre and post crisis (MPre =

5.31, SD = 1.69; Mpost = 4.75, SD = 1.73, t (101) = 2.830, p = .006). For the low- identified fans there was also significant difference in pre/post crisis merchandise

278

consumption score, although the post crisis score in this case was higher than the pre crisis score MPre = 2.27, SD = 1.43; Mpost = 3.14, SD = 1.59, t (119) = -5.089, p < .001.

Table G-21. Merchandise Consumption for High and Low Identification Fans Pre and Post Crisis Time Period High ID Low ID n M SD n M SD Phase 1 (Pre) 102 5.306 1.671 120 2.272 1.428 Phase 2 (Post) 102 4.745 1.726 120 3.136 1.589

There was a significant interaction between fan identification and time period in the media consumption domain, Wilks’ Lambda = .880, F (1, 220) = 29.861, p < .001, partial eta squared = .120. A paired-sample t-test was conducted to compare pre and post crisis scores for high and low-identified fans. For the high-identified fans there was a significant difference in media consumption pre and post crisis (MPre = 5.75, SD =

1.36; Mpost = 5.29, SD = 1.41, t (101) = 2.652, p = .009). For the low-identified fans there was also significant difference in pre/post crisis media consumption, although the post crisis score in this case was higher than the pre crisis score MPre = 3.32, SD = 1.49;

Mpost = 4.09, SD = 1.50, t (119) = -5.261, p < .001.

Table G-22. Media Consumption for High and Low Identification Fans Pre and Post Crisis Time Period High ID Low ID n M SD n M SD Phase 1 (Pre) 102 5.745 1.357 120 3.322 1.492 Phase 2 (Post) 102 5.289 1.405 120 4.092 1.501

279

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BIOGRAPHICAL SKETCH

Annelie Schmittel received her PhD from the College of Journalism and

Communications at the University of Florida in 2015. Although she is a German native she has called the U.S. her home for the past eleven years. Annelie graduated with two

Bachelor of Arts degrees in Mass Communication: Broadcast Journalism and German

Literature & Language from Winona State University in Minnesota. She also received her Masters of Science in Sport Management from WSU.

Annelie's primary research area is crisis communication in sports. She maintains secondary research interests in the influence of social media technologies in the sports environment (sports organizations, athletes, fans, media outlets), athlete development, and other sport management and sports media topics.

Her work has been presented at numerous national and international conferences ranging from the International Association for Communication and Sport

(IACS), the North American Society for Sport Management (NASSM), the Association for Education in Journalism and Mass Communication (AEJMC), the College Sport

Research Institute Conference (CSRI), and the Popular Communication

Association/American Culture Association (PCACA). In 2014 two of her papers were awarded “Top Papers” at the national Association for Education in Journalism and Mass

Communication (AEJMC) conference in Montreal, Canada.

Annelie’s manuscripts have been published in some of the leading journals in sports, such as the Journal of Sports Media, the International Journal of Sport

Communication and the Journal of Sports and Social Issues, as well as Visual

Communication Quarterly.

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While at the University of Florida, Annelie has received several awards for her work in the classroom and in scholarship. She received the University of Florida

Graduate Student Teaching Award, the Outstanding International Graduate Student

Award, the College of Journalism and Communications Outstanding Graduate Teacher

Award, as well as the College of Journalism and Communications Graduate Researcher

Award. In addition, she was an instructor for several courses in the Department of

Journalism and the Department of Telecommunication. Annelie has taught Sports,

Media and Society, Introduction to Electronic Media Writing, Introduction to Sports

Media, Sports Reporting, as well as lab sections of Fundamentals of Production.

In the Fall of 2015, Annelie transitioned to the Department of Tourism, Recreation and

Sport Management where she is currently developing a course in Athlete Development, specializing in public relations, media and social media training.

Annelie was once a collegiate track and field athlete and is a passionate fan of all sports, particularly American Football. In the future she hopes to work in the professional sports setting as a player development specialist, providing guidance, education and support for the personal and professional growth of players throughout their athletic careers.

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