ATHLETES BEHAVING BADLY TEAM IDENTIFICATION AND THE OFF-FIELD BEHAVIOUR OF ATHLETES: EFFECT ON CONSUMER INTENTION IN TRADITIONAL AND NON-TRADITIONAL SPORTING CONTEXTS

Benjamin Tarr Bachelor of Business Management

Submitted in fulfilment of the requirements for the degree of

Master of Philosophy

School of Advertising, Marketing and Public Relations

QUT Business School

Queensland University of Technology

2020

Supervisory Team:

Professor Larry Neale (Principal Supervisor)

Associate Investigator and Principal Supervisor

Director of Studies at the QUT Business School

[email protected]

Dr. Louise Kelly (Supervisor)

Associate Investigator and Supervisor

Senior Lecturer at QUT

[email protected]

Keywords

Consumer Evaluations, Established Professional Sport, New Teams and Leagues,

Sport Fan Behaviour, New Sporting Teams, Non-traditional Professional Sport, Off-

Field Athlete Behaviour, Team Identification, Professional Sport Across Contexts,

Athletes Behaving Badly Team Identification and the Off-field Behaviour of Athletes: Effect on Consumer Intention in Traditional and Non-traditional Sporting Contexts i

Abstract

Off-field behaviour by athletes has tended to attract significant attention from the press, fans, marketers and researchers. To date, academic research has primarily been confined to traditional sports and limited to off-field occurrences. This study questions whether fans react differently to off-field athlete behaviour based on their support for traditional or non-traditional sporting teams. Established professional leagues and teams are referred to as ‘traditional’, verses newer leagues and teams, often within regions where the sport does not have a traditional footprint, being referred to as ‘non- traditional’. The research extends team identification and social identity theory literature around how athlete behaviour interacts with team identification and consumer intentions on a comparative basis, in traditional and non-traditional sport settings. Three independent samples are drawn upon to provide context, with a focus on fan reaction to findings that a key athlete or athletes have engaged in either antisocial, benign or prosocial behaviour.

A total of 473 participants were drawn from two traditional professional sports leagues and one non-traditional sports league. These included the traditional sports leagues of Rugby Union (n=125) and the National Football League (NFL; n=122), and the non- traditional professional sports league, Major League Rugby, USA (n=226). Participants responded to three developed stimuli articles. Correlation and Regression analyses were used in an experimental study to unveil the relationship between team identification, consumer intention and off-field athlete behaviour evaluations.

Identification with the sporting team was used as a covariate as to whether respondents’ perception of off-field behaviour was impacted by ‘in’ or ‘out’ group membership. Social identity theory suggests that attitudinal modification exists around in/out group identification. The results provide evidence as to the impact of prosocial and antisocial off-field athletes behaviour in traditional and non-traditional professional sport contexts.

The off-field behaviour of athletes was found to significantly influence consumer intention in a non-traditional setting, with results indicating the less identified a person is with their team the more susceptible they are to off-field athlete behaviour changing their consumer intention levels. Conversely, off-field athlete behaviour of athletes did not significantly impact consumer intention at all among the traditional participants.

This research supports the view that because established teams and leagues enjoy a full complement of causes and antecedents of team identification the off-field athlete behaviour is not as powerful a variable as it is in non-traditional or emerging teams and leagues.

Athletes Behaving Badly Team Identification and the Off-field Behaviour of Athletes: Effect on Consumer Intention in Traditional and Non-traditional Sporting Contexts ii

Table of Contents

Keywords ...... i Abstract ...... ii Table of Contents ...... iii List of Figures ...... iv List of Tables ...... v List of Abbreviations ...... vi Statement of Original Authorship ...... vii Acknowledgements ...... viii Chapter 1: Introduction ...... 1 Background ...... 1 Purpose ...... 4 Significance, Scope, and Definitions...... 5 Thesis Outline ...... 6 Chapter 2: Literature Review ...... 9 Chapter 3: Research Methodology ...... 21 Methodology and Research Design ...... 21 Participants ...... 24 Instruments ...... 28 Analysis ...... 30 Ethics and Limitations ...... 35 Chapter Summary ...... 36 Chapter 4: Results ...... 38 Summary of Key Outcomes ...... 55 Chapter 5: Discussion ...... 56 Chapter 6: Conclusions...... 67 Bibliography ...... 69 Appendices ...... 83 Appendix A Questionnaire ...... 83 Appendix B ...... 86 Surveys ...... 86 Appendix C ...... 95 Facebook Postings ...... 95

Athletes Behaving Badly Team Identification and the Off-field Behaviour of Athletes: Effect on Consumer Intention in Traditional and Non-traditional Sporting Contexts iii

List of Figures

Figure 1 Antecedents and Causes of Team Identification (Wann, 2006)...... 12 Figure 2 Moderating Effect of Off-field Athlete Behaviour ...... 33 Figure 3 Age variable ...... 40 Figure 4 Team identification score ...... 40 Figure 5 Team Identification Scores across all participants ...... 42 Figure 6 Age breakdown across all participants ...... 45 Figure 7 Hypothesised Model ...... 48 Figure 8 Simple Slopes Graph of Moderating Effect ...... 50 Figure 9 Team Identification and consumer intention ...... 52

Athletes Behaving Badly Team Identification and the Off-field Behaviour of Athletes: Effect on Consumer Intention in Traditional and Non-traditional Sporting Contexts iv

List of Tables

Table 1 Partipant Breakdown ...... 39 Table 2 Participant Response Distribution of Off-field Athlete Behaviour Assessment ...... 41 Table 3 Dependant Variables Mean Score, Skewness & kurtosis Coefficient ...... 43 Table 4 Bivariate Correlations for Perception of off-field athlete behaviour and Consumer Behaviour Intention Items ...... 46 Table 5 Mean Intention Scores among Groups based grouped by Behaviour Assessment ...... 46 Table 6 Model Summary from Two-way Moderated Regression ...... 49 Table 7 Simple Slopes Analyses ...... 50 Table 8 Pearson Correlation coefficients, Team Identification and Consumer Intention in a Non-traditional Sport ...... 51 Table 9 Model Summary from Two-way Moderated Regression ...... 51 Table 10 Simple Slopes Analyses ...... 52 Table 11 Pearson Correlations Coefficients across traditional and non-traditional fans and Intention items ...... 53 Table 12 Model Summary from Two-way Moderated Regression ...... 54

Athletes Behaving Badly Team Identification and the Off-field Behaviour of Athletes: Effect on Consumer Intention in Traditional and Non-traditional Sporting Contexts v List of Abbreviations

BIRG Bask in reflected glory

CORF Cut off reflected failure

IV Independent variable[s]

MLR Major League Rugby

NCAAA National Collegiate Athletic Association

NFL National Football League

NOLA Gold New Orleans Gold

NRFL National Rugby Football League

RAN Rugby Americas North

RSL Rugby Super League

SSIS Sport Spectator Identification Scale

TID Evidence-based talent identification

TII The Team Identification Index

TMS Total mean substitution

USMLR United States Major League Rugby

Athletes Behaving Badly Team Identification and the Off-field Behaviour of Athletes: Effect on Consumer Intention in Traditional and Non-traditional Sporting Contexts vi Statement of Original Authorship

The work contained in this thesis has not been previously submitted to meet requirements for an award at this or any other higher education institution. To the best of my knowledge and belief, the thesis contains no material previously published or written by another person except where due reference is made.

Signature: QUT Verified Signature

Date: 1[1[2-020

Athletes Behaving Badly Team Identification and the Off-field Behaviour of Athletes: Effect on Consumer Intention in Traditional and Non-traditional Spo1ting Contexts vu Acknowledgements

This thesis is the product of the unflagging support, guidance, good will and humour of people across several continents and both hemispheres. This topic and the production process that generated it evidence the impact ‘globalisation’ has on a daily basis across realms as diverse as sport, academia and business. Written and researched while I was a foundation player for the US Major League Rugby’s NOLA Gold team, questions around how players’ conduct can help shape fan perceptions in relation to an emerging, non-traditional sport were very ‘live’ points of contemplation for me.

The expertise, enthusiasm and exceptional guidance of my supervisors,

Professor Larry Neale and Dr Louise Kelly therefore has been deeply appreciated.

Similarly, the QUT Business School faculty – and in particular Professors Stephen

Cox and Paula McDonald – have been remarkable in creating a sufficiently flexible program to make this ‘real world’ learning process possible for me.

Similarly, without the incredible support of Major League Rugby teams, especially my New Orleans ‘home team’ family in relation to both my playing and research activities, this wouldn’t have been possible. Like all things “Southern’, their generosity was extraordinary, making the balancing act of professional training schedules, workload, research and academic studies viable. My thanks should go in particular to Fitz, DJ, Scott Alexander and the rest of the NOLA Gold front office who helped make the connects necessary to stay on track in all respects.

Finally, I would be remiss if I didn’t acknowledge my parents (lifelong) contribution:

They have not only always held the faith that ‘Forwards Thinking’ is not an oxymoron but have always gone out of their way to make it a reality in all respects.

Athletes Behaving Badly Team Identification and the Off-field Behaviour of Athletes: Effect on Consumer Intention in Traditional and Non-traditional Sporting Contexts viii

Chapter 1: Introduction

Background

Professional sport is big business and forecast to increase exponentially in the near future. Indeed, as Forbes analysts recently observed “There has never been a better time to own a sports franchise in a major pro league” (Badenhausen, Forbes, 2018). The global sport of soccer, for example, claims three of the world’s four most valuable sports franchises collectively valued at US$13 billion, with 20 of its team’s worth $US1.69 billion on average (Ozanian, 2018; Forbes 2018). However, this figure is dwarfed by the projected 2019 value for the North American sports industry. It is expected to reach US$73.5 a figure up US$13 billion from its 2014 value (PwC, 2018). With the National Football League (NFL) dominating that market through entities such as the world’s most valuable sports franchise, the US$4.8 billion Dallas Cowboys, and 29 of the 50 most valuable sports teams averaging US$3 billion individually (Forbes, 2018; PWC, 2018) the potential for further market growth is apparent, particularly in the context of the impact of globalisation on this market.

Critical to the sector’s bottom lines are spectators and supporters – its fans. For example, NFL TV network rights alone in 2018 were US$8.2 billion, and individual NFL teams reported revenue lines of up to US$840 million around merchandising, tickets, advertising, and other associated revenue (Forbes, 2018). Supporter numbers for a team therefore constitute a tangible asset. Beyond this, research evidences the extent to which supporters’ psychological attachment to ‘their’ team is in itself a vital asset. Termed ‘team identification’ the level of psychological attachment felt by a team supporter is a powerful and stable predictor of consumer behaviour (Kim & Kim, 2009; Wann, Melnick, Russell, & Pease, 2001). In tangible terms lower levels of team identification correlate with lower involvement resulting in less emotional and financial commitment. Higher levels of team identification therefore translate to higher profit (Sutton, McDonald, Milne & Cimperman, 1997). Therefore, it is beneficial to not only have a large support base but to also have one that is highly identified.

Team identification as a valid predictor of consumer behaviour rests on multiple environmental, psychological, historical and team related drivers. Research literature examining isolated variables in this context is relatively extensive and includes documentation around contributors such as achievement and winning records (Fink, Trail & Anderson, 2002; Sutton et al., 1997), the teams’ stadiums (Underwood, Bond & Baer, 2001), and perceived group and team prestige (Gwinner & Swanson, 2003; Wang & Tang, 2018 ).

Antisocial behaviour as an impact driver

With the proliferation of 24-hour news and social media coverage an increasingly relevant variable has become athletes’ unscrupulous, bad, or as used in this study, ‘antisocial’ behaviours. So often the exhibited behaviour generates enough discussion and interest among the community to completely overshadow the performance of the individual or team in question. High profile examples over the last decade have not

Chapter 1: Introduction 1

been scarce, ranging from the Atlanta Falcon’s two-year suspension of NFL superstar Michael Vick for dog fighting (New York times, 2007) to Alex Rodriguez’s 2014 suspension from baseball for the Biogenesis doping scandals (USA today, 2014). Both scandals had significant financial consequences for the players, teams and leagues involved, resulting in dramatic revenue loss around sponsorship endorsements and advertising, ticket sales, merchandising and fan support (CBS Sports, 2013; CNN, 2018).

In the case of Tiger Woods, after details emerged outlining his infidelities, he similarly suffered large reputational and financial damage. Separate from, and potentially much greater than his own damages, sponsors shareholders suffered estimated collective losses between US $5-12 billion (Stango & Knittel, 2009). Examples closer to home that received extensive media coverage in recent times include the 2018 Australian cricket team ball-tampering scandal, and controversial Instagram posts by rugby union player Israel Folau (The Washington Post, 2018; BBC, 2019). The ball-tampering scandal involved the development and execution of a plan by Australian national cricketers to alter the condition of the cricket ball with sandpaper to disadvantage the South African batsmen. All parties were caught and exposed, subsequently experiencing severe backlash. The three Australian cricketers involved were stood down from leadership positions and received large suspensions from playing professional cricket (Cricket Australia, 2018). Arguably the Australian cricket team suffered as well, having lost three major contributors for a lengthy spell. Folau, a prominent rugby union player for both Australia and New South Wales, posted controversial anti-gay comments on his Instagram account and consequently had both his playing contract and a major sponsorship deal terminated (BBC, 2019). At the time of conducting this research, the complete fallout from Folau’s social media postings is yet to be seen, but inevitably given the evidence of the cricket example cited above, significant damage will be incurred to a range of interested parties.

Conversely, certain types of antisocial off-field conduct in arguably less-established professional sports contexts have been deemed to have resulted in outcomes associated with enhanced team identification levels. These included New York Yankee's Wade Boggs' notable consumption of 64 beers on a cross-country flight, former Australian cricketer Shane Warne’s numerous misdemeanours and AFL footballer Ben Cousins highly publicised off-field incidents involving recreational drug use, traffic convictions and association with criminal elements (Friken, 2008; “Cousins boosts membership sales”, 2009; “Australia's enduring infatuation with Shane Warne”, 2016).

Prosocial behaviour as an impact driver

Extensive reporting of athlete behaviour, especially negative, that has become commonplace in tabloids, newspapers, and social media outlets has heightened research interest and focus upon athlete behaviour. As discussion and interest grows, governing bodies often move quickly to control the situation and punish the offending parties. Publications also do appear detailing athletes engaging in prosocial off-field athlete behaviour, for example, players supporting and/or participating in charitable events ranging from hospital visits through to volunteering at a local animal shelter. In these instances, either by their own accord or through the direction of their sporting

Chapter 1: Introduction 2

body, the athletes have positive engagements with third parties. This positive behaviour also has the propensity to gain coverage and be a topic of discussion.

Structure and contribution

This research was motivated by my interest to make a contribution to the critical understanding of the impact that both forms of behaviour have on the most important stakeholder, the fans. How off-field antisocial athlete behaviour impacts supporter perceptions in non-traditional sports and newer professional leagues, however, is presently unknown. Similarly, how off-field prosocial athlete behaviour impacts fan perceptions in non-traditional sports also remains unknown.

The study is designed to explore whether sport consumers react differently to off-field athlete behaviour depending on whether they are supporting a traditional or non- traditional sporting team. It will do so by conducting research in three settings: two traditional and one non-traditional. The traditional supporter results will be drawn from fans within 1) professional rugby leagues in Europe and Australasia and 2) the National Football League (NFL), a professional American football competition based in the same region as the non-traditional league. The non-traditional data set will be derived from supporters of Major League Rugby (MLR) franchise fans in the United States, the majority of which comes from the New Orleans Gold (NOLA Gold) rugby union team. It will provide data around the experience of a recent emergent professional league operating in direct competition with numerous traditional mainstream professional sports. NOLA Gold were part of the 2017 launch of the United States’ second professional rugby union league, the MLR. The league completed its inaugural 2018 season with seven teams and in 2019 added a further two more teams to the league roster. A further three teams are set to join the league in 2020.

As NOLA Gold is a relatively unknown sport start up in the U.S. context outcomes will extend fan identification and social identity theory literature, as well as contribute to the research gap around media coverage as an influencing variable on general public perceptions and fan identity impact in relation to non-traditional sports. Whether consumer reactions parallel findings in established sports is significant for two reasons. First, it validates this variable as a potentially valuable predictive consumer behaviour tool for decision making in relation to strategic planning, financial and managerial decisions. Given the significant market advantage traditional competitors hold, maximising returns and mitigating risk are of utmost importance. Recognising how unscrupulous transgressions enhance or alienate spectator support during the critical transitional period of a new sporting codes' start-up is important therefore to this calculation.

Second, as non-traditional sporting teams lack the complexities an historical presence may create, they may be more vulnerable than established teams to this specific dynamic. Lacking known contributors to fan identity such as winning outcomes, high performance records and perceived group prestige, this variable stands to be far more significant as a driver than might otherwise be the case (Fink, Trail & Anderson, 2002; Sutton et al., 1997; Gwinner & Swanson, 2003). This study therefore will extend team identification and social theory literature around how this behaviour impacts team

Chapter 1: Introduction 3

identification and consumer intentions on a comparative basis, in traditional and non- traditional sport settings.

Purpose

This research aims to provide a contextual examination of off-field athlete behaviours association with team identity and the effect team identification and off-field athlete behaviour has on consumer intentions. This will further address an under-researched contributor towards fan identity. The research will extend the literature on fan identification and social identity theory, providing further explanation on why and how fans identify with sporting teams. Previous studies on identification show it to be a stable measure, especially amongst those who are more identified (Cialdini et al., 1976; Wann & Branscombe, 1990). Research obtained by collecting data from a newly formed league could yield new insights, as most previous studies have taken place in traditional established sport settings.

In terms of prosocial off-field athlete behaviour, research indicates that fans value the socially responsible efforts of their teams and use this information especially when considering product selection and/or favourably speaking of the organisation (Walker & Kent, 2009). This research thus will expand upon this understanding by evaluating the link between the individual athletes’ prosocial behaviour and their connection to the larger organisation and team. Having players engage in prosocial behaviours such as supporting and attending grassroot and youth level events has been suggested, amongst many other player accessibility and community involvement strategies, as means for professional teams to build fan identification (Sutton et al.,1997). The efficacy of some of these practices is evaluated over the due course of this research. The effect of antisocial athlete behaviour is similarly evaluated through the research.

Consequently, to address the above-mentioned areas, the following three research questions are proposed:

RQ1: How differently do fans react to off-field athlete behaviour depending on whether they are supporting a traditional or non-traditional sporting team?

RQ2: What is the relationship between team identification and consumer outcomes for a non-traditional sport?

RQ3: How is the relationship between team identification and consumer intention different in a non-traditional sport context?

By answering these research questions, the study aims to make a practical contribution to current disciplinary knowledge. A contextual examination of off- field athlete behaviours’ effect on the outcomes of fan identity could be especially beneficial to non-traditional sport start-ups and traditional sports alike. Resources towards developing a new sport or league may be limited and decisions around the allocation of these resources can be especially complicated (Azimzadeh, Morteza,

Chapter 1: Introduction 4

Pitts, Ashani & Kordnaeji, 2013). With limited resources and often in a vulnerable position, maximising return whilst mitigating risk is of utmost importance. While athletes frequently partake in prosocial behaviours by their own accord as they really want to and/or are passionate about a cause or program, sporting teams will often direct and instruct their participation. The study could assist management and marketers in practice by highlighting the potential benefits of encouraging athlete community engagement promoting prosocial behaviours, against the potential costs of players engaging in antisocial behaviour.

If the benefits are significant, encouraging and supporting players so that they can actively involve themselves in prosocial behaviour is a relatively low-cost exercise against alternative methods. Also, by employing players who exhibit prosocial behaviour, strategic recruitment practices can be better streamlined with marketing practices (Babiak & Wolfe, 2009). However, if the results of the study show that these behaviours do not make a significant positive contribution towards attainment of managerial outcomes, it presents an opportunity for practitioners to better allocate resources. It gives less cause for athletes’ focus and attentions to be drawn away from the playing arena and reduces the spectrum in which marketers strategise activities.

Beyond this, a deeper understanding of the effect off-field behaviour has on identity outcomes could impact how firms exploit commercial sponsorship opportunities. It is not uncommon to see firms immediately cut ties with athletes and teams who have been caught out in antisocial behaviour. Recognising that fan identity is a product of many experiences over an extended period, perhaps these effects are everlasting and the immediate reaction to behaviour does not affect intended outcomes. Knowledge about this relationship would better inform firms about whether decisions to cut ties are too rash. The results would also assist firms in identifying athletes and a team by providing an indication as to whether an athlete or team that is renowned for demonstrating prosocial behaviours is more valuable as a corporate agent than an athlete or team that is renowned for their sporting abilities, along with exhibiting antisocial behaviour (Conway, 2014).

Significance, Scope, and Definitions

While literature considering fans reaction to on-field occurrences is substantive, that being the play of the team, Fink (2009) argued that “how athletes’ off-field behaviour impacts fan identification is an under-studied area” (p.143). Research evaluating consumer reactions to off-field athlete behaviour is largely absent, while its relative importance and proclivity remains widely understood (Sato et al., 2015; Sato, Ko & Kellison, 2018).

Equally the role contextual factors play in the relationship remains underexplored; for example little is known about whether antisocial and prosocial behaviour in established professional leagues have an equal effect in sports and leagues that are less traditional or in infancy in terms of being a professional sporting code. Throughout the study established professional leagues and teams are referred to as ‘traditional’, verses newer leagues and teams, often within regions where the sport does not have a traditional footprint, being referred to as ‘non-traditional’(O’Hanley, 1999; Donghun & Schoenstedt, 2011). Differences between the two are discussed in depth in the

Chapter 1: Introduction 5

following chapter. Empirical research evaluating the direct and interaction effects of team identification and athlete off-field behaviours on outcomes of fan identity has therefore the potential to make a significant contribution to this field of research.

To robustly answer the research questions, the scope of the research was narrowed during the design stage. The three Australian cricketers embroiled in ‘Sandpaper Gate’, Tiger Woods and Israel Folau were all raised earlier in this chapter as high- profile examples of athletes who engaged in a form of antisocial behaviour. These incidents, like all other incidents of athlete behaviour, can be distinguished by whether they occurred ‘on-field’ or ‘off-field’. The Australian cricketer was caught ‘red handed’ by television cameras with sandpaper in hand amidst an international match, thus constituting ‘on-field’ behaviour. Woods on the other hand did not commit his scandals in view of spectators but was outed through an investigative journalist from a U.S. supermarket magazine therefore constituting ‘off-field’ behaviour (National Enquirer, 2008). Folaus’ behaviour, likewise, is categorised as ‘off-field’ behaviour as he posted his controversial anti-gay comments on his private Instagram account (Rychter, New York Times, 2019).

To understand the impact that athlete behaviour has on the fans from an empirical perspective, the research will focus purely on the off-field athlete behaviour. On-field athlete behaviour or incidents occurring within the field of play and can be closely intertwined with the actual performance of the athlete, introducing any number of extraneous variables. This is despite research indicating that on-field and performance related scandals are perceived as more severe, eliciting more negative responses from consumers (Kwak, 2016). They are similarly more negatively impactful on the athlete themselves (Sato et al., 2015). By focusing on off-field behaviour, the behaviour in question can be better isolated, and measured accordingly. This was reflected in the experimental design of the research study. Chapter Three further outlines this.

Thesis Outline

This thesis is comprised of six chapters. Chapter One outlines the background and provides context for the research. It addresses the purpose of the research by outlining the research questions that directed lines of inquiry for subsequent examination. Critical ways in which the experiment conducted here addresses these research questions are introduced.

Chapter Two provides a review and an analysis of research that has previously been conducted on the topics of team identification, athlete behaviour, consumer intention and the differences between new and established professional sporting teams. Following an examination of the literature, the hypotheses are presented.

Chapter Three presents the methodology. It opens with a discussion of the research philosophy underpinning the research project. The research objectives are highlighted, and the methodology is then explained, including details around why experimental design and quantitative research are employed. Chapter Three also outlines both the independent and dependant variables. Lastly the participants, instruments used, procedure and timeline, and means of data analysis are covered.

Chapter Four presents the results of the study.

Chapter 1: Introduction 6

Chapter Five contains a full discussion, interpretation and evaluation of the results with reference to the literature. It also contains discussion around the limitations to the research, and recommendations for future research and practitioners.

Chapter Six provides a brief overview of the derived conclusions, limitations to the research, and recommendations for future research and for practitioners.

Chapter 1: Introduction 7

Chapter 2: Literature Review

This project builds off a rich body of research around team identification, fan attachment, and drivers that contribute to fan loyalty and more broadly enhance their own sense of social identity. In this context the study extends this field by testing existing hypotheses and evidence as to whether the relationship observed between team identification and consumer behaviour is replicated in a non-traditional professional sport setting. The study similarly extends this field by empirically testing hypotheses related to prosocial and antisocial athlete behaviour, and its impact upon fan bodies across contexts. The foundations on which this research rests are outlined below.

This chapter commences by canvassing the construct of team identification, its origins and known influencing factors. Research examining 1) athletes’ behaviour and 2) athletes in the context of team identification and fan behaviour, is then reviewed. The chapter concludes by identifying differences that may exist between traditional and non-traditional sports. Research hypotheses are integrated against relevant and corresponding literature throughout this chapter.

Team Identification

Team identification has been defined as spectators’ perceived connectedness to a team and the experience of the team’s successes and failings as their own (Ashforth and Mael, 1989; Wann, Melnick, Russell & Pease, 2001; Kim & Kim, 2009). It is a connectedness that extends beyond a mere level of psychological attachment felt by a sports fan for the team and entails actual impact on the sport fan’s own sense of self- worth and self-value (Ashford & Mael, 1989, Tajfel and Turner, 1986). Gladden and Milne (1999) contend that team identification is a long-term factor that is built up over time by varied experiences relating to geographic location, facilities, tradition, media coverage, reputation, conference, schedule, entertainment, values, coaches, players and past performances of the team.

Team identification originates in early research conducted by Tajfel (1974, 1978, 1982) and Tajfel and Turner (1979) aimed towards understanding different attitudes and behaviours towards distinct groups of people. A key objective was dispelling Sherif’s (1954, 1958, 1961, 1966) findings that group conflict, negative prejudices, and stereotypes were the result of competition between groups for desired resources. Social identity theory was first coined in 1979 and was even proposed to help explain the intergroup conflict and discrimination in Europe following WWII (Tajfel & Turner, 1979). The theory highlights differences that exist between interpersonal and intergroup relationships, focusing on antecedents and characteristics of group behaviour that can be present without actual physical group conflict (Tajfel, 1982). Social identity being defined as “that part of an individual’s self-concept which derives from his knowledge of his membership of a social group (or groups) together with the value and emotional significance attached to that membership” (Tajfel, 1978, p. 63).

Chapter 2: Literature Review 9

Social identity theory suggests that individuals have both a personal identity and a social identity (Tajfel & Turner 1979; Tajfel & Turner, 1986). While social identity rests on traits derived through membership in particular groups, such as around classification as to citizenship, demographics (gender, sex, age, etc), or organisational membership (religion, education) personal identity consists of distinctive attributes unique to that individual such as interests, abilities and talent. Put alternately, social identity goes to traits inherited from the groups to which an individual naturally or by election belongs including, most relevantly for this study, sports team membership/affiliation (Turner, 1982).

The terminology of ‘in-group’ and ‘out-group’ was made popular by Tajfel and his colleagues during his research formulating social identity theory. ‘In-group’ being used to describe the member group to which an individual has acquired a sense of belonging, with other distinct comparable or rival groups being labelled as ‘out- groups’ (Tajfel, 1982; Tajfel & Turner, 1979; Turner, Brown, & Tajfel, 1979). An ‘in- group’ could be members of a family, citizens of a country, or most relevant to this research, fans of a specific sporting team. An example of an ‘out-group’ for sporting team fans may be fans of a rival team.

For self-image enhancement purposes, Tajfel‘s work documented that in group members will actively seek to enhance their group’s status to create further positive self-perception. Aside from magnifying their own positives, part of this process includes as standard the highlighting of negative aspects of an out-group. Tajfel and Turner (1979) credit this social process as fundamental to the development of prejudiced views across cultures and even racism. In the context of sports fandom, building higher self-esteem translates into social comparisons made as against out groups: that is, ‘our’ team versus ‘their’ (opposition) team(s). In-group members may even ridicule out-group members from a rival sports team for reasons not directly relevant to on-field competition. Through this process personal self-esteem is melded and enhanced. By logical extension, Tajfel and Turner hypothesise fans can then regulate self-esteem by emphasising positive aspects of the group and downplaying negative information (Wann and Branscombe, 1995; Chien, Kelly & Weeks, 2016). In the sports context, therefore, fans with strong team identification should be expected to manifest positive biases towards their own team players’ conduct while being hyper- critical of conduct of opposing team players.

Sport fans can also regulate self-esteem through their response to team successes and failures. Individuals will celebrate and draw attention to their association with others (their teams) successes, even in circumstances where they are unable to claim to have contributed towards the success (Cialdini et al, 1976; Hirt, Zillman, Erikson & Kennedy, 1992). This process is called ‘basking in reflected glory’ (BIRGing) (Cialdini, Borden, Thorne, Walker, Freeman & Sloan, 1976; Cialdini & De Nicholas, 1989; Cialdini & Richardson, 1980). The opposite regulatory phenomenon is ‘cutting off reflected failure’ (CORFing) (Snyder, Lassegard & Ford, 1986). During CORFing individuals may distance themselves and supress their associations with unsuccessful third parties (their teams). This is done in order to avoid a negative self-reflection which ultimately threatens their self-concept (Sherman et al., 2007).

Self-esteem regulatory mechanisms, such as individuals’ responses to successes and failures are heavily impacted by the degree to which an individual has identified with

Chapter 2: Literature Review 10

that team. Higher fan identification levels resulting in increased tendencies to BIRG and decreased tendencies to CORF (Wann & Branscombe, 1990). Fans with high team identification are more likely to use terms such as “we” when speaking about team wins and losses (Wann & Branscombe, 1990, 1993). Colloquially a fan possessing these tendencies would be referred to as ‘hardcore fan’ or ‘die-hard fan’ opposed to a ‘fair-weather fan’ or ‘bandwagon supporter’.

Team Identification and Consumer Behaviour

Beyond the degree individuals may employ regulatory mechanisms such as BIRGing and CORFing to protect self-worth and self-esteem, team identification can account for varying levels of consumption among fans. Sport fans engage in a wide range of consumer behaviours such as purchasing sport branded merchandise, listening to radio commentary, reading the sports pages of the daily newspapers, watching live television broadcasts, dialling into sport websites and traveling extensively to attend events among other activities (Horne, 2006).

Team identification has been found to be a strong predictor of sport fan consumption behaviours along with intention (Robinson et al., 2004; Sloan, 1989; Wann & Branscombe, 1993). Fans with elevated levels of identification behave differently than those with lower levels because highly identified fans are more likely to have a strong sense of attachment and belonging to the team (Sutton et al., 1997). They are more likely to possess elevated commitment and loyalty levels (Ashwork & Mael, 1989). Beyond this, team identification also positively impacts an individual’s sense of belonging, their level of openness, loneliness, depression and fatigue levels (Wann, 2006).

Fans that possess higher levels of team identification have been found to be more likely to engage in consumer behaviours than those with lower levels. Specifically, fans with higher team identification levels purchase merchandise, attend live games, pay more for tickets, be satisfied, and to stay loyal to a team during a period of underperformance (Madrigal, 1995; Wakefield, 1995; Wann & Branscombe, 1993). Conversely, fans with low levels of team identification are more likely to reduce their connectivity and association with a team following adverse team performances (Snyder, Lassegard, & Ford, 1986).

Team Identification Antecedents

Most professional sport teams in the modern era have diversified revenue streams with media revenue and gate receipts comprising most total revenue, and sponsorship, merchandise, and other making up the balance (Mason, 1999). Fans underpin the sports industry through either directly or indirectly driving these revenue streams through consumer behaviours such as merchandise and attendance sales (direct), or even by providing a market for media and television (indirect) (Mason, 1999). As Taylor (1992) comments, “the crowd is the supreme authority without which the golden core of the game has no currency'' (p.188). Thus, fans that possess higher levels of team identification have been found to be more likely to engage in these revenue driving behaviours than those with lower levels. Given the response differential, not

Chapter 2: Literature Review 11

only is it important to have a large fan base, but it is important to have a committed one as well. As a result of the differential it is viable in sports context to create or undermine team value by manipulating drivers that impact team identification strength (Wann and Branscombe, 1995, Hogg & Abrams, 1990).

The capacity of numerous on-field and off-field fan experiences to shape identity outcomes is the subject of extensive literature. This has been driven by scholars’ interest in the factors that encourage an individual to identify with a team. Given the abundance of research relating to the outcomes around causes and antecedents of team identification, they have been further grouped into three categories: psychological, environmental and team related factors (Wann, 2006) as indicated in Figure 1:

Figure 1 Antecedents and Causes of Team Identification (Wann, 2006)

Beyond these, Sutton, McDonald, Milne and Cimperman (1997) have similarly nominated four factors that impact fan identification that are subject to direct influence by managers; team characteristics, organisational characteristics, affiliation characteristics and activity characteristics. Intangible aesthetic factors have similarly been found to influence identification in on-field contexts, such as around artistic appreciation of a sport’s inherent beauty (Fink et al., 2002), and around player attributes such as attractiveness and player similarity to the fan (Greenwood, 2001; Fisher, 1998). Given the ‘grey’ zone that unscrupulous behaviour occupies as to moral compasses it would again be reasonable to hypothesise that this type of intangible aesthetic factor would be relevant. Environmental factors such as exposure to the sport and number of associations (Gwinner & Swanson, 2003), the physical proximity to the team to where someone lives, or has grown up around (Greenwood, 2001), and unique stadiums (Underwood, Bond & Baer., 2001) all positively contribute towards team identity as do socialisation and psychological factors.

Socialisation agents such as other fans and friends (Crawford, 2003; Kolbe & James, 2003), and family who are existing fans of the team, also contribute towards identification (Funk & James, 2001). When asking U.S. university students to list how they came about supporting their favourite sporting team the most common reason was that their parents or family also supported that team (Wann et al., 2006). Updating these findings for the tech savvy, Hyatt (2018) even asserts that due to the prevalence and access to teams and sports from all over the globe through technology, children even influence their parents own sport fandom (Hyatt, 2018).

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Psychological factors relate to the fans desire to feel unity and cohesion, as well as a desire to feel a part of distinctive groups (Sutton et al., 1997; Gwinner & Swanson, 2003; Ashforth & Mael, 1989). They include group status, prestige, domain involvement and fan ritual (Ellemers, Van Knippenberg, de Vries & Wilke, 1988; Wang & Tang, 2018; Gwinner & Swanson, 2003). Group status broadly relates to the positive contribution a group can make towards a member’s personal identity; for example, high status groups make a significant contribution to their members’ social identity (Ellemers et al., 1988). Prestige, in this context, refers to the opportunity for a fan to enhance self-esteem by identification with a prestigious team or community (Gwinner & Swanson, 2003). Domain involvement refers to an individual’s interest in a particular sports genre, in addition to their interest in a specific team (Gwinner & Swanson, 2003).Fan rituals also impact identification: fan rituals include multiple behaviours, often expressive and symbolic, that are periodically demonstrated by fans (Wang & Tang, 2018; Boyle & Magnusson, 2007; McDonald & Karg, 2014; Watkins, 2014).

Taken collectively these intangible aesthetic factors influence behaviour, which in turn contributes to the shaping of team identity.

Athlete Behaviour as an antecedent of Team Identification and as an Impact Driver

Athlete behaviour has the capacity to heavily influence fan and sponsor activities. Athlete behaviour, depending on its form and nature, also arguably has the capacity to make an impact in all three of the team identification antecedent categories proposed by Wann (2006). Its capacity to influence is not surprising given the keen interest paid by the public towards controversial, unscrupulous and scandalous athlete behaviour. Satisfying the public interest, news-media reporting of off-field athlete behaviour is at a level significantly higher than the behaviours occurrence in the broader population (Bloxsome, 2015). Fans similarly are more likely to publicly shame athletes over social media for violations in social and legal norms than sport specific violations (Macpherson & Kerr, 2019). Specific interest in this behaviour is especially interesting considering that on-field and performance related scandals receive more critical to consumer evaluations and are impactful on the offending athlete (Lee & Kwak, 2017; Sato, Ko, Park & Tao, 2015).

Athlete incidents have been found to generate anger with the degree of perceived responsibility having a strong effect on behavioural intention (Sato et al., 2018). Such activities have resulted in reduced game attendance and merchandise sales and sponsorship activities, and negatively impact the athlete themselves (Hughes & Shank, 2005; Lee, Bang, & Lee, 2013; Bacon & Busbee, 2010; Prior, O'Reilly, Mazanov; Huybers, 2013; Sato et al., 2015). Research supporting hypotheses that consumer response to scandal differs based upon whether people identify with the team involved (Chien, Kelly & Weeks, 2016) aligns with research highlighting the importance of exposure to the team over time in building identification whether that be via number of games attended, media, internet, or other environmental factors being highlighted (Mahoney, Nakazawa, Funk, James, & Gladden, 2002; Sutton, et al, 1997; Gwinner & Swanson, 2003). However, given the damages to the athlete, their teams, and

Chapter 2: Literature Review 13

associated sponsors proportionately little research has been conducted to understand the sport consumers' emotional, cognitive, and behavioural reactions to athlete scandals (Sato, Ko & Kellison, 2018). This study will therefore contribute towards addressing this gap.

Williams and Greenwell (2019) examined how three distinct cases of sexual misconduct in the 2015-2016 Division I National Collegiate Athletic Association (NCAAA) Women’s Basketball season impacted fan consumption and team success. Despite critical assessments of the coach attendance levels remained stable across all three institutions. Fink, Parker, Brett and Higgins (2009) also explored the effect of unscrupulous athlete off-field behaviour on team identification. They conducted an experiment depicting a key athlete engaging in antisocial behaviour such as being charged with drunk and disorderly conduct and assault and battery. Following the revelation team identification scores amongst the fan set were markedly lower, Chien, Kelly and Weeks’ experiment (2016) found that antisocial off-field athlete behaviour can adversely affect attitude towards a team across groups. They exposed in-group and out-group fans to revelations that one or multiple members of a team had been implicated in a doping scandal. Both groups attitudes toward the team were adversely affected by the scandal, however the more highly identified in-group fans’ attitudes remained more positive than those in the out-group. These findings could be expected as more highly identified individuals can be more resistant to threatening and negative information about the team because group membership is more central to their social identity (Doosje, Branscombe, Spears, & Manstead, 1998; Iyer, Jetten, & Haslam, 2012).

Fink et al.’s (2009) study found that antisocial athlete off-field behaviour reduces team identification. Sato et al.’s (2008) research determined that they can reduce game attendance and merchandise sales. It remains unclear whether this relationship will replicate itself across differing professional sporting contexts. Chien, Kelly and Weeks’ (2016) study was within the same professional sports context, however it does provide support that antisocial off-field athlete behaviour can engender negative attitudes across groups, hence the first hypothesis is framed as:

H1: Antisocial off-field athlete behaviour will have a negative association with intention to buy merchandise, attend subsequent live games and follow the success of the team in both a non-traditional and traditional sport setting.

Conversely, prosocial off-field athlete behaviour potentially has the capacity to bring about polarising effects. Trust has been found to be important when building team identification and can be created through interactions between the team and its fans, along with team management and customer relations management strategies (Wu, Tsai & Hung, 2012). Prosocial behaviour observed through interactions can help fans make assessments around the trustworthiness of both individual players as well as the organisations they represent. Trust in a team positively contributes towards team identification and trust in a player can improve player identification, which in turn drives team identification (Wu et al., 2012). Athlete participation with non-profit organisations and adhering to off-field behaviour guidelines are examples of prosocial behaviour that are cited as good trust building activities (Morgan & Hunt, 1994).

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Beyond trust, on an individual level research indicates that the specific traits of a team’s players can be a compelling cause of team identification. Fisher (1998) found that both player attractiveness and player similarity to the fan are important predictors of team identification. A player who is exhibiting prosocial behaviours is potentially engaging with people who may similarly be involved in helping, sharing, donating, co-operating or volunteering activities. Equally, involvement in these prosocial activities could be increasing his or her attractiveness. Funk, Mahony and Ridinger (2002) found in their study of women’s professional soccer that role modelling is a key motivator of spectator support. Again, the presence of role models was found to be a motivational factor in the Women’s National Basketball League (Funk, Ridinger and Moorman, 2003).

These findings contribute to a solid base of literature highlighting the positive impact that prosocial off-field athlete behaviour engenders. Hence it can be hypothesised that prosocial off-field athlete behaviour will positively affect consumer intention in all professional sport contexts.

H2: Prosocial athlete off-field behaviour will have a positive association with intention to buy merchandise, attend subsequent live games and follow the success of the team in both a non-traditional and traditional sport setting.

Athlete Behaviour as a moderator

Extreme examples of unscrupulous off-field athlete behaviour produced changes of mean team identification scores among participants (Fink et al., 2009). Mean changes were substantive, with differences being recorded among low identified fans and among highly identified fans. Changes were the results of team identification scores being recorded immediately after the participants had reacted to the mock articles that were used as the stimulus. This was credited as being a potential limitation in the research. In relation to the change in team identification scores the authors noted that “it is plausible that fans experience and immediate reaction that subsides after time” (Fink et al., 2009, pp.152). This suggests that while unscrupulous athlete behaviour provokes a fan response, perhaps the change in team identification scores captured the reactionary response, rather than the genuine change in team identification level among the subjects. Team identification is thus a composite variable comprised of a broad range of antecedents. It is for this reason that team identification has been found to be quite a stable measure, especially among those who possess high identification levels (Cialdini et al., 1976; Wann & Branscombe, 1990; Lock, Funk, Doyle & McDonald, 2014), as well as being a very strong predictor of consumer behaviour (Robinson et al., 2004).

Taking this into consideration, while athlete behaviour certainly can impact team identification levels, its impact is not likely to the same extent that was measured in Fink et al.’s (2009) study. That was potentially a better reflection of the reactionary response of the consumer. Nonetheless the findings suggest that athlete behaviour is powerful variable. Extent literature supports this, having determined that antisocial athlete behaviour has the capacity to impact consumer behaviours such as crowd attendance and merchandise sales (Sato et al., 2018). Despite recognising this as being an important variable “little research has been conducted to understand sport

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consumers' emotional, cognitive, and behavioural reactions to athlete scandals” (Sato, 2018 p107).

To address the above points arguably the reaction to the off-field behaviour does not cause powerful changes in team identification scores, rather it has a powerful effect on the already well-established relationship between team identification and consumer behaviour. Hence, along those lines the degree to which an individual believes an off- field athletes’ behaviour is very prosocial - or very antisocial – may see their reaction manifest itself as a significant alteration to the strength of their association between their team identification score and their consumer behaviours. The strength alteration is being the reaction to the off-field behaviour. To further examine the impact that athlete off-field behaviour has on the relationship between team identification and consumer behaviour hypothesis 3 is framed as:

H3: Athlete behaviour will have a significant moderating effect on the relationship between Team Identification and Consumer Intention

New versus Established Leagues and Teams

The context in which research has evaluated the relationship among key variables such as the antecedents of team identification and corresponding consumer behaviours has been largely conducted in a ‘traditional’ context. Traditional in this research refers to established leagues and teams, while ‘non-traditional’ refers to new teams and leagues. Potential differences between the contexts are outlined below.

As mentioned, there are numerous reasons why an individual might choose to identify with a sporting team. Wann (2006) broadly categorises the causes and antecedents of sport team identification into psychological, environmental and team categories. Underpinning all antecedents of identification is the key understanding that “... individuals strive to achieve or maintain positive social identity” (Tajfel & Turner, 1979, p. 40). Extending this key understanding is Ellemer’s (1988) experiments that found group identification levels vary based upon the contribution a group can make to towards and individuals’ social identity. High status groups make a larger contribution than low status groups, hence the members tend to have higher identification scores.

New leagues and new teams do not possess many of these causes and antecedents of sport team identification. Arguably then they may not be as ‘high status’ as a team or league with a full complement (Sutton, Mcdonald & Milne, 1997). Specifically, new teams and leagues without an existing footprint lack many team related factors such as traditions, a rich history and winning legacy (Lock, Taylor & Darcy, 2011). They also do not possess many environmental causes of team identification such as the existing presence of socialisation agents that increase exposure to the sport and build team identification. Whilst some psychological factors may also be missing such as prestige, new teams certainly are not devoid of contributors. A desire to support the sport, the home city of the members and the match day occasion have been found to be important in building new team identification (Lock, Taylor & Darcy, 2011). Additionally, the opportunity to support a new team provides a rare and distinctive consumption

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experience which attracts fans (Doyle, Lock, Funk, Filo & McDonald, 2017; Harada & Matsuoka, 1999).

Arguably individuals differentiation could also motivate their fandom with new team and leagues. Social identification indicates that individuals will attempt to differentiate themselves from others (Tajfel & Turner, 1985) because this makes them feel good about themselves. These contributors however do not account for the fact that the most salient part of identity development and strength in relation to identity with sporting teams and leagues is argued to be duration (Lock, Darcy & Taylor, 2009). Duration allows identity strength to increase and develop as traditions, team experiences and history accumulates (Lock, Darcy, Taylor, 2009. New non-traditional professional sporting teams and leagues lack the same complex interwoven web of fan experiences to create identity.

Amidst the absence of antecedent’s, questions arise evaluating the transferability of existing knowledge to a non-traditional sporting context. While an absence of antecedents is not enough to suggest that fans behave differently, the following hypotheses is proposed to determine whether fans in a non-traditional context abide by the same pattern.

H4: Fans with high identity levels will have a positive association with intention to buy merchandise, attend subsequent live games and follow the success of the team in a non-traditional sport setting.

Athlete Behaviour across traditional and non-traditional contexts

Building upon hypothesis 3, in traditional sport contexts the impact of athlete behaviour as a variable interacting with team identification and consumer behaviour would necessarily be influenced by the existing impact of other variables that drive positive, or negative, team identification. Fink, Trail & Anderson (2002) have shown that fan motivation is strongly tied to vicarious achievement through analysis of tangible factors such as high performance and winning records. However, in the context of a non-traditional sport in start-up stages with no marquee players it can be hypothesised that the behavioural factor will be weighted potentially far more heavily one way or the other. This ultimately has a larger effect on the strength of the relationship between team identification and the consumer behaviours. Hence hypotheses 5 and 6 are framed as:

H5: Prosocial athlete off-field behaviour will have a stronger moderating effect on the relationship between team identification and consumer intention in a non- traditional professional sport setting than in a traditional setting

H6: Antisocial athlete off-field behaviour will have a stronger moderating effect on the relationship between team identification and consumer intention in a non- traditional professional sport setting then in a traditional setting

Summary and Implications

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Significantly less research has considered off-field events, particularly how athletes’ off-field behaviour impacts fan identification (Fink, et al. 2009). Research evaluating consumer reactions to off-field athlete behaviour is largely absent, while its relative importance remains widely understood (Sato et al., 2015; Sato et al., 2018). Equally, the role contextual factors play in the relationship remains under-explored. More specifically in whether antisocial and prosocial behaviour in established professional leagues have an equal effect in sports and leagues that are less traditional, or in infancy, in terms of being a professional sporting code. Empirical research evaluating the direct and interactional effects of team identification and off-field athlete behaviours on outcomes of fan identity has therefore the potential to contribute to this field of research.

A contextual examination of off-field athlete behaviours association with team identity, and the effect team identification has on consumer intentions, will further address an under-researched contributor towards fan identity. The study will extend the literature on fan identification and social identity theory, providing further explanation on why and how fans identify with sporting teams. Previous studies on identification show fan identification and social identity theory to be a stable measure, especially amongst those who are more identified (Cialdini et al., 1976; Wann & Branscombe, 1990). This research, by collecting data from a newly formed league yields new insights as most previous studies have taken place in traditional established sport settings.

In terms of prosocial off-field athlete behaviour, research indicates that fans value the socially responsible efforts of their teams and use this information especially when considering product selection as well as, or when favourably speaking of the organisation (Walker & Kent, 2009). This proposed research presents an opportunity to expand upon this understanding by evaluating the link between the individual athletes’ prosocial behaviour and their connection to the larger organisation or team. Having players engage in prosocial behaviours such as supporting and attending grassroot and youth level events has been suggested, amongst many other player accessibility and community involvement strategies, as a means for professional teams to build fan identification (Sutton et al.,1997). The efficacy of some of these practices was evaluated over the due course of this research. The effect of antisocial athlete behaviour was similarly evaluated through the research.

Practically, a contextual examination of off-field athlete behaviours’ effect on the outcomes of fan identity could be especially beneficial to non- traditional sport start- ups and traditional sports alike. Resources towards developing a new sport or the league may be limited and decisions around the allocation of these resources can be especially complicated. With limited resources and often in a vulnerable position maximising return whilst mitigating risk is of utmost importance. The study is anticipated to assist management and marketers in practice by highlighting the potential benefits of encouraging athlete community engagement, prosocial behaviours, against the potential costs of players engaging in antisocial behaviour. If the benefits are significant encouraging and supporting players so that they can actively involve themselves in prosocial behaviour is a relatively low-cost exercise against alternative methods. Also, by employing players who exhibit prosocial behaviour strategic recruitment practices can be better streamlined with marketing practices. However, if the results of the study show that these behaviours do not make

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a significant positive contribution towards attainment of managerial outcomes it presents an opportunity for practitioners to better allocate resources. It gives less cause for athletes’ focus and attentions to be drawn away from the playing arena and reduces the spectrum in which marketers strategise activities. Alternatively, this is an indication that the specific prosocial behaviours on display are not resonating with the fanbase and resources should be allocated to identifying the causes and behaviours that would.

Beyond this a deeper understanding of the effect off-field behaviour has on identity outcomes could impact how firms exploit commercial sponsorship opportunities. It is not uncommon to see firms immediately cut ties with athletes and teams who have been caught out in antisocial behaviour. Recognising that fan identity is a product of many experiences over an extended period perhaps these effects are everlasting and the immediate reaction to behaviour does not affect intended outcomes. Knowledge about this relationship will potentially better inform firms about whether decisions to cut ties are too rash. The results may also assist firms in identifying athletes and a team by providing an indication as to whether an athlete or team that is renowned for demonstrating prosocial behaviours is more valuable as a corporate agent than an athlete or team that is renowned for their sporting abilities along with exhibiting antisocial behaviour.

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Chapter 3: Research Methodology

Methodology and Research Design

Introduction Chapter Two provided an overview of team identification, consumer intention and off- field athlete behaviour and outlined the questions and hypotheses that this research seeks to test and answer. This chapter will present the methodology and methods used to undertake the research. The chapter begins by outlining the research philosophy underpinning this research with a re-evaluation of the research questions that were formulated during the literature review. Next the design and justification, techniques of data analysis and ethical considerations are discussed and linked explicitly to the research questions.

Research Paradigm Research paradigms in social sciences are underpinned with philosophical roots that provide a basic orientation for theory and research (Neuman, 2014). A research paradigm is the base used to construct a scientific investigation; “a loose collection of logically held together assumptions, concepts, and propositions that orientates thinking and research” (Bogdan & Biklen, 1982, p. 30). It guides and deals with the source, nature and development of knowledge (Bajpai, 2011). Research can be directed from a range of paradigms, the four most common paradigms being Pragmatism, Positivism, Realism and Interpretivism.

Interpretivism research assumes that “access to reality (given or socially constructed) is only through social constructions such as language, consciousness, shared meanings, and instruments” (Myers, 2008, p.38). It is a subjective approach and cannot be generalised, hence was an unsuitable perspective for adoption in the undertaking of the research. A Realism perspective “focuses on explaining what we see and experience, in terms of the underlying structures of reality that shape the observable events” (Saunders, 2009, p.138). Realist research recognises that beyond what is observed, much exists that cannot be observed, but is nonetheless real. Qualitative methods such as case studies and convergent interviews are commonly used when conducting research from a realism paradigm (Sobh & Perry, 2006). Pragmatics “recognise that there are many different ways of interpreting the world and undertaking research, that no single point of view can ever give the entire picture and that there may be multiple realities” (Saunders, Lewis & Thornhill, 2012, p.23). The research question is the crucial determinant of research philosophy. This may mean that methods from mutually exclusive paradigms such as positivism and interpretivism may be mixed in order to address the research question. Identifying the research paradigm of the research helps in designing a suitable methodology to answer the research questions (Neuman, 2014).

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Given this research projects intention to explore whether consumers react differently to off-field athlete behaviour depending on whether they are supporting a traditional or non-traditional sporting team, these three approaches are arguably not the most suitable. Instead a Positivism perspective will be adopted to drive subsequent methodology. Positivism is an objective perspective adhering to the view that only what is gained through observation is trustworthy. The perspective is supported by the scientific method. The role of the researcher is limited to data collection and interpretation. The research is also largely dependent on quantifiable observations that lead to statistical analyses. This requires a highly structured approach, large samples and quantitative measurements. Commonly this manifests itself as the manipulation of reality with variations in a single independent variable. This manipulation of reality can lead to identifying regularities in and forming relationships between some of the constituent elements of the social world. Through these methods generalisations causal explanation and prediction constitute acceptable knowledge contributions. This perspective suitably aligns to the research objectives of making both a determination and a contribution to knowledge around the relationships between team identification, consumer intention and off-field athlete behaviour assessments across traditional and non-traditional sport contexts. This perspective heavily influenced the subsequent methods that were developed to fulfil the research objectives.

Research Methodology The methodology underpinning this research will be guided by the intended theoretical contributions to be made and the research perspective. Adherence to these cornerstones is important in achieving good methodological fit, or internal consistency between key elements of research. This can encompass previous work, the intended theoretical contribution, the research questions, and the research design (Edmondson & McManus, 2007).

As a positivist research perspective will be adopted, suitable research methods needed to be evaluated in respect to their capacity to be highly structured, deductive and involve large samples and involve measurement (Marczyk, DeMatteo & Festinger, 2005). These are qualities that are widely accepted as being the cornerstones of good quantitative research designs. Quantitative research is optimally used “where control of variables, randomization, and valid and reliable measures are required and where generalizability from the sample to the population is the aim” (Jha, 2008, pp.13). In this research project the aim is to generalise the research finding to the broader populations of tradition and non-traditional sport fans. Examples of quantitative research designs include quasi-experimental studies, experimental studies, pre-test and post-test designs. Consequently, these options would all be evaluated for their appropriateness in achieving good methodological fit.

Qualitative methods were overlooked for their lack of appropriateness for this research project. Qualitative research is exploratory by definition and is often best used when unable to anticipate, define a problem or develop an approach (Walliman, 2017). As outlined in Chapter Two the extent literature has already broadly canvassed areas relevant to this research project and facilitated the development of the key hypotheses. The hypotheses formulated during previous work are outlined below:

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H1: Antisocial off-field athlete behaviour will have a negative association with intention to buy merchandise, attend subsequent live games and follow the success of the team in both a non-traditional and traditional sport setting.

H2: Prosocial off-field athlete behaviour will have a positive association with intention to buy merchandise, attend subsequent live games and follow the success of the team in both a non-traditional and traditional sport setting.

H3: Athlete behaviour will have a significant moderating effect on the relationship between Team Identification and Consumer Intention.

H4: Fans with high identity levels will have a positive association with intention to buy merchandise, attend subsequent live games and follow the success of the team in a non-traditional sport setting.

H5: Prosocial off-field athlete behaviour will have a stronger moderating effect on the relationship between team identification and consumer intention in a non-traditional professional sport setting than in a traditional setting.

H6: Antisocial off-field athlete behaviour will have a stronger moderating effect on the relationship between team identification and consumer intention in a non-traditional professional sport setting then in a traditional setting.

To test the above hypotheses data needs to be collected measuring the several key variables. To extract the necessary data an experimental design was selected over descriptive, correlational, quasi-experimental methods. It was retained on the basis it was the most appropriate framework to obtain the necessary quantitative data measuring each key variable across groups. Experimental design allows researchers to evaluate causal relationships among variables while all other variables are eliminated or controlled (Zikmund, D’Alessandro, Winzar, Lowe, Babin, 2015). Exercising this control over the variables is important as it provided an opportunity to collect data capable of supporting the formal hypothesis testing, and statistical inference and analysis. The use of experimental research is also consistent with prior research in the off-field athlete behaviour field (e.g. Fink et al., 2009).

The required data will be obtained by operationalising all the variables in structured questions that will be distributed through online surveys. The structure of the survey, questions and scales are discussed later in this chapter at depth and both time and cost were major factors that supported the use of online surveys. The key variables subject to operationalisation are team identification, participant assessment of off-field athlete behaviour, and the relevant intention items assessing the participants intention level to undertake subsequent consumer activities.

The three intention items relate to purchasing merchandise, attending live games and following the team on social media and television while the fourth intention item is a composite intention measure. Beyond these key variables demographic information will also be collected from all participants. The survey was structured so that initial items captured basic demographic information such as gender and age and asked participants to volunteer their favourite professional team or league. Following these

Chapter 3: Research Methodology 23

questions, the dependent variable ‘team identification’ will be measured over three items.

Participants assessment of off-field behaviour will then be the next key variable measured. The survey will then randomly assign the participant one of three fictitious articles to read. They will be asked to imagine that the perpetrator of the behaviour in in each article was a key athlete from their favourite team. When attempting to capture fan response to athlete behaviour, fictional articles have been employed to great effect in previous studies (Fink et al, 2009; Chien et al., 2016). Each article describing an unnamed athlete from an unnamed team engaging in either antisocial, benign or prosocial off-field behaviour. A benign off-field athlete behaviour article was included as a third intervention item to provide a control group from within the participant pool, hence providing a normal distribution of responses across the perception of off-field athlete behaviour measure.

After reading the article and completing the off-field athlete behaviour assessment item, the participants will complete the survey by answering the three remaining intention items. This experiment will then be replicated across three major groups in order to ascertain whether the association between off-field athlete behaviour and team identification changes between contexts.

Participants

To discover how the association between off-field athlete behaviour and team identification changes between contexts the sampling method needed to include fans from traditional and non-traditional professional sports teams. As discussed in the literature chapter traditional professional sports often enjoy a full complement of psychological, environmental and socialisation causes and antecedents of sport team identification (Wann, 2006). Non-traditional professional sport teams, although not without their own unique contributors such as the distinct consumption experience of supporting a new team, are devoid of many other significant contributors (Doyle, Lock, Funk, Filo & McDonald, 2017; Harada & Matsuoka, 1999).

In order to examine the research question and evaluate how impactful the absence of contributors such as traditions, a rich history and winning legacy are, a purposive sampling technique will be employed. Purposive sampling is a non-random technique that does not need underlying theories or a set number of participants (Etikan, Sulaiman & Musa, 2016). Critical to effective purposeful sampling is the role of the researcher whose task is deciding what needs to be known and identifying participants who can and are willing to provide the information (Bernard, 2011).

Three major groups of sports fans were purposively targeted during the research project, with a target of 120 participants sought from each of the three participant pools. This meaning that successful recruitment will end up with upwards of 360 participants total for the research project. Given the experimental studies format of a 2 (high vs. low identification X 3 (prosocial vs. antisocial vs. benign off-field athlete behaviour), 120 participants allow for a minimum of 20 participants to be allocated to each of the six groups. This figure was consistent with Fink et al.’s (2009)

Chapter 3: Research Methodology 24

experimental study that employed a 2 (high vs. low identification X 2 (strong vs. weak leadership response). Fink et al. (2009) similarly allocated a minimum of 20 participants to each of four groups.

The first group will be drawn from traditional sports and included professional American Football fans from the United States. The second group, also traditional, will include Rugby Union fans from major teams and leagues around the world. The third group will be drawn from the non-traditional sports sector and shared a single likeness with both the previous groups one and two. They are also Rugby Union fans but are drawn from the emergent professional rugby league in the United States. The justification for their purposive selection is discussed below.

Traditional Sport Participants

Traditional fans consist of members of the following three closed Facebook groups:

• New Orleans Saints Fans • Rugby Fanatics • All Blacks & NZ Rugby Supporters

New Orleans Saints Fans

The ‘New Orleans Saints Fans’ group has over 90,000 members and is a dedicated place for fans to discuss all aspects of the NFL team. The membership consists of predominately New Orleans Saints fans, but many other avid fans of rival teams also enjoy membership. Outside of New Orleans Saints fans, a further 14 NFL teams participated in the survey.

The NFL is the most popular professional sports league in the United States with 34 percent of Americans identifying professional football as their favourite sport (The Harris Poll, 2016). The Harris Poll reported professional baseball the next most popular with 16 percent, and college football third-most popular with 11 percent, reaffirming footballs popularity among the U.S. public (2016). Without a Major League Baseball team, and the minor league team, the ‘Baby Cakes’ set to relocate to Wichita in 2020, the New Orleans Saints remain unthreatened by the second most popular professional sport (Vargas, 2019). Having been established in New Orleans, Louisiana in 1966 the Saints have a long-standing history with the community. While the Saints are only the 26th most valuable team in the NFL, they are nonetheless valued at US $2.1 billion, generating an annual revenue of US $413 million dollars in 2017 (Forbes, 2018).

Rugby Fanatics

‘Rugby Fanatics’ has a membership over 30,000 who are dedicated towards following rugby union internationally and in the well-established leagues in both Europe and Australasia. These leagues include the Premiership, the Top 14, Pro14, the

Chapter 3: Research Methodology 25

Championship, Mitre 10 and Super Rugby. Fans supporting teams within all these leagues were accounted for in the survey results.

International Rugby comprises of 120 national unions across Africa, Asia, Europe, North America, South America and the Oceania. The national teams compete against each other in a range of competitions and ‘friendlies’, or non-competion games. The Rugby World Cup is the sport’s most recognised competition and occurs every four years. The Rugby World Cup is the third-largest global sporting event behind the FIFA World Cup and the Summer Olympics (Nikkei Asian Review, 2015)

The Premiership consists of twelve clubs and is the top division of the professional English rugby union system. The competitions origins date back to the 1997-1998 season when the game became professional (Premiership Rugby, 2019) but all twelve competing teams were established prior to 1899 with the oldest teams, Bath and the Sale Sharks, founded in 1865 and 1861 respectively (Bath Rugby, 2019; Sale Sharks, 2019). In the 2017-2018 season the competition had a total crowd attendance of 1.9 million, averaging 14.1 thousand spectators per match (Evans, 2018).

Founded in 1982, the Top 14 is the premier professional rugby union club competition in France. High spectator attendance, government subsidies and large broadcasting deals in recent years have massively increased the economic strength of the competition and its fourteen membership clubs (SportsPro, 2015; SportsPro. 2019; Cleary, 2009). Due to the strong economic performance of the league many of the world’s best players including Dan Carter, Johnny Wilkinson, Bismark Du Plessis and Matt Giteau have all been attracted to French clubs (Ruck, 2018; BBC Sport, 2014).

Originally founded in 1999 as the Welsh-Scottish League the league has evolved to its current form, the Pro14. The professional rugby union competition involves fourteen sides from Italy, Ireland, South Africa and Wales. In 2017-2018 season attendance figures reached 1.3 million, averaging 8,500 spectators a match (Morrison, 2018).

Super Rugby is the premier professional rugby union competition in the southern hemisphere. It involves teams from Australia, New Zealand, Argentina, Japan and South Africa. Super Rugby started as the Super 12 in the 1996 with 12 teams from Australia, South Africa and New Zealand. The league has expanded in recent years to include teams from Japan and Argentina (Super Rugby, 2014; Mckay, 2019). The standard of play in the competition is widely recognised as being extremely high. This is reflected in the selection of the Argentina, New Zealand, Australia, Japan and South African national team squads, all teams who feature within the top 11 nations worldwide as of July 2019 (World Rugby, 2019). These squads significantly feature, and sometimes entirely draw from, participating players in the league (Worthington, 2019; Guinness, 2015).

All Blacks & NZ Rugby Supporters

The ‘All Blacks & NZ Rugby Supporters’ group has over 79,000 group members who follow rugby in New Zealand. Rugby Union is the National sport of New Zealand. The final of 2011 Rugby World Cup was the single most watched television event in New Zealand history, with the national team, the ‘All Blacks’, defeating France (Winks,

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2011). New Zealand also had a dedicated television channel screening classic games and documentaries relating to the sport when live matches are not being broadcasted (Keall, 2019).

The national team, the All Blacks, are the most successful international team in world rugby. They have maintained a 77% winning record in all international matches since 1903, had three world cup victories, and retained the number one word ranking longer than all the other nations combined (World Rugby, 2019; New Zealand Rugby, 2019). The All Blacks are comprised of players from the Crusaders (Christchurch), Highlanders (Dunedin), Blues (Auckland), Chiefs (Hamilton) and Hurricanes (Wellington). All the clubs compete in the Super Rugby competition.

Non-Traditional Sport Participants

Non-traditional sport fans consist of fans from the emergent professional rugby union teams in the United States. These teams include:

- Nola Gold, New Orleans LA - Houston SaberCats, Houston TX - Seattle Seawolves, Seattle WA - Toronto Arrows, Toronto - San Diego Legion, San Diego CA - Austin Elite, Austin TX - Rugby ATL, Atlanta GA - Utah Warriors, Utah UT - RUNY, New York - Colorado Raptors, Denver CO

Rugby is not entirely new to the United States. American Football, said to share common roots with modern rugby until Rugby Sevens made its debut at the 2016 Rio Games, were the reigning Olympic champion in Rugby having won gold at the 1920 and 1924 Summer Olympics. Beyond this, rugby has been played in U.S. universities since as early as the 1800s. However, it was the 1960s when rugby really found a foothold in colleges. It was led by Catholic colleges such as Notre Dame and particularly the Jesuit universities such as Boston College and St. Joseph's in Philadelphia, and Ivy League universities such as Harvard, Yale and Princeton. Currently there are over 32,000 college players registered with USA Rugby, making college rugby the largest section of USA Rugby's membership. Notwithstanding this, college rugby remains governed by USA Rugby, and does not fall under the auspices of the NCAA with the exception of 15 NCAA women's programs. The NCAA have only indicated Women's Rugby as being a contender for admittance to the National Collegiate Athletic Association. Despite recognition it has remained classified as an NCAA Emerging Sport since 2002.

Several attempts have been made to develop a professional Rugby Union competition in the United States. In most instances these attempts have been unsuccessful. Several entities have explored the viability of developing a professional competition. For example, a fifteen-a-side rugby competition, the Rugby Super League began play in 1997 as a national competition and was discussed as potentially becoming a

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professional competition. However, the RSL struggled financially with several teams exiting the competition from 2009 to 2012 before the league folded after the 2012 season (Willoughby, 2014). In 2012, the U.S Professional Rugby Competition was reported to be exploring the possibility of launching a ten-team league for 2015, but nothing emerged from this speculation. The National Rugby Football League (NRFL) also announced its intention to begin a professional rugby competition in 2015 and scheduled the Independence Cup, but again the venture did not launch. More recently, PRO Rugby, owned and operated by Mr Doug Schoninger, did launch a competition in 2016 with five teams but this folded by the end of that year (Pengelly, 2016; Wassell, 2017). Schoninger blamed the collapse on the lack of suitable venues and support from local media. As these examples demonstrate there are many risks associated with Rugby Union start-ups in the United States.

Professional rugby union in the United States had its most recent re-emergence with the establishment of Major League Rugby (MLR) in 2018. MLR was an initiative of the North American rugby community in partnership with private investors with seven teams that competed in the inaugural 2018 season. The league is structured as a single entity with each club owned by the league and club operators owning a share of the league. The inaugural clubs invested approximately $500,000 to join and expanded to include teams from Toronto and New York in 2019. In 2020 further expansion is set to occur with the addition of teams from New England, Washington DC and Atlanta (Major League Rugby, 2019).

Having only both just completed their second respective seasons the Nola Gold and Houston SaberCats are foundation teams that featured in the inaugural season. They provide a solid basis for the collection of non-traditional professional sport fan data. Situated in Houston and New Orleans respectively, neither location had a history of professional rugby prior to the commencement of the MLR and only possessed a very limited footprint in terms of amateur club, high school and college rugby.

Instruments

Three online surveys will be created to capture the data used in this research project. The only differences between the three surveys occurs in league/team specific questions (Appendix A) and a slightly modified information sheet to suit the different participant groups (Appendix B). Beyond that they all similarly capture fan reaction to the same fictitious articles describing an unnamed athlete from an unnamed team engaging in either antisocial, benign or prosocial off-field behaviour.

The surveys will be created and hosted on Key Survey, a survey management solution offered by WorldAPP. The decision that an online survey was the most appropriate mechanism to extract data for this research project was supported by several factors including time and cost. Where sports fans sets have been the targeted population numerous studies have often opted to collect data at home-game match venues (Matsouka et al., 2003; Neale & Funk, 2006; James, Ridinger, 2002). Collecting data at the playing arena, or even the region of locality of the team, can provide a scenario where researchers are face-to-face with a high concentration of potential participants. Given the research targeted a wide range of fans across the world from two major

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sports attending live games or even the region that the team resides in was impractical. This was even more so with the non-traditional sports teams. Unlike traditional professional sporting teams whose fans may be highly concentrated within a geographic area non-traditional fans are much fewer in number and thus may be much more difficult to gain access to outside of games and events.

Online surveys were selected on the basis they offer a mechanism through which a researcher can gain access to people who share specific interests, attitudes, beliefs, and values regarding an issue, problem, or activity (Wright, 2005). The surveys were distributed through two teams marketing channels online, as well posting on three online Facebook Groups (Appendix C). This provided access to rugby fans not from one major established competition, but from seven around the world. Fans of fifteen NFL teams and eight newly formed teams were also accessed through the online surveys. Online surveys may also save time by allowing researchers to collect data while they work on other tasks (Llieva, Baron, & Healey, 2002).

The key variables measured included participant team identification scores, their perceptions of the off-field athlete behaviour and lastly their intention to subsequently engage in three consumer behaviours.

Team Identification refers to spectators’ perceived connectedness to a team and the experience of the team’s successes and failings as their own (Ashforth and Mael, 1989; Wann, Melnick, Russell & Pease, 2001; Kim & Kim, 2009). Many measures have been developed to aid in the assessment of team identification including Trail and James’ (2001) measure of team identification that will be used during this research project. This measure, named The Team Identification Index (TII), consists of three items (1) I already consider myself a fan of the [team name]; (2) I would feel at a loss if I had to give up being a [team name] fan; and (3) Others recognise that I am a big [team name] fan. Participants respond to the items on a seven-point Likert scale anchored by “strongly agree” and “strongly disagree”. The score from all three items will be totalled then divided by the number of items to produce a fan identification score. The TII is the second most used measure of team identification by sports scholars behind Wann and Branscombe’s (1993) Sport Spectator Identification Scale (SSIS) (Wann & James, 2018). Despite the SSIS similarly being a three-item scale, the TII will be retained over the SSIS on the basis it was the measure of choice during Fink et al’s (2009) similar study exploring unscrupulous athlete behaviours effect on team identification.

Evaluations of off-field athlete behaviour and the corresponding fan reactions, or intention items, will be generated using a manipulation. The manipulation will involve the participants reading one of three fictional newspaper stylized articles describing an unnamed athlete from an unnamed team engaging in examples of prosocial, antisocial and benign off-field athlete behaviour (Appendix A). The three articles were loosely based on true events reported in an Australian and U.S. newspaper. The participants will be asked to imagine that the articles describe the actions of a prominent player from within their favourite team and respond accordingly. The prosocial article describes an athlete walking to school with a bullied 12-year old. The antisocial article describes an athlete refusing to take a photograph with a fan which rapidly escalates into a confrontation. The benign article describes athletes actively undertaking recovery sessions at a community pool.

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Following the article participants will be asked: “If prosocial behaviour is defined as ‘behaviour with intent to benefit others’ and antisocial behaviour is defined as ‘behaviour causing harm or lacking consideration for the well-being of others’, where on this scale would you place the behaviour described in the scenario?”. Participants will respond to this item on a five-point scale anchored by ‘very antisocial’ and ‘very prosocial’, with ‘neutral’ sitting on the midpoint. A 5-point scale was retained over a larger scale on the principles expressed by Hammersley: “the more precise the scale, the much more difficult it is to achieve high levels of validity” (Hammersley, 1987, pp.77). This score is critical as it will serve as the assessment of the athlete’s behaviour. The articles were deliberately written to align with the three critical points on the scale which were key to subsequent analysis. Conversely the scale similarly will ensure that the articles are correctly capturing antisocial, benign and prosocial behaviour constructs.

Immediately following the manipulation check participants will complete the survey by answering three questions measuring consumer intention. For this study the intention items will be adapted from Matsuoka et al’s. (2003) study exploring the effects team identification and satisfaction have on intention to attend games. Matsuoka et al (2003) when measuring intention asked, “how likely are you to attend the [team’s] games during the remainder of the season?”. This question will be rephrased as “How likely are you to continue attending your favourite team’s games?”. The intention item will be accompanied with a further two items; (1) “How likely are you to continue following the team this/next season? i.e. on social media and television”; and (2) “How likely are you to buy more merchandise this/next season?” Both documented by-products of elevated identification levels (Madrigal, 1995; Wakefield, 1995; Wann & Branscombe, 1993). As per Matsuoka et al’s (2003) study, participants will respond to the items on a seven-point Likert scale anchored by “definitely would” and “definitely would not”.

Analysis

Once the surveys are closed, in line with the positivist research perspective adopted, the collected data will be uploaded to the software package SPSS Statistics. As a positivist perspective relies specifically on observable and quantifiable scientific evidence, such as statistics and experiments. Accordingly, SPSS statistics is a suitable program to undertake the hypothesis testing. As a statistics package, SPSS Statistics fulfils all the functions of statistics including the collection, organisation, displaying, analysis, interpretation and presentation of data (Romijn, 2014).

Before the analysis and hypothesis testing can occur, the data will be prepared and cleaned. This will involve conducting descriptive statistics to identify missing data sets. If data is missing the individual cases will be either retained or deleted. Maximising the total data collection while mitigating the introduction of bias will be the underlying principles when deciding upon whether to retain or delete cases.

Assumptions

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Once the data is prepared, cleaned and coded, an evaluation of key assumptions will be undertaken. The key assumptions will be evaluated in respect to statistical analyses relevant to the formulated hypotheses. Confirmation of independence, homoscedasticity, linearity and normal distribution will provide ideal conditions under which type I error is controlled and statistical power is maximised (Mellinger & Hanson, 2016). Simple frequencies analyses will be conducted in SPSS enabling a simple preliminary review of all the variables within the data set. This will be completed in order to check that a normal distribution of the data is experienced across all key variables.

Special attention will be paid to the off-field athlete behaviour response item. Assessments will be made evaluating the measure’s validity and reliability. It is critical to subsequent analysis that participant assessments of the three fictitious articles, the prosocial, benign and antisocial articles are effective in capturing the desired participant behaviour assessment on the scale. If so, it would suggest that the construct of interest was being accurately captured. Beyond this, an assessment must be made evaluating whether the item produced consistent repeatable results.

Tests will be then conducted testing for differences between group means. Homogeneity across the data sets for the control variables is desirable. T-tests will be conducted on demographic variables before proceeding to the analyses relevant towards the focus of the research. The t-test will determine whether gender and age have a significant effect on key variables such as team identification and off-field athlete behaviour assessment scores. Any significant differences between the independent samples relevant to extraneous factors such as gender and age, could influence the subsequent interpretation of the hypotheses.

Cross-tabulations will then be conducted assessing whether the three independent samples varied in terms of their demographic composition. Crosstabs, being a non- parametric method of gauging how two non-metric (nominally scaled) variables are related will assess whether the three samples significantly differed in terms of their age and gender composition. If the cross-tabulations are insignificant, meaning that the three independent samples do not vary significantly in terms of their composition, all individuals within the samples will be grouped together for the subsequent analyses.

Prosocial and Antisocial Athlete Off-field Behaviour and Consumer Intention

H1: Antisocial athlete off-field behaviour will have a negative association with intention to buy merchandise, attend subsequent live games and follow the success of the team in both a non-traditional and traditional sport setting.

H2: Prosocial athlete off-field behaviour will have a positive association with intention to buy merchandise, attend subsequent live games and follow the success of the team in both a non-traditional and traditional sport setting.

Hypothesis one and two evaluate prosocial and antisocial off-field athlete behaviours association with consumer intention. Specifically, the hypotheses are centred around an evaluation of whether the relationship between the athlete behaviour and the four consumer intention items were statistically dependent on each other.

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The bivariate Pearson correlation method is the first statistical analysis deemed appropriate for an evaluation of hypotheses. Bivariate analysis is one of the most common and useful statistics used in quantitative research in the fields of education and social sciences research (Frey, 2018). The analysis provides a measure of the linear relationship between two variables, X and Y, or in this case the athlete behaviour score and the consumer intention item(s). This measure is represented as a value between +1.0 and −1.0, where 1.0 is a perfect positive correlation, 0.0 (zero) is no correlation, and −1.0 is a perfect negative correlation. In addition, the correlation coefficient can be interpreted in terms of its statistical significance (Frey, 2018). Limitations exist in terms of its capacity to provide inferences about causation.

The analyses will provide an indication of the following:

- The strength of a linear relationship between the athlete behaviour assessment and the consumer intention item (i.e., a determination of how close the relationship is to being in a perfectly straight line) - Whether a statistically significant linear relationship exists between the behaviour assessment item and the consumer behaviour item (s) - The direction of a linear relationship

Next, as the focus of the research is on the effect prosocial and antisocial off-field athlete behaviour has on the intention items, different responses on the perception scale will be grouped. Prosocial assessments ‘very prosocial’ and ‘prosocial’ (scores 4 & 5) and antisocial assessments ‘very antisocial’ and ‘antisocial’ (scores 1-2) will be grouped together so that they could be compared to the neutral assessments ‘neutral’ (score 3).

Two independent samples t-tests will then be executed in order to evaluate H1 and H2.

Independent samples t-tests’ compare the means of independent groups to determine whether the associated population means are significantly different. The first will compare the prosocial evaluations group and their mean intention items with the neutral group and their mean intention items. The second t-test will compare the antisocial assessments group with the neutral group.

Athlete Behaviour as a Moderator

H3: Athlete behaviour will have a significant moderating effect on the relationship between Team Identification and Consumer Intention.

As was theorised in the literature review, while team identification has a direct effect on consumer intention, this relationship is expected to be different among those impacted by off-field athlete behaviour. The degree to which the behaviour is perceived as being antisocial verses prosocial was anticipated to have varying degrees of influence on this relationship. When a variable is thought to temper or modulate the magnitude of the effect between the independent and dependent variables, it is said to

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be a moderating variable (Judd, 2015). Accordingly, the hypothesis is centred around evaluating athlete off-field behaviour as a moderating variable (see Figure 2 below).

Figure 2 Moderating Effect of Off-field Athlete Behaviour

Multiple regression analysis examines the relationship between a dependent (response) variable and p independent variables (predictors, regressors, IV’s). Multiple regression is one of the most widely used statistical procedures for both scholarly and applied marketing research because of its ease of use and robustness to violations of the underlying assumptions (Mason & Perreault, 1991). Hierarchical multiple regression is the statistical test used when assessing whether a moderator is significantly impacting the direction or magnitude of the relationship between the independent and dependant variable. Consequently, a hierarchical multiple regression will be the statistical procedure undertaken to evaluate the hypotheses.

Central to the procedure is an evaluation of the interaction effect between the independent variable (team identification) and the moderator (off-field athlete behaviour). A determination that the interaction effect is significant in predicting the dependant variable (Consumer Intention) would confirm and validate the hypotheses. The regression was conducted using SPSS statistics. Team Identification will be specified as the independent variable and a composite variable for intention will be the dependent variable within the model (see Figure 1).

The perception of the off-field athlete behaviour was the moderator variable. The composite intention item will be calculated by averaging the three intention items. Hierarchical multiple regression will be used to assess the moderating effect that perception of athlete behaviour had on intention items 1,2,3. Beyond the creation of a composite variable for intention, team identification scores will be mean-centred, and an interaction term will be created for the moderation effect. Following Aiken and West’s (1991) recommendations the independent and moderator scores will all be mean centred to reduce the collinearity between those variables and the interaction term.

The control variables of age, gender and fan base will be specified as covariates in step 1. The main effects team identification and perception of athlete behaviour will be then entered in step 2. Lastly, the interaction term will be specified for step 3.The regression will be executed in SPSS and then the model summary reviewed to determine if the entry of the different models is significant. Provided the interaction effect was significant, a hierarchical multiple regression slopes analysis will be conducted to help

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interpret the results. The analysis will be in accordance with the methods proposed by Jaccard, Turrisi, and Wan (1986).

Team Identification and Consumer Behaviour in a non-traditional sport

H4: Fans with high identity levels will have a positive association with intention to buy merchandise, attend subsequent live games and follow the success of the team in a non-traditional sport setting.

As the focus of this hypothesis is on the relationships among the variables team identification and the three intention items bivariate correlations will first be computed. Bivariate correlations can provide an assessment of the strength among the variables and the composite intention item. The sample will be filtered to only include participants within the non-traditional sample (MLR) for all subsequent analysis.

To account for the impact perception of athlete behaviour has on the results a moderated regression will then be conducted on the non-traditional sample. The control variables of age, gender and fan base will be specified as covariates in step 1. The main effects consumer intention and perception of athlete behaviour will then be entered in step 2. Then the interaction term will be calculated for step 3 (Team Identification x Perception Athlete Behaviour). If the interaction effect is significant a slopes analysis will be conducted in accordance with the methods proposed by Jaccard, Turrisi, and Wan (1986).

By specifying perception of athlete behaviour as the moderator and team identification as the independent variable a graph will then be generated. It will display the linear relationship between team identification and consumer intention and the moderating effects of athlete behaviour. This should also be useful in the assessment of hypothesis four

Differences Across Contexts

H5: Prosocial off-field athlete behaviour will have a stronger moderating effect on the relationship between team identification and consumer intention in a non- traditional professional sport setting than in a traditional setting

H6: Antisocial off-field athlete behaviour will have a stronger moderating effect on the relationship between team identification and consumer intention in a non- traditional professional sport setting then in a traditional setting

In order to test hypotheses 5 and 6 the main data file will be split into two subfiles based upon whether the participant had been drawn from MLR, traditional rugby, or the NFL. NFL and Rugby participants will be grouped together as ‘traditional’, while MLR fans will be grouped in the ‘non-traditional’ file. The separation will be undertaken to facilitate the subsequent comparative analyses assessing H4 and H5. Bivariate correlations will be calculated for both groups evaluating the varying strength among the perception of athlete behaviour and intention variables.

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To further examine H4 and H5, a hierarchical multiple regression will be created for the purposes of comparison against the previous one evaluating H3. Whereas the focus of the previous hierarchical regression will have lay in making a determination around the relationship between team identification and consumer intention among non-traditional fans, this subsequent regression will be executed on the data obtained from the traditional fan set. From there the moderating effect of athlete behaviour could be assessed and compared across a traditional and non-traditional setting.

The entry conditions will be held constant across both regressions to allow comparison. The dependent variable in both cases will be the composite measure created for intention. In both cases age and gender will be specified as covariates in step 1. The main effects of team identification and perception on athlete behaviour will be entered in step 2. Then the interaction terms will be calculated for step 3. The composite variables for intention and team identification scores will be mean centred to reduce the collinearity between those variables and the interaction term (Aiken & West, 1991). The results from the traditional hierarchical regressions will then be analysed and compared. Special attention will be paid towards the interaction term (step 3) of the model summary with these values indicating the significance of the moderating effect athlete behaviour has on the model.

Ethics and Limitations

The research will be conducted in accordance with both the National Statement on Ethical Conduct in Human Research and QUT’s Research Governance Framework (NHMRC, 2018; Research Governance Framework, 2015). The major ethical issues considered as part of the undertaking of this study were the participants’ consent, anonymity, confidentiality, risk of harm and the reciprocity of results. These issues have all been addressed in the design stage with assistance provided from a QUT Business School Ethics advisor. After a final review, the Ethics committee concluded the research project did not exceed the ‘low risk’ threshold and provided clearance (Approval number 1900000208). Some key features of the research that were designed with ethical issues in mind include:

- Inclusion of a participation sheet explicitly outlining the required involvement. - Informed consent being sought before any information is collected. - Participants being asked to imagine that the articles describe the actions of a prominent player from within their favourite team and respond accordingly. - Participants could freely withdraw from the project at any time up until the anonymous submission of their result. - No personally identifiable data would be used in the research project.

While successful in mitigating many potential risks, limitations in the research could arise out of the precautionary measures deployed in the study. Previous studies such as Fink et al. (2009) and Doyle et al. (2014) relied on limited disclosure during the intervention. In their studies participants were temporarily deceived believing that the article was factual. They also identified a specific athlete from a specific team. In this study participants will be asked to imagine that the article relates to a key athlete from their favourite professional sporting team. As the participants in this research are made

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aware of the fictitious nature of the articles, the full purpose of the research and its expected duration and procedures, response bias could influence the experiment.

Response bias is an overarching term for a wide range of tendencies that can consciously or subconsciously cause participants to respond inaccurately or falsely to questions (Furnham, 1986). They are a common phenomenon in survey research (Furnham, 1986). Specifically, the response bias, ‘demand characteristics’ could possibly have been more impactful on this research than its more deceptive counterparts (Fink et al., 2009; Doyle, Pentecost & Funk, 2014). Demand characteristics are when participants alter their response to conform with expectations. This can occur when participants become aware of the experiment (Orne, 1962). During this study, through recognition of the full purpose of the research, participants may have been pushed towards taking on a ‘role’ during the experiment. Weber and Cook (1972) have described many of these roles, but they range from the ‘good participant’ to the ‘negative-participant role’. In both instances the participant has discerned the experiments hypotheses and is either attempting to confirm them or destroy the credibility of the research (Weber & Cook, 1972).

A further limitation could arise out of the use of online surveys. Self-selection bias is a common phenomenon during online survey research (Stanton, 1998; Thompson et al., 2003; Wittmer et al., 1999). Self-selection being that some individuals among a group will be much more likely than others to access and complete a survey that is hosted on the internet. This has the effect of potentially restricting the ability to generalise the results from the research study to the rest of the group.

By recruiting participants online through existing fan-oriented groups and mailing lists, mean team identification scores could be very high. If the scores are not normally distributed with an overwhelming number of participants clustered around the high end of the TII scale, then the generalisability of the research could be limited. As a distinct lack of low-medium identified fans would limit the ability to generalise the results of the research study to the population at large.

Chapter Summary

This chapter provided a detailed overview of the research methodology employed in this study utilising a positivist paradigm choice and an experimental quantitative methodology with cross-sectional online surveys as an instrument of data collection. The surveys capture basic demographic information and team identification scores. The surveys then present the participant with a fictionalised article describing an athlete engaging in either a benign, antisocial or prosocial off-field behaviour. They then complete the survey by completing the behaviour assessment item and the relevant intention items. Purposive sampling is used to deliberately target three major groups of sports fans. The first group includes professional American Football fans from the United States (traditional). The second group includes Rugby Union fans from major teams and leagues around the world (traditional). The third group shared a single likeness with both the previous groups one and two. They are also rugby union fans but are drawn from the emergent professional rugby league in the United States (non-traditional). The purposive selection of these groups will allow for a determination of whether the effect of antisocial and prosocial off-field athlete

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behaviour changes between established professional leagues and leagues that are less traditional, or in infancy in terms of being a professional sporting code.

Further considerations of data collection were discussed, and corresponding statistical analyses were matched with the hypotheses. These include independent samples t- tests, cross tabulations, Bivariate correlations and hierarchical multiple regressions. Lastly the robust ethical practises to be adhered by were reviewed along and potential limitations arising out of the design were addressed. The following Chapter Four will reveal the data analysis of the study.

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Chapter 4: Results

This chapter mirrors the analysis section discussed in the Chapter Three, then builds by presenting the results of the relevant statistical analyses. First the sample characteristics are presented. Following on, the results relevant to a determination of whether the articles were successful in capturing benign, prosocial and antisocial off- field behaviour among respondents are presented. Results from the assumption checking, including the independent samples t-tests and cross tabulations are then introduced. This provides a determination of whether potential extraneous variables have significantly influenced subsequent evaluations and analysis of the hypotheses. Following on, hypotheses one through to six are then introduced in sequential order. Corresponding with the hypotheses are the results from matching statistical analyses used.

Participant Overview

Across the three independent samples, 485 participants completed the survey. The largest response came from the non-traditional group, MLR with 232 responses. Traditional sports saw 126 (NFL) and 127 (Rugby) responses respectively. Descriptive statistics was run to identify missing data sets.

This process was especially critical as “missing data can reduce the statistical power of a study and can produce biased estimates, leading to invalid conclusions” (Kang, 2013 p.403). Descriptive statistics revealed that across the 485 surveys a total of 18 participants had left key items incomplete. After investigating where the missing data occurred, most of the missing items were confined to 12 participant entries. A further six participant entries accounted for a total of seven missing responses.

As missing data is a very common problem in survey research, numerous methods are proposed that deal with missing values. In Raaijmakers (1999) study evaluating the effectiveness of different missing data treatments in surveys with Likert-type data, he commented that “missing data (is) a practical problem, and a practical solution has to be found” (1999, p729 Adherence to those principles saw the adoption of a combination of missing data methods in the study. The principles behind, and the justification of, the selection of two methods during this study was to maximise total data collection while mitigate the introduction of bias.

First, the pairwise deletion method was specified. Alongside the listwise method, the pairwise deletion method is one of the most common techniques of handling missing data (Peugh & Enders, 2004). While the listwise method deletes cases when a single item is missing, the pairwise method retains cases where only some data is missing, hence attempting to minimise the losses that occur in listwise deletion. Provided the participant had entered a single response for a key team identification or consumer behaviour item they were retained. Cases where the individual had provided no useable data for a key variable (such as a recorded team identification or consumer intention

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item response) were deemed to be incomplete – and the cases and were erased from the data file. This resulted in a total loss of 12 entries from the data set. These 12 entries accounted for a large majority of the missing items.

In cases where participants had failed to record a single response for a multi-item section, such as failure to record a response for one of the three team identification or intention items, the total mean substitution (TMS) replacement method was used to impute replacement figures. The TMS method is the most commonly used replacement procedure in surveys with Likert-type data (Raaijmakers, 1999). The TMS method involved replacing the missing variable with the series mean. This was easily achieved in SPSS by running the ‘replace missing values’ function and specifying the ‘series mean’. This function generated the series mean for a total of seven missing entries across three team identification items and the three intention items. Team Identification scores were then generated by averaging all three items in Trail and James (2001) Team Identification Index (TII). A composite variable was also created for Intention by averaging the three individual items.

The final composition for analysis is displayed below in Table 1. A target of 120 participants was set from each of the three participant pools. When the two non- traditional samples were grouped together, the non-traditional and traditional sample were comparable in terms of the number of participants.

Table 1 Participant Breakdown

Sample Participant Number Percentage NFL 122 25.8 Traditional Rugby 125 26.4 MLR 226 47.8 Total 473 100

Across the three independent samples they were comprised of majority males, with Rugby consisting of the highest percentage of male respondents (81.6%) and NFL with the lowest (72.1%). MLR was in between with 73.8% male. Most participants across all three samples fell between 45-54 years of age (23.9%), with 65+ (7.9%) and 18-24 (11%) making up the smallest groups (see Figure 3). Equivalence across the sample groups was encouraging for the purpose of contrast and population representativeness. The samples closely mirrored sport consumer demographics in the broader population, with Lombardo and Broughton (2016) reporting that the median age of U.S. TV viewers ranged from 40 to see 64 years old depending on the U.S. sport. The predominately male sample was not unexpected, as although studies have shown men and women to be equally likely to be a fan of a team, men are more engaged in fan behaviour than females (Dietz-Uhler, Harrick, End & Jacquemotte, 2000).

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Figure 3 Age variable

The overwhelming number of participants were clustered around the high end of the TII scale, indicating that most participants could be closer categorised towards ‘die- hard fans’ end of the spectrum rather than towards the ‘fair-weather fans’ end (Wann & Branscombe, 1990). This was reflected with a mean team identification score of 5.63 across all three independent samples (see Figure 4).

Figure 4 Team identification score

Chapter 4: Results 40

Off-field Athlete Behaviour Assessment

As highlighted in Chapter Three it was critical that the intervention used, or in this case the articles produced, were effective in capturing the desired participant behaviour assessment on the scale. Specifically, an effective measurement would foremost accurately reflect the presence and magnitude of the target property with a high degree of precision (Hammersley, 1987). Second, an assessment of ‘reliability’, or the achievement of consistency of scores would provide a further indication of validity (Hammersley, 1987).

An evaluation of responses regarding the three-criterion suggested that the measurement was sound. After reviewing participant assessments of the three fictitious articles, the prosocial and antisocial articles were effective in capturing the desired participant behaviour assessment on the scale. As highlighted earlier, a 5-point Likert scale was retained over a larger one to ensure a higher level of validity. Divergence occurred around the evaluation of the ‘benign’ behaviour article. Participants often categorising the article that depicted athletes engaging in pool recovery sessions as ‘prosocial’ rather than ‘neutral’ behaviour. This resulted in a slightly elevated mean of 3.34.

Simple frequencies analysis showed that 31.6% of the participants viewed the behaviour in question as either ‘very antisocial’ (19.8%) or just simply ‘antisocial’ (11.7%). 53.8% of the respondents perceived the article to be either ‘prosocial’ (22.2%), or ‘very prosocial’ (31.3%). Only 14% of participants perceived the article as describing ‘neutral’ off-field athlete behaviour. The online survey was configured to display one of the three articles on rotation, enabling an equal but random generation of each article. This having the effect that articles were programmed so that only one of the three would generate for the participant on a random basis, therefore equal distribution across prosocial, antisocial and neutral behaviour was anticipated for a mean value of 3.

Table 2 Participant Response Distribution of Off-field Athlete Behaviour Assessment

Very antisocial Antisocial Neutral Prosocial Very Prosocial 19.7% 11.6% 14.6% 22% 31.3%

Assessment of Normality, Linearity and Homogeneity of Variance

Violations of normality are credited as a major contributor towards statistical error in scientific literature (Ghashemi & Zahadiasl, 2012). This is due to its underlying importance to many commonly used statistical procedures, namely parametric statistical procedures such as correlation, t-tests, regression and analysis of variance, which all assume that the data follows a normal distribution (Ghashemi & Zahadiasl, 2012). Given that the testing of the formulated hypotheses involved the above- mentioned statistical procedures, an assessment of normality was undertaken on the key variables. The results are reported below.

As previously discussed, team identification scores were not normally distributed with an overwhelming number of participants clustered around the high end of the TII scale.

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This indicated that most participants could be categorised closer towards the ‘die-hard fans’ end of the spectrum rather than towards the ‘fair-weather fans’ end. This was reflected with a mean team identification score of 5.63 across all three independent samples. An assessment of the asymmetry of the distribution of team identification scores returned a skewness value of -1.078. It is desirable that for the normal distribution of data the values of skewness should be near to 0, which would indicate a perfectly symmetrical distribution (Tabachinick & Fidell, 2013). This value reflected the bulk of scores were aggregated towards the higher end of the team identification scale. This coefficient, while not exceeding the range of -2 to 2 which is cited as a point of reference for substantial departure from normality, did not come within a ‘fairly symmetrical’ range of -0.5 to 0.5 (West, Finch & Curran, 1996; George & Mallery, 2010). This skewness figure was larger than twice the standard error rate of .122 indicating a significant departure from normality. This distribution is evidenced visually in the histogram (see Figure 5).

Figure 5 Team Identification Scores across all participants

An assessment of the dependant variables revealed that they were similarly negatively skewed (see Table 3). The most negatively skewed variable was ‘Intention to follow the team’, with a skewness coefficient of -1.42. In this case most participants indicated that they were either ‘likely’ or ‘very likely’ to continue following the team. This negative skew was reflected with a mean intention score of 6.11. The variable that most closely resembled a symmetrical distribution was ‘Intention to purchase merchandise’, with a mean of 5.25 and a skewness coefficient of -0.72.

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Table 3 Dependant Variables Mean Score, Skewness & kurtosis Coefficient

N = 473 Mean Skewness Kurtosis Int Atten 5.91 -1.415 1.245 Int Follow 6.11 -1.720 2.617 Int Merch 5.25 -.721 -.332

While no key variables came close to achieving perfect symmetry, all skewness coefficients were within the range of -2 and +2 range and were therefore considered acceptable in order to prove normal univariate distribution (West, Finch & Curran, 1996; George & Mallery, 2010). Furthermore, concern about the negatively skewed data was abated by the large sample size. Tabichnick & Fidell (2013) argued that with large enough sample sizes the violation of the normality assumption should not cause issue during subsequent analysis (Ghashemi, Zahadiasl, 2012; Pallant, 2007). After the determination that there was not a substantial enough departure from normality within the data set to undermine subsequent analyses, an evaluation of the assumption of linearity was undertaken. The results of the evaluation are discussed below.

The assumption of linearity is that a straight-line relationship can be observed between two key variables. As per the recommendations of Tabichnick & Fidell (2013), bivariate scatterplots were generated displaying the relationships among key variables. These were created to assess linearity by visual inspection. Scatterplots were first generated for the variables team identification and the three consumer intention items. Following on, scatterplots were generated for the behaviour assessment variable and the three intention items. In all instances the data was distributed normally with no scatterplots displaying an ‘oval-shaped’ pattern which would suggest a violation of the assumption (Gu & Kingston, 2010; Tabichnick & Fidell, 2013).

The final evaluation before proceeding with the hypotheses related statistical analyses involved an assessment of the equality, or homogeneity of variance, across the three independent samples. The purpose of this evaluation is to ensure that “when one of the variables is discrete (the grouping variable), the other is continuous (the DV); the variability in the DV is expected to be about the same at all levels of the grouping variable” (Tabichnick & Fidell, 2013, p.119). Any significant differences between the independent samples relevant to extraneous factors such as gender and age, could influence the interpretation of the hypotheses (Davis, 2010). In the context of this research project it was important to determine first whether demographic variables such as gender and age had a significantly impact on subsequent results and second if so, whether the three independent samples significantly varied in terms of their demographic composition. These questions were answered by conducting Levene’s tests and cross tabulations.

The Levene’s test is the standard test for determining homogeneity of variance (Davis, 2010). Accordingly, Levene’s tests were run testing for differences between group means. Homogeneity across the data sets for the control variables being desirable. Through the t-test procedures in SPSS, Levene’s tests were conducted on key demographics gender and age. The focus of the test was to determine whether the demographic variables had a significant effect on team identification and off-field athlete behaviour assessment scores. Following these assessments, cross tabulations

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were conducted to compare the independent samples in terms of their demographic composition. The results of these analyses are discussed below.

Female mean team identification scores were clearly elevated with scores 5.87 verses 5.59 for males. Similarly mean perception of off-field athlete behaviour was also slightly different. Females had a slightly elevated mean of 3.61 versus males 3.25 Consequently, two independent samples t-tests were conducted to determine whether gender would affect mean team identification scores and the perception of off-field athlete behaviour.

The first t-test indicated that gender had a significant effect on team identification scores. The Levene’s test was non-significant with p = .104 (p < .05) and an F statistic of 2.66. This meaning that the assumption of homogeneity of error variances was upheld and that the result of the t-test could be trusted (Stone, 2010). Assuming equal variances evidence-based team identification (TID) scores were significantly different between males and females with a t-value of 2.09 a significance value of p < .037, below the cut-off value of 0.05.

The second t-test concluded that a significant difference existed in athlete behaviour perception scores between males and females. Levene’s test of equality of variance was insignificant at .148, exceeding the cut-off value of 0.05, with variances not significantly different from each other. Assuming equal variances, TID scores were significantly different between males and females with a t-value of 2.2 and a significance value of p < .028, below the cut-off value of 0.05.

With both tests indicating that gender had a significant effect on two critical variables relevant to the research hypotheses a review of gender composition across the three independent samples was conducted using cross tabulation. Cross tabulation being a non-parametric method of gauging how two non-metric, or nominally-scaled, variables are related (Lewis-Beck, Bryman & Futing, 2004). In this case it was used to assess whether the three samples significantly differed in terms of their gender composition. All three independent samples were comprised of majority males, with Rugby consisting of the highest percentage of male respondents (81.6%) and NFL with the lowest (72.1%). MLR was in between with 73.8% male. These figures were sufficiently close to suggest that no difference existed between the participant samples in terms of ration males to females.

When the observed distribution of counts was compared against the expected distribution, a non-significant Pearson chi-square statistic of 14.42 (figure is the magnitude of the discrepancy between observed and expected) was calculated (p = 0.07). This meant that no significant difference existed between the three groups in terms of their gender composition allowing males and females to be grouped together during subsequent analyses.

After eliminating concern that gender differences between groups would impact on the comparison of groups, cross-tabulation analysis was conducted on age, which was the remaining demographic variable. Most participants across all three samples fell between 45-54 years of age (23.9%), with 65+ (7.9%) and 18-24 (11%) making up the smallest groups (see Figure 6). No significant difference was found to exist between the three samples in terms of age composition as the Pearson chi-square was calculated

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to be 17.020, at a non-significant value of .255 (P > 0.05). This meant all individuals were grouped together for subsequent analyses.

Figure 6 Age breakdown across all participants

Prosocial and Antisocial Off-field Athlete Behaviour and Consumer Intention

H1: Antisocial off-field athlete behaviour will have a negative association with intention to buy merchandise, attend subsequent live games and follow the success of the team in both a non-traditional and traditional sport setting.

H2: Prosocial off-field athlete behaviour will have a positive association with intention to buy merchandise, attend subsequent live games and follow the success of the team in both a non-traditional and traditional sport setting.

As outlined in Chapter Three, hypothesis one and two evaluate prosocial and antisocial off-field athlete behaviours association with consumer intention. Specifically, the hypotheses were centred around an evaluation of whether a relationship between the athlete behaviour and the four consumer intention items were statistically dependent on each other.

Bivariate correlations were calculated on the basis that they provide a measure of the linear relationship between two variables, X and Y, or in this case the athlete behaviour perception score and the consumer intention item(s). While unable to provide inferences about causation, they can provide a strong measure of the statistical relationship, or association, between two continuous variables (Frey, 2010; Sheskin, 2010). Bivariate correlations verifying the relative influence of the perception of off-field athlete behaviour had on the three intention items are presented below in Table 4.

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Table 4 Bivariate Correlations for Perception of off-field athlete behaviour and Consumer Behaviour Intention Items

Attend Follow Merchandise

Behaviour .384 .324 .327 Assessment Score

* All correlations significant at the .001 level (2-tailed).

A positive correlation was observed between perception of athlete behaviour and all three intention items at a significant level (P < .001). The strongest correlation was with intention to continue attending live games with a Pearson correlation coefficient of r = .384. Large correlations are often reported at the r = .50, while medium correlations are reported at the r = .30 level (Cohen, 1992). Therefore, by adopting the much-cited thresholds proposed by Cohen (1992), a moderate to strong correlation was observed between the participants behaviour assessment score and their intention to attend subsequent live games. A medium correlation was observed of 3.24 and 3.27 with intention to continue following on social media/television and buy more merchandise (P < .001). Beyond the size of the correlation, the coefficient may be interpreted in terms of its level of statistical significance. All three correlations were calculated at a statistical significance level of P < .001. At this significance level the probability that the sample result was derived from a population in which no correlation exists is less than 1 in 10,000, a very unlikely scenario indeed (Frey, 2018).

As discussed in Chapter Three, different responses on the perception scale were then grouped to isolate the individual behaviour assessments. Prosocial assessments ‘very prosocial’ and ‘prosocial’ (scores 4 & 5) and antisocial assessments ‘very antisocial’ and ‘antisocial’ (scores 1-2) were grouped together so that they could be compared to the neutral assessments ‘neutral’ (score 3). They were grouped to further evaluate hypotheses one and two.

After grouping independent samples t-tests were conducted to first compare the prosocial evaluations group and their mean intention items with the neutral group and their mean intention items. The second t-tests this time compared the antisocial assessments group with the neutral group. Independent samples t-tests were specified due to their ability to determine whether the means between two groups were statistically significant (Stone, 2010, pp.1552-1556). The mean intention scores for each of the groups are displayed below in Table 5.

Table 5 Mean Intention Scores among Groups based grouped by Behaviour Assessment

Prosocial Neutral Antisocial n252 n69 n152 Attend Mean 6.35 5.74 5.26 Std. Deviation 1.05 1.40 1.87 Follow Mean 6.45 6.03 5.59 Std. Deviation 1.01 1.22 1.65 Merchandise Mean 5.66 5.29 4.55 Std. Deviation 1.51 1.42 1.89

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Prosocial and Neutral

There was a significant difference between the mean intention to continue attending live games between the prosocial and neutral groups (t90.046 = 3.391, p < .001). The average intention to continue attending live games was 0.61 scale points, or 11% higher in the prosocial assessments group than the neutral group. Similarly, the differences between mean intention to continue following the team was significant between the groups (t94.389 = 2.612, p < .001). Participants who had recorded a prosocial evaluation scored on average .42 scale points higher or 7%. The results however did suggest that no significant difference existed between the mean intention to purchase merchandise scores between groups (P > 0.05). This was reflected with only a .37 difference between mean scores (7% elevated prosocial).

There was a significant difference in mean intention to continue attending and following the team between the prosocial and neutral groups. The mean scores of intentions to continue attending and follow the team were significantly different between prosocial and neutral groups (P <.001; P <.001). The results however did suggest that no significant difference existed between the mean intention to purchase merchandise scores between groups (P > 0.05). Consequently, no significant difference existed between groups in terms of their mean scores of intentions to purchase merchandise.

Antisocial and Neutral

The second t-test comparing mean intention scores from the antisocial group with the neutral group concluded that all three mean intention item scores were significantly different from one another. There was a significant difference between the mean intention to continue attending live games between the antisocial and neutral groups (t171.794 = -2.129, p < .05). The average intention to continue attending live games was 0.48 scale points, or 8.4% lower in the antisocial assessments group than the neutral group. Similarly, the differences between mean intention to continue following the team was significant between the groups (t173.222 = -2.195, p < .05). Participants who had recorded an antisocial evaluation scored on average .44 scale points lower or 7.3% than the average neutral assessment participants. A significant difference also existed between the mean intention to purchase merchandise scores between groups (t171.685 = -3.248, p < .001). This was reflected with only a .37 difference between mean scores (6.5% below the neutral group).

Athlete Behaviour as a Moderator

H3: Athlete behaviour will have a significant moderating effect on the relationship between Team Identification and Consumer Intention

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Regression analyses are a set of statistical techniques that permit an evaluation of the relationship between one dependent variable and several independent variables. It is one of the most commonly used statistical procedures. Its popularity stems from its ease of use and interpretation, applicability to a wide range of data and problems, and robustness to violations of underlying assumptions (Mason & Perreault, 1991). Allen (2017) argued that “Researchers use multiple regression as a statistical procedure to analyse quantitative data with the goal of explaining relationships between variables” (p.1041). While team identification has a direct effect on consumer intention, this relationship was expected to be different among those impacted by off-field athlete behaviour (see Figure 7). Specifically, it was expected to temper and modulate the magnitude of the effect between the team identification and the consumer intention items. Adherence to those properties would then have the off-field athlete behaviour variable defined as a moderating variable (Judd, 2015).

Figure 7 Hypothesised Model

Following the recommendations provided by Aiken & West (1991) and Fairchild & MacKinnon (2009) hierarchical multiple regression analysis was conducted, with all predictor variables and their interaction term being centred prior to model estimation to improve the interpretation of regression coefficients.

Hierarchical regression allowed for the IV’s to enter the equation at a specified order to assess what each adds to the equation at its own point of entry. The control variables of age, gender and fan base were specified as covariates in step 1. The main effects team identification and perception of athlete behaviour were entered in step 2. Then the interaction term was calculated for step 3. The regression was executed in SPSS and then the model summary was reviewed to see if the entry of the different models was significant. The two-way moderated regression was run and results from the model summary are presented in the table below.

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Table 6 Model Summary from Two-way Moderated Regression

Adjusted Std. Error Sig. F Model R R Square R Square of the Estimate R Square Change F Change df1 df2 Change

1 .276a .076 .070 1.31668 .076 12.761 3 466 .000

2 .618b .382 .376 1.07872 .306 115.137 2 464 .000 3 .626c .392 .384 1.07124 .010 7.501 1 463 .006 *p < .05; **p < .01; ***p < .001.

The entry of the control variables (age, fan base and gender) explained a significant amount of variance in the DV (composite intention item). As can be seen below, entry of the covariates accounted for a significant amount of variance, Adj. R2 = 0.070, F(3, 466) = 12.761, p < .001.

Step two, the main effects, of the model summary was reviewed to assess the mean- centred main effects for the IV and the Moderator. A substantial change in R-Square from Model 1 was apparent and was attributable to the variables entered at Step 2 of the regression. In this case, the addition of team identification and perception of athlete behaviour at Step 2 of the regression significantly explained further variance in the DV (intention) (R2 Ch. = .306, F(2, 464) = .115.137, p < .001).

Step 3 of the model also revealed that the interaction term explained a significant amount more variance in intention, R2 Ch. = .01, F(1, 463) = 7.501, p < .006. This meaning the interaction explains 1% over the existing effects. Furthermore, this increment was significant (P < 0.05).

As the interaction effect was significant a hierarchical multiple regression slopes analysis was conducted to help interpret the results. The graphical presentation and analysis were in accordance with the methods proposed by Jaccard, Turrisi, and Wan (1986). As Jaccard and Turrisi (2003) noted, “if there were no interaction effect, the three lines would be parallel” (p.32). Upon visual inspection of the generated graph (see Figure 8), it was evident that this was not the case as the moderation effect was clearly visible. These analyses found that the slope for low behaviour fit (antisocial behaviour), medium behaviour (neutral), and high behaviour (prosocial) were all significant (P < .001) (see Table 7 for slopes).

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Figure 8 Simple Slopes Graph of Moderating Effect

Table 7 Simple Slopes Analyses

Slope t value Antisocial Behaviour 0.552 11.469 Neutral Behaviour 0.474 14.981 Prosocial Behaviour 0.395 8.184 *P < .001

*Narrowing of points at top meaning High team identification individuals less impressionable *analyse the slopes

Team Identification and Consumer Behaviour in a non-traditional Sport

H4: Team Identification will have a positive association with intention to buy merchandise, attend subsequent live games and follow the success of the team in a non-traditional sport setting.

To evaluate hypothesis four bivariate correlations were calculated on the basis that they provide a measure of the linear relationship between two variables, X and Y, or in this case team identification scores and the consumer intention item(s). While unable to provide inferences about causation they can provide a strong measure of the statistical relationship, or association, between two continuous variables (Frey, 2010; Sheskin, 2010). Bivariate correlations verifying the relative influence of team

Chapter 4: Results 50

identification had on the three intention items in a non-traditional were calculated. The results of the bivariate correlations among team identification and the four intention items among participants within the non-traditional sample (MLR) are listed below in Table 8 .

Table 8 Pearson Correlation coefficients, Team Identification and Consumer Intention in a Non- traditional Sport

Merchandise Follow Attend Composite

Team .324 .353 .342 .367 Identification

* All correlations significant at the 0.01 level (2-tailed).

Results of the Pearson correlation indicated that there was a significant positive association between team identification and all intention items within the non- traditional sample. The correlations for the individual items ranged from .324 to .353, indicating a moderate to strong association was present between team identification and the three consumer intention items, (r (226) = . p = < .001).

Accounting for the impact perception of athlete behaviour had on the results a moderated regression was then conducted on the non-traditional sample. The control variables of age, gender and fan base were specified as covariates in step 1. The main effects consumer intention and perception of athlete behaviour were entered in step 2. Then the interaction term was calculated for step 3, (Team Identification x Perception Athlete Behaviour).

The model summary was reviewed (see Table 9). The entry of the control variables (age, fan base and gender) explained a significant amount of variance in the DV (composite intention item). As can be seen below, entry of the covariates accounted for a significant amount of variance, Adj. R2 = 0.078, F(2, 221) = 10.395, p < .001.

Step two of the model summary, the main effects model, was reviewed to assess the mean-centred main effects for the IV and the Moderator. A substantial change in R- Square from Model 1 was apparent and was attributable to the variables entered at Step 2 of the regression. In this case the addition of team identification and perception of athlete behaviour at Step 2 of the regression significantly explained further variance in the DV (intention) (R2 Ch. = .271, F(2, 219) = .271, p < .001).

Step 3 of the model also revealed that the interaction term explained a significant amount more variance in intention, R2 Ch. = .017, F(1, 218) = 5.767, p < .017.

Table 9 Model Summary from Two-way Moderated Regression

Adjusted Std. Error R Square

Model R R Square R Square of the Estimate Change F Change df1 df2 Sig. F Change

1 .293a .086 .078 1.32986 .086 10.395 2 221 .000 2 .598b .357 .346 1.12009 .271 46.266 2 219 .000 3 .612c .374 .360 1.10809 .017 5.767 1 218 .017 *p < .05; **p < .01; ***p < .001.

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As identification and consumer intention and the moderating effects of athlete behaviour (see Figure 9). A positive linear relationship was evident upon visual inspection between team identification and consumer behaviour regardless of whether a prosocial or antisocial athlete behaviour had been observed.

Figure 9 Team Identification and consumer intention

The simple slopes analyses determined that all three slopes were statistically significant (see Table 10). Low behaviour fit (antisocial behaviour), medium behaviour (neutral), and high behaviour (prosocial) were all significant at P levels < .05.

Table 10 Simple Slopes Analyses

Slope T value Sig. Antisocial Athlete behaviour 0.563 5.963 0.0000 Neutral Athlete behaviour 0.408 6.447 0.0000 Prosocial Athlete behaviour 0.252 2.663 0.0083

Team Identification levels were positively associated with the composite consumer intention value regardless of the behaviour observed. The perception of the athlete behaviour impacted the level of intention. Prosocial behaviours corresponded with higher intention scores than antisocial behaviour.

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Differences Across Contexts

H5: Prosocial off-field athlete behaviour will have a stronger moderating effect on the relationship between team identification and consumer intention in a non- traditional professional sport setting than in a traditional setting.

H6: Antisocial off-field athlete behaviour will have a stronger moderating effect on the relationship between team identification and consumer intention in a non- traditional professional sport setting then in a traditional setting.

Bivariate correlations were calculated for both the traditional and non-traditional groups. All Pearson’s correlations among the intention items and the athlete behaviour variable were statistically significant across both groups (P < .001). The correlation coefficients for both the traditional and non-traditional groups are displayed in Table 11 below.

Table 11 Pearson Correlations Coefficients across traditional and non-traditional fans and Intention items

Merchandise Follow Attend Composite Intention

Traditional .289 .247 .321 .324

Non-traditional .372 .395 .446 .435

all correlation reported Correlation is significant at the 0.01 level (2-tailed).

The relationship was similarly positive across each of the groups with intention to attend live games having the strongest positive association. Purchasing merchandise and following the team on television and social media had a weaker correlation in both instances. While the direction of the correlation was consistent across both samples, divergence occurred in the strength of the associations. Intention items in the non- traditional sample had a stronger positive correlation than the traditional intention items. The strongest positive correlation in the traditional group: ‘intention to attend live games’, was still weaker than the weakest correlation in the non-traditional group ‘intention to purchase merchandise’.

To further examine H4 and H5 the hierarchical moderated multiple regression was created for the purposes of comparison against the previous one evaluating H3. Whereas the focus of the previous hierarchical regression was the determination of the relationship between team identification and consumer intention among non- traditional fans, this subsequent regression would execute on the data obtained from the traditional fan set. From there the moderating effect of athlete behaviour could be assessed and compared across a traditional and non-traditional setting.

The dependent variable in both cases was the composite measure created for intention. In both cases age and gender were specified as covariates in step 1. The main effects team identification and perception of athlete behaviour were entered in step 2. Then the interaction terms were calculated for step 3. The composite variables for intention

Chapter 4: Results 53

and team identification scores were mean centred to reduce the collinearity between those variables and the interaction term (Aiken & West, 1991).

As was determined during the non-traditional analysis, the covariates, main effects and interaction terms all explained a significant amount of variance in consumer intention (see Table 9). Subsequent simple slopes analysis also determined that all three slopes were statistically significant (see Table 10). Low behaviour fit (antisocial behaviour), medium behaviour (neutral), and high behaviour (prosocial) were all significant at P levels < .001.

The results from the traditional hierarchical regression differed in several respects. Critical figures from the model summary are displayed in Table 12. The entry of the control variables (age and gender) also explained a significant amount of variance in the DV (composite intention item). As can be seen below, entry of the covariates accounted for a significant amount of variance, Adj. R2 = 0.056, F(2, 243) = 8.218, p < .001.

Step two of the model summary, the main effects model, was reviewed to assess the mean-centred main effects for the IV and the Moderator. Again, a substantial change in R-Square from Model 1 was similarly apparent and was attributable to the variables entered at Step 2 of the regression. In this case, the addition of team identification and perception of athlete behaviour significantly explained further variance in the DV (intention) (R2 Ch. = .352, F(2, 241) = 72.5, p < .001).

In this case though, the addition of the interaction term at Step 3 of the regression did not significantly explain further any variance in the DV (R2 Ch. = .005, F(1, 240) = 2.05, ns).

Table 12 Model Summary from Two-way Moderated Regression

Model R Adjusted R Std. Error of the R Square F Sig. F R Square Square Estimate Change Change df1 df2 Change 1 .25 .063 .056 1.30253 .063 8.218 2 243 .000 2a 2 .64 .415 .405 1.03347 .352 72.500 2 241 .000 4b 3 .64 .420 .408 1.03122 .005 2.052 1 240 .153 8c *p < .05; **p < .01; ***p < .001.

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Summary of Key Outcomes

The final composition for analysis included a total of 473 participants. Of these participants, 47.8% hailed from MLR (non-traditional) and the remaining percentage were quite evenly divided between the traditional segments of rugby and NFL. All three independent samples were mostly comprised of male participants (between 72- 82%) and the largest age category fell between 45-54 years (23.9%). A key figure was the team identification scores. The overwhelming number of participants were clustered around the high end of the TII scale, indicating that most participants could be closer categorised towards ‘die-hard fans’ end of the spectrum rather than towards the ‘fair-weather fans’ end (mean = 5.63).

The results of the bivariate correlations showed that antisocial off-field athlete behaviour perceptions were significantly negatively correlated with consumer intention items. Similarly, positive behaviour assessments were associated with higher consumer intention levels. The association was stronger in the non-traditional sample than it was in the traditional sample. The hierarchical multiple regression analyses found that the moderation effect was significant in the combined and non-traditional sample. The hierarchical multiple regression found that the moderating effect was non- significant in the traditional sample. These results are discussed in detail in the next chapter.

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Chapter 5: Discussion

This chapter discusses the theoretical and practical contributions of this research, including potential limitations. Additionally, this chapter provides recommendations for future research. A summary of findings and the hypotheses is first presented. This is followed by a discussion in relation to consequential implications for theory discussing off-field athlete behaviour consumer intention. Practical implications will be reviewed, and the chapter concludes with an evaluation of its limitations and recommendations for future research.

Summary of Findings

The study both confirmed and extended previous research around team identification, off-field athlete behaviour and fan reaction. New insights were provided highlighting the importance of the context in which an individual fan exists. Due to a variety of factors, many related to the absence of causes and antecedents of team identification, the effect of off-field athlete behaviour is far more profound in a non-traditional setting. Furthermore, the financial and time related cost of consumer activities were also found to be an important factor when considering the impact of athlete off-field behaviour; namely, the smaller the financial or time related cost is of the consumer behaviour, the less likely it is to be influenced by off-field behaviour.

Athlete behaviours association with Consumer Intention

The first examination was into the association between off-field athlete behaviour and consumer intention. Bivariate correlations were calculated with the results across all three samples. The results showed that off-field athlete behaviour has a positive relationship with intention to buy merchandise, attend subsequent live games and follow the success of the team in both a non-traditional and traditional sport setting. While the association between off-field athlete behaviour, following the team and purchasing merchandise was moderate, the correlation with attending subsequent live games was moderate to strong going by the thresholds proposed by Cohen across the entire sample (1992).

This association was apparent with average intention to continue attending fixture scores among was the prosocial participant assessments being 11% elevated over those who believed that the athlete behaviour was benign (neutral). Similarly, individual’s intention to continue following the team on social media and purchase merchandise was 7% higher than the neutral assessments group. Individuals who perceived the described behaviour as antisocial also recorded depressed intention levels across all three items when compared to the neutral group. Again, intention to continue attending

Chapter 5: Discussion 56

the games suffered the biggest swing being depressed by 8.4%. Intention scores for following the team and purchasing merchandise were depressed by 7.3% and 6.5% respectively.

An evaluation of the intention item scores themselves was useful in making sense of the shifts. Intention to follow the team had the highest mean intention score of 6.11. Four out of the six major consumers behaviours that Horne (2006) nominated fall within this intention category. These four consumer behaviours engaged in by sport fans include listening to radio commentary, reading the sports pages of the daily newspapers, watching live television broadcasts and dialling into sport websites (Horne, 2006). Relatively speaking, when compared against the other two activities which include purchasing sport branded merchandise and traveling extensively to attend events, this segment involves the least expenditure in terms of both price and time commitment.

High scores are also accounted for through the increasing prevalence and access to teams and sports from all over the globe through technology (Hyatt et al., 2018). The ability for individuals’ access to the web has substantially increased with sport related media becoming widely available multiple websites (Loakimidis, 2010). This provided further explanation of why such a high mean score would be collected and why only a small difference would be recorded based upon whether the participants read the prosocial or antisocial article.

Conversely, the largest swing and strongest correlation occurred on the intention to attend item. The mean score for this item among the entire sample was 5.91. This indicated that a very strong desire to attend subsequent live games was already present among the participants. Watching the game on television does however offer similar benefits to the consumer as the attending without the significant costs associated. Studies have found that television broadcasting is a rivalrous activity against match day attendance (Baimbridge, Cameron & Dawson, 1995; Allan & Roy, 2008). As stated above, attending the fixture is becoming less and less relevant when it comes to following the team performance.

The remaining item, intention to purchase merchandise, recorded the lowest mean score of 5.25 and a moderate correlation across the sample. Research indicates that purchasing merchandise is an infrequent expenditure and identification is the strongest predictor of this behaviour (Ozer & Argan, 2006; Castro, 2014). Despite its infrequent purchase, merchandise sales represent a significant segment to firms’ bottom lines. Alongside intention to attend, this item can involve a significant cost. Merchandise is also a reusable good.

Based upon these results, the extent to which the consumer behaviour requires substantial expenditure or the investment of time the more associated it will be with the perception of off-field athlete behaviour. In other words, free and easy fan activities will be less related to the perception of athlete behaviour than expensive and time- consuming activities.

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Athlete behaviours association intention across contexts

When comparing the non-traditional professional sport fans to the traditional fan sets the associations between off-field athlete behaviour and the intention items differed substantially. Within the traditional sport context only a weak correlation was present between off-field athlete behaviour and intention to follow the team on social media and television. When taking into consideration the mean intention score for this item among the sample was very high at 5.99, this suggests that in fans of traditional professional sports team are most likely to continue following their team on social media or television regardless of their perception of the off-field behaviour or conduct demonstrated by an athlete from their team. Among the non-traditional sample the mean intention item was similarly high, being recorded at 6.06. However, the association between off-field athlete behaviour and intention to follow the team was 60% stronger (moderate-strong) among the non-traditional participants. Explanation can be derived from the fact that traditional teams have highly developed websites and social media accounts, the local media are highly involved in day to day operations, and they leverage well off word of mouth from the existing fan base. This coupled with the rising ability of, and access to technology has meant that it is very easy for individuals to stay connected with their teams (Loakimidis, 2010; Hyatt et al., 2018). In many instances, without actively seeking the information, a fan could inadvertently become aware of pertinent team related information without looking for it. This is certainly not the case for non-traditional teams. Lacking large marketing budgets, established broadcasting agreements and developed fan bases that warrant constant media attention, they do not possess an equivalent platform to disseminate information. This makes following the team significantly more challenging to the fan than their traditional counterpart. These results mirrored the finding from the first sample that being the degree to which the consumer behaviour requires substantial expenditure or the investment of time, the more related it will be to the perception of off-field athlete behaviour.

As for the last two consumer behaviours, in the traditional sample a weak correlation was calculated between off-field athlete behaviour and intention to purchase merchandise (mean of 5.03). The strongest association lay with intention to attend subsequent live games (mean of 5.67) and off-field athlete behaviour. This was deemed to be moderate going by the thresholds proposed by Cohen (1992). Again, the same association were significantly stronger among the non-traditional professional sport fans. The association between off-field athlete behaviour and intention to buy merchandise was 29% stronger (moderate-strong) among the non-traditional participants. Intention to attend subsequent live games was 39% higher and had also possessed a moderate to strong relationship.

Certainly, literature can substantially account for the discrepancy between the intention to attend live games levels. Research has found that television impact has a negative impact on match-day attendance (Baimbridge, Cameron & Dawson, 1995; Allan & Roy, 2008). Indeed, in the case of MLR and many other developing leagues, television deals are still being formed with no league-wide deals in place. Most teams have negotiated with local television networks to distribute the games. Live broadcasts are uncommon as prime-time slots are reserved for sports that generate the greatest appeal among the wider public.

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Moderation Effect

The second key finding came from the hierarchical multiple regression exploring how off-field athlete behaviour tempers of modulates the relationship between team identification and consumer behaviour. Specifically, how powerful the effect of off- field athlete behaviour is on the already well-established relationship between team identification and consumer behaviour. As discussed in Chapter Two, while athlete behaviour can undoubtedly impact team identification levels, its impact is not likely to the same extent that was measured in Fink et al.’s (2009) study. That was potentially a better reflection of the reactionary response of the consumer. This was because team identification is a composite variable comprised of a broad range of antecedents which make it quite a stable measure. This is especially so among those who possess high identification levels (Cialdini et al., 1976; Wann & Branscombe, 1990; Lock, Funk, Doyle & McDonald, 2014), as well as being a very strong predictor of consumer behaviour (Robinson et al., 2004).

This investigation was the first step in evaluating whether fans react differently to off- field athlete behaviour depending on whether they are supporting a traditional or non- traditional sporting team. To first understand the relationship the regression included all participants across the entire research project, with the results showing that athlete behaviour does in fact moderate the relationship between team identification and consumer intention.

The moderation effect saw prosocial off-field athlete behaviour increasing and antisocial off-field athlete behaviour decreasing intention levels among the combined sample of participants. The moderation effect was far more profound among individuals possessing lower team identification levels. In other words, the results indicated the less identified a person is with their team the more susceptible they are to off-field athlete behaviour changing their consumer intention levels.

These findings were consistent with the extent literature. In Fink et al.’s (2009) study the changes in pre-test and post-test scores following an athlete scandal were much more substantive among the low identified individuals. Highly identified fans are more likely to have a strong sense of attachment and belonging to the team and will behave differently than low identified fans (Sutton et al., 1997). With increased loyalty and commitment levels they are more likely already engaging in higher levels of consumer behaviour (Madrigal, 1995; Wakefield, 1995; Wann & Branscombe, 1993) and are more likely resistant to threatening and negative information about the team because (Doosje, Branscombe, Spears, & Manstead, 1998; Iyer, Jetten, & Haslam, 2012).

Moderation effect in a Traditional and Non-traditional Professional Sport Context

The final major finding surfaced when isolating the non-traditional and traditional professional sport samples in order to compare the moderating effect. The moderating

Chapter 5: Discussion 59

effect being the effect of off-field athlete behaviour across contexts. The two hierarchical multiple regressions found that in a non-traditional professional context athlete behaviour has a significant moderating effect, but that the same effect is non- significant within the traditional fan sample. The results suggest that in a non- traditional professional sport setting prosocial off-field athlete behaviour elevates intention levels with antisocial behaviour reducing them. The results shared the same characteristics of the first combined sample hierarchical moderated regression with the moderating effect being more profound among individuals who were less identified with their team than those who were highly identified.

Among the traditional sample the moderating effect was insignificant. When attempting to explain why no discernible relationship was present among the traditional sample several factors were considered. As noted, the moderating effect was far more profound among individuals who were less identified with their team in the combined and non-traditional sample regressions. Taking this into account team identification scores were compared across the non-traditional and traditional sample. While both very high, the mean scores from the groups were comparable with the traditional sample marginally higher at 5.66 verses 5.59 for the non-traditional sample. Intention scores were also evaluated and compared as an irregularity could have influenced the results. However, the mean individual intention items were similarly comparable across the two samples. While all three mean intention items were higher among the non-traditional sample, the differences were not substantive enough to justify further exploration. The biggest difference between samples was an elevated score by 6.4% for intention to purchase merchandise. Mean intention items for attending live games and following the team were only higher by 5.6% and 1.2% respectively among the non-traditional sample. These figures were all in line with the marginally higher mean team identification score calculated for the non-traditional sample. These results were in line with Williams & Greenwell’s (2019) study where, despite critical assessments, fan attendance numbers across the three scandals impacted Division I NCAA Women’s basketball teams remained stable.

Taking these factors into account, the results of this research project suggest that the context in which the off-field athlete behaviour is exhibited is quite crucial when calculating its associated fan impact. The reasons as to why this is the case can likely be attributed to the composite nature of team identification. Wann (2006) identified three broad categories of team identification antecedents, those being psychological, environmental and team related factors. The non-traditional sample being comprised of professional rugby union fans within the United States were namely without contributing factors lying within all three encompassing categories.

Exposure to a sport and the number of associations (Gwinner & Swanson, 2003), the physical proximity to the team and how close an individual has grown up around it (Greenwood, 2001), and the uniqueness of stadiums (Underwood et al., 2001) are all important contributing antecedents to team identification. Major League Rugby team fans, who would have been supporting their team for only a maximum of two seasons at the time of the research collection, wouldn’t have been influenced by any of these contributing factors. As a recent start-up and being a relatively small sport in comparison to the market leaders NFL, NBA and MLB, the MLR many fans will have had little exposure to the sport growing up. While they possibly are living quite proximate to the new teams, they most certainly did not grow up around a professional

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team within the United States. As it currently stands the Colorado Raptors are the only team that play out of what Underwood et al. (2001) would define as a ‘unique stadium’. In this case it is the only purpose-built rugby stadium in the United States.

Socialisation agents such as other fans and friends (Crawford, 2003; Kolbe & James, 2003), and family who are existing fans of the team also contribute towards identification (Funk & James, 2001). When asking U.S. university students to list how they came about supporting their favourite sporting team, the most common reason was that their parents or family also supported that team (Wann et al., 2006). As stated, earlier rugby in the United States is not without a footprint. Many colleges adopted rugby in the 1960s and there are currently over 32,000 college players registered with USA Rugby. Correspondingly, many of these players past and present would likely make up a large section of the total fan membership of MLR. With varied levels of exposure, it follows that this section provides strong evidence that the sport of rugby is not without its socialisation agents within the United States. Given only the recent emergence of professional team in the past couple seasons (Roar, 2020), this research project argues that the socialisation agents would be much less prolific and less influential than those occurring in rival sports such as football and basketball.

Group status, prestige, domain involvement and fan ritual are all examples of psychological antecedents of team identification (Ellemers, Van Knippenberg, de Vries & Wilke, 1988; Wang & Tang, 2018; Gwinner & Swanson, 2003). Again, arguably the non-traditional sample do not possess many of these causes and antecedents of sport team identification. As a significantly smaller sport within the context of the United States, they may not be as ‘high status’ and they undisputedly lack many team related factors such as traditions, a rich history and a winning legacy.

Without these listed causes and antecedents of team identification the results of this project suggest that off-field athlete behaviour becomes a more powerful variable. Otherwise put, the more complex and interwoven the web of fan experiences that creates identity is, the more impervious a fan is to the off-field behaviour of athletes. Within the context of a newly established professional sport team, or an emergent league in a new sport, off-field athlete behaviour becomes a far more powerful variable than in an established league or team.

The literature does propose an alternative narrative that relates to unique causes and antecedents of team identification that may only be relevant to non-traditional sport teams. As discussed in the literature review chapter, differentiation can be a significant motivator of fandom. Social identity theory indicates that individuals will attempt to differentiate themselves from others because this makes them feel good about themselves (Tajfel & Turner, 1985). Supporting a new team provides a rare and distinctive consumption experience which has been shown to attract fans (Doyle, Lock, Funk, Filo & McDonald, 2017; Harada & Matsuoka, 1999). Traditional sport team fan membership is arguably not as effective as a means of differentiating oneself.

This membership is largely rooted in the before mentioned causes and antecedents of team identification that are centred around accumulated traditions, team experiences and history (Lock, Darcy, Taylor, 2009). Group membership is already often large. Certainly, within the case of some regional areas, more residents would consider themselves a fan of the local team than not. Many new fans of established teams have

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a pretty well-developed idea of what membership will entail. Seemingly fans electing to engage with newly established teams could be seeking to identify with teams for reasons outside of accumulated traditions, team experiences and history. Non- traditional sports and developing leagues often present greater opportunities for fans to have much closer interactions with the athletes than their traditional counterparts. Perhaps the opportunity for interpersonal interaction with athletes is far more pertinent in their identification with the team. Correspondingly, to that extent knowledge of athletes engaging in prosocial or antisocial behaviour would have a greater impact in this context. In other words, possibly the distinct and unique antecedents and causes of team identification in non-traditional settings result in a construct with a higher propensity to be influenced by athlete behaviour.

Implications for Theory

A key implication for theory relates to the understood effect of athlete behaviour on team identification. Fink et al. (2009) noted the lack of research around off-field events, particularly how athletes’ off-field behaviour impacts fan identification, and correspondingly conducted research within this space. Fink et al. (2009) determined that extreme examples of unscrupulous off-field athlete behaviour produced changes of mean team identification scores among participants. These mean changes were substantive with up to a 13% difference being recorded among low identified fans and a 11% difference among highly identified fans.

These changes were the results of team identification scores being recorded immediately after the participants had reacted to the mock articles that were used as the stimulus. This was credited as being a potential limitation in the research. As Fink et al. (2009) noted in relation to the change in team identification scores, “it is plausible that fans experience and immediate reaction that subsides after time” (2009, p.152). This suggested that while unscrupulous athlete behaviour provokes a fan response, perhaps the change in team identification scores captured the reactionary response rather than the genuine change in team identification level among the subjects. This reactionary response notion is supported by the extent that literature has found team identification to be a very stable measure due to its composite nature, especially among those who possess high identification levels (Cialdini et al., 1976; Wann & Branscombe, 1990; Lock, Funk, Doyle & McDonald, 2014), as well as being a very strong predictor of consumer behaviour (Robinson et al., 2004).

While athlete behaviour certainly can impact team identification levels, its impact is not likely to the same extent that was measured in Fink et al.’s (2009) study. That was potentially a better reflection of the reactionary response of the consumer. A contribution is made around the power of this variable by being more suitably aligned to measure the reactionary response. This has been done by capturing intention items for three key consumer behaviours. This added further clarity around how key variables in this space interact and remedies a potential misconception that a single incident of off-field athlete behaviour will always result in drastically inflated or depressed team identification levels.

As for the reactionary response, while its relative importance remains widely understood, “little research has been conducted to understand sport consumers'

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emotional, cognitive, and behavioural reactions to athlete scandals” (Sato et al., 2018 p108). The empirical research evaluating the direct and interaction effects of team identification and off-field athlete behaviours on outcomes of fan identity has therefore contributed towards this area of research. Specifically, the research has indicated the prosocial off-field athlete behaviour is related to higher levels of consumer intention.

Reciprocally, antisocial off-field athlete behaviour is associated with lower levels of consumer intention. Beyond this, the research has provided evidence to suggest that the more identified an individual is with their respective team the less impact the off- field athlete behaviour variable will have on their intention levels. A further contribution to theory comes through the determination based upon these results that smaller the financial or time related cost is of the consumer behaviour, the less likely it is to be influenced by off-field behaviour.

The role contextual factors play in the relationship has also been explored. Strong evidence is presented supporting the finding that the context in which the behaviour is exhibited in is critical towards its associated impact. Evidence suggests that antisocial and prosocial off-field athlete behaviour in established professional leagues do not have equal effect in sports and leagues that are less traditional or in infancy in terms of being a professional sporting code. Due to a variety of factors, many related to the absence of causes and antecedents of team identification, the effect of off-field athlete behaviour is far more profound in a non-traditional setting.

Implications for Practice

The results shed some light by suggesting that in the context of established professional teams’, off-field athlete behaviour is not as important a variable. In line with the results of this research project, professional sporting teams in established leagues should focus on other well documented antecedents of team identification to achieve commercial outcomes. In pursuit of these, the teams can afford to sign individuals who have a bad history provided they justifiably perform.

For non-traditional sports or emerging professional teams that do not enjoy a full complement of team identification, antecedents of the conduct of their athletes is a very important variable towards achieving positive commercial outcomes. The results of this study were presented to an administrative and marketing team within an emergent professional rugby team. Many of these recommendations were ‘brainstormed’ and evaluated with some subsequently incorporated into their marketing plan going forward. In terms of player management stringent player conduct policies should be in place to reduce the potential impact of antisocial athlete behaviour. In the event policy be breached then a strong leadership response would be required from the team leaders. For example, this could include action either from coaches, athletic directors, and management, or multiple levels of leadership. As Fink et al. (2009) concluded quick action denouncing the behaviour of the athlete can reduce the negative impact of the behaviour.

The firms should also encourage and support players so that they can actively involve themselves in prosocial behaviour. It is a relatively low-cost exercise against alternative methods. Athlete participation in charitable events, getting local schools

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involved, and other grass roots programs are cited mechanisms in which athletes can positively interact with the public (Lachowetz et al., 2009). In terms of player recruitment non-traditional firms need to look beyond an athlete’s credentials, skills, accomplishments and capacity to contribute within the field of play. Strategic recruitment practices can be better streamlined with marketing practices by targeting players who consistently exhibit prosocial behaviour. Despite potentially less publicity and scrutiny around athletes in this context the results suggest individuals who are renown for exhibiting antisocial behaviour should be overlooked regardless of their level of talent and ability.

Non-traditional teams and leagues should pay careful attention to the many diverse causes and antecedents of team identification. By strategically developing several antecedents the results suggest that the impact of a single variable such as athlete behaviour can significantly be reduced. This would be so providing there was an enhanced level of operational stability to the organisation and a good strategic threat mitigation mechanism. Some recommendations of how that can be achieved are provided below.

Environmental factors as the team’s stadiums should be a point of focus. Spa cabanas and a dog park (Jacksonville Jaguars), a fully functional stadium app (San Francisco 49ers), the entertainment arena inside a replica pirate ship (Tampa Bay Buccaneers) and adding winning scores to tombstones situated outside the ground (Clemson) are all examples of how a team might make their stadium unique, which in turn contributes to team identity (Underwood et al., 2001).

Socialisation agents should also be a focal point for sport marketers and practitioners. Other fans, friends and family are powerful drivers of fan identification (Crawford, 2003; Kolbe & James, 2003; Funk & James, 2001). Offering concessions and prizes to existing fans who encourage fellow friends and family to attend live games or engage with the team are effective means of incentivising socialisation agents.

Lastly, psychological factors should be addressed through a wide array of methods. A considerable effort should be made to help the fans feel a sense of unity and cohesion with the team. Using words such as ‘we’ and ‘us’ rather than the ‘team name’ is useful in satisfying those needs. A huge opportunity exists for new teams and leagues to establish a unique team or fan related rituals. The ‘Viking thunderclap’ is a ritualistic clapping sequence that Icelandic soccer players and fans alike delight in partaking following big on-field performances. The West Ham soccer team blows an array of bubbles out among the fans before each final home game of the season. The All Blacks perform a Maori war dance before competition. These are all examples of team rituals. These often expressive and symbolic behaviours have the capacity to also increase identification by fulfilling fans desire to feel unity and cohesion (Wang & Tang, 2018; Boyle & Magnusson, 2007; McDonald & Karg, 2014; Watkins, 2014).

The results also suggest that during challenging times or in the aftermath of an off- field athlete scandal the results suggest that the consumer behaviours that require the least commitment, whether that be time or financial, are most likely to continue at a consistent level. This means that despite diminished attendance and merchandise figures the reduction will not necessarily be fully reflected in the numbers who continue to monitor the team through radio, social media, television and online

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websites. This indicates that marketers should; 1) make it as easy as possible to follow the team during challenging times, 2) increase the quantity of free and easy team related content disseminated by the organisation in the aftermath of scandal. They can do this by devoting more time and resources towards the team related social media avenues, more frequent press releases, more public interviews with associated media distributors, and an increased volume of highlights footage. This could be an effective means of re-enticing fans.

Furthermore, the results suggest that firms should carefully consider the ease in which fans can access and engage in consumer behaviours. Specifically, purchasing merchandise and attending live games should be made as easy as is economically viable. Merchandise sales should not be confined to event booths or home game marquee sales. Online ecommerce functionalities should be developed as a top priority and products she be advertised through the online channels. Similarly, the effort required to attend home live games needs to be carefully considered from the perspective of the consumer. Reducing the effort required by fans to undertake these consumer behaviours should result in greater organisational stability during periods of turmoil and scandal. Stadium locality, efficient ticket checking processes, parking and public transportation considerations, and chartered busses are all examples of mechanisms that can ease the difficulty often experienced by fans when attending home games.

While the recommendations mirror the key results of the research project largely focus on the findings arising out of the non-traditional setting it is important to note a key limitation of the study. The majority of participants were highly identified with their prospective teams. It is possible that these recommendations will hold true in a traditional setting among individuals with low-medium levels of team identification where an off-field athlete behaviour could have a significant moderating effect.

Limitations

Potential limitations to this research could have arisen out of the design and the sampling method of the research project. As discussed at length in Chapter Three, several responses biases could have impacted the generalisability of the research findings. Specifically demand characteristics, as they relate to participants altering their response to conform with expectations. This can occur when participants become aware of the experiment (Orne, 1962). As discussed in Chapter Three, in accordance with ethical requirements participants were made fully aware of the fictitious nature of the articles, the full purpose of the research and its expected duration and procedures.

Additionally, recruiting participants through existing fan Facebook groups could have introduced bias. Self-selection bias is very common when using online surveys for research (Stanton, 1998; Thompson et al., 2003; Wittmer et al., 1999). In this case individuals who actively engage with their teams via social media or fan newsletters were able to first become aware, then subsequently access the survey.

The distribution of the surveys via the fan newsletters and designated fan related Facebook pages presents a further limitation. Fans with lower team identification

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scores are much less likely to read the weekly team newsletters, be an existing member or check the online team related fan group pages where the surveys were disseminated. This was clearly reflected in the sample characteristics through an extremely high mean team identification score of 5.63. This meant a very large section of participants were omitted from the sample. Low-medium identified fans or ‘fair weather’ fans were largely absent. As has been documented in this research, the impact of off-field athlete behaviour was more profound on those possessing lower levels of team identification. The statistical significance and impact that athlete off-field behaviour has upon consumers therefore could have been underestimated due to the inability of the distribution method to effectively canvas the entire fan populations.

A further limitation relates to the use of fictional articles that exclusively described incidents taking place away from the sporting arena. This was a deliberate move to mitigate the influence of extraneous performance related factors that could be linked to athlete behaviour on-field or within the field of play. As has been shown on-field and performance related scandals are perceived as more severe, eliciting more negative responses from consumers (Kwak, 2016).

Recommendations for Future Research

As has been noted the sample was largely confined to fans who were highly identified with their respective teams. A sample canvassing fans possessing low to moderate levels of identification may yield new insights. The effects of off-field athlete behaviour were more profound among those who possessed lower levels of team identification. The mean team identification score for the traditional sample was very high at 5.66; it is likely that the moderating effect would be significant among participants possessing low to moderate levels of team identification.

Further research exploring the reasons why fans choose to identify with new and developing teams and leagues could also yield interesting insights. New leagues and teams often provide frequent opportunities for fans to freely intermingle with the athletes. The player-fan relationship could be a far more salient variable among these fans, hence why examples of prosocial and antisocial behaviour have had a significant effect.

While the absence of many causes and antecedents of team identification are discussed and used to explain the findings in the non-traditional sample, further research should seek to better understand the unique psychological factors that contribute towards team identification within these contexts. It is conceivable that the appeal is less rooted in performance related factors and more geared towards the unique elements the help differentiate the individual. An effort should be made to better understand these unique factors.

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Chapter 6: Conclusions

The research project aimed to provide a contextual examination of athlete off-field behaviours association with team identity and the effect team identification has on consumer intentions. By doing so it has helped address an under-researched contributor towards fan identity. It has extended the literature on fan identification and social identity theory by providing further explanation on why and how fans identify with sporting teams.

Corresponding with the research questions and hypotheses a few major findings arose from the research project. The first key finding being that the extent the consumer behaviour requires substantial expenditure or the investment of time the more associated it will be with the perception ofa off-field athlete behaviour. In other words, the results suggested the higher the cost or inconvenience of the consumer activity, the more consumer intention levels for that activity will be related to athlete off-field behaviour.

The second major finding was that the results suggested that off-field athlete behaviour only tempers and modulates the relationship between team identification and consumer behaviour in a non-traditional professional sports context. Prosocial evaluations of off- field athlete behaviour lead to increased intention levels to engage in consumer behaviour. Conversely antisocial perceptions of athlete off-field behaviour lead to reduced intention levels. The moderation effect was far more profound among individuals possessing lower team identification levels. Therefore, the results indicated the less identified a person is with their team the more susceptible they are to off-field athlete behaviour changing their consumer intention levels. As to why no discernible relationship was present among the traditional sample several factors were considered. This research proposed that because established teams and leagues enjoy a full complement of causes and antecedents of team identification the off-field athlete behaviour is not as powerful a variable as it is in non-traditional or emerging teams and leagues.

Following these findings, the limitations of the research and subsequent implications for practice were discussed at length. The limitations discussed were centred around various design and sampling issues. The abundance of ‘die hard’ fans among all three samples is a particularly noteworthy factor that may have a potentially significant impact upon the extrapolation or generalising of the research findings.

Subsequent to discussion around the findings and potential limitations, several recommendations for future research were also proposed. Namely, further research evaluating the effects among fans possessing low-medium levels of team identification could yield new insights. Further research exploring the reasons why fans choose to identify with new and developing teams and leagues could also reveal unique factors as to why individuals identify with sporting teams.

Chapter 6: Conclusions 67

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Bibliography 81

Appendices

Appendix A

Questionnaire

Question 1: To which gender identity do you most identify? (ALL)

Question 2: What is your age? (ALL)

Question 3:

3.1 What is the name of your favorite Major League Rugby team? (survey 1)

3.2 What is the name of your favorite NFL team? (survey 3)

3.3.1 What league does your favorite professional rugby team compete in? (survey 2) 3.3.2 What is the name of your favorite professional rugby team? (survey 2)

Question 4: (ALL)

Please rate the following statements about your favorite Major League Rugby/NFL/professional rugby team?

Scale: 1. Strongly Disagree - 2. - 3. - 4. Neutral - 5. - 6. - 7. Strongly Agree

4.1 I already consider myself a fan of (my favorite team)

4.2 I would feel at a loss if I had to give up being a (my favorite team) fan

4.3 Others recognise that I am a big (my favorite team) fan

Now please read the fictional article on the next page and imagine that it relates to a key player from your favorite Major League Rugby/NFL/Professional Rugby team. Once you have completed reading the article please continue by answering the final questions.

Only one of the following articles will be randomly generated for the participant. (ALL)

Rugby/Football Player Refuses to Take a Photo

An altercation has ensued following the refusal of an athlete to sign a piece of supporter merchandise or take a photo with the fan.

The professional rugby player/football player was dining at a local restaurant in early May when a fan approached him and asked for a photograph or a signature. The athlete reportedly refused resulting in a heated argument. Roger, a witness, told reporters that he and his friends saw the fan become quite agitated when the athlete told the fan to “F—k off, I am eating lunch”. The fan initially began to apologise, but the athlete interrupted him and exclaimed, “I told you to leave, now get the hell out of my sight”.

Appendices 83

Roger claims that the fan was clearly taken back by the second outburst, “The fan then demanded an apology from the prominent local athlete, to which he responded by standing up and saying, ‘if you don’t leave now I am going to beat the s—t out of you’”.

By this point security guards intervened jumping in to keep the high-profile athlete away from the fan.

Pool Recovery Key to Success

This week players from your favourite professional MLR/NFL/Professional Rugby team have been spotted frequenting the local swimming pool as they strive to “win the recovery”.

Pool manager Roger explained, “Swimming is a terrific way to recover after a hard day or to get in some fat-burning cardio. In fact, studies have found that athletes who hit the pool for a moderate workout on a recovery day were able to subsequently work out longer than those who took it easy”.

“Active recovery in a pool helps to reduce the soreness, flushes out lactic acid and prevents a drop-off in performance”, Said one of the players following his swim.

While some regular pool goers may be upset with the potential lane shortage, others are very pleased to see the players taking the initiative to recover well on their ‘off days’. “The players are always very courteous and are respectful of the other people in the pool” Cynthia said, a regular patron at the pool.

Professional MLR/Rugby/NFL player Supports Bullied 12-year-old

A key player from your favourite MLR/NFL/Professional Rugby team spent part of the weekend making good on a promise he made to support 12-year-old resident Roger, who has had to endure taunts from fellow school kids on the playground and in the classroom for close to three years, according to his father.

"They just seem like a wonderful family," the athlete told the reporter on Monday.

"Roger is a really polite, intelligent and enthusiastic kid. With any place, there is always going to be some people that make things not go as well as you would like."

"It had reached the point where Roger couldn't deal with it anymore," Richard told on Monday.

"As a parent, you want to do so much more in these situations, but you really can't. So, I thought maybe I could get somebody else involved." That somebody turned out to be one of the key players for the team. Richard reached out to several players on social media to see if they would help restore his son's confidence.

It’s understood that more than one player from the team agreed to walk him to school. "I was shocked they got back to me so quick," Richard said.

Richard said that the meeting has done “wonders for his sons’ confidence, and that Roger is doing really well now”.

Appendices 84

Question 5: If prosocial behaviour is defined as ' behaviour with intent to benefit others' and antisocial behaviour is defined as 'behaviour causing harm or lacking consideration for the well-being of others', where on this scale would you place the behaviour described in the scenario? (ALL)

• Scale: 1. very antisocial - 2. antisocial - 3. neutral - 4. prosocial - 5. very prosocial

Question 7: Please imagine that the previous article is true and relates to a key player on your favorite MLR team. Imagining this, please rate the following statements about this team... (ALL)

- Scale: 1. Definitely Would Not - 2. - 3. - 4. Neutral - 5. - 6. - 7. Definitely Would

7.1 How likely are you to continue attending your favorite team's games?

7.2 How likely are you to continue following the team this/next season? i.e.. on social media and television

7.3 How likely are you to buy more merchandise this/next season?

When complete please submit and you will be redirected to the random prize draw. Thank you

Clicking submit takes participant to second prize draw survey.

Thank you for participating! To thank you, you have an opportunity to enter a random prize draw for a $20.00 voucher to Whole Food Market/Autographed jersey by the Nola Gold!

Question 1: Would you like to go into the prize draw? (ALL)

Answers: yes – enter email address and name – reproduction of participant information sheet No – Reproduction of participant information sheet

Appendices 85

Appendix B

Surveys

1. NFL FAN DATA SURVEY https://survey.qut.edu.au/f/193226/185a/ DATA

PARTICIPANT INFORMATION FOR QUT RESEARCH PROJECT – Survey –

Team Identification and the Off-field Behaviour of Athletes: Effects on Consumer Intention in Traditional and Non-traditional Sporting Contexts

QUT Ethics Approval Number 1900000208

Research team

Principal Researcher: Mr Benjamin Tarr Masters Student Associate Researchers: Professor Larry Neale Principal Supervisor Dr Louise Kelly Associate Supervisor School of Marketing, QUT Business School Queensland University of Technology (QUT) Brisbane, Australia

Why is the study being conducted? This research project is being undertaken as part of a master’s study for Benjamin Tarr.

The study examines the effects of off-field athlete behaviour on fan intention. The study compares how fan reaction might be different amongst and across varying professional sport contexts.

You are invited to participate in this research project because you are a fan of a team in the National Football League (NFL).

What does participation involve? Participation will involve completing a 6-item survey and being asked to read and respond to a fictitious news article depicting an athlete engaging in either antisocial, prosocial or benign behaviour .

You will be asked to read and complete the survey imagining that the article has been written about a key athlete from your favorite NFL team.

Two questions will capture demographic information (age & gender), and a further two questions will ask about your favorite sporting team. Lastly a further 7 items will use a Likert scale to capture team identification and consumer response data.

Appendices 86

Answering the questions and reading the article will take approximately 10 minutes of your time.

Questions will include: 1. I already consider myself a fan of the (team name)? 1 = Strongly Disagree to 7 = Strongly Agree

2. How likely are you to attend the (team’s) games during the remainder of the season? 1 = Definitely would Not to 7 = Definitely would

Your participation in this research project is entirely voluntary. If you agree to participate you do not have to complete any question(s) you are uncomfortable answering.

Your decision to participate or not participate will in no way impact upon your current or future relationship with QUT or the associated professional sporting team. If you do agree to participate you can withdraw from the research project during your participation without comment or penalty by exiting the survey window before clicking ‘submit’ on the final page. No information up to that point will be obtained.

What are the possible benefits for me if I take part? To recognise your contribution should you choose to participate the research team is offering you the chance to win a $20.00 Amazon eGift Voucher.

Please note the opening date for entries is 21 July 2019, the closing date for entries is 15 September 2019.

The Terms and Conditions of the prize draw can be located at: https://survey.qut.edu.au/survey-data/67/67667/media/62/6254.pdf

What are the possible risks for me if I take part? There are minimal risks associated with your participation in this research project.

Participation could however put you at risk of experiencing feelings of disappointment and anger in response to the behaviour described in the articles.

Participants in the United States who may experience discomfort or distress as a result of their participation in the research are able to contact the Crisis Call Center on (775) 784-8090. The Crisis Call Center provides 24-hour, 7 day, 365 days a year crisis line support for individuals in any type of crisis. IMALIVE online chat is another option providing instant support messaging to individuals in moments of crisis or intense emotional pain. The IMALIVE online chat can be accessed at https://www.imalive.org/.

Participants in Australia can contact Lifeline who provide access to online, phone or face-to-face support, call 13 11 14 for 24 hour telephone crisis support. If you are aged up to 25, you can also call the Kids Helpline on 1800 551 800.

What about privacy and confidentiality? It is not necessary for you to provide any personally identifying information to participate. This meaning that your responses are anonymous i.e. it will not be possible to identify you at any stage of the research because personal identifying information is not sought in any of the responses and no traceable information is collected via the server or survey tool.

Appendices 87

If you do however wish to be entered in the random prize draw for a Amazon eGift voucher you will need to provide an email address after submitting the survey. Your email address will only be accessible to the research team and will be deleted upon the conclusion of the prize draw. Your email address will not be included in the data file so cannot be linked to your individual responses.

Any data collected as part of this research project will be stored securely as per QUT’s Management of research data policy. Data will be stored for a minimum of 5 years, and can be disclosed if it is to protect you or others from harm, if specifically required by law, or if a regulatory or monitoring body such as the ethics committee requests it.

How do I give my consent to participate? The submission or return of the completed survey is accepted as an indication of your consent to participate in this research project.

What if I have questions about the research project? If you have any questions, require further information or would like to receive result feedback please contact one of the listed researchers:

Benjamin Tarr [email protected] Larry Neale [email protected]

What if I have a concern or complaint regarding the conduct of the research project? QUT is committed to research integrity and the ethical conduct of research projects. If you wish to discuss the study with someone not directly involved, particularly in relation to matters concerning policies, information or complaints about the conduct of the study or your rights as a participant, you may contact the QUT Research Ethics Advisory Team on +61 7 3138 5123 or email [email protected].

Appendices 88

2. RUGBY FAN DATA SURVEY https://survey.qut.edu.au/f/193225/8b55/

PARTICIPANT INFORMATION FOR QUT RESEARCH PROJECT – Survey –

Team Identification and the Off-field Behaviour of Athletes: Effects on Consumer Intention in Traditional and Non-traditional Sporting Contexts

QUT Ethics Approval Number 1900000208

Research team

Principal Researcher: Mr Benjamin Tarr Masters Student Associate Researchers: Professor Larry Neale Principal Supervisor Dr Louise Kelly Associate Supervisor School of Marketing, QUT Business School Queensland University of Technology (QUT) Brisbane, Australia

Why is the study being conducted? This research project is being undertaken as part of a master’s study for Benjamin Tarr.

The study examines the effects of off-field athlete behaviour on fan intention. The study compares how fan reaction might be different amongst and across varying professional sport contexts.

You are invited to participate in this research project because you are a Rugby Union supporter.

What does participation involve? Participation will involve completing a 6-item survey and being asked to read and respond to a fictitious news article depicting an athlete engaging in either antisocial, prosocial or benign behaviour .

You will be asked to read and complete the survey imagining that the article has been written about a key athlete from your favorite Professional Rugby team.

Two questions will capture demographic information (age & gender), and a further two questions will ask about your favorite sporting team. Lastly a further 7 items will use a Likert scale to capture team identification and consumer response data.

Answering the questions and reading the article will take approximately 10 minutes of your time.

Questions will include: 1. I already consider myself a fan of the (team name)? 1 = Strongly Disagree to 7 = Strongly Agree

2. How likely are you to attend the (team’s) games during the remainder of the season? 1 = Definitely would Not to 7 = Definitely would

Appendices 89

Your participation in this research project is entirely voluntary. If you agree to participate you do not have to complete any question(s) you are uncomfortable answering. Your decision to participate or not participate will in no way impact upon your current or future relationship with QUT or the associated professional sporting team. If you do agree to participate you can withdraw from the research project during your participation without comment or penalty by exiting the survey window before clicking ‘submit’ on the final page. No information up to that point will be obtained.

What are the possible benefits for me if I take part? To recognise your contribution should you choose to participate the research team is offering you the chance to win a $20.00 Amazon eGift voucher.

Please note the opening date for entries is 21 July 2019, the closing date for entries is 15 September 2019.

The Terms and Conditions of the prize draw can be located at: https://survey.qut.edu.au/survey-data/67/67667/media/62/6254.pdf

What are the possible risks for me if I take part? There are minimal risks associated with your participation in this research project.

Participation could however put you at risk of experiencing feelings of disappointment and anger in response to the behaviour described in the articles.

Participants in the United States who may experience discomfort or distress as a result of their participation in the research are able to contact the Crisis Call Center on (775) 784-8090. The Crisis Call Center provides 24-hour, 7 day, 365 days a year crisis line support for individuals in any type of crisis. IMALIVE online chat is another option providing instant support messaging to individuals in moments of crisis or intense emotional pain. The IMALIVE online chat can be accessed at https://www.imalive.org/.

Participants in the United Kingdom can visit the NHS Moodzone section on the NHS for a range of interactive tools, videos and audio guides to improve mental and emotional wellbeing (https://www.nhs.uk/conditions/stress-anxiety-depression/).

A comprehensive list of mental health helplines available to participants in the UK can be accessed at https://www.nhs.uk/conditions/stress-anxiety-depression/mental-health- helplines/?fbclid=IwAR2vAmO0peYLKv0c- 6tSGTQp3tYvtlHpFs1jluoyQXqHigay1GKoZ3bekPc.

Participants in New Zealand may be able to access a range of mental health support services. A comprehensive list of 24 hour, seven days a week helplines may be found at https://www.mentalhealth.org.nz/get- help/in-crisis/helplines/.

Participants in Australia can contact Lifeline who provide access to online, phone or face-to-face support, call 13 11 14 for 24 hour telephone crisis support. If you are aged up to 25, you can also call the Kids Helpline on 1800 551 800.

Appendices 90

What about privacy and confidentiality? It is not necessary for you to provide any personally identifying information to participate. This meaning that your responses are anonymous i.e. it will not be possible to identify you at any stage of the research because personal identifying information is not sought in any of the responses and no traceable information is collected via the server or survey tool.

If you do however wish to be entered in the random prize draw for a Amazon eGift card you will need to provide an email address after submitting the survey. Your email address will only be accessible to the research team and will be deleted upon the conclusion of the prize draw. Your email address will not be included in the data file so cannot be linked to your individual responses.

Any data collected as part of this research project will be stored securely as per QUT’s Management of research data policy. Data will be stored for a minimum of 5 years, and can be disclosed if it is to protect you or others from harm, if specifically required by law, or if a regulatory or monitoring body such as the ethics committee requests it.

How do I give my consent to participate? The submission or return of the completed survey is accepted as an indication of your consent to participate in this research project.

What if I have questions about the research project? If you have any questions, require further information or would like to receive result feedback please contact one of the listed researchers:

Benjamin Tarr [email protected] Larry Neale [email protected]

What if I have a concern or complaint regarding the conduct of the research project? QUT is committed to research integrity and the ethical conduct of research projects. If you wish to discuss the study with someone not directly involved, particularly in relation to matters concerning policies, information or complaints about the conduct of the study or your rights as a participant, you may contact the QUT Research Ethics Advisory Team on +61 7 3138 5123 or email [email protected].

Appendices 91

3. MLR FAN DATA SURVEY https://survey.qut.edu.au/f/193057/2628/

PARTICIPANT INFORMATION FOR QUT RESEARCH PROJECT – Survey –

Team Identification and the Off-field Behaviour of Athletes: Effects on Consumer Intention in Traditional and Non-traditional Sporting Contexts

QUT Ethics Approval Number 1900000208

Research team Principal Researcher: Mr Benjamin Tarr Masters Student Associate Researchers: Professor Larry Neale Principal Supervisor Dr Louise Kelly Associate Supervisor School of Marketing, QUT Business School Queensland University of Technology (QUT) Brisbane, Australia

Why is the study being conducted? This research project is being undertaken as part of a master’s study for Benjamin Tarr.

The study examines the effects of off-field athlete behaviour on fan intention. The study compares how fan reaction might be different amongst and across varying professional sport contexts.

You are invited to participate in this research project because you are a supporter of a team in Major League Rugby.

What does participation involve? Participation will involve completing a 6-item survey and being asked to read and respond to a fictitious news article depicting an athlete engaging in either antisocial, prosocial or benign behaviour .

You will be asked to read and complete the survey imagining that the article has been written about a key athlete from your favorite Major League Rugby team.

Two questions will capture demographic information (age & gender), and a further two questions will ask about your favorite sporting team. Lastly a further 7 items will use a Likert scale to capture team identification and consumer response data.

Answering the questions and reading the article will take approximately 10 minutes of your time.

Questions will include: 1. I already consider myself a fan of the (team name)? 1 = Strongly Disagree to 7 = Strongly Agree

2. How likely are you to attend the (team’s) games during the remainder of the season?

Appendices 92

1 = Definitely would Not to 7 = Definitely would

Your participation in this research project is entirely voluntary. If you agree to participate you do not have to complete any question(s) you are uncomfortable answering. Your decision to participate or not participate will in no way impact upon your current or future relationship with QUT or the associated professional sporting team. If you do agree to participate you can withdraw from the research project during your participation without comment or penalty by exiting the survey window before clicking ‘submit’ on the final page. No information up to that point will be obtained.

What are the possible benefits for me if I take part? To recognise your contribution should you choose to participate the research team is offering you the chance to win a $20.00 Amazon eGift card.

Please note the opening date for entries is 21 July 2019, the closing date for entries is 15 September 2019. The Terms and Conditions of the prize draw can be located at: https://survey.qut.edu.au/survey-data/67/67667/media/62/6254.pdf

What are the possible risks for me if I take part? There are minimal risks associated with your participation in this research project.

Participation could however put you at risk of experiencing feelings of disappointment and anger in response to the behaviour described in the articles.

Participants in the United States who may experience discomfort or distress as a result of their participation in the research are able to contact the Crisis Call Center on (775) 784-8090. The Crisis Call Center provides 24-hour, 7 day, 365 days a year crisis line support for individuals in any type of crisis. IMALIVE online chat is another option providing instant support messaging to individuals in moments of crisis or intense emotional pain. The IMALIVE online chat can be accessed at https://www.imalive.org/.

Participants in Australia can contact Lifeline who provide access to online, phone or face-to-face support, call 13 11 14 for 24 hour telephone crisis support. If you are aged up to 25, you can also call the Kids Helpline on 1800 551 800.

What about privacy and confidentiality? It is not necessary for you to provide any personally identifying information to participate. This meaning that your responses are anonymous i.e. it will not be possible to identify you at any stage of the research because personal identifying information is not sought in any of the responses and no traceable information is collected via the server or survey tool.

If you do however wish to be entered in the random prize draw for a $20.00 Amazon eGift card you will need to provide an email address after submitting the survey. Your email address will only be accessible to the research team and will be deleted upon the conclusion of the prize draw. Your email address will not be included in the data file so cannot be linked to your individual responses.

Appendices 93

Any data collected as part of this research project will be stored securely as per QUT’s Management of research data policy. Data will be stored for a minimum of 5 years, and can be disclosed if it is to protect you or others from harm, if specifically required by law, or if a regulatory or monitoring body such as the ethics committee requests it.

How do I give my consent to participate? The submission or return of the completed survey is accepted as an indication of your consent to participate in this research project.

What if I have questions about the research project? If you have any questions, require further information or would like to receive result feedback please contact one of the listed researchers:

Benjamin Tarr [email protected] Larry Neale [email protected]

What if I have a concern or complaint regarding the conduct of the research project? QUT is committed to research integrity and the ethical conduct of research projects. If you wish to discuss the study with someone not directly involved, particularly in relation to matters concerning policies, information or complaints about the conduct of the study or your rights as a participant, you may contact the QUT Research Ethics Advisory Team on +61 7 3138 5123 or email [email protected]

Appendices 94

Appendix C

Facebook Postings

PARTICIPATE IN RESEARCH PROJECT Faculty of Business, Queensland University of Technology (Australia)

If you are above the age of 18 you are Invited to complete the 10-minute survey examining the effects off-field athlete behaviour has on fan intention. Off-field behaviour by athletes attracts significant attention by the press, fans, marketers – and researchers. This research is looking to further explore this impact and the differences that may exist across different professional sporting bodies. To recognise your contribution should you choose to participate the research team is offering you the chance to win a $20.00 Amazon eGift voucher. If you are interested in participating in this study, please follow this link to the Key Survey site where you will find a detailed information sheet and the survey: (insert link)

Appendices 95

Appendices 96