Understanding Sport Consumers within Competitive Markets

Understanding Sport Consumers within Competitive Markets

Hunter Fujak

Doctor of Philosophy UTS Business School

University of Technology Sydney

Submitted 15 November 2018

i

Certificate of original authorship

I, Hunter Fujak declare that this thesis, is submitted in fulfilment of the requirements for the

award of Doctor of Philosophy, in the Business School at the University of Technology

Sydney.

This thesis is wholly my own work unless otherwise referenced or acknowledged. In addition,

I certify that all information sources and literature used are indicated in the thesis. This

document has not been submitted for qualifications at any other academic institution. This

research is supported by the Australian Government Research Training Program.

___________________________________________

Date: 15 November 2018

Production Note:

Signature removed prior to publication.

ii

Acknowledgements I would like to thank and acknowledge the following people that have made the completion of

this research project possible. First, I would like to thank my primary supervisor Dr Stephen

Frawley. I am forever indebted for both your professional and personal guidance, which now

extends beyond a decade. The potential you saw in me as an undergraduate and the backing

provided since has been truly responsible for me living my best life.

I would also like to thank all those in the broader UTS and academic community. I wish to

acknowledge my supervisors, Associate Professor Daryl Adair and Dr Stephen Bush for your

support and expertise during this project. Thanks also to Dr Daniel Lock, Dr David Bond and

Professor Heath McDonald for sharing your time and wisdom as external co-contributors.

Thanks also to Associate Professors Nico Schulenkorf, Hussain Rammal and Dr Katie

Schlenker for your advice and support. A large debt of gratitude goes to Top Tier Editing for

their thorough and punctual editing toward this thesis. Finally, thanks to my PhD comrades

Jack, Greg, Loic, Paul and Natasha, who I’ve spent more time with in the last four years than

my own family and friends. I will look back fondly on this journey as a shared one.

Finally, thanks to those people whose quality time I have most sorely missed in the process of

completing this dissertation. To Mum and Dad, thank you for all the numerous ways you have

helped me survive this period. To my closest friends Nathan, Ra, Trent, Carly, Foxy, Sandman,

Will, Kat & Oneg, thank you for not forgetting about me during my periods of prolonged

disappearance. To Rachel, thank you for putting up with my stress-related messiness, lack of

quality of time and inconsistent working hours. Thank you for putting up with me in general.

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Statement of thesis format This thesis is submitted by compilation format, including five published or under review

journal articles. Chapter one introduces the thesis before chapters two through six presents the

five studies. The studies are presented in accordance with the format required by their

corresponding journal submission, however attempts have been made to standardise the thesis

to British English where appropriate. Chapter seven provides a discussion of the overarching

thesis and concludes the document.

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Publications arising from the project

Journal articles

Fujak, H., Frawley, S., McDonald, H., & Bush, S. (2018). Are sport consumers unique? Consumer behavior within crowded sport markets. Journal of Sport Management. 32(4), 362-375.

Fujak, H., Frawley, S., & Bush, S. (2017). Quantifying the value of sport broadcast rights. Media International Australia, 164(1). 104-116.

Conference presentations

Fujak, H., Frawley, S., & Adair, D. (2018). Conceptualisation and sizing the sport market.

Paper presented at 24rd Sport Management Associaton of Australia and New Zealand Conference, 21-23 November, Adelaide, Australia.

Fujak, H., Frawley, S., & Adair, D. (2017). Quantifying the sport consumer’s shopping basket.

Paper presented at 23rd Sport Management Association of Australia and New Zealand Conference, 29-1 December, Gold Coast, Australia.

Fujak, H., Frawley, S., & Joachim, G. (2017). Measuring sport consumption across the East-

West Sydney divide. Paper presented at Sporting Traditions XXI: The Business of Sport, 3-6 July, Sydney, Australia.

Fujak, H., Frawley, S., & Schulenkorf, N. (2016). Ethnicity and sport preference: Implications

for future Australian sport consumption. Paper presented at 22nd Sport Management Associaton of Australia and New Zealand Conference, 22-25 November, Auckland, New Zealand.

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Contribution of authors Paper Authors Contribution Signature Fujak, H., Frawley, S., & Adair, D. (2019). Conceptualising and Sizing the Sport Market. Sport Management Review.

(Under Review)

(ABCD List: A)

Fujak, H Primary data collector, analysis and write up of manuscript.

Frawley, S PhD panel supervisory support and proofreading.

Adair, D PhD panel supervisory support and proofreading.

Fujak, H., Frawley, S., McDonald, H., & Bush, S. (2018). Are sport consumers unique? Consumer behavior within crowded sport markets. Journal of Sport Management, 32(4), 362-375.

(ABCD List: A*)



McDonald, H Conceptual and theoretical feedback.

Bush, S PhD panel supervisory support. Guidance with statistical analysis.

Fujak, H., Frawley, S., & Bond, D. (2019). The Relationship Between Revenue and Fan Base Size Within Sport Markets. Sport Management Review.

(Under Review)

(ABCD List: A)



Bond, D Assistance with data collection. Feedback with accounting methodology.

Production Note:

Signature removedprior to publication.

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Fujak, H., Frawley, S., & Lock, D. (2019). Television Audiences as a Measure of Market Acceptance. Marketing Intelligence & Planning.

(Under Review)

(ABCD List: A)



Lock, D Conceptual and theoretical feedback. Proofreading.

Adair, D PhD panel supervisory support and proofreading.

Fujak, H., Frawley, S., & Bush, S. (2017). Quantifying the value of sport broadcast rights. Media International Australia, 164(1), 104-116.

(ERA Rank: A)



Bush, S PhD panel supervisory support. Guidance with statistical analysis.

Production Note:


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vii

Table of Contents

1. Introduction ................................................................................................................................... 1

1.1 Study Background and Context .............................................................................................. 2

1.2 Statement of Problem .............................................................................................................. 4

1.3 Research Justification ............................................................................................................. 5

1.4 Research Design ...................................................................................................................... 7

1.5 Project Outline ...................................................................................................................... 11

1.6 Delimitations of Scope .......................................................................................................... 15

1.7 Conclusion ............................................................................................................................ 16

2. Study 1: The Consumer Market Structure of Australian Sport ............................................. 17

2.1 Introduction ........................................................................................................................... 19

2.2 Literature Review .................................................................................................................. 21

2.3 Method .................................................................................................................................. 29

2.4 Results ................................................................................................................................... 34

2.5 Discussion ............................................................................................................................. 42

2.6 Conclusion ............................................................................................................................ 47

2.7 References ............................................................................................................................. 50

3. Study 2: Are Sport Consumers Unique? Consumer Behaviour within Crowded Sport Markets ................................................................................................................................................ 60

3.1 Introduction ........................................................................................................................... 62

3.2 Literature Review .................................................................................................................. 65

3.3 Methods ................................................................................................................................. 71

3.4 Results ................................................................................................................................... 78

3.5 Discussion ............................................................................................................................. 91

3.6 Conclusion ............................................................................................................................ 97

3.7 References ........................................................................................................................... 100

4. Study 3: The Relationship Between Revenue and Fan Base Size Within Sport Markets .. 107

4.1 Introduction ......................................................................................................................... 109

4.2 Literature Review ................................................................................................................ 112

4.3 Empirical Setting ................................................................................................................ 117

4.4 Methodology ....................................................................................................................... 119

4.5 Results ................................................................................................................................. 126

4.6 Discussion ........................................................................................................................... 131

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4.7 Conclusion .......................................................................................................................... 136

4.8 References ........................................................................................................................... 139

5. Study 4: Consumer Behaviour Toward a New League and Teams: Television Audiences as a Measure of Market Acceptance .................................................................................................... 147

5.1 Introduction ......................................................................................................................... 149


5.3 Method ................................................................................................................................ 160

5.4 Results ................................................................................................................................. 164

5.5 Discussion ........................................................................................................................... 171

5.6 Conclusion .......................................................................................................................... 176

5.7 References ........................................................................................................................... 178

6. Study 5: Quantifying the Value of Sport Broadcast Rights .................................................. 186

6.1 Introduction ......................................................................................................................... 188


6.3 Method ................................................................................................................................ 193

6.4 Results ................................................................................................................................. 197

6.5 Discussion ........................................................................................................................... 204

6.6 Conclusion .......................................................................................................................... 206

6.7 References ........................................................................................................................... 208

7. Discussion and Conclusions ..................................................................................................... 212

7.1 Thesis context and purpose ................................................................................................. 212

7.2 Study linkage and findings .................................................................................................. 214

7.3 Thesis contribution .............................................................................................................. 222

7.4 Practical implications .......................................................................................................... 227

7.5 Future research .................................................................................................................... 228

7.6 Final remarks ...................................................................................................................... 231

Appendix 1 ......................................................................................................................................... 233

Appendix 2 ......................................................................................................................................... 236

Appendix 3 ......................................................................................................................................... 247

Appendix 4 ......................................................................................................................................... 251

Bibliography ...................................................................................................................................... 252

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List of Tables

Table 1: Summary of research methods .................................................................................................. 9 Table 2: Thesis research outputs ........................................................................................................... 12 Table 3: Variables influencing sport avidity (in order of significance) ................................................ 35 Table 4: Model fit statistics ................................................................................................................... 36 Table 5: Latent class probabilities for top 20 sports ............................................................................. 40 Table 6: Latent class membership composition .................................................................................... 41 Table 7: List of generalised marketing principles ................................................................................. 68 Table 8: List of Sydney clubs ............................................................................................................... 73 Table 9: Dirichlet models ...................................................................................................................... 81 Table 10: Duplication of sport attendance ............................................................................................ 87 Table 11: Testing for market partitioning among attendees ................................................................. 89 Table 12: Significant changes to Australia’s sport marketplace between 1998 and 2017 .................. 118 Table 13: Variable description and summary statistics ....................................................................... 125 Table 14: GLS regression estimates .................................................................................................... 127 Table 15: Descriptive and inferential statistics for FTA season 1 (2013/14) by region ..................... 165 Table 16: Hierarchical ANOVA for HomeShare as a function of HomeTeam and Season nested within Region ...................................................................................................................................... 167 Table 17: Audience metrics across the regular BBL season ............................................................... 170 Table 18: Regression upon ratings with NRL ..................................................................................... 199 Table 19: Regression results of total AVH ......................................................................................... 201 Table 20: Friday night football ratings analysis by component .......................................................... 202 Table 21: Sunday afternoon football ratings analysis by component ................................................. 204 Table 22: Thesis research outputs reaffirmed ..................................................................................... 215

List of Figures

Figure 1: Conceptualising the sport market .......................................................................................... 27 Figure 2: Sydney (left) and Melbourne (right) sport market: scatter-plot relationship between brand share and penetration rate ..................................................................................................................... 84 Figure 3: Sydney attendance rate of solely loyal buyers by league ...................................................... 86 Figure 4: Amalgamated interaction effect plot for games involving local teams versus non-local teams ............................................................................................................................................................ 168 Figure 5: NRL broadcast by segment duration and audience size ...................................................... 198 Figure 6: NRL average audience size and advertising concentration ................................................. 200 Figure 7: Conceptual sequencing of the five presented studies .......................................................... 218

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Abstract

The past half century has seen the transformation of many sporting organisations into

sophisticated commercial businesses (Stewart, 2007). The global sport industry itself has

become economically substantial, with underlying finances – revenue and expenditure – the

subject of vigorous scholarship. Unsurprisingly, research into the commercial management of

sport requires an understanding of its market and competitors. Within that context, the

position of consumers has attracted considerable interest from scholars – theorised as

distinctive by virtue of ‘irrational’ passions that command high levels of product and brand

loyalty, relentless optimism and vicarious identification (Smith & Stewart, 2010). Herein,

though, lies a paradox. The sport landscape is now highly congested, with more competitors

than ever, but we know little about how consumers influence sport markets in the context of

unprecedented growth and choice options (Baker, McDonald, & Funk, 2016). This represents

a substantial disconnect, both in terms of theory and practice, given that market behaviour in

other highly competitive repeat-purchase industries has been researched thoroughly

(Ehrenberg, Uncles, & Goodhardt, 2004). As sport increasingly adopts broader management

practices, understanding such market behaviour is critical to defining the position of ‘sport

management’ as a unique discipline (Chalip, 2006).

Given the research gap, this thesis explores the structure of sport markets and their

participants. The research is comprised of five discrete but interconnected studies, utilising a

multimethod quantitative design. Four datasets primarily underpin the project. Surveys of

27,412 and 1,498 respondents comprehensively capture the sport attitudes, preferences and

behaviours of Australian residents, while two television ratings datasets elucidate the

behaviour of the broader market. Evaluation techniques applied include: Latent Class

Analysis (Hagenaars & McCutcheon, 2002), Dirichlet Modelling (Ehrenberg, 1971) and

variant methods of Analysis of Variance and Regression.

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Significant findings emerge. First, the research determines that sport consumers

behave in observable patterns, leading to market structures that are predictable through

Dirichlet modelling. This means that although the sport industry may contain distinctive

consumer characteristics, its consumption patterns and market structures are hardly dissimilar

to many other consumer product categories that are purchased regularly. Second, sport fans

consume sport contests within a repertoire-purchase pattern; hence, they treat sport leagues or

teams as complementary products. This is perhaps the most fundamental behavioural

characteristic of repeat-purchase consumer markets, yet it has been virtually ignored in

commercially-focused sport management research. The finding is also significant for

practitioners as it runs counter to the long-perpetuated portrait of the ‘irrationally’ loyal sport

fan. Rather, sport teams share their fans and must reorient their strategies accordingly. Third,

market segmentation determines that 37% of the population are rejecters of sport. This

finding is significant given that the body of sport consumer research has focussed

inordinately on a narrow subset of highly engaged fans, despite that sport category being far

from ubiquitous within the population. In sum, these findings make it imperative that

researchers of commercial sport adopt a market-level view in which the sport product is

positioned within a broader entertainment context and its consumption evaluated beyond its

most avid customers. By exploring a reconceptualisation of the sports market, the thesis

provides a much-needed framework by which to facilitate new research in commercially-

focused sport management.

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1. Introduction

This doctoral thesis is concerned with choice behaviour and preference in the context

of sport consumerism. It explores how the growing array of both products (teams and

leagues) and methods to consume sport has impacted the nature and structure of the consumer

sport market. Sport fandom has historically been considered a unique form of consumption,

and therefore a pillar upon which the sport management sub-discipline has distinguished

itself (Baker et al., 2016). Yet, the financial growth of the industry requires an increasingly

business-like orientation to its management that, as this thesis infers, suggests the need for a

reconfiguration of the sub-discipline. This line of research is therefore theoretically

significant in the context of the ongoing enquiry into the positioning of the sport product as a

unique discipline distinct from broader management (Chalip, 2006; Costa, 2005).

Although spectator sport can trace its origins to at least the time of the ancient Greeks,

its transformation in the area of mass media represents a relatively modern phenomenon,

commencing in the 1960s and accelerating into its current multi-platform format at the turn of

the millennium (Todreas, 1999; Whannel, 2009). That the sport management sub-discipline

has developed into a comprehensive and robust field of research is in itself a reflection of the

substantial growth of the sport industry, the scale of which may have been unimaginable just

fifty years prior (Rein, Kotler, & Shields, 2006). The contemporary nature and structure of

the sport market represents what this thesis seeks to explore. Accordingly, a background to

the study context is first discussed here to contextualise the identified research gap. This is

followed by presentation of the research problem, the justification for investigating that

problem, and the research design. Finally, project delimitations are discussed, with the

chapter concluding by providing an overview of the structure of the thesis.

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1.1 Study Background and Context

The past half century has seen a transformation of many sporting clubs and

competitions from ‘kitchen table’ operations to sophisticated commercial organisations

(Stewart, 2007). Commercialisation, driven largely by revenue from the development and

expansion of broadcast media, has caused a shift in the sport paradigm away from

amateurism towards sport as a profession, and thus as an economic activity (Rowe, 1996,

2009). As noted by Evens, Iosifidis and Smith: “Fuelled by technological developments in

broadcasting and communications more generally, this repackaging of sport as a commodity

has expanded it into a global business that effectively functions as a specialised division of

the entertainment industry” (2013, p. 13). By the year 2019, the value of the sports industry in

the North American market is forecast to reach USD $73.5 billion (PricewaterhouseCoopers,

2015), while in an Australian context, sport is estimated to produce an annual economic

impact of $50 billion across events, trade, tourism and foreign affairs (Boston Consulting

Group, 2017).

This type of growth in the financial value of the sport industry has resulted in a

corresponding boom in opportunities for the consumption of sport products, both in terms of

improved choice and methods to do so. Commercialisation has led to the growth of many

leagues, including expansion teams in non-traditional markets as well as multi-team markets,

fuelling further competition and demand for limited resources (Stewart, 2014).

Corresponding to increased league size, and in fulfilling broadcaster desires, most major sport

leagues have also endeavoured to produce a greater volume of content (Rowe, 1996). This

repackaging of sporting contests into ‘content’ speaks to its continued transition into an

entertainment/leisure commodity, resulting in a new competitive positioning within a broader

set of entertainment and leisure products (Howard & Burton, 2002). As noted by Byon,

Zhang and Connaughton (2010), “With such a crowded sport marketplace, sport consumers

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have many options in which to spend their leisure time and discretionary dollars. As a result,

professional sport organisations face stiff competition in an effort to gain market share” (p.

143).

Despite continued changes to the size and scope of the sport industry, it remains

predicated upon the same group of individuals – fans, who either through direct or indirect

consumption are responsible for generating revenue for sport leagues and clubs (Biscaia,

Hedlund, Dickson, & Naylor, 2018). According to Mason (1999): “It is the support of the

sports fan that underpins the sports industry” (p. 406). This is echoed by Taylor (1992): “the

crowd is the supreme authority without which the golden core of the game has no currency”

(p. 188). Therefore, the need to understand sport consumers remains the most core sport

market research problem (Filo, Lock, & Karg, 2015). Yet, as the industry becomes more

sophisticated, so too do its consumers who are faced with an increasing potpourri of

consumption choices. Such growing choice, however, appears inconsistent with the historical

stereotypes of sport fans in which the sport experience is “mired in the irrational passions of

fans, commanding high levels of product and brand loyalty” (Smith & Stewart, 2010, p. 3).

Accordingly, what seems evident from the growing number of opportunities and methods to

consume sport is that fandom is becoming an increasingly complex form of behaviour. This

necessitates a change in the study of sport fans toward a lens that evaluates them more

specifically as product consumers (Funk, Alexandris, & McDonald, 2016). Research must

therefore adjust to this shift and consider sport consumption from a broader business and

market perspective. These challenges provide a context for the purpose of the research, which

now follows.

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1.2 Statement of Problem

Given that there has been little scholarship into the overarching structure of sport

consumer markets, a significant and intriguing research opportunity is presented. As such,

this thesis will contribute to the sport management literature by providing empirical evidence

about the collective behaviour of consumers in relation to the structure of markets that such

behaviour creates. The relative scarcity of such research at the market level in a sport setting

will allow the thesis to contribute to a stream of inquiry that has only recently started to

explore the impact of consumer behaviour on sport markets (Baker et al., 2016; Doyle, Filo,

McDonald, & Funk, 2013). By comprehensively exploring the size, composition and

structure of consumer sport markets, this thesis aims to expand upon the limited

understanding of consumption patterns within the sport marketplace and any distinctive

characteristics therein (Smith & Stewart, 2010). In doing so, the study also contributes to

addressing a significant practical problem within sport practice. Given the growing intensity

with which sport organisations compete commercially, it is increasingly vital for practitioners

to understand the consumer structure of the market. Accordingly, as sport organisations

become increasingly sophisticated in their operation, the study addresses the extant need to

further our understanding of sport consumer behaviour in the context of growing choice.

The central research objective addressed by this thesis is therefore to: Understand and

measure the sport market. In order to pursue this prime objective, five subsidiary research

questions were designed, each of which correspond to thesis chapters which represent

discrete studies:

Chapter 2 – Study 1– RQ1: What is a ‘sport market’?

Chapter 3 – Study 2– RQ2: Are sport markets unique from typical industries?

Chapter 4 – Study 3– RQ3: How has sport team revenue and market size adapted to the increased competitive intensity of sport markets?

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Chapter 5 – Study 4– RQ4: How do sport consumers respond to new sport products?

Chapter 6 – Study 5– RQ5: Do consumers exhibit typical media consumption behaviour within sport markets?

Each of the five questions is underpinned by a focus on the market. However, given

the intrinsic connection between markets, products and participants, the project is significant

to the broader enquiry of sport consumers. The broader significance of the research problem,

both theoretically and practically, follows next.

1.3 Research Justification

The following section outlines the justification and significance of the central research

aim and subsidiary questions in terms of the current body of knowledge, as well as the

practical implications of these inquiries. The study is justified on the basis of the significance

of its proposed contribution to literature, which comprises of four parts. First, the research

contributes to a small but vital field that explores the collective market behaviour of

consumers in sport markets. Secondly, by doing so, the research contributes to understanding

a broader array of consumers than hitherto has been explored. Third, this broadening of the

consumer scope leads to a widening of the methodologies through which consumers can be

explored. Finally, this allows for a significant contribution to broader discussion about the re-

positioning of sport management as a sub-discipline.

First, while there appears to be scholarly consensus that sport markets have become

increasingly competitive and crowded (Kim & Trail, 2010; Rein et al., 2006), there is little

research that attempts to empirically measure that phenomenon. This problem corresponds to

an overarching research gap – a failure to quantify and segment consumer behaviour within

such crowded sport markets (Field, 2006; Pelnar, 2009). Anecdotal evidence among fans, for

example, suggests that supporting multiple sports and teams is hardly uncommon, yet

vigorous academic confirmation of this phenomenon has yet to take place (McDonald, Karg,

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& Lock, 2010). Correspondingly, we know little about the degree to which sport market

behaviour is broadly consistent or otherwise with other consumer industries. This thesis,

therefore, contributes to an emergent stream of research that seeks to evaluate sport fan

behaviour from a market perspective (Baker et al., 2016; Doyle et al., 2013).

Second, sport consumer research has typically focused upon what has been described

as ‘more engaged’ and ‘developed’ fans (Park, Mahony, & Kim, 2011). This narrow interest

has necessarily led to an absence of knowledge about ‘less engaged’ and ‘non-engaged’

consumer segments of the sport market (Reysen & Branscombe, 2010). The narrow approach

has obstructed the development of a holistic view of sport markets, including segmental

analysis to allow strategic attitudinal and structural insights (Ehrenberg, 1971). Indeed, the

historical focus on targeted groups of consumers and sport-specific contexts has created a

significant imbalance, with an absence of macro-level market analysis. For example, we

know little about the degree to which the industry is underpinned by the avid consumption of

a few as compared to the general consumption of the many. Consequently, through adopting

a market lens, this research will provide an advancement of knowledge regarding the

characteristics of the consumer base in the sport market.

Third, in focusing upon more engaged and developed fans, the body of research

surrounding sport consumers has also typically focused narrowly upon very limited

behavioural measures, such as attendance and season-ticket holding (Stewart, Smith, &

Nicholson, 2003). While that cohort represents a club’s most passionate and resilient market

segment, it constitutes a relatively small proportion of the overall market. By adopting a

broader market lens, this research expands the scope of sport consumer research by utilising

varied datasets (surveys, television ratings, secondary data) to explore sport consumption and

preference in a much broader way (Tainsky & Jasielec, 2014). In doing so, this research

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addresses a limitation of existing sport consumer research by considering preferences both at

a broader market level and, by engaging with consumers professing very different measures

of fandom, providing a more comprehensive and nuanced picture of the sport market.

Finally, in evaluating the market structure of sport, the research also contributes to

debates about the distinctive characteristics of the sport industry. As the financial significance

of the industry develops, sport organisations continue to adopt more sophisticated

commercial orientations. This continued adoption of broader management practice and theory

increasingly challenges the unique positioning of sport management scholarship (Chalip,

2006). The case for sport management as a field of inquiry has been largely underpinned by

assumptions that it has ‘unique’ and ‘innate’ characteristics, these in turn requiring

scholarship and managerial practice consistent with a distinctive ‘sport’ market (Baker et al.,

2016). As sport management becomes increasingly sophisticated, there has been debate about

whether corresponding research strategies should be based upon broader management

principles or specialised from within the sport management sub-discipline (Chalip, 2006;

Costa, 2005). This thesis therefore contributes to a foundational understanding of the sub-

discipline of sport management, specifically in terms of its commercial nature.

1.4 Research Design

Given the component nature of the thesis, a more robust and detailed explanation of

methodological implementation and practice is provided within the individual studies that are

presented within Studies 1 through 5. However, the overarching research design – which is

utilised to gather primary empirical data – is briefly discussed here.

This thesis adopts a quantitative multimethod design, an approach first proposed by

Campbell and Fiske (1959). Historically, there has been some confusion around multimethod

design given that two components from the same paradigm can themselves be considered a

8

mixed method design (Yin, 2006). Here, the multimethod design is defined epistemologically

as a project in which there is more than one method, but restricted ontologically to one

worldview (Teddlie & Tashakkori, 2003). Multimethod designs appear well suited to doctoral

studies completed by discrete studies, given they involve the conduct of two or more research

methods, each conducted rigorously within discrete projects that are complete in themselves.

The major research problem drives the program, but the program consists of two or more

interrelated studies. Each study can be planned and conducted to answer a particular sub-

question, with the results triangulated to form a complete whole (Morse, 2003). Within this

study, the ‘complete whole’ represents an attempt to further our understanding of the

structure of consumer markets within a commercial sport context. In doing so, three specific

dimensions are focussed upon within the individual studies. The first is the attitudinal

predisposition of consumers toward sport, which is evaluated in Studies 1 and 3. Second is

attendance behaviours which are measured in Study 2. Thirdly are television viewership

behaviours which feature in Study 4 and 5.

Morse (2003) identifies eight types of multimethod designs, to which this doctoral

study can be categorised as a quantitative core component with a simultaneous quantitative

supplementary component (QUAN + quan). Multimethod designs in which the core and

supplementary components come from the same paradigm are advantageous as they allow for

a consistent theoretical drive. This allows the project to follow a singular inductive or

deductive direction of inquiry (More, Niehause, Wolfe, & Wilkins, 2006). Since the studies

are treated as simultaneous, the point of interface for the findings is the discussion and

conclusions chapter, whereby the narrative of the results is brought together. The core

method of this doctoral study is surveying, with two surveys completed and utilised within

Study 1 and 2. The supplementary method is the analysis of secondary data in the form of

financial and fandom data (Study 3) and television ratings data (Study 4 and 5). Table 1

9

provides a summary of the discrete research methodologies adoption within each study, the

interconnection between which is now further described.

Table 1: Summary of research methods Study Analysis Technique Dataset/Instrument Research Context Sample Summary/ Size

1 Latent Class Analysis Primary survey data National 27,412 survey respondents

2 Dirichlet Modelling Primary survey data Sydney & Melbourne 1,498 survey respondents

3 Longitudinal Random-Effects Regression

Secondary panel data, Financial data

National 18 observational units producing 240 data points

4 Hierarchical Analysis of Variance

Television ratings data

National 128 matches by 5 regions for 640 units of analysis

5 Multifactor Analysis of Variance

Television ratings data Sydney &Melbourne 20 matches, analysed in 15 second intervals for 13,324 units of analysis

The research is underpinned by two online consumer surveys, each administered by

commercial research panel operators. Online survey distribution is becoming increasingly

prevalent due to its many benefits over traditional methods. These advantages include higher

response rates, reduced overall costs, increased turnaround times, less respondent error and

improved aesthetic and design capabilities (Bech & Kristensen, 2009; Birnbaum, 2004;

Wright, 2005). The first survey was completed in April of 2015 and placed an emphasis on

capturing a depth of data across few questions. Correspondingly, the survey encompassed

only seven questions but was completed by a national cohort of 27,412 respondents. The

emphasis on depth during first-stage surveying ensured robust information was captured to

measure key sizing metrics of the sport market. The survey elicited demographic information

surrounding age, gender, postcode, ethnicity and languages spoken as well as interest across

37 sports/leisure activities. This dataset in turn influenced the development of the sample

frame and questionnaire for the second-stage survey. It also provided benchmark measures of

reliability against which the second-stage survey could be compared. That survey can be

found in Appendix 1.

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Sampling for the second-stage survey occurred in November 2016 and captured the

specific sport behaviours and attitudes of 2,572 consumers. Of the total 2,572 respondents

who entered the survey, 40% were screened out due to a lack of sport interest, resulting in

1,498 relevant complete surveys. This survey placed an emphasis on breadth of information

captured, including a total of 48 questions. In-built question logic ensured that questions were

only presented to relevant respondents, resulting in an efficient survey length with a median

completion time of 16 minutes. This is significant, as the relationship between questionnaire

length, response rate and quality has been well established (Bogen, 1996; Galesic & Bosnjak,

2009). The second-stage questionnaire contained the following items. First, screening

questions surrounding respondent age, location, sport interest and current consumption were

captured. Second, current respondent sport consumption patterns were elicited utilising a mix

of multiple choice and numeric response questions. Third, psychological commitment and

attitudinal loyalty were measured utilising the Attitudinal Loyalty to Team Scale (ALTS)

developed by Heere and Dickson (2008). Finally, media consumption questions were asked

prior to capturing final demographic information, such as income, education, employment

and ethnicity. This survey can be found in Appendix 2.

The second overarching component of the research consisted of television ratings

broadcast data. Television viewership represents a comparatively emergent methodological

area of enquiry within the sport management sub-discipline (Tainsky & Jasielec, 2014). The

thesis utilised television ratings data collected by OzTAM, a commercial media research

organisation. OzTAM is responsible for producing television ratings across the five mainland

capital cities of Australia, as well as some regional areas. The first television dataset in the

thesis is longitudinal, tracking consumer behaviour in respect of a new sport product over a

five-year period – 2013 and 2017 – encapsulating 128 fixtures across 5 viewing regions to

create 640 units of analysis. The second television data set captured minute-by-minute

11

viewing preferences for twenty football broadcasts that occurred in 2012. The analysis, which

occurs in 15 second intervals, produced 13,324 units of data in respect of market viewing

patterns. A summary of the respective sample frames of the two television broadcast datasets

can be found in Appendix 3 and 4 respectively.

Finally, four secondary quantitative data sources were amalgamated and analysed to

understand longitudinal changes to the sport market structure during the period 2000 to 2017,

as explored within Research Question 3. This particular analysis focussed upon the markets

largest sport teams owing to data availability and quality. Secondary data is suitable in

providing historical information that primary data is often unable to achieve (Vartanian,

2010). The four data sources used were: (a) team attendance and membership rates, (b)

population growth rates, (c) team financial data, and (d) team fan base estimates. Attendance

for individual Australian Football League (AFL) clubs was collected from annual reports,

while the AFL governing body has maintained annual membership figures for all clubs since

1984 (Lenten, 2012). Population data was collected from the Australian Bureau of Statistics

(2017). Financial data was amalgamated from individual financial reports and categorised in

a manner consistent with previous research of this kind. In particular, the revenue structure of

sport organisations was categorised according to its operating nature (Pinnuck & Potter,

2006). Fan base estimates were derived from longitudinal primary survey research conducted

by the Australian commercial media research agency Roy Morgan Research.

1.5 Project Outline

As identified earlier, the project is completed by manner of completed studies, which

constitute chapters of the thesis. Accordingly, an outline of these study chapters is shown

graphically in Table 2 and an overview of each study is discussed in this section. This is

followed by an identification of the linkage between papers, providing an opportunity to

12

foreshadow how they integrate in addressing the core research aim as a collective body of

work. They do so by reiterating the overarching connection in methodology, followed an

articulation of conceptual and theoretic links between the studies.

Table 2: Thesis research outputs Study Study Submission Journal (ABDC Rank) Status 1 The Consumer Market Structure of Australian

Sport

Sport Management Review (A)

Under review

2 Are sport consumers unique? Consumer behavior within crowded sport markets

Journal of Sport Management (A*) Published

3 Testing the Relationship Between Revenue and Fan Base Size Within Sport Markets


Under review

4 Consumer Behavior toward a New League and Teams: Television Audiences as a Measure of Market Acceptance

Marketing Intelligence and Planning (A)

Under review

5 Quantifying the value of sport broadcast rights Media International Australia (AERA) Published

Study 1 begins the major body of work by identifying the significant gap in which this

research sits. By reviewing the literature that underpins sport consumer research, it concludes

there to be a scarcity of market-level sport research. The study therefore begins by proposing

a re-conceptualisation of the competitive sport market. This section then provides evidence of

segmentation in the Australian sport marketplace using latent class analysis to provide vital

insights underpinned by a substantial dataset (n = 27,412) of consumer attitudinal

preferences. Study 2 follows by evaluating the behavioural structure of the Australian sport

market, utilising a multi-site analysis of sport consumers in Sydney and Melbourne. The

study utilises NBD-Dirichlet modelling, which theorises that consumer behaviour within

repeat-purchase markets can be predicted according to consistent patterns that have become

known as generalised marketing laws (Uncles, Ehrenberg, & Hammond, 1995). Repeat-

purchase markets are defined by the seminal work of Ehrenberg (1971) as “any situation

where a person buys the item in question more than once” (p. 2). The ‘item in question’

within this study refers to sport game attendance. The application of this theory within a sport

setting is significant, as considerable academic literature is devoted to identifying and

13

appraising the ‘special features’ of the sporting market, which have acted to narrowly

position commercial research into sport management and limited the sub-discipline itself

(Baker et al., 2016).

Study 3 provides a longitudinal analysis of the Australian sport landscape, capturing

sport market and population characteristics since the year 2000 – when media coverage of

sport began to reach unprecedented heights. The research focuses upon one specific but vital

element of sport market structure; it analyses the association between sport team fan base size

and team financial performance of sport teams. The study explores how fan base size and

financial performance of sport teams have changed over time, in the context of markets that

are becoming increasingly competitive and crowded. This is achieved through a novel

methodology in which four distinct sources of independent secondary data are amalgamated,

analysed, and triangulated.

Study 4 and 5 adopt a different lens in their exploration of sport consumers, focussing

on market behaviour as expressed through television broadcast consumption. Study 4

explores the market acceptance of a new sport product. It draws on social identity complexity

research to examine the extent to which existing group memberships create consumption

biases in television viewership market behaviour. This provides a basis upon which to discern

whether initial consumption of a new team is premised on cognitive biases made salient by a

community or city identity. This study also explores whether new leagues conform to a

consistent pattern of consumer behaviour in which they benefit from a novelty effect during

establishment years before suffering from a post-novelty retraction in interest. Study 5 also

measures and quantifies consumer viewing behaviour within sport telecasts, albeit situated

within a football context. The study evaluates the market behavioural response toward sport

14

broadcasts to determine whether an ingrained perception that sport fans exhibit high levels of

product and brand loyalty results in correspondingly loyalty in viewership patterns.

Despite the unique contributions of the studies in addressing the core research aim,

the studies are underpinned by methodological, conceptual and theoretical linkages. In

respect to the overarching methodology, the thesis is linked across studies in three respects.

First, although the individual methods are distinct, the studies are collectively underpinned by

quantitative methodologies. As identified in section 1.4, the thesis’ multimethod quantitative

approach provides robustness in addressing what is a complex social phenomenon. Second,

each study utilises comparatively large datasets to perform analysis upon market level data.

Finally, the overarching research context remains consistent across all five studies, that being

the Australian sport landscape. Studies 1, 3 and 4 perform analyses at a national level, while

Studies 2 and 5 focus more specifically upon the Sydney and Melbourne markets.

The underpinning methodologically consistency of the studies provides a

corresponding platform for the conceptual and theoretic linkages of the research. As

previously stated, the studies work towards answering particular sub-questions that form a

complete whole (Morse, 2003). The central purpose underpinning the studies is to further our

understanding of the structure of consumer markets within a commercial sport context, with

two core conceptual and theoretic themes linking the studies. In respect to purpose, each

study adopts a market-level view of sport consumers in alignment with the central research

aim. In accordance with this market view, the studies draw heavily from a central source of

theory, that developed from the body of work arising from Ehrenberg’s (1971) well

established framework for market analysis. Finally, each discrete study explores a facet of

sport consumerism utilising a varied mix of attitudinal and behavioural settings with the

purpose of quantifying and explaining the nature of an element of the sport market.

15

1.6 Delimitations of Scope

The three central delimitations of scope for this thesis are defined as follows. First, the

overall thesis is situated within the single Australian setting. As explained previously, the

Australian market was chosen on the basis that it features high competitive intensity.

However, the Australian case does not appear so unique as to represent an ungeneralisable

setting. Congested sport markets are also a significant phenomenon in places like the United

States, Canada and the United Kingdom. Additionally, the Australian context is familiar to

the researcher. This ensured that local contextual factors were not missed in the study design

or execution.

The second delimitation pertains to the captured scope of teams who compete within

the ‘sport market’, which is conceptualised in Study 1 and modelled behaviourally within

Study 2. Acknowledging that leagues and teams in the Australian market operate along a

fully professional to semi-professional continuum across both team and individual sports, the

population of the competitive landscape modelled in Study 2 is restricted to Australian-based

teams that compete within domestically orientated leagues that are broadcast in their entirety

on free-to-air or subscription television. Within this scope, there were 70 teams across 7

competitions utilised to develop the Dirichlet model of competing market brands: AFL,

National Rugby League (NRL), A-League, Super Rugby, National Basketball League (NBL),

Big Bash League (BBL) and Netball.

Third, the research process explores three specific dimensions of the sport market.

First, the attitudinal predisposition of consumers towards sport; second, attendance

behaviours; and finally, the television viewership behaviours of consumers. The surveying

also captured behavioural information surrounding merchandising and membership

16

consumption, as well as sport participation for data completeness; however, they are not

utilised within the thesis given its specific focus and purpose.

1.7 Conclusion

This introduction has provided an outline to the doctoral study, identifying the

context, research aim and justification before illuminating the overarching research design. A

brief description of the individual studies was then provided, as well as a summary of their

collective linkages, before limitations and delimitations were identified. Within the following

sections, these five papers are presented. Chapter 7 then concludes the thesis by first

summarising the key contextual factors underpinning the thesis and its purpose, and then

identifying the key study linkages and findings. This finally leads to the identification of the

thesis contribution and avenues for future research.

17

2. Study 1: The Consumer Market Structure of Australian Sport

18

Abstract

Improvements in media technology, coupled with the continued expansion of leagues, have

contributed to sport markets becoming more dynamic and competitive than ever. Yet, despite

such increasing competitive tension, there has been a scarcity of scholarly research to

understand the consumer structure of sport markets. By extension, existing sport consumer

research has typically focused on more engaged and active sport fans, with less understanding

of non-fans. Through this research, we addressed these two interconnected gaps. First, we

proposed a conceptual approach by which to understand the consumer sport market; then, we

conducted a segmentation of a crowded sport market. To do so, the sport preferences and

attitudes of 27,412 Australians were analysed using latent class analysis to segment the

Australian sport market. The model produced 13 segments that distinguished consumers

within the Australian sport market. Most significantly, the results confirmed that a large

component of the population rejected sport (37%), primarily women and younger individuals.

The size of this group provides a counterbalance to the field’s focus on sport consumers,

identifying that the sport category is not ubiquitous. Accordingly, category-level barriers

exist which inhibit the overall growth of the industry. Furthermore, the segmentation showed

that more avid sport fans were interested in a greater repertoire of sports. The sport

practitioners’ endeavours to achieve more loyal and avid fans for their team is thus

paradoxical, as avid sport fans are less likely to be singularly loyal to individual sports.

Overall, this research suggests that although individual sports compete for market share,

cooperating to grow the sport category could prove particularly beneficial.

19

2.1 Introduction

Broadcast and digital technology innovations, coupled with expanded product

offerings, have in recent years provided sport consumers with unprecedented choice (Mahony

& Howard, 2001; Rein, Kotler, & Shields, 2006). It is now common within many developed

nations for multiple professional sports and teams to operate within cities and compete for

attention from the general public, commercial sponsors, and the media (McDonald, Karg, &

Lock, 2010; Shilbury, Westerbeek, Quick, Funk, & Karg, 2014). Indeed, it is often the case

that national leagues have multiple teams within one city, generating intense local

competition for consumers. In these respects, while competition is at the heart of professional

sport (Shilbury, 2012), it is not just a contest between teams and athletes. The hyper-

commercialisation of sport means that battle for off-field survival can indeed be as intense as

what occurs on the field of play. As Byon, Zhang, and Connaughton (2010) have noted, “with

such a crowded sport marketplace, sport consumers have many options in which to spend

their leisure time and discretionary dollars. As a result, professional sport organizations face

stiff competition in an effort to gain market share” (p. 143).

Sport marketplaces have not only become increasingly crowded, sport also competes

for consumers within a broader set of entertainment and leisure products (Howard & Burton,

2002). Further, technological innovation has not only impacted sport, but also the experience

economy with its varied entertainment and leisure opportunities. As Mauws, Mason, and

Foster (2003) have put it, “what has changed in recent years is not so much the types of

substitutes available but, rather, the variety within each type” (p. 149). Not surprisingly then,

the management, marketing, and financial sustainability of professional sport leagues have

been significant topics of discussion for sport management scholars seeking to understand the

business of sport (Mahony & Howard, 2001; Westerbeek & Smith, 2002). While there have

been substantial contributions by way of understanding sport consumers, the body of research

20

in the management and marketing domains has mainly focused on single sport or team-

specific contexts, and on more avid or attached fans (McDonald & Funk, 2017; Park,

Mahony, & Kim, 2011; Stewart, Smith, & Nicholson, 2003). By comparison, what has too

often been absent is an examination of the macro-view—a broader analysis of sport markets

in national or global contexts (Pelnar, 2009). Indeed, the historic focus on targeted consumer

groups and sport-specific contexts has created an imbalance, with macro-level market

analysis underdeveloped.

Sport consumer, marketing, and management research can be broadly positioned at

three different, though overlapping, levels of analysis. The vast bulk of research has occurred

at a micro-level, focused upon consumers within specific sports and leagues and typically

upon more avid consumers (Park et al., 2011). Sport can, however, be contextualised as a

meso-level market in which individual sport products (e.g., tennis, football, rugby) compete

for consumer interest within the subset of a population interested in sport (Barbour, 2017).

Here, the sports market is becoming increasingly crowded, although the characteristics of the

meso-level sport consumer market have only recently been subject to empirical evaluation

(Baker, McDonald, & Funk, 2016; Doyle, Filo, McDonald, & Funk, 2013). Notably, although

consumers appear to fulfil category needs from a repertoire of sport teams (Fujak, Frawley,

McDonald, & Bush, 2018), our understanding of these repertoires is limited, aside from

comparisons of functional attributes (Gantz, Wang, Paul, & Potter, 2006; Solberg &

Hammervold, 2008; Wann, Grieve, Zapalac, & Pease, 2008). Finally, the sport product

competes at a macro-level within the leisure and entertainment market against other pursuits

such as cinema attendance, concerts, festivals, television viewing, and computer gaming.

Here, it is acknowledged that sport is competing with existing or emergent entertainment and

leisure activities for consumers’ limited time and spend (Howard & Burton, 2002). It is

21

surprising, therefore, that sport as a macro-level consumer product within leisure and

entertainment has yet to be systematically researched (Gemar, 2018).

Through this study we offer exploratory research at the sport market meso-level. The

scarcity of scholarship surrounding the structure of sport markets represents a critical

research gap given that increasing competitive intensity is both changing the structure of the

industry (McDonald et al., 2010) and creating financial pressures for survival (Byon et al.,

2010). We begin by exploring the conceptualisation of the ‘sport market’ in the context of

consumerism within professional sport, with the study henceforth focused upon the

professional sporting landscape. We then perform a quantitative analysis of consumer

preferences in an identified crowded sport market. This is pursued by adopting a

developmental research question: What is the consumer market structure of a crowded sport

marketplace? The research question is explored through an analysis of the sport preferences

from a comprehensive sample of adult Australian residents (n = 27,412). The paper is

presented in four parts. The first section surveys literature relevant to markets and sport

landscapes, providing a foundation for the second section, which outlines the methodology

deployed for this research. Subsequently, the third part of the paper discusses the data

analysis, and the fourth section evaluates research findings and their implications. The paper

concludes with recommendations for further research.

2.2 Literature Review

Conceptualising the Sport Product and Consumer Market Structure

The notion of ‘markets’ has long been the subject of robust debate: Economic and

social interpretations of their meaning can be divergent or complementary (Friedland &

Robertson, 1990). From an economic perspective, markets are simply places of exchange

between buyers and sellers in which products are transacted (Callon, 1998). More formally

expressed, a market is a coordination device in which (a) agents pursue their interests based

22

upon economic calculations that can be seen as an operation of optimisation and/or

maximisation, and (b) agents generally have divergent interests, which lead them to engage in

(c) transactions which resolve the conflict by defining a price (Guesnerie, 1996). According

to the economic interpretation, as critiqued from a social interpretation perspective, “the

market merely becomes a synonym for the universe of traders, since there exists no

specification of its institutional features or instruments of exchange” (Lie, 1993, p. 288). The

marketing domain has largely embraced the long-standing economic viewpoint in principle,

although in practice have tended to use the word market to describe only buyers which has

proven to be a critical distinction as detailed further below (Ferrell & Hartline, 2012; Geroski,

1998).

Social interpretations have largely been developed in reaction to neoclassical

economic conceptualisations of markets. Social interpretations of markets contend that they

form as social institutions that transcend simple transactions, suggesting that all forms of

economic interaction are centered upon social relations (Fligstein, 1996). Granovetter’s

(1985) germinal work referred to this as the embeddedness of markets. Granovetter sought to

avoid an under- or over-socialised view of actors: “actors do not behave or decide as atoms

outside a social context, nor do they adhere slavishly to a script written for them by the

particular intersection of social categories that they happen to occupy. Their attempts at

purposive action are instead embedded in concrete, ongoing systems of social relations”

(Granovetter, 1985, p. 487). The term has gained widespread acceptance and remains largely

unchallenged as the central organising principle within economic sociology (Krippner, 2002).

As markets are constructed through the actions of individuals and groups for their benefit,

thus creating a collection of boundaries and rights, markets exist within a social context

imbued with a history that involves the exercise of political power (Friedland & Robertson,

1990).

23

In a professional sport context, the ‘sport market’ would appear an apt example of the

social institution approach to understanding markets, especially given the use of political

power by select groups to structure and maintain sport markets for their benefit through

rigidly defined property rights and boundaries (Neale, 1964). In their formation, for instance,

sport leagues did not organically coalesce to create a market in which to offer their sport

product to consumers, but rather developed through the collaboration of teams who formed

cartel-like structures to operate (Mason, 1999; Stewart, Nicholson, & Dickson, 2005). The

need to collaborate is innate to sport, given the need for at least two cooperating competitors

to create a sporting contest. Cooperation in sport, however, has expanded beyond the product

as a contest between athletes and has resulted in the implementation of many anti-competitive

practices that are typically outlawed in many industries. This represents one of the unique

features that distinguishes sport from typical businesses (Smith & Stewart, 2010).

Despite the concept’s central importance to both economics and marketing, each

discipline has adopted distinct approaches to understanding ‘market structure’. Economics is

concerned with broad socio-economic issues and accordingly, there are four classical types of

market structures: perfect competition, monopolistic competition, oligopoly, monopoly.

Marketing is more concerned with managerial aspects of market structure, and therefore

focuses upon operationalizing the nature of competition derived from either customer

perceptions of product substitutability (Guiltinan, 1993), market impact (Murphy & Enis,

1986) or a hybrid of the two. Despite well-established unique structural elements, our

understanding of the sport market is underdeveloped (Pelnar, 2009). This is partly because

much of sport management’s foundational analysis of market structures is rooted in sports

economics (Shilbury, 2012).

The operational nature of the marketing view of market structure means that market

structures are fluid and often changing in response to market behaviours. While

24

substitutability is where the two domains largely intersect, marketing’s emphasis on

consumer perception of competition results in a multidimensional view of substitutability that

reflects its centrality to the domain. Multidimensionality supposes that products compete in

ascending and descending product purpose domains (Guiltinan, 1993). A by-product of this

approach is that each level of substitutability may yield a different market definition and there

is therefore no "true" market definition or structure (Lovelock, 1983; Srivastava et al.,

1984). Mason’s (1999) conceptual exploration of the sport product and market appears to

represent a rare attempt at adopting a marketing market structure approach: “Today, teams

compete for those consumers who could choose to attend other entertainment options

available. Thus, rather than competing within a narrow, sport-specific market, league

franchises now compete in a broader entertainment market” (p. 406). This assertion, which

implicitly acknowledges the multidimensionality of competition faced by sport teams as an

entertainment option, has been well accepted (Mahony & Howard, 2001; Rein et al., 2006),

even though it did not generate a large body of empirical research thereafter (Gemar, 2018).

In essence, Mason’s concept of the sport market builds upon Noll’s (1982) development

work: “The most important product markets are the sale of admissions and concessions at

home contests and the sale of the right to broadcast or televise play-by-play accounts of the

games” (p. 348). Accordingly, the sports market is defined as the sum of the discrete

segments in which sport is able to generate revenue, being; fans, media, host cities and

corporations (Mason, 1999). This approach therefore is focused on product segments across

a broad entertainment market rather than on product competitors within a sport market.

Despite the intrinsic connection between products and markets, our understanding of

the sport ‘product’ is more developed. According to Schaaf (1995), “in the context of sports

marketing, the ‘product’ is either the entertainment of competition [the uncertainty], or a

product/service associated with the excitement of the event, or both” (p. 22). As Shilbury

25

(2012) noted, “definitions of sport universally refer to competition as a key characteristic”

(p. 4). Functionally then, the core sport product is a contest from which consumers derive

some form of fulfilment or entertainment value (Mason, 1999). In relation to this fulfilment,

underlying psychological motives have been thoroughly explored (Funk & James, 2001).

Implicit within much of existing sport research discourse (see Figure 1), the sport product

competes across three axes representing levels of competition that, as this study proposes,

conceptualises the market from a competitive perspective according to the ascending

dimensions of product purpose (Guiltinan, 1993).

At the micro-level, sport teams compete among fellow teams within their league,

which represent brand alternatives of functionally similar products. Here, the competition is

not only on the field, but off it too, as teams attempt to attract a larger share of fans and

sponsorship from other teams in the league (Shilbury, 2012). The majority of sport consumer,

marketing, and management research has occurred at this micro-level, focused upon

consumers within specific sports and leagues and typically upon trying to understand more

avid consumers (McDonald & Funk, 2017; Park et al., 2011; Smith & Stewart, 2010). At the

meso-level, leagues compete against other leagues for consumers’ collective attention,

preference, and purchase. Here, sports and leagues represent genres within the overarching

sport category, akin to rock, folk, gospel, and country representing genres of music festivals

or comedy, action, drama, and thriller genres in the cinema film market. With the growth of

digital technology, such genre competition is no longer geographically or physically

constrained, creating even greater market opportunity, but also heightened competition

(Hutchins & Rowe, 2012; Rowe, 2011). Accordingly, a cohort of sport consumers are

extending their team repertoires to offshore leagues (Kerr & Gladden, 2008), while the

embryonic intersection of sport and gaming known as e-sport has emerged as a new sport-

entertainment genre (Funk, Pizzo, & Baker, 2017; Hutchins, 2008). Such phenomena,

26

coupled with the general growth in the absolute volume of commercial sport teams and

leagues, has led to a consensus that the sport market is becoming increasingly “crowded”

(Byon et al., 2010; Cottingham et al., 2014; Rein et al., 2006). Kim and Trail (2010), for

instance, estimated there to be over 600 professional sport teams in America sharing a vast

pool of resources given an estimated $17.1 billion in annual ticket spend on sporting events.

Despite a consensus that sport markets are becoming increasingly crowded, there have been

few attempts to quantify the structure of sport markets (Pelnar, 2009). As McDonald et al.

(2010) noted, “although not widely acknowledged in the sports management research

literature, anecdotally it seems many fans follow multiple sports teams” (p. 68). Gemar

(2018) attempted to quantify the crowded Canadian sport market, finding a small cluster of

sport “omnivores” (6.6%) who had a propensity to consume all five major North American

sport leagues. Indeed, recent research has shown that consumers treat competing sport

leagues as complementary goods that are consumed as part of repertoires to fulfil overall their

consumption needs, consistent with typical repeat-purchase goods (Ehrenberg, Uncles, &

Goodhardt, 2004; Fujak et al., 2018). Previous research has also illustrated that teams within

the same league can also be treated as complementary goods (Baker et al. 2016).

Accordingly, the sport market would appear to be characterised by “polygamously loyal”

consumers that teams within markets share as has been shown to be the case in the context of

many of goods such as retail fuel purchase, breakfast cereals and apparel (Sharp, Wright, &

Goodhardt, 2002).

The sport market, however, represents but one pane within the z-axis that constitutes

all leisure category alternatives at a macro-level. Sport therefore competes at a macro-level

among a suite of substitutable leisure and entertainment products found along the z-axis.

Given the core sport ‘product’ represents some form of contest from which consumers derive

pleasure or fulfilment (Mason, 1999), its underlying level of substitutability is dictated by the

27

degree to which alternatives can satisfy similar motives, needs, wishes, and desires (Hendee

& Burdge, 1974; Pritchard & Funk, 2006). The number of consumption methods, and

therefore substitutes, is undoubtedly growing at the leisure industry level (Mauws et al.,

2003).

Figure 1: Conceptualising the sport market

Segmenting the Sport Market

The scarcity of sport market research belies the prevalence of segmentation

methodologies within sport management, which are primarily used to divide a heterogeneous

market for a product or service into homogenous segments (Mullin, Hardy, & Sutton, 2014;

Shilbury et al., 2014). Accordingly, within a sport context, the objective of most

segmentation research has been to evaluate sport-specific micro-level observation, though at

28

the expense of failing to also explore the meso- and macro-levels. Such studies have focused

on ice hockey (Crawford, 2001; Koo, Andrew, Hardin, & Greenwell, 2009), football

(Alexandris & Tsiotsou, 2012; Nakazawa, Mahony, Funk, & Hirakawa, 1999), and triathlon

(Funk, Toohey, & Bruun, 2007; Wicker, Hallmann, Prinz, & Weimar, 2012). Liu et al. (2008)

have very usefully provided a comprehensive overview of segmentation studies in sports,

indicating which segmentation variables have been used.

As alluded to earlier, the scarcity of meso-level sport market research leaves both

theoretical and empirical questions unanswered. In relation to theory, a key question is the

degree to which sport markets (and their consumers) exhibit patterns of behaviour consistent

with other typical consumer industries, a vital gap that is just beginning to be addressed

(Baker et al., 2016; Doyle et al., 2013; Fujak et al., 2018). This theoretical question underpins

an overarching inquiry into whether sport—in this case its commercial dimensions—should

be treated as a distinct field of academic inquiry (Chalip, 2006). Empirically, the absence of

market-level research results in significant knowledge gaps about the consumer structure of

the sport industry (McDonald et al., 2010). Practical performance measures such as market

share, penetration, purchase rate, and 100% loyalty rate are common management and

marketing metrics used to assess industry structure and competitor performance (Ehrenberg,

2000; Ehrenberg et al., 2004; Uncles, Ehrenberg, & Hammond, 1995), yet they have been

barely utilised in a sport industry context (Baker et al., 2016). For instance, although sport

appears to be becoming increasingly ubiquitous through mediated consumption (Byon et al.,

2010; Hutchins, 2011; Hutchins & Rowe, 2009; Rein et al., 2006), we know little about the

size of the sport market in terms of the support or non-support of the general population

(McDonald & Funk, 2017; Reysen & Branscombe, 2010). Correspondingly, sport consumer

research has often lacked cross-sectional studies across multiple sports that would allow for

29

the development of market-level comparisons and insights (Baker et al., 2016). The present

study seeks to explore that gap.

2.3 Method

Study Background

The decision to examine the Australian sport market is based on several

considerations. First, Australia may be the world’s most concentrated sporting landscape.

The nation is home to 24.5 million residents who sustain more than 70 elite commercial sport

teams, spread across only 12 cities and across seven mainstream sports. Additionally,

participation statistics suggest that Australians take part in a very diverse array of sports

(Eime & Harvey, 2018), perhaps reflecting that the practice of sport has long been considered

a bedrock of Australian cultural values (Cashman & Hickie, 1990). That diversity of sport

choices is mirrored in the diversity of Australia’s population, which may contribute to

heterogeneous sport preferences and behaviours. Only 47% of Australia’s population in 2016

was born to two Australian-born parents, with the nation’s population growth driven by

migration rather than birth rate (Australian Bureau of Statistics, 2016).

Another source of sport preference divergence within the Australian landscape is

geographic. A particularly important element of this is a historical phenomenon known

colloquially as the ‘Barassi Line’. This imagined line is used to illustrate and explain the

nation’s football preferences along a geographic demarcation whereby Northeast Australia

has been most associated with rugby football (of both the League and Union variety), while

Southwest Australia has preferred Australian Rules football (Hess & Nicholson, 2007). The

line also corresponds to the cross-city rivalry between Australia’s two largest state capitals,

Sydney and Melbourne. Despite similar populations, economic strength, and sport team

concentration, differences between the two cities in terms of their sporting cultures have long

been observed and debated (Cashman & Hickie, 1990). Melbourne, self-described as “the

30

sporting capital” of Australia (Misener & Mason, 2009, p. 782) is clearly the more fanatical

sport city based on expressed behaviour, such as attendance. However, efforts to quantify

such geographical distinctions in consumer market structure have been surprisingly scarce.

Data Background

For the present study, a primary survey was conducted by a commercial panel

operator to understand sport preferences. Online panels achieve speed, lower cost, ability to

target niche markets, and access noncustomers and their use has proven beneficial in the sport

consumer research domain (Dickson, Naylor, & Phelps, 2015). In total, the survey captured

27,446 responses from individuals aged 18 to 92 across Australia. Thirty-four incomplete

cases were identified and removed, resulting in a final sample of 27,412. The data were

weighted to be representative across age, gender, and national distribution (Creswell, 2003).

The survey captured the following demographic information: age, gender, postcode,

ethnicity, and languages spoken. In relation to sport interest, participants were asked to

provide a dichotomous response (i.e., yes or no) to a multichoice set of 37 sports/leisure

activities, in addition to “Other” and “None of the above”. The list represents a

comprehensive attempt to capture organised sports that operate with some degree of

commercial presence. Using these criteria, the list reflects Australia’s most participated-in

sports. A total of 23 of the 37 selected sports within the list feature among the top 30

participation sports per Australian Government statistics, with excluded recreational activities

walking, fitness/gym, bush walking, yoga, Pilates and dancing accounting for the difference

(AusPlay, 2018). Other large commercial sports with small participation bases such as Rugby

League (ranked 33rd), Rugby Union (ranked 40th) and Horseracing (ranked 105th) were also

included. Finally, global commercial sports with observed local interest were also included

(Formula 1, American Football, NASCAR, UFC). The complete list of sports and leisure

31

activities included is provided in Table 5, along with the nationally representative response

prevalence for each.

Respondent sport avidity was also measured via a single item 10-point Likert scale

question per the following: “On a scale of 0 (not at all) to 9 (die hard), how big a sports fans

do you consider yourself to be?” to allow for deeper analysis between those who are sport

avid and apathetic. Sport avidity is defined as the level of interest, involvement, passion, and

loyalty a fan exhibits to a particular sports entity (i.e. a sport, league, team, and/or athlete)

(DeSarbo & Madrigal, 2011). Avidity is considered the consequence of passion (the motive),

and although it has been central to public discourse, has received little empirical focus as a

measure of consumption behaviour (Wakefield, 2016). While Wakefield (2016) is critical of

single measure scales of avidity which have previously been utilized in the measurement of

avidity (Apostolopoulou, Clark, & Gladden, 2006), his study testing their efficacy only

utilized a sample of registered, self-selecting NFL fans, resulting in 95% of respondents

identifying as ‘avid’ fans of their team. With the likelihood of such a dense concentration

diminished with the context of a broader population, single item measures provide the

advantage of simplicity and brevity and although not without limitations, have previously

been shown to remain valid and reliable in a sport marketing setting (Kwon & Trail, 2005).

The phrasing of the single measure used within this study, is consistent to Wakefield’s (2016)

first measure of passion (“How passionate are you about the team (no passion- ultimate

passion)”), which was illustrated to associate to the concept of “passion”.

Analysis and Procedure

The analysis was performed using multiple software packages and data analysis

techniques. In respect to software, data preparation was performed within Microsoft Excel

and SPSS version 23, while analysis was performed utilising the Q Research Software

platform developed by Displayr (Displayr, n.d.). Preliminary exploratory analysis for

32

overarching market distinctions in sport avidity was performed using independent complex

samples t testing. Following this, the primary market segmentation was performed using

latent class analysis (LCA).

Lazarsfeld and Henry (1968) introduced LCA and Kamakura and Russell (1989) later

developed it to prominence, corresponding with improved computational power. LCA

endeavours to categorise people into classes (an interchangeable term with ‘segments’)

utilising observed items to best distinguish between cases. A significant advantage of LCA is

that it performs class enumeration probabilistically, as compared to non-probabilistic

traditional cluster analytic techniques that are more reliant upon researcher judgment in

choosing cluster numbers (Hagenaars & McCutcheon, 2002). Despite this significant

advantage and the technique’s increasing application across management and marketing, its

use in the sport domain thus far appears largely limited to sport psychology (von Davier &

Strauss, 2003). To date, Widdop, Cutts, and Jarvie’s (2016) Canadian sport participation

research and Gemar’s (2018) sport consumption work appear to be among the few instances

of the application of LCA to sport market segmentation, albeit from a sociological

perspective.

LCA features many statistical likelihood-based tests and information criteria by which

to determine the optimum number of segments. Such criteria include Akaike information

criterion (AIC), Bayesian information criterion (BIC), consistent Akaike information

criterion (CAIC) and the Lo-Mendell-Rubin test (LMR). Typically, such criteria are used in

combination, although the BIC has been found to been the most accurate and a variety of

resources suggest its prioritisation (Hagenaars & McCutcheon, 2002; Nylund, Asparouhov, &

Muthén, 2007; Yang, 2006). This is because BIC imposes a harsher penalty on an increase in

the number of segments than the AIC as well as log-likelihood values, reducing the risk of

false positives (Walker & Li, 2007). By contrast, LMR and AIC tend to overestimate the

33

number of classes (Nylund et al., 2007). Aside from producing probabilistic classes, LCA

produces two other outputs central to this research. First, LCA produces the class probability

parameters, which specify the relative size of each class. This is central for performing

market sizing for the sport market. Second, LCA models produce item parameters that

correspond to conditional item probabilities. These item probabilities are specific to a given

class and provide the probability of an individual in that class of endorsing the item (Nylund

et al., 2007). These item parameters are key to elucidating distinctions among classes for the

purposes of articulating the various typologies of sport consumers. The accuracy of these

item parameters is measured by model entropy, with a score of 1 indicating certain class

allocation and 80% considered a model benchmark (Hagenaars & McCutcheon, 2002).

The selection of a method and the appropriate number and type of segments is crucial

to segmentation research (Wedel & Kamakura, 2012). There are two distinct methodological

approaches to market segmentation. The first is the conceptual (a priori) approach, whereby

respondents are divided into groups—or segments—in advance, based on attributes, prior

knowledge, and/or demographic variables. The second is an empirical data-driven (posteriori)

approach, whereby attitudes, benefits, and motivations are used in grouping respondents (Liu

et al., 2008). Given the exploratory nature of this research and the presence of a

comprehensive existing dataset, a posteriori approach was adopted. Correspondingly, the

LCA was developed using the classes from the dichotomous set of 37 sports and leisure

activities. LCA is robust for predictions with dichotomous variables, such as those utilised in

a sport participatory context (Widdop et al., 2016). However, while a dichotomous response

format achieves a breadth of input variables (37 sports), it may diminish the entropy of the

model by providing a condensed scale by which to develop item parameters. Model

specification was run to 1,000 iterations utilising Halton draw generation. Respondents were

weighted to achieve nationally representative class probability parameters.

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2.4 Results

Who Are Sport Fans?

To begin exploratory analysis of the data, independent complex-samples t testing was

performed on the sport avidity measure to examine any broad market-level demographic

differences in sport consumer avidity (see Table 3); p values reported within Table 3 were

corrected for multiple comparisons using false discovery rate. The most significant

demographic difference in sport avidity related to gender, with men (M = 5.01, SD = 2.78)

significantly more sport avid than women (M = 3.13, SD = 2.61), t(16, 901) = 47.5, p < .001.

Cohen’s d was .70, suggesting a moderate to high practical significance to this difference.

The significant difference in underlying sport interest corresponds to a large variance in sport

repertoire size, with women averaging 3.62 sports and men 5.70 sports within their repertoire

of interest. At each extreme of the scale, 23.13% of women nominated a sport avidity score

of 0 (not at all interested) compared to only 9.89% of men. Conversely, only 2.22% of

women indicated absolute sport fanaticism compared to 9.17% of men.

With respect to age, distinctions in sport avidity appeared to be most pronounced

among younger and older individuals. Individuals aged 18 to 29 had the lowest sport interest

(M = 3.71, SD = 2.89) and individuals aged 80+ had the highest sport interest (M = 4.67, SD

= 2.87). With respect to geography, there did not appear to be significant differences in sport

avidity across capital cities. While Melbourne held a statistically significant higher avidity,

the practical difference was very minor (d = .06). No significant difference was observed in

sport avidity based on location relative to the Barassi Line.

35

Table 3: Variables influencing sport avidity (in order of significance) Variable Level SAS SD t df p d ATSII Gender Women 3.13 2.61 3.62 Men 5.01 2.78 47.50 16,901 < .001 0.70 5.70 Age 18–29 3.71 2.89 8.16 5,689.01 < .001 0.16 3.89 30–39 4.02 2.85 1.17 7,805.01 .24 4.22 40–49 4.09 2.85 0.48 7,571.7 .63 4.60 50–59 4.02 2.85 1.30 10,797.33 .19 4.87 60–69 4.28 2.80 5.40 9,998.33 < .001 0.08 5.45 70–79 4.45 2.76 6.23 2,784.11 < .001 0.15 5.47 80+ 4.67 2.87 3.20 233.4 < .01 0.22 5.59 Ethnicity SC Asian 4.96 2.61 7.46 559.22 < .001 0.33 4.47 Aboriginal 4.51 2.94 2.11 246.8 .09 5.18 Australian 4.11 2.87 3.50 11,841.6 < .001 0.06 4.76 Oceanian 4.10 2.86 0.20 489.79 .85 5.30 SE European 4.08 2.86 0.20 1,887.12 .84 4.46 Middle East + Africa 4.06 2.69 0.07 299.68 .94 4.12 SE Asian 3.85 2.78 2.05 850.03 .10 3.92 NW European 3.82 2.89 4.54 5,201.72 < .01 0.10 4.72 North American 3.71 2.79 1.61 189.23 0.11 5.46 South American 3.62 2.70 1.56 107.13 .12 4.61 NE Asian 3.61 2.64 5.23 1,163.92 < .001 0.17 3.56 Location Melbourne 4.23 2.86 2.95 8,876.41 .01 0.06 4.48 Sydney 4.18 2.83 1.81 9,981.18 .07 4.60 Adelaide 4.16 2.91 0.70 2,474.28 .49 4.63 Hobart 4.13 2.82 0.12 375.07 .90 4.73 Brisbane 3.91 2.90 3.33 4,031.16 < .001 0.08 4.57 Perth 3.84 2.87 3.48 2,311.59 < .001 0.10 4.30 North Barassi 4.05 2.85 4.74 South Barassi 4.10 2.87 1.14 26480 .25 4.56

Segmenting the Sport Market

Number of classes/segments and model specification.LCA was the primary

methodology used to examine the Australian sport market and underlying segments. The

initial aim was to establish the appropriate number of classes within the population, with

Table 4 providing a summary of model fit statistics to illustrate that determination. The BIC

reached an optimal number of classes with a 13-cluster solution, while the AIC did not reach

an optimal number of classes. Given that the AIC is known to overestimate classes, the

divergence was not surprising and the BIC solution was adopted. Within the 13-class model,

the model entropy was 77%. While this sits marginally below the 80% benchmark desired for

class probability parameters, the dichotomous response format of the input variables acted to

suppress entropy as compared to scaled question formats. The 13-class model produced

segments ranging in size from 0.75% of the population to 37.01% of the population. Previous

research in which LCA has produced high-cluster solutions has relied on a priori guidance or

36

use of interpretive methods such as scree-plots of log-likelihood values to choose models

with fewer classes. Aside from deviating from methodological best practices with BIC, this

would also appear to belie the primary benefit of LCA—its probabilistic method. Given the

broader aim of the present study to segment the entire population based on a substantive list

of sports, such a high number of segments would appear intuitively realistic in segmenting

what could be expected to be a highly heterogeneous cohort. Furthermore, given the

exploratory nature of this research, the large number of classes provides an opportunity to

identify particular nuances that would be lost within a model with fewer and forced clusters.

Table 4: Model fit statistics LL BIC AIC Entropy 1- Cluster -242.666 485.697 485.406 2- Cluster -224.439 449.619 449.029 0.86 3- Cluster -220.849 442.814 441.925 0.83 4- Cluster -218.634 438.758 437.569 0.78 5- Cluster -216.822 435.510 434.023 0.80 6- Cluster -215.647 433.535 431.748 0.79 7- Cluster -214.785 432.186 430.101 0.79 8- Cluster -214.272 431.534 429.149 0.77 9- Cluster -213.875 431.115 428.431 0.75 10- Cluster -213.159 430.059 427.076 0.77 11- Cluster -212.837 429.790 426.507 0.78 12- Cluster -212.596 429.682 426.101 0.73 13- Cluster -212.176 429.219 425.338 0.77 14- Cluster -212.131 429.503 425.323 0.72

Note. LL = model log likelihood; BIC = Bayesian information criterion; AIC = Akaike information criterion; CAIC = consistent Akaike information criterion.

Profile of sports clusters. Table 5 presents the latent class probabilities of the top 20

most supported sports (out of 37) and their probability of cohort membership across the 13

clusters. The top two rows of avidity and repertoire size did not form part of the underlying

LCA calculations, but are reported here as diagnostics to further illuminate the nature of the

clusters such that all sport-related attitudes are presented concisely within the one table.

Table 6 presents corresponding demographic information for age, gender, location, and

ethnicity, which underpinned the composition of each cluster. These two tables formed the

basis for deriving meaning regarding the nature of each cluster, which will now be identified

and discussed in further detail. The largest segment (37.01%) of the population was also

perhaps the most attitudinally distinguishable, being characterised as outright Sport rejecters.

37

They exhibited the lowest level of average sport avidity (2.33) and had the smallest repertoire

of sport interest (1.33). Women were much more likely than men to be sport rejecters,

comprising 61.71% of the segment. Correspondingly, nearly half of Australian women

(45.04%) were sport rejecters. This segment was also skewed toward younger individuals,

with the average age (43.53) below the mean (46.93); 18- to 29-year-olds were particularly

overrepresented within this segment (25.91%). Sport rejecters were relatively evenly

dispersed across the nation. The salience of AFL and Rugby League as leading national

sports was reflected in that these sports retained a conditional probability of 0.19 and 0.14 of

class adoption—even among Sport rejecters.

The next largest segment was labelled Mainstream focused (21.85%), reflecting this

group’s distinct preference toward a repertoire of sports that rank highly as Australia’s most

participated, as compared to larger commercial sports with comparatively smaller

participation rates within the population. This segment exhibited a diminished preference for

all of Australia’s largest commercial sports: AFL (0.34), Cricket (0.30), Rugby League

(0.22), and Rugby Union (0.08). It demonstrated high preference for sports with a large

participatory or recreational focus: swimming (0.67), tennis (0.65), and gymnastics (0.35).

The deemphasising of commercial sport in this segment resulted in a comparatively small

repertoire size (4.78) compared to the average for the remaining non-sport rejecter segments

(6.82). This segment was also dominated by women, who represented 71.86% of the cohort.

Taken together, 76.02% of the Australian female population fell within the sport rejecter or

mainstream-focused segments. This segment was otherwise demographically representative

across age, location, and ethnicity.

The remaining 11 segments (41.14%) ranged in size between 0.75% and 7.74%,

indicating a significant fragmentation in typologies among those who were sport-inclined.

Given the significant overrepresentation of women in the largest two segments, the remaining

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11 segments each displayed male gender dominance. Three overarching characteristics

distinguished the remaining 11 clusters. The first surrounds clusters that exhibited strong

conditional probabilities to specific sport repertoires, which shared particular functional

similarities (Clusters 4 through 7). Oval sport purists (7.74%) retained an absolute preference

for AFL (0.74) and Cricket (0.96), which given their non-overlapping winter and summer

schedules, largely fulfilled their small repertoire needs (3.52). Similar patterns emerged

among the Motor-sport inclined (4.74%), who exhibited high interest in Formula1 (0.88) and

MotoGP (0.68), but were largely disinterested in participatory sports. The Rugby-inclined

(6.03%) segment exhibited a preference for the rugby codes, similarly at the expense of

participatory sports. The Global sport-inclined (2.06%) segment held high rates of interest in

MMA/UFC (0.82), boxing (0.80), soccer (0.51), and basketball (0.40), which were among the

most globalised sports. Correspondingly, this cohort was the youngest segment (38.28),

comprised of over a third of millennials aged 18 to 29 (37.50%).

The second cohort characteristic that conformed to conceptual expectations surrounds

the North Barassian (2.83%) and South Barassian (3.67%) segments. The South Barassian

segment showed a 1.0 probability of interest in AFL, only a 0.17 probability of interest in

Rugby League, and a 0.00 probability of interest in Rugby Union. Validating the Barassi Line

concept, the South Barassian cohort primarily resided south of the Barassi Line (77.28%),

particularly from Melbourne (28.09%) and Adelaide (14.03%). The South Barassians can be

considered the more open-minded cousins of the Oval sport purists, who are

characteristically most similar to this segment. Both segments shared an absolute passion for

AFL and Cricket, but the South Barassian segment were interested in a greater variety of

sports (so long as they did not derive from north of the Barassi Line). By contrast, the Oval

sport purists appeared to concentrate their avidity toward fewer sports and were therefore

likely to be high consumers of these select sports. Antithetically, North Barassians exhibited

39

particularly high preference for Rugby League (0.90) and Rugby Union (0.45) and

comparatively low interest in AFL (0.38). Conceptual validation of this segment derived from

their geographic concentration north of the Barassi Line (77.90%), particularly in Brisbane

(15.66%). Similar to their southern counterparts, the North Barassians represented a more

open-minded extension of the Rugby purists, with whom they shared a passion for the rugby

codes, but remained more open-minded to other sports. Notably, the South Barassians self-

identified as more avid sport fans (6.71) than North Barassians (6.20), but retained a smaller

repertoire of sports in which they were interested (7.72 vs. 8.20).

The final segments (10 through 13) captured sport consumers who could reasonably

be interpreted as fanatical, but whose preferences varied enough to form unique groups.

Absolute fanatics represented the pinnacle sport consumer, yet also the smallest cluster within

the model (0.75%). This group was relatively homogenous geographically, ethnically, and

across age groups. Aside from reporting high self-identified sport avidity (7.63), what made

this group of consumers remarkable was their absolute repertoire size (28.34). Diverse

fanatics and Focused fanatics differed in their application of sport interest. Diverse fanatics

had a lower avidity score (6.92) but were interested in a larger repertoire of sports (15.58)

than focused fanatics, who had a strong sport interest (7.33) but were focused on a smaller

repertoire of sports (11.49). Participatory generalists had an avidity score similar to rugby

purists, but had an interest in more than double the number of sports (12.76). This cohort

retained an interest across all sports, but had a higher interest in the participatory sports than

their fanatical counterparts.

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Table 5: Latent class probabilities for top 20 sports LC1 LC2 LC3 LC4 LC5 LC6 LC7 LC8 LC9 LC10 LC11 LC12 LC13 Population 37.01% 21.85% 3.70% 7.74% 6.03% 4.74% 2.06% 3.67% 2.83% 4.66% 2.95% 2.02% 0.75% 100%

Sport/Metric Sport Rejecters

Mainstream Focused

Mainstream Rejecters

Oval Sport Purists

Rugby Purists

Motor Inclined

Global Sport Inclined

South Barassian

North Barassian

Participatory Generalists

Focused Fanatics

Diverse Fanatics

Absolute Fanatics

Avidity 2.33 3.93 3.94 5.48 6.09 4.75 5.68 6.71 6.20 6.33 7.33 6.92 7.63 4.07 Rep Size 1.33 4.78 6.82 3.52 5.54 5.53 7.83 7.72 8.20 12.76 11.49 15.58 28.34 4.66 AFL 0.19 0.34 0.22 0.74 0.51 0.45 0.45 1.00 0.38 0.61 0.86 0.76 0.89 0.39 Cricket 0.09 0.30 0.10 0.96 0.68 0.42 0.34 0.97 0.85 0.72 0.97 0.81 0.98 0.38 Tennis 0.15 0.65 0.16 0.33 0.37 0.18 0.45 0.77 0.34 0.84 0.74 0.56 0.95 0.38 RugbyLeague 0.14 0.22 0.26 0.25 0.93 0.30 0.46 0.17 0.90 0.54 0.88 0.75 0.92 0.31 Swimming 0.05 0.67 0.28 0.00 0.19 0.06 0.20 0.45 0.36 0.91 0.48 0.55 0.96 0.28 Soccer 0.11 0.25 0.21 0.23 0.38 0.14 0.51 0.26 0.23 0.46 0.74 0.45 0.87 0.23 Formula1 0.04 0.09 0.16 0.20 0.12 0.88 0.19 0.41 0.27 0.28 0.56 0.89 0.80 0.18 RugbyUnion 0.03 0.08 0.12 0.06 0.88 0.17 0.19 0.00 0.45 0.38 0.88 0.54 0.86 0.17 Golf 0.02 0.11 0.06 0.18 0.24 0.13 0.12 0.58 0.54 0.44 0.59 0.48 0.90 0.16 Fishing 0.06 0.09 0.33 0.08 0.09 0.21 0.19 0.22 0.86 0.30 0.18 0.65 0.80 0.14 Gymnastics 0.06 0.35 0.27 0.00 0.01 0.02 0.10 0.04 0.03 0.56 0.03 0.13 0.72 0.14 Cycling 0.03 0.15 0.24 0.06 0.05 0.11 0.00 0.32 0.07 0.56 0.46 0.47 0.92 0.13 Horseracing 0.03 0.09 0.07 0.09 0.16 0.08 0.14 0.38 0.36 0.39 0.37 0.39 0.77 0.12 Netball 0.03 0.18 0.05 0.02 0.09 0.01 0.05 0.21 0.05 0.41 0.35 0.18 0.76 0.10 Basketball 0.03 0.11 0.07 0.07 0.09 0.04 0.40 0.33 0.02 0.26 0.34 0.30 0.74 0.10 MotoGP 0.02 0.03 0.21 0.02 0.02 0.68 0.05 0.15 0.17 0.16 0.28 0.76 0.66 0.10 MMA/UFC 0.05 0.06 0.42 0.01 0.05 0.11 0.82 0.04 0.10 0.16 0.07 0.45 0.65 0.10 Boxing 0.02 0.02 0.24 0.01 0.10 0.05 0.80 0.14 0.35 0.21 0.28 0.52 0.77 0.09 Snow Sport 0.02 0.15 0.35 0.00 0.03 0.05 0.06 0.06 0.04 0.44 0.11 0.31 0.75 0.09 Surfing 0.02 0.09 0.31 0.01 0.05 0.06 0.05 0.05 0.20 0.35 0.10 0.38 0.71 0.08

Note. LC = latent class; Rep Size = sport repertoire size. Bold = Significant difference from population mean at the 95% level. Remaining sports surveyed (ordered 21 through 37): Lawn Bowls, Pool/Billiards, Hockey, Marathon, American Football, NASCAR, Equestrian, Boating, Extreme Sports, Volleyball, Wrestling, Athletics, Weightlifting, Badminton, Mountain Biking, Rowing and MotorCross.

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Table 6: Latent class membership composition LC1 LC2 LC3 LC4 LC5 LC6 LC7 LC8 LC9 LC10 LC11 LC12 LC13 Population 37.01% 21.85% 3.7% 7.74% 6.03% 4.74% 2.06% 3.67% 2.83% 4.66% 2.95% 2.02% 0.75% 100% Sport

Rejecters Mainstream Focused

Mainstream Rejecters OSP

Rugby Purists

Motor Inclined GSI

South Barassian

North Barassian

Participatory Generalists

Focused Fanatics

Diverse Fanatics

Absolute Fanatics

Gender Women 61.71 71.86 46.46 29.36 23.27 20.97 18.79 25.74 14.07 50.64 11.88 10.14 17.24 50.70 Men 38.29 28.14 54.54 70.64 76.73 79.03 81.21 74.26 85.93 49.36 88.12 89.96 82.76 49.30 Location Metropolitan 72.83 73.86 65.18 69.22 72.17 63.61 76.20 65.59 61.43 70.16 73.48 64.30 75.22 71.30 Regional 27.17 26.14 34.82 30.78 27.83 36.39 23.80 34.41 38.57 29.84 26.52 35.70 24.78 28.70 N. Barassi 54.67 55.35 57.39 40.43 73.89 49.77 55.03 22.72 77.90 56.45 62.18 55.86 59.32 54.45 S. Barassi 45.33 44.65 42.61 59.57 26.11 50.23 44.97 77.28 22.10 43.55 37.82 44.14 40.68 45.55 Sydney 20.38 20.95 15.95 12.19 24.22 11.85 25.06 7.33 20.42 17.44 22.50 17.98 25.55 19.07 Melbourne 17.57 19.62 14.05 23.29 8.28 17.01 18.99 28.09 5.52 14.11 15.21 12.46 20.70 17.58 Brisbane 10.62 10.61 10.80 8.64 14.71 10.53 11.54 3.38 15.66 10.36 10.26 8.91 8.73 10.52 Adelaide 7.63 6.69 6.36 9.35 3.37 9.58 7.54 14.03 2.50 8.97 6.31 7.45 6.36 7.48 Perth 7.42 6.45 6.09 7.81 5.40 6.08 6.65 8.94 3.03 7.07 5.38 5.72 3.10 6.81 Age 18–29 25.91 22.03 31.48 14.44 15.97 12.85 37.50 11.10 4.57 13.69 12.26 10.99 12.69 21.00 30–39 20.59 18.95 23.24 16.81 12.70 11.89 24.57 13.42 7.93 14.26 13.19 14.08 14.39 18.00 40–49 18.87 16.97 19.93 18.30 14.78 22.80 15.33 15.62 14.25 14.14 16.31 25.93 23.42 18.00 50–59 16.24 16.15 12.89 16.08 18.19 23.42 10.97 16.90 26.06 17.67 16.56 25.39 19.01 17.00 60–69 9.66 13.12 6.45 15.16 16.70 18.58 6.57 19.91 24.50 17.96 16.88 15.56 20.20 13.00 70–79 5.32 8.31 3.53 11.76 11.90 7.92 2.40 14.76 11.62 13.79 14.88 5.39 6.74 8.00 80+ 3.42 4.46 2.49 7.45 9.76 2.55 2.66 8.28 11.07 8.49 9.91 2.66 3.56 5.00 Mean 43.53 46.43 40.09 50.78 52.42 50.03 38.28 54.13 58.0 52.71 53.92 49.10 49.81 46.93 Ethnicity Australian 72.03 71.34 78.30 77.41 72.24 80.48 67.39 86.57 80.93 75.55 76.33 81.38 72.54 74.05

NW European 14.22 15.00 15.52 14.50 20.98 14.02 11.53 11.65 16.01 17.43 19.05 14.33 11.28 14.98

SE European 6.62 6.47 6.76 5.20 4.43 5.42 11.14 4.30 3.09 5.02 3.66 5.64 6.73 6.04 NE Asian 5.66 6.48 2.81 1.60 2.21 1.83 6.06 0.58 0.16 2.35 2.32 1.61 2.32 4.38 SE Asian 4.20 4.54 2.67 1.93 0.99 1.09 10.71 0.82 0.46 1.86 1.84 1.81 2.36 3.39 SC Asian 2.14 2.84 0.77 4.50 1.55 0.64 0.96 0.97 0.00 2.31 3.00 0.87 2.91 2.22 Oceanian 1.46 1.26 1.50 0.53 3.63 2.27 1.83 0.14 3.29 2.52 3.16 1.98 1.86 1.62

Middle East+ Africa 1.34 1.22 1.28 0.76 1.40 0.53 2.91 0.40 0.43 1.91 0.76 0.80 0.00 1.19

Aboriginal 0.90 0.53 1.56 0.93 1.08 1.04 2.96 0.78 1.76 1.23 0.54 1.62 1.30 0.95 N. American 0.64 0.69 1.39 0.48 0.59 0.50 1.69 0.89 0.06 1.14 0.87 0.56 1.35 0.70 S. American 0.37 0.54 0.41 0.05 0.21 0.25 1.23 0.00 0.11 0.27 0.26 0.58 0.00 0.36

Note. GSI = global sport-inclined; OSP = oval sport purist. Bold = Significant different from population mean at the 95% level.

42

2.5 Discussion

The exploratory nature of this research approach and corresponding methodology

produced a segmentation model that offers considerable insights into the consumer structure

of crowded sport marketplaces. This in turn addresses an identified scarcity of sport

management research at the meso and macro industry levels. Two key findings emerged:

First, the absolute size and nature of the sport rejecters segment corresponded to a gender

divergence, which was also found in the dichotomy between participatory and commercial

sport preference. The second key finding was the strong geographic-culture influence that

impacted the national sport market. These findings are now discussed in terms of their

significance to relevant sport management literature and theory.

Sport Rejecters and Market Sizing

A key empirical marketing question within every consumer product category is

determining the proportion of the population that purchases within the category (Ehrenberg et

al., 2004). In a sport setting, this question has not been thoroughly explored because of an

emphasis upon micro-level analysis within sport scholarship on either specific sport and

teams or specific consumer segments (Park et al., 2011). A second contributing factor to this

absence has been the lack of an appropriate conceptual framework to define the category such

as to allow its quantification. Previous conceptualisations of the sports market have

segmented it according to its sources of revenue (Mason, 1999). Rather, the approach

proposed by this study is to conceptually organise the sport market according to its

participants on the seller’s side of the market. In doing so, the study’s segmentation is able to

address the above key empirical marketing question. By conceptualising sport as a meso-

level market within the broader leisure and entertainment market, the study is able assess the

prominence of sport as a leisure activity within the broader population. Although sport

appears to be increasingly ubiquitous (Byon et al., 2010; Rein et al., 2006; Rowe, 2011),

43

evidence from typical repeat-purchase consumer markets has illustrated that the entrance of

new market competitors does not necessarily grow the underlying consumer base of a product

category (Ehrenberg et al., 2004). Quantifying the degree of category support is particularly

significant as the body of sport consumer research has largely focused on single-sport or

team-specific contexts, with an emphasis on more highly developed fans (Stewart et al.,

2003). By contrast, quantifying product category support allows for the identification of non-

consumers, who remain an under-researched segment of the sport population (McDonald &

Funk, 2017; Reysen & Branscombe, 2010). Given an increasingly crowded sport market in

which existing sport consumers may be fully leveraged (Mahony & Howard, 2001; Mauws et

al., 2003), a better understanding of non-fans may provide the greatest opportunity for

practitioners to grow their respective fan bases (McDonald & Funk, 2017).

The LCA segmentation identified 37.01% of the Australian adult population as Sport

rejecters, characterised by low sport avidity (2.33) and particularly low sport adoption

rates/repertoire sizes (1.33). Two significant demographics were found to characterise this

group: the overrepresentation of women (62%) and the overrepresentation of youth (mean

age of 43.53 vs. 46.93 for the population). The overrepresentation of women among sport

rejecters is not surprising, as women and girls continue to exhibit lower levels of sport

involvement (Eime & Harvey, 2018). Previous studies have suggested that the gender

difference in sport repertoires is related to functional elements, with men preferring

‘masculine-coded’ sports like the football, baseball, and ice hockey and women preferring

more gender-equal sports such as tennis or gender-neutral and stylistic activities like

gymnastics and figure skating (Gantz, Wang, Paul, & Potter, 2006; Solberg & Hammervold,

2008). This finding about experiential and aesthetic elements of sport is also consistent with

the preferences of the Mainstream Focused segment (21.85%), which was made up of

71.86% women.

44

With respect to Sport rejecters, however, the key sport management and marketing

issue was whether these non-consumers represented an innately disinterested and low value

segment to the industry, or whether barriers to sport interest among this cohort were

surmountable through changes to the marketing mix, product orientation, and strategic

adaptability of sport organisations (Mauws et al., 2003). Given that the core sport ‘product’

represents some form of contest from which consumers derive pleasure or fulfilment (Mason,

1999), the underlying level of consumer interest toward sport (as well as its substitutability)

is dictated by the degree to which it (and its substitutes) can satisfy consumer motives, needs,

wishes, and desires (Hendee & Burdge, 1974; Pritchard & Funk, 2006). Further research is

required to understand this key management issue. Given that existing sport consumers may

already be highly leveraged (Mahony & Howard, 2001; Mauws et al., 2003), industry growth

may be best achieved by better understanding how to turn sport rejecters into sport consumers

(McDonald & Funk, 2017).

With respect to age, the diminished levels of sport avidity among youth and their

overrepresentation within the Sport rejecter, Mainstream rejecter, and Global sports-inclined

segments highlights the need to understand generational changes in the sport market. This

was particularly evident by juxtaposing the Global sports-inclined (2.06%) cohort with the

Barassian cohorts (6.50%). The Global sports-inclined cohort was the youngest segment

(mean age = 38.28) and the Barassian cohorts were the oldest segments (mean age = 58.00,

54.13) in the model. These three segments appeared to exemplify the impact of the growth in

mediated sport availability on sport preferences (Rowe, 2011). Even when considering the

fanatics segments, the Global sports-inclined segment exhibited the highest probability of

showing interest in MMA/UFC (0.82), the second highest interest in basketball (.40), and the

third highest in soccer (.51). This segment therefore appeared to match the archetype of a

satellite fan (i.e., geographically distant from their sport passion), a form of mass-mediated

45

consumption that benefits sport economies outside Australia (Hutchins & Rowe, 2012; Kerr

& Gladden, 2008). By contrast, golf (0.58, 0.54), swimming (0.45, 0.36), and horseracing

(0.38, 0.36) featured more prominently within the Barassian repertoire, but were considerably

less salient among the Global sport-inclined segment. More recent developments in eSport

may become responsible for generational changes in sport preferences (Hutchins, 2008) and

an explanatory factor with respect to the higher prevalence of youth in the Sport rejecters

segment. Whether new phenomena such as e-sport become embraced as genres in the sport

market or the gaming market therefore has considerable implications for the positioning of

the sport industry (Funk et al., 2017)—especially given that revenue for eSports flows

overwhelmingly out of Australia, contributing little to the local entertainment revenue base.

Another significant implication arising from the segmentation output pertains to the

relationship between sport avidity and repertoire size in relation to the concept of customer

loyalty. Fostering loyalty among customers has historically been a key goal of sport

marketers (Shilbury et al., 2014). Mullin et al.’s (2014) escalator model, for instance, was

premised upon the pattern that people increase their consumption and loyalty in a collinear

fashion as they escalate up the fandom model. Yet, the segmentation showed that more avid

sport fans tended to exhibit a greater repertoire of sports in which they were interested and

were thus less likely to be singularly loyal to individual sports. The findings of the research

determine 9.7% of the Australian population exhibit particularly elevated sport repertoire

preference consistent with the “sports omnivore”, of which the Canadian population is

similarly comprised of 6.6% (Gemar, 2018). From a broader management domain, Dirichlet

modelling (which focuses on behaviours rather than attitudes) suggests that consumers who

are solely loyal to one brand are typically smaller consumers of the product category overall

(Sharp et al., 2002). This latter contention has been previously supported by such modelling

in the sport context (Fujak et al., 2018). Although Fujak et al.’s (2018) research did not adopt

46

the same methodological approach, the model and its segments appeared to provide

complimentary findings from an attitudinal perspective. Oval sport purists (7.74%), for

instance, appeared to be among the most loyal toward a small cluster of sports (average

repertoire of 3.52), but were less avid toward sports overall (5.48); thus, they are unlikely to

be large contributors to the category as a whole. Accordingly, a paradox becomes evident:

Sport marketers desire highly avid and loyal fans, yet as fans become more avid toward sport,

they are more likely to fulfil their consumption needs from a wider repertoire of sport

opportunities.

Social-Cultural and Geographic Influences on Market Structure

A second key implication of this segmentation pertains to the confirmation of an

interaction effect between social-cultural characteristics and geographical influences in

shaping the sport market. Beyond the underlying transactional exchange, markets are social

institutions that are inseparable from their social context (Friedland & Robertson, 1990).

Within context of this study, the Barassi Line has been both the longest standing and perhaps

most significant social construct to geographically demarcate the Australian sports market

(Hess & Nicholson, 2007). To test the prominence of this social-cultural construct,

respondents were coded with their Barassi affiliation (North or South) based on postal codes,

with latent class membership then tested within the diagnostics of the LCA (see Table 6).

The results indicated that among the 11 smaller segments, five segments (20.27%) were

strongly skewed toward a Barassi side (LC4, LC5, LC8, LC9, and LC11). Accordingly, one

in three sport fans (excluding sport rejecters) appeared to exhibit archetypal preference

patterns in line with those espoused by the Barassi Line (Hess & Nicholson, 2007).

Notable here is that the Barassi Line and its underpinning geography do not influence

sport avidity itself, but rather underpin preferences within sport repertoires. Table 3

illustrated little practical difference in sport avidity across Australia’s capital cities; there was

47

also no statistical difference between the Barassi groupings. This represents a somewhat

surprising outcome given that Melbourne is largely accepted as the “sporting capital” of

Australia, with many major sport events and particularly high attendance rates (Misener &

Mason, 2009, p. 782). Such an assertion has been supported by previous research indicating

that only 34.6% of Sydney residents attend sport per annum compared to 43.8% Melbourne

residents (Fujak et al., 2018). Accordingly, despite Sydney and Melbourne sharing similar

populations, economic strength, and sport team concentration, differences in sporting culture

appear to manifest behaviourally rather than attitudinally (Cashman & Hickie, 1990). This is

particularly significant for sport practitioners, as it suggests that while populations across

locations appear relatively consistent in their sporting amenability (attitudes), differences in

attendance may be due to other barriers such as infrastructure, transportation, or value

proposition—which are more within a practitioner’s strategic control than category-level

attitudinal interest toward sport (Shilbury et al., 2014).

2.6 Conclusion

Understanding the consumer market structure of sport landscapes represents an

underdeveloped yet vital area of sport management research. This paper has made two

significant contributions to remedy this. First, necessitated by competitive industry pressures

that have brought sport markets to the forefront (Byon et al., 2010; Rein et al., 2006), the

paper revisited the concept of ‘sport markets’. In doing so, this research articulated and

formalised an approach to conceptualising sport markets by delineating the three axes on

which sport competes from a management perspective (Shilbury, 2012). Delineating these

axes further served to reinforce the overwhelming emphasis placed on micro-level research

within the sport management domain, which was addressed by the second major contribution

of this paper.

48

The research addressed the shortage of market-level (meso) research by undertaking

an explanatory analysis of a crowded sport market, achieved through quantitative analysis of

survey data on sport preferences. The research methodology was novel in several respects.

First, the volume and quality of the data within this research was notable, capturing sport

preferences comprehensively across 37 sport and recreational activities among 27,412 adult

Australians. Second, the segmentation process was achieved through LCA, a probabilistic

technique that overcomes limitations associated with traditional cluster-analytic processes

(Hagenaars & McCutcheon, 2002). Despite the advantages of this approach, the present study

appears to be one of the few applications of LCA within sport management research.

Several significant findings emerged from this exploratory research. First, the

segmentation was able to successfully size the Australian sport market, determining that 37%

of the population are sport rejecters, with the remaining 63% attitudinally engaged in sport

across 12 distinct typologies. Given the intensity of competition with the local marketplace, a

key question deserving further attention is whether (or to what degree) sport rejecters can be

enticed to take interest in sport through some adjustment in product offering. Alternatively,

are such non-consumers unobtainable to the sport market due to an innate incompatibility

with the core sport product (Mason, 1999; McDonald & Funk, 2017)? The overrepresentation

of women and youth in the sport rejecter segment indicates that cohort membership does not

occur by pure random chance, which is a strategic challenge for the sport industry. Second,

the segmentation highlighted the influence of social-cultural phenomena in shaping the

structure of sport markets. Almost a third of the non-rejecting sports market population fell

into segments determined by the Barassi Line. Despite the increasing proliferation of sport

covered by the media (Rowe, 2011), the findings confirm that the Australian sports market

remains characterised by two divergent and competing football cultures (Hess & Nicholson,

2007).

49

Finally, the segmentation confirmed that higher attitudinal sport avidity was

associated with increased sport repertoire size. This finding was consistent with broader

marketing theory, which has identified an inverse relationship between brand loyalty and

overall category spend patterns (Ehrenberg, 2000; Uncles et al., 1995). As individual sports

represent genres of the overall category, and this research broadly supports previous findings

that sport consumers are polygamously loyal (Fujak et al., 2018; Sharp et al., 2002), sport

practitioners who successfully develop more avid fans for their respective team are also

inadvertently developing more valuable sport consumers overall. Therefore, although

competition remains the ‘heart and soul’ of sport management (Shilbury, 2012), avid sports

fans show a propensity toward interest in a greater array of sports. Therefore, industry

practitioners could benefit from collaborating in order to grow the overall sport category for

the collective benefit of all market participants.

Despite the insights delivered by this research, there are limitations. The research

focused on attitudinal data and, accordingly, further exploration is warranted surrounding

how such attitudes manifest behaviourally. The attitudinal data was also captured

dichotomously. While this allowed for responses to a breadth of sports, it came at the expense

of depth associated with scale questions, impacting model entropy. In general, further

research of sport markets is required, especially among non-fans.

50

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3. Study 2: Are Sport Consumers Unique? Consumer Behaviour within Crowded Sport Markets

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Abstract

Sport consumers and markets have traditionally been thought to exhibit unique behaviours

from traditional consumer products, particularly in respect to perceptions of loyalty. Yet,

despite sport landscapes becoming increasingly crowded, there has been scant research

measuring consumers’ repeat behaviour in the context of the dense sports market. Through

this research we address this gap by applying Dirichlet modelling against the behaviours of

1,500 Australian sport consumers. Two questions are explored: First, do sport attendance

markets exhibit purchase characteristics distinct from typical consumer markets? Second, do

consumers treat sport leagues as complimentary or substitutable goods? The results provide

evidence that consumer patterns within the sport attendance market are consistent to other

repeat-purchase consumer markets. This finding further diminishes the long-held notion that

sport requires unique methods of management. Furthermore, it was found that fans consume

sport teams as complimentary products. As sport teams largely share their fans with other

teams, practitioners must reorient their expectations around fan loyalty.

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3.1 Introduction

Commercial and technological developments within the sport and media industries

have facilitated considerable growth in the opportunities to consume sport. Accordingly, the

value of the North American sport market is projected to be valued at $71.6 billion in

2018 (PricewaterhouseCoopers, 2015). Along with new consumption formats,

commercialisation has also led to an expansion in volume of teams and leagues competing for

consumer hearts and wallets (James, Kolbe, & Trail, 2002). As Byon, Zhang, and

Connaughton (2010) noted, “with such a crowded sport marketplace, sport consumers have

many options in which to spend their leisure time and discretionary dollars. As a result,

professional sport organisations face stiff competition in an effort to gain market share” (p.

143).

While there appears to be consensus that sport markets are increasingly competitive

and crowded (McDonald, Karg, & Lock, 2010), there appears to be scant research that

attempts to quantify the behaviour and structure of such crowded sport markets (Field, 2006).

The scarcity of such research is particularly surprising given the centrality of competition to

the sport sector: “Managing the implications of competition, both on and off the field, is a

critical success factor and a strategic imperative in its own right. Competition, therefore, is

the heart and soul of sport management” (Shilbury, 2012, p. 2). Although sport consumption

has emerged as a vital area of research, the field has largely focused on fan behaviour within

individual sports rather than the consumer markets in which teams compete (Pelnar, 2009).

Through this research we begin to remedy this shortcoming by undertaking an analysis of

sport consumer behaviour within sport markets that feature a high degree of consumption

choice.

Corresponding to an increase in off-field competition, sport has continued along a

path away from leisurely pastime toward organised business practice, resulting in

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increasingly professional management strategies (Robinson, 2008). Yet, as sport management

becomes increasingly sophisticated, contention surrounds whether corresponding strategies

should be based upon broader management principles or specialised from within the sport

management discipline (Chalip, 2006; Costa, 2005). In relation to whether sport belongs as a

distinct field of academic enquiry, Chalip notes (2006): “The fundamental concern has

therefore been whether sport management is a unique discipline or is one that merely derives

applications from theories originating in the so-called “home disciplines” (p. 2).

The defense of sport management as a distinct field has largely been underpinned by

the articulation of unique attributes innate to the discipline which require distinct

management practices (Baker, McDonald, & Funk, 2016). Neale’s (1964) identification of

the peculiar economics of professional sport confirms that such articulation does not

represent a new endeavour. However, more contemporary management orientated research

by Stewart and Smith (1999, 2010) have identified that although sport retains unique

attributes, these unique elements are often over-stated, can be found in other products and

markets and have diminished over time. Nonetheless, these unique attributes appear to still

largely underpin sport management. Baker et al. (2016) point to numerous widely used

introductory sport management and marketing textbooks (e.g., Pedersen & Thibault,

2014, Mullin, Hardy, & Sutton, 2014) that include chapters discussing the uniqueness of

sport, suggesting the uniqueness remains an integral component of the sport management

self-narrative.

One feature of the sport market that has historically been considered to distinguish it

from other industries is the perceived loyalty and passion of sport consumers. Distinct from

the typical rational decision-making consumer, the sport product has historically been

positioned as an “ephemeral experience mired in the irrational passions of fans, commanding

high levels of product and brand loyalty, optimism and vicarious identification” (Smith &

http://www.sciencedirect.com.ezproxy.lib.uts.edu.au/science/article/pii/S144135231600019X#bib0190



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Stewart, 2010, p. 3). While such characterisation of sport fans provides for a simple narrative,

the degree to which sport consumers in fact exhibit particularly unique consumer behaviours

is becoming increasingly disputed. Observational evidence in fact suggests that supporting

multiple sport teams is possible, if not common, although vigorous academic confirmation of

such has yet to occur (Baker et al., 2016; McDonald et al., 2010). This represents a

significant theoretical disconnect, given that consumer buying behaviour in other highly

competitive repeat-purchase industries, such as Fast Moving Consumer Goods (FMCG) and

professional services, has now been well defined (Ehrenberg, Uncles, & Goodhardt, 2004).

Significantly, such research has provided evidence that consumer behaviour across many

varied competitive industries conform to consistent behavioural patterns that result in

predictable market structures (Bound, 2009). Whether such behavioural predictability occurs

in a sporting context has largely yet to be addressed though it is highly significant given the

long-held belief that sport consumers in fact display unique behaviours.

Thus, through this research we attempt to address this critical gap by providing a

quantitative analysis of consumer behaviour in two crowded sport markets where multiple

teams and leagues compete. This is achieved by adopting Ehrenberg’s (1971) well

established framework of buyer behaviour within repeat-purchase markets, utilising the

negative binomial distribution (NBD) Dirichlet Model of market analysis (Bassi, 2011). The

core research purpose, therefore, is to understand sport consumption patterns within selected

geographic markets and is underpinned by two key research questions (RQ):

RQ1: Do sport consumer markets exhibit purchase characteristics typical of repeat-

buying consumer markets?

RQ2: Do consumers treat sporting teams as complimentary or substitutable goods?

The paper is presented in five parts. The first part examines the relevant literature in

respect to consumer markets and sport landscapes. The second part outlines the methods

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deployed in this study. Subsequently, the third part of the paper includes the data analysis,

and the fourth part has the research findings and their implications. The fifth and final part

the paper concludes with ideas for future research.


Consumer Behaviour in Repeat-Purchase Markets

Owing to its financial significance, consumer behaviour in repeat-purchase markets

represents a comprehensively researched academic field (Sharp, Wright, & Goodhardt, 2002).

Critical to the field is the work of Ehrenberg (1971), who found that an NBD was well fit to

analyse the market level data of industries in which consumers made repeat purchases.

Goodhardt, Ehrenberg, and Chatfield (1984) developed this into the functional “Dirichlet”

model—a model theorising that buyers have steady buying propensities, and that these

buying propensities vary across the population according to certain statistical distributions

(Bound, 2009). To measure this, the Dirichlet adopts a stochastic distribution in predicting

probabilistically both the number of purchases a buyer will make and the probability of each

brand being bought on each purchase occasion in a particular time period (Goodhardt et al.,

1984). This model would later be developed into accessible Excel-based software by Kearns

(2000) and later into R programming language by Chen (2008).

The Dirichlet model has been found to be highly generalisable and is considered one

of the most validated models in the business marketing domain (Uncles, Ehrenberg, &

Hammond, 1995). Sharp et al. (2002) noted that Dirichlet-type patterns have been found

across over 50 varied product and service categories and remain valid both across countries

and longitudinally. Ehrenberg et al. (2004) provide a comprehensive summary of the breadth

of such research, although some illustrative examples are provided further below.

Considerable focus however, has centered on the FMCG market given its repeat-purchase

nature (Dawes, 2016; Ehrenberg, Goodhardt, & Barwise, 1990; Ehrenberg et al., 2004;

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Uncles et al., 1995). Aside from being highly generalisable, Dirichlet modelling has also been

found to be relatively robust when applied in settings that depart from the model’s underlying

assumptions. One such assumption toward which the model appears robust is that the market

of analysis is stationary in nature, which does not reflect commercial reality in most instances

(Ehrenberg, 2000; Wright & Sharp, 1999).

A key to the Dirichlet model is the parsimonious manner by which it validates

multiple empirical marketing generalisations and/or principles. Sharp et al. (2002)

distinguished five such generalisations the body of research has validated and which the

NBD-Dirichlet model accurately predicts: First, differences in market share are largely due to

differences in penetration—higher share brands are bigger largely because they have more

customers than lower share brands. This is illustrated within Erhenberg et al.’s (2004)

analysis of the United States coffee market from 1992. The third (Tasters Choice) and fourth

(Nescafe) largest brands held distinct market shares of 17% and 11% despite similar average

annual purchase rates (2.8 vs 2.7). Rather, the source of their divergent market share was

resultant from their differing annual penetration rate: 9% as compared to 6%. Second, the

comparatively small differences between brands in average purchase frequency and other

loyalty statistics follow the double-jeopardy pattern identified by McPhee (1963): Not only

do small brands have fewer buyers, but also these buyers are slightly less loyal. This was the

case in the Italian beer market between 2001 and 2004 (Bassi, 2011). Market leading brand

Moretti (market share of 14.48%) held a 12.05% proportion of solely loyal buyers, compared

to market laggard Bud (0.81% market share) with 8.79% solely loyal buyers (Bassi, 2011).

Third, a brand’s customers, on average, buy other brands more often. This is because most

customers buy from a repertoire of brands. This generalisation is evident within Singh and

Uncles’ (2016) analysis of the United Kingdom breakfast cereal market. Although Kellogg’s

Cornflakes was the market leading brand (9% market share), it accounted for only a 16%

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share of its customer’s annual cereal consumption requirements. Fourth, solely loyal buying

(i.e., the proportion of customers who only buy one brand) is relatively rare and declines over

time. Within the Australian retail fuel industry for instance, the average rate of solely loyal

buying was found to be 8.3% (Sharp et al., 2002). Solely loyal buyers are also lighter buyers

of the overall category while, by contrast, heavier buyers tend to buy more brands but are less

likely to be solely loyal. Fifth, brands share their customers with other brands in line with

each brand’s penetration—this is known as the duplication of purchase law. These empirical

principles represent the key measures tested within RQ1 (see Table7).

In relation to the fourth empirical-marketing generalisation, Sharp et al. (2002)

observed that repeat-purchase markets are polarised by either repertoire- or subscription-

buyer behaviours. Repertoire-pattern markets are characterised by consumers who satisfy

their consumption requirements from within a repertoire of brands. Notably, these buyers are

described as exhibiting polygamous loyalty, which represents a departure from much of

traditional marketing literature classifying consumers dichotomously as either “loyal” or

“switchers.” In contrast, subscription-market patterns differ in that consumers typically

allocate most of their category to one provider. This has been found to be the case for

instance in the credit card market, in which the average rate of solely loyal usage was found

to be 79% in New Zealand (Sharp et al., 2002). Notably, from empirical observation to date,

there do not appear be any markets which occupy the middle ground between these two

extremes. The distinction between repertoire and subscription markets has significant

implications for marketing practice. Brands competing within repertoire markets are more

likely to share customers with competitors, impacting the strategic orientation of marketing

initiatives such as loyalty programs (Uncles, Dowling, & Hammond, 2003). Within repertoire

markets, a brand is better served to increase its penetration within the market than attempting

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to develop solely loyal buyers. Brands within subscription markets should focus on

minimising customer switching and maximising new customer gain (Sharp et al., 2002).

Table 7: List of generalised marketing principles Principle Description

1 Differences in market share are largely due to differences in penetration—higher share brands are bigger largely because they have more customers than lower share brands.

2 The comparatively small differences between brands in average purchase frequency and other loyalty statistics follow a double-jeopardy pattern: Not only do small brands have fewer buyers, but also these buyers are slightly less loyal.

3 A brand’s customers, on average, buy other brands more often. This is because most customers buy from a repertoire of brands.

4 Solely loyal buying (i.e., the proportion of customers who only buy one brand) is relatively rare and declines over time.

5 Brands share their customers with other brands in line with each brand’s penetration- this is known as the Duplication of Purchase Law.

Note: Adopted from Sharp et al. (2002)

Sport-Consumer Behaviour in Crowded Sport Markets

Despite the application of the previously discussed generalised marketing principles

in a variety of empirical settings, sport markets are only beginning to receive similar

academic attention (Baker et al., 2016; Funk, Alexandris, & McDonald, 2016). More

typically, research surrounding sport consumers has focused upon developing typologies and

continuums to define their connection to individual teams and sports (Funk & James, 2001;

Giulianotti, 2002; Mahony, Madrigal, & Howard, 2000; McDonald & Milne, 1997; Mullin,

Hardy, & Sutton, 1993, 2014; Tapp & Clowes, 2002). However, while such sport

segmentation models have become robust in understanding fandom toward single sports and

teams, they do not address consumer behaviour in the context of choice across sport brands at

a market level.

The scarcity of holistic sport market research is perhaps of some surprise, given that

the sector represents a particularly noteworthy field for such endeavour due to widely debated

contention around the degree to which sport contains unique product and marketing

characteristics that distinguish it from other industries (Baker et al., 2016). Researchers have

previously postulated that such empirical generalisations may not necessarily hold in the case

of professional sport team brands (Gladden & Funk, 2001). In contrast, some researchers

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consider sport team supporters to exhibit loyalty patterns similar to those in non-sport

contexts (Tapp, 2004). Smith and Stewart (2010) provided an evaluation of these special

features and their advocates, conflating 10 distinct features from the original work of Stewart

and Smith (1999) into four dimensions in their follow-up critique. These are:

1. Sport is a heterogeneous and ephemeral experience mired in the irrational passions of

fans, commanding high levels of product and brand loyalty, optimism and vicarious

identification.

2. Sport favors on-field winning over profit.

3. Sport is subject to variable quality, which in turn has implications for the management

of competitive balance and anti-competitive behaviour.

4. Sport has to manage a fixed supply schedule. (Smith & Stewart, 2010, p. 3)

Overall, Smith and Stewart’s (2010) critique considered the uniqueness of sport to be

overstated and having diminished since their initial postulations. In relation to the first

dimension, while they now consider sport consumption behaviour to be an exemplar rather

than exception of contemporary consumer behaviour, they note: “Sport is still characterized

by fierce, loyal and passionate fans who experience a strong, vicarious identification with

their players and teams. It remains one of the few products that delivers engaging experiences

that become part of our collective memory” (p. 10). Despite broad acceptance that sport to

some degree retains idiosyncratic features, it is unclear whether sport markets do, in fact,

behave differently than other industries in real-world settings.

Among the first such papers to have tested broader consumption patterns is that of

McDonald and Stavros (2007), who observed that the Season Ticket Holder (STH) product

category appears to be characteristic of a subscription market. They noted that “in sporting

clubs, consumers rarely ‘switch’ teams, thus the issue is not one of attracting customers away

from competitors, but rather re-engaging, maintaining, or increasing the level of participation

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of supporters” (2007, p. 219). The authors, however, largely measured the attitudes of

existing and lapsed members rather than consumers’ propensity to hold multiple

memberships, therefore precluding the possibility of Sharp et al.’s (2002) polygamous

loyalty. Similarly, McDonald (2010) measured the churn rates of STHs among several

Australia Football League (AFL) teams, once again capturing consumers’ propensity to shift

along the continuum of casual ticket buyer to STH status within a single club, rather than

supporting multiple clubs.

Focusing on broader notion of “support” for sport teams, Doyle, Filo, McDonald, and

Funk’s (2013) research suggested that sport markets behave as repertoire markets. The

researchers explored the validity of the double-jeopardy principle in the Australian sport

context market in the context of attitudinal loyalty, finding partial support that the principle

holds in a sport setting. However, their research was limited to only National Rugby League

(NRL) and AFL fans as two broad groups, excluding the remaining two football codes and

other sport leagues that compete within the market. This represents a significant limitation, as

Wann, Grieve, Zapalac, and Pease (2008) observed, clustering in fans’ motivational profiles

toward sports that share functional attributes. The sport market may, therefore, be partitioned

into sub-segments according to such functional similarities and differences.

Baker et al. (2016) also successfully measured double jeopardy in a sport setting,

utilising STH data to track AFL attendance across the 10 Melbourne-based clubs. Notably,

the Dirichlet model was inaccurate at predicting 100% loyalty rates, indicating one potential

way that sport markets differ from other kinds. These findings, however, were constrained to

attendance within one league and were unable to capture consumer-attendance behaviour

across the three remaining football codes that compete in the market. Support was also found

for the duplication of purchase theory among Australian sport consumers, but once again this

analysis was limited to AFL teams rather than the broader sport market. However, Baker et

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al. (2016) noted these limitations to be an opportunity to further expand the topic, stating

“further replication should be undertaken to establish evidence for double jeopardy patterns .

. . across multiple sports and national borders and in more typical settings” (p. 388). This

acknowledgement represents the gap that this research endeavours to address.

From within the identified literature, it becomes apparent that a significant gap exists

in the underlying theory developed to understand sport markets. Drawing from a considerable

stream of work, researchers have identified and validated the unique characteristics of sport

management that distinguish it from other industries. This has perhaps acted as partial

justification for the development of sport-specific theories and models to measure sport

consumption (Baker et al., 2016). Yet, broader marketing theory has been shown to hold true

in many empirical settings (Sharp et al., 2002). Whether broader marketing theories are

applicable in a sport management context has significant implications for the research

approaches adopted by the discipline going forward.

3.3 Methods

Research Context

The study included an evaluation of sport consumer behaviour within two highly

competitive sport markets located within Australia’s two biggest cities, Sydney and

Melbourne (ABS, 2016). Sydney, Australia’s most populous city (5.09 million residents) and

largest from an economic standpoint (responsible for 24.1% of Gross Domestic Product)

represents the primary case and was accordingly allocated a larger sample of consumers (n =

2,039) (ABS, 2017). Melbourne, Australia’s second largest city, represents the secondary

case (n = 459) and provides method replication and a point of case comparison. These two

cities represent logical points of comparison given they are not only similar in size but also in

professional sport team concentration. Sydney was chosen as the primary case on the basis

that it not only has a greater number of competitors within its market, but has been shaped by

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a range of physical, historical, and socioeconomic factors that has led to greater competitive

intensity (Cashman & Hickie, 1990).

Acknowledging that leagues and teams in the Australian market operate along a fully

professional to semi-professional continuum, the population of the competitive landscape for

this study is restricted to leagues that are broadcast in their entirety on free-to-air or

subscription television. Within this scope, competing for Sydney residents’ attention are 14

top-tier football clubs across four football codes, in addition to a further four professional

clubs across the sports of netball, basketball and cricket. Sydney represents a particularly

noteworthy case, given its mix of established and emerging competitors. Rugby Union was

Sydney’s first football code, with the city founding the country’s first governing body in

1874. The sport however, remained amateur until 1996 when the transnational “Super

Rugby” competition established the NSW Waratahs as the sole and apex Rugby club in the

region (Horton, 2009). Rugby League, can similarly lay claim to first-mover status with the

Sydney sport marketplace, being formed as a breakaway Rugby competition in Sydney

featuring nine local teams in 1908 (Cashman, 2010). Today, the NRL consists of nine

Sydney-based clubs (two of which are inaugural) within a 16-team national competition

(Low, 2008).

Soccer and AFL represent newer entrants to the Sydney sporting landscape. The AFL

began its expansion into the Sydney market in 1982 as part of a greater strategic push to

nationalise the sport (Stewart & Dickson, 2007). In 2012 a second AFL team was created

based in Western Sydney, making its first finals appearance in 2016. After a considerable

period of poor off-field governance, a new soccer league known as the “A-League”

commenced in 2005/2006 featuring eight single-city based, de-ethnicised clubs (Georgakis &

Molloy, 2016; Hay, 2011). Accordingly, the city’s two top-tier A-League soccer clubs are

comparatively fledgling (5 and 13 years old) and similar to the AFL model, demarcate along

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an East/West geographic border (Knijnik, 2015). Similarly, three of Sydney’s four non-

football professional teams were established after 2007. Netball’s ANZ Championship was

established in 2008 while cricket’s Big Bash League (BBL), whose two Sydney teams also

follow an East/West geographic divide, was formed in 2011 (Cricket Australia, 2011). In

2016, Sydney’s 18 teams played in 14 different Sydney stadiums, with the greatest distance

between stadiums being 77 kilometers between Brookvale Oval (northern Sydney) and the

Penrith Stadium (western Sydney). A complete list of clubs is presented in Table 8.

Table 8: List of Sydney clubs Club Established Average Attendance Facebook Followersc Rugby League: NRL (men’s)

Souths 1908 14,331 430,017 Easts 1908 10,235 211,741 Canterbury 1935 15,202 283,520 Manly 1947 14,431 182,396 Parramatta 1947 13,929 314,526 Penrith 1967 12,818 140,335 Cronulla 1967 14,578 162,636 St George-Illawarra 1921/1999a 13,632 164,216 Wests 1908/1999a 15,390 256,066

Australian Rules Football: AFL (men’s) Sydney Swans 1982 33,425 270,998 GWS Giants 2012 12,333 89,924

Soccer: A-League/W-League Sydney FC (men’s) 2005 16,071 203,010 Sydney FC (women’s) 2008 Western Sydney (men’s) 2012 14,297 103,009 Western Sydney (women’s) 2012

Rugby Union: Super Rugby Waratahs 1882/1996b 20,280 168,163

Cricket: Big Bash League Sydney Sixers (men’s) 2011 27,956 897,373 Sydney Sixers (women’s) 2015 — Sydney Thunder (men’s) 2011 19,333 622,386 Sydney Thunder (women’s) 2015 —

Netball: ANZ Championship (women’s) NSW Swifts 2008 6,540d 30,689

Basketball: NBL/ WNBL Sydney Kings 1988 6,500d 43,574 Sydney Uni Flames (women) 2003 — 3,034

aBecame merged entities in 1999. Premierships based on merged entities. bCreation of Super Rugby. Premierships based on Super Rugby. cAs at 1/30/2017. dEstimates based on league average.

In comparison to Sydney, competition within the Melbourne sport market has been a

more recent phenomenon and accordingly the market appears more established (Fujak &

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Frawley, 2013). Melbourne is the birthplace of AFL, with the first formal set of rules (known

as Melbourne Rules) conceived in 1859 and the Victorian Football League established in

1896 (Hess, Nicholson, Stewart, & de Moore, 2008). Nine AFL teams operate out of

Melbourne, five of which are inaugural and the remaining four having joined by 1925. Rugby

League added their only Melbourne team to the competition in 1998, Soccer’s two top flight

clubs were founded in 2004 and 2008 while Super Rugby included a local team in 2011.

Melbourne’s BBL cricket (2011), netball (2008) and basketball (rebranded in 2014) teams

were also introduced more recently.

Participants and Materials

An independent panel provider was commissioned to collect survey responses

surrounding sport consumption within the cities of Sydney and Melbourne. An online panel

was desirable specifically for its access to noncustomers, as is of key interest here, and has

proven beneficial in the sport consumer research domain (Dickson, Naylor, & Phelps, 2015).

In total, 2,572 respondents entered the survey, with 39% screened out for a lack of sport

interest, resulting in 1,572 complete surveys. From the remaining 1,572 complete surveys,

another 74 were removed accordingly to quality control procedures, leaving a final sample of

1,498. As the primary case, the final Sydney sample size was 1,191 sport consumers while

the final Melbourne sample size was 307 sport consumers.

The final sample had a slight male skew (52%), with an average age of 44.

Importantly, when compared against the Australian Bureau of Statistics (ABS, 2010) on the

basis of statistical local areas, the sample was distributed geographically evenly across both

Sydney and Melbourne regions. This is particularly significant from a methodological

perspective in the primary case given Sydney’s geographic, social, and cultural diversity.

North and East Sydney are home to Sydney’s wealthier suburbs and residents, characterised

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by higher incomes and lower unemployment, while West and Southwestern Sydney have

historically been more working-class regions (ABS, 2016).

Participants were recruited by the independent panel provider TEG Rewards to

complete an online questionnaire hosted through the Purkle platform. The median complete

time of completed surveys was 16 minutes. The questionnaire contained the following items:

First, a combination of screening and demographic questions surrounding respondent age,

gender, location, and sport interest were captured. As the Dirichlet framework utilises

unsegmented market level data, such diagnostics were primarily used to ensure the

underlying data reflected a representative sample (Ehrenberg et al., 2004). Second,

respondents were asked to list the teams they supported. To avoid listing an overwhelming

array of teams, survey logic was built in to exclude teams from sports in which respondents

reported having no interest. However, an open-ended response was also provided to capture

any further teams not listed.

Third, respondents’ consumption behaviours were captured for their five favourite

teams. Pilot testing indicated that a consumer’s fifth most supported team accounted for only

10% share of spend and thus, appeared an appropriate cutoff point to minimise respondent

fatigue (Gray, 2013). However, a supplementary question was also asked at a sport-wide

level measuring any other consumption behaviours outside of the top five listed, thus

capturing any residual consumption as well as the behaviours of those with no favorite teams.

Although data was captured at a team level, the models are developed at a league

level. Often within FMCG industries individual brands exist under a master brand and

significantly, the additive nature of the Dirichlet means that such brand variants may be

validly grouped together for analysis (Bound, 2009). In this study we focused on competing

leagues as master brands as it allows for sample pooling, which in turn allows for more

robust model predictions. Given the behavioural emphasis of Dirichlet modelling,

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consumption behaviour was measured comprehensively, capturing both spend ($) and

frequency via an open-response numeric format. Although this research was focused upon

sport attendance and utilised the frequency data, the behaviours measured included

attendance (home and away), television viewership, digital streaming, membership, and

merchandise. We also captured psychological and attitudinal perceptions of respondents,

although such information was superfluous to the requirements of the modelling method

given the study’s behavioural focus.

The self-reported nature of consumer behaviour data represents a limitation of the

study. Although self-reported behavioural data is known to have limitations associated with

consumers’ ability to accurately recall purchase behaviours, there are few superior

alternatives in the absence of propriety panel datasets (Wright, Sharp, & Sharp, 2002). While

the Juster Scale has been proposed as one such alternative, this study utilised self-reported

attendance behaviour. Given the now fifty-year history of Dirichlet modelling, behavioural

measures were captured in a manner consistent to previous studies (Bound, 2009). As sport

seasons are of a consistent, limited and fixed supply, and sport attendance is experiential in

nature, self-reported data in a sport context may be more accurate than in typical FMCG

categories (Wright et al., 2002).

Analysis and Procedure

The analysis was performed using multiple software packages, with SPSS version 23

as the primary software tool for data preparation and validation. The Dirichlet model was

built utilising the Excel-based software developed by Kearns (2000). An explanation of

Dirichlet model input requirements and output interpretation follows.

From few data input and measures, Dirichlet modelling is able to provide theoretical

estimates around a number of significant market behaviour metrics which can then be utilised

to test generalised marketing principles as outlined in Table 7. Two estimates are required for

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both the overall category and each brand within the category: the penetration rate and average

purchase rate. The penetration rate is a percentage figure calculated as “the number buying

the brand [or category] at least once divided by the total number of potential customers”

(Ehrenberg et al., 2004, p. 1309). From these estimates, the model is able to derive predicted

values (T) for seven key brand level metrics; ‘% Buying Once’, ‘% Buying 5+’, ‘Purchases

Per Buyer of the Brand’, ‘Purchase Per Buyer of the Category’, ‘Share of Category

Requirements’, ‘% of Solely Loyal Buyers’ and the ‘Purchase Rate of Solely Loyal Buyers’.

Comparing observed behaviour collected through the survey responses against these

theorised predictions allows for interpretation of model fit (Bhattacharya, 1997). Closely

predicted values imply a good model-fit and a lack of systematic bias in the predictions.

Singh and Uncles (2016) note that between-brand correlations (BBC) for predicted and

observed values of between 0.7 and 0.9 represent good model fit. Accordingly, determining if

Dirichlet modelling provides accurate estimates of these seven brand level metrics provides a

mechanism to address the principles which underpin RQ1. Specifically in relation to the

Duplication of purchase law, Dirichlet modelling is also able to provide estimates for the rate

of cross-purchasing between brands. This is achieved by deriving a D estimate to calculate

theorised purchase rates. By doing so, actual cross-purchase rates can be compared to

theorised rates to determine whether patterns of preference exist, known as market

partitioning (Ehrenberg et al., 2004).

The Dirichlet also provides an S parameter for the overall model, a measure of buyer

heterogeneity between choice probabilities. The S parameter can range from zero to infinity,

with an S of zero indicating that a buyer makes the same choice each purchase (i.e., 100% of

consumers are loyal to one brand, although which brand varies between consumers). Sharp et

al. (2002) noted that subscription markets are characterised by S parameters of less than 0.2,

while repertoire markets have S parameters almost always greater than 0.8. The S parameter

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therefore provides an efficient measure to address RQ2. If a sport market exhibits an S

parameter of less than 0.8, its buyers are consuming from within a repertoire of brands in a

manner that is complimentary. If a sport market exhibits an S parameter of less than 0.2, its

buyers are loyal to singular brands and therefore outright substitution is more likely to occur.

3.4 Results

RQ1: Do Sport Consumer Markets Exhibit Purchase Characteristics Typical of Repeat-

Buying Consumer Markets?

To determine whether sport consumer markets contain the characteristics of repeat-

buying consumer markets, the NBD Dirichlet model was fitted to the attendance data. Five

models were tested and are presented in Table 9. The first four focus upon the Sydney market

and includes all seven team sports as a complete sport market, followed by natural sub-

segments being the football market, winter competitions, and summer competitions. Given

that each individual model is derived from common inputs (brand penetration and buying

rate), the four model fits are inherently similar. Lastly, the complete Melbourne sport market

model is presented.

Model consistency across the Sydney and Melbourne sport consumer markets.

The Sydney and Melbourne sport markets are underpinned by innately different

consumer behaviour. A greater proportion of Melbourne residents attend sporting fixtures

(44% vs 35%) and do so in greater annual frequency (10.2 vs 7.5). Melbourne’s apparent

stronger desire for sport consumption does not however translate into greater variety in

preferences. In Melbourne, AFL retains a leading market share (63%) that is 4.5 times larger

than its nearest competitor (A-League). By contrast, the NRL retains a Sydney market share

(39%) that is only twice that of its next largest competitor (A-League). Melbourne’s

demonstrative passion for AFL is evident by virtue that 17.21% of the Melbourne population

consumes AFL to the exclusion of all other competition leagues.

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Despite innately different structures, both the Sydney and Melbourne sport consumer

markets appear to behave in largely predictable patterns that the NBD Dirichlet model is

robust toward modelling. In respect to elements of the model which are less predictive,

patterns of behaviour within the Sydney and Melbourne models nonetheless remain

consistent. This is significant as it may be concluded that both markets conform to a

consistent underlying structure. Model interpretation however needs to be considered in

conjunction with sample size, as Sydney benefits from a larger sample compared to

Melbourne (n = 2,039, 459). The eight predicted values derived by the Dirichlet cascade from

utilising a base of all sport attendees to derive brand penetration (n=1,119, 201), to then sport

specific base sizes for the remaining seven predicted values. The smallest individual sport

specific sample size in Sydney was 49 (ANZ Champs), compared to 14 in Melbourne (Super

Rugby).

Both Sydney and Melbourne models provide highly accurate estimates of league level

penetration. The BBC for Penetration values with the Sydney and Melbourne models was .99

and .98 respectively. In respect to the % Buying, both models show a similar trend of over

prediction of consumers who purchase once and under prediction of those who purchase on

five plus occasions and correspondingly still yet show a strong BBC value. The BBC for the

Sydney model for % Buying Once and Five+ was .96 and .93 respectively. The

corresponding values in Melbourne were .48 and .95. The Melbourne model suffered from an

anomaly in respect to the NRL value, likely influenced by limited sample in the secondary

case. Each market model also provided accurate predictions for the purchase rate per buyer,

with a BBC of .93 and .96 in Sydney and Melbourne respectively.

The models under predicted the category purchase rate of consumers (i.e., the sum all

league attendance), although the model did so in a consistent manner across brands and

models (BBC = .77, .72 respectively). The share of category requirements percentages is

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calculated by dividing the two aforementioned Purchases Per Buyer metrics.

Correspondingly, their predictive power is relational to the aforementioned values. Finally,

the model provided relatively accurate predictions for the proportion of solely loyal

consumers (BBC = .92, .98), although their rate of consumption was consistently under

predicted. The model was perhaps least predictive of the rate of buying among 100% loyal

fans of the larger brands within each model.

.

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Table 9: Dirichlet models

Brand Share

Penetration % buying once

% buying five+

Brand purchases

Category purchases

Share of category requirements

100% loyal % 100% loyal rate

O T O T O T O T O T O T O T O T Complete Sydney Sport Marketa

NRL 39% 22% 23% 23% 36% 38% 25% 4.8 4.6 8.7 7.6 55% 61% 31% 28% 4.8 2.6

A-League 19% 12% 14% 29% 43% 30% 19% 4.2 3.7 10.1 8.3 42% 44% 22% 19% 5.5 2.0

AFL 18% 15% 14% 30% 43% 21% 18% 3.3 3.6 10.1 8.3 33% 44% 16% 19% 4.8 2.0

BBL 8% 8% 7% 38% 47% 14% 15% 2.5 3.2 10.6 8.8 24% 36% 11% 15% 1.9 1.7

Super Rugby 7% 7% 6% 41% 47% 19% 15% 2.9 3.2 9.5 8.8 31% 36% 18% 15% 2.0 1.7

NBL 6% 5% 5% 39% 48% 19% 14% 2.9 3.1 11.8 8.9 24% 34% 13% 14% 2.6 1.7

ANZ Champs 3% 2% 2% 40% 50% 12% 13% 2.6 2.9 11.0 9.1 24% 32% 12% 14% 2.3 1.6

Sydney Football Marketb NRL 47% 22% 22% 23% 36% 37% 29% 4.8 4.6 7.5 8.2 64% 56% 39% 39% 4.9 3.3 A-League 23% 12% 14% 31% 43% 29% 23% 4.2 3.7 8.7 9.2 49% 40% 27% 26% 5.4 2.4 AFL 21% 15% 13% 31% 43% 19% 22% 3.3 3.6 8.1 9.3 41% 39% 27% 25% 4.5 2.4

Super Rugby 9% 7% 6% 43% 48% 18% 19% 2.9 3.2 8.1 10.0 36% 32% 21% 21% 2.4 2.1

Sydney Winter Competitionsc NRL 58% 22% 23% 23% 36% 37% 29% 4.8 4.6 6.5 6.8 74% 67% 50% 50% 4.7 3.8 AFL 27% 15% 13% 31% 43% 19% 22% 3.3 3.6 6.9 7.8 48% 47% 31% 31% 3.9 2.7 Super Rugby 11% 7% 6% 43% 47% 18% 19% 2.9 3.2 7.3 8.4 40% 38% 24% 24% 2.9 2.3 ANZ Champs 4% 2% 2% 35% 50% 14% 17% 2.6 3.0 8.1 8.8 32% 34% 16% 16% 2.1 2.1

Sydney Summer Competitionsd A-League 59% 12% 14% 31% 40% 29% 24% 4.2 3.7 5.2 4.8 80% 76% 65% 65% 4.5 3.4 BBL 24% 8% 6% 38% 46% 13% 19% 2.5 3.1 4.4 5.4 57% 57% 56% 56% 2.6 2.8 NBL 17% 5% 45 38% 48% 20% 18% 2.9 3.0 5.2 5.6 55% 54% 38% 38% 2.6 2.6

Complete Melbourne Sport Markete

AFL 63% 39% 39% 30% 26% 54% 39% 7.3 7.3 10.6 9.2 69% 79% 44% 33% 6.9 3.0

A-League 14% 14% 19% 29% 43% 32% 18% 4.7 3.6 15.6

12.1 33% 30% 13% 10% 5.5 1.4

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BBL 9% 13% 13% 39% 47% 21% 14% 3.0 3.1 15.8

12.8 19% 25% 3% 8% 2.0 1.3

NRL 6% 8% 10% 24% 49% 21% 13% 3.3 2.9 16.5

13.0 20% 23% 7% 8% 2.0 1.3

NBL 4% 7% 7% 41% 51% 18% 12% 3.0 2.8 17.3

13.3 18% 21% 7% 7% 1.0 1.3

ANZ Champs 2% 4% 4% 40% 52% 12% 11% 2.5 2.7 14.6

13.5 17% 20% 3% 7% 0.0 1.2

Super Rugby 1% 3% 3% 41% 53% 5% 10% 2.2 2.6 13.4

13.6 16% 19% 0% 7% 2.0 1.2

a Penetration of category = 34.6%, Rate of category buying = 7.5, S = 1.8 , b Penetration of category = 32.8%, Rate of category buying = 6.7, S = 1.3, c Penetration of category = 29.9%, Rate of category buying = 5.9, S = 1.1, d Penetration of category = 19.2%, Rate of category buying = 4.4, S = 0.6, e Penetration of category = 43.8%, Rate of category buying = 10.2, S = 3.9

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Differences in market share are largely due to differences in penetration. Table 9

demonstrates strong support for Principle 1. Six of the seven leagues conformed to the

proposed pattern between market share and penetration. In this respect, A-League in the

Sydney model appears to be the only confounding league, with an average purchase rate

among buyers 14% above the predicted value. Correspondingly, the A-League records a

higher market share than the AFL despite a smaller penetration. One potential explanation for

this deviation may relate to the semi-fixed supply of sport matches, a trait that appears

relatively unique to sport (Smith & Stewart, 2010). In a sport setting, supply deviates

between leagues based on season structure. Between the A-League’s two Sydney teams, a

total of 43 matches were played locally (within NSW) during the 2015/2016 season across

pre-season, domestic, and continental championships. In contrast, the AFL’s two Sydney

teams competed locally 29 times across pre-season and premiership fixtures.

The relationship between market share and penetration is further illustrated visually

within Figure 2. The relationship between penetration and market share follows a linear

pattern and accordingly, exhibits Pearson correlations of .97 and .99 in the Sydney and

Melbourne market respectively. A standard regression upon the Sydney market yields an

unstandardised coefficient (B) of 1.792 for penetration upon market share (t = 8.37, p < .001).

Therefore, a 1% increase in consumer penetration can be expected to yield a 1.8% increase in

market share in the Sydney sport-attendance market.

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Figure 2: Sydney (left) and Melbourne (right) sport market: scatter-plot relationship between brand share and penetration rate

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Double jeopardy. The results support Principle 2, presented in Table 9. This is most

apparent at the extremes of each market. The Sydney market leader NRL has an average

purchase rate 84% larger than the smallest share brand (4.8 vs. 2.6). Melbourne market leader

AFL has an average purchase rate 230% larger than the smallest share brand (7.3 vs. 2.2).

The relationship between market share and purchase rate returns a Pearson correlation of .94

in Sydney and .96 in Melbourne. Overall, the models provide accurate predictions for

purchases-per-buyer of the brand, with the coefficient of variation between the predicted and

observed purchase rate equating to 11.4% and 12.8% of the observed mean in the Sydney and

Melbourne model respectively.

Customers buy from a repertoire of brands. Table 9 provides overall support

towards Principle 3. In the complete models, the NRL is the only code that supplies its

customers with more than half their category requirements in Sydney (55%) while the AFL

behaves similarly in Melbourne (69%). Within the summer sub-segment of the sport market,

each of the three competing leagues provide more than half of consumer category

requirements, thus violating Principle 3. This however, is a reflection of the small number of

competitors competing within this sub-segment

Solely loyal buying is relatively rare. The Dirichlet model provides accurate

predictions for the rate of loyal buying in the Sydney models, particularly within the football

market, supporting Principle 4. In the case of Rugby League, 39% of the league’s fan base

exclusively attends NRL fixtures, with Super Rugby holding the smallest share of loyal fans

at 21%. Solely loyal buying metrics cannot be interpreted from within the Melbourne model

as there are only a cumulative 15 solely loyal buyers within the sample among the remaining

six competitors below the AFL. The generalisation that solely loyal buyers are lighter buyers

of the overall category holds within a football attendance context, although the model’s

predicted values are considerably lower than the observed values.

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left most side of the table, diminishing in a rightward order. With a penetration rate of only

2.4%, resulting in a sample of 49 attendees, the ANZ Netball Championship has been

included within the bottom row of Table 10 for completeness rather than analysis.

Both Table 10 and corresponding correlational analysis provide strong support for the

duplication of purchase law within a sport attendance context. Given that the ranked order of

penetration and average duplication are perfectly aligned, the non-parametric Spearman

correlation provides a perfect correlation of 1.0. The corresponding Pearson correlation

returns a correlation value of 0.97. Notably, the D estimates of duplication provide highly

accurate predictors for the observed data. With the exception of NRL buyers (and

disregarding the small sample of ANZ Netball Championship buyers), the predicted

duplication falls within 1 or 2% of the observed data. In the case of NRL buyers, although the

D estimate under-predicts observed data, it does so at a consistent rate of 20% under-

prediction for Brands 2 through 6.

Table 10: Duplication of sport attendance Percentage who also bought: Average

Duplication Buyers of NRL AFL AL BBL SR NBL ANZ NRL 39% 33% 22% 19% 13% 5% 22% D estimate (2.2) 31% 26% 17% 15% 10% 5% 18% AFL 58% 36% 30% 23% 20% 8% 29% D estimate (2.9) 63% 35% 23% 20% 14% 7% 27% A-League 58% 44% 23% 17% 17% 3% 27% D estimate (2.7) 58% 39% 22% 19% 13% 6% 26% BBL 58% 54% 34% 26% 16% 11% 33% D estimate (3.3) 71% 48% 40% 23% 15% 8% 34% Super Rugby 58% 48% 29% 30% 11% 7% 31% D estimate (3.1) 66% 45% 37% 25% 14% 7% 32% NBL 58% 63% 45% 28% 17% 7% 36% D estimate (3.6) 79% 53% 44% 29% 25% 9% 40% ANZ Champs 47% 51% 16% 20% 37% 14% 31% D estimate (3.1) 67% 45% 38% 25% 21% 14% 35% Penetration 22% 15% 12% 8% 7% 5% 2% 10%

Note. D estimates represent the predicted rate of duplication of purchase, calculated by multiplying observed average duplication against competitor penetration.

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Table 11 tests for partitioning within the Sydney sport attendance market. Markets in

which brands are directly substitutable will not show evidence of special clustering, known as

partitioning (Ehrenberg et al., 2004). However, markets with functional sub-categories may

attract a segmented consumer group, resulting in the clustering of similar brands and

deviation away from predicted D estimates. As displayed in Table 9, the overarching sport

market can be potentially distinguished into several sub-categories, notably by season (winter

vs. summer) and additionally by sport type (football vs. non-football) and as such these

categories made for logical partitions to evaluate.

Table 11 does not suggest a segment-level partitioning trend exists within either the

sport type of season markets. For true partitioning to be evident, there must be a consistent

pattern of over- or under-purchase within and between partitions. Notably, however, the

consistent under-consumption of NRL games by each of the remaining six codes suggests a

form of partition between the NRL as market leader and the remaining six leagues.

Conversely, the AFL is overconsumed among supporters of other leagues relative to

predicted values. Perhaps most surprising is that there does not appear to be any particular

partitioning between the NRL and Super Rugby competitions, despite being variant forms of

the same underlying sport (rugby) and, therefore, the most similar in nature.

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Table 11: Testing for market partitioning among attendees Partitioning based on sport type

Football Non-football NRL AFL AL SR BBL NBL ANZ Football

NRL +7% +7% +4% +4% +2% 0% AFL -5% +1% +2% +6% +7% +1% AL 0% +4% -2% +1% +5% -3% SR -8% +3% -8% +6% -3% 0%

Non-football BBL -14% +5% -6% +3% +1% +3% NBL -21% +10% +1% -8% -1% -1% ANZ -20% +6% -21% +15% -5% 0%

Partitioning based on season Winter Summer NRL AFL SR ANZ AL BBL NBL

Winter NRL +7% +4% 0% +7% +4% +2% AFL -5% +2% +1% +1% +6% +7% SR -8% +3% 0% -8% +6% -3% ANZ -20% +6% +15% -21% -5% 0%

Summer AL 0% +4% -2% -3% +1% +5% BBL -14% +5% +3% +3% -6% +1% NBL -21% +10% -8% -1% +1% -1%

RQ2: Do consumers Treat Sporting Teams as Complimentary of Substitutable Goods?

Sharp et al. (2002) refer to three components by which to determine whether a market

behaves as a repertoire (complimentary) or subscription (substitutable) market. First,

subscription markets violate Principles 2, 3, and 4 of typical repeat-buying consumer markets

as previously outlined (see Table 7). Second, it is common to expect rates of solely loyal

buying to exceed 70% within subscription markets. Finally, the Dirichlet model’s S

parameter provides a definitive metric by which to assess the market structure. Subscription

markets typical hold an S parameter value of less than 0.2 These criteria are now applied

against the sport attendance market data.

Subscription markets violate Principles 2, 3, and 4 of typical repeat-buying

consumer markets. Results pertaining to RQ 1 confirmed that each of the three principles

(and the five overall) hold true within the sport attendance market. Two particular

characteristics of a typical subscription market that were not evident within the model, as

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seen in Table 7, relate to the rate of loyal buyers (Principle 3) and the share of category

requirements each brand provides (Principle 4).

It is common to expect rates of solely loyal buying to exceed 70% within

subscription markets. In the complete Sydney model, market leader NRL achieved the

highest rate of loyalty (31%) while the seven brands held a collective average of 18%.

Although sample size prohibits valid interpretation of the metric in the Melbourne market,

market leader AFL recorded a rate of loyal buying rate of 44% (n = 79), also far below the

expectations of a subscription market. Furthermore, individual brands should provide a

significant majority of a consumers spend/usage, commonly exceeding 60% to 70% of

customer category requirements (Sharp et al., 2002). Within the complete Sydney model, the

NRL recorded the highest share (55%) while the market averages 33%. In the Melbourne

model, the AFL is able to secure a high share (69%), although this is not replicated across the

market (average 27%).

The estimate of the Dirichlet model’s S parameter. Sharp et al. (2002) noted that

subscription markets are characterised by S parameters of less than 0.2, while repertoire

markets have S parameters almost always greater than 0.8. The complete Sydney market

resulted in an S parameter of 1.8, while the complete Melbourne market model resulted in an

S parameter of 3.9. The smallest S parameter was 0.6, being the Sydney summer model.

Brands within this model also had higher rates of solely loyal consumers and share of

category requirements; however this is expected in a model that features only three

competitors. Upon interpreting the results across the three components in respect to the sport

attendance market, it appears conclusive that consumers treat sport teams as

complimentary/repertoire goods.

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3.5 Discussion

The core research aim of the study was to develop a theoretical understanding of

consumption within competitive sport markets. This was underpinned by two research

questions, by which the results and discussion have been demarcated.

RQ1

This study has provided evidence that the sport attendance market exhibits the

purchasing characteristics of repeat-buying consumer markets. All five proposed marketing

generalisations hold with the sport attendance markets tested, each with implications for sport

marketing theory and practice.

Loyalty and solely loyal buying. A significant amount of the literature in sport

management has placed an emphasis on identifying the unique elements that differentiate

sport as an industry. Of particular emphasis is further understanding the sport fan, who is

perceived to have an irrational commitment to his or her team that is unmatched within other

consumer products categories (Smith & Stewart, 2010). Yet, despite this commonly-held

belief, the Dirichlet model provides robust predictions for the rate of solely loyal buying

within the sport-attendance market. Accordingly, the rate of loyal buying within this market

does not differ significantly from many previous validated consumer good product lines that

do not claim to have irrationally loyal customers (Sharp et al., 2002).

The use of the Dirichlet model to evaluate “loyalty” in sport markets is novel, as it

utilises distinct measures of loyalty compared to many pre-existing definitions in both a sport

and broader management context (Dawes, 2016; Funk & James, 2001). Importantly, the

Dirichlet model does not presuppose a connection between commitment and loyalty, nor does

it require exclusive consumption (Bassi, 2011; Ehrenberg et al., 2004). Loyalty within the

Dirichlet framework is therefore, in part, measured by the share of category requirements

provided by the brand, as this translates directly to sales revenue and therefore profits

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(Dawes, 2016). This represents a critical theoretical distinction from many sport fandom

models. Mullin et al.’s (2014) escalator model, for instance, is premised by a pattern in which

people increase their consumption and loyalty in a collinear fashion as they escalate up the

fandom model. Yet, Figure 2 illustrates that sole loyalty is not a pre-condition to high

customer value, as this group encompasses a component of consumers who are, in fact, very

small brand and category consumers. Therefore, within an escalator model, the most

behaviourally loyal (solely) consumers exist at both the low- and high-value ends of the

escalator. Here, an interesting hypocrisy emerges: Although sole loyalty is an intuitive

indicator of strong support for a specific team/league, solely loyal consumers are lesser

consumers of the overall sport category (than non-loyals). In fact, non-solely loyal consumers

are more sport-orientated overall, yet their desire to consume sport diversely can result in

their categorisation into negatively toned typologies such as the “flaneur” who is “more likely

to be bourgeois and thus in pursuit of a multiplicity of football experiences” (Giulianotti,

2002, p. 39).

The relationship between market share and penetration. The relationship between

penetration and market share is particularly significant in a sport setting, since the sport-

attendance market poses particular structural constraints upon the practitioner who aspires to

increase his or her team’s attendance penetration. Specifically, unlike typical repeat-purchase

contexts, such as those in FMCG, the sport attendance product is tied to a physical location

and cannot be freely distributed. Therefore, the physical location of stadiums and the size of

major metropolitan cities are likely to be strong additional influences that shape consumer

propensity to attend, which in turn will impact penetration.

The Sydney case provides a compelling empirical example of both the

aforementioned challenge and the corresponding benefits of adopting a multi team market-

expansion strategy. Within both the Sydney and Melbourne markets, league penetration

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neatly aligns with the number of respective clubs. Focusing upon Sydney, Rugby League has

nine Sydney-based clubs and is correspondingly able to draw deeply from emotional

attachments to physical place (Low, 2008). The next three largest leagues by penetration

(AFL, A-League, and BBL) have all adopted dual-team expansion strategies that have

attempted to leverage tribalism along Sydney’s East–West geographic and social divide

(Knijnik, 2015). Conversely, the three smallest leagues by penetration (Super Rugby, NBL,

ANZ Championship) each had just a single team within the Sydney market at the time of the

study.

The relevance of penetration in shaping competitive sport markets emerges in several

specific management case studies here. First is Rugby Union, which via the advent of Super

Rugby in 1995 adopted the North American ‘one team, one city’ model of sport league

franchising (Horton, 2009). Therefore, despite the first mover advantage of being the first

football code established in Sydney, the code has only one top-level club within its heartland

market (Cashman, 2010). This has limited the geographic accessibility of the sport, resulting

in penetration and market share that lags behind less established competitors. Next is football,

which similarly adopted a one team, one city model when relaunching the A-League

competition in 2005 (Hay, 2011). In initially conforming to this policy, the league expanded

its competition with teams from regional centers, all of which would fail by 2012. Critics

have suggested that had the A-League adopted two Sydney-based footballs teams from

inception, the league would be in a much healthier position today (Georgakis & Molloy,

2016). Last is netball, which in 2017 launched a new re-branded Super Netball competition

which now features two Sydney-based clubs. However, unlike the A-League, BBL, and AFL,

who clearly delineate their respective two teams’ geographical catchment, the two Sydney-

based netball teams play from the same Western Sydney-based venue and offer little such

delineation. Therefore, it remains highly questionable whether the addition of this new team

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will successfully improve the penetration of the sport in the absence of geographic

diversification.

Confirmation of double jeopardy and duplication of purchase law. The study

provides further confirmation of the existence of the double-jeopardy pattern of market share

first proposed by McPhee (1963) within sport-attendance markets. This study represents the

most complete confirmation of the phenomena within the sport industry to date, thus

improving the generalisability of previous studies. The first such study by Doyle et al. (2013)

explored attitudinal loyalty, but did so for only two sport leagues, an incomplete set of

competing brands. Baker et al. (2016) further observed a strong double-jeopardy pattern in

membership attendance data for Melbourne-based AFL clubs, but this result was limited by

nature to attendance of teams within one league. This study not only captured a more

complete competitive set, but did so across two markets.

Although the study also showed strong support for the duplication of purchase law,

perhaps the more significant finding relates to the lack of partitioning within the market.

Departure from predicted rates of duplication of purchase often indicates that a market is

comprised of partitions in which brands share particular functional similarities. In such

instances, there is a coherent structure to a broad product category with subtypes competing

more intensely with each other (Dawes, 2016). This has been shown to be the case in

numerous product categories such as gasoline (unleaded vs. leaded) and the automobile

market (e.g., premium vs. sport) (Ehrenberg et al., 2004). One could expect the four football

codes to constitute such a partition within the overarching sports market; however, the data

did not support this expectation, which has significant implications for sport practitioners in

terms of understanding competitive sport landscapes. Football administrators, for instance,

who may view other football codes as more direct competitors, must also concern themselves

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with the performance of non-football leagues as fellow market participants, given that all

leagues appear to compete as one non-partitioned competitive set.

The absence of duplication is perhaps most surprising when evaluating the cross-

attendance between Rugby League and Rugby Union, which given that the former is derived

from the latter, represent the most functionally similar sports. Research into the motivational

profile of fans across sports suggests that fans of aggressive sports share significantly

different motivations from fans of nonaggressive sports (Wann et al., 2008). Therefore, one

could expect the shared motivational drivers of aggressive sports to coincide with greater

cross-attendance in similar such leagues. Conversely, given that the two rugby codes have

diametric social and cultural identities, in which the divide “assumes a class basis, with rugby

league fixtures being heavily supported by the working class” (Horton, 2009, p. 969), one

might expect a suppression of cross-attendance despite obvious functional similarities. The

duplication rates illustrated in Tables 3 and 4, however, do not provide compelling support

for either the functional (increased cross-attendance) or sociological (decreased cross-

attendance) proposition.

RQ2

This study has provided evidence that sport attendance is a repertoire market and

therefore consumers treat sport-team attendance as complimentary goods. This determination

is consistent with existing literature, although it significantly expands the application of the

theory. Baker et al. (2016), for example, reached a similar conclusion; however, their study

focused solely on AFL members and, therefore, focused on a sub-segment of attendees and

only measured cross-attendance within a single sport- a limitation noted by the authors.

Moreover, in performing a segmentation of football supporters, Tapp and Clowes (2002)

developed a segment titled “repertoire fans” that attended matches not involving their team

and this group accounted for a quarter of the sample. This study represents a significant

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advancement on such findings, as the first to examine a sports market in its entirety, being

across multiple sports and measuring the behaviour of an entire market.

A further significance of Dirichlet loyalty measurement is that it does so at a market

level (macro), adding multi-dimensionality missing in existing team-level segmentation

models. Within Funk and James’s (2001) psychological continuum model, it is proposed that

the most advanced “allegiant” fans display behavioural loyalty through a biased behavioural

response with respect to one or more alternative brands in a set of brands, resulting in repeat

purchasing over time. This biased behavioural response, therefore, requires the preclusion of

other brands (Funk & James, 2006). Yet, consumers within repertoire markets are capable of

exhibiting polygamous loyalty to several brands (Sharp et al., 2002). Therefore, while sport

team practitioners should strive to develop a fan base that is “allegiant,” such a strong

psychological connection would not necessarily equate to a fan base that is solely loyal to the

team in question.

From a practitioner perspective, repertoire and subscription markets require distinct

marketing strategies. Furthermore, Dirichlet modelling allows practitioners to develop

realistic data-driven performance benchmarks to develop and measure such strategies (Bassi,

2011). The repertoire nature of the sport-attendance market has implications for the

expectations practitioners should set in attempting to capture solely loyal consumers. The

modelling accurately predicted rates of solely loyal buying ranging between 14% and 28% in

the Sydney market, far below the rates typically seen in subscription markets (Sharp et al.,

2002). Thus, evidently, the vast majority of a sport league’s customers are in fact shared.

This conforms to Ehrenberg’s (1971) important observation that customers are really other

people’s customers who occasionally buy from you. As repertoire market brands share their

customers with other brands, a greater emphasis must be placed on increasing penetration and

share of category requirements (Ehrenberg et al., 2004).

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From an academic viewpoint, the repertoire market nature of sport attendance has

significant theoretical and practical implications. Theoretically, the model supported all five

marketing theory principals, indicating that sport markets are relatively typical of repeat-

buying consumer markets. While this does not contradict the existence of sport markets’

“unique” features, it may diminish their significance. The fit of the model suggests that

mainstream business approaches may have greater application within a sport business

context, which in turn has implications for the legitimacy of sport management as a distinct

field of research (Baker et al., 2016). This, however, also provides opportunities for scholars

to apply previously untested methods and principles in a sport setting, creating a plethora of

opportunities for future research.

3.6 Conclusion

The researchers endeavoured to quantify the consumer structure of sport markets. To

do so, a highly generalisable and parsimonious model called the Dirichlet was tested upon

sport attendance to determine whether the market behaved characteristically of other repeat-

purchase goods. Significantly, this research represents the most substantive attempt yet at

performing such a market level analysis in a sport setting, advancing upon previous attempts

in two respects. Firstly the study provided a multi-market comparative analysis. Here, the

Australian cities of Sydney and Melbourne were chosen as the markets of analysis, owing to

the presence of numerous competitors creating crowded sport markets (Fujak & Frawley,

2013). Secondly the study more comprehensively captured the behaviour of consumers than

previously attempted, with consumption data measured across Australia’s seven largest

professional sport leagues.

Five generalised marketing principles were tested and shown to remain valid in a

sport setting, confirming that although the sport industry may contain unique characteristics,

these do not result in consumer behaviour that is distinct from many other repeat-purchase

98

goods. This finding represents a significant contribution to the field given the on-going

contention surrounding the positioning of sport management as a standalone discipline

(Baker et al., 2016; Chalip, 2006). In confirming that sport consumers behave in predictable

patterns replicated in many other industries, the research runs counter to much of the field’s

foundational research and instead contributes to a growing body of work which is eroding the

basis by which the sport product can be justified as unique (Baker et al., 2016; Smith &

Stewart, 2010). Although this has considerable implications for the positioning of sport

marketing and management as specialised disciplines, it also facilitates opportunities for

future research to further apply business principles from non-sport contexts that are yet to be

considered within the discipline. This represents a further contribution, given the findings

contribute to remedying the scarcity of strategy related research in competitive sport settings

(Shilbury, 2012). One such area deserving further strategic exploration is the choice between

prioritising consumer frequency (increasing existing fan consumption) or penetration

(creating new fans). The field of Dirichlet modelling espouses the prioritisation of penetration

to increase market share and profitability (Ehrenberg et al., 2004) while sport theories of

escalating commitment favour developing fan commitment to increase consumption

frequency (James et al., 2002; Mullin et al., 1993).

Within the five generalised marketing principles analysed, this study also confirmed

that consumers attend sport matches within a repertoire-purchase pattern and therefore treat

sport teams as complimentary products. This determination is theoretically significant as it is

perhaps the most fundamental behavioural characteristic of repeat-purchase consumer

markets, yet has been rarely investigated in a sport market setting. While competition may be

at the “heart and soul of sport management” (Shilbury, 2012, p. 2), sharing is in fact what

characterises sport consumer markets. Rather than considering sport consumers to be

disloyal, this finding necessitates a fundamental shift in the interpretation of sport fan

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behaviour away from a dichotomous view of loyalty toward a polygamous one (Sharp et al.,

2002). From a practitioner perspective, recognition of the fundamental structure of the market

may also require an adjustment in expectations, objectives, and strategy development.

Despite the advancements to theory and practice offered within this research, it is not

without limitations. Given sample size restrictions, models were aggregated to league-level

master brands. While this is methodologically valid (Bound, 2009), further research is

warranted at a team level across multiple sports. Additionally, the sport market encompasses

many product categories, and the research has focused upon attendance. In particular, while

attendance and STH markets have now received attention, an opportunity exists for further

research in respect to merchandise and television consumption market behaviour.

100

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4. Study 3: The Relationship Between Revenue and Fan Base Size Within Sport Markets

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Abstract

Sport markets are becoming increasingly crowded. Yet, despite significant managerial

implications associated with increasing competitive tension, there have been limited attempts

to understand the consumer structure of sport markets from a management perspective. We

address this gap by performing longitudinal analysis using generalised least-squares

regressions to empirically test the relationship between sport team fan base size and financial

performance. The research extends upon existing theorisation of market size by quantifying

the influence of consumer preferences, as well as testing a novel attitudinal measure of

market support towards teams. This attitudinal measure of team support was highly predictive

of team financial performance, supporting an emerging view that the consumer structure of

sport markets is predictable and generalisable. The modelling also identified the presence of

strong localised consumer preferences, resulting in high local market share teams with

significant market advantages despite smaller populations. The existence of such teams belies

the existing theorised relationship between population and team revenue, indicating that

additional measures of consumer dynamics are required to more robustly measure the

conceptual components of ‘market size’.

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4.1 Introduction

With the value of the global sport market projected to have reached a record USD

$90.9 billion in 2017, the financial significance of the sport industry continues to reach new

heights (KPMG, 2016). Yet, as the industry has continued to grow financially in recent

decades, so too has competitive intensity which is reshaping the structure of the sports

market. This increase in competitive intensity within the sport industry has manifested on

multiple fronts. First, sport is increasingly competing with other forms of entertainment and

leisure for consumers’ limited time and budgets (Howard & Burton, 2002). Second, the

competitive intensity and concentration of sport markets is increasing with the advent of new

teams and leagues (Byon, Zhang, & Connaughton, 2010). It is now common for multiple

professional sports and teams to operate within cities, competing for attention from the

general public, commercial sponsors, and the media (Foster, O'Reilly, & Dávila, 2016). In

2010 it was estimated there were over 600 professional sport teams in the United States (Kim

& Trail, 2010). Perhaps Byon and colleagues (2010) best expressed the confluence of

competitive pressures, noting that “with such a crowded sport marketplace, sport consumers

have many options on which to spend their leisure time and discretionary dollars. As a result,

professional sport organizations face stiff competition in an effort to gain market share” (p.

143).

This heightened competition for market share has resulted in intensified struggle for

off-field survival that metaphorically parallels the intensity teams display on the sporting

field. Yet, despite the importance of management in increasingly competitive sport

marketplaces, sport consumer research has typically narrowly focused upon single sports and

more avid fan groups (McDonald & Funk, 2017; Park, Mahony, & Kim, 2011; Reysen &

Branscombe, 2010; Smith & Stewart, 2010). In doing so, the field has neglected to perform

the more holistic analyses of sport consumers required to understand the increasing influence

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of consumer choice in shaping sport markets (Pelnar, 2009). Therefore, although there is a

growing stream of management research which has empirically tested theorised consumer

market behaviours in a sport setting (Baker, McDonald, & Funk, 2016; Doyle, Filo,

McDonald, & Funk, 2013; Fujak, Frawley, McDonald, & Bush, 2018), the structure of sport

markets from a consumer perspective remains comparatively underexplored compared to

other consumer goods industries (McPhee, 1963; Sharp, Wright, & Goodhardt, 2002). Rather,

the origins of sport research on competitive behaviour and market structures has largely

originated from the economics discipline (Shilbury, 2012). Although such scholarship has

robustly modelled the significant structural components of the industry, research on

competition in a consumer context appears to have fallen in a gap between the economics and

management/marketing domains. This gap is a significant one, as “managing the implications

of competition, both on and off the field, is a critical success factor and a strategic imperative

in its own right” (Shilbury, 2012, p. 2).

One conceptual specification that appears divergent across the economic and

management/marketing domains relates to the relationship between population, market size

and team revenue. Within sport economics literature, it is “universally agreed” (Gustafson &

Hadley, 2007, p. 251) that the size of a team’s local population area has a positive impact

upon team win percentage by virtue of leading to higher team revenue. Yet from a sport

management and marketing perspective, it is the size of the supporter base rather than

population which is central to organisational survival (Shilbury, Westerbeek, Quick, Funk, &

Karg, 2014). As noted by James, Kolbe, and Trail (2002): “A team's financial success is

predicated, in large part, on the creation of an adequate income stream. This necessitates that

sport teams attract, develop, and maintain a relationship with a substantial number of sport

consumers” (p. 251). Although it is perhaps implicit within economic modelling that larger

populations provide a greater pool of potential supporters, it would appear both theoretically

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and empirically intuitive that there is a significant distinction between population size and fan

base. Technological advancement since Rottenberg’s (1956) seminal economic work means

that sport teams are less geographically constrained in their pursuit of both supporters and

revenue (Hutchins & Rowe, 2012). High profile teams such as Manchester United, Real

Madrid and the New York Yankees provide examples of globalised sport teams whose fan

base extend far beyond their metropolitan population catchments (Kerr & Gladden, 2008;

Lock, Taylor, & Darcy, 2011; McDonald, Karg, & Lock, 2010). Yet despite consensus within

economic studies that market support is a key driver of team financial revenue (O'Reilly &

Nadeau, 2006), operationalisation of the concept has been largely confined to sports event

attendance and local population data (Lenten, 2012). This appears inconsistent with broader

sport consumer theory and practice, as it is evident that team support is no longer only drawn

from localised geographic catchments, nor is sport fandom limited to simply attending

fixtures (Funk & James, 2001; Jones, 1997).

Thus, through this research we begin to explore this divergence by providing a

longitudinal quantitative analysis of the consumer structure of one such crowded sport

market. The research focuses on a specific but vital element of market structure: the

association between sport team fan base size and financial performance of sport teams. This

is achieved through a novel methodology in which four sets of secondary population and

consumer data were amalgamated and analysed for the period 2000 to 2017. The core

research purpose is to explore how the relationship between fan base size and sporting team

financial performance has changed over time in the context of increasingly competitive and

crowded markets (McDonald et al., 2010). In doing so, the research endeavours to adopt a

broader market lens to address underexplored yet vital management questions about the

competitive structure of sport markets (Shilbury, 2012). The research questions are further

elucidated through identification of relevant literature.

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The paper is presented in six parts. The first part examines the relevant literature on

consumer markets and sport landscapes. The second part outlines the methods deployed in

this study. Subsequently, the third part of the paper provides a brief description of the

empirical setting. The fourth and fifth parts present the results and a discussion of findings.

The six and final part of the paper concludes with ideas for future research.


Crowded sport markets: a sport management and marketing perspective

Classical interpretations of markets define them as places of exchange between buyers

and sellers in which products are transacted (Callon, 1998; Guesnerie, 1996). Although

marketers have typically focused on the customer side of markets (Ferrell & Hartline, 2012;

Geroski, 1998), an evaluation of the literature suggests that sport markets globally are

becoming increasingly crowded from a supply perspective (James et al., 2002). Furthermore,

sport marketplaces are not only becoming increasingly crowded, but sport competes

increasingly for consumers within a broader field of entertainment products—an overarching

industry which is also said to be facing increasing competitive and environmental pressures

(Howard & Burton, 2002). Increasing competition and pressure within the sport industry

began to be discussed within the literature from the early 2000s (Byon et al., 2010; Carroll,

Connaughton, Spengler, & Byon, 2014; Cottingham et al., 2014; Kim & Trail, 2010; Rein,

Kotler, & Shields, 2006). Ballouli and Bennett (2012) claimed Houston to be a crowded sport

market in their case study on the development of the University of Houston athletic team.

The authors noted Houston was home to 2.1 million residents and five professional sport

teams in additional to many other college athletic programs. Similarly, Field (2006) identified

Toronto as Canada’s most crowded sport market on the basis of 14 teams and six sports

operating in the city. In an Australian context, the number of elite commercial sporting teams

grew 75% between 2005 and 2017 (see Table 12).

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As sport markets become increasingly crowded, significant managerial implications

emerge due to increasing competition for consumer support (Shilbury, 2012). This is because

the ability of sport teams to attract, develop, and maintain a substantial supporter base is core

to their financial success (James et al., 2002). Although the significance of this relationship

has perhaps been most explicitly acknowledged in the context of new team formation

research (James et al., 2002; McDonald et al., 2010), it is a foundational tenet which appears

consistently in sport management and marketing textbooks (Mullin, Hardy, & Sutton, 1993;

Pedersen & Thibault, 2018; Shank & Lyberger, 2014; Shilbury et al., 2014). To be successful

in competitive market environments, organisations have increasingly adopted a marketing

orientation which prioritises the satisfaction of consumer needs (Shank & Lyberger, 2014).

Accordingly, competition within sport markets has not only been driven by an increase in the

absolute number of competitors, but also by the increasingly sophisticated commercial

strategies of sports organisations (Shank & Lyberger, 2014). As noted by Shilbury and

colleagues (2014): “The identification and nurturing of new markets brought recognition that

the customer is central to ongoing organisational survival” (p.14). Accordingly the

development of fans, who collectively form the fan base, has become the central objective of

the sport organisation.

The increasingly competitive nature of sport markets is routinely linked to the

viability of sport organisations by virtue of their capacity to maintain a sufficiently sized fan

base (Rein et al., 2006). In their trends analysis of the forthcoming decade, Mahony and

Howard (2001) predicted elite sport team franchises to have achieved maximum leverage of

their supporting corporations and fans: “A sad but growing consensus at the close of the

1990s is that the average fan can no longer afford to attend a major league sporting event . . .

there are signs that fans are close to being tapped out” (p. 282). Regarding corporate support,

they noted that “there is mounting concern that suite renewals are anything but automatic” (p.

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283). Mauws and colleagues (2003) similarly speculated about the likely impact of increased

competitive tension on sport firms. Complementing the work of Mahony and Howard (2001),

they considered consumer power to be increasing, given increases in substitute availability

and consumer price sensitivity. While less competition in markets in the early years of

professional sport favoured owners, fans are more likely to benefit in an era of crowded sport

marketplaces: “[T]eams battle not just for wins on the field, court, or ice, but for the biggest

share of the world’s existing and potential fans of professional sports. They will do so with

the intention of deriving revenue from these fans in the future” (Mauws et al., 2003, p. 158).

Evident in the prognostications above is that understanding the longitudinal changes

to the structure of sport markets and the effects on team revenue and viability represents a

vital topic of exploration for sports management (Mahony & Howard, 2001; Mauws et al.,

2003). Therefore, a key question is whether the evolving commericial sophistication of sport

teams and/or the increasing intensity of competition has resulted in a saturation of developed

sport markets (Hendee & Burdge, 1974; Mahony & Howard, 2001; Mauws et al., 2003).

Accordingly, the first research question is an empirical one: Does the Australian sport market

exhibit financial evidence of consumer saturation?

Market size and financial performance: a sports economics perspective

Market size has been accorded central importance in the analysis of the structure of

professional sport leagues from an economic perspective (Buraimo & Simmons, 2009). A

survey of literature illustrates that the impact of market size has been of particular concern in

relation to three interconnected elements of organisational performance: attendance, revenue

and team performance (Gustafson & Hadley, 2007). Scholarly enquiry into the relationship

between market size and organisational performance can be traced back to the seminal work

of Rottenberg (1956), which was further developed by El-Hodiri and Quirk (1971), and

popularised by Quirk and Fort (1997). This work, as well as later work by others (Bruggink

115

& Eaton, 1996; Demmert, 1973; Noll, 1974) focused upon the connection between market

size, team win percentage and attendance due to data availability (Gustafson & Hadley,

2007).

Despite differences in the dependent variable of interest (attendance, win percentage,

revenue), the scholarly consensus on the impact of market size on organisational performance

is that operating in larger markets leads to higher attendances and revenue, which can be

spent upon improving team performance (Buraimo & Simmons, 2009; Gustafson & Hadley,

2007). Despite the emphasis on the central importance of market size within sport economics

literature, there appears to be general acknowledgement that operationalising the concept in

modelling remains challenging. Thus far, economic research has focused upon measures of

local population as the core operationalisation of ‘market size’, yet this contains several

observed limitations (Schmidt & Berri, 2001). Four inadequacies are detailed below.

The adoption of population as a proxy for market size can be traced to Rottenberg’s

(1956) observation that most baseball club revenue derived from attendance, which is a

positive function of the size of population or territory in which the team has the monopoly

right to play. Since this seminal work however, it has been acknowledged that the growth of

media has diminished the importance of physical attendance as the central driver of team

revenue (Shilbury, 2012). The growth in media technology has facilitated satellite fandom

that has allowed sport teams to reach distant populations far removed from local catchments

(Kerr & Gladden, 2008; Lock et al., 2011; McDonald et al., 2010). Additionally the

increasing mobility of populations may have also reduced the connection between local

population and local teams since Rottenberg’s (1956) foundational work. Second, the use of

population figures can result in model misspecification, as a metropolitan area with twice the

population cannot be considered as having double the market size. This is because it will

likely cover a wider physical area, resulting in different travel costs for consumers that

116

influence the demand curve for attendance (Buraimo & Simmons, 2009). Third, Villar and

Guerrero (2009) observe that the definition of a “potential” market cannot be made in any

precise way as not all individuals within the population are potential followers of a particular

sport or team. Heterogeneity in sport tastes and preferences can arise due to changing

demographic and social factors and in an Australian context, it has been illustrated that the

popularity of the country’s seven largest sports varies significantly between genders and

across geographic locations (Fujak & Frawley, 2013, 2016). To overcome such problems,

some studies have utilised gender-targeted populations (Dobson & Goddard, 1992), or the

population of a specific ethnic group (Burdekin & Idson, 1991; Hynds & Smith, 1994).

Finally in respect to competitive intensity and crowded sport markets, numerous

practical issues arise in research when multiple teams share a market (Villar & Guerrero,

2009). Some studies have divided area populations by the number of teams within the

specified area without weighting, while others use weighted populations based on metrics

such as relative season ticket holdings (STH) to account for the differing popularity of teams

within a shared market (Garcia & Rodriguez, 2002). Shared markets are further complicated

in contexts where stadiums are also shared. In an English context, Buraimo and Simmons

(2009) was able to successfully utilise granular data of 175,000 enumerated districts to

develop radial distances to local football stadiums. In an Australian context however, nine

Melbourne-based Australian Rules football (AFL) teams operate out of two centralised inner-

city stadiums, precluding a similar approach (Stewart, Nicholson, & Dickson, 2005). Such

highly concentrated contexts make it difficult to specify the ‘local’ catchment. For instance,

the iconic Melbourne-based AFL team Collingwood had an average match attendance of

46,188 in 2016, despite the state suburb Collingwood being home to only 8,513 residents as

per that year’s census (Australian Bureau of Statistics, 2016).

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Market size and fan base size: a conceptual divergence

From the literature, it becomes evident that both conceptual divergence and practical

specification challenges surround ‘market size’ as a measure of a team’s underlying

commercial potential. From the economic perspective, larger populations are thought to

enhance a team’s ability to capture revenue. Implicit within this reasoning is that larger

markets have greater populations from which to attract consumers, known as a ‘fan base’

from the marketing perspective. Yet, populations have been shown to contain vast sport and

team preference heterogeneity that complicates this relationship (Fujak & Frawley, 2013;

Garcia & Rodriguez, 2002). In a globalised media context, the potential fan base for a team is

less determined by local population size, and further influenced by fragmentation in the

nature and frequency of various consumption methods (McDonald, 2010; McDonald, Karg,

& Vocino, 2013; Stewart, Smith, & Nicholson, 2003). The sport marketer may therefore have

less reason to be concerned about the size of the local population as compared to the absolute

number of people who are prepared to support a team across an array of platforms and in a

variety of locations. Accordingly, our second research question relates to the conceptual

specification of market size: can market size be more accurately estimated through alternative

measures other than population in a specified area?

4.3 Empirical Setting

The decision to focus on the Australian sport market is based on several

considerations. Firstly, Australia has traditionally self-identified as a sporting nation. Perhaps

reflecting the great variety in available options, sport has long been considered a bedrock of

Australian cultural values (Cashman, 2010). Secondly and perhaps correspondingly, Australia

can contend to being the world’s most crowded sport marketplace, with 24.5 million residents

sustaining more than 70 elite commercial sport teams (Fujak et al., 2018). A distinct element

of the Australian marketplace contributing to its crowdedness is the diversity and growth in

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available sport consumption choices. Australia sustains seven sports that maintain an elite

commercial presence. These seven sports are adjudged to be both elite and commercial in that

their athletes are paid competitors who participate within leagues in which all matches are

broadcast on television in exchange for a rights fee.

The market has seen the introduction of many new teams and leagues in recent years,

with the landscape changing considerably since the turn of the millennium. To help

contextualise the case study, a concise summary of changes to the Australian sport market is

provided in Table 12.

Table 12: Significant changes to Australia’s sport marketplace between 1998 and 2017

Period AFL Rugby League Cricket

Rugby Union Soccer Basketball Netball

1998 to 2002

Formation of a new, unified

competition in 1998 (20

teams rationalised

to 15 by 2000)

1 merger, 1 addition, 1 ceased

NBL team

2003 to 2007

Addition of

1 NRL team

(2007)

Launch of Twenty20 Big Bash


teams)

Addition of 1 Super Rugby team

(2006)

Launch of A-league in

2005 (7 teams),

replacing the

National Soccer

League (13 teams)

2 added teams, 3 ceased

NBL teams

Cessation of the

Commonwealth Bank Trophy in 2007 (8 teams)

2008 to 2012

Addition of 2 AFL

teams (2011, 2012)

Launch of Twenty20 Big Bash League


teams, replacing 5

teams)

Addition of 1 Super Rugby team

(2011)

Launch of women's W-league in 2008 (8 teams), 4 A-league

teams added, 2

cease

3 ceased NBL teams

Launch of trans-

national ANZ

Championship in 2008

(5 Australian teams, first commercial

league)

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2013 to 2017

Launch of

women's national

AFWL in 2017 (8 teams)

Launch of women's

Twenty20 Big Bash League


teams)

Launch of National Rugby

Championship in 2014 (8 teams).

1 ceased NBL team

Launch of national Super

Netball championship in 2017

(3 new teams + 5 existing)

Total

fixtures: AFL + AFLW NRL BBL +

WBBL Super Rugby

A + W-League NBL Super

Netball 2000 185 191 0 33 0 175 0

2017 236* 201 91** 75 189*** 120 60

Note. *Men’s: 207 + Women’s: 29. **Men’s: 35 + Women’s: 56). ***Men’s: 135 + Women’s: 54)

The research focuses specifically upon the performance of teams within Australia’s

most supported, most culturally embedded and commercially largest sport league: the

Australian Football League (AFL) (IBISWorld, 2017). Comprised of 18 teams, the AFL is

among the nation’s oldest leagues. The first formal set of AFL rules (known as Melbourne

Rules) were conceived in 1859 in Melbourne and the Victorian Football League was

established in 1896 (Hess, Nicholson, Stewart, & de Moore, 2008). Six of the AFL’s current

18 teams were founded in 1896, while an additional four had joined the Victorian Football

League by 1925. The remaining eight current AFL teams were progressively relocated or

added to the competition between 1982 and 2012 and in doing so, transforming the Victorian-

based VFL into a national league. Accordingly, despite a long history the league retains a

distinct south-western heartland which is demarcated along a geographic and social divide

known as the Barassi Line (Stewart & Dickson, 2007). The league has introduced teams

progressively, with most recent additions focused upon expanding the game into non-

heartland regions in New South Wales and Queensland where the game has a less established

presence (Turner & Shilbury, 2005).

4.4 Methodology

Data

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This research draws from an array of secondary data sources for the period 2000 to 2017.

Four categories of data were analysed to address the research questions: (a) team financial

data, (b) team attendance and membership rates, (c) fan base estimates, and (d) market data.

These sources are further detailed below.

Team financial data. The model dependent variable Revenue is derived from team

financial data extracted from publicly available annual reports as well as those procured

through the government regulator, the Australian Securities and Investments Commission

(ASIC). A total of 295 of a possible 302 reports were reviewed, with only reporting for the

Adelaide Crows Football Club between 2000 and 2006 missing due to confidential

disclosures to ASIC. Consistent with previous research utilising AFL financial data, several

categories of revenue were identified and categorised (Pinnuck & Potter, 2006). For the

purposes of this research, revenue was distinguished as either team-specific (the focus of this

research) or non-team specific (excluded). Team-specific revenue included all match day gate

receipts, memberships, marketing, merchandise, and sponsorship receipts. It has been posited

that these income sources are influenced by the size of a team’s underlying fan base. Non-

team-specific revenue included AFL league distributions, gaming/wagering,

government/community grants, and all other non-football-related revenue, which we consider

to be non-core to the individual performance of the football team itself. Revenue was

standardised against inflation (to 2017 values) to account for the change in time value of

money (Szymanski, 2012).

Attendance, membership, and population figures. The Attendance variable derives

from aggregate AFL regular season attendance figures collated by afltables.com. AFL teams

received 11 regular season home fixtures throughout the analysis period, providing

longitudinal consistency. Membership (STH) derives from annual tallies collated from AFL

governing body annual reports, which report the audited figures (Lenten, 2012). Annual

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reports were also utilised to identify the Broadcast Cycle for each respective season of the

dataset. The dataset features four broadcast cycles, commencing with the 1998 to 2001 rights

deal. Local Population estimates are drawn from the Australian Bureau of Statistics (2017),

utilising a mixture of census and estimates data across Metropolitan Statistical Areas (MSA).

Fan base estimates. Fan base estimates were derived from longitudinal panel data

developed by commercial research agency Roy Morgan Research. Founded in 1941, the

organisation is a well-respected Australian market research company with a rich history in

collecting quantitative data surrounding sport consumers. Although the credibility of figures

derived from market research agencies is often questioned within academic settings

(Anderson, 2002), their use is not without precedent (O’Reilly & Nadeau, 2006). Television

viewership behaviours represent one such domain in which commercial data has been

commonly embraced (Tainsky & Jasielec, 2014) while O’Reilly and Nadeau (2006)

demonstrated Forbes estimates to be a credible source of financial data.

Roy Morgan has produced an annual football supporter survey since the turn of the

millennium, a component of which has included tracking fan support toward AFL teams.

From a methodological perspective, their annual survey has remained longitudinally

consistent, thus ensuring a degree of reliability typically lacking in secondary data

(Vartanian, 2010). The firm adopts an attitudinal approach to quantifying fan base size,

utilising a single-response multiple-choice question: “Which one Australian Football League

team do you support?” Their annual survey findings are derived from a robust annual sample

of approximately 14,000 respondents, comprised of a demographically representative cohort

of Australians aged 14 years and over.

The fan base estimates are distinct from typical measures of market size in two key

respects. Firstly, a fan can reside anywhere within Australia and thus team fandom is not

constrained to immediate localities. Secondly, as an attitudinal construct, the measure does

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not have capacity restraints as with the use of stadium attendance data. Of the 18 years of

survey data, 15 are available for complete analysis (2012, 2014 and 2015 excluded). In

conjunction with the available financial annual reports, 256 of 302 observations are therefore

complete with corresponding fan base, revenue, membership and attendance estimates.

Market data. Several key market characteristics were quantified to become

independent variables of interest within the dataset. The competitive intensity of a market

was coded across two components, reflecting differing forms of market competition.

CompetitorsAFL reflects internal competitive pressure, capturing the number of rival AFL

teams within the local market. Second, given the presence of six rival national leagues, three

of which are competing forms of football, the variable CompetitorsM captured the number of

non-AFL sport teams within the local market. Additionally, a measure of market support

towards the sport of AFL was included within the dataset in the form of Heartland. This

time-invariant variable operationalises the well-established division in football preferences

associated with the Barassi Line (Stewart & Dickson, 2007). Geography is likely to influence

individual team revenue by virtue of the overall competitive positioning of AFL in a given

geographic region (Stewart & Dickson, 2007).

Econometric Model

Longitudinal panel data presents challenges in implementing linear regression, owing

to correlation and independence violations associated with repeated observations that result in

inefficient estimates of coefficients and misleading standard errors (Ware, 1993). To

overcome this limitation, the model employs generalised least-squares (GLS) regressions

with random effects to estimate results from the panel data. A random effects model is

adopted because a potential key explanatory variable, the geographic team location variables,

is time invariant and therefore would not be identifiable within a fixed effects ordinary least-

squares regression model (Gujarati, 2009). Teams located in heartland regions benefit from

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greater media exposure and cultural centrality, which is likely to benefit these team’s

financial performance (Fujak & Frawley, 2013). Another key benefit of the GLS regression

method is that it allows for unbalanced samples, which is the case within this dataset given

the creation of new AFL teams throughout the time period as well as some missing values in

the dataset. The GLS regression also ensures the weight and importance of each observation

are taken into account, yielding more accurate estimates than ordinary least squares (Gujarati,

2009).

We used the “xtreg” command from the Stata program to account for the panel nature

of the dataset, as has previously been adopted within a sport management setting (Watanabe,

Yan, Soebbing, & Pegoraro, 2017). The xtreg function is considered the most

computationally efficient among similar alternatives (xtmixed, gllamm), although is limited

in its post-analysis testing (Rabe-Hesketh & Skrondal, 2008). To overcome this, we followed

Stata’s recommended guidelines by testing the uniformity of the random-effects model

against a maximum likelihood estimator (MLE), using the latter’s likelihood statistics for

post-analysis testing where appropriate (StataCorp, 2013). The Breusch-Pagan Lagrange

multiplier (LM) test was performed to determine whether a random effects regression was

appropriate rather than an Ordinary Least Squares (OLS) regression. The test was performed

upon the baseline model, returning a significant result (p = .000, chi = 278.21). We used the

‘robust’ function command to control for and obtain heteroskedasticity-robust standard errors

(StataCorp, 2013).

Eight GLS regressions were estimated using an unbalanced panel dataset of team

performance (n = 240). The models were then constructed in a multi-phase process to provide

a more complete understanding of the influence of variables upon team financial performance

(Stinson & Howard, 2007). In the first step, only ‘baseline’ variables were entered in the

model (broadcast cycle, population, market competition concentration and heartland). These

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baseline variables are indirect market-related predictors of football team revenue that can be

derived with no pre-existing knowledge of team-specific information. On-field football team

performance was then added in Model 2 via the variables Finalist and Premiership, with the

FanBase variable added in Model 3. It was important to distinguish these factors as

components of the baseline model prior to testing the inclusion of attendance and

membership figures. This is because attendance and membership are simultaneously

predictors of team revenue as well as specific operational components of team revenue.

Models 4 and 5 utilise single consumer performance metrics to predict financial performance

(attendance, membership). Model 6 provides the full model, using all available information to

predict team revenue. Models 7 and 8 provide the most parsimonious baseline and complete

models using Akaike information criterion (AIC) and Bayesian information criterion (BIC)

measures. From this, the following function is estimated:

Revenueit = θj + β1BroadcastCycleit + β2LocalPopulationit + β3CompetitorsAFLit + β4CompetitorsMit + β5Heartlandit + β6Finalistit + β7Premiershipit + β8FanBaseit + β9Attendanceit + β10Membershipit + νi + εij

where i indexes team and t indexes years, ν is team fixed effects and θ controls for season

random effects. Given BroadcastCycle covers multiple years and teams, θ does not present an

identifiability issue. Complete variable descriptions and corresponding summary statistics are

provided in Table 13.

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Table 13: Variable description and summary statistics Variables Description M SD Min. Max. Revenue Team specific football related total revenue 17,112,20

9 8,993,455 1,367,586 50,337,212

LocalPopulation Total MSA population 3,261,132 1,137,990 1,142,726 4,872,233 CompetitorsAFL Total competing AFL teams within the local MSA 6.53 4.50 0 10 CompetitorsM Total competing non-AFL teams within the local MSA 5.37 3.28 1 17 Broadcast Cycle Cycle 1 Indicator variable for the period was in the first broadcast cycle: 1998-2001 (1 =yes) 0.13 0.33 0 1 Cycle 2 Indicator variable for the period was in the second broadcast cycle: 2002-2006 (1 =yes) 0.31 0.46 0 1 Cycle 3 Indicator variable for the period was in the third broadcast cycle: 2007-2012 (1 =yes) 0.34 0.47 0 1 Cycle 4 Indicator variable for the period was in the fourth broadcast cycle: 2013-2017 (1 =yes) 0.23 0.42 0 1 Heartland Indicator variable for whether team is located in an AFL heartland market (1=yes) 0.85 0.36 0 1 Finalist Indicator variable for whether team reached the finals in the current year (1 =yes) 0.48 0.50 0 1 Premiership Indicator variable for whether team won the premiership in the current or previous year (1 =yes) 0.12 0.32 0 1 FanBase Team fan base size as measured by Roy Morgan Research 507,996 305,063 64,000 1,709,000 Membership Total team membership base (STH) 35,565 13,169 11,270 78,427 Attendance Aggregate regular season home game attendance 375,729 100,548 106,715 695,816

Note. MSA = Metropolitan Statistical Area.

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4.5 Results

Econometric Model

Eight GLS regressions with random effects were estimated using the unbalanced

panel datasets. Identical models were run using MLE, with comparative stability in the

coefficients and standard errors indicating that it was appropriate to utilise and report MLE

likelihood statistics of corresponding GLS models to assess model fit. The results of the

analysis are presented in Table 14. The models are divided into two groupings: firstly the

baseline models (1, 2, 3 and 7), whereby only observable market-related predictors are

inputted, excluding predictors with a direct operational connection to financial performance

(attendance and membership). Likelihood-ratio testing of Model 7 against Model 3 found it to

be the most parsimonious (Chi2 = 1.65, p = .4387), confirming it as the most efficient

baseline model (R2 = .7618, BIC = 8,031.4). Models 4, 5, 6 and 8 are complete models in

which full information is used to predict team revenue. Comparison of Models 3, 4 and 5

confirm that Membership is the strongest singular predictor of team revenue (R2 = .838, BIC

= 7948.591), compared to both FanBase (R2 = .766, BIC = 8,040.738) and Attendance (R2 =

.754, BIC = 8,014.708). Likelihood-ratio testing of Model 8 against Model 6 found it to be

parsimonious (Chi2 = 3.38, p = .7598), confirming it the most efficient complete model (R2 =

.827, BIC = 7,912.092).

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Table 14: GLS regression estimates Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8

Coefficient SE Coefficient SE Coefficient SE Coefficient SE Coefficient SE Coefficient SE Coefficient SE Coefficient SE

LocalPopulation 3.99 2.12** 2.80 1.80 0.55 1.49 1.07 1.31 2.21 1.63 0.52 1.44 1.57 0.84* - -

CompetitorsAFL -1,804,055 538,061*** -1,439,932 438,944*** -641,528 302,530** -538,006 306,168* -1,285,947 408,946*** -488,882 320,271 -870,914 260,870*** -491,522 150,970***

CompetitorsM 182,372 299,277 208,694 218,508 186,821 305,546 -257,059 267,899 78,328 221,616 -94,595 270,942 - - - -

BroadcastCycle 2 2,665,604 579,262*** 2,938,134 544,108*** 2,502,963 377,739*** 2,149,670 488,125*** 2,727,208 619,822*** 2,184,545 481,634*** 2,397,238 320,890*** 2,257,583 514,662***

BroadcastCycle 3 7,714,919 1,147,031*** 8,180,345 1,014,555*** 9,236,185 826,700*** 5,894,114 870,599*** 7,281,403 826,171*** 6,023,724 952,913*** 9,404,489 974,160*** 5,802,863 889,316***

BroadcastCycle 4 16,200,000 2,228,575*** 16,900,000 1,921,245*** 19,000,000 1,689,594*** 10,200,000 1,743,236*** 16,900,000 1,519,528*** 11,700,000 1,923,150*** 19,200,000 1,720,336*** 11,600,000 1,835,815***

Heartland 19,000,000 4,255,683*** 16,400,000 4,489,482 *** 14,100,000 2,630,543*** -1,095,706 3,002,268 8,591,852 4,537,561* -275,775 2,784,759 14,800,000 2,557,377*** - -

Finalist - - 1,951,152 396,249*** 1,235,659 460,318*** 965,261 332,462*** 333,064 676,548 466,686 450,778 1,393,117 449,576*** - -

Premiership - - 2,619,604 936,129*** 924,820 945,053 1,058,198 647,627* 1,689,072 799,865** 608,202 730,690 - - - -

FanBase - - - - 13.41 2.15*** - - - - 1.76 2.22 13.36 2.25*** - -

Membership - - - - - - 429.04 46.88*** - - 342.84 52.24*** - - 340.73 53.67***

Attendance - - - - - - - - 37.04 6.59*** 13.63 6.12 ** - - 19.49 4.90***

Constant -8,872,434 5,193,291** -6,919,897 5,329,993** -9,280,415 3,086,948*** -1,494,063 2,654,941 -11,000,000 4,367,150** -4,637,287 2,822,755* -10,700,000 3,243,860*** -4,565,902 -1,902,658**

R2 .578 .627 .766 .838 .754 .8398 .7618 .827

Wald x2 206.46*** 289.22*** 543.27*** 341.53*** 687.96*** 530.38*** 325.27*** 324.49***

AIC 8047.486 8023.251 7995.489 7903.343 7969.459 7889.385 7993.137 7880.766

BIC 8082.292 8065.018 8040.738 7948.591 8014.708 7941.595 8031.424 7912.092

Note. SE = standard error. Team RE = team random effects. *p < .10. **p < .05. ***p < .01.

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The models are highly predictive of AFL team revenue, with the R2 values for the

eight models ranging from .578 to .8398. The baseline models ranged in their R2 from .578 to

.762. Unsurprisingly, the complete models were superior in their predictiveness, ranging from

.754 to .840. The parameter estimates were for the most part stable across models, exhibiting

signs that were consistent to that which was conceptually expected. Comparison of the

significant predictors within parsimonious baseline and complete models confirm that the

introduction of direct operational predictors result in quite different parameter estimates.

With complete information, the addition of membership and attendance variables increases

model predictiveness by 6% despite the removal of four market-related variables that are

significant predictors in the parsimonious baseline model. Within the complete model, one is

able to estimate an AFL team’s football revenue with approximately 83% accuracy from

inputting the season’s membership and attendance tally with only two existing variables

pertaining to market conditions.

The complete models are able to provide insight into the value of populations of

consumers. Model 6 provides a linear estimate for the financial conversion rate of population

($0.52) to fan ($1.76) to attendee ($13.63) to member ($342.84). Without direct operational

measures of financial performance, the baseline model nonetheless performs strongly,

utilising six significant predictors to reach a model R2 of .762. Given the complete models

utilise direct operational components of revenue as predictors, the baseline model is the more

illuminating model in explaining how market factors contribute to team revenue and these are

further described below.

The influence of market factors upon team revenue. The influence of market

factors was measured by including measures of population, competitive intensity and

consumer preferences. Results indicate that the use of coherent coefficients in the modelling

provides insights into the influence of these market factors upon team revenue. The influence

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of geography upon team revenue was measured through the predictor Heartland, which

dichotomously demarcates AFL teams according to whether they operate within a heartland

or expansion market in accordance with the Barassi Line principle. Modelling confirms that

operating within a heartland market results in a significant increase in team revenue, worth

$14,800,000 per annum (z = 5.79, p = .000). Regarding competitive market intensity, the

number of fellow AFL teams within a local market was found to have a significant impact

upon team revenue (z = -3.34, p = 0.001). The addition of an AFL competitor into the local

market equated to a reduction in team revenue of $870,914 per additional team. In contrast,

the number of non-AFL teams within a local market did not prove to be a significant

predictor in any of the models, resulting in its removal from the parsimonious models.

Dividing the CompetitorsM variable into football competitors (Rugby League, Rugby Union

and Soccer) and non-football competitors (Cricket, Basketball and Netball) did not result in

improved predictiveness or model quality.

Taking into account these factors jointly provides an opportunity to better understand

the influence of markets on team financial performance. The teams with the most beneficial

market circumstances are the West Coast Eagles and Fremantle Dockers, who are both based

in the city of Perth. These two teams benefit from operating within a heartland market (1) that

has a low AFL team concentration (2) with a reasonable population (1,963,300), resulting in

market dynamics that are worth $20,611,466 to team revenue in 2017. As confirmed by the

modelling, these market conditions assist the West Coast Eagles to be the league’s most

lucrative team, being the first team to exceed $50 million in team-specific revenue in 2017.

They were able to do so despite only having the league’s fifth highest fan base (547,000),

fifth highest membership (65,064) and seventh highest aggregate home game attendance

(404,258) in 2017. The AFL’s two Adelaide teams benefit from a similar market structure,

albeit with a smaller local population (1,308,669), resulting in a marginally smaller market

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value ($19,583,697). Melbourne represents the most populous AFL heartland market

(4,530,062) but features a high concentration of AFL teams (10), resulting in a smaller

market value ($16,803,057) than less populous counterparts. Within the non-heartland

regions, Sydney features Australia’s largest population (4,872,233) and has only two teams,

resulting in a market value of $6,778,492. The Brisbane market is the least valuable for AFL

teams to operate in, contributing $2,694,253 to team revenue. Comparing Australia’s most

and least valuable AFL markets, Perth-based teams derive a seven and half-fold financial

market advantage compared to those based in Brisbane.

Broadcast cycle, time and team revenue. As television revenue is collected

centrally and distributed to all AFL teams equally, broadcast income is a non-team-specific

revenue that is excluded from the calculation of Revenue. However, the variable Broadcast

Cycle is included as an independent predictor of Revenue on the basis that cyclical changes to

broadcasting conditions, as well as increases in central revenue distribution, may influence

the ability of AFL teams to generate team-specific revenue. The results confirm that, despite

holding all other factors equal, AFL teams are generating more revenue over time. AFL

teams individually generated an additional $16,200,000 in revenue during cycle 4 (seasons

2013 to 2017) as compared to cycle 1 (season 2000 and 2001). BroadcastCycle is the most

significant factor of the model, with each of the three periods showing an increasing rate of

revenue generation (z = 7.47 [cycle 2], z = 9.65 [cycle 3], z = 11.17 [cycle 4]).

Population verses fan base as predictors of team revenue. Inspection of the

baseline models confirm that the use of an attitudinal measure of team support (FanBase) is a

significant predictor of team revenue. FanBase is first included in Model 3, with its

introduction increasing R2 by 14%. This improved predictiveness is confirmed through

likelihood-ratio testing, which confirmed the incremental improvement in model quality

between Models 2 and 3 was significant (chi2 = 29.76, p = .000). Within the parsimonious

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baseline model (7), FanBase is the second largest contributor (z = 5.93, p = 0.000) after

broadcast cycle, exceeding that of population (z = 1.87, p = 0.061). Comparison of the

coefficients for LocalPopulation (1.57) and FanBase (13.36) provides an insight for sport

practitioners into the financial importance of converting local populations into identified fans.

Within the complete models, neither LocalPopulation nor FanBase are significant predictors;

strength of membership is the strongest single predictor of team revenue. However,

comparison of Model 3 and 5 suggests that FanBase is as strong a predictor of team revenue

as Attendance. This is particularly notable given FanBase is an attitudinal variable and

Attendance is a behavioural variable that directly links to team financial performance.

4.6 Discussion

This study’s core research aim was to begin to develop an understanding of the

financial and competitive structure of crowded sport markets. This was underpinned by two

research questions, by which the discussion has been demarcated.

Research Question 1

The first research question is an empircal one: does the Australian sport market

exhibit financial evidence of consumer saturation? The first research question is underpinned

by a seemingly international consensus that sport markets are becoming increasingly crowded

due to greater supply (Byon et al., 2010; James et al., 2002; Kim & Trail, 2010). In an

Australian context, the number of elite commercial sport teams grew 75% between 2005 and

2017, providing anecdotal support to the possibility of an increasingly saturated local market

which could impact team revenue (see Table 14).

The GLS regression model provides mixed support for the notion that the increase in

teams within the Australian sport market has financially impacted AFL team revenue.

Observing the financial performance of the 15 teams for which there is complete data

between 2000 and 2017, team cumulative revenue grew year on year for sixteen consecutive

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seasons to its peak in 2017. From base revenue of $122.4 million in 2000 (inflation adjusted),

AFL teams generated $461.4 million in revenue in 2017. This represents an annualised

growth rate of 6% over and above the rate of inflation, indicating that AFL team revenue is

outgrowing the general economy despite increasing market competition. This phenomenon

was captured in the model by the significant predictor BroadcastCycle. By exhibiting a

positive and increasing coefficient across cycles, ceteris paribus, AFL teams are deriving

more revenue over time. The predicted shift in market power to buyers and maximised

leverage of consumers would appear to have potentially has therefore not eventuated in the

Australian market (Mahony & Howard, 2001; Mauws et al., 2003). Rather, an explanation

may be that Australian Rules football clubs continue to become ever more sophisticated in

their customer orientation in terms of leveraging consumers, consistent with a view that sport

professionalism and associated revenue have grown symbiotically (Shilbury et al., 2014).

Indeed, a growing marketing orientation within sporting organisations appears to have

resulted in continued growth in revenue despite competitive pressures upon financial

performance (Shank & Lyberger, 2014).

Despite the AFL reaching record levels of collective team revenue, the model did

provide evidence for the influence of market competition in the Australian setting. Notably,

only the presence or addition of other AFL competitors were found to have a significant

influence on team revenue, while the presence of teams from other sports within the local

market was not found to have a significant impact (Stewart & Dickson, 2007). This

modelling outcome suggests that a team’s greatest market competitors are those within-

league rather than across-league (Turner & Shilbury, 2005). It would therefore appear that, at

a sport level, the AFL resists substitutability (Hendee & Burdge, 1974; Mauws et al., 2003).

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Research Question 2

The second research question was as follows: can market size be more accurately

specified through alternative measures of the concept? A significant contribution of the study

has been the utilisation of a novel measure of market size in the modelling, therefore bringing

a management perspective to a field which has largely been dominated by an economic

perspective (Pinnuck & Potter, 2006; Shilbury, 2012). The use of an attitudinal measure of

market support resulted in the development of a model which is highly predictive, with

implications for how market size is understood. Although “a myriad of factors contribute to

the revenue generation ability of major professional sport teams” (O’Reilly & Nadeau, 2006,

p. 311), the modelling illustrates that teams must attract fans who in turn produce revenue for

teams (James et al., 2002). Specifically, several key findings emerge from the complete and

baseline models in relation to our understanding of the connection between consumers,

markets and team revenue. Firstly, with complete information, a team’s STH tally is the

single strongest predictor of their revenue. This gives credence to the emphasis placed by

both academia and practitioners upon understanding STH behaviour and motivations

(McDonald, 2010; McDonald et al., 2013).

Secondly, the significance of the FanBase variable within the parsimonious baseline

model (7) is striking, consistent with the thesis of this study that technological advances and

societal changes have weakened the nexus between local population and team revenue (Kerr

& Gladden, 2008; Lock et al., 2011; McDonald et al., 2010). Furthermore, model comparison

illustrated that FanBase (Model 3) was marginally more predictive than Attendance (Model

5) as single measures, reflecting that sport teams have become less reliant on attendance

revenue in absolute terms (Shilbury, 2012) since the seminal work of Rottenberg (1956). That

an attitudinal measure of consumer support is a significant predictor of team financial

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revenue further illustrates that attitudinal measures can predict behavioural outcomes at a

market level in a sport setting (Doyle et al., 2013). This adds to the diversity of fandom

measures given that research has typically focused on attendance and STH (Lenten, 2012;

Stewart et al., 2003). This is of importance because existing measures are not perfect

reflections of fandom. Attendees, for instance, may not be fans or supporters of the teams

they are spectating, nor are all supporters capable of physical attendance (Jones, 1997). Also,

attendance behaviour is not representative of all fan consumption, given that consumption

occurs on an escalating scale through an array of methods and frequencies (Mullin et al.,

1993). While the FanBase variable overcomes these limitations by measuring fans in totality,

the variable must then encompass a spectrum of varied consumers to one model coefficient,

who can vary from indirect to heavy users (Mullin et al., 1993) and attracted to allegiant fans

(Funk & James, 2001). That FanBase is a significant predictor of financial value confirms

there to be a consistent distribution of fan consumption behaviour across the individual 18

units of observation, aligning to emergent research that sport markets are underpinned by

predictable patterns of consumer behaviour (Baker et al., 2016; Doyle et al., 2013; Fujak et

al., 2018). Predictable market patterns consistent with theorised marketing norms are also

evident in the financial success of the Perth and Adelaide based teams. Fans of higher market

share sport teams report higher attitudinal loyalty (Doyle et al., 2013), consistent with broader

double-jeopardy marketing theory that dictates that larger brands benefit from higher

purchase frequency and loyalty (McPhee, 1963; Sharp et al., 2002). This research appears to

support and advance this theorised application within a sporting setting, illustrating that these

high local market share teams convert market advantages into financial success despite

comparatively smaller supporter bases.

It is apparent that, despite specification differences across disciplines, market support

is a key driver of team financial revenue (O'Reilly & Nadeau, 2006). In operationalising

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market support within this study, this research tested the influence of a key Australian

sociological phenomenon known as the Barassi Line. Previous studies have successfully

identified the significance of the Barassi Line in shaping football preferences (Doyle et al.,

2013; Fujak & Frawley, 2013; Stewart & Dickson, 2007), however this study extends such

research by quantifying its direct economic impact upon teams in markets. Model results

confirm two key conceptual points. Firstly, consumer interest toward AFL differs across

cities such that Australian sport markets cannot be treated as nationally homogeneous. This

finding aligns to Villar and Guerrero’s (2009) critique that challenges exist in defining a

market in any precise way as not all individuals within a population will hold a consistent

propensity to consume a particular sport or team. Secondly, operating in heartland markets

provides a significant financial advantage to teams within those markets. This provides a

resolution to the contention raised in previous qualitative research as to whether being located

in an isolated or concentrated heartland is beneficial, with the former proven to be so (Turner

& Shilbury, 2005).

The identification of strong preferences based on geography complicates what

originated as a simple relationship between population and revenue generation capacity

(Rottenberg, 1956). The accepted wisdom is that operating in larger markets leads to higher

attendances and revenue, which can be spent upon improving team performance (Buraimo &

Simmons, 2009; Gustafson & Hadley, 2007). Yet, under current operating conditions, the

modelling identifies Perth and Adelaide to be the two most lucrative markets for AFL teams

to operate in despite containing the two smallest capital city populations of mainland

Australia. In 2017, this resulted in the four teams within these two markets all ranking in the

top five AFL teams for team revenue. These four teams therefore benefit from a strong

financial market advantage compared to their AFL counterparts elsewhere (Turner &

Shilbury, 2005). Significantly, a component of their advantage would appear to derive from

136

factors that relate to the competitive and consumer structure of their market, which existing

theorisation and discourse relating to market size fail to account for.

4.7 Conclusion

With competition considered central to sport management both on and off the field,

the increasingly crowded nature of sport markets suggests significant questions about the

structure and future of the industry (Mahony & Howard, 2001; Mauws et al., 2003). This has

required sporting organisations to become increasingly sophisticated in their attempts to

develop sustainable fan bases (Shilbury et al., 2014). Correspondingly, while the practical

challenges associated with crowded sport markets have been well identified, corresponding

empirical testing and application of theory at a market level has remained underdeveloped in

a sport setting (Baker et al., 2016; Fujak et al., 2018). This research endeavoured to address

this gap by utilising novel data to longitudinally explore financial performance and fandom in

one such crowded sport market. The study is significant in several respects. First, it is among

the more substantive attempts at performing market level analysis of a sport market (Baker et

al., 2016). Second, it does so by triangulating financial and management data with modelling,

which has largely been within the domain of economists (Shilbury, 2012). Finally, the

research is longitudinal, addressing a weakness within previous modelling research (O'Reilly

& Nadeau, 2006).

Secondary data was collected and a model developed to test two identified sport

management suppositions about the relationship between market support and revenue. First,

would longitudinal changes to the financial value of sport consumers be evident, given the

contention that sport markets are becoming increasingly crowded and fans fully leveraged

(Mahony & Howard, 2001)? Second, given the well-established contention that fans are the

underlying driver of team income (James et al., 2002), could the financial performance of

teams be predicted more parsimoniously through inclusion of a variable that attitudinally

137

captured the size of team supporter bases? With respect to the former contention around

longitudinal changes to fan value, the findings run counter to both theory and the applied

prognostications made by sport management academics (Mahony & Howard, 2001; Mauws

et al., 2003). Despite the Australian sport market becoming increasingly crowded, the

financial value derived by AFL teams from their fans when accounting for inflation has

continued to increase. While the study captures only a subset of sport teams within the

market, the finding is nonetheless surprising. Given the longitudinal nature of the data, the

data provides complementary evidence supporting the contention that sports organisations are

becoming more sophisticated in their operations, and achieving continued commercial growth

notwithstanding competition pressures (Shank & Lyberger, 2014).

The model yielded a strong fit, with fan base size an influential predictor of team

revenue. From this, several significant conceptual and theoretical implications emerge around

the understanding of sport markets. Firstly from a methodological perspective, that an

attitudinal measure of consumer support is a significant predictor of financial team revenue

further illustrates that attitudinal measures can predict behavioural outcomes at a market level

in a sport setting (Doyle et al., 2013). This provides further justification for using alternative

methods of fandom measurement apart from attendance and STH (Stewart et al., 2003). In

respect to theoretical implications, consumer interest toward AFL was found to differ across

cities such that the Australian sport market cannot be treated as a homogeneous national

market (Stewart & Dickson, 2007; Villar & Guerrero, 2009). Within the research context, the

modelling identified Australia’s two least populous mainland capital cities as the most

lucrative markets to operate in, inconsistent to foundational conceptualisations of market size

(Rottenberg, 1956). The identification of strong localised consumer preferences complicates

the theorised relationship between population and revenue generation capacity of sport teams

(Buraimo & Simmons, 2009; Gustafson & Hadley, 2007). Perhaps owing to the ubiquity of

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Association Football in many European contexts, it would appear that we have largely

focused upon the size of markets rather than the influence of consumer preferences and

characteristics within them. Finally, the accuracy of the modelling further contributes to an

emerging view that the consumer structure of sport markets are predictable and generalisable

(Baker et al., 2016; Fujak et al., 2018). This allows us to quantify the commercial value of

sport fandom for sport teams, which is of considerable value to practitioners.

139

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5. Study 4: Consumer Behaviour Toward a New League and Teams: Television Audiences as a Measure of Market Acceptance

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Abstract

Uncovering the embryonic aspects of sport fandom represents a vital area of sport

management and the key research goal regarding new sport teams and leagues. Yet, the field

retains several notable gaps. Significantly, sport management researchers have largely

maintained a narrow focus on the season ticket holder and regular attendee as the

observational unit. Correspondingly, a greater emphasis has been placed upon understanding

consumption of individual new teams, rather than new leagues as a whole. This research

addresses these gaps by longitudinally analyzing a new sport league’s television ratings data

to focus upon market-level behaviour. Two core questions are investigated. First, do

consumers, at an aggregated city level, exhibit instantaneous viewing preferences for new

local teams? Second, how does market consumption behaviour of new sport leagues during

its formative years change over time? Results indicate that local markets show an immediate

preference for local teams. Accordingly, teams appear to be born to an identity by virtue of

consumer cognitive bias. Notably, markets did not grow increasingly interested in their home

team over time in terms of consumption preference. Finally, the novelty effect of new

products was tested, with market consumption appearing to normalize around season four of

competition.

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5.1 Introduction

The introduction and growth of Twenty20 (T20) cricket is, arguably, one of the most

significant developments to the global sport landscape this century. Since the inception of the

T20 format in 2003, the shortest version of the game has evolved from an experimental

novelty to a substantial part of the international cricket calendar. In the process, it has driven

the revitalization of a sport that had shown signs of stagnation (Kitchin, 2008). This

revitalization has centered on harnessing T20 cricket as a new sport product targeted toward

new and weakly attached fans (Paton & Cooke, 2011). T20 leagues, therefore, provide an

ideal setting to research sport consumer behaviour toward new sport products, given they

involve the formation of many new teams at such a commercial scale to allow for the

exploration of consumer market response to their creation.

The Big Bash League (BBL), on which we focus in this article, was introduced to

Australia’s crowded sport marketplace in the 2011/2012 season. Significantly, the leagues’

strategic shift to national free-to-air (FTA) television coverage during the 2013/2014 season

affords an opportunity to extend current understanding of consumer behaviour in relation to

new sport products through the use of a novel dataset to examine two significant areas of

enquiry. Consumer connections and identification with new teams have been studied

extensively over the last 20 years (James, Kolbe, & Trail, 2002; Katz & Heere, 2016; Lock,

Darcy, & Taylor, 2009; Lock, Taylor, & Darcy, 2011; Lock, Taylor, Funk, & Darcy, 2012;

Lock, Funk, Doyle, & McDonald, 2014). This work, however, has typically used quantitative

or qualitative data to explore the behaviour of season ticket holders (STH), to draw

conclusions about “consumers” of new teams, broadly. From a methodological perspective,

this cohort represents a logical focus of analysis given their direct consumption; however,

they are only one segment of a new team’s consumers. Adopting a broader lens, marketers

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must remain concerned with understanding the totality of buyers within a market (Ehrenberg,

Uncles, & Goodhart, 2004). Yet, broader market-level behaviour has yet to receive the

attention of researchers examining new leagues and teams (Smith & Stewart, 2010). To

advance previous work, the present study uses television ratings data from this national

distribution channel to further our understanding of market consumption towards relatively

new leagues and their teams (Tainsky & Jasielec, 2014). In doing so, the study responds to

Kunkel, Funk, & King’s (2014) call to address a deficiency in league-level marketing

research. The combination of the BBL context and market-level methodological approach

allow examination of two significant problems.

First, we draw on social identity complexity research (Heere & James, 2007; Lock &

Funk, 2016; Roccas & Brewer, 2002) to examine the extent to which existing group

memberships create consumption biases in television viewership markets. This provides a

basis to discern whether initial consumption of recent teams is premised on cognitive biases

made salient by a community or city identity. In doing so, we retest previous findings from

cross-sectional studies that suggest the community in which a team is situated plays a

powerful role in the consumer identity development process (e.g., Heere, Walker, Yoshida,

Ko, Jordan, & James, 2011; Kolbe & James, 2000; Lock et al., 2011). In the establishment of

its marketing strategy, the BBL consciously deviated from traditional cricket structures,

norms, and fans in favor of the attraction of new consumers and markets (Cricket Australia,

2011). By de-emphasizing tradition and forming city-based franchises to target new

consumers, Cricket Australia realigned the cues it provided about the group identities

surrounding BBL teams to distance the BBL from cricket’s (traditional) sport identity.

Instead, the organization took steps to emphasize each team’s geographic identity. As such,

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the BBL offers a prime context to examine whether geographic identity creates biases in

market-level viewership patterns for new teams.

Second, the response of markets to sport product launches is unclear. Consumers seek

out new and different experiences in accordance with novelty seeking behaviour (Hirschman,

1980) and according to some previous research, consumers of new sport leagues conform to

this phenomenon (Mahony, Nakazawa, Funk, James, & Gladden, 2002). Conversely, research

from the marketing domain that has explored market and brand performance metrics

advances the antithesis: that new repeat-purchase consumer products display “near-instant

loyalty” and behave like established brands within the short term (Ehrenberg & Goodhardt,

2000). Accordingly, new buyers, once buying, make the brand a habitual part of their

ongoing repertoire (Trinh, Romaniuk, & Tanusondjaja, 2016). It is particularly vital to

understand consumer behaviour toward new leagues and teams, such as the BBL, as sport

markets become increasingly crowded and competitive (Byon, Zhang, & Connaughton,

2010). By analyzing television audience data over the five seasons the league has been

exposed to a new national audience, we specifically explore two contrasting explanatory

theories surrounding the consumer adoption of new sport products: novelty seeking and near-

instant loyalty hypotheses of market behaviour. We test whether a period of initial novelty

and subsequent retraction in interest can be detected and in doing so, extend upon extant

theory on the durability of interest in new teams.


External Group Identities and New Sport Teams

A major question facing consumer researchers and sport marketers is why consumers

develop preferences for specific teams. Responses to this question are vast and clearly

elucidate the importance of vicarious achievement (Cialdini, Borden, Thorne, & Walker,

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1976; Trail et al., 2012); domain involvement (e.g., Fisher & Wakefield, 1997; Kahle,

Kambara, & Rose, 1996; Lock et al., 2011; Funk, Mahony, & Ridinger, 2002); tradition and

community (Heere, Walker, et al., 2011; Jones, 2000; Kolbe & James, 2000); and

socialization (James, 2001) in the development of consumer preferences. Each of these

factors has been analyzed from a social identity perspective to some extent (cf. Tajfel &

Turner, 1979). The central point of social identity theorizing is that groups are important

social frames of reference helping individuals to make sense of their self in relation to other

people and groups (Turner & Reynolds, 2008). Furthermore, group identification creates

cognitive biases that, when salient, lead members to display preferences for their own group

at the expense of others (Turner, Oakes, Haslam, & McGarty, 1994). This underscores why

consumer researchers have used the social identity approach to make sense of the biases and

consumption patterns fans demonstrate toward their own team (Lock & Heere, 2017). We use

this strand of social identity theorizing to make sense of in-group favoritism in relation to a

specific group identity.

In early social identity theorizing, Tajfel (1982) alluded to the emotional value of

group membership. That is, identities that are internalized and self-important to an individual

play a crucial role in self-definition and behaviour (Lock & Heere, 2017). Perhaps because of

this observation, sport consumer researchers have tended to concentrate on studying

identified fans in order to understand the motives and behaviours of the most committed

supporters as a proxy to understand all team consumers (Park, Mahoney, & Kim, 2011). Yet,

as Smith and Stewart (2010) have noted, “while ‘die-hard’ and passionate fans are obviously

an appealing cohort to examine, the elucidation of their motivations and behaviours provides

an imbalanced picture of sport consumption. . . . Sport consumers are not all passionate and

fanatical” (2010, p. 5). While it is widely accepted that sport fandom exists along a

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continuum from casual spectators through to die-hard fans (Funk & James, 2001; Trail,

Robinson, Dick, & Gillentine, 2003), it is not mutually inclusive that a casual spectator must

identify with a team. In reference to casual spectators, Bernache-Assollant, Laurin, and Bodet

(2012) noted that team “identity is only a peripheral component of their self-concept” (p.

123). In this regard, we retain a weak understanding of the behaviours of weakly identified or

non-identified consumers.There has also been a focus on the facets of teams that lead

consumers to identify, rather than more expansive theoretical positions that take account of

the associated groups and communities that interrelate with team identities (Heere & James,

2007; Heere, Walker, et al., 2011). This ignores work on social identity complexity, which

states that a person’s social self-concept is formed of a repertoire of groups upon which the

individual places significance (Roccas & Brewer, 2002). The work on social identity

complexity demonstrates that sport teams can be both a direct source of group identity and a

symbolic representation of other communities (Heere & James, 2007). Such thinking is well

established in sport management, although scantly tested empirically (Heere, James, Yoshida,

& Scremin, 2011).

The relationship between sport teams and other groups has received some attention,

albeit in small-scale studies of one team. Kolbe and James (2000) conducted a quantitative

study of motives leading to attachment for Cleveland Browns STH. They found that

attachment to city and community had the strongest influence on team attachment. Similarly,

Jones (2000) conducted a qualitative investigation of the reasons fans identified with Luton

Town Football Club, an underperforming lower league club in the United Kingdom. He

found that a tradition of support for Luton Town Football Club, and broader identification

with Luton as a community, drove the formation of identification. More recently, Uhlman

and Trail (2012) indirectly reached a similar conclusion in developing and testing a model of

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fan superiority. Utilizing the case of the Seattle Sounders, a new team with immediately

strong local fan support, Uhlman and Trail found that attachment to the Seattle community

itself was the strongest predictor of team identification amongst STH. Although attachment to

community appears to be a consistently strong driver of team identification for established

teams, Uhlman and Trail largely failed to recognize the significance of their study as a rare

case in which it was tested and confirmed in a new team setting. Their study, however, was

limited to a small sample (N = 328) and targeted a highly identified group of STH fans.

In this paper, we utilize television ratings data to understand market level consumer

behaviour. Within the fan typology literature, television viewership retains a relatively low

status in the hierarchy of expressed fan consumption and, correspondingly, has been an

underdeveloped component of fan behaviour research (Tainsky & Jasielec, 2014). Although

attention toward market level television viewing behavior has been comparatively limited, it

is both anecdotally intuitive and academically supported that behavioural preferences are

observable through patterns of sport television consumption. Noll (2007), for instance, has

noted, “Because every team is likely to be more popular at home than in other areas, local

rights can capture most—perhaps nearly all—of the value of the national rights for many

teams” (2007, p. 23). Work in an Australian setting support this perspective, demonstrating

that local audiences prefer to view local football teams (Fujak & Frawley, 2013). Tainsky and

Jasielec (2014) have also observed that the single greatest determinant of local National

Football League viewership was the telecast of local teams, although not to the extent that

such viewers generated the majority of broadcast rights value.

Although it is unsurprising that local teams generate higher local viewing audiences in

established leagues, whether such phenomenon occurs from the inception of a competition is

unknown. It has been shown that individuals can develop deep psychological connections

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with new sport products/teams before they have physically entered the market or played a

match (James et al., 2002; Kunkel, Doyle, Funk, Du, & McDonald, 2016) and such

connections should lead to a corresponding viewing preference. However, such individuals

represent a small group of highly attached fans which represent only a fraction of the overall

market. Given existing research has focused near exclusively on highly engaged fans,

exploring whether the theorized behaviours associated with social identity theory can be seen

in a broader sport consumption setting represents a significant test of the boundary conditions

of the theory’s generalizability in a sport context (Busse, Kach, & Wagner, 2017).

At the market level, where most consumers may know little about a new team,

external group identities such as geographic connection could potentially be most impactful

upon team identity during the formative years of new teams. In contrast, if the market does

not perceive a genuine connection between a new team and its geographical region, there

may be little reason to exhibit a viewing preference toward a local team compared to non-

local teams. This was feasible in the context of the BBL competition, given the league was

designed and conceived entirely through market research consultancy rather than community

driven initiatives. Despite this, we hypothesize that the presence of geographic group identity

will result in local viewers showing an immediate viewing preference for local teams, despite

such teams having an embryonic team identity:

H1: Local teams will generate significantly larger audiences in their local market in

the first season of free-to-air broadcast.

Extending upon this initial hypothesis, we consider whether the viewing preferences

of fans toward their local team changes longitudinally. Whether home cities exhibit an

instantaneous viewing preference for their local teams, it would appear intuitive that new

sport teams develop loyalty and preference among their fans over time (Funk & James, 2006;

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Mahony et al., 2002; Olson & Jacoby, 1971). Connection to a team may therefore grow

iteratively through direct experiences, consistent with theories of escalating commitment

(James et al., 2002; Mullin, Hardy, & Sutton, 1993). It is noteworthy that escalating

commitment is a behavioural manifestation of fandom (Lock et al., 2012). Previous research

has confirmed that fans can exhibit psychological connections to teams in the absence of

game experiences (James et al., 2002). These connections can often commence from a strong

starting point and remain stable even during extended periods of poor performance (Lock,

Funk, Doyle, & McDonald, 2014). We therefore hypothesize that viewer preference toward

local teams should increase over time, given that time allows such teams to develop their

identity within their market.

H2: Consumer preference towards local teams will increase over time.

New Sport Leagues: Consumer Novelty or Stability?

Understanding team identification and consumer preference ought to be a key

objective for those establishing new sport leagues and teams. There is, however, robust

debate among academics about the immediacy with which consumer preference and loyalty

can develop toward new products. Some scholars contend that high levels of loyalty cannot

be created instantaneously, but must be developed over time (Funk & James, 2006; Mahony,

Nakazawa, Funk, James, & Gladden, 2002; Olson & Jacoby, 1971). In place of loyalty during

a new product’s formative period appears to be novelty. According to Hirschman (1980),

consumer preference towards new products is said to be impacted by novelty seeking

behaviour, reflecting an inherent human desire to seek out the new and different. The

influence of novelty in the context of consumer behaviour has been extensively examined

(Baumgartner & Steenkamp, 1996; Hansen, 1972; Manning, Bearden, & Madden, 1995;

Sheth, Newman, & Gross, 1991). Sheth, Newman, and Gross (1991) argue that novelty is

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encapsulated in the epistemic value of a product: “the perceived utility acquired from an

alternative’s capacity to arouse curiosity, provide novelty, and/or satisfy a desire” (p. 162).

As consumers strive to optimize their stimulation and arousal (Berlyne, 1960), the novelty

effect results in a pattern of increased initial interest toward new products followed by a

corresponding reduction due to inherent novelty seeking behaviour external to the product

itself.

There has been limited exploration of novelty in a sport context as a method of

understanding behavioural patterns. As noted by Park, Mahony and Kim (2011): “In addition

to more thoroughly examining trait and state curiosity, understanding sensation and novelty

experience seeking in sport is also important” (p. 46). Research surrounding the novelty

effect in sport thus far has appeared more conclusive in relation to the effect of superstar

athletes (Shapiro, DeSchriver, & Rascher, 2017; Jewell, 2017; Lawson, Sheehan, &

Stephenson, 2008). Shapiro et al. (2017) for instance determined that the novelty effect of

David Beckham’s signing to Major League Soccer (MLS) was largely confined to the first of

his six seasons. In relation to the introduction of new leagues, Mahony et al. (2002) noted that

many consumers view new leagues as a novelty in early years, allowing for exploratory

interest and experimental consumption. However, once this novelty period fades, more

sophisticated marketing strategies are necessary in order to maintain—and hopefully grow—

the fan–team relationship. This line of reasoning appears to be supported by some, albeit

comparatively limited, empirical sport case studies. The establishment of the first

professional soccer league in Japan in 1993 (J-League) was followed, in its third year, with

an average attendance of 19,679—a number that has not been exceeded since (Nakazawa,

Mahony, Funk, & Hirakawa, 1999). Crucially, there was a substantial, unexpected decline in

Year 4, after which modest attendances became normative. The fan–team relationship had

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failed to maintain momentum in the wake of consumers’ early flirtation with the J-League.

Additionally, boosted by hosting the 1994 FIFA World Cup, America’s National Professional

Soccer League experienced a similar trend of immediate interest followed by a period of

subdued interest thereafter (Collins, 2006; Trecker, 1998).

The impact of novelty upon new product consumption however remains contested

within consumer behaviour research. Ehrenberg, Uncles, and Goodhart (2004) articulate the

contention that surrounds new products: “the general view for new brands is that loyalty

grows slowly . . . but no generalizable results of this have been reported” (p.1314). In line

with this observation, Wright and Sharp (2001) found that new brands behaved like existing

brands quickly, within 6 to 8 weeks of market entrance. Ehrenberg and Goodhart’s (2000)

study determined that new brands exhibit “normal” levels of consumer purchase rates

virtually from inception. Similar findings were reported in other studies applying Dirichlet

modeling (Hoek, Kearns, & Wilkinson, 2003; Wellan & Ehrenberg, 1988). Notably, these

predictable modelling patterns have also been found to hold true in the context of television

viewing behaviours, despite the comparative low barriers to purchase/consumption (Barwise

& Ehrenberg, 1988).

That consumption behaviour of new products in fact normalizes quickly is

underpinned by the premise that buyers of new brands are still likely to be experienced

buyers of the product category, and so the event of buying a new brand is unlikely to be a

radical departure from existing behaviour (Trinh, Romaniuk, & Tanusondjaja, 2016). Given

recent studies have begun to confirm that sport markets conform to consistent consumer

behaviour patterns and generalizations as predicted by Dirichlet market analysis consumer

modelling (Baker, McDonald, & Funk, 2016; Fujak, Frawley, McDonald, & Bush, 2018), it

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would appear plausible that the BBL competition would not experience the effects of

consumer novelty. Correspondingly, it is proposed that:

H3: BBL television viewership exhibits longitudinally stable patterns of consumption.

The literature reviewed demonstrates three substantial gaps that exist in the

underlying research on new sport teams and leagues. Firstly, due to the focus of extant

research on STH or fans as the target populations within the majority of new team and league

research, there is an opportunity to analyze market-level data to develop understanding of

consumer behaviour towards new teams and leagues on a broader-level. STH are likely to be

the most loyal consumer group and generally represent a small proportion of a team’s overall

fan base.

In contrast to existing STH research, this study evaluates behavioural preferences

among perhaps the broadest group of consumers, television viewers, who represent a melting

pot of low loyalty, high loyalty and even non-fans. Despite its scarcity in the new sport

product setting, the evaluation of theorized market behaviour is well established across many

consumer product categories (Ehrenberg, Uncles, & Goodhart, 2004). Secondly, despite the

nascence of a new team’s identity, there has been limited empirical testing of the extent that

external group identities influence consumer television consumption preferences specifically

in the context of new teams. Thirdly, there appears a lack of consensus as to the effect of

novelty on fan interest within new leagues. While some empirical examples suggest a novelty

effect (Mahony et al., 2002), findings in marketing are equivocal (Ehrenberg et al., 2004). We

seek to provide a test of consumer television preferences to add to current knowledge of the

novelty effect in relation to new sport teams.

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5.3 Method

Research Context

The BBL was launched in 2011, in response to a global cricket trend surrounding

declining levels of fan interest and engagement with the sport, particularly among youth and

families (Paton & Cooke, 2011). The launch of the BBL into Australia’s competitive sport

marketplace was aided by cricket’s pre-existing institutionalized popularity, which

differentiates the BBL from most typical leagues which commence with low market share

and weaker financial positions. As of 2016, cricket retained the third largest participation

base among all team-based ball sports (behind football and basketball) while Cricket

Australia is the third largest Australian sport organization on the basis of revenue (AusPlay,

2017; IBISWorld, 2017). The BBL now represents the entry point for exposing non-

traditional fans and children to cricket in Australia and although the success of the strategy is

likely to be generational, early reports suggest that the BBL has successfully attracted non-

traditional cricket audiences to the sport.

The period of analysis spanned five BBL seasons from 2013/2014 through to

2017/2018. These constitute Seasons 3 through 7 of the BBL tournament in which saw all

BBL matches on FTA television throughout Australia as part of an AU$100 million-per-year

broadcast contract (Cricket Australia, 2015). Prior to this, the first two seasons were telecast

on pay television, which has a subscription rate across Australia of around 30%, with sport

channel subscribers only a sub-group therein (OzTAM, 2013). Two considerations preclude

Season 1 and 2 from inclusion within this study’s analysis. First, as subscription television

ratings are reported as an aggregated national rating, they are unable to be dissected to

illuminate the audience contribution of individual regions as desired by Hypothesis 1 and 2.

Secondly, subscription audiences are considerably more likely to be sport fans given that

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sport content is a primary driver of subscription television. The first season of national

coverage on FTA television provided the league its first exposure to national audiences who

may not have had previous exposure to the league or the sport as had been idenitifed within

the sports marking plan (Cricket Australia, 2011). Given the desire to measure market

response at a broader consumer level, the new national distribution channel utilized to

broadcast Seasons 3 to 7 provided a representative sample of the Australian population not

achieved during Seasons 1 and 2. Reference to market responses to these relatively new BBL

teams is therefore within the context of this new national distribution channel, which

commenced in Season 3. Season 1 within the results and discussion refers to the first season

of FTA coverage from hereon given this exclusion.

Data Source and Type

The study utilized television ratings data collected by research agency OzTAM, the

industry-standard aggregator of television ratings data across Australia’s major metropolitan

cities. This market sector is substantial, accounting for 78% of Australia’s $3.8 billion

spending on television advertising in 2015 (FreeTV Australia, 2015, 2016). OzTAM has a

sample of approximately 3,150 households comprised of 8,280 individuals, distributed across

the five capital cities of mainland Australia (OzTAM, 2015, 2016). Seven of eight BBL

teams reside within these five capital metropolitan cities, with the exception being the

Hurricanes who are based in the regional city of Hobart.

A brief description of the metrics utilized within this analysis is provided here. First,

the “average” audience provides a measure of the size of the audience during the entirety of a

program. The average audience provides the most valid measure of the absolute popularity of

a program and, correspondingly, is the most widely publicly reported viewership metric.

Second, “reach” captures “the number of unique viewers who have seen at least one minute

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of an event or time band across its total duration” (OzTAM, 2010, p. 3). This figure can be

used as a proxy for the maximum possible audience, given that it includes people who may

have only watched a small part of a program. Lastly, “viewer duration” measures the average

amount of the telecast minutes watched per viewer, measuring audience commitment to the

telecast. This can provide insight into whether an audience is composed of a small

concentration of loyal viewers or many light viewers who watch only a portion of the

program.

Data Analysis

In order to perform the analysis required of this study, transformations were

performed on the raw ratings data. Broadcasters divide standard T20 cricket telecasts into two

distinct sessions; however, in keeping with the goals of this research the sessions were

combined to reflect the overall match audience. This was calculated as follows: Session

duration (SD) was divided by total match duration (MD) to calculate the contribution of each

innings to the total broadcast. This share percentage was then applied against each session’s

average audience (AUD) to create a valid weighted average viewership for the entirety of the

match (WAV). This formula is illustrated in Equation 1.

(SD1/MD x AUD1) + (SD2/MD x AUD2) = WAV (1)

In respect to reach figures, the larger of the two innings was utilized as the overall

program reach in the study. While that represents a limitation of the dataset, any

underreporting should be consistent across regions and therefore not impact the underlying

purpose of the analysis. Finals matches were also excluded to allow for more valid

longitudinal comparisons (i.e., finals matches did not include all teams and, more

importantly, create abnormal peaks of interest within related geographical regions). Given

that the team and fixture structure of the BBL has remained near-consistent since inception,

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focusing on the regular season allowed for a balanced study with an equal sample of 32

fixtures across teams and seasons until season 5, where the regular season was expanded to

40 fixtures.

Independent samples t tests were performed to ascertain whether fans’ ‘behavioural

team preference’ could be observed as a measure in the ratings from Season 1. A hierarchical

analysis of variance (ANOVA) design was then implemented to test the latent strength of

behavioural preference among BBL television audiences over time. ANOVA is an effective

analysis technique when there is a small number of categorical independent variables and

each variable has a small number of levels, as is the case within the design of the present

study (Leech, Barrett, & Morgan, 2012). Specifically, the independent variable “season” has

five ordinal levels (2013/2014, 2014/2015, 2015/2016, 2016/2017, 2017/2018) while “home

team” is a dichotomous categorical variable (Yes, No). Given the presence of five distinct

broadcast markets within the study, a third categorical variable of “region” was utilized

(Sydney, Melbourne, Brisbane, Adelaide, and Perth) within which “season” and “home team”

are both nested. Nesting allows for the aggregation of the five individual regional models,

thus reducing the risk of Type I error (Fowler, 2013). The dependent variable, “homeshare” is

a scale variable that reflects the average viewing audience as a proportion of the total

population within the market. Each market holds a varied population size, and therefore

utilizing a share of the population metric rather than absolute audience size standardizes

audience viewing propensity across the five regions. This is critical to the modelling as it

normalizes the error variances.

The interaction effect between season and home team nested within region is central

to testing whether behavioural preference has developed in BBL audiences. A significant

interaction effect between the two independent variables would suggest that the size of a local

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audience for home team matches is contingent on season, thus inferring that the degree of

home team support has changed over time. An insignificant interaction between the

independent variables would suggest that local interest in watching home team matches has

not changed over time when accounting for changes in overall interest in watching BBL.

Contrasts were performed upon HomeShare to test for intra-season viewership differences, in

accordance with Hypothesis 3.

5.4 Results

H1: Teams will have significantly higher television audiences in their home city than

teams from other markets in the first season of free-to-air broadcast.

Table 15 displays the descriptive and inferential statistics required to address

Hypothesis 1. Despite small sample sizes within each of the five t tests (N = 32), each region

had a statistically significant difference between the mean audience of matches that feature a

local team compared to matches not involving a local team. All local markets therefore

appear to have developed a preference toward their local team(s) near immediately upon

broadcast on FTA television. The performance of the Perth and Brisbane markets is

particularly notable, in that positive team performance in the first two pre-FTA seasons

helped both clubs lead the competition in terms of local viewer preference in Season 1.

The city of Perth appeared to exhibit the largest immediate preference toward to its

local team, corresponding with the average audience for the Perth Scorchers being 76%

higher than for matches not involving their team. Evaluation of Cohen’s effect size (d = 2.18)

suggests a high practical significance to this difference. Although failing to win the

competition in the first two seasons, the Perth Scorchers were the most successful BBL team

leading into Season 1, given that they reached and hosted the final in the first two seasons.

Brisbane recorded the second largest effect size (d = 1.28), generating audiences for home

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matches involving the Brisbane Heat that were 43% higher than matches that did not feature

the local team. Although the Brisbane Heat did not qualify for the finals in the inaugural

BBL season, they were the premiers of the second season, and therefore entered Season 1

with strong momentum in terms of fan support. The statistical significance of each local

audience for local teams across the five markets provides strong support for Hypothesis 1.

Local Share, as presented in Table 15, provides an alternative measure of team

support. The figure is calculated as the average audience size for local team matches relative

to total viewing universe of the local audience as measured by OzTAM (2013). This allows

for the standardization of audience size to account for the varying population of each region,

given that Sydney is Australia’s largest mainland capital city with 4,734,400 viewing

residents, while Adelaide is the smallest with 1,434,000. Applying this metric, Adelaide

viewership appears particularly strong, with 5.5% of the Adelaide population watching each

Strikers match. Conversely, the aforementioned performance of the Brisbane Heat appears

less significant, with only 3.6% of the Brisbane population watching each Heat match. The

Brisbane case, therefore, provides a juxtaposition, as the market is among the most

comparatively loyal to their team, but the least embracive of the new league overall.

Table 15: Descriptive and inferential statistics for FTA season 1 (2013/14) by region Local Audience Differential

Region Local

Team/s Non-Local

Teams

Num. % Local Share

a t df p d Sydney 164,50

9 133,73

7 30,77

2 23.01 3.5% 2.3

5 29.7

2 .03 0.8

2 Melbourne

235,879

189,501

46,378

24.47 5.1% 2.06

28.99

.05 0.73

Brisbane 109,196

76,616 32,580

42.52 3.6% 3.88

30.00

< .01 1.28

Adelaide 79,072 63,064 16,008

25.38 5.5% 2.51

30.00

.02 0.95

Perth 92,435 52,586 39,849

75.78 4.8% 6.02

30.00

< .01 2.18

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aSize of audience for local team matches relative to the region’s population.


To address Hypothesis 2, Table 16 presents a hierarchical ANOVA for HomeShare

(TV Audience) as a function of HomeTeam and Season, nested within five distinct regions.

The overall model was highly predictive, with an adjusted R-squared value of .671. Each of

the three main effects and one interaction effect were individually significant: (a) Region,

F(4, 840) = 231.095 , p = .000, η2= .537; (b) Season within Region, F(20, 840) = 16.259, p =

.000, η2= .287; HomeGame within Region, F(5, 840) = 108.105, p= .000, η2= .404; and (c)

the interaction of HomeGame and Season within Region, F(20, 840) = 1.982, p= .006, η2=

.046. The hypothesis is specifically tested through the observation of the interaction effect

between HomeTeam and Season. For viewers’ behavioural preference within the respective

markets to have changed over time, a significant interaction effect between these two

variables would be present within the ANOVA test. Table 16 provides support for Hypothesis

2 as the interaction between HomeGame and Season (Region) was significant, F(20, 840) =

1.982, p = .006, η2 = .046.

Given the significance of the interaction effect within the overall hierarchical model,

the five regions were disaggregated into individual market models to further evaluate the

interaction. Levene’s test of equality for error variance was satisfied within each model (see

Table 16). Upon further examining the five disaggregated region-based models, the Adelaide,

F(4, 168) = 3.321, p = .012, ηp2 = .078, and Brisbane, F(4, 168) = 3.718, p = .006, ηp2 = .086,

markets were determined to be the source of this significance, reflecting a change in relative

local team interest to matches not featuring a local team.

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Table 16: Hierarchical ANOVA for HomeShare as a function of HomeTeam and Season nested within Region

Variable SS df MS F p η2 Corrected model 0.138 49 0.003 35.848 .000 .690 Intercept 1.300 1 1.300 16595.239 .000 .954 Region 0.072 4 0.018 231.095 .000 .537 Season (Region) 0.025 20 0.001 16.259 .000 .287 HomeGame (Region) 0.042 5 0.008 108.105 .000 .404 HomeGame × Season (Region) 0.003 20 0.000 1.982 .006 .046

Error 0.062 790 7.833E-5 Total 1.564 840

Note. R2 = .690; Adjusted R2 = .671. Levene’s test of equality for error variance was passed within each of the five disaggregated models: Sydney (.465), Melbourne (.071), Brisbane (.069), Adelaide (.236), Perth (.052).

Figure 4 illustrates the amalgamated interaction effect plot for the five markets by

season. In the 3 years between Season 1 and Season 3, viewers within four markets

(excluding Adelaide), in fact, became comparatively less interested in the local team

compared to the overall competition in relative terms. In both the Adelaide and Brisbane

instances, where a significant interaction was observed, it appears that a spike in team

performance resulted in a corresponding surge in local team viewership. In the case of

Adelaide’s local team, the “Adelaide Strikers,” the audience size of the telecasts increased

from a differential of 125% in Season 1 to 173% in Season 2 (compared to non-Strikers’

telecasts). The Strikers finished seventh in Season 1, before finishing first during the regular

season (i.e., before the finals series) in both Season 2 and 3. Given that the local market

preference for the Strikers’ fixtures diminished after the team’s period of success, increased

local favoritism appears to have been fleeting. Similarly, Brisbane’s local team, the “Brisbane

Heat,” experienced a differential in audience size between matches and non-local teams

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which increased from 119% to 154% in Seasons 3 and 4, during which their ladder position

improved from sixth to second.

Figure 4: Amalgamated interaction effect plot for games involving local teams versus non-local teams


Analysis of the BBL’s seasonal changes in ratings determines the competition to have

been volatile, counter to the prediction of Hypothesis 3. Although the BBL tournament grew

its viewership over the duration of its FTA contract, this growth was not incremental (see

Table 17). Most audience growth occurred in Season 3, and across all five regions. The

cumulative average metropolitan audience of the BBL during the regular season in Season 1

was 18,283,724, increasing only 3.03% in Season 2, followed by a 28% increase in Season 3

before a 4% retraction in Season 4. Season 5 included an additional two rounds of fixtures

(eight games), but the average audience per fixture declined by 11%. Reverse Helmert

contrasts reveal that there is a significant statitistical intra-season variance in average

viewership across all regions, resulting in a national average audience (Combined

Metropolitan) that varies significantly. Three of four Season contrasts upon the Combined

100%

110%

120%

130%

140%

150%

160%

170%

180%

190%

13/14 14/15 15/16 16/17 17/18

Sydney Melbourne Brisbane Adelaide Perth

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Metropolitan audience are highly significant, with only the increase from Season 1 to 2

narrowly missing the .05 significance threshold (see Table 17). The results of these contrasts

result in the rejection of Hypothesis 3.

Measurement of market consumption stability through the average audience however,

is comprised of two underpinning components of potential and distinct variance which can be

further explored. The average audience is the sum of the aggregate number of people who

watched the program at any point (Reach) and the amount of minutes watched per person

(Viewing Minutes). Table 17 illustrates that the growth in average audience size over the

period was predominantly caused by an increase in consumption per viewer (Viewer

Minutes), rather than an increase in the total number of individuals viewing (Reach). While

average viewership increased by 13% between Seasons 1 and 5, driving this increase was

Viewer Duration,which grew 19%, whereas Reach increased only 5%. Viewership growth

has therefore been driven by increased audience commitment rather than a growth in the base

of consumers. In previously addressing Hypothesis 2, a hierarchical ANOVA on average

audience produced a highly predictive model, with an adjusted R-squared of .671. Re-running

this model upon the dependant variables of Reach and Viewing Minutes provides insight into

the predictability of viewing markets. A hierarchical ANOVA upon audience Reach under

the same conditions described in Table 2 provides an adjusted R-square of .861 while the

Viewing Minutes model generates an adjusted R-Squared of .454. The determinents of

market penetration are therefore considerably more predictable than the determiments of

consumption strength and commitment.

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Table 17: Audience metrics across the regular BBL season

Region Season Average Audience

Audience Reach

Viewer Duration

Contrast sig*

Sydney 2013/2014 147,200 384,347 67.09 - 2014/2015 145,130 394,728 69.55 .784 2015/2016 168,326 391,657 78.90 .004 2016/2017 154,360 412,334 72.89 .927 2017/2018 150,641 385,967 75.51 .305 Melbourne 2013/2014 209,791 515,552 70.40 - 2014/2015 223,535 554,391 76.13 .286 2015/2016 291,471 616,958 86.57 .000 2016/2017 261,575 599,561 85.15 .046 2017/2018 213,501 511,135 80.48 .000 Brisbane 2013/2014 84,761 239,987 61.69 - 2014/2015 81,963 235,928 65.48 .375 2015/2016 102,332 250,263 74.93 .004 2016/2017 121,638 281,007 83.76 .000 2017/2018 115,138 264,106 83.02 .000 Adelaide 2013/2014 67,066 170,026 69.65 - 2014/2015 74,366 180,201 76.72 .002 2015/2016 87,989 187,954 84.97 .000 2016/2017 85,087 198,037 83.31 .027 2017/2018 77,648 179,676 82.08 .003 Perth 2013/2014 62,548 145,463 74.03 - 2014/2015 63,670 133,840 89.09 .961 2015/2016 103,273 189,511 99.55 .000 2016/2017 97,426 194,987 96.53 .000 2017/2018 85,934 182,632 89.77 .052 Combined Metropolitan

2013/2014 571,366 1,455,375 68.36 - 2014/2015 588,664 1,499,088 73.95 .053

2015/2016 753,391 1,636,343 84.27 .000 2016/2017 720,086 1,685,926 83.02 .000 2017/2018 642,862 1,523,517 81.49 .000

*Reverse Helmert contrasts, comparing current level to previous level of Average Audience.

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5.5 Discussion

It is well accepted that establishing a loyal fan base is the key challenge faced by sport

organizations and their marketing teams (James et al., 2002). This becomes an even greater

challenge with increased competitive pressure between sports leagues (and their teams) in

crowded markets (Byon et al., 2010). The establishment of a fan base is particularly difficult

for new sport leagues and teams who must also contend with novelty during formative years.

Despite this, novelty in a sport setting has largely yet to be explored (Park et al. 2011).

Previous research surrounding new team consumerism has largely focused on STH, which

typically constitute the most avid fan group. The present study has thus broadened the scope

of the research field by measuring team and league behaviour at a wider level of consumer

behaviour and over a longer time period, addressing previous calls by Kunkel, Funk and King

(2014).

H1: Teams will have significantly higher television audiences in their home city than

teams from other markets in the first season of free-to-air broadcast.

The results of the study support the stated proposition in Hypothesis 1. The findings

confirmed that local viewers exhibited a behavioural preference for fixtures involving their

local home team in the first season of FTA coverage. This conclusion is consistent with

previous media research in both international (Tainsky & Jasielec, 2014) and domestic (Fujak

& Frawley, 2013) settings, which confirm fan preferences toward local teams within

established leagues. That consumers express an immediate preference for new teams is also

consistent with existing research on new team identification. Specifically, Lock and

colleagues (Lock, Darcy, & Taylor, 2009; Lock et al., 2011) and James et al. (2002) have

confirmed that members of new teams are able to generate strong identification in their first

seasons, although such studies have focused on more active and highly attached fans. The

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present study, therefore, advances our understanding of team identification toward new teams

by illustrating that near immediate identification can occur at market level as well as among

the highly attached subset of fans.

Given that the vast majority of consumers did not have direct viewing access to BBL

teams until the introduction of FTA coverage, the immediacy with which local markets

adopted a viewing preference for local teams is consistent with Heere and James’s (2007)

conceptual model of the relationship between external communities and team identification.

Although loyalty toward a new team has been observed at a city level among passionate fans

(Uhlman & Trail, 2012), these findings illustrate that consumers exhibit localized preferences

at a market level early within a team’s existence. Teams do not develop an identity in

isolation, but rather do so within a confluence of external identities. Although nearly all sport

clubs name themselves after their city region as a matter of custom, the findings confirm that

a team’s name can have an immediate effect upon shaping team identity. This ability to

observe the influence of geographic external group identity upon consumer preference

reveals a significant advantage of utilizing market level broadcast data: Game attendance

preferences, by nature, are constrained by physical barriers which restrict fan consumption

choices. In contrast, match broadcasts (in particular FTA broadcasting) have few barriers to

consumption and therefore provides a fairer measure of consumer preferences.

Perhaps more fundamentally, the findings confirm that consumers perceive metropolis

cities to represent a legitimate source of identity. For some consumers this identification may

not necessarily be a strong one, with a preference for local team consumption perhaps created

solely by associative comparison to non-local teams. For others, it may represent an

embryonic conception to deeper psychological connection (Funk & James, 2001). This is

none the less significant because, although Heere and James’s (2007) conceptual model

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identifies three levels of geographic identity (city, regional, national), the importance of such

identities are a reflection of the underlying communities of which an individual perceives

themselves to be a part (Heere, James, et al., 2011). Within the BBL, attachment to city

identity was evident across each of the five capital cities, despite an average population size

of 3.1 million residents (OzTAM, 2016). The implication for sport practitioners may be

simple but nonetheless significant: At a market level, consumers appear influenced by the

geographic connection purported by teams. Given the receptiveness of the market to such

cues, new teams need to carefully consider whether to align themselves to a suburban, city,

state, or regional identity.


Although viewer interest in the league increased only moderately during the five

seasons under investigation, the results presented in Table 16 and Figure 4 provide mixed

support for Hypothesis 2. Growth in FTA viewership largely occurred across all teams,

confirming that consumption growth has been driven by increased overall league interest

rather than toward individual local teams. Only two markets, Brisbane and Adelaide, were

observed to have achieved a significant increase in local team viewership. However, in each

case the increase in local team consumption was momentary, appearing to respond to a

winning local team. Aside from these momentary piques of interest, viewer behaviours

toward local teams appeared longitudinally consistent. Consistent with the behavioural

market patterns predicted by Ehrenberg et al. (2004), the differential in audience interest

between local and non-local teams normalized from the inception of the BBL competition

(Ehrenberg & Goodhardt, 2000) .

The largest differential in local audiences during the five seasons was in Perth,

whereby the Perth Scorchers played in fixtures that generated 35% of the cumulative Perth

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audience despite accounting for only 25% of games. However, this was no doubt also a

function of their time zone, which suppresses the audience of East Coast matches (played 3

hours later) not involving the local team. In Brisbane, meanwhile, the Heat were involved in

fixtures that accounted for 31% of cumulative Brisbane viewership, despite accounting for

25% of fixtures. While these figures reinforce that local teams drive local audiences (Tainsky

& Jasielec, 2014), such small differentials in the audience contribution of local teams belies

Noll’s (2007) assertion that local rights “capture most—perhaps nearly all—of the value of

national rights for many teams” (p. 23). The BBL is not consistent with that assumption,

deriving its ratings via a relatively diverse national spread that reflects the longstanding

national interest in various forms of cricket. This observation supports the view of Kunkel et

al. (2014) that a greater league-level rather than team-level orientation toward marketing can

often be beneficial for sport leagues.


BBL viewer interest during the five seasons telecast on FTA television was found to

be volatile, resulting in the rejection of Hypothesis 3. Notably, within a five season span, the

league had already exhibited evidence of growth, plateauing and retraction.

A potential explanation as to why BBL viewership did not conform to theorized

marketing norms may relate to the seasonal nature of the BBL sport product. Ehrenberg and

Goodhardt’s (2000) research suggests that brands reach a regular repeat purchase rate with

final penetration levels stabilizing within approximately 30 to 36 weeks. These findings were

based off purchase behaviour patterns from within the prescription drugs, food, drink,

personal and household cleaning product categories, which are available year-round and thus

do not suffer from scarcity. Given the BBL is played annually within a six to seven week

window, the totality of its duration over five seasons has equated to 30.5 weeks of

175

availability. As Viewer Duration appeared to normalize between Seasons 3 and 5, it is

plausible that the theorized norms predicted by this body of marketing literature may only

have begun to emerge towards the end of the analysis period (Ehrenberg, & Goodhardt, 2000:

Trinh, Romaniuk, & Tanusondjaja, 2016).

The findings supports the proposition of Mahony et al. (2002) that new leagues

initially face challenges in developing loyalty in the face of product novelty among

consumers. This is reflected in the longitudinal growth of Viewer Duration for BBL telecasts,

which is an effective measure of commitment to viewing. In Season 1, viewers watched an

average of 68 minutes per typical 175 minute BBL broadcast, the lowest of the five seasons.

In Season 2, this increased to 74 minutes before increasing and plateauing above 80 minutes

from Season 3 onwards. BBL television consumers are therefore shown to be becoming

heavier consumers of the product over time (Mullin, Hardy, & Sutton, 1993).

An empirical aspect in which new sport leagues may differ from other settings may be the

timeliness with which novelty seeking behaviour onsets and peaks. Novelty seeking

behaviour is said to reflect human desire to seek out the new and different (Hirchsman,

1980), with the epistemic value of product linked to the utility derived from the product’s

capacity to arouse curiosity and novelty (Sheth, Newman, & Gross, 1991). The novelty effect

is therefore associated with short-term time horizons and this has certainly appeared true in

the context of the impact of superstar athletes, where the effect has been most pronounced in

the first season (Shapiro, DeSchriver, & Rascher, 2017; Jewell, 2015; Lawson, Sheehan, &

Stephenson, 2008). It is contentious however whether BBL novelty peaked in Season 1 or

across Seasons 3 and 4. Season 1 exhibited the lowest commitment to watching telecasts,

indicating a greater propensity for curiosity based consumption that was transient in nature

(Berlyne, 1960; Park, Mahony, & Kim, 2011). Season 3 and 4 exhibited the highest number

176

of people consuming the BBL, resulting in seemingly abnormally high average ratings which

would align more strongly with the collection of previous empirical cases of new sport

leagues (Nakazawa et al., 1999; Trecker, 1998).

5.6 Conclusion

The BBL represents an opportune sport context in which to evaluate consumer

behaviour within new leagues and teams. This study’s focus on television ratings as the

dataset for analysis was also opportune given that previous research on new teams and

leagues has almost singularly focused on club STH. While that cohort represents a club’s

most passionate and resilient market segment, it constitutes a relatively small proportion of

the overall market. The present study addresses that limitation by considering fan behaviour

and preferences at a market level, as reflected by television ratings.

The research offers new contributions to our investigation and understanding of new

sport leagues and teams. First, whereas previous research focused solely upon STH, the

current study extended the data set to focus on television viewership preference. Local

audiences were found to exhibit an instantaneous viewing preference toward local teams,

generating viewership approximately 30% larger than for non-local games. This finding

expands the boundary conditions upon which social identity theory has been tested upon new

sport teams, confirming that entire markets (cities) exhibit biases towards new local teams.

This expands upon the works of Lock et al, 2009; Lock et al., 2011, James et al., 2002) and is

consistent with Heere and James’s (2007) conceptual model. Second, local audiences did not

become more interested in local teams over time but rather largely fluctuated concurrently

with interest in the league as a whole. This was significant because it illustrated that growing

television audiences is a league-led rather than a team-led task (Kunkel et al., 2014). Only

two identified cases of local audience growth were identified, which in both cases occurred

177

when the team in question was winning. The effect of winning on local team market

preference may have been particularly amplified due to the absence of established history or

performance to define brand associations to develop ingrained customs.

Finally, the present study explored consumer novelty in the context of a new league,

adding to a small pool of scholarship to explore sport curiosity and novelty (Park, Mahony, &

Kim, 2014). It concluded that the league appears to have experienced an initial period novelty

seeking behaviour by consumers (Hirschman, 1980), with potential stabilization of market

consumption towards the end of the analysis period (Ehrenberg & Goodhardt, 2000). These

findings were consistent with the limited pool of existing empirical enquiry into initial

fortunes of new sport leagues as a whole (Mahony et al., 2002), providing some evidence

toward an empirical generalization that the fourth season of a new sport league represents a

turning point in respect to novelty and embeddness.

Despite an ability to capture interest at perhaps the broadest level, television ratings

represent an underutilized data resource within the study of fan preferences more broadly.

The research approach therefore warrants further application, with strong potential to address

longitudinal questions around the development of new leagues and teams.

178

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6. Study 5: Quantifying the Value of Sport Broadcast Rights

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Abstract

Although sport broadcasting has received a considerable amount of academic attention, how

sport content is valued and monetised from a broadcaster perspective remains relatively

underdeveloped. This article adopts multisided market theory to test the broadcast value of

Australia’s two most valuable sport media properties, the Australian Football League and

National Rugby League. To do so, a content and ratings analysis was performed to quantify

the interaction between content and viewership within broadcasts. The article concludes that

innate game dynamics have a significant bearing on the value generated for broadcasters

from sport content. Advertising aired during intermissions generated audiences 23% smaller

than advertising within the match itself. Notably, the National Rugby League’s most valuable

timeslot was a delayed telecast, which although potentially reducing the audience size,

allowed for an increase in the concentration of advertising within the telecast.

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6.1 Introduction

Given the dramatic growth in the valuations placed on sport content and the evolving

diversity of broadcast media, effective management of sports rights is an increasingly central

strategic issue within sport management (Turner, 2007). Growth in the value of sports rights

has also been a catalyst for a surge in scholarship surrounding broadcast rights (Gratton and

Solberg, 2007). To date, there appears to be a consensus that the demand for sports media

rights is largely contingent upon the size and demographic characteristics of the potential

audience (Solberg and Gratton, 2000). Correspondingly, a large number of demand studies

has, therefore, focused on understanding the drivers of potential audience size (Buraimo and

Simmons, 2009; Johnsen and Solvoll, 2007; Tainsky and McEvoy, 2012; Tainsky et al.,

2014; Alavy et al., 2010).

However, given that free-to-air (FTA) commercial broadcasters primarily generate

their financial return via the advertising component of sport telecasts (and public broadcasters

to varying degrees), the emphasis placed on audience demand modelling explores only one

element in the valuation of sports rights. As noted by Solberg and Hammervold (2004): ‘It is

important to bear in mind that [advertising] income corresponds with the ratings figures

during the commercials, not during the programs themselves. Thus, it is necessary to estimate

the correlations between the ratings figures during the core program and the commercials’ (p.

86). Yet, few models or theoretical frameworks have been developed, or have attempted, to

quantify the role of game dynamics in shaping advertising opportunities as an underlying

driver in the financial value of sports leagues (Dietl and Hasan, 2007; Késenne, 2014). The

research addresses this significant gap by introducing multisided market (MSM) theory as an

approach to evaluating the relationship between sport audience and advertisers, focused

specifically on FTA broadcast markets (Evans and Schmalensee, 2007).

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The application of MSM theory in evaluating the audience-advertiser relationship is

contextualised via comparison of the broadcast structure of Australia’s two largest

commercial sports, the Australian Football League (AFL) and the National Rugby League

(NRL). The central objective of the paper is therefore to quantify and to compare the

broadcast composition of Australia’s most valuable sporting broadcast properties. This

objective is achieved by analysing the broadcast structure of each organisation through the

synchronisation of minute-by-minute television ratings data with a corresponding broadcast

content analysis for a select sample of matches during the 2012 competition season.

Specifically, the research is focused on assessing the structure of content in respect to the

concentration of advertising within broadcasts, the distribution of advertising and content

within broadcasts, as well as intra-broadcast ratings fluctuation.


Sport displays unique characteristics that make it specifically desirable to

broadcasters. Notably, sport content not only generates improved advertising revenue and

subscriber rates via its appeal among lucrative demographics, but it can also provide positive

spill-over effects for a broadcaster’s brand and other programming (Hoehn and Lancefield,

2003). Additionally, the commitment of sports fans to their team and sport provides

broadcasters with a relatively loyal audience in an era where new technologies and platforms

are exacerbating audience fragmentation (Szymanski, 2006). Sport has been shown to be not

only resilient to fragmentation, but also adaptive to new audience cultures and forms of

consumption (McCosker and Dodd, 2013). In 2015, sports programming accounted for 1.4%

of American television content but represented 49.7% of Twitter TV activity (Nielsen,

2016b). The growing intersection between social and sport media was made further apparent

in 2016, when the National Football League (NFL) announced that it had partnered with

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Twitter to stream 10 Thursday night football games (National Football League, 2016). Such

arrangements reflect the changing patterns in sport and media consumption more broadly.

Hutchins (2014) has noted that the rise of mobile technology has not only led to ‘on the go’

consumption of sport media but it has also facilitated the development of multi-screen

behaviour. Nielsen (2016a) noted that in 2016, 76% of Australians aged 16 and over had

multi-screened while 90% of consumers aged 16-34 had also done so. Notably, the average

Australian home now features 6.4 screens, one screen more than had been the case in 2012

(Nielsen, 2016a).

The final desirable characteristic of sport content relates to its live and perishable

nature, which provides resistance to the practice of digital recording, thus protecting against

consumers fast-forwarding through content in order to avoid advertisements. According to

Deninger (2012), 90% of viewers who watch a sport broadcast will do so live. The need to

telecast live, however, has also brought about challenges and in response, it has been sport

that has historically adapted to the needs of broadcasters. Rowe (1996) noted that

broadcasters progressively pressured sport into modification, resulting in rule adaptation,

television-friendly schedules, and restrictions on overtime. Such modification has seen sport

shift from a traditional spectator-based model into a global media model termed ‘Media-

Corporations-Merchandising-Markets’ (Andreff and Staudohar, 2000). As noted by Evens,

Iosifidis and Smith; ‘Fuelled by technological developments in broadcasting and

communications more generally, this repackaging of sport as a commodity has expanded into

a global business that effectively functions as a specialised division of the entertainment

industry’ (2013: 13).

The exact financial value of sports rights is determined by a combination of micro and

macro factors, as well as by unique sport-specific considerations. According to Gratton and

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Solberg (2007), such determinants include the size and purchasing power of the population,

the popularity of the sport among the general audience, the level of competition on the supply

side as well as the demand side, and the clarity of juridical ownership. An additional factor

that shapes the value of sport relates to legislative regulation. Australian broadcast protocols

surrounding sport content (known as ‘anti-siphoning’) are among the world’s most stringent,

dictating which events must be shown on FTA television (Rowe and Gilmour, 2009). As

Australia’s largest sport properties are constrained to a large degree when appearing on FTA

television, they must take a particular interest in understanding how to economically

maximise the value of such rights.

From an economic viewpoint, one of the few proposed sport broadcast valuation

models is that of Noll (2007). Noll’s research suggests that for advertising-supported

programs such as FTA and public television, revenue is determined ‘by the size of the

audience and its distribution across demographic categories’ (p. 404). This definition,

however, largely disregards the role of the content and advertising in shaping rights fees,

which appears potentially vital in driving sport broadcast valuation (Solberg and

Hammervold, 2004). Unlike pre-recorded programming that carries a planned concentration

and schedule of commercials, sport broadcasting contains variability in terms of the volume

and timing of advertising presented and must be telecast live or near live to maximise ratings

(Gaustad, 2000; Cowie and Williams, 1997). Cricket, for example, provides strong television

content due to the regular intermissions inherent to the sport (Whannel, 2000). In contrast,

soccer, due to its fluid pace, does not create opportunities for in-game advertising breaks,

restricting advertising segments to intermissions during which time the audience diminishes

significantly (Késenne, 2014). Further complications arise when considering other match

dynamics. The duration of a cricket (or tennis, volleyball) match is contingent on the

192

competitors, whereas football telecasts are guaranteed a minimum duration irrespective of

weather and score progression. Yet, the role of advertising concentration in sports value has

been scarcely considered within existing modelling literature. In observing significant

differences in the sport broadcasting market structures of North America and Europe, Dietl

and Hasan (2007) noted:

A soccer match consists of two respectively uninterrupted halves of 45 minutes which

are separated by a 15 minute half-time break. This half-time break thus is the primary

interval in which networks are able to air commercials without causing very high

disutility for viewers. The North American Major League sports however, are

interrupted significantly more often . . . adding to a total potential commercial time

significantly exceeding the 15 minutes in soccer. (p. 416)

This shortcoming is overcome through the theoretical identification that media

products are multisided (Anderson and Gabszewicz, 2006). An industry is characterised as

multisided when a supplier serves distinct customer groups. This is in contrast to one-sided

markets where there is a homogeneous group of customers and transaction volume holds a

relatively linear relationship to price charged. Evans and Schmalensee (2007) observe three

criteria that characterise MSM:

The existence of at least two distinct customer groups

An indirect connection between these groups by indirect network effects (externalities),

Difficulty in sufficiently internalising externalities

Media products are multisided by nature because broadcasters serve two distinct

customer groups: viewers and advertisers (Budzinski and Satzer, 2011). Each stakeholder

however, has divergent objectives. Viewers make a discrete choice of which station to watch,

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that maximises their utility according to their preferences. Importantly, viewers are assumed

to be averse to watching advertising (a disutility) with few exceptions such as the Super

Bowl. Advertisers endeavour to reach a target market to shift demand for their product.

Broadcasters attempt to reach equilibrium between these two participants, in turn maximising

their financial return. Reaching this equilibrium is a particular challenge for FTA

broadcasters as they have greater control over a viewer’s ability to consume advertising.

Unlike print media, where a consumer can easily bypass advertising, FTA broadcasters have

the ability to control both the dispersion and concentration (within legislative parameters) of

advertising (Anderson and Gabszewicz, 2006). Notably, the sport industry is itself a MSM

and, therefore, sport broadcasts represent a rare intersection between two multisided products

(Budzinski and Satzer, 2011). Given that sport content by its nature contains considerable

variability, the scheduling of advertising within this form of content poses a particularly

distinct challenge to FTA broadcasters and is thus the core challenge addressed in this

research.

6.3 Method

Contextual Setting

The Australian football landscape is underpinned by a long-standing cultural

phenomenon known as the ‘Barassi Line’, a metaphoric demarcation of the country’s football

preferences (Hess and Nicholson, 2007). This line is geographic in nature, with North-East

Australia (including the city of Sydney) preferring the Rugby codes while in South-West

Australia (including the city of Melbourne) AFL is the dominant code (Cashman, 2010).

Although many factors have influenced the popularity of football in specific areas, it is

apparent that interest in the football codes is still strongly linked to these heartlands (Fujak

and Frawley, 2015). Research by Fujak and Frawley (2013) has illustrated that 81% of AFL

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and 93% of NRL audiences are derived from respective heartland markets found on each side

of the Barassi Line.

The cities of Sydney and Melbourne are particularly notable as they represent two of

the most crowded sport markets in the world (Fujak and Frawley, 2016b). In total, 28 top-tier

commercial football clubs compete across four football codes within these two cities, each

with a population nearing five million residents (OzTAM, 2016). The NRL and AFL,

however, maintain commercial dominance in these respective markets. Within the 16 team

NRL competition, 9 teams are based in Sydney, with the city responsible for 33% of

cumulative NRL television viewership (Fujak and Frawley, 2013). Within the 18 team AFL

competition, 10 are Melbourne based, with the city responsible for 37% of aggregate AFL

television viewership (Fujak and Frawley, 2013). Given that Sydney and Melbourne

cumulatively account for 50% of national advertising spend on FTA television, the popularity

of these two codes in these two cities is a large contributor to their media broadcast rights

value (FreeTV Australia, 2016). The AFL and NRL respectively hold the two largest media

rights deals in Australia, with the AFL signing in 2015 a AUD $2.5 billion six year

agreement and the NRL signing in 2016 a AUD $1.8 billion five year agreement (National

Rugby League, 2016; Australian Football League, 2016)

Broadcast Setting

The research adopted a quantitative methodological design utilising television

broadcasts and ratings from 20 AFL and NRL matches broadcast on FTA television during

the 2012 season. The decision to examine both the AFL and NRL was based on several

critical considerations. Firstly, the study was focused on evaluating the broadcast structure of

FTA television, of which the AFL and NRL retain the dominant share of coverage, as they

were the only football codes televised on FTA on a weekly basis during the timeframe under

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examination (Fujak and Frawley, 2016a). Secondly, given the parity between the two leagues

in terms of their most recent broadcast valuations and market leading positions, this approach

best allowed for comparison of commonalities and distinctions between the leagues (Bryman,

2008). Additionally, combined analysis of the leagues allows for the opportunity to compare

broadcast structure across different timeslots and broadcast types (i.e. live versus delayed

broadcasts). Finally, historical fixtures were analysed due to more recent changes in

broadcast structure that hamper analysis of more recent seasons. In 2015 the NRL introduced

live Sunday afternoon fixtures and in 2016 replaced delayed Friday fixtures with live

Thursday fixtures.

Data background

The study utilised minute-by-minute ratings data collected by television ratings

research organisation OzTAM, who are the sole providers of television ratings information

across metropolitan Australia and can, therefore, lay claim to being the central medium by

which billions of dollars of television media are bought and sold (Fujak and Frawley, 2013).

Given the financial significance of the organisation’s output, OzTAM adopt stringent

methodological guidelines in their ratings collection process to achieve timely, valid and

representative data. Their methodology is consistent with those utilised in other developed

markets across North America and Europe (OzTAM, 2010). Minute-by-minute data was

utilised as it represents the most granular level of ratings information available.

FTA television ratings within Australia’s five capital cities are particularly important

due to their financial significance. Metropolitan markets accounted for 79% of the

approximate $4 billion expended on FTA advertising in 2015 despite representing 69% of the

population (FreeTV Australia, 2016). Although subscription platforms also contain

advertising, income from this source is secondary to subscription revenue. In their last

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publicly available financial reporting, Consolidated Media Holdings (2012) reported that

subscriptions accounted for 70% of total Fox Sports Australia revenue ($348.8 million). This

was followed by advertising (18%) and other revenues (12%).

Analysis

Analysis was limited to a sample of 20 matches played on Friday night and Sunday

afternoon, due to the substantive coding depth required. In total, the twenty fixtures contained

56 hours of content that, in turn, created 13,324 units of analysis (15-second intervals). The

20 matches were recorded directly from the live public broadcast, ensuring that coded footage

was the same as that seen by home audiences and, specifically, those within the OzTAM

sample. Given the popularity of each league in specific local markets, NRL broadcasts were

analysed specifically against Sydney television ratings, while AFL broadcasts were analysed

against Melbourne television ratings. As Australia’s largest capital cities, Sydney and

Melbourne are allocated the largest sample size in measuring television ratings, improving

the reliability of the data (OzTAM, 2016). To improve sampling validity, a stratified sample

was utilised to ensure a fair representation of matches. The selection of fixtures has attempted

to control confounding variables within the limitations of empirical observational data.

Season is consistent given that all fixtures come from within one season. Furthermore, no

fixtures that played on public holidays or that were marquee in nature were included in

analysis. The sample is representative across match score line with a balanced distribution

between even and more convincing victories.

Recordings of the corresponding fixtures were analysed, coded, and aligned with

ratings data. The alignment of ratings with content allows for the standardisation of telecast

analysis that facilitates several significant comparisons. Firstly, the analytical method allows

for the comparison of structurally variant broadcasts within a league. For the purposes of this

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research, the method allows for the comparison of three NRL broadcast timeslots to

determine their relative advertising performance despite differing telecast times, days and

structures (live or delayed). The analytical method also allows for comparison between the

AFL and NRL, despite the leagues having different broadcast durations (3 hours versus 2

hours) and match structures (quarters versus halves).

In order to test the statistical significance of observed relationships, analysis was

performed in the R statistical package. To allow for comparison of audience exposure to

advertising with programs across the different timeslots and leagues, a metric titled ‘Ad View

Hours’ (AVH) was calculated. The AVH metric is derived by multiplying the audience size

against the duration of advertising within each broadcast, therefore accounting for both the

concentration of advertising within a program and the size of the audience during such

programs. The AVH measure creates a standardised unit, similar in nature to the method

historically adopted by the International Olympic Committee (2014) to compare viewership

across Olympics Games. Factors influencing the audience were analysed both in 15-second

intervals as well as at an aggregate game audience level using multifactor analysis of

variance. The factors considered in this analysis include the timeslot of the game, the code

played, the content type being displayed in a given interval, and whether the 6pm news was

due to commence within 10 minutes of the measurement.

6.4 Results

Comparing NRL broadcast timeslots utilising MSM framework

Each of the NRL’s three fixture types generated distinctly different audience viewing

and advertising concentration distributions. The Friday night live (FNL) fixture generated the

strongest average Sydney audience of 437,529 viewers (n = 4), considerably more than the

Sunday afternoon delayed (SAD) coverage with 290,049 viewers (n = 4) and the Friday night

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delayed (FND) coverage of 208,342 viewers (n = 4). However, while FNL was able to

generate the strongest average audience, this timeslot was also the weakest for the FTA host

broadcaster with reference to advertising concentration. The FNL telecast contained an

advertising concentration of 15.6%, significantly lower than SAD (24.8%) and FND telecasts

(22.1%).

Minute-by-minute television ratings illustrate that there is also considerable intra-

broadcast viewing variance within sport broadcasts. Each fixture type displays a unique

ratings pattern, reflecting both the varying timeslots of telecasts and a distinction between

live and delayed content. The FNL broadcast viewership is characterised by a double peak

and trough pattern whereby there is a significant uplift in viewing for in-game content and

noticeable declines during intermissions (see Figure 5). Within the FNL timeslot, in-game

content generated an average audience of 470,052, while the intermissions (pre-game, half-

time and post-game) recorded an average audience of 355,536 (a 25% reduction in compative

ratings).

Figure 5: NRL broadcast by segment duration and audience size

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FND fixtures are characterised by a rapid and progressive decline. At the end of the

first half of the delayed match, the audience has declined by 37.4% (214,395) from the first

minute of telecast (342,635). By the final minute of the entire telecast at 11:29 pm, the

audience has declined by 76% (81,599) from the first minute of telecast at 9:31 pm. In

contrast, SAD fixtures display a growth trajectory as the broadcast culminates. The last 10

minutes of the broadcast (17:50 to 17:59) recorded a significantly larger average viewership

of 396,811 as compared to 281,362 for the first 110 minutes of the telecast (ANOVA, p =

0.014) (see 4.1 in Table 18). Given that the broadcaster uses the last minute of the broadcast

to select a sponsored ‘man of the match’, this advertising segment is perhaps among the more

valuable within their inventory.

Table 18: Regression upon ratings with NRL

Variable Estimate Std. Error t value

Pr(>|t|) Pr(>F) 4.1 Intra-broadcast ratings variance (NRL) (Intercept) 261,051 3,641 71.692 < 0.001 CodeNRL 3,880 5,249 0.739 0.45976 0.45976 Ad.SpaceContent 43,121 4,004 10.769 < 0.001 < 0.001 Before News 15,969 6,898 2.315 0.02063 0.02063 CodeNRL:Ad.SpaceContent 17,804 5,840 3.048 0.00231 0.00231

4.2 Broadcast structure and advertising dispersion (NRL)

(Intercept) 191,052 3,367 56.748 < 0.001 < 0.001 Ad.SpaceContent 22,991 3,882 5.922 < 0.001 < 0.001 Match Type: Friday Live 178,054 5,470 32.55 < 0.001 Match Type: Sunday Delay 85,983 4,958 17.343 < 0.001 < 0.001 Ad.SpaceContent:Match.TypeFriday Live 63,320 6,082 10.411 < 0.001 Ad.SpaceContent:Match.TypeSunday Delay -6,293 5,663 -1.111 0.267

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Analysis of the timing of advertising content within telecasts illustrates that

advertising is not dispersed homogeneously within broadcasts. In FNL fixtures, for instance,

84% of the program’s advertising occurred during intermissions, despite accounting for only

28% of the telecast time (see Figure 6). Given that intermissions generated reduced ratings

compared to in-game content (355,536 versus 470,052), it is therefore of little surprise that

ratings achieved for advertising were less than that for content. Overall, audiences for

advertising are 18% smaller than for content (369,106 versus 450,153) while the audience for

advertising aired during intermissions are 22% smaller compared to those aired within the

game when in-play (353,376 vs. 451,689). This decrease in audience during advertising

content on timeslot is statistically significant (ANOVA, p<0.0001) (see 4.2 Table 18).

Figure 6: NRL average audience size and advertising concentration

455,417

214,043

293,734

369,106

191,052

277,035

- 100,000 200,000 300,000 400,000 500,000

Friday Live

Friday Delay

Sunday Delay

Intermission

In-game

% of Advertising: 19%

81% 26%

74% 84%

16%

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In contrast, the delayed nature of the FND and SAD fixture provides the broadcaster

greater flexibility to in-build advertising within content and reduce their reliance on

intermissions. Each 40-minute half of football within these two fixtures takes approximately

54 minutes to view, with advertising accounting for the discrepancy. As a result, 74% of

advertising within FND fixtures and 81% of advertising during SAD fixtures occurred

ingame. Correspondingly, advertising content generated an average rating of 96% of the

match average for SAD matches (277,035) and 92% of FND fixtures (191,052).

The SAD fixture appears to be the league’s most valuable timeslot. The SAD telecast

generated 121,491 hours of viewed advertising compared to 115,346 hours for FNL and

97,912 hours for FND. Due to a small sample size of aggregated data (n = 4 per fixture type),

such a conclusion was unable to be statistically validated (ANOVA, p = 0.313) (see Table

19). Nonetheless, there appears to be strong indicative evidence that SAD telecasts provide a

good balance between high advertising concentration (via a delayed telecast) and a

reasonably strong core rating (aided by a low small audience decline for advertising content).

In contrast, the FNL fixture did not convert its high ratings into advertising, while the FND

telecast suffered from lower audiences.

Table 19: Regression results of total AVH

Variable Estimate Std. Error t

value Pr(>|t|) Pr(>F) (Intercept) 352,491,319 38,235,875 9.219 < 0.001 Match Type: Friday Live 62,753,213 54,073,693 1.161 0.276 0.313 Match Type: Sunday Delay 84,877,954 54,073,693 1.57 0.151

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Comparing NRL to AFL broadcast value utilising MSM framework

Friday Night Football. The structure of each league’s Friday night football telecast is

summarised in Table 20, with analysis indicating that the AFL is the structurally superior

telecast product in the timeslot. Given the AFL’s greater game length, the league is able to

deliver a 225-minute broadcast from a single game (including distinctly reported pre and

post-game commentary segments), whereas the NRL delivered two games (one live, one

delayed) for the duration of 244 minutes. Evidently, AFL broadcasters benefit from structural

game elements of the sport which help maximise their ability to monetise content. An

average of 28 goals was scored per AFL match within the sample (compared to 7 tries in an

NRL match), each providing the potential for a 30-second advertising break. Such breaks are

also potentially more amenable to viewers, aware that the game clock stops between scoring

and the restart of play. As a result, 26% of advertising within AFL game telecasts happen in-

game (compared to only 16% for live Friday games).

Table 20: Friday night football ratings analysis by component Broadcast

Duration Broadcast

Rating Content Rating

Adv. Rating

Aud Disc.

Adv. Con. AVH AVH

P/H

AFL

Pre-game 20 295,575 294,223 299,453 1% 26% 25,890 77,429

Game 159 449,802 455,028 419,293 −7% 15% 162,476

61,312

Post-game 46 211,533 210,089 216,028 2% 24% 40,505 52,547

Combined 225 387,160 390,431 366,898 −5% 18% 228,871

60,948

NRL

FNL game 120 442,313 455,417 369,106 −17% 16% 115,346

57,493

FND game 124 208,342 214,043 191,052 −8% 25% 97,914 47,378

Combined

244

323,592

332,940

278,759

−14%

20%

213,260

52,361

Aud Disc. = Audience Discount (Content Rating/Adv Rating), Adv. Con. = Advertising Concentration, AVH = Ad View Hours, AVH P/H = Ad Viewer Hours Per Hour

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The by-product of greater in-game advertising within AFL broadcasts is that the

differential between the average television rating for content and advertising (the “audience

discount”) is smaller compared to NRL broadcasts. A broadcaster benefits from content that

suffers only a small audience discount as this ensures that their advertisers reach an audience

that is close to the reported ‘average’ audience of the entire program. AFL advertising

operates on a 7% advertising discount to content for live matches compared to 17% for NRL.

Comparing Friday night fixtures, for example, while the AFL and NRL broadcasts record

similar overall viewership audiences in their home markets (450,302 vs. 442,313, p-value =

0.883), they record significantly different average advertising audiences (392,469 vs.

369,106, p-value<0.0001). Furthermore, on an AVH per hour basis, the AFL outperformed

the NRL by 16%.

Sunday Afternoon Football. In contrast to Friday night football, the NRL was able

to generate a superior advertising return than the AFL for their host broadcaster in the Sunday

afternoon timeslot. The NRL telecast is able to generate a higher AVH in 2 hours of content

(121,491) than the AFL’s 3-hour telecast (117,271; see Table 21). The NRL’s superiority

reflects that Sunday afternoon matches were broadcast on a one-hour delay, providing the

host broadcaster with greater control over advertising dispersion and concentration within the

telecast. The NRL also recorded a stronger average television rating for Sunday football,

although even with an equal television rating, the NRL would remain the better advertising

property on a per hour basis. Another by-product of delayed NRL Sunday afternoon coverage

was that the audience discount for advertising content was considerably smaller as compared

to the FNL fixture (4% versus 17%).

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Table 21: Sunday afternoon football ratings analysis by component

Broadcast Duration

Broadcast Rating

Content Rating

Adv. Rating

Aud Disc.

Adv. Con. AVH AVH

P/H

AFL

179

224,672

228,828

206,950

−8%

19%

117,271

39,309 NRL 119 290,049 293,734 277,035 −4% 22% 121,491 61,128

Aud Disc. = Audience Discount, Adv. Con. = Advertising Concentration, AVH = Ad View Hours, AVH P/H = Ad Viewer Hours Per Hour

6.5 Discussion

Applying MSM to Sport Broadcasts

To date, scholarly evaluation of sport broadcasting has been largely oriented towards

the audience side of the transaction, with considerably less emphasis on the advertiser

component or the interaction between the two (Solberg and Hammervold, 2004).

Furthermore, while there has been general acknowledgement of the importance of game

dynamics shaping broadcast fees, there has been little empirical testing of it to date (Késenne,

2014; Dietl and Hasan, 2007). To address this gap, the application of a relatively new theory

was adopted, that of multisided markets. As noted, sport broadcasting potentially provides a

special case of MSM due to its intersection of two distinct multisided markets, that being

both sport and media (Budzinski and Satzer, 2011).

The data, albeit from a small but robust sample, illustrated the potential application of

MSM theory in sport broadcast markets. Most notably, the results illustrated a method by

which practitioners and academics alike could more effectively evaluate sport content. In

particular, the research expands on Noll’s (2007) framework for sports broadcast rights fee

evaluation by further defining revenue as a construct driven by two distinct components

(advertising concentration and audience size). To facilitate this theoretical development, it

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was proposed that the audience size be further demarcated between program and advertising

content and that the advertising concentration of telecasts be calculated. By multiplying

broadcast duration by advertising concentration and average advertising rating, it was

possible to derive a figure that reflects a program’s advertising performance (AVH), thus

allowing for comparison of sports.

More broadly, the research also expands our understanding of how sport broadcasts

are consumed. Although television ratings are often announced as a single, aggregate average

figure when reported in the media, it is evident that there is significant intra-broadcast

variance in audience viewership. It was determined that advertising content and intermission

content both significantly reduce audience size (consistent with the assumption that

advertising causes disutility), consistent with existing findings by Solberg and Hammervold

(2004), who observed that advertising achieved only an average of 45% of the audience size

within European football coverage. The research illustrates that the structural elements and

game dynamics of a sport have a significant impact on the return on investment (ROI) that

can be achieved by a broadcaster (Whannel, 2000). AFL broadcasters benefit from more

natural intermissions that allow for natural advertising dispersion, thus mitigating audience

leakage during advertising. By comparison, the NRL conforms to the European soccer model

identified by Dietl and Hasan (2007), in which audiences for advertising diminish

considerably due to their concentration during intermissions.

NRL shift to live Sunday football

Significantly, utilising consumer revealed preferences (through television ratings) via

the adoption of a MSM framework can also have implications for how sports modify their

structure to maximise broadcast value (Rowe, 1996). In 2014, the NRL announced a change

in structure for Sunday afternoon football, resulting in the telecast being broadcast live rather

206

than on a one-hour delay. Given that this research identified this delay as key to the timeslot

achieving the strongest ROI of the league’s three timeslots, the impact of live telecasting can

be hypothesised through the MSM framework. The new live structure is likely to result in a

decline in advertising concentration consistent with FNL telecasts (from 22% to 16%), as

well as a greater audience discount for advertising content (from 4% to 17%). With these

factors in mind, the average broadcast rating would need to increase 62% from 290,049 to

470,903 to offset the loss associated with the other calculation elements. This would seem to

be an unlikely proposition as it would result in the host broadcaster losing value from the

change in structure.

6.6 Conclusion

In this study multisided market theory was empirically applied to Australia’s two

largest sport broadcasting properties and Noll’s (2007) conceptual framework for FTA sport

broadcast rights valuation was expanded upon. The analysis illustrated that cumulative

television ratings alone do not provide a complete or accurate portrayal of the value generated

for respective broadcasters. This determination was best typified within the NRL, for which it

was found that the delayed Sunday afternoon fixture was the most valuable broadcaster

timeslot despite the aggregate program rating being only 66% of the rating for live Friday

night football. Overall, however, the AFL was shown to be the structurally superior live

broadcast property, being capable of inserting a greater proportion of advertising within

match content as well as having more intermission time in which to place advertising blocks.

Despite the advancements offered by the expanded framework, it is not without

limitations. The framework currently measures value in units of advertising exposure

generated, thus only allowing for comparison between timeslots and sports in relative terms.

Television advertising rates are needed to develop the framework and provide an avenue of

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further research. Furthermore, the study was limited in sample size, prohibiting some

statistical validation. Nonetheless, this research represents one of the first attempts to

empirically apply MSM theory within a sport context and, therefore, provides an

advancement of scholarly understanding of sport broadcast viewing behaviour and economic

value.

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7. Discussion and Conclusions

The following conclusion brings together the discrete studies presented within Study

1 through 5 to provide an integrated discussion of findings in relation to the overarching

research question. To do so, the thesis context and purpose is first briefly restated, followed

by identification of the links between studies and their collective response to the central

research question. Finally, the chapter identifies the contribution to literature and practice

before reflecting upon the research process and the implications for future work in the field.

7.1 Thesis context and purpose

This thesis explored the nature and structure of sport markets, in relation to consumer

behaviour and preferences within such markets. The thesis was underpinned by a contextual

observation that such markets in many developed Western nations have exhibited a growing

competitive intensity, increasingly referred to as ‘crowded’ within academic literature. This

growth in the absolute volume of sport teams as well as the transformation of sporting

organisations into increasingly sophisticated commercial entities has corresponded to

continued significant growth in the financial value of the industry (PricewaterhouseCoopers,

2015). Yet, while there has been corresponding growth in sport management scholarship

surrounding sport consumers, such research has focused largely on the micro level of sport in

relate to the connection between fans (typically more fanatical fans) and individual teams

(Park et al., 2011; Park, Mahony, & Greenwell, 2010). Against this background, this research

takes a more holistic perspective and explores the increasing array of sport fan consumption

choices to understand the broader sport market. Given much of our understanding of the

structural elements of sport management has in fact been developed by sport economists

(Shilbury, 2012), the adoption of a management perspective toward the exploration of

consumers within markets represents the basis of the work’s contribution.

213

Two particularly noteworthy phenomena have shaped sport markets. The first is the

growth of methods to consume sport, shaped by an increasingly diverse array of media

technologies. Commencing in the early 1990s, sport’s digital era now allows mediated sport

consumption that has expanded the methods through which fans can consume sport (Hutchins

& Rowe, 2009; Rowe, 1996, 2011; Todreas, 1999). Streaming technologies now allow sport

consumption on devices such as phones and tablets, continuing to fragment the focus of

consumers, while further advances in Virtual Reality and ancillary products around gaming

and fantasy sport continue to expand the horizon of sport media consumption. In relation to

the competitive positioning of the sport product, Mauws et al. (2003, p.149) already noted 15

years ago: “What has changed in recent years is not so much the types of substitutes

available, but, rather, the variety within each type”. Secondly, around the world there has

been growth in the number of leagues and teams that compete for market share (Byon et al.,

2010). Kim and Trail (2010) for instance estimated there to be over 600 professional sport

teams in America. In a local context, there has been a 66% increase in the total number of

fixtures produced by Australia’s top seven leagues between 2000 and 2017 (see Table 12).

Correspondingly, there have been 31 new sport teams added to the Australian sport landscape

to 2017 since 2005, representing an 80% increase in market competitors. Inclusive of the

recent commercial invention of women’s sport teams, which largely sit under the same team

branding umbrella, there has been a 140% increase in the volume of teams within the

Australian sport market over this time.

While there appears to be consensus that sport markets are increasingly competitive

and crowded (McDonald et al., 2010; Rein et al., 2006), there has been scant research that

attempts to quantify the behaviour and structure of such crowded sport markets (Field, 2006;

Pelnar, 2009). The scarcity of such research is particularly surprising given the centrality of

214

competition to the sport sector: “Managing the implications of competition, both on and off

the field, is a critical success factor and a strategic imperative in its own right. Competition,

therefore, is the heart and soul of sport management” (Shilbury, 2012, p. 2). Yet while the

effect of competition within markets is generally well understood, its impact upon sport

markets may not conform, given the sport experience is “mired in the irrational passions of

fans, commanding high levels of product and brand loyalty” (Smith & Stewart, 2010, p. 3).

This research set out to remedy this shortcoming by undertaking an analysis of sport

consumer behaviour within sport markets that feature a high degree of consumption choice.

This was achieved through the conduct of five studies that are interconnected thematically,

conceptually and methodologically to address the overarching thesis research questions. The

interconnection of the studies is now addressed to further elucidate the overall contribution to

theory and practice.

7.2 Study linkage and findings

The thesis presented five studies that collectively contributed to addressing the core

research aim and questions of the thesis. These studies, as were presented in Chapters 2

through 6, are reaffirmed within Table 22 including their submission journals and status.

Their methodological, conceptual and findings interconnection is now identified and further

discussed below.

215

Table 22: Thesis research outputs reaffirmed Study Study Submission Journal (rank) Status 1 The Consumer Market Structure of

Australian Sport


Under review

2 Are sport consumers unique? Consumer behavior within crowded sport markets

Journal of Sport Management (A*) Published

3 Testing the Relationship Between Revenue and Fan Base Size Within Sport Markets


Under review

4 Consumer Behavior toward a New League and Teams: Television Audiences as a Measure of Market Acceptance

Marketing Intelligence and Planning (A)

Under review

5 Quantifying the value of sport broadcast rights

Media International Australia (AERA) Published

Methodological

The thesis is underpinned by a quantitatively orientated multimethod design. The

purpose of the multimethod design is to improve the strength of the overall research design

(Morse, 2003). Multimethod designs typically, although not exclusively, incorporate a

mixture of qualitative and quantitative methods. This thesis employs multimethod by virtue

of the multiple distinct datasets and analysis approaches which are incorporated into the

discrete studies. The diversity of methodologies utilised within the thesis is a reflection of the

exploratory nature of the research question. The overarching methodology of the thesis, then,

is replicated across studies in three respects. Firstly, each study is underpinned by a

quantitative methodology. Secondly, each study performed analysis upon market level data.

Finally, the study context remained consistent across all five studies.

In respect to the analysis of market level, Studies 1 and 2 utilised primary survey data

consisting of representative Australian populations. Studies 4 and 5 performed market level

analysis by utilising television ratings data from the Australian broadcast market. The data

within Study 1 comprised of survey responses from 27,412 Australian residents, followed by

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a second survey targeting Sydney and Melbourne residents with a sample size of 2,572. The

two datasets in conjunction allowed the thesis to address the central research question from

different perspectives. The dataset of Study 1 has considerable depth in respect to its volume

of respondents and is among the largest primary data samples analysed within the field.

However, counterbalancing this volume and depth was the limited pool of questions, with the

survey containing only five demographic questions and two sport questions. This survey

however, remained attitudinally focused. By contrast, Study 2 utilised a smaller sample of

respondents, but included a greater volume and variety of questions. The second primary

survey posed 48 questions across a range of demographic, behavioural and attitudinal

questions. These behavioural questions were particularly vital for performing the Dirichlet

modelling within Study 2.

Finally, the thesis is set within one central research context: the Australian sport

marketplace. The decision to set the study within an Australian context was based on several

considerations. First, Australia is one of the world’s most concentrated sporting markets. The

nation is home to 24.5 million residents who sustain more than 70 elite commercial sport

teams, spread across only 12 cities and across seven mainstream sports. Additionally,

participation statistics suggest that Australians take part in a very diverse array of sports

(Eime & Harvey, 2018), perhaps reflecting that the practice of sport has long been considered

a bedrock of Australian cultural values (Cashman & Hickie, 1990). Significantly, while

Australia may be among the world’s most competitive sporting landscapes, it was not

considered so unique as to produce ungeneralisable results. Other scholars have noted places

such as Houston (Ballouli & Bennett, 2012) and Toronto (Field, 2006) to be crowded sport

marketplaces while cities such as London, Los Angeles and New York appear also to host a

diverse range of sport teams and leagues. While the overarching research context was the

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Australian marketplace, the studies fit into two categories. Study 1, 3 and 4 utilise a mix of

survey and media ratings data to evaluate sport consumer behaviour at a national level. Study

2 and 5 are focused on the Sydney and Melbourne markets, owing to the centrality of these

cities to overall Australian sport culture because of their populations and team concentrations.

Overall, the use of a multimethod-based design in a focused context was able to achieve a

methodological coherency that underpinned the results and findings of the thesis.

Conceptual

The studies were organised and presented in a structure that reflected a specific

conceptual sequence by which each study contributed to addressing the core research

question. This sequence is identified within Figure 7 and described below. Study 1

represented both the starting point of the thesis, formalising a conceptualisation of the

competitive sport market that thematically framed and underpinned the remaining studies.

The sport market was identified as a meso level market within the macro leisure and

entertainment market that is composed of brands (teams) and genres (leagues/sports) that

compete for the interest of consumers. Study 1 represents a significant departure from

existing research, which then permeates through the remaining studies, by introducing a

market focus that evaluates sport management from a broader consumer population

perspective. Study 2 tested theorised patterns of behaviour within the identified sport market

framework identified within Study 1. Studies 3 through 5 continued to then empirically test

discrete components of sport market structures using varied techniques and settings.

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Studies 1 through 3 provided insight into the structure of sport market, providing

highly interconnected findings. Study 1 determined that approximately a third (37%) of the

Australian population were not within the sport market (attitudinally), dubbed ‘Sport

Rejecters’. Study 2 determined that about a third of the population were actively engaged

consumers of the market (behaviourally). Modelling within Study 3 determined that AFL

clubs have been successfully increasing their financial return from fanbases, rather than

growing them in absolute terms. These studies in combination suggest the Australian sport

market is characterised by a neat symmetry, whereby the market is split into three roughly

even groups: rejecters, active fans and non-active fans.

The second conceptual link between the studies is the exploration of the nature of

collective consumer behaviour within sport markets. This was achieved across a mix of

attitudinal and behavioural settings with the purpose of quantifying and exploring the nature

of the market. Collectively, Studies 2 through 5 link conceptually by virtue of their consensus

that sport consumers behave rationally and consistently in a manner that produces predicable

market structures. Study 2 made this determination in relation to attendance behaviour in the

Sydney and Melbourne sports markets. Study 3 did so by producing a highly predictive

model of fandom financial value. Study 4 achieved this by illustrating that a new sport

product launch was adopted by consumers in a predictable adoption pattern. Study 5 achieved

this by illustrating sport consumer preference sensitivity towards media consumption typical

of media programming. The connection between these findings is further elucidated below.

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Findings

The relationship between the studies is perhaps most apparent in the consensus they

reach surrounding the core structure and nature of sport markets and their consumers. Two

pre-existing notions shaped the research question, which in turn shape the implications of the

findings. Firstly, sport markets have been considered to be becoming increasingly

competitive and crowded, owing to growth in both the competitive set and methods to

consume sport (Byon et al., 2010; Mauws, Mason, & Foster, 2003; Rein et al., 2006). This

growth has been suggested to have resulted in existing sport consumers holding more power

within the market but becoming fully leveraged (Mahony & Howard, 2001; Mauws et al.,

2003). Secondly, the archetype of the irrationally loyal and passionate sport fan has remained

stubbornly pervasive, both as a perceived unique component of sport (Baker et al., 2016;

Smith & Stewart, 2010) and as a focus within sport consumer research (McDonald & Funk,

2017).

The collective research findings however largely run counter to many sport

management notions. The overarching conclusion that can be drawn from across the studies

is that sport consumers behave rationally, leading to predictable market structures.

Accordingly, each study identified behavioural patterns that largely conformed to the

predicted behaviours from within the Dirichlet market analysis field of research, as distinct

from the sport management field. Consumer rationality in maximising personal utility was

evident across the studies. In the most significant instance, Study 2 illustrated through

Dirichlet modelling that although the sport industry may contain unique characteristics, these

do not result in sport attendance consumption behaviour that is distinct from many other

repeat-purchase goods. Correspondingly, consumers attend sport matches within a repertoire-

purchase pattern and therefore treat sport teams as complementary products. That consumers

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make sport attendance decisions to maximise their personal utility through ‘polygamous

loyalty’ rather than archetypal sport fanatic loyalty is consistent with the consumption

behaviour observed in Study 4 within a media setting. Study 5 quantified consumer behaviour

within sport broadcasts, observing a consumer dislike toward intermissions, advertising and

poor scheduling that is typical of consumer media behaviour.

The behavioural repertoire pattern observed in Study 2 was also synchronous with the

attitudinal patterns of sport preferences observed within the Latent Class segmentation

performed in Study 1. Study 1 illustrated that as sport consumers become increasingly

attitudinally fanatical, their sport repertoire set grows co-linearly. Accordingly, a paradox

becomes evident: sport marketers desire highly avid and loyal fans, yet as fans become more

avid toward sport, they are more likely to fulfil their consumption needs from a wider

repertoire of sport opportunities. This observation is consistent with the fourth empirical law

of marketing which was first shown to hold true in a sport setting in Study 2, and is the case

across multiple repeat-purchase consumer categories: solely loyal buyers are lighter buyers of

the overall category. By contrast, heavier buyers tend to buy more brands and are less likely

to be solely loyal (Sharp, Wright, & Goodhardt, 2002).

Dirichlet market analysis has been comprehensively adopted within the thesis to

understand the behaviour and structure of sport markets. However apart from the five

longstanding empirical generalisations which have been shown to underpin the sport market

across previous studies, Study 4 explored the entrance of a new product within a sport

market. As was the case across the previous studies, there was observed divergence between

Dirichlet and sport literature in respect to the theorised predicted behavioural outcomes. The

immediacy with which local markets exhibited a preference for local teams, which then

222

remained longitudinally steady, again appeared consistent with predictable market behaviour

of new entrants within typical product categories (Ehrenberg & Goodhardt, 2000; Hoek,

Kearns, & Wilkinson, 2003)

That sport consumers are polygamously loyal has significant implications for

commercial sport strategy. The field of Dirichlet modelling espouses the prioritisation of

penetration to increase market share and profitability (Ehrenberg et al., 2004) while sport

theories of escalating commitment favour developing fan commitment to increase

consumption frequency (James, Kolbe, & Trail, 2002; Mullin, Hardy, & Sutton, 1993). Study

3 confirmed AFL teams have appeared to indeed focus on the latter strategy. Consequently,

Australia’s largest football clubs were shown to have significantly grown their revenue based

on increasing their yield per fan, rather than achieving underlying growth in their fan base

size this millennium. The absence of growth in the sport fan base of Australia’s largest league

belies the assumed ubiquity with which sport is perceived, a finding consistent with the

segmentation perform in Study 1 in which over a third (37%) of the population were classed

as sport rejecters.

7.3 Thesis contribution

This thesis was concerned with the characteristics and behaviour of consumers in

context of the burgeoning sport consumption opportunities with which we are currently

privileged. Such consumption opportunities however, exist through the creation and

maintenance of markets which represents the central economic and social mechanism through

which consumers transact (Callon, 1998). Accordingly, this thesis has contributed towards

our understanding of the sport market and the behavioural patterns of its participants, making

several major contributions that are clustered into four theoretical and conceptual themes.

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From these themes emerge corresponding practical contributions, which are then identified

and collectively discussed. These contributions frame the following section.

Conceptualising the ‘sport market’

Competition is considered to represent “the heart and soul of sport management”, the

strategic management of which is a critical success factor for sport organisations (Shilbury,

2012, p. 2). Significantly, the body of literature displays a universality with which sport

landscapes are considered to be becoming increasing competitive and crowded (Byon et al.,

2010; Mauws et al., 2003; Rein et al., 2006). Despite the strategic imperative to understand

the nature of competition in sport, the body of research has thus far focussed overwhelmingly

upon micro-level research at the expense of broader market-level research (McDonald &

Funk, 2017; Park et al., 2011; Stewart et al., 2003). That sport has been widely acknowledged

as competing for consumers as part of a suite of entertainment and leisure options (Mahony

& Howard, 2001; Mason, 1999; Rein et al., 2006; P. Smith, Evens, & Iosifidis, 2015) has

also yet to be a catalyst for sport research at a market level within a management context

(Baker et al., 2016).

The lack of such research, despite regular references to the functional characteristics

of ‘sport markets’, reflects the absence of a comprehensive conceptualisation of the ‘sport

market’. This was rectified in Study 1 and represents the first significant contribution of the

thesis. In doing so, this research articulates and formalises an approach to conceptualising the

competitive structure of sport markets by delineating the three axes on which sport competes

for consumers. The ‘sport market’ is composed of brands (teams) who compete within and

across genres (leagues/sports) for the interest of consumers who display an interest in sport as

a category. The sport market therefore represents one unexceptional component of the

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broader leisure and entertainment category. This conceptualisation represents a significant

contribution in the context of the ongoing discourse surrounding sport’s status as a distinct

field of academic enquiry (Chalip, 2006; Costa, 2005).

The ‘sport market’ is not unique

The second major theoretical contribution to emerge was confirmation that the sport

market did not display unique consumer behavioural patterns. Study 2 determined that the

behavioural patterns of Australian sport attenders exhibited behavioural patterns that could be

predicted utilising Dirichlet modelling. Accordingly, the sport market was determined to

behave predictably, in a manner that is consistent with many other repeat-purchase consumer

goods markets (Ehrenberg, 2000; Ehrenberg et al., 2004). In confirming that sport consumers

behave in predictable patterns replicated in many other industries, the research runs counter

to much of the field’s foundational research and instead contributes to a growing body of

work which is eroding the assertion that the sport product is unique (Baker et al., 2016; Smith

& Stewart, 2010).

The ‘sport market’ is not ubiquitous

Building upon the conceptualisation of the ‘sport market’, the thesis quantified the

prominence of sport within the Australian landscape. Australia is considered a sport-obsessed

nation in which sport is a bedrock of its culture (Cashman, 2010). This perception is largely

reinforced by the diversity of available consumption choices and the saturation of sport

across media channels and technological mediums (Hutchins & Rowe, 2012; Rowe, 1996,

2011). This thesis, through Studies 1, 2 and 3 achieved a triangulated approach to quantify

the pervasiveness of sport fandom within the Australian marketplace. In doing so, the

collective findings are amongst the most comprehensive scholarly quantifications of the

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pervasiveness of sport consumer culture. Overall, the findings confirm that sport fandom is

far from ubiquitous, with only a third of the adult population engaging in more active forms

of sport consumption and another approximate third of the population rejecting sport entirely.

This has significant implications for the sport management field as the overwhelming

majority of sport consumer research has focussed comprehensively on more avid fans

(McDonald & Funk, 2017; Reysen & Branscombe, 2010). Correspondingly, a significant

scholarly gap in our understanding of non-fans has begun to be addressed through this

doctoral study and the presented findings.

Sport consumers are behaviourally rational and predictable

Study 2 confirmed that consumers attend sport matches within a repertoire-purchase

pattern and therefore treat sport teams as complementary products (Sharp et al., 2002). This

determination is theoretically significant as it is perhaps the most fundamental behavioural

characteristic of repeat-purchase consumer markets, yet had rarely been investigated in a

sport market setting. It also runs counter to the long-perpetuated image of the irrationally

loyal sport fan. This finding also has implications for sports organisations given the

divergence between Dirichlet modelling and sport literature in regards to optimising strategic

orientation. The field of Dirichlet modelling espouses the prioritisation of penetration to

increase market share and profitability (Ehrenberg et al., 2004) while sport consumer theories

of escalating commitment favour developing fan commitment to increase consumption

frequency (James et al., 2002; Mullin et al., 1993). The divergent strategic recommendations

observed between sport-specific and broader management/marketing research are once again

significant here, given the aforementioned tension surrounding the unique positioning of the

sport management discipline (Baker et al., 2016).

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Confirmation of the behavioural predictability of sport consumers provided

theoretical validity that the financial performance of sport teams could be accurately

modelled by adopting a consumer-orientated conceptualisation of fan bases. This is

significant because the conceptual link between supporter bases and sport team revenue (see

James et al., 2002) had yet to be explicitly tested. Accordingly, Study 3 was successful in

quantifying the financial value of fandom within the Australian sport market, producing a

parsimonious predictive model that connects sport team financial performance to its

underlying fan base. This represents a significant conceptual contribution to the modelling

literature, which has been primarily driven by a sport economist perspective at the expense of

a management orientation (Shilbury, 2012; O'Reilly & Nadeau, 2006).

Finally, Study 4 and 5 confirmed the behavioural predictability of sport consumers in

the context of mediated sport consumption. Study 4 illustrated an instantaneous preference

within the local market toward new local sport teams. Significantly, that this loyalty was

instantaneous before becoming longitudinal and steady was consistent with broader

management research in which purchase patterns of new brands across many typical repeat-

purchase industries is often near-instant (Ehrenberg & Goodhardt, 2000; Hoek et al., 2003).

Study 5 evaluated the market behavioural response toward sport broadcasts. Despite an

ingrained perception that sport commands high levels of product and brand loyalty (Smith &

Stewart, 2010), sport broadcast viewers demonstrated a significant disutility towards

advertising and intermissions that is consistent with typical programming content.

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7.4 Practical implications

Stemming from these theoretical and conceptual themes, significant practical

implications for sport practitioners emerge in relation to the management and marketing of

consumers. Significantly, the research assists sport practitioners navigate perhaps the most

fundamental decision in marketing strategy: choosing whether to focus on obtaining new

customers (penetration) or on increasing purchase loyalty among existing ones (Sharp,

Wright, & Goodhardt, 2002). Although a sole emphasis upon either strategy in isolation is

likely flawed in a real-world practical context, the research confirms Australian sport

consumers fulfil their sport category needs from within repertoire bundles of teams.

Consistent with other industries, this would confirm that increasing penetration is the key to

increase market share and profitability (Ehrenberg et al., 2004). Sport practitioners should

focus their scarce marketing resources towards attracting new consumers to increase their

market penetration.

Notably, sport consumers are polygamously loyal and therefore sport teams do not so

much compete for fans as share them. Although an increasingly crowded sport market can be

conflated with growing competitive tension, there is merit in practitioners retaining a

category level philosophy in understanding consumer behaviour. Here, the aphorism ‘a rising

tide lifts all boats’ appears appropriate. This is because although sport marketers desire highly

avid and loyal fans, the fourth empirical law of marketing states that solely loyal buyers are

lighter, less valuable overall consumers (Sharp, Wright, & Goodhardt, 2002). Sport marketers

who grow their brand through valued consumer experiences are simultaneously growing the

category which, unavoidably, can benefit all market participants. This reinforces that sport

management engages in a delicate act of competition and cooperation (Shilbury, 2012;

Stewart, Nicholson, & Dickson, 2005).

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Given that sport retains a consumer structure consistent with many other repeat-

purchase products, it is increasingly evident that sports practitioners must view their product

as but one component of the broader leisure and entertainment category. Here, the strategic

value of a category level philosophy is furthermore accentuated as the research found that a

third of Australians are rejecters of sport and absent from the category. Therefore perhaps the

greatest opportunity for sport practitioners to grow their respective brands sits outside of the

sport market itself. A modernised practitioner orientation in which sport is understood as a

broader consumer product may help to find solutions to overcome barriers to sport

consumption among non-consumers, who represent a large and potentially untapped group

(McDonald & Funk, 2017). As such, this research supports the view that practitioners who

attempt to develop marketing plans for such non-consumers would benefit from exploring

and adopting strategies which may originate from outside the sporting domain.

7.5 Future research

As outlined above, this thesis has addressed a significant research gap by

investigating the nature, structure and behaviour of sport markets. Although the findings of

the thesis provide a significant contribution to the sport management body of knowledge, the

research uncovers questions requiring further exploration. Accordingly, a number of agendas

for future research stemming from this thesis have emerged, including methodology,

sampling, the exploration of sport rejecters and the entertainment and leisure repertoire.

Methodology

In confirming that sport consumers behave in predictable patterns replicated in many

other industries, the research runs counter to much of the field’s foundational research and

instead contributes to a growing body of work that is eroding the basis by which the sport

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product can be justified as unique (Smith & Stewart, 2010). Although this has considerable

implications for the positioning of sport marketing and management as specialised

disciplines, it also facilitates opportunities for future research to further apply business

principles from non-sport contexts that are yet to be considered within the discipline.

Accordingly, this research supports previous calls that further experimentation with

mainstream business methodologies and principles is warranted in a sport setting (Baker et

al., 2016). For instance, although game attendance and season ticket holding behaviours have

been well explored and now tested against theorised marketing generalisations, similar such

approaches are warranted in less explored consumer domains such as merchandising

(Stewart, Smith, & Nicholson, 2003). Similarly, our understanding of market behaviour

toward the introduction of new teams and leagues could benefit from the adoption of broader

marketing theories such as those as those provided by Dirchlet modelling or the various Bass

inspired models of new product diffusion (Mahajan, Muller, & Wind, 2000). This however is

acknowledged to be a potentially controversial proposal, given the existence of a diametric

view that sport management should develop distinctive methodologies to justify its status as a

unique discipline (Chalip, 2006).

Sampling

Building upon a limited pool of prior research (Baker et al., 2016; Doyle et al., 2013;

McDonald & Stavros, 2007), this doctoral study represents a comprehensive attempt to utilise

Dirichlet modelling and broader empirical marketing principles to understand the structure of

the sport market. However, the body of research retains an Australian focus. Although the

research context utilised within this study is justified as relatively typical of the competitive

sport environment found in most commercially developed and populous nations, further study

of competitive sporting landscape is warranted within, North American, European and Asian

230

contexts that appear ‘crowded’ (Ballouli & Bennett, 2012; Byon et al., 2010; Field, 2013;

Kim & Trail, 2010). Replication in such international contexts would help identify

similarities and differences in the competitive structure of sport markets globally. This is

significant because at present, there exists scant research through which sport consumer

behaviour in differing international contexts can be validly compared. Further Dirichlet

modelling can overcome this by providing objective market benchmarks and statistics that

allow for the valid comparison of sport consumers in diverse cities such Auckland, London,

Singapore or New York in terms of market structure and consumer behaviour.

Understanding sport rejecters

Another key contribution of this thesis was the finding that the sport market did not

hold ubiquitous acceptance among consumers. This was noted to have significant

implications for the sport management field as the overwhelming majority of sport consumer

research has focussed comprehensively on avid fans (McDonald & Funk, 2017; Reysen &

Branscombe, 2010). Accordingly, given an increasingly crowded sport market in which

existing sport consumers may be fully leveraged (Mahony & Howard, 2001; Mauws et al.,

2003), a better understanding of sport rejecters may provide the greatest opportunity for

practitioners to grow their respective fan bases (McDonald & Funk, 2017). The key sport

management and marketing issue is whether these non-consumers represent an innately

disinterested and low value segment to the industry, or whether barriers to sport interest

among this cohort are surmountable through changes to the marketing mix, product

orientation and strategic adaptability of sport organisations (Mauws et al., 2003). Further

research is critical in this space to understand and address this key management issue.

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Exploring the entertainment and leisure repertoire

Finally, this research conceptualises a widely held scholarly proposition that the sport

product is increasingly competing within a broader entertainment and leisure sector.

Accordingly, while the extant body of sport consumer literature has been heavily weighted

toward the micro level of research, what is required is an increased focus on understanding

consumption of the sport product from within the broader set of competing entertainment and

leisure pursuits. While this research has illustrated sport to be consumed within repertoires, it

is intuitive that consumers are likely to fulfil their overarching leisure needs from within a

repertoire of varied pursuits. Confirmation of such, as well as understanding the composition

of such repertoires among the mass consumer market, would be particularly worthwhile in

sport management’s continued search to understand its place and positioning.

7.6 Final remarks

The sport management discipline in some respects appears to be a victim of its own

success. While the industry’s growing financial significance has provided a strong

justification for its scholarship, it has also necessitated an increasingly business-like

orientation to its management. This increasingly commercialised orientation however

represents a potential source of identity crisis for the discipline, with the continued adoption

of broader management practice and theory increasingly challenging the unique positioning

of sport management scholarship. Correspondingly, while some scholars have embraced the

opportunity to cross-pollinate across fields, others have called for what appears a

protectionary emphasis upon the development of localised theory from within the discipline.

This thesis falls into the former camp, utilising an array of generalised management and

marketing methods and theory to evaluate patterns of behaviour within the sport market that

appear typical of standard consumers.

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Although sport management undoubtedly retains unique features and idiosyncrasies,

its fundamental challenges are in fact typical of many industries. It has been observed to be

competing within a growing competitive set, facing both opportunities and threats posed by

technology as well as globalisation. Despite such category level challenges, as well as

continued postulation around the scholarly positioning of sport management, there has been a

distinct scarcity of sport research at the market level to which this thesis contributes to

addressing. This complete imbalance in research focus has rendered it difficult for

scholarship to contribute to addressing the significant market challenges faced by the

industry. For instance, in the context of growing the sport industry, the existing research that

focuses upon highly engaged fans appears narrowly targeted as compared to understanding

the 37% of the population that reject sport and who remain under-researched. That this

doctoral study finds the sport market to appear characteristic of most typical repeat-purchase

consumer markets suggests that the strategic focus of the sport market should be to grow

penetration, rather than frequency, as has been typically the emphasis.

Furthermore, while conceptualising the sport market as but one segment of the larger

entertainment and leisure industry may risk contributing to further reducing the discipline’s

perceived distinctiveness, such an approach is called for by this thesis. Such a positioning is

also vital to our understanding of the category in the context of an increasingly competitive

consumer leisure environment. Consumers fulfil their sport needs from within repertories and

the same is intuitively likely in respect to overarching entertainment and leisure consumption

patterns. Therefore, rather than seeking to maintain a siloed and inwardly focused research

agenda, the sport management discipline must embrace the opportunity to better understand

its place within the broader leisure and entertainment market for the betterment of the

discipline.

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Appendix 1

First survey completed by 27,412 respondents as utilised within Study 1.

Q1 Please indicate your gender? Male ........................................................................................................ 1 Female ................................................................................................... 2

Q2 Please type in your age: _____ (please enter below)

Q3 Please enter your residential postcode: ____________________ Q4 What is your ethnicity? Select up to two. Q5 Besides English, do you speak any other languages? Tick all that apply

Yes/No

Aboriginal 1/0 Australian 1/0 Other Oceania 1/0 North or West European (e.g. United Kingdom, France, Germany, Sweden, Norway) 1/0 South or East European (e.g. Spain, Italy, Greece, Romania, Hungary, Ukraine) 1/0 North African or Middle Eastern 1/0 South-East Asian (e.g. Vietnamese Filipino, Indonesian) 1/0 North-East Asian (e.g. Chinese, Japanese, Korean) 1/0

South and Central Asian (e.g. Pakistani, Indian) 1/0

North American 1/0

South American 1/0

African 1/0

Other 1/0

Prefer not to say 1/0

Arabic Yes/No

Cantonese 1/0

French 1/0

German 1/0

Greek 1/0

Hindi 1/0

Italian 1/0

Japanese 1/0

Korean 1/0

Mandarin 1/0

Portuguese 1/0

Russian 1/0

Serbian 1/0

Spanish 1/0

234

Q6 In general, how big a sports fan would you say you are?

0 – Not at all

interested in sport

2 3 4 5 6 7 8 9 9- I live and breathe sport

Q7 Please indicate which of the following sports you take an interest in.

Vietnamese 1/0

Other 1/0

I do not speak any other languages besides English 1/0

Yes/No

AFL 1/0

American Football 1/0

Athletics 1/0

Badminton 1/0

Basketball 1/0

Boating 1/0

Boxing 1/0

Cricket 1/0

Cycling 1/0

Equestrian 1/0

Extreme Sports 1/0

Fishing 1/0

Formula1 1/0

Golf 1/0

Gymnastics 1/0

Hockey 1/0

Horse Racing 1/0

Lawn Bowls 1/0

Marathon 1/0

MMA/UFC 1/0

MotorCross 1/0

MotorGP 1/0

MountainBiking 1/0

NASCAR 1/0

Netball 1/0

Pool/Billiards 1/0

Rowing 1/0

Rugby League 1/0

Rugby Union 1/0

Snow Sports 1/0

Soccer 1/0

Surfing 1/0

Swimming 1/0

Tennis 1/0

Volleyball 1/0

235

Weightlifting 1/0

Wrestling 1/0

Other 1/0

None of the above 1/0

236

Appendix 2

Second sport survey completed by 2,572 respondents as utilised within Study 2

SECTION 1: SCREENER

SURVEY INTRO: The purpose of this research /online survey is to find out about how you consume sport. This survey will take approximately 15 minutes to complete. You can change your mind at any time and stop completing the survey without consequences. If you agree to be part of the research and allow research data gathered from this survey to be published in a form that does not identify you, please continue with answering the survey questions. If you have concerns about the research that you think I or my supervisor can help you with, please feel free to contact us: Hunter Fujak: [email protected] Stephen Frawley: [email protected] If you would like to talk to someone who is not connected with the research, you may contact the Research Ethics Officer on 02 9514 2478 or [email protected] and quote this number ETH16-0488 S1 Please indicate your gender?

Male ........................................................................................................ 1 Female ................................................................................................... 2

S2a Please type in your age: _____ (please enter below)

S2b AGE HIDDEN QUESTION: PLEASE CODE AGE INTO THE FOLLOWING:

17 or less ................................................................................................. 1 18-21 years ............................................................................................. 2 22-24 years ............................................................................................. 3 25-29 years ............................................................................................. 4 30-34 years ............................................................................................. 5 35-39 years ............................................................................................. 6 40-44 years ............................................................................................. 7 45-49 years ............................................................................................. 8 50-54 years ............................................................................................. 9 55-59 years ........................................................................................... 10 60-64 years ........................................................................................... 11 65 years or more .................................................................................. 12

S3a Please enter your residential postcode: ____________________

S7a SPORTS FANDOM: In general, how big a sports fan would you say you are?

0 – Not at all

interested in sport

2 3 4 5 6 7 8 9 9-

I live and breathe sport

S6a SUBSCRIPTION TELEVISION: Do you have Foxtel in your home?

mailto:[email protected]



237

Yes .......................................................................................................... 1 No .......................................................................................................... 2

ONLY SHOW S6b TO PEOPLE WHO HAVE SELECTED 1 AT S6a, OTHERWISE SKIP TO S7a

S6b Do you have Fox Sports channels as part of your package? (SR)

Yes .......................................................................................................... 1 No .......................................................................................................... 2

S6d Do you subscribe to, or share access with, any of the following streaming services? (MR)

Netflix ..................................................................................................... 1 Stan ........................................................................................................ 2

Presto ..................................................................................................... 3

Optus Sport (English Premier League)…………………………………………………..4

Other (please specify)___________________ ....................................... 5

I do not use an online streaming service ............................................... 6

G1a Please select your level of interest in the following sports:

ROTATE STATEMENTS, ONLY ONE TO EQUAL 6

0- No interest whatsoever

1 2 3 4 5 6

Rugby League 0 1 2 3 4 5 6

Soccer (Football) 0 1 2 3 4 5 6

Rugby Union 0 1 2 3 4 5 6

AFL 0 1 2 3 4 5 6

Cricket 0 1 2 3 4 5 6

Netball 0 1 2 3 4 5 6

Basketball 0 1 2 3 4 5 6

TERMINATE IF RESPONDENT ANSWERS 1 to 3 AT S7a TERMINATE IF RESPONDENT NOT A SYDNEY OR MELBOURNE POSTCODE AT S3A

SECTION 2: TEAM LANDSCAPE

SL1 CAPPED AT 5 TEAMS

SL1 Earlier, you mentioned that you were interest in [INSERT LIST OF SPORTS 1+ BASED ON G1A]. Which

specific teams do you support?

My Favourite Team is: DROP DOWN BOX OF SPORTS Team:_DROP DOWN BOX OF TEAMS

My second favourite team is: DROP DOWN BOX OF SPORTS Team:_DROP DOWN BOX OF TEAMS

My third favourite team is DROP DOWN BOX OF SPORTS Team:_DROP DOWN BOX OF TEAMS My fourth favourite team is: DROP DOWN BOX OF SPORTS Team:_DROP DOWN BOX OF TEAMS

My fifth favourite team is: DROP DOWN BOX OF SPORTS Team:_ DROP DOWN BOX OF TEAMS

SL2a You mentioned that you supported the following teams. How many of their games HOME games did

you ATTEND in their most recently completed season?

[SL1a]: ______________ [SL1b]: ______________ [SL1c]: ______________ [SL1d]: ______________

238

SL2aa In the most recently completed seasons, how many games did you attend OVERALL?

Display answer in SL2a Total games

[SL1a SPORT]: ______________ ___________ [SL1b SPORT]: ______________ ___________ [SL1c SPORT]: ______________ ___________ [SL1d SPORT]: ______________ ___________

[Sport not chosen at SL1]: ___________ [Sport not chosen at SL1]: ___________ [Sport not chosen at SL1]: ___________ [Sport not chosen at SL1]: ___________

SL2b How many HOME and AWAY games of your favourite teams did you watch on television in their

most recently completed season?

[SL1a]: ______________ [SL1b]: ______________ [SL1c]: ______________ [SL1d]: ______________

DISPLAY SPORTS 1+ AT G1A,

SL2bb In the most recently completed seasons, how many games did you watch on television or stream

OVERALL?

Display answer in SL2b Total Games

Rugby League ________________ ______________ Soccer ________________ ______________ Rugby Union ________________ ______________ AFL ________________ ______________ Cricket ________________ ______________ Netball ________________ ______________ Basketball ________________ ______________

SL2c In the most recently completed seasons, did you STREAM any of the following sports online?

YES NO

Rugby League ________________ ______________ Soccer ________________ ______________ Rugby Union ________________ ______________ AFL ________________ ______________ Cricket ________________ ______________ Netball ________________ ______________ Basketball ________________ ______________

DISPLAY YES AT SL2C

SL2cc How many games did you stream??

Total Games

Rugby League ________________

239

Soccer ________________ Rugby Union ________________ AFL ________________ Cricket ________________ Netball ________________ Basketball ________________

SL2d You mentioned that you supported the following teams. Did you hold a club

membership during the most recently completed season?

YES NO

[SL1a]: ______________ ______________ [SL1b]: ______________ ______________ [SL1c]: ______________ ______________ [SL1d]: ______________ ______________

IF YES

SL2dnew How much did your membership cost?

SL2dnew2 How many games does your membership entitle you to?

SL2e You mentioned that you supported the following teams. How much did you spend on merchandise

during most recently completed season?

[SL1a]: ______________ [SL1b]: ______________ [SL1c]: ______________ [SL1d]: ______________

LOOP SL2F/G BASED ON NUMBER OF TEAMS

SL2f PSYCHOLOGICAL COMMITMENT:

a) I am a committed fan of the [insert team]

1- Disagree Strongly

2 3 4 5 6 7-

Strongly Agree

b) I am a loyal supporter of the [insert team]


2 3 4 5 6 7-

Strongly Agree

c) Win lose or draw I’m a loyal fan of [insert team]


2 3 4 5 6 7-

Strongly Agree

SL2g ATTITUDINAL LOYALTY:

a) I could never switch my loyalty from [insert team] even if my close friends were fans of another team

1- 2 3 4 5 6 7-

240

Disagree Strongly

Strongly Agree

b) It would be difficult to change my beliefs about [insert team]


2 3 4 5 6 7-

Strongly Agree

c) I would still be committed to the [insert team] regardless of the luck of any star players


2 3 4 5 6 7-

Strongly Agree

d) I would still be committed to my [insert team] regardless of the lack of physical skill among the players


2 3 4 5 6 7-

Strongly Agree

G3c Thinking of sports you participate in, can you please nominate which sports you have ever played in

either a formal competition or socially.

G3d When did you participate in the sports you mentioned?

LIST ONLY 1’s AT G3C

Yes/No Social/ Formal/ Both

Rugby League 1/0 1/2/3

Soccer (Football) 1/0 1/2/3

Rugby Union 1/0 1/2/3

AFL 1/0 1/2/3

Cricket 1/0 1/2/3

Netball 1/0 1/2/3

Basketball 1/0 1/2/3

Touch Football/Oz Tag 1/0 1/2/3

Child 0-12 Yes/No

Adolescent 13-18 Yes/No

Recently or Currently Yes/No

Rugby League 1/0 1/0 1/0

Soccer (Football) 1/0 1/0 1/0 Rugby Union 1/0 1/0 1/0

AFL 1/0 1/0 1/0 Cricket 1/0 1/0 1/0 Netball 1/0 1/0 1/0

Basketball 1/0 1/0 1/0 Touch Football/Oz Tag 1/0 1/0 1/0

241

SECTION 4: MEDIA CONSUMPTION

ONLY SHOW S6c TO PEOPLE WHO HAVE SELECTED 1 AT S6b

S6c To what extent do you agree with this statement:

Disagree strongly

Disagree slightly

Neither agree nor disagree

Agree slightly

Agree strongly

Without sports channels, I would not consider it worthwhile subscribing to Foxtel

1 2 3 4 5

MTR1a You mentioned earlier that you had watched [G3b] number of [INSERT FAVOURITE SPORT BASED ON

G1] games during the most recently completed season. What proportion of these games were

watched in a private household and what proportion were viewed at a public pub/club or other

venue?

At a private household: ________________ In a public club/pub or venue: ________________

= 100%

FOR THOSE IN SYDNEY, IF RUGY LEAGUE IS NOT FAVOURITE AT G1A, BUT 1+ AT G1A

MTR1b You mentioned earlier that you had watched [G3b] number of Rugby League games during the most

recently completed season. What proportion of these games were watched in a private household

and what proportion were viewed at a public pub/club or other venue?

At a private household: ________________ In a public club/pub or venue: ________________

= 100%

G4 Thinking back to the sports you nominated as your favourites, if [Sport] was no longer telecast on pay

television, how would it affect your desire to keep Foxtel and Fox Sports?

ASK ONLY IF FOOTBALL CODE IS FAVOURITE AT G1A

Not change

I would

unsubscribe

Without [Sport] on Foxtel, my desire to pay extra for the sports channel bundle package would:

1 2 3 4 5

Without [Sport] on Foxtel, my desire to subscribe to my overall Foxtel package would:

1 2 3 4 5

MTR2 Which one of the following statements best reflects your attitude towards watching [INSERT

FAVOURITE SPORT BASED ON G1] on television:

I never make plans to watch, but will watch if it’s on ......................................................... 1 I will generally try to watch my team play, but not so interested in other games. ............ 2 I look forward to watching footy, but only catch a game or two a week ............................ 3 I love watching the footy and will watch as many as I can .................................................. 4

IPB1a If [INSERT FAVOURITE SPORT BASED ON G1] offered their content to you via an online subscription,

whereby you were able to watch matches and content live and on replay via the internet on your

phone, computer OR TV, how interested would you be in subscribing to such a service FOR SEASON

242

2017?

0 – Not at all interested

1 2 3 4 5 6 7 8 9 10 – Very

interested

IPB2a Now, please imagine this same service offered in the future when internet speeds are faster and more

reliable, download limits are higher and televisions have easy or inbuilt connection to the internet on

your phone, computer OR TV. How interested would you be in subscribing to this service IN THIS

FUTURE ENVIRONMENT?

0 – Not at all interested

1 2 3 4 5 6 7 8 9 10 – Very

Interested

IPB2b Ignoring your own personal interest level, what do you think would be a fair monthly price for such a

service?

$__________ (please enter below)

SECTION 5: CHOICE MODEL

SHOWN ONLY TO THOSE WHO ARE 1+ AT G1A FOR RUGBY LEAGUE IN SYDNEY & MELBOURNE

Intro:

In the next section of the survey we’re going to show you nine different scenarios around how you could potentially buy sport content in the future. For this exercise, please imagine that we are living in the near-future where internet speeds are consistently fast and there are no limits on downloads. Please also imagine that NRL is still telecast on Channel 9 and Fox Sports in its current structure. In each scenario, the options and the prices at which these are available will change. It is important that you review all of the information provided in each scenario before making your decision. Below is an example of what a single choice scenario screen will look like. It gives you three potential products that differ in some way across price and content. The task in each scenario is to select which of the options, at the prices provided, you would be most interested in purchasing. If after reviewing all of the different options, you would choose not to buy any of these, you can select the “None” option down the bottom of the screen. Please click next to start the first scenario.

LEVELS/DESCRIPTIONS:

Levels (main display) Level Detail (to be hovered over)

Cost ‘$10 per month’ $10 per month. Cancellable at anytime

‘$20 per month’ $20 per month. Cancellable at anytime

‘$35 per month’ $35 per month. Cancellable at anytime

Content ‘Choose your own 26 game bundle’

‘Your choice of any 26 games throughout the season’

‘All games ’ ‘All games from the current season live and on replay’

All games PLUS classic matches ‘All games live and on replay

243

PLUS commentary shows plus access to 1000’s of classic matches as well as exclusive NRL related weekly commentary shows’

Advertising ‘None’ ‘No ads whatsoever’

‘Medium’ ‘No ads during play. Ads during breaks in play (pre-game, post-game, half time, try scoring, scrums).’

‘High’ ‘Ads during breaks in play. Occasional digital ads during play (displayed at the bottom of the screen)’

COMBINATIONS: SET POSITION TO BE RANDOMISED, POSITION IN SET (1,2,3) TO BE RANDOMISED

LEVEL Cost Content Advertising

000 000 000

0 $10 26 games None

1 $20 All games Medium

2 $35 All games PLUS High

Set 1 2 3

1 000 122 211

2 011 100 222

3 022 111 200

4 101 220 012

5 112 201 020

6 120 212 001

7 202 021 110

8 210 002 121

9 221 010 102

FOLLOW UP QUESTIONS:

1) Which would you be most interested in purchasing?

2) Would you purchase this product? .......................................... YES/NO

ONLY SHOW Q3 TO PEOPLE WHO SELECT YES AT Q2 AND YES AT S6A

3) How would this impact your subscription to Foxtel and Fox Sports?

I would keep my Fox Sports subscription ............................................... 1 I would cancel my Fox Sports package but keep my Foxtel package .... 2

I would cancel Foxtel entirely ................................................................ 3

244

SECTION 6: DEMOGRAPHICS

D1 Which of the following best describes your household?

Living on my own or with housemates ................................................... 1 Live with a partner, no kids .................................................................... 2 Family, with youngest child under 12 years ........................................... 3 Family, with youngest child aged 12-18 years ........................................ 4 Family, with adult children living at home ............................................. 5 Older single or couple with children living out of home ........................ 6 Older single or couple, no children ......................................................... 7

D2 Which of the following best describes your highest level of education?

Year 10 or below ..................................................................................... 1 Year 11 or 12 .......................................................................................... 2 Diploma or certificate from a college or TAFE (including apprenticeships) .... 3 Degree or diploma from a university ...................................................... 4 Post graduate degree ............................................................................. 5

D3 What is your total personal annual income (before tax)?

Up to $30,000 ......................................................................................... 1 $30,001 to $50,000 ................................................................................. 2 $50,001 to $70,000 ................................................................................. 3 $70,001 to $100,000 ............................................................................... 4 $100,001 to $125,000............................................................................. 5 $125,001 to $150,000............................................................................. 6 $150,001 to $200,000............................................................................. 7 More than $200,000 ............................................................................... 8 I would rather not say ............................................................................ 9 Not sure ................................................................................................ 10

D4 Which of the following best describes your current employment status?

Employed full time .................................................................................. 1 Employed part time ................................................................................ 2 Self-employed ......................................................................................... 3 Small business owner or partner ............................................................ 4 Not employed, but looking for work ...................................................... 5 Not employed, and not looking for work ............................................... 6 Retired .................................................................................................... 7 Student ................................................................................................... 8 Homemaker ............................................................................................ 9 Prefer not to answer ............................................................................. 10

D5 What is your occupation?

Community or Personal service worker ................................................. 1 Entrepreneur/Business owner................................................................ 2 Full time home duties ............................................................................. 3

245

Middle Management (e.g. department head, senior manager) ............. 4 Professional (e.g. doctor, lawyer, accountant) ....................................... 5 Retired .................................................................................................... 6 Sales/Service worker .............................................................................. 7 Self-employed ......................................................................................... 8 Student ................................................................................................... 9 Technician or Trades Worker ............................................................... 10 Unemployed ......................................................................................... 11 Upper management ............................................................................. 12 White-collar worker .............................................................................. 13 Prefer not to answer ............................................................................. 14

D6a What type of internet do you currently have at home?

I don’t have an internet connection at home ......................................... 1 3G or 4G wireless broadband ................................................................. 2 ADSL/ADSL2 ............................................................................................ 3 Cable/HFC ............................................................................................... 4 Fibre/NBN ............................................................................................... 5

D6a How satisfied are you with your current internet’s speed and reliability?

Very Dissatisfied-

1 2

3 4

Satisfied-

5

S3c CHILDHOOD: Where did you spend the majority of your childhood (between ages 6 and 18)? Sydney (please specify) (closed listed to be provide) ............................. 1

Non-Sydney NSW: (please specify) (open ended) .................................. 2 Interstate: (please specify) (closed list to be provided) ......................... 3 International (please specify) (open ended) .......................................... 4

S4a ETHNICITY: In which country were you born? (SR)

Australia .................................................................................................. 1 England ................................................................................................... 2 New Zealand ........................................................................................... 3 India ........................................................................................................ 4 China ....................................................................................................... 5 Other- please specify .............................................................................. 6

ONLY SHOW S4b TO PEOPLE WHO HAVE NOT SELECTED 1 AT S4a, OTHERWISE SKIP TO S5

S4b ETHNICITY: Please type in how long you have lived in Australia: _____ (please enter below)

S4c ETHNICITY HIDDEN QUESTION: PLEASE CALCULATE RESIDENCE IN AUSTRALIA BASED ON:

S2a (AGE) – S4b (Ethnicity) =…………………………………………………………………

S5 ANCESTRY: What is your Ancestry? (MR)

North-West Europe ................................................................................ 1 Southern & Eastern Europe .................................................................... 2 North Africa & Middle East ..................................................................... 3 South-East Asia ....................................................................................... 4 North-East Asia ....................................................................................... 5

246

Southern and Central Asia ...................................................................... 6 Americas ................................................................................................. 7 Sub-Saharan Africa ................................................................................. 8 New Zealand & Pacific Islander .............................................................. 9 Indigenous Australian ........................................................................... 10 United Kingdom .................................................................................... 11 Other (Please Specify) .......................................................................... 12

247

Appendix 3

Sample frame of analysed broadcast ratings as utilised within Study 4

Match number Season Match up 1 2013/2014 Melbourne Stars vs Melbourne Renegades 2 2013/2014 Sydney Sixers vs Sydney Thunder 3 2013/2014 Hobart Hurricanes vs Adelaide Strikers 4 2013/2014 Brisbane Heat vs Perth Scorchers 5 2013/2014 Perth Scorchers vs Melbourne Renegades 6 2013/2014 Sydney Thunder vs Adelaide Strikers 7 2013/2014 Brisbane Heat vs Hobart Hurricanes 8 2013/2014 Sydney Sixers vs Melbourne Stars 9 2013/2014 Melbourne Renegades vs Brisbane Heat 10 2013/2014 Adelaide Strikers vs Perth Scorchers 11 2013/2014 Hobart Hurricanes vs Melbourne Renegades 12 2013/2014 Sydney Thunder vs Melbourne Stars 13 2013/2014 Brisbane Heat vs Sydney Sixers 14 2013/2014 Perth Scorchers vs Sydney Thunder 15 2013/2014 Melbourne Renegades vs Melbourne Stars 16 2013/2014 Adelaide Strikers vs Sydney Sixers 17 2013/2014 Perth Scorchers vs Hobart Hurricanes 18 2013/2014 Sydney Thunder vs Brisbane Heat 19 2013/2014 Melbourne Stars vs Adelaide Strikers 20 2013/2014 Sydney Sixers vs Perth Scorchers 21 2013/2014 Hobart Hurricanes vs Sydney Thunder 22 2013/2014 Brisbane Heat vs Melbourne Stars 23 2013/2014 Melbourne Renegades vs Sydney Thunder 24 2013/2014 Sydney Sixers vs Hobart Hurricanes 25 2013/2014 Perth Scorchers vs Adelaide Strikers 26 2013/2014 Adelaide Strikers vs Brisbane Heat 27 2013/2014 Melbourne Renegades vs Sydney Sixers 28 2013/2014 Melbourne Stars vs Hobart Hurricanes 29 2013/2014 Adelaide Strikers vs Melbourne Renegades 30 2013/2014 Hobart Hurricanes vs Brisbane Heat 31 2013/2014 Sydney Thunder vs Sydney Sixers 32 2013/2014 Melbourne Stars vs Perth Scorchers 33 2014/2015 Adelaide Strikers vs Melbourne Stars 34 2014/2015 Sydney Sixers vs Melbourne Renegades 35 2014/2015 Melbourne Stars vs Hobart Hurricanes 36 2014/2015 Sydney Thunder vs Brisbane Heat 37 2014/2015 Perth Scorchers vs Adelaide Strikers 38 2014/2015 Hobart Hurricanes vs Sydney Sixers

248

39 2014/2015 Perth Scorchers vs Melbourne Renegades 40 2014/2015 Sydney Thunder vs Sydney Sixers 41 2014/2015 Brisbane Heat vs Melbourne Stars 42 2014/2015 Sydney Sixers vs Perth Scorchers 43 2014/2015 Melbourne Renegades vs Sydney Thunder 44 2014/2015 Adelaide Strikers vs Hobart Hurricanes 45 2014/2015 Perth Scorchers vs Sydney Thunder 46 2014/2015 Hobart Hurricanes vs Brisbane Heat 47 2014/2015 Melbourne Renegades vs Melbourne Stars 48 2014/2015 Brisbane Heat vs Adelaide Strikers 49 2014/2015 Melbourne Stars vs Sydney Sixers 50 2014/2015 Adelaide Strikers vs Perth Scorchers 51 2014/2015 Melbourne Renegades vs Hobart Hurricanes 1 52 2014/2015 Perth Scorchers vs Brisbane Heat 53 2014/2015 Sydney Thunder vs Hobart Hurricanes 54 2014/2015 Melbourne Stars vs Melbourne Renegades 55 2014/2015 Hobart Hurricanes vs Perth Scorchers 56 2014/2015 Brisbane Heat vs Sydney Sixers 57 2014/2015 Adelaide Strikers vs Sydney Thunder 58 2014/2015 Melbourne Renegades vs Brisbane Heat 59 2014/2015 Sydney Sixers vs Adelaide Strikers 60 2014/2015 Hobart Hurricanes vs Brisbane Heat 61 2014/2015 Sydney Thunder vs Melbourne Stars 62 2014/2015 Melbourne Renegades vs Adelaide Strikers 63 2014/2015 Melbourne Stars vs Perth Scorchers 64 2014/2015 Sydney Sixers vs Sydney Thunder 65 2015/2016 Sydney Thunder vs Sydney Sixers 66 2015/2016 Adelaide Strikers vs Melbourne Stars 67 2015/2016 Brisbane Heat vs Melbourne Renegades 68 2015/2016 Sydney Sixers vs Hobart Hurricanes 69 2015/2016 Melbourne Stars vs Sydney Thunder 70 2015/2016 Perth Scorchers vs Adelaide Strikers 71 2015/2016 Hobart Hurricanes vs Brisbane Heat 72 2015/2016 Melbourne Renegades vs Sydney Sixers 73 2015/2016 Perth Scorchers vs Brisbane Heat 74 2015/2016 Sydney Sixers vs Melbourne Stars 75 2015/2016 Sydney Thunder vs Adelaide Strikers 76 2015/2016 Brisbane Heat vs Hobart Hurricanes 77 2015/2016 Melbourne Renegades vs Perth Scorchers 78 2015/2016 Adelaide Strikers vs Sydney Sixers 79 2015/2016 Hobart Hurricanes vs Sydney Thunder 80 2015/2016 Melbourne Stars vs Melbourne Renegades 81 2015/2016 Perth Scorchers vs Sydney Sixers 82 2015/2016 Brisbane Heat vs Sydney Thunder

249

83 2015/2016 Hobart Hurricanes vs Melbourne Renegades 84 2015/2016 Adelaide Strikers vs Perth Scorchers 85 2015/2016 Melbourne Stars vs Hobart Hurricanes 86 2015/2016 Sydney Thunder vs Perth Scorchers 87 2015/2016 Brisbane Heat vs Adelaide Strikers 88 2015/2016 Melbourne Renegades vs Melbourne Stars 89 2015/2016 Hobart Hurricanes vs Perth Scorchers 90 2015/2016 Sydney Sixers vs Brisbane Heat 91 2015/2016 Sydney Thunder vs Melbourne Renegades 92 2015/2016 Adelaide Strikers vs Hobart Hurricanes 93 2015/2016 Melbourne Stars vs Brisbane Heat 94 2015/2016 Sydney Sixers vs Sydney Thunder 95 2015/2016 Perth Scorchers vs Melbourne Stars 96 2015/2016 Melbourne Renegades vs Adelaide Strikers 97 2016/2017 Sydney Thunder vs Sydney Sixers 98 2016/2017 Adelaide Strikers vs Brisbane Heat 99 2016/2017 Melbourne Renegades vs Sydney Thunder 100 2016/2017 Sydney Sixers vs Hobart Hurricanes 101 2016/2017 Perth Scorchers vs Adelaide Strikers 102 2016/2017 Hobart Hurricanes vs Melbourne Stars 103 2016/2017 Sydney Sixers vs Perth Scorchers 104 2016/2017 Sydney Thunder vs Brisbane Heat 105 2016/2017 Melbourne Renegades vs Perth Scorchers 106 2016/2017 Brisbane Heat vs Hobart Hurricanes 107 2016/2017 Adelaide Strikers vs Sydney Sixers 108 2016/2017 Melbourne Stars vs Melbourne Renegades 109 2016/2017 Perth Scorchers vs Sydney Thunder 110 2016/2017 Hobart Hurricanes vs Adelaide Strikers 111 2016/2017 Brisbane Heat vs Sydney Sixers 112 2016/2017 Sydney Thunder vs Melbourne Stars 113 2016/2017 Perth Scorchers vs Brisbane Heat 114 2016/2017 Adelaide Strikers vs Hobart Hurricanes 115 2016/2017 Melbourne Renegades vs Melbourne Stars 116 2016/2017 Hobart Hurricanes vs Sydney Thunder 117 2016/2017 Sydney Sixers vs Melbourne Renegades 118 2016/2017 Melbourne Stars vs Adelaide Strikers 119 2016/2017 Brisbane Heat vs Perth Scorchers 120 2016/2017 Melbourne Renegades vs Hobart Hurricanes 121 2016/2017 Sydney Sixers vs Sydney Thunder 122 2016/2017 Perth Scorchers vs Melbourne Stars 123 2016/2017 Adelaide Strikers vs Melbourne Renegades 124 2016/2017 Melbourne Stars vs Brisbane Heat 125 2016/2017 Sydney Thunder vs Adelaide Strikers 126 2016/2017 Brisbane Heat vs Melbourne Renegades

250

127 2016/2017 Hobart Hurricanes vs Perth Scorchers 128 2016/2017 Melbourne Stars vs Sydney Sixers

251

Appendix 4

Sample frame of analysed broadcast ratings as utilised within Study 5

Code Fixture Type Round Date Match Up NRL Friday Live 1 2/03/2012 Parramatta v Brisbane Friday Live 8 27/04/2012 Canterbury v Manly Friday Live 13 1/09/2012 Manly v St George Illawarra Friday Live 24 17/08/2012 Canterbury v Wests Friday Delay 4 23/03/2012 South Sydney v Brisbane Friday Delay 8 27/04/2012 Brisbane v Gold Coast Friday Delay 13 1/06/2012 Gold Coast v North Queensland Friday Delay 24 17/08/2012 Brisbane v Melbourne Sunday Afternoon 6 8/04/2012 Newcastle v Parramatta Sunday Afternoon 12 24/05/2012 Wests v North Queensland Sunday Afternoon 19 15/07/2012 St George Illawarra v Cronulla Sunday Afternoon 24 19/08/2012 Manly Sea Eagles v Newcastle AFL Friday Live 1 30/03/2012 Hawthorn v Collingwood Friday Live 8 18/05/2012 Collingwood v Geelong Friday Live 16 13/07/2012 North Melbourne v Carlton Friday Live 21 17/08/2012 Geelong v St Kilda Sunday Afternoon 4 22/04/2012 Sydney v North Melbourne Sunday Afternoon 8 20/54/2012 Carlton v Adelaide Sunday Afternoon 9 27/05/2012 Carlton v Melbourne Sunday Afternoon 18 29/07/2012 St Kilda v Western Bulldogs

252

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