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Understanding Sport Consumers within Competitive Markets
Hunter Fujak
Doctor of Philosophy UTS Business School
University of Technology Sydney
Submitted 15 November 2018
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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.
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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*)
Fujak, H Primary data collector, analysis and write up of manuscript.
Frawley, S PhD panel supervisory support and proofreading.
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)
Fujak, H Primary data collector, analysis and write up of manuscript.
Frawley, S PhD panel supervisory support and proofreading.
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)
Fujak, H Primary data collector, analysis and write up of manuscript.
Frawley, S PhD panel supervisory support and proofreading.
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)
Fujak, H Primary data collector, analysis and write up of manuscript.
Frawley, S PhD panel supervisory support and proofreading.
Bush, S PhD panel supervisory support. Guidance with statistical analysis.
Production Note:
Signature removedprior to publication.
Production Note:
Signature removedprior to publication.
Production Note:
Signature removedprior to publication.
Production Note:
Signature removedprior to publication.
Production Note:
Signature removedprior to publication.
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Signature removedprior to publication.
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Signature removedprior to publication.
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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
5.2 Literature Review ................................................................................................................ 151
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
6.2 Literature Review ................................................................................................................ 189
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
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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
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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
Sport Management Review (A)
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.
34
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
61
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 &
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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.
3.2 Literature Review
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
75
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
96
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
99
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
108
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
110
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
111
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.
112
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.
4.2 Literature Review
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
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& 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
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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
competition in 2005 (5
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
competition in 2011 (8
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
competition in 2015 (8
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).
133
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
138
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.
5.2 Literature Review
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.
H2: Consumer preference towards local teams will increase over time.
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
H3: BBL television viewership exhibits longitudinally stable patterns of consumption.
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
169
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.
170
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.
171
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
172
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
173
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.
H2: Consumer preference towards local teams will increase over time.
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
174
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.
H3: BBL television viewership exhibits longitudinally stable patterns of consumption.
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
187
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.
6.2 Literature Review
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
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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
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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.
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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
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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.
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Table 22: Thesis research outputs reaffirmed Study Study Submission Journal (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
Sport Management Review (A)
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
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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
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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
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Weightlifting 1/0
Wrestling 1/0
Other 1/0
None of the above 1/0
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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: hunter.fujak@student.uts.edu.au Stephen Frawley: Stephen.frawley@uts.edu.au 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 Research.ethics@uts.edu.au 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?
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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]: ______________
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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 ________________
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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]
1- Disagree Strongly
2 3 4 5 6 7-
Strongly Agree
c) Win lose or draw I’m a loyal fan of [insert team]
1- Disagree Strongly
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-
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Disagree Strongly
Strongly Agree
b) It would be difficult to change my beliefs about [insert team]
1- Disagree Strongly
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
1- Disagree Strongly
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
1- Disagree Strongly
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
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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
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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
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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
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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
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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
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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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