Examining the behavioralmanifestations of fan avidity

in sports marketingWayne S. DeSarbo

Smeal College of Business, Pennsylvania State University,University Park, Pennsylvania, USA, and

Robert MadrigalLundquist College of Business, University of Oregon, Eugene, Oregon, USA

Abstract

Purpose – The sports industry is one of the fastest growing business sectors in the world today and itsprimary source of revenue is derived from fans. Yet, little is known about fans’ allocation of time, effort,and/or financial expenditures in regard to the sports they care so desperately about. The purpose of thispaper is to explore the multidimensional aspects of such manifestations of fan avidity and examine thenature of heterogeneity of such expressions.

Design/methodology/approach – Data were collected from a student sample of football fans from awell-known US university.

Findings – In total, 35 different expressions of fan avidity are developed related to how fans follow andsupport their favorite team. A spatial choice multidimensional scaling model is developed to uncoverfour latent dimensions of fan avidity expression.

Originality/value – The managerial aspects of these empirical findings are provided, and the authorssuggest several directions for future research.

Keywords United States of America, Sports, Consumer behaviour

Paper type Research paper

The sports industry is one of the ten largest and fastest growing business sectors in theUSA. The Sports Business Journal (2009) has estimated the size of the industry in 2008 tobe approximately $213 billion, more than twice the size of the US auto industry andseven times the size of the movie industry[1]. A sizable proportion of these revenues areattributed either directly or indirectly to the sports fans. Without question, spectatorsports are big business. And, like any business, it depends principally on its most loyaland dedicated consumers/sports fans.

Fan avidity is defined (DeSarbo, 2009, 2010) 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). As noted by Syracuse (2008, p. 1):

Avid fans are those that have an emotional connection to the game – people whose interest,enthusiasm, and passion for the product defy the norm. From a Marketing standpoint, theseindividuals are dream customers because they are eager consumers of all things associatedwith the sport.

Avid sports fans have been found to spend considerably more money, time, and effort forsports-related activities and goods than their non-avid fan counterparts, as indicated in thevarious survey-based sports polls conducted by Taylor Nelson Sofres (TNS)

The current issue and full text archive of this journal is available at

www.emeraldinsight.com/1746-5664.htm

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Journal of Modelling in ManagementVol. 6 No. 1, 2011

pp. 79-99q Emerald Group Publishing Limited

1746-5664DOI 10.1108/17465661111112511

and TURNKEY Sports & Entertainment. The concept helps explain why such teams asthe New York Knicks National Basketball Association (NBA) basketball team, theToronto Maple Leafs National Hockey League (NHL) hockey team, the Chicago Cubsmajor league baseball (MLB) baseball team, and the Cleveland Browns National FootballLeague (NFL) football team have high game attendance figures, yet have not won majorleague championships in their respective sport in decades. Avid fans are more likely tostick with their team(s) even through long droughts of losing seasons. Indeed, the notion offan avidity and its measurement has been the focus of applied research in the sportsindustry for the past decade. Research suppliers such as TNS, TURNKEY Sports& Entertainment, Scarborough Sports Marketing, etc. have been instrumental inpromoting and implementing the concept of fan avidity. For example, Scarborough SportsMarketing has quantified the various major US cities and has identified Columbus (Ohio)as having the greatest percentage of overall avid sports fans (66 percent), followed byBoston (64 percent), Pittsburgh (63 percent), and Buffalo (63 percent). According to ThePress Box, in 2006, some 32 million fans identified themselves as avid NFL fanswith college football second at 23 million, followed by MLB (19.6 million), collegebasketball (15.8 million), National Association for Stock Car Auto Racing (15.1 million), theNBA (14.3 million), figure skating (13.1 million), extreme sports (11.4 million), boxing(10.2 million), the Professional Golf Association (10 million), and the NHL (6.5 million).And the fan avidity concept has caught on with the individual teams where, for example,the Cleveland Browns NFL professional football organization had recently created aformal organizational management position titled “Coordinator of fan” avidity (ClevelandBrowns web site). While there is a rather extensive literature in the Marketing and SportsBusiness journals on fan loyalty (Hill and Green, 2000; Bauer et al., 2008; Funk and James,2006; Bee and Kahl, 2006), very little is published in the academic literature dealing withthe topic of fan avidity.

For the most part, previous research measuring fan avidity has utilized surveymethodologies that employ discretely scaled single attitude or self-description measures(e.g. avidity, interest, involvement, etc.). Thus, the focus of these studies has beenpredominantly on psychological constructs. Missing in much of this published academicresearch is the extensive actual fan manifestations or behavioral expressions of fanavidity, i.e. games attended, merchandise purchased, watching the games on TV,reading about the team in the newspaper, playing fantasy sports, etc. In this manuscript,we explore the dimensionality of the many expressions of fan avidity, as well as theheterogeneity of these manifestations across a sample of sports fans. In particular,we demonstrate empirically that there are many different pathways to becoming an avidfan. And these alternative pathways have different revenue implications and marketingimplications for the team or league involved. We show that single-item attitudinalmeasures are insufficient to capture this complexity of actual avid sports fan behaviors.We address this shortcoming by presenting a spatial choice model that uncovers thelatent dimensionality in expressions of fan avidity. By so doing, we demonstrate howbehavioral measures pertaining to specific sports fan actions may be employed bypractitioners to model the richness and diversity of the fan experience.

The manuscript proceeds as follows: we begin by examining the behavioralfoundation of fan avidity by conceptualizing sports fandom as a type of consumersubculture that is defined by specific rituals, symbols, and a distinct hierarchical socialstructure. We then discuss that underlying fans’ commitment to a sports entity

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is a sense of social identification characterized by certain normative behaviors intended tostrengthen their sense of connectedness to the group. We discuss the measurement of themany expressions of fan avidity, and introduce a spatial choice multidimensional scaling(MDS) methodology as a means of uncovering the latent dimensionality of sportsfans behavior and expressions of fan avidity. We then describe the study conductedwith student college football fans from a large US university, and present the results ofthe spatial analysis. Finally, we discuss the implications of our results for marketingpractice.

Fan avidity: backgroundConsumer fanaticism represents an affective attachment that is sometimes so profoundthat it implies a religious fervor in which the object takes on aspects of the “sacred”(Belk et al., 1989). Like any consumer subculture, sports fanatics have a set of sharedbeliefs and values, unique jargons, rituals, and modes of symbolic expression (Schoutenand McAlexander, 1995). For example, Pimentel and Reynolds (2004) found that highlyaligned fans regularly engage in a variety of proactive sustaining behaviors such asparticipating in game rituals (e.g. watching games with friends, tailgating), displayingteam symbols (e.g. team memorabilia, licensed clothing), and even constructingshrines in their homes paying allegiance to their favorite team (Pimentel andReynolds, 2004).

Also typical of a subculture, fan avidity is characterized by a hierarchical socialstructure in which expressions of attachment vary. For example, Hunt et al. (1999)developed a conceptual typology based on fans’ attachment to a sports team. At thelowest level are temporary fans whose attachment is time constrained. These fans followa team only when it is winning or when a favorite player is on the roster. Local fans arebounded by geographic constraints. Fans attachment at this level is with theirgeographic region rather than the team and will fade if they were to move away. Devotedfans have a stronger sense of attachment that is not bounded by temporal or geographicconstraints. This fan remains committed regardless of team performance. At the highestlevel of attachment is the fanatical fan. These fans engage in ritualistic expressions ofavidity that far exceed those of even the devoted fan. Yet, family and friends accept theseexpressions because they are considered to be supportive of the sports property.

Underlying fans’ attachment to a sports property is social identity theory whichsuggests that people define themselves in part by their memberships and affiliations tovarious social groups (Hogg and Abrams, 1988; Tajfel and Turner, 1986; Turner, 1982).According to Abrams and Hogg (1990, p. 2), “(S)ocial identity is self-conception as agroup member.” Increased levels of identification are related to a greater sense ofoneness or connectedness to the salient group. An individual’s self-concept is comprisedof many self-identities, each varying along a continuum ranging from individualcharacteristics at the personal end of the spectrum to social categorical characteristics atthe extreme social end. An individual’s identification with a particular team or athleterepresents a single aspect of social identity.

People behave as group members in those circumstances where social categorizationis made salient and as individuals when personal identity is made salient (Turner, 1982).As with personal identity, people seek to maintain a positive social identity in order toenhance their own self-esteem. In those situations where group identity is made salient,in-group members engage in a variety of normative behaviors aimed at maintaining

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positive distinctiveness. Social identification is enhanced through positivedistinctiveness when one’s own group is perceived to be different and better thanthat of an out-group on dimensions generally considered to have social value or whichmatter to the in-group.

In an effort to achieve positive distinctiveness, members will be compelled by referentinformational influence to conform to group norms (Hogg and Turner, 1987). Suchnormative behavior is intended to promote the group’s welfare. For example, researchhas found that the largest contributor to intentions to buy products from a corporatesponsor of a favorite team was group norms, followed by team identification (Madrigal,2000). In addition, a structural equation model by Fisher and Wakefield (1998) found thatteam identification mediated the effect of sport involvement, team performance, andplayer attractiveness on a number of normative behaviors including attendance,purchase of licensed merchandise, and displays of team support (e.g. wearing clotheswith the team logo). The normative influence of team identification has also been linkedto intentions to engage in aggressive and violent behaviors (Branscombe and Wann,1992a, b; Wann and Pierce, 2003).

Aside from the normative influence of team identification on intentions and behavior,a number of other topics related to fan avidity have appeared in the sports marketingliterature. One such topic is the motives underlying fans’ interest in watching sports(Gantz and Wenner, 1991; Hunt et al., 1999; Sloan, 1989; Wann, 1995). Research has alsoconsidered the in situ consumption of sporting events using ethnography (Holt, 1995)and survey (Madrigal, 2006) methods. Studies have also considered fans use ofcounterfactual thinking and hindsight judgments (Roese and Maniar, 1997), and howbiased processing affects their interpretation of game information (Hastorf and Cantril,1954; Madrigal, 2008; Wann and Schrader, 2000).

Empirical research concerning the measurement of fan avidity expressions haveutilized check lists of various fan consumption behavior involving the allocation ofmoney and/or time towards following a specific sport, league, team, and/or player. Forexample, the Entertainment and Sports Programming Network (ESPN)/TNS sports poll,a syndicated continuous tracking survey of US teenagers and adults that runscontinuously (360 days/year) considered by many as the “industry standard” formonitoring the overall “health” of sports, collects such binary responses to a limited setof fan behaviors by type of sport. Subsequent analysis of such data are then typicallylimited to examining the descriptive frequencies of each measure. Our primary researchobjective is to examine the multidimensionality and heterogeneity of more extensiveexpressions/manifestations of fan avidity. To do this, we collect survey data fromstudent sports fans of a large US university’s football team to measure fan avidity andthe various activities undertaken by these students in following and supporting theirfootball team. We apply a spatial choice MDS model to uncover these latent dimensions,and individual student representation in the resulting joint space to explore theheterogeneity of such fan avidity expressions.

The spatial choice MDS modelOur primary objective here is to uncover the latent dimensionality in the binary (checklist) expressions of fan avidity in terms of the different activities selected by sports fans(as in the ESPN/TNS sports poll) with respect to their avidity for a specified sports team.As mentioned in Bowles et al. (2005), responses to binary items are not normally

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distributed and therefore violate a key assumption of standard factor analysis (i.e. thenormality of the unique factors). Previous research has demonstrated that factoranalyses of dichotomous items can yield biased factor loadings (Parry and McArdle,1991) or generate spurious factors (McDonald and Ahlawat, 1974). Here, we seek a jointspatial representation of both selected activities and sports fans such that theirgeometric relationship implies aspects of each fan’s activity selection process. Thespatial procedure utilized should be tied into a well-accepted theory of how sports fansmake actual activity choices. For these purposes, we adapt the DeSarbo and Cho (1989)spatial MDS choice model which we summarize in some detail below for the convenienceof the reader in order to interpret the resulting analyses[2].

Let:

t ¼ 1, . . . ,T dimensions (extracted in an MDS context);

i ¼ 1, . . . ,I fans;

j ¼ 1, . . . ,J sports activities;

yij ¼1 if fan i chooses activity j;

0 otherwise;

(

Pij ¼ the probability that fan i choses activity j;

ait ¼ the t-th vector terminus coordinate for fan i;

ci ¼ an additive constant for fan i; and

bjt ¼ the t-th coordinate for activity j.

DeSarbo and Cho (1989) assume that the choice process of fan i choosing or expressingactivity j is Bernoulli, with probability of choice given by Pij. As mentioned in DeSarboand Cho (1989), unlike the conditional logit

PjPij ¼ 1 (McFadden, 1976) and conditional

probit (Hausman and Wise, 1978), there need not be the constraint that since the sum ofthe probabilities across activities is the expected numbers of picks for fan i which in mostcases will exceed 1. By not requiring this constraint, the model can be utilized toaccommodate choice situations where multiple activities are engaged in withcorrespondingly high probabilities.

Let us define a latent, unobservable utility index mij as:

mij ¼XT

t¼1

aitbjt þ ci þ eij; ð1Þ

where eij is an error term assumed to have a N 0;s 2ij

� �distribution with:

Covðers; etuÞ ¼s 2

ij if r ¼ t ¼ i; s ¼ u ¼ j;

0 else:

(ð2Þ

The right-hand side of equation (1) contains the scalar product of the i-th fan’s vectorcoordinates with the j-th activity’s coordinates. That is, equation (1) defines an MDS vectormodel (Tucker, 1960) of utility where fans are represented by vectors and sports activitiesby coordinate points in a T-dimensional joint space. The orientation of a fan’s vectorprovides information as to the direction in the derived joint space of higher utility

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or probability of being selected. The projection of an activity onto a fan’s vector indicatesthe degree or magnitude of utility – the larger the scalar products (i.e. the higher theprojection of an activity onto a fan’s vector), the higher is the utility of that activity for thatfan (Slater, 1960). Figure 2 shows the workings of this spatial choice model for twodimensions, three fans (labeled 1-3), and four activities (labeled A-D). Each fan isrepresented in this hypothetical latent space by a vector whose direction indicatesincreasing utility for that fan. Each sports activity is represented by a point. Oneorthogonally projects each activity onto each fan vector (i.e. their scalar product), and theintersection onto each fan vector indicates a cardinal utility value. For example, considerfan 1 in Figure 2. Projecting the four activities onto this fan’s vector indicates that activityB has the highest probability of expression or choice by this fan since it projects thefurthest in the direction of increasing utility, followed by activities C, A, and D,respectively. The threshold value (for fan 1) denoted in Figure 2 shows the utility barrierthat must be passed for there to be a choice for this particular fan. In Figure 1, activities Band C are predicted to be chosen, while activities A and D not chosen. In two dimensions,the iso-probability contour is a straight line perpendicular to the fan vector, i.e. any activitylying on such a perpendicular line has equal probability of being expressed by that fan.Fan heterogeneity is represented by the variation in these vector orientations across fans.Given only the activity choice data and a trial value of the dimensionality, the objective isto estimate this type of joint spatial representation.

Thus, in the DeSarbo and Cho (1989) model framework, uij is specified such that ifuij # d2

i then we observe yij ¼ 0 (no choice), and if uij . d2i we observe yij ¼ 1 (a choice).

Here, d2i is a threshold parameter (confounded with the additive constant) which varies

by fan. Therefore:

Figure 1.An illustration of thespatial choice model

Dimension II

Dimension I

Fan 1

ActivityB

ActivityA

Activity D

ActivityC

Fan 2

Fan 3

Threshold δ1

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Pðyij ¼ 0Þ ¼ P uij # d2i

� �¼ P

XT

t¼1

aitbjt þ ci þ eij # d*i

!

¼ P eij # 2XT

t¼1

aitbjt þ di

!¼ FðsÞ;

ð3Þ

where di ¼ d2i 2 ci , and F(s) represents the standard normal cumulative distributionfunction evaluated at:

s ¼di 2

PTt¼1aitbjt

sij: ð4Þ

One must restrict sij ¼ si above since there is insufficient data to estimate all the modelparameters. As such, one can set si ¼ 1 for all i here without loss of generality as anindividual level scale factor can be normalized out of expression (4) above. That is, thereis no need to estimate the variance term since it is not identifiable. Similarly:

Pðyij ¼ 1Þ ¼ P uij. d2i

� �¼ P eij . 2

XT

t¼1

aitbjt þ di

!¼ 1 2FðsÞ ¼ Pij: ð5Þ

Thus, one can assume that a latent utility variable exists which, after reaching anindividual specific threshold value, “produces” the observed choice yij ¼ 1. As noted inDeSarbo and Cho (1989), this general specification is quite common in the econometricsliterature (Chow, 1983; Maddala, 1986) where discrete choice models are tied into latent,indirect utility scores, and threshold values. In fact, this specification above can beviewed as a bilinear spatial probit type of model. The theoretical justification for such aspecification can be found in Lewin et al. (1944), Siegel (1957), Simon (1959), and otherresearch on aspiration levels and decision making. According to Simon (1959, 1978),economic agents engage in satisfying behavior rather than maximizing behavior. Simon(1959, p. 264) claims that economic agents form thresholds or aspiration levels which“defines a natural zero point in the scale of utility”. When the economic agent hasalternatives to it that are at or above its aspiration level, this theory predicts that theagent will choose amongst these alternatives as opposed to those alternatives below thislevel. This appears to also be congruent with multistage decision-making/choiceprocesses which combine compensatory and conjunctive rules (Coombs, 1964; Dawes,1964; Einhorn, 1970; Green and Wind, 1973).

The resulting likelihood function can be expressed as:

L2 ¼YI

i¼1

YJ

j¼1

½1 2FðsÞ�yij½FðsÞ�ð1 2 yijÞ; ð6Þ

and the log likelihood as:

K2 ¼ ln L* ¼XI

i¼1

XJ

j¼1

½yijlnð1 2FðsÞÞ þ ð1 2 yijÞlnFðsÞ�: ð7Þ

Thus, given Y ¼ (yij) and a trial value of the dimensionality (T), one wishes to estimateA ¼ (ait), B ¼ (bjt), and d ¼ (d1) to maximize K * in expression (7).

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The DeSarbo and Cho (1989) spatial model can accommodate external analysis whereA and/or B are given/specified from, say, some previous analysis, or it can estimate bothsets of coordinates (internal analysis). Several options also exist with respect to estimatingd, including fixing these thresholds all to zero, estimating one common threshold, orestimating a separate threshold value per fan. Note, the model defined in expressions (3)and (5) can also be generalized to incorporate additional data in the form of fan and/oractivity background variables. The coordinates for fans (vector termini) and/or activities,as the case might be, can be reparameterized as linear functions of background variables(see deLeeuw and Heiser (1980) for constraining MDS spaces). If activity attribute data isavailable, then bjt can be reparameterized as:

bjt ¼XK

k¼1

Xjkgkt; ð8Þ

where xjk is the value of attribute k(k ¼ 1,. . . K , J) for activity j, and gkt is the impact offeature k on dimension t. As in CANonical DEcomposition with LINear Constraints(Carroll et al., 1979), three-way multivariate conjoint analysis (DeSarbo et al., 1982), andGENFOLD2 (DeSarbo and Rao, 1984, 1986), one can model the location of the activities tobe a direct function of their respective features or attributes if available. Thus, the xjk arequantified features which are related to subjective attributes (Lancaster, 1966, 1979).Similarly, when fan background data are available, ait can be reparameterized as:

ait ¼XR

r¼1

zirart; ð9Þ

where zir is the value of characteristic r(r ¼ 1, . . . ,R , I) for fan i, and art is the impact ofthe r-th individual characteristic on dimension t. When both activity and fan backgrounddata are available, both sets of coordinates can be reparameterized. Note, one always hasthe option of performing general property fitting analyses in the non-reparameterizedmodel with A and/or B (via correlation and/or regression analysis) to enhance theinterpretation of the derived dimensions and A and/or B.

Maximum likelihood methods are utilized by DeSarbo and Cho (1989) to maximizeK * ¼ ln L * in expression (7) with respect to the given set of unknown parametersspecified in the particular model of interest. The method of conjugate gradients (Fletcherand Reeves, 1964) with automatic restarts (Powell, 1977) is utilized for this optimizationproblem. The specific steps or phases of the algorithm are described in DeSarbo and Cho(1989). The Akaike information criterion (AIC) statistic (Akaike, 1974) is recommendedby DeSarbo and Cho (1989) as a method of model selection (e.g. choice of dimensionality,constrained vs unconstrained models, etc.). Here, it is defined as:

AIC ¼ D þ 2Q; ð10Þ

where Q is the effective number of independent or free parameters estimated in themodel, and D is the deviance measure (22K2 ). Such an information heuristic trades offgoodness of fit (D) with model complexity (the number of independent modelparameters). In this context, one selects the model/solution with the minimum value ofAIC as the “best” or most parsimonious solution.

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College football fan avidity studyThe studyInitially, a number of in-depth interviews were conducted with various students at a largeUS university (which we will designate as University XXX) to examine the nature ofstudent fan avidity for the university’s football team. It is a university where football isthe dominant sport on campus (average home game attendance averages over 100,000fans) and where national prominence is achieved each year as reflected by typical top20 ratings in the national polls each year. In 2009, it is estimated that this sport wasresponsible for the generation of nearly $100 million in revenue to this university. Thehighly visible head coach of the football team has achieved iconic status in the sport.Here, we wish to examine the different ways or activities student fans choose to expresstheir avidity for their football team. Over 50 in-depth interviews were initially conductedwith representative undergraduate students[3], as well as athletic staff associates fromthis university, to understand how student fans of the university’s football programfollow their team. Questions were asked to evaluate their interest, involvement, avidity,attitudes, and opinions about themselves and the XXX football program, backgroundinformation, as well as open questions to tally the various ways they stated theyfollow the team (if at all). Initially, some 42 different activities were stated from thisprocess. We focused on the subset of activities that were mentioned by at least two ofthese 50 individuals and reduced the set to some 35 different activities orexpressions/manifestations of fan avidity as it pertained to their following of thefootball team. These activities are listed in Table I with their associated labels and wereworded (in line with a number of major sports polls) in terms of whether or not (i.e. binary)the student selected that particular activity in following the football team during the 2008season. This list in Table I is much more extensive than those utilized in any commercialsports poll. Note that there was no constraint placed as to the number of activities to beselected by the student respondent so that this data can be characterized as pick any/N.

A questionnaire was developed and pretested with a set of 20 different students forwording, comprehension, and relevance. The questionnaire contained measures of fanavidity, interest, involvement, the inventory of these 35 activities, attitudinal andpsychographic questions, and demographics. After several revisions and additionalpretesting, we distributed the questionnaire to undergraduate students at this universityin a number of subject pools for which they received additional credit towards theirgrade in a marketing course they were all taking at the time. In all, 307 completedquestionnaires were obtained.

Figure 2 shows the percentage of the 307 students who selected each of the35 activities utilized in the study and listed in Table I. As shown, there is substantialvariation in these proportions ranging from 1 percent who actually stated they tried outfor the dance team that entertains stadium fans to 96 percent who state they watch theteam play on television. Other highly selected activities include wearing the schoolfootball colors during the football games (92.51 percent), reading the college newspaperabout the football team (90.81 percent), attending at least one home football game(89.58 percent), and tailgating at the stadium (88.60 percent).

AnalysisThe analysis employing the DeSarbo and Cho (1989) spatial choice MDS model to examinethe latent dimensionality of this data vis-a-vis a joint space representation of both fans

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and activities was conducted in T ¼ 1, . . . ,5 dimensions. Table II presents the associatedgoodness-of-fit values including the log likelihood and AIC values. Using the AIC criterionto select the dimensionality, we see its minimum value occurs at four dimensions.

Figure 3 shows the four-dimensional space (presented one dimension at a time) for justthe activities in order to best interpret these derived latent dimensions. For dimension I, letus concentrate on those activities which load heavily positive such as tries out or becomesa member of the marching band, tries out or becomes a member of the dance team, tries outor joins the football team, tries out or becomes a member of the cheerleading squad, orhelps to clean the stadium after the football game. These all define on-field activeparticipation in the university’s football-related activities. Note also that these activitieshave the lowest frequency of being selected by this sample as seen in Figure 2. At theopposite extreme on this dimension define non-field participation activities which tend tobe much more frequently selected. We therefore label this dimension as distinguishing

Plot code Activity description

A1 Attends at least one home game?A2 Attends at least one away game?A3 Listens to the games on local radio?A4 Reads about the XXX football team in the school newspaper?A5 Purchases XXX football merchandise?A6 Attends the post-season bowl game?A7 Reads about the XXX football team in the local newspaper?A8 Purchases 2008 XXX football season tickets?A9 Plays as XXX football team in video games?A10 Watches XXX football game on TV at home?A11 Collects XXX football memorabilia?A12 Joins or tries out for the XXX football team?A13 Purchases sports magazines to read on the league’s football?A14 Uses the internet to follow XXX football or the league?A15 Participates in friendly wagers on the results of the XXX game?A16 Watches XXX football game on TV at restaurant or bar?A17 Attends the open practice football game in spring?A18 Purchases XXX football clothing?A19 Listens (radio) to the XXX football games over the internet?A20 Watches (streaming video) the XXX football games over the internet?A21 Watches the league channel’s football coverage?A22 Attends XXX football pep rallies?A23 Tailgates at the stadium at games?A24 Purchases or subscribe to XXX football magazine?A25 Uses school color face or body paint during a XXX football game?A26 Participates in post-game celebrations or parties?A27 Tries out or becomes a member of the XXX cheerleading squad?A28 Tries out or becomes a member of the XXX marching band?A29 Tries out or becomes a member of XXX dance team?A30 Wears school colors (clothing) during the XXX football game?A31 Helps tutor XXX football players with their studies?A32 Joins the football sports club (you or a member of your family)?A33 Camps out for good seats the night before the game?A34 Works at XXX football games?A35 Helps to clean the stadium following the game?

Table I.Football activitydescriptions

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on-field participation from more non-active involvement. Note, it is also inversely relatedto choice share. Regarding dimension II, we see high loadings for reading about the teamin the local newspaper, following the team and its division on the internet, listening to thefootball games over the internet, reading about the team in the college newspaper, andlistening to the games on the local radio. This dimension defines a very passive followingof the football team. Dimension III is defined at the positive extreme by such items aspurchasing the school’s football merchandise, purchasing the school’s football clothing,

Figure 2.Sample percentages forcollege football-related

activities

A35

A34

A33

A32

A31

A30

A29

A28

A27

A26

A25

A24

A23

A22

A21

A20

A19

A18

A17

A16

A15

A14

A13

A12

A11

A10

A9

A8

A7

A6

A5

A4

A3

A2

A1

0.00 10.00 20.00 30.00 40.00 50.00(%)

60.00 70.00 80.00 90.00 100.00

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purchasing/collecting football team memorabilia, and joining an expensive footballbooster club. Clearly, this dimension describes a purchasing construct which obviouslyresults in active revenue generation for the university. Finally, dimension IV ischaracterized at the positive end by purchasing season football tickets, attending at leastone away game, using school-colored body paint during the football game, subscribing tothe football magazine, attending at least one home game, and attending the pep rallies.This describes a social dimension related to the stadium experience which is also related torevenue generation (e.g. ticket sales, concessions, etc.) for the university. Thus, these fourdimensions describe very different manifestations of fan avidity amongst these students.And, as seen in the correlations between these activities dimensions presented in Table III,the derived dimensions are nearly orthogonal lacking any significant correlation betweeneach other indicating they are describing rather independent aspects of the data[4].

Figures 4 and 5 show the four-dimensional joint space via two joint space plotsdisplaying dimensions 1 vs 2 in Figure 4, and dimensions 3 vs 4 in Figure 5. As discussed inthe illustration in Figure 1 earlier, the activities are represented by coordinate points withtheir plot label, whereas each fan is represented by a vector whose orientation points in thedirection of greater utility and correspondingly higher probability of activity selection. Asis common in most vector MDS joint space plots, we normalize the length of the studentfans’ vectors to equal length in each plot for convenience. Exploring first Figure 4 andthis first two-dimensional joint space, we see that the fan vectors appear to be mostlyconcentrated in a 758 arc located in the center of quadrants 3 and 4 indicating that the vastmajority of student fans here do not typically engage in the on-field participation activities.This is also verified by the frequency plot in Figure 2 where such aggressive participationactivities are much smaller expressions of fan avidity. We do witness much highervariation/heterogeneity along the second dimension where this student fan sample appearsto display a wider variety of passive vs non-passive expressions of fan avidity. In Figure 5,we see much wider variation/heterogeneity in the vector orientations across all 3608,although there is a somewhat higher concentration of fan vectors in quadrant 3. It is clearthat watching the football on TV at home is highly preferred by most of these student fans.

As mentioned in the introduction, many sports marketing research firms measureoverall fan avidity by means of survey measurement single-item attitude scales onavidity, interest, or involvement. In order to examine the association of such items wecollected[5] and the four dimensions of fan avidity expression, we correlated thesemeasurements as shown in Table IV. We also created a variable called sum, which totalsthe number of activities chosen by each respondent, and included that variable in theanalysis as well to illustrate a point. A number of interesting findings can be gleaned from

T Q K * AIC

1 649 22,995.9 7,289.92 990 22,645.1 7,270.23 1,330 22,156.0 6,972.14a 1,669 21,766.3 6,870.55 2,007 21,521.1 7,056.14c 457 23,210.7 7,335.4

Note: aDenotes minimum AIC solutionTable II.Goodness-of-fit values

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this table of correlations. One, the study confirms the industry standard of using interest inthe sport as a single-item proxy for overall fan avidity since the two measures have thelargest association in the table. Two, the fan avidity variable is more highly correlatedwith the sum of the chosen activities than with any of the four derived dimensionsindividually. It appears that the sum measure is indicative of avidity intensity which canexplain this finding. Three, the strongest correlations (negative here) between fan

Figure 3.College football activity

dimensional space

3.500

A28

A31

A29

A19

A7 A14 A3A18A5

A19A2

A8A25

A24

A1

A22A11

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A24A28A22A34

A27A18

A6

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A5

A32

A17A9

A27A31A14A29

A28A13A12

A4A35A34

A23A26A19A20

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A10

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A31A1A30A23A12A14A15A33

A20

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A21

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A18A35

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A10

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3.000

2.500

2.000

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Dimension I: Dimension II: Dimension III: Dimension IV:On-field

participationPassive

followingPurchasing Social

Fan avidityin sports

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91

avidity, interest, and involvement measures vs the four derived latent dimensions of fanvectors occur with respect to the first dimension (on-field participation). Given that thisdimension is also inversely related to choice share, this finding makes intuitive sensegiven what we see with the sum variable and avidity. While other correlations with theother dimensions are statistically significant, these single-item measures fail to explainmore than 5 percent of the variation in dimensions II, III, and IV. In particular, given thetwo major dimensions related to revenue generation are the purchasing and socialdimensions (dimensions III and IV), we see very low levels of association between thesetwo revenue-related dimensions and overall fan avidity, interest, and involvement.

In a different way of looking at this issue, we reran the spatial choice model in fourdimensions where we constrained the fan vectors to be linear functions of fan avidity,interest, and involvement using expression (9). Table II displays the associatedgoodness-of-fit statistics obtained by this solution (designated by 4c in the table).As clearly indicated by the comparison of the AIC heuristics, the four-dimensionunconstrained solution clearly dominates this reparameterized, constrained solution. Thisindicates that fan avidity, interest, and involvement measures are incapable of properlycapturing the variations and heterogeneity in expressions of fan avidity vis-a-vis the actualbehaviors of fans regarding these 35 activities. In particular, these single-item aggregatemeasures alone are incapable of discriminating between the revenue vs non-revenuegenerating dimensions of the different expressions of fan avidity. As such, researchers andpractitioners must be very guarded about using such aggregate measures of fan avidity,interest, and/or involvement alone in attempting to explain the variation of expressions offan avidity, or to infer revenue potential by activity.

DiscussionWe have described the sports industry as one of the top 10 business entities in the USAgenerating some $213 billion in annual revenue. Much of this revenue is generated fromsports fans who consume tickets, merchandise, concessions, memorabilia, etc.We introduced the concept of fan avidity which is currently a very popular topic in thisparticular industry given the fact that avid fans consume much more than non-avid fans.The behavioral framework underlying the concept has been described in detail. We havepresented a study involving the student fans of a well-known university football programand examined some 35 different expressions or manifestations of their fan avidity.Employing a vector MDS model for binary choice data, we uncovered four very distinctdimensions of such avidity expressions: on-field participation, passive following, social,and purchasing. And, we illustrate the nature of student heterogeneity vis-a-vis the differentvector orientations in this four-dimensional joint space. In essence, we demonstrate howthere are different pathways to fan avidity. We aptly illustrated how single-item measures

CorrelationsDim 1 Dim 2 Dim 3 Dim 4

Dim 1 0.018 0.015 0.013Dim 2 20.023 2 .020Dim 3 20.017Dim 4

Table III.Correlations between thefour dimensions of thesports activities

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currently in use by many research practitioners regarding overall fan avidity, interest, andinvolvement do not adequately explain the heterogeneity of such avidity expressions.However, interest and involvement are more parsimonious indicators of overall fan aviditythan the various dimensions derived from this spatial choice MDS model.

Sports fans are consumers who express their avidity for their sport, team, league, player,university, etc. in a variety of different ways. Some of these expressions involve expenditureof money, while others involve more an expenditure of time and/or effort. As such, there arerevenue implications associated with these various consumer behaviors. Our study hasuncovered four distinct dimensions underlying the 35 different manifestations or

Figure 4.Joint space plot of

dimensions 1 and 2

A14

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Passivefollowing

On-fieldparticipation

Activites Student fans

Fan avidityin sports

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93

expressions of fan avidity. Two of the four dimensions relate more closely to revenuestreams for the university as noted above. One can use such an analysis as a basis forsegmenting fans, and evaluating the value of these fans and derived market segments.

This research can be extended in a number of interesting directions. One, furtherresearch needs to be aimed at understanding the theoretical process of fan avidity andhow it develops and alters over time. Our purpose is to explore the outcomes of fanavidity in this manuscript, its multidimensionality, and the heterogeneity amongst fans.Two similar studies need to be conducted at the professional level and across differentsport types. Obviously, the manifestations of fan avidity will vary depending uponthe type and level of sport, as so will the derived dimensions. Our study is not meantto generalize to any other domain in the sports arena. Finally, cross-cultural analyseswould be fascinating in terms of comparing how fans express their avidity, especiallygiven the strong international interest in sports (e.g. soccer).

Figure 5.Joint space plot ofdimensions 3 and 4

A8 A2

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Social

Activities Responses

A14A12

A9

A10

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94

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Table IV.Correlations between thefour fan dimensions and

various single-item scales

Fan avidityin sports

marketing

95

Notes

1. Plunkett Research, Ltd forecasts the total size of the sports industry in the USA to be muchhigher at $410.6 billion in 2009.

2. Correspondence analysis is another spatial procedure that could have been employed herefor the analysis of two-way binary data (see Ferreira et al. (2009) for an illustration of its use).However, this data analytic procedure is not tied into any theory of choice, is deterministicwithout decisive model selection tests, and contains issues with interpreting geometricrelationships between row (fan) and column (activities) objects in the derived space. See alsoDeSarbo (2010) for a spatial unfolding model adapted to such binary data.

3. The athletic department of University XXX estimates that over 25 percent of their totalrevenue is attributable to its undergraduate students with respect to ticket sales.

4. The spatial choice model does not constrain the derived dimensions to be orthogonal unlikemany MDS procedures based on singular value decomposition methods.

5. For the fan avidity scale, use a seven-point semantic differential scale to indicate the level ofagreement with the statement “I consider myself to be an avid XXX football fan”. We usedfour-point response scales for interest (overall, how interested are you in XXX football?) andinvolvement (how would you describe your level of involvement with XXX football?).

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About the authorsWayne S. DeSarbo is the Smeal Distinguished Research Professor of Marketing at the SmealCollege of Business of Pennsylvania State University in University Park, PA. Wayne S. DeSarbo isthe corresponding author and can be contacted at: wsd6@psu.edu

Robert Madrigal is Associate Professor of Marketing at the Lundquist College of Business atthe University of Oregon in Eugene, Oregon, USA.

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