Marketing & Business Law Faculty Works College of Business Administration

2006

A Mixed-Method Approach for Developing Market Segmentation A Mixed-Method Approach for Developing Market Segmentation

Typologies in the Sports Industry Typologies in the Sports Industry

Andrew J. Rohm Loyola Marymount University, arohm1@lmu.edu

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Recommended Citation Recommended Citation Rohm, Andrew J., George R. Milne, and Mark McDonald (2006), “A Mixed-Method Approach for Developing Market Segmentation Typologies in the Sports Industry,” Sport Marketing Quarterly, 15 (1), 29-39.

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Sport MarHeting Quarterly, 2006, 15, 29-39, © 2006 West Virginia University

A Mixed-Method Approach forDeveloping Market SegmentationTypologies in the Sports Industry

Andrew J. Rohm, George R. Milne, and Mark A. McDonald

Abstract

This study presents a mixed-method approach forsegmenting a sports product-market using participa-tion motivation data. Qualitative data are used to seg-ment a national sports product-market—runningfootwear—using qualitative analysis software as well asmultivariate statistical approaches. This studydescribes a systematic approach to developing a con-sumer segmentation typology using both demographicvariables as well as self-expressed motivations for sportand fitness participation. The mixed-methodapproach reported here employs qualitative data tohelp validate subsequent quantitative cluster analysis,and draws upon cluster profiles to establish the struc-ture for market segmentation. The findings from thisstudy offer implications for marketing research andmarketing communications in the sport industry.

A Mixed-Method Approach for DevelopingMarket Segmentation Typologies in theSports Industry

Consumers possess myriad and complex motivationsfor sport and fitness participation (Shank, 2002;Stewart, Smith, & Nicholson, 2003). The complexity

Andrew J. Rohm, PhD, is an assistant professor inMarketing Group in the College of BusinessAdministration at Northeastern University. His researchinterests include branding and integrated marketingcommunications.

George R. Milne, PhD, is an associate professor ofmarketing in the Isenberg School of Management at theUniversity of Massachusetts Amherst. His research inter-ests include privacy, database marketing, electronic com-merce, and consumer consumption experiences.

Mark A. McDonald, PhD, is an associate professor inthe Department of Sport Management in the IsenbergSchool of Management at the University ofMassachusetts Amherst. His research interests includesport marketing and sport management.

"An important question facing hoth sportresearchers and marketers, however, is not onlyhow to generate a deeper understanding of theirconsumers, hut also how to analyze and use this

information (such as motivation and participationdata) that involves multiple dimensions."

of understanding consumers' underlying motivationsfor sports or fitness participation points to both theopportunity and challenge for marketers in developingeffective and meaningful market segmentation prac-tices that are based on consumer typologies. Pastresearch has shown that understanding consumers'underlying motivations for product (Mehta, 1999;Hong & Zinkhan, 1995) and sport spectator (Trail,Fink, & Anderson, 2003) consumption is important todeveloping advertising appeals. Gaining a more com-plete understanding of individuals' participation moti-vations in activities such as running and sportsproduct-markets such as running shoes, through thedevelopment of consumer typologies, can also enablemarketers to develop more meaningful and effectivesegmentation and marketing communications strate-gies. The development of market segmentation strate-gies can be particularly important in such industries asathletic footwear, where brands such as Nike, NewBalance, and Reebok compete fiercely for "share offeet" in widely diverse markets of both young and oldand casual- and performance-based consumers.

Recent studies (e.g., McDonald, Milne, & Hong,2002) involving sports consumers as well as assess-ments of sport consumer research (Funk, Mahoney, &Havitz, 2003) suggest that effective segmentation prac-tices can result from developing a deep understanding,beyond mere demographic profiles, of the consumerand the psychological reasons driving motivations andparticipation. An important question facing bothsport researchers and marketers, however, is not onlyhow to generate a deeper understanding of their con-sumers, but also how to analyze and use this informa-tion (such as motivation and participation data) that

Volume 15 • Number 1 • 2006 • Sport Marheting Quarterly 29

"... consumers possess multiple and unique motivations—including achievement, competition, socialfacilitation, physical fitness, skill mastery, physical risk, affiliation, aesthetics, aggression, value develop-ment, self-esteem, self-actualization, and stress release—for participating in particular sport activities."

involves multiple dimensions. In analysis, multi-dimensional data such as this can result in too fme asegmentation approach, revealing consumer profilesthat may not be sufficiently distinct from each other towarrant the execution of unique marketing communi-cations approaches targeting the derived consumergroups. Given this, there is a growing realization thatthe benefits of finer consumer typologies should beweighed against the efficacy and cost of executingthose typologies in marketing strategy (Stewart, Smith,& Nicholson, 2003). Ideally, analysis of participantmotivation data would involve both qualitative data, toelicit in-depth information about participation motiva-tion, as well as quantitative data in order to reduce thedimensionality of consumer types and to better under-stand the underlying structure of the data.

The purpose of this study is to develop a consumertypology based on analysis of consumer participationmotivation data through a mixed-method approach.In doing so, this study seeks to extend the method-ological basis for conducting sport research and devel-oping market segmentation strategies. We present anddemonstrate an approach for using qualitative data tosegment a national sports product-market—runningfootwear—using multivariate statistical approaches.This research represents an approach to segmentingconsumers not only by demographic background databut also by participation motivation data providedthrough qualitative responses to an open-ended surveyquestion. We demonstrate this mixed-methodapproach in a market segmentation study using anational survey conducted in partnership with a well-known running shoe and apparel brand and Runner'sWorld magazine.

The remainder of the paper is in four sections. First,we review the literature regarding participation moti-vation in the sports and leisure behavior context. Wealso review applications and benefits of mixed-methodresearch and its use in sports marketing. Next, wepresent a mixed-method segmentation approach forquantifying qualitative data from open-ended ques-tions. This approach includes using computer-basedqualitative analysis software (QSR NVivo, 1999) tocode and organize the open-ended responses as well asmultivariate methods such as principal componentsand k-means clustering. We demonstrate this mixed-method research using data collected from Runner'sWorld subscribers. We then review the implications of

this study to research and practice as well as the limita-tions of such a study, and offer suggestions for futureresearch.

Literature Review

Participation MotivationThe central premise to our study is, given the com-

plex mindset of consumers in sport and fitness activi-ties such as running, where rational and emotionalmotivations play into not only product consumptionbut also participation, marketers need to understandthe underlying nature of participation motivation inorder to develop meaningful and effective communica-tion strategies, including the development of advertis-ing messages. In developing market segmentationmodels one needs to also recognize the wide array ofsocial and psychological factors and motivations thatunderlie benefits sought from sport consumption. Forthe purposes of this research, we conceptualize partici-pation motivation as the drive to satisfy physiologicaland psychological needs and wants through consump-tion of products and activities (Lindquist & Sirgy,2003). Whereas socio-demographic variables, such assex, income, age, household size, category, and brandusage may be useful in understanding consumerbehavior, classifying sport consumers based on under-lying participation and consumption motivations canbe more revealing (e.g.. Trail & James, 2001; Green-Demers, Pelletier, Stewart, & Gushue, 1998).

Numerous sport consumer studies evaluating sportparticipation and consumption are based on motiva-tional factors (e.g.. Brooks, 1994; Weissinger &Bandalos, 1995; Green-Demers et al., 1998; Trail &James, 2001; Gladden & Funk, 2002; McDonald,Milne, & Hong, 2002; Trail, Fink, & Anderson, 2003).For instance. Brooks (1994) outlines the relationbetween the underlying extrinsic and intrinsic motiva-tions for sports participation. Brooks examines whatfactors motivate adults to participate in various sportsand fitness activities and proposes that such externalstimuli as marketing and advertising messages helpconsumers to form images of what the activity meansto them at a personal level. These images then lead toboth judgments and feelings associated with the activi-ty as well as participation in that activity.

Related to intrinsic motivation and leisure activity,Weissinger and Bandalos (1995) develop the IntrinsicLeisure Motivation (ILM) scale involving constructs

30 Volume 15 • Number 1 • 2006 • Sport MarHeting Quarterly

such as self-determination, competence, commitment,and challenge. Further, Green-Demers et al. (1998)suggest that continued participation in certain sportscharacterized by monotonous and repetitive traininginvolves interest-enhancement elements as well asintrinsic and extrinsic motivating factors. The authorspropose and test a model of interest-enhancement andmotivation and illustrate significant relationshipsbetween interest-enhancement strategies such as chal-lenge, variety, and self-relevant rationale for participa-tion and underlying intrinsic and extrinsic motivation.

"...researchers recognize the contribution thatmixed method research—combining qualitativeand quantitative methods—can lead to strongerinferences and enhance overall knowledge ofthe

research issue."

McDonald, Milne, and Hong (2002), drawing uponMaslow's human needs hierarchy, present evidenceillustrating that consumers possess multiple andunique motivations—including achievement, competi-tion, social facilitation, physical fitness, skill mastery,physical risk, affiliation, aesthetics, aggression, valuedevelopment, self-esteem, self-actualization, and stressrelease—for participating in particular sport activities.In other sports consumption contexts such as sportsteam identification that are categorized by high con-sumer affective and cognitive involvement, motiva-tions for consumption may be captured in numerousways, including investigating the affect- and cognitive-based motivations for attitude formation or participa-tion (Gladden & Funk, 2002).

Related to sport consumption. Trail and James(2001), in developing the Motivation Scale for SportGonsumption (MSSG), identify motives such asachievement, skill, escape, and social elements thatdrive sport spectator behavior; subsequent studies(e.g.. Trail, Fink, & Anderson, 2003; James & Ross,2004) applied modified versions of the MSSG in vari-ous sport spectator contexts.

This review of the related literature suggests thatsport participation and consumption motives shouldbe viewed as a multidimensional construct composedof a broad range of both environmental as well as psy-chological elements, and that understanding con-sumers at levels deeper than mere demographicprofiles is important to brand positioning and market-ing communications practice. It points to the impor-tance of understanding the resulting motivations forparticipation in such sport and fitness activities as run-ning in the development of segmentation and market-ing communication strategies, as well as theimportance of reducing these multiple dimensions in a

structured approach in order to better interpret andunderstand the findings.

Mixed-Method ResearchA wide array of recent published work in sport

research (Funk, Mahoney, & Havitz, 2003; Lachowetz,McDonald, Sutton, & Hedrick, 2003; Mason & Slack,2003; Silk & Amis, 2000; Stewart, Smith, & Nicholson,2003) reflects the importance and role of qualitativeresearch in studying consumer behavior.Notwithstanding the well-recognized benefits ofemploying quantitative research methods, one relativeadvantage of qualitative research is that it can be asource of rich descriptions and explanations of livedexperiences. In their review of sport consumer typolo-gies, Stewart, Smith, and Nicholson (2003) argue thatqualitative methods should form the basis of sportconsumption models and that "there are stronggrounds for undertaking more qualitative research...totease out some of the more subterranean behefs andmotivations..." (p.214).

While information gained from purely qualitativeresearch may be useful, combining qualitative andquantitative approaches can help the researcher tobenefit from the relative advantages of each method(Teddlie & Tashakkori, 2003). Accordingly, researchersrecognize the contribution that mixed methodresearch—combining qualitative and quantitativemethods—can lead to stronger inferences and enhanceoverall knowledge of the research issue.

For this study, we define mixed method research asthe integration of both quantitative and qualitativemethods in a single study in order to achieve a greaterlevel of knowledge regarding the research issue.Among others, Rossman and Wilson (1984) have sug-gested that combining qualitative and quantitativeapproaches can assist elaborate analysis and lead toricher findings and corroborate findings via triangula-tion (i.e., the support that each method offers the oth-ers' findings). Moreover, Teddlie and Tashakkori(2003) present three areas in which mixed methodapproaches are superior to single approach designs: (1)mixed methods research provides insights to researchissues that single methods cannot, (2) mixed methodsresearch offers stronger inferences, and (3) mixedmethods research can help to capture a greater diversi-ty of respondent views.

Given the multidimensionality of constructs such asparticipant motivation in sport research, the challengefor researchers is how to combine qualitative insightswith quantitative data for reduced dimensionality, andto do so in a manner that is understandable and mean-ingful, while retaining the richness found in the origi-nal qualitative data. Particularly in research

Volume 15 • Number 1 • 2006 • Sport MarHeting Quarterly 31

investigating sport participation, the challenge inherentin traditional survey-based approaches is how touncover underlying meanings behind or motivationsfor sport participation. Likewise, the challenge inher-ent in purely qualitative approaches is to offer sometype of inferential statistic that defines directions orrelationships within the data.

In this study, we employ a mixed-method approachto segment running participants based upon participa-tion motivations. We describe the integration of quan-titative and qualitative methods and the mixed-methoddesign employed in this study next.

"Perhaps most important for sport research and thedevelopment of market segmentation approaches,

this mixed-method approach illustrates how the useof qualitative data can help validate subsequentquantitative cluster analysis, as well as how the

established cluster profiles help to set up the struc-ture for market segmentation models."

Method

Research Setting and Data CollectionThe research setting for this study involved sub-

scribers to Runner's World magazine, a publication tar-geting running enthusiasts with a circulation in the USof approximately 500,000 subscribers. This researchcontext and subsequent sample size is appropriate forthe application ofthe mixed-method research designdescribed here because it involves a consumer segment

whose participation motives regarding running may berational, or emotional, or both. In order to effectivelysegment this market, relying on quantitative approach-es would risk losing the richness of the open-endedqualitative responses, and to rely solely on qualitativeapproaches would risk leaving the researcher with anunwieldy and less meaningful array of segmentationtypes.

Data for this study were gathered as part of a largerdata collection effort. A four-page questionnaire wasmailed to 2,000 Runner's World subscribers. A coverletter, a small incentive (a running pace wheel), andpostage-paid return envelope were included in themailing. A follow-up postcard was sent approximatelytwo weeks after the initial questionnaire mailing. Thisgenerated 864 (43.2%) responses.

Glosed-ended questions in the questionnaire exam-ined the following: running shoe purchase influences,running shoe and apparel brands last purchased by therespondent, perceptions of various running shoe andapparel brands and running shoe and apparel tech-nologies, and background questions regarding shoeand apparel purchase location and running history(years running, miles run per week, races participatedin during previous year, age, and sex). The question-naire also included an open-ended question (finalquestion of the survey) that asked respondents"Finally, how important is running to you and why?"Space was provided on the questionnaire for 15 lines ofresponses. Ofthe 864 questionnaires returned, 815respondents (94.3% ofthe total responses) answered

Figure JA Mixed-Method Approach for Integrating Qualitative and Quantitive Anaiysis

ourvcjT

QUALITATIVE ANALYSIS-*- I. Form Initial Gategories

2. Analyze with NVivoa. Gode text data electronicallyb. Generate coding reportsc. Gompare generated codes with the initial

codes3. Independent judges code open-ended

responses for each ofthe 10 categories witha binary score

4. Assess interjudge reliability and resolvediscrepancies. Greate data file forsubsequent analysis.

1 0 A<i p*i<i ("rpninilifv C\T tnp rlii*\tprs witn ^

open-ended data.

f Design 1

GJ.

QUANTITATIVE ANALYSIS—^" 5. Data reduction with principal

components analysis6. Greate factor scores for motivations7. Segment runner types with K-Means

Glustering Algorithm8. Assess reliability of cluster solution9. Assess external validity by profiling -<

clusters with close-ended data. Look formanagerially meaningful differences.

32 Volume 15 • Number 1 • 2006 • Sport MarHeting Quarterly

Table IInter-Judge Reliabilities

Frequency ofAgreements

AddictionFitnessCompetitionSelf-EsteemMental HealthWeight ControlSocial ReasonsSpiritualIt's who I amGoal striving

Overall

728731707724721786768524678715

7082

PercentageAgreements

.89

.90

.87

.89

.88

.96

.94

.64

.83

.88

.87

Reliability (I^)

.87

.89

.86

.88

.88

.96

.94

.53

.81

.87

.86

Table 2Rotated Factor Pattern'

AddictionFitnessCompetitionSelf-EsteemMental HealthWeight ControSocial ReasonsSpiritualIt's who I amGoal striving

% Variance

'Extracted with+.30 shown in

Factor 1

-.569.663.193-.051.618

1 .202-.096.044-.454.036

14.5%

Principalbold.

Factor 2

.164-.092.161.107.360-.616-.154.747.068-.082

12.1%

Factor 3

.054

.010

.008

.709-072-.069.093-.029-.231.754

11.7%

Factor 4

.023

.079

.660-.094.126.074.711.071.351.185

10.7%

components; loadings >

this question. The purpose of including the open-ended question is that, generally, open-ended ques-tions are best used when exploratory information isgathered and when a complete set of closed responsesis not known a priori. Further, given the nature of thequestion, it is important for the respondent to be will-ing to think about and provide complete responses(Dillman, 2000). The high response rate and quality ofresponses (evidenced by the length ofthe response)suggest that this condition was met.

Mixed-Method DesignA schematic diagram for the mixed-method design

employed in this research is illustrated in Figure 1. Thisdesign is based upon independent coding of the open-ended responses enabled by QSR NVivo (QSR NVivo,

1999), a qualitative analysis software program, and sub-sequent principal components and cluster analysis.

Qualitative analysis of open-ended responses.Initially, analysis ofthe 815 open-ended responses

involved a multiple-step process outlined by Miles andHuberman (1994). The first step involved reading theopen-ended responses and analyzing the text data ofthe open-ended responses for specific themes. The 10categories were determined using standard proceduresoutlined in the literature (Kassarjian, 1977; Kolbe &Burnett, 1991). The process entailed having two oftheauthors read all the comments and then mutuallyagreeing on which categories were present in the data.Thus, the categories were not predetermined a priori,but rather were generated from the data.

The second step involved analysis with NVivo. Afterthe responses were electronically imported into anNVivo text database, they were re-read and coded bytwo of the authors together using NVivo to summarizethe responses into specific categories that reflected whyrunning was important to the respondents.

From the open-ended responses, this initial codingprocess produced 10 summary categories that describedindividuals' motivations for running. These categorieswere addiction, fitness, competition, self-esteem, men-tal health, weight control, social, spiritual, "it's who Iam," and goal striving. The use of NVivo initially inthe coding was particularly valuable given the iterativenature of the coding process in which the text responseswere read, electronically coded, and then re-coded.NVivo allowed for key words or themes to be coded, orattached to the text. These segments of text could thenbe retrieved for further analysis.

The second step involved electronically generatingcoding reports in NVivo that aggregated similarresponses across the 10 categories identified in the ini-tial coding process. These coding reports were print-able and enabled access to the data, specifically theopen-ended responses by category, which helped tocross-check the categorization of responses for themat-ic accuracy.

The third step involved assigning each of the respons-es to one or more ofthe 10 derived categories. Giventhe 10 derived categories for running motivation andbecause of the multi-faceted reasons provided by therespondents, two independent coders (not authors ofthe paper) placed the 815 open-ended responses intoone or more ofthe 10 derived categories. Each ofthe815 open-ended responses was given a binary score(1/0) for inclusion in each ofthe 10 categories.

The fourth step involved assessing the reliability ofthe coding. The inter-judge reliabilities between thetwo independent judges for the 10 categories are

Volume 15 • Number 1 • 2006 • Sport MarHetIng Quarterly 33

Table 3Cluster Centroids of Factor Scores

Factor 1Factor 2Factor 3Factor 4

Total

0.00.00.00.0

HealthyJoggersN=315

.46-.32-.51-.56

SocialCompetitors

N=153

.39-.09-.011.67

ActualizedAthletesN=167

.02

.181.55-.41

Devotees

N:=181

-1.15.48

-.55-.07

F

182.830.5

527.6592.9

Prob.

.000

.000

.000

.000

shown in Table 1. Overall, the coders agreed on 87% oftheir judgments. The percentage of agreement for eachof the categories ranged from 96% agreement on theweight control category to 64% on the spiritual catego-ry. The overall reliability (Perreault & Leigh, 1989)was .87, and ranged from .96 to .53 on individualitems. After discrepancies were resolved by the authors,a data file was created for subsequent analysis.

Quantitative analysis of qualitative data.The fifth step involved reducing the dimensionality

of the 10 categories. A principal components analysis(PCA) of the 10 response categories was then conduct-ed to better understand the underlying structure of thedata and create orthogonal linear composites of moti-vations to serve as metric inputs to the clustering algo-rithm.

While principal components is most often done onmetric data, violating this assumption and usingdummy variables [0-1] can be done (Hair, Anderson,Tatham, & Black, 1995, p. 226). With large samplesizes, this application of PCA produces robust results.The rotated results of the principal components analy-sis are shown in Table 2. The analysis produced fourfactors with eigenvalues greater than 1, whichexplained 49% of the variance in the data.

The sixth step involved calculating factor scoresbased on the rotated loadings for subsequent analysis.The seventh step involved segmenting the runners toreduce the large number of respondents (based upontheir responses for motivations for running) into amore meaningful and interpretable number of smallersubgroups. In this step, a k-means clustering algorithmwas applied to the factor scores for the four principalcomponents.

The eighth step involved assessing the reliability ofthe cluster solutions. For this step, ranges of clustersolutions ranging from three to five clusters wereexamined. A four-cluster solution was selectedbecause it produced the most interpretable results. Asnake-plot of the four segment solution by the under-lying 10 motivations indicates a rich solution that cap-

tures differences across groups. The cluster centroidsfor the factor scores and the F-tests for centroid differ-ences are reported in Table 3. Based on the pattern ofthe data shown in Table 3, we labeled the clusters theHealthy Joggers, the Social Competitors, the ActualizedAthletes, and the Devotees.

The ninth step involved assessing the external validi-ty of the cluster solution. We were able to do this bymerging the closed form data with the cluster solutionsbased on qualitative data. The profile of the four clus-ters by closed-form background variables is shown inTable 4. Statistical differences among clusters arefound in terms of miles ran per week (F=16.7, p<.001),days ran per week (F=13.6, p<.001), and 5Ks (F=12.6,p<.001), lOKs (F=3.8, p<.05), 1/2 Marathons (F=8.0,p<.001), and Marathons (F=4.8, p<.01) entered peryear. In addition, differences were found in terms ofthe number of years they have been running, as well asage and sex. Table 5 profiles the clusters by reportingthe percentage of members who ascribed why they ranto each of the 10 motivations. Statistically significantdifferences were found for all groups.

The 10th step involved assessing the credibility of thecluster profiles with representative quotes from thequalitative data. This step helped support the labelingeffort and reinforce the insights provided by the quan-titative data.

Results

Cluster Profiles Using Quantitative and QualitativeData

As shown in Table 4, the first cluster—the HealthyJoggers—ran the least amount of miles per week (21.0),and their participation levels in races (e.g., 2.0 5Ks/year,and 1.1 lOKs/year) is below average. Similar to theoverall response rate, over 70% of this group has beenrunning for 5+ years. In this group, 32.7% of its mem-bers are 40-49 years old, and 55.6% are male. The datafrom Table 5 show that this group is motivated by fit-ness (70%) and mental health (52%) reasons and, to alesser extent, spiritual (23%) and weight control (21%)

34 Volume 15 • Number 1 • 2006 • Sport MarHeting Quarterly

Table 4Profile of Clusters by Runner Background

Miles ran/weekDays ran/week5Ks/yearlOKs/year1/2 Marathons/yearMarathons/yearTriathlons/year

How long haveyou run?1 year or less2-5 years5+ years

Age< 25 years old25-39 years old40-49 years old50+ years old

SexFemalesMales

Total

24.84.52.91.3.43.32.21

(%)

4.424.970.6

21.032.629.516.8

46.353.7

HealthyJoggersN=315

21.04.02.01.1.31.25.24

(%)

5.423.570.8

17.131.132.719.0

44.455.6

SocialCompetitors

N=153

29.04.94.61.6.67.42.26

(%)

.719.679.7

22.927.528.121.6

37.362.7

ActualizedAthletesN=167

24.64.73.01.2.35.23.09

(%)

6.633.559.9

28.738.324.0

9.0

59.940.1

Devotees

N=181

28.24.82.81.6.50.44.19

(o/o)

3.923.872.4

18.834.330.416.0

44.855.2

F

16.713.612.63.88.04.80.7

Pr(%2).014

.009

.000

Prob.

.000

.000

.000

.011

.000

.003

.554

Cramer's V.092

.104

.149

"One of the benefits of the mixed-methods designreported here lies with its ahility to infer multipledimensions of motivation within eluster profiles(e.g., the Soeial Competitor cluster identified in

this study), whereas in sport motivation researchindividuals are ofien times classified hy distinct

motivators (i.e., a person is either primarily drivenhy social or hy competition needs)."

reasons. This cluster was labeled "Healthy Joggers"because of their propensity to run for physical andmental fitness, while running relatively the fewest milesper week of all four groups. As one respondent stated:

"The older I get running becomes more impor-tant. It helps me stay fit and healthy. It helps meto maintain my weight. It's a great stress relieverand my two dogs love it too. I am very much arecreational runner and do not worry too muchabout times. Consequently I don't run too manyorganized races."

The second cluster—the Social Competitors—ranthe most miles per week (29.0) and days per week (4.9)of any group. They were also very active competitors,ranging from 4.6 5Ks/year to .46 marathons per year.This group was the most experienced—79.7% of thissegment has been running for 5+ years. Interestingly,this group has the highest percentage of runners 50+years (21.6%) and males (62.7%). As with theHealthy Joggers cluster, these runners were highlymotivated by fitness (68%) and mental health (61%).While it is reasonable to expect that fitness and mentalhealth are important motivations for most runningtypes, as the name suggests, the Social Competitorscluster is differentiated by two dimensions which didnot register for the Healthy Joggers —social reasons(61%) and competition (52%)—and which help todefine this individual. In addition, this group claimedfitness (68%) and spiritual reasons (38%) as motiva-tions for running. For one individual, running is asocial enabler: "It is at the top of my list... I have metso many wonderful people at races and from running!"

Volume 15 • Number 1 • 2006 • Sport MarHeting Quarterly 35

Table SProfile of Clusters by Runner Motivation

AddictionFitnessCompetitionSelf-EsteemMental HealthWeight ControlSocial ReasonsSpiritualIt's who I amGoal Striving

Total

%

12.152.810.417.845.814.316.538.4

8.917.8

HealthyJoggersN=315

1.070.2

0.01.9

51.721.0

0.023.2

3.02.2

SocialCompetitors

N=153

7.268.052.3

9.861.420.361.438.611.826.1

ActualizedAthletesN=167

10.252.7

1.867.142.5

7.28.4

42.51.2

58.1

Devotees

^%^^

37.69.91.16.6

25.44.4

14.960.828.7

0.6

Pr(x ) Cramer's V

.000

.000

.000

.000

.000

.000

.000

.000

.000

.000

.430

.477

.659

.659

.249

.213

.600

.294

.398

.588

And for another: "I have not experienced in anyother sport or activity such enjoyment and supportfrom those you run with or against. It is a personalactivity that only those involved in understand."

The third cluster—the Actualized Athletes—ran 24.6miles per week and 4.7 days per week. This group runsan average amount of 5Ks (3.0), but is much less likelyto participate in triathlons (0.9). This group is the leastexperienced, with 40% of its members who have run lessthan 5 years. The group is also the youngest (28.7% lessthan 25 years old) and contains more female runners(59.9%) than any other segment. This cluster waslabeled "Actualized Athletes" because of their relativelyhigh motivations for self-esteem (67.1%), fitness(52.7%), mental health (42.5%) and spirituality(42.5%). Accordingly, one respondent stated:

"Running is important to me because it hashelped me feel a great sense of accomplishment. Irun before work and it makes me feel good toknow that even if I have a horrible day at work, Ialready accomplished something great before Istepped through the door."Another talked about the spiritual aspects of running

and mental well being: "Running trail ultras hasbecome my spiritual refuge and source of renewal ofthe soul. It is my meditation, my retreat to innerpeace, the place where I become one with the universe.It rests onto my soul."

The fourth cluster—the Devotees—logs a lot ofmiles (28.2 miles per week) and days (4.8 days perweek). Interestingly, the Devotees do not run as many5Ks as other groups, but rather prefer the longer races.They run more marathons than any other group (.44per year). About 65% of this group is between 25 and

50 years old and 55% are males. Runners in this groupare more likely than others to claim they run becausethey are addicted to the activity (37.6%)—they statethey run because "it's who I am" (20.8%). In addition,this group has the highest spiritual reason for running(60.8%). Perhaps running has an addictive quality forthis group because running and its benefits occupysuch a central role in their lives. One individual talkedabout running and the self: "When I run I feel morewhole than with any thing else. Eventually I becomethe run and the run becomes me. That's the greatestfeeling in the world. To stop running would be to sep-arate myself from me." And a second spoke of run-ning's paramount importance in his or her life:

"Running—it is not life or death—it's more

important than that."An important step in the mixed-method design

described here is that these open-ended responses(additional representative quotes for each of these clus-ters are shown in Table 6) were compared to the clus-ter profiles to validate the resulting profiles.

Implications for Research and PracticeThis study offers several findings and implications

for research involving sport participant motivationsand the development of sport consumer typologies.

Implications for ResearchImplications of this study for sport marketing

research are that, given the complex mindset of sportparticipants (in activities such as running) whererational and emotional, as well as extrinsic and intrin-sic motivations, may play into sport consumption andparticipation (see Funk & James, 2001; Milne &

36 Volume 15 • Number 1 • 2006 • Sport Marheting Quarterly

Table 6Representative Quotes by duster Profile

Healthy Joggers

"I find running to be both relaxing and is the primary way along with a good diet that I keep up my plan forgood health and fitness." -Female 50+ years old, 18 miles/week, 4 days per week

"Running is a very important because I use running to relieve stress and to think about what is bothering me.I use running to clear my head. Running is important to maintain fitness and to counteract my poor diet oflate." -Male, < 25 years old, no mileage reported.

Social Competitors

"Running is one ofthe greatest joys of life. Keeps the body mind, and spirit soaring. Running with friends isspecial. Competition pushes me to new levels. Can travel to races and see new places. I can share stories withrunners from all over the world." -Female, 25-39 years old, runs 40 miles per week, 5 days/week

"I just recently started running 3 yrs ago. I used to weigh 317 lbs I'm now down to 245. Before I leave workI change and go directly to a 1/2 mile track located on the way home. My running is very important; it relievesa lot of stress and is something that is within my control. I have made many acquaintances at the track. We allmotivate each other. If someone misses one day everybody is aware and concerned. That alone motivates youto keep going. Besides I am trying to get down to 199lbs." -Male, 40-49, runs 24 miles/week, 6 days week

Actualized Athletes

"I quit smoking at age 33, in 1978, and took up running and I will never stop running. I bike & kayak butrunning is my first love. It makes me feel good about myself and it gave me a lot of confidence. I've run manymarathons in my past yrs and many races and you cannot describe the feeling of accomplishment at the end. Itgave me the confidence to go back to school at the age of 40 and get a degree in nursing." -Female, 50+ yearsold, runs 30 miles, 6 days/week

"I love to run. I've always been athletic and enjoyed team sports. But running is different. It's a solitarysport. It pits me against me. I'm 42 yrs old and I know I've yet to reach my potential as a runner. My best yrsare behind me and I know I'll never be world class but I still have room to improve and I'll keep trying, train-ing, testing. It makes me fit, it makes me happy. I love to run." -Male, 40-49 years old, runs 35 miles/week, 5days/week

Devotees

"It is a big part of my life. It's like brushing your teeth—it's a gift I give myself every day or almost everyday.It is who I am and I never want not to run. It's the most wonderful total feeling in life. It has made me growin so many ways and also appreciate life so much more. You can do it anywhere at any time—no expense." -Male, 50+ years old, runs 38 miles/week, 6 days/week

"It's part of who I am. Running is the most important free time activity I have besides spending time withmy kids. I'm a happier person when I get my running." -Female, 25-39, runs 20 miles/week, 4 days/week

McDonald, 1999; Creen-Demers et al., 1998; Brooks,1994), the integration of qualitative and quantitativemethods in a segmentation model adds both richnessand rigor to the findings. The mixed-methodapproach illustrated here enables a more detailedunderstanding of consumer sport participation moti-vation than would either purely qualitative or quanti-tative research.

The motivation types uncovered in this study aresimilar to those suggested by past sport participationand consumption research. Intrinsic and extrinsic par-ticipation motives (e.g.. Brooks, 1994; Green-Demers,

1998; McDonald, Milne, & Hong, 2002) can be relatedto the idea of running for spiritual, self-actualization,or physical fitness and weight loss. The concepts ofcommitment and challenge are similar to the addictiveand competitive qualities of running conveyed in thisstudy. Interestingly, motives examined in previoussport spectator research, such as achievement, escape,and social interaction (see Trail & James, 2001), aresimilar to the characteristics found in the clusters pro-filed in this study that are related to physical and emo-tional health, competition, stress release, andinteraction with other runners at races and events.

Volume 15 • Number 1 • 2006 • Sport MarHeting Quarterly 37

One of the benefits of the mixed-methods designreported here lies with its ability to infer multipledimensions of motivation within cluster profiles (e.g.,the Social Competitor cluster identified in this study),whereas in sport motivation research individuals areoften times classified by distinct motivators (i.e., a per-son is either primarily driven by social or by competi-tion needs).

Perhaps most important for sport research and thedevelopment of market segmentation approaches, thismixed-method approach illustrates how the use ofqualitative data can help validate subsequent quantita-tive cluster analysis, as well as how the established clus-ter profiles help to set up the structure for marketsegmentation models. Given the wide array of open-ended responses containing numerous variations inthemes, the qualitative analysis of such data is aninherently complex process. The coding of theresponses for mutually exclusive participation motiva-tion themes, along with the subsequent cluster analysis,helped us to better understand and classify the respon-dent comments by grouping and conceptualizingresponses with similar patterns and characteristics.The use of cluster analysis shows how the 10 cate-gories, originally derived from the qualitative data, arestructurally interrelated. Through cluster analysis, andby reducing the number of dimensions from 10 (10original categories for running motivation) to four, welower the dimensionality of the data and move to high-er levels of abstraction to enhance the interpretabilityof the data.

Implications for PracticeImplications of this research for sport marketing

practice is that this the mixed-method approach andfindings reported here can enable organizationsinvolved in the promotion of running (e.g., athleticfootwear and apparel brands such as Nike, Reebok,adidas; running clubs; running event directors) to dis-criminate between groups of runner types and developadvertising or promotional messages to effectivelycommunicate with these groups.

For instance, marketing communications efforts(e.g., advertising messages, in-store or on-site displays)targeting the Healthy Joggers segment might be basedon a message or theme that portrays the benefits ofrunning for physical and mental health (e.g., to loseweight, have more energy, reduce stress). For SocialCompetitors, the message might be based on thematicelements involving "a community of runners" and thesocializing nature of competition at running events.Marketing communication efforts targeting ActualizedAthletes might focus on female runners (perhapsyounger mothers with children) and stress the feelings

of accomplishment, empowerment, and control overone's life that result from running. For Devotees,marketing communications themes could focus on theidea of running as a central element to one's daily life,or the concept that this person's self-identificationprominently includes himself or herself as a "runner."

The approach and findings reported here can assistsport brands in the development of more insightfuland relevant market segmentation and marketing com-munication efforts designed to reach a specific market(e.g., the running market) whose members may pos-sess complex and multiple motivations for participa-tion as well as product consumption. Thismethodological approach can also benefit other organ-izations such as health care providers as they developcommunications programs geared towards individualsfor whom regular exercise (such as running or walk-ing) may be beneficial.

Study Limitations and Future ResearchIn interpreting these findings it is important to con-

sider that the data for this research are based upon thethoughts of subscribers to a magazine targeting runningenthusiasts and may not be representative of othergroups of runners. The relevance of these findings maybe limited to those sport consumers that share charac-teristics similar to the sample. Because of the qualita-tive component of this research used to elicitparticipation motivations, the reporting of thesemotives does not reflect the relative strength of the vari-ous motives indicated. Additionally, it should be notedthat some of the questionnaire items or backgroundquestions asked in the questionnaire could conceivablyhave influenced the open-ended responses regardingthe importance of running. Some of these items mayhave led respondents to overexaggerate their runningroutine and hence its importance to the respondent.

Future research examining segmentation modelsmight explore questions directly related to personalaspirations and sport participation. Further, themixed-method research design outlined in this studycan serve as a stepping stone for future studies thatseek to empirically demonstrate the effectiveness ofsuch segmentation models linking self-reported partic-ipation motivation data to advertising response andeffectiveness.

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