building strong brands in retailing

rch 60 (2007) 1–10
Journal of Business Resea
Building strong brands in retailing☆

Arch G. Woodside a,⁎, Martin G. Walser b

a Boston College, Carroll School of Management, 140 Commonwealth Avenue, Chestnut Hill, MA 02467, USAb University of Innsbruck, School of Management, Marketing, A-6020 Innsbruck, Austria/Europe, Austria

Received 1 December 2005; received in revised form 1 July 2006; accepted 1 September 2006

Abstract

Brand strength is the relative power of attraction of a given brand versus other brands and the levels of other product attributes. “Brand” is anencompassing concept that includes retail firms as well as physical products and services. We propose and empirically test a behavior-based model ofbrand strength that is particularly relevant for competing retail enterprises. Eleven propositions in the model include the following points. P1: thebrand strength of a given retailer is relative to the customer's desire for levels of other attributes, such as the names of competing retailers, specificprice points, and performance characteristics built into competing products. P2: brand strength depends in large part on customer experience with agiven retailer. P3: increasing customer experience decreases the impact of competitive contexts on brand strength. P4: a retailer's accessibility intoworking memory from long term memory influences brand strength. P5: a consumer's experience with a given retailer influences accessibility of theretailer from memory. A research method merging purchase histories with perceptual and judgment data was used to test the model. The resultsprovide strong support for the propositions. The study includes an adaptation and empirical examination ofWind's [Wind (1977), “Brand Loyalty andVulnerability,” in Consumer and Industrial Buying Behavior, Arch G. Woodside, Jagdish N. Sheth, and Peter D. Bennett, eds., New York: North-Holland] basic vulnerability matrix.© 2006 Elsevier Inc. All rights reserved.

Keywords: Retailing; Brand strength; Brand influences

For a specific customer or market segment, brand strength isthe relative power of attracting customers to a given brandversus other brands and the levels of other product attributes.Implicit in this definition is the proposition that competingbrands are not equally strong. “Brand” is encompassing conceptthat includes retail firms as well as physical products andservices. The relevant literature includes several definitions ofbrand strength and brand equity (e.g., see Aaker, 1996; Françoisand MacLachlan, 1995; Keller, 1993; Srivastava and Shocker,1991; Park and Srinivasan, 1994). Defining brand strengthrestrictively to relative power of attraction has advantages sim-ilar to Fishbein and Ajzen's (1975) recommendation for a singledimensional definition of attitude to mean affection. Ambiguityis reduced and the meaning of the brand strength concept in

☆ The authors thank Elizabeth J. Wilson, Sawyer Business School, SuffolkUniversity, for research assistance and for suggestions on previous versions ofthis paper.⁎ Corresponding author. Tel.: +1 617 552 3069.E-mail addresses: [email protected] (A.G. Woodside),

[email protected] (M.G. Walser).

0148-2963/$ - see front matter © 2006 Elsevier Inc. All rights reserved.doi:10.1016/j.jbusres.2006.09.009

terms of its relations to other constructs in a theoretical networkbecomes clear.

Increases in brand strengthmay lead to increases in brand equity(François and MacLachlan, 1995). Brand equity is defined as afinancial estimate of the value of a brand identity (e.g., the estimatedof the price premium a customer is willing to pay to acquire aspecific brand identity versus others, cf. Park andSrinivasan, 1994).The focus here is on building and empirically testing models toexplain brand strength in the field of retailing to consumers. Ourcontribution is in developing and testing a behavior-basedmodel ofbrand strength. The model is based on extending Smith andSwinyard's (1983) hypothesis that only after purchasing and usinga brand is anything resembling beliefs and attitudes formed. Themodel we propose includes the hypothesis that purchase and use inearlier time periods increases brand strength. The findings providestrong support for the main propositions in the model.

Also this article reports on a unique test of Wind's (1977)“basic vulnerability matrix” applied to brand strength in retail-ing. Wind (1977) develops the theoretical view that some seg-ments of customers are found who: (1) regularly buy the brandthey dislike and (2) never buy a brand they profess to like. The

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http://dx.doi.org/10.1016/j.jbusres.2006.09.009

2 A.G. Woodside, M.G. Walser / Journal of Business Research 60 (2007) 1–10

present article extends this paradigm to the concept of brandstrength and supports the view empirically.

1. A basic model of brand strength

This section presents a basic model of brand strength. Thisbasic model (see Fig. 1) includes the proposition that brandexperience (e.g., extensive prior brand usage) influences brandstrength positively-shown in Fig. 1 as path P2. Such a proposi-tion begs the question, what are the causes of early brand expe-rience? Such issues are important for developing an extendedmodel of brand strength. However, the present report is restrictedto presenting and testing a basic model of brand strength.

1.1. P1: a brand's strength is contingent on specific competitivesituations

The brand strength definition implies a core proposition of theconcept, the strength of a given brand identity is relative to thecustomer's desired levels of other attributes, such as the names ofcompeting brands, specific price points, and performance char-acteristics built into competing products. The notion of “buildingstrong brands” (Aaker, 1996) implies that all the brands competingfor a share of customers' purchases are not equally strong. Brandstrength is viewed best as being contingent on a given set ofalternatives, the feature levels built into these alternatives, and acustomer's preferences for each of these feature levels.

1.2. P2: brand experience influences brand strength positively

P2: Brand strength depends in large part on customer brandexperience. For most customers the hypothesis here is thatbrand strength grows following use of a brand and/or exposureto the brand over several months or years. This propositionreflects Zajonc's (1978) and Fazio, Powell, and Herr's (1983)proposals regarding the mere exposure influence on affect andEhrenberg's (1971, 1972) views that trial purchase/use and re-peat buying reinforces a brand's desirability (also see Muthuk-

Fig. 1. Behavior-based brand strength model.

rishnan, 1995). More importantly, the proposition is grounded inSmith and Swinyard's (1983) hypothesis and empirical resultsthat only after purchasing and using a product is anything re-sembling beliefs and attitudes formed.

1.3. P3: brand experience influences the impact of competitivesituations on brand strength

P3: Increasing brand experience decreases the impact ofcompetitive situations on brand strength. Buyers purchasing brandX over several occasions are likely to have experienced positivereinforcementswith using the brand (Bennett andMandell, 1969);consequently, brand X may represent a special trusted friend thatshould be purchased in most competitive situations (see Fournier,1998; Muthukrishnan, 1995).

1.4. P4: a brand's accessibility into working memory influencesits strength

Brand strength is one indicator of brand attitude: while it isuseful to view the two constructs as being distinct, brandstrength and brand attitude are likely to be associated positively.A brand's accessibility into working memory from long termmemory affects a consumer's attitude and behavior toward thebrand (for a review of this literature, see Kardes, 1994). Sim-ilarly, P4: a brand's accessibility into workingmemory from longterm memory influences brand strength. Thus, the more readilyaccessible a brand, the higher its brand strength.

1.5. P5: brand experience influences brand accessibility

P5: A consumer's prior brand experience influences brandaccessibility. For example, trial and repeat usage influences con-sumer top-of-mind awareness of a brand name. Consequently, earlyand repeat brand usage (i.e., experience) results in readyaccessibility, positive attitude, and high brand strength (cf. Fazioet al., 1989).

1.6. P6a, b: (a) share of purchases devoted to a brand influencesbrand accessibility and (b) brand strength

The prior share of purchases (SOP) made by a consumer fora given brand is one measure of brand experience. The numberof occasions and years of usage of the same brand are othermeasures of brand experience. As a brand's SOP increases (i.e.,approaches 100%) for a consumer, (P6a) the brand may beexpected to be highly accessible and (P6b) strong because theconsumer is likely to equate the brand with the productcategory. A possible example is a consumer's wearing only theLevi's brand of jeans; thus, the name Levi's means jeans andshe finds this meaning highly attractive.

1.7. P7: brand experience influences a brand's sop positively

Brand experience is defined here tomean frequency of selectingthe same brand over a number of buying occasions, even if otherbrands are also purchased during some or all of these buying

3A.G. Woodside, M.G. Walser / Journal of Business Research 60 (2007) 1–10

occasions. Because of the desire to simplify buying decisions(Payne et al., 1993) and habit (Meyer and Kahn, 1991), thefollowing proposition is included in the model of brand strength.P7: Consumers tend to increase the SOP of the brand they buy onmost purchase occasions. However, the desire for variety (Meyerand Kahn, 1991), may limit the number of consumers whoeventually single source their purchases for any given brand, forexample, Brand X (i.e., SOPX=1.00).

1.8. P8–11: brand vulnerability segments of consumers do exist

Wind (1977) proposed, but did not test empirically, a “basicbrand vulnerability matrix.” The matrix includes the counterin-tuitive views that some consumers dislike a given brand, X, andyet buy X regularly; another segment of consumers like brand Xand never buy it-buying competing brands only. Wind (1977)defines “loyal” consumers as the segment who like X and buy Xregularly. An extension to Wind's hypotheses can be made hereto reflect brand strength. P8a: Some customers with high brandstrength for X do not buy X. P8b: Some customers withsubstantially low brand strength for X buy X on a regular basis.One rationale for high brand strength and not buying: X may beperceived to be priced too high for purchase. One rationale forlow brand strength and buying: X may be perceived as offeringexclusive or hard-to-find features desired by the consumer.

While Wind (1997) formulated the basic vulnerability matrixas a 3 X 3 matrix, the paradigm is expanded in Fig. 2 to a 3 X 4matrix. Consumers having substantially positive brand strengthfor brand X are divided into two segments: significantly pos-itive but moderate brand strength, and high brand strength.

Note that the labels for each cell in Fig. 2 do not matchSternberg's (1986) or Shimp and Madden's (1988) terminology.“Consummate” is restricted in use in Fig. 2 to brand X buyersusing the brand on a regular basis, for example, consumersbuying the brand in the current year and in most previous years.“Trial” and “puppy love” refers to segments of first-time brandbuyers. For prospect segments labeled in cells 9–12, none of

Fig. 2. Brand strength an

these consumers purchased the brand but “took a test drive,” orrequested a catalog, or made some other initial step towardbuying the brand but did not buy.

The following core proposition relates to the brand strength/experience matrix: (P9) a substantial segment of customers existin each of the 12 cells, even though a greater proportion of corebuyers are expected to have moderate and high brand strengthcompared to new buyers and non-buyer prospects. Also, (P10)analogous to Wind's (1977) proposal, brand X core buyers incells 1 and 2 should be more vulnerable to competing brandsthan brand X core buyers in cells 3 and 4. Similarly (P11) non-buying prospects in cells 11 and 12 are more vulnerable to brandX than non-buying prospects in cells 9 and 10.

2. Method

To test the brand strength model shown in Fig. 1 and therevised brand vulnerability matrix shown in Fig. 2, the co-operation of a U.S. national retail firm was requested andreceived. For competitive reasons the name of the firm andindustry is not identified. The firm's products are sold in bothretail stores and via catalogs. In the firm's primary SBU, 75%of sales are made via mail order catalogs. The study focuses onexamining the brand strength model relating to the firm'scustomers (i.e., both buyers and non-buying prospects). A mailsurvey was sent to representative samples of 12 segments of thefirm's customers. The firm's customers include more than twomillion U.S. households. The decision to create and examine 12segments was based on the objectives of the study.

Separate samples from three segments of “core buyers” weredrawn. A core buyer is defined to mean a customer buying in thecurrent period and in two or more years within the previous fouryears. Core A buyers purchase only relatively low-pricedproducts from the firm; core B buyers purchase only relativelyhigh-priced products form the firm; and core C buyers buy bothlow-and high-priced products from the firm. Separate samples ofthree new buyer segments were drawn. Similar to core buyers,

d experience matrix.


new A, B, and C buyers represent customers buying for the firsttime from the firm segmented by the price range of productspurchased. Five samples of “non-buying requesters” from fivepopulations of households in the firm's database were drawn.The five populations represent five known categories of personsrequesting a free catalog from the cooperating firm during thebuying seasons who did not buy any products from the firm'scatalogs, and who have never purchased previously from thefirm. The cooperating firm was able to identify the specificsources used by these non-buyer requesters in making theirrequests, for example, an advertising coupon offer placed in aspecific magazine. Consequently, five category-source pop-ulations were created for the purposes of studying brand strengthsfor each population, as well as related issues.

A twelfth sample of respondents was drawn from the pop-ulation of former buyers of the cooperating firm's products.This sample was used to test the hypothesis that brand strengthfor X, the cooperating company, would be lower among formerbuyers compared to core buyers and new buyers. Members ofthe twelfth sample had not purchased any products from thecooperating firm for the current year and the prior two years, buthad made purchases from the firm in two or more of theprevious four years.

The complete buying history for each household in the 12samples was made available by the firm. Buying behavior datawere collected relevant to all known competitors, as well as allmajor U.S. firms marketing by mail order catalogs (e.g., LLBean, Land's End, Spiegel, Talbots, andWinterthur). The studywas identified in a cover letter to sampled households as ahousehold lifestyle and consumption study, not as a study aboutbrand strength. To gain cooperation in completing the survey, asummary of findings was offered and provided to participantsin the study. Also, an offer was made to participate in tenrandom drawings each for $50.00 gift certificates for productsrelated to the study.

2.1. The survey instrument

Following a three-month planning period that included fourthree-hour meetings with a committee of executives from thecooperating firm and two rounds of pretests, six versions of an eightpage survey were created for the study. Both pretests includedface-to-face interviews with five product category users; theobjectives of the pretests were to reduce priming effects (e.g., seeKahneman and Miller, 1986), and to increase clarity in questionwording. Also, a simplified response procedure was also tested inthe pretests: subjects were asked to complete two buying exerciseswith unique orthogonal, fractional, factorial designs constructedfrom the same factors and factor levels. The instructions for one ofthese exercises required more complex answers, that is, eachsubject was required to select and circle the three to five productss/he selected for purchase. The subject was asked to cross-out thetwo to three products s/he definitely would not buy, and to checkmark the two to five products s/he found attractive but s/hewas notbuying today. The instructions for the second exercise were lesscomplex: the subject was asked to check mark the items selectedfor purchase and cross-out three to five products s/he would

definitely not buy. The request to complete the two exercises wasseparated by 20 to 30 min and counter-balanced: half the pretestsubjects completed the complex response exercise first followedby the later administration of the simple response exercise, andvice versa. Examining the respondents' ease of completion andusefulness in modeling the data from the two procedures resultedin the conclusion that the simple response exercise was moremanageable and about as useful as the more complex procedure.Consequently, the simple instructionswere used in themain study.

One of six versions of a product “buying exercise (game)” wasincluded in each survey. These buying exercises were fractionalfactorial, orthogonal, conjoint designs. Each conjoint designincluded 5 of 8 product-service factors. Four of the six designsincluded brand X, that is, the name of the mail order firmcooperating in the study. Brand Y, brand X's main competitor andthe industry leader, was included as one of four brands in two of thefour games which also included brand X. To further examine theimpact of brand names on preference, two control conjoint designswere incorporated into the study. One of the control conjointdesigns did not include brand names; another one of the conjointdesigns included four imaginary (i.e., placebo) names of brands.

The eight product-service features used in one or more of thesix conjoint exercises are described in Fig. 3. Each of the sixconjoint exercises included five factors, for example: 3 pricelevels by 4 brands by 3 levels of usage instructions by 3 productcategories (or 3 sub-product categories) by 3 quantity discounts(or by 4 message benefits or 3 levels of product benefits). A totalof 16 unique product offers was needed to achieve orthogonalrelationships among the five factors per conjoint exercise. Furtherdetails of the eight product-service factors used in one or more ofthe conjoint exercises are summarized in Fig. 3.

At the top of the page in the buying exercise, abovedescriptions for each of the 16 product offers, the followinginstructions were provided:

Please complete the following buying exercise, the [ProductCategory] Buying Game. Please assume that you have amaximum of $[dollar amount] to spend on [product cate-gories A, B, and C]. All the items listed [offer a desiredperformance]. Some items appear similar to others but eachis unique in some way. Please “buy” a maximum of [dollaramount] of the items listed by checking (✓) the items youselect. Please cross-out (X) about 4 items you definitelywould not buy. Remember this is only a game; no items areactually being bought by you.

2.2. Procedure

The data were collected following the annual buying seasonsfor the product category. A total of 34 sampled population mem-bers in each of the 12 groups received one of the six versions ofthe questions containing one of the six conjoint exercises. Thus,the total households included in the study were 2448(34×6×12). The first-class postage mailing to each subjectincluded a hand-signed, personalized cover letter, the question-naire, and a post-paid, first-class, return envelope. A “roomnumber” in the return address was used to identify each

Fig. 3. Eight products-service features used in one or more of the conjoint designs.


respondent; this procedure was used for three reasons: in orderto merge the data files (i.e., the survey data with the buying andinquiry records) for each respondent, to reduce self-selectionbias in requesting name and address, and to mail a second copyof the survey only to non-respondents.

No statement was made in the cover letter that participationin the study would be anonymous. Using the same procedurethat was used for the first mailing, a second mailing to all non-respondents to the first mailing was completed. Comparisonsfor all responses were made between first and second mailingrespondents; the number of significant differences between thetwo groups was not greater than expected by chance alone. Atotal of 2431 questionnaires were delivered (17 were returnedbecause of inaccurate, insufficient addresses and related rea-sons). A total of 1288 questionnaires were returned; and 1276were partially or fully answered. A total of 1187 respondentscompleted the conjoint exercises and all other sections of thequestionnaire. Thus, the response rate for completely filled-insurveys was 49% (i.e., 1187/2431).

2.3. Analysis

Each subject's responses in the conjoint exercise was codedinto three ordinal categories: −1=crossed-out product offer-ings; 0= left blank; +1=selected for purchase. For the indepen-dent variables effect coding was used for the factor levels.Correlations for each attribute variable and the dependentchoice variable were examined and several multiple regressionanalyses (ordinary least squares MRA) were performed on thedata from the conjoint exercise for each subject. Because use ofresults from MRA is based on experience as well as science(Pedhazur, 1982), several algorithms were developed andapplied in selecting the one most representative and usefulmodel for each subject. For example, the model selected wasrequired: (1) to have the signs of the partial regression

coefficients in the same direction as indicated in the correlationsof the factor level and the dependent variable; and (2) to have atleast one attribute level with a significant standardized (Z) scoreof an absolute value of .08 or higher. In most cases the selectedmodel for each subject had 6 to 8 independent variables with 16observations used for each model. Thus, sufficient degrees offreedom were available and at least a 2:1 ratio of observations toparameter estimates were achieved in order to test for, andobtain, reliable estimates of brand strength. A total of 91% ofthe models had interpretable results — 9% of the subjectsapparently provided random responses in completing theexercise.

For analyzing the results related to the brand strength andexperience matrix (Fig. 2), using the regression model for eachsubject, the subjects were segmented into four brand strengthcategories among the subjects responding to the four conjointexercises that included the cooperating firm's brand name (i.e.,brand X). The four brand strength categories included:

(1) negative brand strength: βXb− .099 ( pb .05)(2) brand strength absent: βX between − .099 and +.099(3) moderate brand strength: βX between +.10 and +.299

( pb .05)(4) high brand strength: βXN+.30 ( pb .001).

Thus, related to brand X, the subject's standardized partialregression coefficient for brand X was used as the estimated brandstrength of X for the subject. The procedure used is one form ofhybrid conjoint analysis because the combined results are based ondata from four conjoint designs each unique to some extent in thefactors examined. The results may be viewed usefully as a meta-analysis weighted by sample sizes. Such an analysis provides auseful measurement and classification procedure for identifyingconsumers according to the relative value held for specific brandsacross a set of competing brand-related purchase situations. As the

Fig. 4. Path (Beta) results from testing relationship in the behavior-based brandstrength model. Note all paths shown are betas for statistically significant partialregression coefficients except for P1 and P3. For P1, .15 is the range of the betasfor BSX across four conjoint exercises. For P3, .13 is the range of the ranges ofthe betas for BSX for core buyers versus non-buying requesters for the fourconjoint exercises.


measure of brand strength, the individual respondents' standard-ized partial regression coefficients for brand X (βX) were used as adependent variable in testing the model summarized in Fig. 1.Also, the four strength categories were used to segmentrespondents into the four brand strength segments by the threepurchase experience segments as displayed in Fig. 2.

2.4. Other operational definitions of constructs

2.4.1. Share of purchases from firm X (SOPX)A crude index was used to estimate each respondent's brand

X share of purchases (SOPx). The index value was based on theresponse to the following question in the questionnaire, “Didyou buy from any of the mail order firms for this year's [productpurchases]? Please check (✓) all that apply.” These instructionswere followed with a listing of all known U.S. firms marketingthe product categories relevant to the study; more than 30 firmswere listed alphabetically. If the respondent check marked thecooperating firm, the SOPX index value was equal to 1 dividedby the total number of firms checked. Thus, if no other firm wascheck marked, SOPX=1.00. If three additional firms werecheck marked, then SOPX=0.25. If the respondent did notcheck mark firm X, but did check mark other firms, thenSOPX=0.00. If no firms were check marked, then therespondent was not included in the analysis.

2.4.2. Brand X accessibility (BAX)A top of mind awareness question was used to estimate

brand accessibility. After asking about whether or not the res-pondent considered by the product category by mail order, thesurvey includes asking the following open-ended question,“When thinking about buying [products] via mail order, whatcompany first come-to-mind? Please name one or twocompanies.” A conservative measure was used for brand Xaccessibility. Among respondents listing one or more firms, iffirm X was listed first, BAX=1.00, if firm X was not listedfirst or not listed at all, BAX=0.00; if no firm was listed, therespondent's case was not used in analyses involving BAX.

3. Findings

The following findings are limited to examining the propo-sitions in the behavior-based brand strength model describedearlier and summarized in Figs. 1 and 2.

3.1. P1 supported: a brand's strength is contingent on specificcompetitive situations

The comparisons of the average βX across the four conjointexercises, which included Brand X, products in the buyingexercises indicated significant differences (means: .07, .09, .14,and .22, pb .01). That is, competitive environments faced bybrand X affected the brand strength of X. Given that any onebrand will face several different competitive environments (e.g.local contingencies with different brands, prices, product designs,service offers), testing a given brand's strength in three, four, ormore scenarios may be necessary for gaining deep knowledge of

a brand's strength. The brand strength range for the four means(.22–.07= .15) is included in Fig. 4 for path P1 as an indication ofthe impact of specific competitive situation on brand strength.

3.2. P2 supported: brand experience influences brand strengthpositively

The relationship between brand experience and brandstrength for X was examined in several ways. First, the Pearsonproduct-moment correlation for the two variables is highlysignificant statistically (r=.25, n=1175, pb .01): brand strengthincreases as brand experience increases from non-buyingrequesters to new buyers to core buyers.

Second, related to Fig. 1 the BEX–BSx path coefficients inthe full and trimmed models are highly significant statistically.For the model trimmed of a non-significant SOPX–BSX path,the BEX–BSX is equal to .21 ( pb .01). This result and relatedresults are summarized in Fig. 4. Third, comparisons of theaverage brand strength scores among core, new, and non-buyerprospects support the proposition. Here are the average βXvalues (brand strengths for X), standard errors for these mean(se), and number of respondents (n) for the three brand Xexperience customer segments:

BEX
βX se n
Core customers
.23 .02 218 New customers .11 .02 193 Non-buying requesters .02 .02 227.
Fourth, a “tipping point” analysis (see Gladwell, 1996;McClelland, 1998) indicates the correlation coefficient under-states the significance of the relationship between brand expe-rience and brand strength. The rationale for tipping analysis:behavioral scientists have observed that societal variables make

Fig. 6. Brand experience and brand strainght: group evel correlation and simpleregression analysis for the eleven customer segments.

Fig. 5. Shares of core, new, and non-buyer prospects at various brand strength levels in percent.


little impact on a dependent variable until they reach a certaincritical level (e.g., McClelland, 1998; also see Bass et al., 1968for a related discussion). Consequently, a linear correlationcoefficient misrepresents such relationships. Identifying highbrand strength as one such tipping point, a total of 40% of coreX buyers versus 18% of non-buyer prospects have high brandstrength ( pb .001). Further details appear in Fig. 5.

Fifth, group-level correlation and simple regression analysiswere computed for actual brand X experience (BEX) and brandstrength (BSX); see Bass, Tigert, and Lonsdale (1968). Theaverage BSX was estimated for each of the eleven (of 12)customer segments used in the analysis: the five non-buyingprospect segments, the 3 new-buyer segments, and the 3 corebuyer segments. Findings: for the group-level analysis theBEX–BSX >correlation is .73 ( pb .001). Additional details ofthe results from this analysis are summarized in Fig. 6.

3.3. P3 supported: brand experience influences the impact ofcompetitive situations on brand strength

Among core buyers, the range for brand strength (BSx)across the four conjoint exercises equals .11; among new buyerthe brand strength range (BSx) equals .19; and among non-buying prospects the range equals .24. The differences in rangesare in the direction hypothesized. Conclusion: increases inbrand experience are associated with decreases in variability ofbrand strength in specific competitive situations.

3.4. P4 supported: a brand's accessibility into working memoryinfluences its strength

The BAX–BSX correlation was .26 ( pb .01, df=683). TheP4 coefficient for BAX predicting BSX included in the brandstrength model is a highly significant ( pb .001) indicator ofbrand strength, even with prior brand experience included inthe trimmed model; see Fig. 4. Conclusion: ready mentalaccess to a brand is associated strongly with building strongbrands.

3.5. P5 supported: brand experience influencesbrand accessibility

The BEX–BSX correlation was .25 ( pb .01, df=683). TheP5 coefficient for BEX predicting BAX included in the brandstrength model is highly significant ( pb .001); see Fig. 4.Conclusion: as proposed previously (Fazio et al., 1989), lev-el of prior brand experience strongly influences brandaccessibility.

3.6. P6a,b supported: (a) share of purchases (SOPX ) devoted toa brand influences brand accessibility and (b) brand strength

Share of purchases for brand X was associated with brandstrength toward X (r= .21, pb .01). However the direct pathbetween these two variables does not contribute an increase inthe variance explained for brand strength beyond that explainedby brand experience and brand accessibility. Consequently,

Fig. 7. Finding the brand strength and experience matrix: row and total percents per cell. Note. Chi-square=45.67; df=6; pb .001. a For example 16.5% of the 218 corebuyers of brand X held significantly negative brand strength toward X; a 5.6% of the total 638 repondents for whom brand strength was measured were core Xcustomers having significantly negative brand strength toward X.


the path coefficient (P6a) for SOPX to BSX has been trimmedfrom the model as shown in Fig. 4. Conclusion, the moreconcentrated prior experience with a single brand, the higherthe brand strength and the more accessible the brand in workingmemory.

3.7. P7 supported: brand experience influences a brand's soppositively

The findings include a highly positive correlation betweenprior brand experience (BEX) and the respondent's reported shareof purchase for X (SOPX) in the current buying season (r=.27,

Fig. 8. For 12 brand stregnt toward x customer segments: (a) Average bradn stregth t

pb .01). In the path model summarized in Fig. 4, a strong directinfluence is indicated from BEX on SOPX (P7= .28).

3.8. P8a and P8b supported: customers found with highlypositive BSX who do not buy, as well as customers found withsignificant negative BSX who do buy

The shares of core and new customers with negative brandstrength toward X were 17% and 25%, respectively. The sharesof non-buying requesters with significantly positive and highlysignificant positive brand strength toward X were 16% and18%, respectively. For further details see Fig. 7.

oward Y; (b) Standard deviation; (c) Standard error; (d) Number of respondents.


3.9. P9 supported: a larger share of core buyers have positivebrand strength compared to newbuyers and non-buying requesters

While a substantial segment of customers exist in each of the12 cells shown in Fig. 7, a greater proportion of core buyershave high brand strength compared to new buyers and non-buyer prospects. Note that in the distributions of the threecustomer segments in Fig. 7, only the distributions for corebuyers and non-buying requesters depart from the expectedchance distribution of respondents (i.e., 25% row percent percell). The distributions for both core and non-buying requestersare skewed in directions expected by the behavior-based modelof brand strength.

3.10. P10 supported, core buyers with negative brand strengthmore vulnerable to competitors than core buyers with positivebrand strength

The competitor's brand strength among different customerX segments is one indicator of brand's X's vulnerability. “Y”is the brand name used here to identify X's main competitorin the minds of executives at firm X. Firm Y's market shareis estimated by these executives to be 1.5 times firm X'smarket share. Related to P10, most core buyers of X withnegative brand strength toward X (cell 1 in Fig. 2) are likelyto have positive brand strength toward Y — a main competitorfor X.

3.11. P11 supported: non-buying requesters differing in theirbrand strength toward X differ in the opposite direction in theirbrand strength toward Y

In Fig. 8 the non-buying prospects in cells 11 and 12 havenegative brand strength toward Y (βY=− .14, se=.08, pb .03),while the non-buying prospects in cell 9 have positive brandstrength toward Y (βY=.28, se=.05, pb .001). Clearly a substantialshare of the non-buying requesters in cell 9 is highly unlikely to beconverted into buyers of firm X products.

Fig. 9. Extended behavior-bas

Related to cell 9 customers, gaining deep knowledge about thedetails of the attributes about firm X that turns cell 9 customersoff, as well as the attributes about firm Y that turns them onprovides very useful competitive information. Similar in depthknowledge about customers in cells 1, 4, and 12 would be veryuseful competitive information. Wind (1997, p. 315) states therelevant point well, “The vulnerability matrix in its simplest formdoes not reveal the nature of or reason for the brand'svulnerability.”

4. Implications for consumer behavior theory andmanagingbrands in retailing

We view the main contribution of the present study to be inextending Smith and Swinyard's (1983) proposition (i.e., onlyafter purchasing and using a product is anything resemblingbeliefs and attitudes formed) to the study of brand strength.Unlike the financial value view described earlier for the brandequity concept, the construct of brand strength is a psycho-logical construct that is grounded in the consumers' behavioralexperiences with competing brands (cf. Fournier, 1998; Françoisand MacLachlan, 1995). Consequently, collecting multiple datasets in different time periods is particularly useful for examiningthe extent of influence of prior behavior on brand strength.The use of such a multiple time period data collection methodis helpful in reducing the problems associated with self-gen-erating validity (Feldman and Lynch, 1988): questions ap-pearing early in a survey instrument influence responses to laterquestions. The study described in the present article is unique inmerging data files of real-life buying behavior with a current-yearsurvey data file.

Particularly intriguing are customers found in cells 1 and 12.Learning the unique products purchased by cell 1 customersmay provide clues for developing an on-going marketing–buying relationship with customers having negative brandstrength with respect to the firm. Still the question is leftunanswered, why do most cell 1 customers have significantlynegative brand strength toward the brand (i.e., firm) they are

ed brand strength model.


buying from? Interview data from applications of the longinterview method (McCracken, 1988) are likely to provideuseful answers to this question.

Many customers in cell 12 may be consumers just starting toplan product purchases in the category under study. Modeling-based preference (Bandura, 1977) and a mere exposure effect(Zajonc, 1968) are two possible reasons for their high brandstrength toward X: these customers' brand strength may bebased on having noticed family member and friends using brandX products across many years.

5. Suggestions for further research

Further research on building and testing behavior-basedmodels of brand strength in three areas is considered in thissection: antecedents, consequences and measurement issues. Indiscussing the results of the empirical study with seniorexecutives at firm X, one of their main concerns was under-standing the role of the firm's marketing efforts, demographics,and the product-specific lifestyles and motivations (e.g., seeMehrotra and Wells, 1977) of consumers in influencing brandstrength. These executives expressed willingness to conductcustomer-based field experiments to learn how the firm couldstrengthen brands and shift customers from negative brandstrength positions.

Possible impact routes of these antecedent variables on brandstrength are shown in Fig. 9. Four indirect marketing routes tobuilding brand strength are included:

• M1:e.g., offer free samples to influence trial experience withthe brand

• M2:offer quantity and variety discounts to influence lesspurchases of competing brands

• M3:increase advertising placements and mailings to increasebrand accessibility in working memory

• M4:create additional brands; buy competing brands, to achievegreater control over buying contexts.

A core proposition implied in behavior-based models ofbrand strength should be made explicit and tested empirically:(P12) brand strength affects future brand purchase behavior. SeeFig. 9. Even with all the attention devoted to the topic, to dateresearch is scant on the influence on brand strength on real-life,future purchase behavior of brands.

In what specific ways does changes in competitive situations(i.e., buying contexts) influence brand strength? How do con-sumers explain their choices that lead to different brandstrengths in different buying contexts? Combining conjointdesigns with the think aloud method (Someren et al., 1994)offers a useful approach to explore such issues.

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