Consumer evaluation of retail wine stores

This article was downloaded by: [USC University of Southern California]On: 08 December 2014, At: 00:57Publisher: RoutledgeInforma Ltd Registered in England and Wales Registered Number: 1072954Registered office: Mortimer House, 37-41 Mortimer Street, London W1T 3JH, UK

Journal of Wine ResearchPublication details, including instructions for authors andsubscription information:http://www.tandfonline.com/loi/cjwr20

Consumer evaluation of retail winestoresLarry Lockshin a & Pierris Kahrimanis aa University of Adelaide, Wine and Food Business Group,Department of Horticulture, Oenology and Viticulture , PMB1, Glen Osmond, SA, 5064, Australia Phone: 08 8303 6764Fax: 08 8303 6764 E-mail:Published online: 21 Mar 2007.

To cite this article: Larry Lockshin & Pierris Kahrimanis (1998) Consumer evaluation of retailwine stores, Journal of Wine Research, 9:3, 173-184, DOI: 10.1080/09571269808718146

To link to this article: http://dx.doi.org/10.1080/09571269808718146

PLEASE SCROLL DOWN FOR ARTICLE

Taylor & Francis makes every effort to ensure the accuracy of all the information(the “Content”) contained in the publications on our platform. However, Taylor& Francis, our agents, and our licensors make no representations or warrantieswhatsoever as to the accuracy, completeness, or suitability for any purpose of theContent. Any opinions and views expressed in this publication are the opinions andviews of the authors, and are not the views of or endorsed by Taylor & Francis. Theaccuracy of the Content should not be relied upon and should be independentlyverified with primary sources of information. Taylor and Francis shall not be liablefor any losses, actions, claims, proceedings, demands, costs, expenses, damages,and other liabilities whatsoever or howsoever caused arising directly or indirectly inconnection with, in relation to or arising out of the use of the Content.

This article may be used for research, teaching, and private study purposes.Any substantial or systematic reproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in any form to anyone is expressly

http://www.tandfonline.com/loi/cjwr20

http://www.tandfonline.com/action/showCitFormats?doi=10.1080/09571269808718146

http://dx.doi.org/10.1080/09571269808718146

forbidden. Terms & Conditions of access and use can be found at http://www.tandfonline.com/page/terms-and-conditions

Dow

nloa

ded

by [

USC

Uni

vers

ity o

f So

uthe

rn C

alif

orni

a] a

t 00:

57 0

8 D

ecem

ber

2014

http://www.tandfonline.com/page/terms-and-conditions

http://www.tandfonline.com/page/terms-and-conditions

Journal of Wine Research, 1998, Vol. 9, No. 3, pp. 173-184

Consumer Evaluation of Retail Wine Stores

LARRY LOCKSHIN and PIERRIS KAHRIMANIS

Original manuscript received, 28 May 1998Revised manuscript received, 20 August 1998

ABSTRACT This research investigates what attributes wine shoppers use to evaluate retail wine shops.

Interviews were used to elicit a range of wine store attributes and then a survey was constructed

incorporating those attributes. Respondents rated the importance of attributes and then rated a specific wine

shop's performance on the same attributes. Affective attributes relating to staff performance rated highly,

as did functional attributes such as the range of wines and pricing. The 45 attributes were formed into

six general factors and these factors were used in a discriminant analysis to produce a positioning map

of eight different wine stores. The managerial significance of the results are discussed in the context of the

proper positioning of wine stores.

Introduction

Consumers today are faced with a large array of stores in which to purchase wine.Consumers' store choice depends in part on the perceived image of those stores, whichin turn relies on the positioning efforts of management. Positioning involves creating animage in the mind of the buyer (Ries and Trout, 1982). The more distinct the image,the more differentiated it is from those of competitors. If the perceived image iscongruent with buyer expectations, patronage is much more likely to result (Sirgy andSamli, 1985). Managers and owners of retail shops must accurately understand consumerperceptions of their stores and work to properly position the stores in order to maintainand expand their businesses.

How do consumers evaluate retail wine shops? What attributes of those shops aremost important in creating a store's image in the consumer's mind? The answers to thesequestions have not been explored in the context of wine purchase behaviour and theirinvestigation will provide insights for store managers. This paper begins the search toanswer these questions. Firstly, the literature on store image and positioning is coveredto provide cues to how to understand and measure image and position. These aspects ofwine retailing are then explored in a two-stage research process using qualitativeinterviews to generate a list of store attributes used by wine shoppers and then examiningthe attributes quantitatively using a questionnaire. Finally, the results are presented anddiscussed in the context of both managerial action and future research.

Dr Larry Lockshin (to whom correspondence should be addressed) and Pierris Kahrimanis, University of Adelaide,Wine and Food Business Group, Department of Horticulture, Oenology and Viticulture, PMB 1, Glen Osmond,SA 5064, Australia (Tel: 08 8303 6764 (office); Fax: 08 8303 7116; e-mail: [email protected]).

Dow

nloa

ded

by [

USC

Uni

vers

ity o

f So

uthe

rn C

alif

orni

a] a

t 00:

57 0

8 D

ecem

ber

2014

174 L. LOCKSHIN AND P. KAHRIMANIS

Literature Review

The positioning of products and services is one of the fundamental activities of marketingmanagement (Kotler el al., 1998). Faced with a confusing array of choices, consumersestablish 'images' in their minds for the various choices the market provides. These imagestypically are simplified rankings of the stores across a set of salient attributes. Differentconsumers may have different sets of salient attributes. However, rather than storing listsof attributes, consumers usually simplify their ranking to a relative position based on themost important attributes. It is the job of marketers to select a target market, understandthe relevant attributes and either position their products or stores closest to the consumer'sideal points or try and change the consumer's ideal points to match their offerings.

Ries and Trout, two of the most famous positioning strategists, define positioning asfollows: "positioning starts with a product: a piece of merchandise, a service, a company,an institution, or even a person. It is not what you do to the product, it is what you doin the mind of the prospect" (1982, p. 2). Positioning can be measured by askingconsumers to rate various alternative product or store choices across sets of attributes.These ratings are then presented graphically, typically using the two most importantdimensions to generate perceptual maps (MacKay and Drogc, 1990). Managers can usethese maps to compare their product or store positions with those of their competitorsand can then develop strategics to move their products or stores to a different position(Dillon el al, 1986). They would choose to reinforce the existing positions of well-positioned products and stores through selected communication strategies.

The consumer perception of the positioning of stores is often called 'store image'. Thedifference between image and position is merely that an image is of one store, whileposition involves the comparison of images across multiple stores. Martineau (1958) firstdefined store image as "the way in which the store is defined in the shopper's mind, partlyby its functional qualities and partly by an aura of psychological attributes". Martineauseparated functional elements of store image, such as the range of products, store layoutand store location, which can be compared objectively with competitive offerings, fromthe feelings generated by these functional elements.

Subsequent research into store image has concentrated mainly on functional attributes(e.g. Fisk, 1961-62); Lindquist, 1974-75; May, 1974-75; Sirgy and Samli, 1985).Although Kunkel and Berry (1968) explored some non-functional perceptions of stores,it was not until Zajonc (1980) that a theory linking functional attributes and feelingsemerged. Zajonc suggested that feelings developed from functional attributes and thatboth types of images were then tied to the object of interest. He theorised that consumerssum up the functional attributes of objects with an affective (emotional) evaluation of theirfeelings towards those objects, such as whether these are good or pleasant feelings, andso on.

Jackson and Konell (1993) used a list of functional attributes derived from theliterature and a set of general feelings (Mehrabian and Russell, 1974) to test consumers'ratings of discount, department and specialty stores. Specialty stores rated highest on mostof the functional and positive affective attributes, with discount stores lower anddepartment stores in between. However, subjects only rated specific examples of storesof each type. No attempt was made to understand whether these ratings were involvedin the store images or positioning. Babin and Darden (1993) showed how the affectivequality of a store's environment helped the understanding of the differences in storeattractiveness. Consumers responded to stores as being exciting or relaxing. Differentstores were clustered using these scales but no functional store characteristics were testedin this study.

Dow

nloa

ded

by [

USC

Uni

vers

ity o

f So

uthe

rn C

alif

orni

a] a

t 00:

57 0

8 D

ecem

ber

2014

CONSUMER EVALUATION OF RETAIL WINE STORES 175

More recent research by Woodside and Trappey (1992, 1996) examined therelationship between top of mind awareness and store choice. Store attributes werementioned to subjects and the first store that came to mind was recorded. Resultsindicated that top of mind awareness corresponded to the subjects' first-choice store andwere related to the store's positioning in the marketplace. This methodology presupposesthat the researcher knows what are likely to be the 'hot buttons' that trigger top of mindawareness, and that either the stores are visited frequently or the decision is one of lowinvolvement. These cues are not known for wine shops.

The literature seems clear that consumers have both functional and affective oremotional attributes as part of their overall image of retail stores. Prior research hascompared consumer perceptions of discount, department and specialty stores anddifferences have been found as would be expected. However, within a single category ofstores, little research has compared the relative importance of functional and affectiveattributes for shoppers, nor has previous research used these attributes to map therelative positions of competing stores.

Methodology

The methodology for this research is divided into two parts: qualitative and quantitative.The qualitative research was conducted in order to gain an understanding of the rangeof attributes wine shoppers use in choosing and evaluating wine shops. These attributeswere then used in a quantitative questionnaire to understand their relative importanceand the ways in which consumers perceived the wine stores on each attribute.

A list of exploratory questions regarding how customers evaluated wine stores wasdrawn up. The order and wording of the questions were piloted at several wine storesbefore the final version was designed. The key question was "Describe the overall imageof this particular store". Other questions leading up to this one seemed to "warm therespondent up" to the subject, but this question provided the most useful items for thesubsequent survey.

A total of 127 personal interviews were conducted during a 3 week period in a majorAustralian capital city. Interviews were conducted in four different wine shops duringdifferent times of the day and the week in order to get a broad representation ofshoppers. Convenience sampling was used. This was deemed adequate to develop the listof store attributes for the exploratory purposes of this project. Customer responses wererecorded on paper for each question in each interview. The data from all the interviewswere collated and the attributes grouped into categories. These categories correspondedto the ones derived from the literature (Martineau, 1958; Kunkel and Berry, 1968;Zimmer and Golden, 1988; Babin and Darden, 1993; Jackson and Konell, 1993). Thecategory assignments were reviewed by three independent academics and no disagree-ments were found. These attributes were then used to design the questionnaire.

There were seven attribute categories in the questionnaire: price, wine range(including accessories and gourmet foods), staff qualities, wine tastings, presentation andstore layout, overall image of the store, and miscellaneous (including membership clubs,parking, opening hours and distance to home and work). Within each category therewere multiple items as generated by the interviews. Respondents rated the importanceof each attribute for their own wine store choice. They also rated the store in which theywere shopping on the same attributes. For example, buyers would rate how importantlow prices were and then rate the store on how well the store offered low prices. Thismulti-attribute method was compared to open-ended questions by Tigert (cf. Woodside

Dow

nloa

ded

by [

USC

Uni

vers

ity o

f So

uthe

rn C

alif

orni

a] a

t 00:

57 0

8 D

ecem

ber

2014


and Trappey, 1992) and a correlation greater than 0.80 for the two methods was foundin grocery stores, discount stores and do-it-yourself stores.

Eight wine stores agreed to participate in the study. Four of the stores were part ofthe same discount chain; two had been recently renovated and two had not. One storewas a member of a national premium wine chain, two stores were part of a localpremium wine chain and the final store was an independently owned fine wine shop.The stores were chosen to represent a range of suburbs and styles of store in order toassure enough variance in the study. Trained research assistants collected the data ineach store over two successive Thursdays and Saturdays between 10 a.m. and 4 p.m.,which arc considered peak shopping times. For exploratory research, an interceptmethodology has proved both efficient and reliable for uncovering predicted differences(Calder el ai, 1982). Customers in the store were approached and asked to fill out thesurvey. As many customers as possible were approached, and multiple clipboards withsurveys were available in each store. Finished surveys were placed in a closed box. Therespondents could then fill out an entry form for a draw for a free case of wine as anincentive for taking part in the survey.

The data were checked and tabulated. The means of the importance attributes werecalculated and a ranking of the attributes was prepared.1 The ratings of each of the storesacross the attributes were also computed. However, because there were 45 differentattributes, even these rankings became cumbersome. Factor analysis was used to reducethe attribute list to six factors, accounting for 72% of the variance. An eigenvalue greaterthan 1.0 was used as the criterion. New variables were constructed using the six factorsand these were used as input to the discriminant analysis and accompanying mappingprocess. The stores were also compared across the six factors.

The best way of understanding the way shoppers actually sec their wine stores is toplace the stores on a two-dimensional map. Woodside and Trappey (1992) discuss topof mind attributes as a means of understanding a store's position in the market. However,they also state that "to some extent, unique store-image dimensions arc likely to be foundfor each competing store in a given metropolitan area that do affect the store's selectionas a primary store". The only way to measure these would be by comparing specificstores on the same attributes as presented here. It should be noted as well that the systemproposed by Woodside and Trappey (1992, 1996) is explicitly geared to routinedecision-making for such stores as grocery and convenience. It is not clear whether wineshops would fall into this model or not.

As stated above, a perceptual map is akin to the way in which buyers actuallyvisualise stores, reducing lists of differences to imagined positions on two dimensions. Aperceptual map of the eight wine stores was created using discriminant analysis. Thedependent variable was the store and the independent variables were the six factors plussix other variables that added discriminating power. The added variables were selectedfrom the 15 most important attributes that were not included in the derived factors.These attributes would probably correspond to top of mind attributes as used byWoodside and Trappey (1992, 1996). The first two discriminant functions accounted for82% of the variance, so the stores were mapped using these two functions.

Results

The rank order of the importance weights of the attributes customers use in choosing awine shop is provided in Table 1. Although functional attributes dominate the list (32 of45) the two most important attributes were affective. These top-ranked affective at-tributes are responses to the staffing of the store. Four of the top 10 attributes were

Dow

nloa

ded

by [

USC

Uni

vers

ity o

f So

uthe

rn C

alif

orni

a] a

t 00:

57 0

8 D

ecem

ber

2014

CONSUMER EVALUATION OF RETAIL WINE STORES 1 77

Table 1. Rank order of store choice attributes

Rank

12345678

910111213

1415

16

1718

192021222324252627282930

31323334

35

36

37

3839

40

4142434445

Attribute

Friendly staffHelpful staffRange of wine availableCompetitive pricesPrompt serviceVisibility of labelsStaff knowledge of winesVisibility of pricesCustomer/staff relationshipEase of finding wineCar parking availabilityAble to give recommendationsDiscounts availableAlways have what I wantRange of prices availableCheerful atmosphereInformal atmosphereEase of walking through aislesPrice of wines'Location signs' for winesFine wine storeInformation about wineExtended opening hoursWine tastings availableInteresting storeRange of wines being tastedDiscount wine storeImage of storeBoutique wine availableStore displaysSalesperson help while browsingPrccoolcd wine availableDistance of store to houseNewspaper advertisingExciting storeColourful storeGift ideas availableTraditional decorSimple decorMembership availabilityModern decorPlain storeDistance of store to workBar accessoriesGourmet foods sold

N

334335336336335335336336

335336335336334

335334

335331335335

336

333335336

333335332333

334

333336

335334334

335330

336335333329331331319330

334333

Mean"

4.464.344.194.114.104.034.003.993.973.933.913.913.763.683.653.623.593.583.573.483.443.303.243.183.143.123.042.972.922.912.702.702.622.562.482.422.402.332.302.192.071.801.791.721.59

Standarderror

0.0380.0410.0420.0440.0490.0410.0510.0430.0500.0450.0530.0520.0550.0570.0480.0540.0530.0530.0500.0550.0590.0560.0680.0660.0590.0640.0640.0650.0620.0550.0620.0750.0680.0660.0640.0610.0660.0630.0590.0700.0580.0500.0580.0520.048

Type

AffectiveAffective

FunctionalFunctionalAffective

FunctionalFunctionalFunctionalAffective

FunctionalFunctionalFunctionalFunctionalFunctionalFunctionalAffectiveAffectiveAffective

FunctionalFunctionalFunctionalFunctionalFunctionalFunctionalAffective

FunctionalFunctionalAffective

FunctionalFunctionalFunctionalFunctionalFunctionalFunctionalAffectiveAffective

FunctionalFunctionalFunctionalFunctionalFunctionalAffective

FunctionalFunctionalFunctional

"Scale 1—5, with 1 ~ not important and 5-very important.

affective, which emphasises the importance of the emotional reaction of customers tomanagement's positioning of the store. This confirms previous research on the import-ance of staff training and staff/customer relationships in wine retailing (Macintosh andLockshin, 1997). The other top 10 attributes concerned the range and pricing of the

Dow

nloa

ded

by [

USC

Uni

vers

ity o

f So

uthe

rn C

alif

orni

a] a

t 00:

57 0

8 D

ecem

ber

2014


wines, the visibility of labels and prices and the ease of finding wine in the store. Theseare more typical functional attributes relating to merchandise and pricing.

It is also useful to consider the attributes ranked at the bottom of the list. Six of thelowest 11 attributes relate to the decor and feelings the store provides (e.g. exciting/plain). Surprisingly, two of these attributes relate to how far the store is from home orwork. Distance has been considered to be an important variable in store choice (Tigert,1983; Finn and Louviere, 1990). However, as the results show, when there are severalchoices in reasonable proximity to each other, the exact location is less important thanother attributes. This would tend to be the case for wine shops in most large Australiancities. The other low-ranked attributes concern accessories and gourmet foods, andmembership clubs. One of the chains in this research has a strong promotional campaignfor its membership club, but this did not seem important to the consumers surveyed inthis research.

The ranking of attributes does not indicate whether the stores actually differ in theeyes of the consumers across these attributes. Table 2 presents the consumer comparisonof the stores across the 15 top-ranked attributes. A series of tests of one-way analysis ofvariance (ANOVAs) were run for each of the 15 most important attributes across theeight stores. There is at least one significant difference across the stores for 11 of the 15attributes. An examination of the actual differences between each of the stores for eachvariable revealed that the source of most of the variance was store 6, the privately ownedpremium wine store, and stores 7 and 8, the locally owned chain stores. Store 6 wasconsistently the highest performing store in all aspects of staffing, which seems to indicatebetter human resource management for the independent store compared to the chainstores. Store 6 also had the highest rating on parking availability, but two of the chainstores were next in order, with the two local chain stores rated last. It is also interestingto note that for pricing, all the stores were considered similar; that is, whether the storewas positioned as a discount chain or fine wine shop did not seem to influence thebuyers' perception of prices. The speed of service and the range of wines available werenot seen as significantly different between the stores. These attributes were important, asprevious research in grocery shopping has shown (Woodside and Trappcy, 1992).However, no store was rated statistically better than any other on these two areas.

Table 2. One-way ANOVA differencesamong the stores on the top 15 attributes

Attribute

Parking availabilityStaff knowledge of winesRange of wine offeredAble to give recommendationsAlways have stock I wantVisibility of labelsHelpful staffCustomer/staff relationshipEase of finding wineFriendly staffVisibility of pricesPrice ranges availablePromptness of serviceDiscounts availablePrice competitiveness

F

18.376.706.555.193.903.903.473.433.363.082.741.471.430.900.22

Significance

0.000.000.000.000.000.000.000.000.000.000.010.180.190.510.98

Dow

nloa

ded

by [

USC

Uni

vers

ity o

f So

uthe

rn C

alif

orni

a] a

t 00:

57 0

8 D

ecem

ber

2014


With 45 attributes, it is difficult to conceptualise the differences among the stores oreven the rankings of the individual attributes. Factor analysis was used to reduce thenumber of attributes by inferring that there was some latent structure to the data. Thisis similar to the approach Zimmer and Golden (1988) used in their content analysis ofstore images. This approach makes sense, because the attributes were originally placedinto seven groups after the qualitative analysis. The initial factor analysis contained largenumbers of variables that had loadings higher than 0.3 on more than one dimension,which indicated that some of the dimensions and variables were not distinct. An iterativeprocess was employed to eliminate these items and create a clear-cut set of factors thathad reasonable loadings and small cross-loadings and were interpretable (Rummel,1970). The resultant analysis is presented in Table 3.

Six factors with eigenvalues greater than 1.0 were extracted using principal compo-nents analysis and varimax rotation. The total variance accounted for is 72%. Thefactors contained easy-to-interpret dimensions based on price, staffing, the presentationof merchandise, wine tasting, non-wine accessories and the range of wines offered.Newspaper advertising is linked to wine tasting because most wine shops advertise thewines available for tasting before each weekend. The only set of original attributes thatdid not come into the final six factors were the various image attributes, such ascolourful, exciting, interesting and boring. These affective perceptions cross-loaded onseveral factors, indicating that a specific feeling could be linked to several different

Table 3. Rotated store performance factors"

Attribute

Helpful staffFriendly staffStaff help without harassmentCustomer/staff relationshipPromptness of service

'Location signs' for winesEase of finding wineStore displaysEase of walking through aisles

Value for moneyPrice competitivenessSpecials oifcrcd on winePrice ranges available

Bar accessories availableGift ideas offeredGourmet foods sold

Range of wines being tastedWine tasting sessionsNewspaper advertising

Boutique wine availableRange of wine available

Stafperf

L 0.821: o^8i;': 0.78:-;10.78 •

0.76 j}

0.240.230.27

0.18

0.36

0.17

0.210.27

0.19

Prcspcrf

0.22

0.190.34

::to.82f; U78tb.73':;;(0.73J

0.170.27

0.24

0.31

Factor

Priccperf

0.150.17

0.20

0.19

0.24

i 0 -.87;i 0.86:1

•0.65:

Accspcrf

0.21

0.18

0.19

1:0.87:;;P0.82!

0:73:

0.210.19

Tastperf

0.15

0.25

0.19

0.20

0.30

;0.82i:

1 0.810.70

Rangperf

0.17

0.22

0.81 '; 0.76 :

"Loadings below 0.15 not shown.

Dow

nloa

ded

by [

USC

Uni

vers

ity o

f So

uthe

rn C

alif

orni

a] a

t 00:

57 0

8 D

ecem

ber

2014


Table 4. One-way ANOVA differences among thestores on the six factors

Factor

Tasting performance (Tastpcrf)Range of wines performance (Rangpcrf)Accessory performance (Accspcrl)Presentation performance (Prcspcrf)Staff performance (Stafpcrf)Price performance (Pricpcri)

F

12.269.908.494.352.700.33

Significance

0.000.000.000.000.010.94

functional attributes. This summation of functional attributes as feelings is similar to whatZajonc (1980) predicted.

The factor analysis provides a clear way to group the various store attributes (Table3). If the stores differ across the attributes, one would expect differences to occur on thefactors as well. Table 4 shows the one-way ANOVA results across the eight stores foreach of the factors. Only price performance is not significant across the eight stores,which is not surprising, since the single price attributes were not significant cither. Theother factors represent areas where shoppers perceive the stores to differ.

The six dimensions and the six most highly ranked variables not part of the factorsolution were used in the discriminant analysis. Only the first two discriminant functionswere significant and they accounted for 82% of the total variance (Table 5). Labels wereconstructed by looking at the weightings of the different attributes on each factor. Theccntroids for each of the eight stores arc mapped against the functions in Figure 1. Theperceptual map clearly shows that stores 7 and 8 are perceived relatively similarly. Thisis not surprising, since they are part of the same locally owned chain. Store 6 appearsby itself. Again, this is not surprising, since this store is a single privately owned high-end

Table 5. Structure matrix for the first twodiscriminant functions

Factor/attribute

Presentation performanceParking availabilityTasting performanceStaff performanceAccessory performancePrice performanceVisibility of labelsStaff knowledge of winesVisibility of pricesRange of wine offeredRange of wines performanceAlways have stock I want

EigenvaluePercentage varianceWilks lambdaChi squareSignificance

Function

1

-0.39-0 .49

0.330.003

0.46-0.08-0 .12

0.27-0 .18

0.220.370.23

0.8646.500.24

209.970.00

2

0.100.670.600.290.07

-0 .040.400.380.300.180.210.26

0.6535.200.44

119.840.00

Dow

nloa

ded

by [

USC

Uni

vers

ity o

f So

uthe

rn C

alif

orni

a] a

t 00:

57 0

8 D

ecem

ber

2014

"5to•a

O) O

II


2i

Chain upscale•

Chain not refurb-1.3 -0.3

Chain refurb

Chain refurb•

Chain not refurb•

1

Private upscale

0.7 1.7Local chain

Local chain•

Range of products(Function 1)

Figure 1.

wine shop. Stores 1-5 cluster relatively together and are spread mainly along function 2but similar on function 1. This is more surprising even though they are members of thesame national chain. Stores 1 and 4 are closer than 1 and 2 or three and 4, even though1 and 2 have been recently refurbished and 4 and 5 have not. Store 5 is also close, butit has a different name and, from management's perspective, a different position in themarket, even though it has the same corporate parent as stores 1-4. It is obvious thatthe wine shoppers surveyed in this research perceive those five stores as being fairlysimilar overall.

Table 5 provides the structure matrix for the first two discriminant functions. Thismatrix shows the strength of the loadings of each factor or variable on the two functions.These matrixes are more stable and are generally preferred for interpretation rather thanthe discriminant weights (Hair el ai, 1995, p. 206). For example, stores 7 and 8 arerelatively high on function 1. This would be interpreted as meaning that those storeshave a relatively low perceived performance on presentation and parking availability(negative loadings) and a high perceived performance on accessories, range and staffknowledge. Store 6 is high on function 2, which can be interpreted as meaning that itis perceived as strong on parking, tastings, visibility of labels and prices, and staffing, andslightly less so on range. Store 5 is close to 0 on function 2 but not far from — 1 onfunction 1. This is interpreted as meaning that it is perceived as relatively strong onparking and presentation, but less so on tasting, accessories and staff knowledge. Pricing,which was seen as relatively equivalent between the stores in the one-way ANOVAs, hasa loading of close to 0, as expected.

Dow

nloa

ded

by [

USC

Uni

vers

ity o

f So

uthe

rn C

alif

orni

a] a

t 00:

57 0

8 D

ecem

ber

2014


Conclusion

This research specifically tried to explore the questions of what attributes wine shoppersuse to evaluate retail wine shops and how those attributes are used to create an overallimage and position of the stores in buyers' minds. Interviews with over 100 wineshoppers were used to generate a list of attributes. These attributes were then ranked inimportance by a different group of wine shoppers. The results indicated that affectiveattributes relating mainly to buyers' perceptions of the staff were very important inchoosing a wine store. Although the range of wines available and pricing were also seenas important, further analysis revealed that most of the stores were not perceived to bedifferent on these attributes. The ease of locating wines and of viewing the labels andprices was also deemed important and the wine shops in this survey differed on thesecharacteristics.

Managers can learn from this in allocating time and resources to the most effectivefeatures. It seems that to have competitive prices and a wide range of wines to choosefrom is merely a given; a store must have these to be competitive. Stores that want todifferentiate themselves from the competition must focus on hiring and training their staffto be friendly and knowledgeable. It is also important to pay attention to store layout anddesign for ease of shopping.

These desired qualities most probably stem from the unique attributes of wine as aconsumer product. Rather than three to five top brands in the product category as istypical of most fast moving consumer goods (Aakcr, 1996), wine has thousands of brandscreated and marketed by a wide range of countries, regions, co-operatives, majorcorporations and small producers. This complexity is partly remedied by well-trainedstaff. Customers desire basic attributes, such as available parking and reasonable prices,but they seem to place equal importance on the staff and the layout of the shops.

Wine buyers in this research did not distinguish perceptually between stores that haddifferent names and a different managerial control if those stores had similar attributes.There is more to positioning than the store or brand name. Four discount stores andone fine wine store owned by the same company but using different names appearedvery close together on the perceptual map. On the other hand, an independentlyowned fine wine shop was able to differentiate itself through its staff knowledge andtasting programmes. Also, an expensive store refurbishment campaign did not seem toaffect shoppers' perceptions of the store's image. The refurbishment had occurred onlya few months before this research, so it may not have affected customers' perceptions asyet.

This research does have some limitations. The study was done in one city among arange of only eight stores. It is reasonable to assume that the attributes uncovered in thequalitative portion represent the important factors in wine store choice in most markets.However, the structure of this market does not include wine sold through supermarkets.It is likely that in places where wine is sold through grocery stores (e.g. the UK, Franceand many US states), the attributes uncovered may only be applicable to wine specialtystores and not to wine departments in larger stores.

The sample was too small to do an adequate job of segmenting the buyers. Previousresearch on segments of wine shoppers (Lockshin el al., 1997) showed that differentgroups of wine buyers reacted differently to different retail tactics. One would assumethat the importance rating of various attributes would differ among different consumersegments and that this would determine the proper marketing and positioning strategyfor different types of wine stores. However, recent research seems to indicate that thepurchase occasion may be a better determinant of shopping behaviour than customer

Dow

nloa

ded

by [

USC

Uni

vers

ity o

f So

uthe

rn C

alif

orni

a] a

t 00:

57 0

8 D

ecem

ber

2014


segmentation (Hall el al., 1997). If this is the case, stores will have to provide moreinformation regarding the use of different wines for different occasions. A wine store, likeany product or brand, must choose a positioning strategy aimed at a target market, if itwants to differentiate itself.

This research explored what attributes wine shoppers use in deciding where to shop.It would be useful to conduct a similar project in a region where wine is also sold insupermarkets to ascertain whether the same attributes and the same ranking occur. Awider range of wine store types surveyed would also enhance our knowledge of this area.A larger sample would provide more ability to segment the buyers and see how differentthe attribute rankings would be among different segments.

This research did provide an investigation of what attributes consumers find import-ant when choosing a wine shop. Qualitative research provided a group of 45 attributes,and although 32 were functional attributes, affective or feeling attributes were almost halfof the 10 most important attributes. Store performance on these attributes was used toproduce a positioning map for eight wine shops. The positioning map was useful forunderstanding which stores were differentiated and which were not. Merely changing thename of a store or refurbishing it was not enough to create differentiation. Managersmust understand the needs of their customers and provide a range of products andservices to meet those needs. The people element of wine retailing had a large effect onstore position. This is where small independent shops can best compete with large chainstores and even major supermarkets which sell wine.

Note

1. Note that a five-point rating scale was used. Although different consumers might use the scaledifferently, the smaller number of choices, as compared to a 10-point scale for example, allowsmore accurate determination of means across a wide range of individuals.

References

AAKER, D.A. (1996) Building Strong Brands, New York: The Free Press.BABIN, B.J. and DARDEN, W.R. (1993) Mapping the affective quality of retail stores: some practical

insights, in: DARDEN, W.R. and LUSCH, R.F. (eds) The Cutting Edge: Proceedings of the SecondSymposium on Patronage Behaviour and Retail Strategy, American Marketing Association, 215-224.

CALDER, B., PHILLIPS, L.W. and TYBOUT, A.M. (1982) The concept of external validity, Journal ofConsumer Research, 9, 240-244.

DILLON, W.R., DOMZAL, T. and MADDEN, T.J. (1986) Evaluating alternative product positioningstrategies, Journal of Advertising Research, 26, 29-35.

FINN, A. and LOUVIERE, J. (1990) Shopping-center patronage models: fashioning a consideration setsegmentation solution, Journal of Business Research, 21, 259-275.

FISK, G. (1961-62) A conceptual model for studying customer image, Journal of Retailing, 37, 1-8.HALL, J., LOCKSHIN, L. and WINCHESTER, M. (1997) Qualitative analysis of the influence of

situational components in the consumer decision making process, in: REED, P., LUXTON, S. andSHAW, M. (eds) Proceedings of the Australian New Zealand Marketing Educators' Conference, Vol. 3,1281-1282.

JACKSON, A.L. and KONELL, S. (1993) A tale of two images: functional versus psychologicalcomponents of three store types, in: DARDEN, W.R. and LUSCH, R.F. (eds) The Cutting Edge:Proceedings of the Second Symposium on Patronage Behaviour and Retail Strategy, American MarketingAssociation, 193-202.

KOTLER, P., ARMSTRONG., G., BROWN, L. and ADAM, S. (1998) Marketing, 4th edition, Sydney:Prentice Hall.

KUNKEL, J.H. and BERRY, L.L. (1968) A behavioral conception of retail image, Journal of Marketing,38, 21-27.

LINDQUIST J . D . (1974-75) Meaning of image, Journal of Retailing, 39, 21-35.

Dow

nloa

ded

by [

USC

Uni

vers

ity o

f So

uthe

rn C

alif

orni

a] a

t 00:

57 0

8 D

ecem

ber

2014


LOCKSHIN, L.S., SpAWTON, A.L. and MACINTOSH, G. (1997) Using product, brand, and purchasinginvolvement for retail segmentation, Journal of Retailing and Consumer Services, 4, 171-183.

MACINTOSH, G. and LOCKSHIN, L. (1997) Retail relationships and store loyalty: a multi-levelperspective, International Journal of Research in Marketing, 14, 87-97.

MACKAY, D.B. and DROGE, C. (1990) Extensions of probabilistic perceptual maps with implicationsfor competitive positioning and choice, International Journal of Research in Marketing, 7, 265-282.

MARTINEAU, P. (1958) The personality of the retail store, Harvard Business Review, 36, 47-55.MAY, E.G. (1974-75) Practical applications of recent retail image research, Journal of Retailing, 39,

212-225.MEHRABIAN, A. and RUSSELL, J. (1974) An Approach to Environmental Psychology, Boston, MA, MIT

Press.RiES, A. and TROUT, J. (1982) Positioning: The Battle for Your Mind, New York: Warner Books.RUMMFX, RJ. (1970) Applied Factor Analysis, Evanston, IL: Northwestern University Press.SiRGY, MJ. and SAMLI, A.C. (1985) A path analytic model of store loyalty involving self-concept,

store image, geographic loyalty and sociocconomic status, Journal of the Academy of MarketingScience, 13, 265-291.

TIGERT, D. (1983) Pushing the hot buttons for a successful retail strategy, in: DARDEN, W. andLUSCH, R. (eds) Patronage Behavior and Retail Management, New York: Nortli Holland.

WOODSIDE, A.G. and TRAIPPEY III , R.J. (1992) Finding out why customers shop your store and buyyour brand: automatic cognitive processing models of primary choice, Journal of AdvertisingResearch, 32, 59-77.

WOODSIDE, A.G. and TRAPPEY III, R.J. (1996) Customer portfolio analysis among competing retailstores, Journal of Business Research, 35, 189-200.

ZAJONC, R.B. (1980) Feeling and thinking preferences need no inference, American Psychologist,February, 151-175.

ZiMMER, M.R. and GOLDEN, L.L. (1988) Impressions of retail stores: a content analysis ofconsumer images, Journal of Retailing, 64, 265-279.

Dow

nloa

ded

by [

USC

Uni

vers

ity o

f So

uthe

rn C

alif

orni

a] a

t 00:

57 0

8 D

ecem

ber

2014

Documents

Consumer evaluation of retail wine stores