e-retailers’ competitive intensity: a positioning mapping ... · of why consumers prefer one...

struggle to survive today’s harsh economicrealities. The slow economy, coupled withthe crowded field of competitors inbusiness-to-consumer (B2C) e-commercemake it apparent that achieving long-termsuccess in web retailing requires a store toadhere to traditional economic andmarketing principles and apply traditionalmarketing strategies. In Patton’s words‘the companies and individuals that havesucceeded on the Web aren’t necessarilythose that jumped in first or developedthe most novel strategies. Instead, manyhave focused on simplicity and onapplying traditional business thinking to anew channel’.3

These trends increase the importance

INTRODUCTIONThe introduction of e-commerce hasdramatically changed the rules of thegame of marketing.1 Thanks to the powerof telecommunications and informationtechnologies, consumers can now accessinformation about more vendors moreeasily than ever before. Moreover, newsoftware tools make it easy for consumersto compare and assess quality, image andprice. The end result of this might beshrinking the already diminishing profitsof today’s vendors even further.2 Therecession in general and that of thedot.com economy in particular intensifythe sense of urgency among e-retailerswho face stiff competition in their

114 Journal of Targeting, Measurement and Analysis for Marketing Vol. 12, 2, 114–136 � Henry Stewart Publications 1479–1862 (2003)

E-retailers’ competitive intensity:A positioning mapping analysisReceived (in revised form): 8th August, 2003

Noam Tractinskyis a senior lecturer in the Department of Information Systems Engineering Sciences, Faculty of Engineering at Ben-GurionUniversity of the Negev. His research interests are in the area of human–computer interfaces, management informationsystems, decision support systems and the social dimension of web retailing. His papers have appeared in high-qualityacademic journals. He has a PhD from the University of Texas at Austin in the USA.

Oded Lowengartis a senior lecturer at the Department of Business Administration at the School of Management at Ben-Gurion University ofthe Negev, Israel. His research interests are in the areas of pricing effects on consumer choice, international marketing,product positioning and market share forecasting and diagnostic. His papers have appeared in high-quality academic journals.He has a PhD from the University of Wisconsin-Milwaukee in the USA.

Abstract The downturn of the internet economy has made it clear that e-retailers needto become more competitive if they are to survive. One way for retailers to gauge theircompetitiveness is by considering their position on various key perceptual dimensions ofthe retail environment. This study suggests the use of two analytical tools — perceptualmaps and gap analysis — to help managers improve their ability to gauge and improvetheir competitive position. In this study, data from a previous study are used todemonstrate the use of the two tools in two different competitive environments. Thepaper describes how the tools can be used to reveal similarities and differencesbetween e-retailers, to identify the ideal combinations of important perceptual factors inthe context of the retail domain of these stores, to locate the e-retailer’s positionrelative to the ideal combination and to suggest directions for improving website design.

Oded LowengartDepartment of BusinessAdministration, School ofManagement, Ben-GurionUniversity of the Negev,PO Box 653, Beer-Sheva84105, Israel.

Tel: �972 8 6472774;Fax: �972 8 6477691;e-mail:[email protected]

deals with how consumers evaluatespecific online retailers and how theycompare virtual stores.12–15 Of the threelines of research delineated above, onlythe latter deals with issues close to thequestion of how web-retailers arepositioned relative to their competitors.Within this line it is possible to identifystudies that concentrate on rating variousstores,16 studies that focus on decidingwhether to buy from a specific, single,store,17 and studies that concentrate onconsumer decisions regarding which storeto buy from, given a set of alternativestores.18 Again, it is this latter type ofresearch, ie research that studies howconsumers choose between differentstores, that is pertinent to the study ofweb store positioning vis-a-vis thecompetition.

In addition to this thematicclassification of consumer behaviour ine-commerce research, it is also importantto note that findings might be contingentupon the type of products sought. Thus,Jarvenpaa et al.19 found that consumersweigh various store attributes differentlywhen shopping for low or high-riskproducts. Zhang and her colleaguesfound that user evaluations of internetsites depend on the site domain.20,21 Thatis, different attributes were weighteddifferently depending on the type ofproduct or service offered by those sites.Similarly, Lowengart and Tractinsky22

have compared the consumer choiceprocess of buying online computers andbooks. They found that consumersweighted various perceptualcharacteristics of the e-retailer differentlydepending on the product for whichthey were shopping.

Perhaps due to the boominge-economy and the perception that theend of its exponential growth rate is notyet in sight, retailers as well asresearchers were not as concerned aboutintra-industry competition as they used

of how potential consumers view retailstores. Previous studies have indicatedthat consumer perception of websites areaffected by various design decisions madeby the retailer and at the same time arean important determinant of userintentions to shop at those sites.4–6

Despite increasing academic interest inconsumer internet shopping behaviour,however, much of this researchconcentrates on evaluations of web-basedretailers rather than on selection of oneretailer over another. This studydemonstrates how the application of twoanalytical tools — perceptual maps andgap analysis — can serve web-basedretailers by allowing them to understandhow they are perceived by consumers onkey store characteristics, and how theseperceptions position them vis-a-vis thecompetition. In the following sectionsthe groundwork for this study is laid bysurveying the general characteristics ofresearch on consumer behaviour in thee-retail environment and the idea ofcompetitive positioning.

STUDIES OF CONSUMERBEHAVIOUR IN THE E-RETAILENVIRONMENTThere is a growing body of research onconsumer behaviour in electronic retailsettings. Little research has been done todate, however, on the specific problemof why consumers prefer one web-basedretailer to another. Most of the academicresearch on e-retail has focused on threethemes. One line of research has studiedthe general tendencies of consumers toshop on the web.7–10 This line ofresearch is most commonly concernedwith questions regarding consumerreadiness to buy from online stores ingeneral. Another branch of B2C researchhas focused on how users evaluatespecific brands that are offered throughthe web.11 The third type of research

� Henry Stewart Publications 1479–1862 (2003) Vol. 12, 2, 114–136 Journal of Targeting, Measurement and Analysis for Marketing 115

E-retailers’ competitive intensity: A positioning mapping analysis

Thus, providing mapping tools forretailer positioning can enhance theability of managers to improve theirbusiness and attract and retain customers.

Wind and Mahajan suggest that manyfailed dot.com companies had a flawedunderstanding of their customers.29 Theyalso failed to apply traditional marketingtools to assess the challenges they werefacing. For example, e-retailers try toprofile users.30,31 Yet, e-retailers also needto be aware of how their profile isperceived by consumers. This truism ofthe old economy has not been attendedto by past studies of web-based retail. Itis the purpose of this study to propose amethod for studying this aspect ofe-retail. Thus, the current studyconcentrates on how consumers perceivealternative e-retailers. The paper proposesand demonstrates two analytical tools thatcan assist in conveying user perceptualprofiling of e-retailers in two differentdomains. It is demonstrated that suchprofiling is feasible and is also sensitive tothe domain within which the e-retailer issituated. More specifically, the techniquesof gap analysis and positioning maps areused to reveal similarities and differencesbetween e-retailers. Then the idealcombinations of important perceptualfactors are shown in the context of theretail domain of these stores.

The analytical tools are presented inthe next section. Following that, howthis methodology can be effectivelyimplemented in different domains ofe-commerce is demonstrated using dataregarding three bookstores and threecomputer stores.

METHOD

Database

To illustrate the viability of thecompetitive positioning analysis toolsproposed in this study, data from a study

to be in the age of the ‘old economy.’The downturn of the internet economyhas necessitated a shift in this approach.It is clear now that e-retailers need tobecome more competitive if they are tosurvive. One way in which retailers cangauge their competitiveness is byconsidering their position on various keyperceptual dimensions of the retailenvironment.23

COMPETITIVE POSITIONINGProduct positioning is a well-establishedconcept in marketing literature.24 Looselydefined, positioning is the way a firmdesigns and presents its image topotential customers such that the targetaudiences understand what the firm offersrelative to other firms in the samemarketplace. Positioning is in the mindof the customer, something that isbrought about by a combination ofreality and image: ‘Competitivepositioning is the totality of offer andimage of the company relative tocompeting companies’.25 For positioningto be effective, a retailer has to offertangible, important and communicablebenefits to target customers.26 Theconcept of positioning is broad enoughto be extended beyond the product andthe firm, eg to the realm of the countryand its resources27 or even to encompassan entire continent.28

Constructing a map that reflects thepositioning of a commercial entity allowsmanagers to achieve two main objectivesin defining their marketing strategies.The first objective is gaining moreinsight into the competitive positioningof their firm compared to that of otherrival firms. In addition, by studying thismap, managers may be able to find‘open’ spaces in the competitiveenvironment, allowing them, forexample, to reposition themselves asmore attractive than the competition.


Tractinsky and Lowengart

purchase, the quality of the product itselfand the size and reputation of the store(see Table 3). The main differencesbetween the two types of merchandiseindicate that when consumers shoppedfor computers, they placed greateremphasis on the risks associated withpurchasing the product than whenshopping for books. Such differences arecommensurate with the differencesbetween experience-quality, expensiveproducts (eg computers) and search-quality, inexpensive products (eg books).Indeed, the literature on e-retailingportrays a central role for risk inconsumer behaviour. Risk in this contextcan stem from two main sources:perceptions of the uncertainty associatedwith the buying process and themagnitude of a loss (eg monetary, ofeffort or time, or emotional) that mighttranspire eventually.34 Further, theliterature on risk in internet shoppingalso distinguishes between channel risk(ie the general risk of buying throughthe internet), store risk (ie the specificrisk associated with a particular vendor)and product risk.35 These issues closelyoverlap with general issues of consumer

by Lowengart and Tractinsky32 are used.The data in that study were obtainedfrom 114 participants using asemi-experimental procedure.Seventy-two of the participants weremale (63 per cent) and 42 female (37 percent), with an average age of 23.Participants in that study evaluated threee-retailers in the computer hardwaredomain and three e-retailers in thebookstore domain across 21 differentstore characteristics (see Table 1).

Using factor analysis,33 four factors thataffect consumer evaluations of onlinebookstores and six factors that affectevaluations of online computer storeswere identified. The four factors thatemerged from the data on bookstoresrepresent the shopping process, thequality of the product, risks involved inshopping from the store and the size andreputation of the store (see Table 2). Thesix factors affecting intent to buy fromthe computer stores only partiallyoverlapped with those of the bookstore.These factors were the shopping process,the quality of the information about theproduct, monetary risks involved in thepurchase, other risks involved in the



Table 1: Items used in the study questionnaire

1 The site belongs to a large store2 The site belongs to a reputable store3 Finding information on the site is easy4 The website makes shopping easy5 The site’s design is beautiful6 This site uses good mechanisms of data security7 This site provides good service8 The site’s operators will not put my privacy in jeopardy9 The site provides complete information about the costs associated with a purchase

10 I am interested in the products that are sold on this site11 The site has a wide range of products12 The site provides attractively priced merchandise13 Shopping on this site is fun14 The site’s operators are reliable15 Shopping on this site might lead to monetary losses for me16 The site provides detailed description of the products17 The information on this site is reliable18 This site is a source of high-quality products19 Products on this site are displayed in a visually appealing manner20 Products on this site do not meet my expectations21 The site provides up-to-date information to potential customers

tools is demonstrated by using the datagathered by Lowengart and Tractinsky.37

Gap analysis

Perceptual gap analysis determines theproximity of internet shopping sites toeach other. The tool investigates thespatial difference among the stores in thecompetitive space, by examiningconsumer perceptions of the competinge-retailers in terms of key storecharacteristics. Thus, this tool facilitatesthe discovery of similarities anddifferences among the competing internetshopping sites.

Positioning maps

The second analytical tool enablescomparison of the differences in relativeperceptions of the different characteristicsof each store.38,39 This analysis allows

behaviour on the internet,36 as discussedabove. Whereas this paper does not dealwith the issue of internet shopping riskrelative to other channels, the study wasdesigned to address the other two riskissues mentioned above.

Analysis tools

In this study two analysis tools areemployed to demonstrate how e-retailerscan assess their positioning relative to thecompetition. The outcomes of theanalyses can be presented in a visualform, and the values that each e-storescores on any of the analyses can bejuxtaposed against the scores of itscompetitors to facilitate understanding ofits position. The two tools are discussedin the following subsections.Subsequently, the effectiveness of said



Table 2: Rotated component matrix — bookstores

Factors and per cent of explained variance

141.69%

28.50%

37.56%

44.87%

The site belongs to a large storeThe site belongs to a reputable storeFinding information on the site is easyThe website makes shopping easyThe site’s design is beautifulThis site uses good mechanisms of data securityThis site provides good serviceThe site’s operators will not put my privacy in jeopardyThe site provides complete information about the costs

associated with a purchaseI am interested in the products that are sold on this siteThe site has a wide range of productsThe site provides attractively priced merchandiseShopping on this site is funThe site’s operators are reliableShopping on this site might lead to monetary losses for

me [R]The site provides detailed description of the productsInformation on this site is reliableThis site is a source of high-quality productsProducts on this site are displayed in a visually appealing

wayProducts on this site do not meet my expectations [R]The site provides up-to-date information to potential

customersCronbach Alpha

0.090.070.750.690.720.340.670.190.52

0.350.570.400.790.25

�0.06

0.720.240.180.77

0.180.60

0.90

0.180.110.290.130.090.000.350.150.26

0.550.590.560.350.370.09

0.400.620.680.23

0.600.54

0.71

0.020.150.080.280.010.680.350.760.38

�0.100.070.060.120.620.64

0.180.420.240.10

0.220.20

0.57

0.910.910.050.000.260.220.100.150.02

0.140.060.130.060.05

�0.10

�0.060.080.160.07

0.04�0.01

0.76*

* Pearson correlation coefficientNote: The bolded entries under each factor (column) indicate which attributes (rows) belong to this factor

‘open’ spaces in the competitiveenvironment, giving them, for example,the ability to reposition themselves asmore attractive than the competition.This competitive positioning can varyacross different store types. For example,consumers may look for different storecharacteristics when buying onlinehigh-risk products (eg computers) thanwhen buying low-risk products (egbooks). Thus, marketers can use varioustools, such as changing the perception ofcertain characteristics via the design oftheir site, to reposition their firm. Thus,it is clear that examining the use ofpositioning mapping as a tool formanagers in designing their strategies toattract consumers is indeed important.

determination of which characteristics donot have similar impact on customerperception in evaluating specific internetshopping sites. It also enables the relativeimportance of a specific characteristic inthe competitive domain to bedetermined.

The construction of a positioning mapthat reflects this type of concept allowsmanagers to achieve two main objectiveswhen implementing their marketingstrategies. Namely, gaining more insightinto the competitive positioning of theirbusiness compared to that of rivale-retailers by comparing the distance oftheir e-store to the others. In addition,by studying this map, managers may beable to find ‘holes’ that might indicate



Table 3: Rotated component matrix — Computer stores

Factors and per cent of explained variance

136.82%

27.92%

36.14%

45.56%

55.04%

64.71%

The site belongs to a large storeThe site belongs to a reputable storeFinding information on the site is easyThe website makes shopping easyThe site’s design is beautifulThis site uses good mechanisms of data

securityThis site provides good serviceThe site’s operators will not put my privacy

in jeopardyThe site provides complete information aboutthe costs associated with a purchaseI am interested in the products that are sold

on this siteThe site has a wide range of productsThe site provides attractively priced

merchandiseShopping on this site is funThe site’s operators are reliableShopping on this site might lead to monetary

losses for me [R]The site provides detailed description of the

productsInformation on this site is reliableThis site is a source of high-quality productsProducts on this site are displayed in a

visually appealing mannerProducts on this site do not meet my

expectations [R]The site provides up-to-date information to

potential customersCronbach Alpha

0.150.160.580.720.820.16

0.570.19

0.38

0.09

0.300.13

0.740.14

�0.04

0.38

0.140.130.72

0.20

0.41

0.88

0.140.170.420.130.140.09

0.360.00

0.61

0.20

0.580.68

0.290.410.07

0.55

0.610.310.28

�0.11

0.65

0.83

0.250.270.070.160.110.69

0.400.75

0.10

0.14

0.050.18

0.200.670.07

0.01

0.350.380.08

�0.31

0.00

0.71

0.850.810.29�0.010.060.22

0.110.18

0.11

�0.03

0.30�0.08

0.140.090.02

0.38

0.150.200.24

0.13

0.24

0.76*

0.060.070.200.100.110.06

0.080.04

0.00

0.76

0.330.15

0.100.12

�0.07

0.06

0.230.57

�0.01

0.56

�0.01

0.46

0.020.06

�0.050.000.00

�0.04

0.16�0.03

0.02

�0.13

�0.04�0.04

0.020.160.91

�0.05

0.210.02

�0.04

0.45

0.06

NA

* Pearson correlation coefficientNote: The bolded entries under each factor (column) indicate which attributes (rows) belong to this factor

preferences with respect to onlineshopping. There are still two missingpieces: (1) the distance between theperceived site characteristics and thepreferred characteristics (the closer the gapbetween perceptions and preferences, themore likely consumers are to purchasefrom the site); and (2) the relativecontribution of the specific characteristicto the differences in the positioning of thee-retailers. Note that the factor-basedpositioning maps consider the similaritiesand differences between the different sitesat the factor level. Such an analysis,therefore, lacks diagnostic informationwith respect to the contribution of thevarious characteristics to these positioningdifferences.

To address the first missing piece inthe puzzle (that is the proximity ofconsumer ideal preferences to theirperceptions of the e-retailer), an idealvector that represents the relative effectof each of two factors on the desiredpositioning of an ideal internet shoppingsite was identified. This was done byregressing the relevant factor scoresagainst consumer preferences for thedifferent internet shopping sites, as perthe method proposed by Urban andHauser.41 This vector can be viewed asthe ideal combination of both factors thatconsumers will prefer when makingonline purchases from a specifice-retailer. The closer the position of aspecific e-retailer to this ideal vector, thecloser it is to what consumers prefer.

The second missing piece (ie how toobtain the desired diagnostic informationregarding the similarities and differencesamong the perceived characteristics ofthe different e-retailers) was addressed byconducting a perceptual gap analysis ofthe characteristics comprising each factor.This allowed those characteristics thatmight have contributed the most to theperceptual discrimination at thepositioning map level between the

Approach

The methodology included the followingsteps: in the first stage positioning mapswere constructed. After constructing themaps, the consumer preference data wereused to determine the ideal vector of thestores’ positions in the particularcompetitive settings (ie bookstores orcomputer hardware stores). That is, theideal combination of web storedimensions that consumers seek whenmaking purchase decisions was identified.Once this vector is superimposed on thepositioning map, the proximity of eache-retailer to that vector determines itsattractiveness to consumers. In thesecond stage perceptual gaps were usedto identify the specific site characteristicsthat contributed to perceptualdiscrimination between alternativee-retailers. Below, a more in-depthexplanation of these analyses and howthey were employed in the current studyis provided.

Positioning maps were constructed inthe first stage to evaluate the differences(or similarities) among the three internetshopping sites. Because positioning mapsinvolve the graphic representation ofdata, their legibility and effectiveness arelimited to the use of only a fewdimensions. Thus, the general perceptualfactors of the e-retailers that emergedfrom Lowengart’s and Tractinsky’s study40

were used rather than the 21 rawcharacteristics that formed the basis ofthe general factors. The positioning mapswere constructed on the basis of thefactors’ scores of different extractedfactors. The outcome of this analysisenables the determination of consumerperceptual discrimination among thethree e-retailers in a specific domain (inthis case, either computer hardware orbookstores).

Positioning maps, however, cannotcompletely solve the puzzle ofunderstanding consumer perceptions and



time mapped. In Figures 1A, 1B and 1Cthe perceptual positioning maps of thebookstores are presented. Naturally,similar representations can be maderegarding the relations of each of thethree factors with the fourth factor, butthis will not be displayed here in theinterest of space. In addition, one mightcontemplate a presentation of three factorsat a time, but such a presentation is likelyto hamper understanding of the results.

Figure 1A presents the location of thethree e-retailers (the black circles) andthe ideal positioning vector with regardto the two first factors: shopping processand product quality. The figure clearlyshows distinct consumer perceptions ofthe actual benefits from the twodimensions for the three internetshopping sites. While this suggests thatthe consumers distinguish among thestores, it can also be seen which store is

various internet shopping sites to beteased out.

RESULTSThe analyses are presented first for thebookstore e-retailers and then for thecomputer hardware e-retailers.

Bookstore results

Positioning maps at the factor level

Using principal component analysis, fourfactors with eigenvalues greater than 1were extracted from the participantevaluations of the bookstores. The fourfactors, which explained 63 per cent ofthe variance (see Table 2), can further beused to construct the positioning maps.For expository purposes, only the firstthree factors are used and two factors at a



Figure 1A Positioning map for bookstores — Product quality vs shopping process

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�0.15

�0.1

�0.05

0

0.05

0.1

0.15

0.2

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Product quality

Shopping process

Site C

Site A

Site B

terms of Euclidean distance) the positionof a given e-retailer to this vector, thecloser the e-retailer is to the positionconsumers view as ideal.

Based on this analysis, one can seethat site A is the closest to the idealvector, indicating that this e-vendor isconsidered to be the best in terms ofaddressing consumer preferences withrespect to the combination of ease of theshopping process and the quality ofproducts offered by an e-retailer. It canalso be seen that site C at its presentstate is far from fulfilling consumerpreferences. The marketing implicationsof these results are elaborated on in thefollowing section.

In a similar fashion the positioningmap and the ideal vector for thecombination of the store risk and theproduct quality factors were constructed(Figure 1B). The coefficients obtained forthe ideal vector by the regression analysiswere: y � 0.7F2 � 0.468F3 with

perceived as closer to the ideal vectorthat combines the two factors. Whenextracting the ideal vector, the followingsignificant linear regression results wereobtained: y � 1.321F1 � 0.7F2 withR2 � 57.8%. (For expository purposesthe factors’ coefficients only arepresented, leaving out the constant). Theconstruction of the ideal vector wascarried out by regressing the overallpreference for shopping from an internetsite against the factor scores of therelevant two dimensions. The ratio ofthe estimated coefficients determined theslope of the vector.42 Using F1 and F2coefficients, the ideal vector can bedrawn and superimposed on thetwo-dimensional space of the first twofactors (ie the arrow stretching from theorigin of the axes in Figure 1A). Thisfacilitates better insights as to thepreference of consumers for an idealcombination of product quality andshopping process. Thus, the closer (in



Figure 1B Positioning map for the bookstores — Store risk vs product quality

�0.4

�0.3

�0.2

�0.1

0

0.1

0.2

�0.2 �0.15 �0.1 �0.05 0 0.05 0.1 0.15 0.2

Site A

Site B

Site C

Product quality

Store risk

(ie the more general level). To analysethe differences at the site-characteristiclevel (ie a more specific level), aperceptual gap analysis was conductedwithin each of the four factors thatportray an e-retailer in the bookstoremarket.

The first factor, the shopping processis addressed first. In Figure 2A a snakeplot of consumer perceptions of thespecific characteristics of the productquality factor is presented. The lines inFigure 2A represent the three e-retailersin the bookstore market. The variouscharacteristics that comprise factor 1 areplotted on the Y axis, and the values onthe abscissa reflect the perceptualattribute ratings of the variouscharacteristics.

The perceptual gap analysis vividlydemonstrates the intricate differences inconsumer perceptions of the three sites,while at the same time providing a moregeneral picture of these perceptions.Thus, it can be clearly seen thatconsumers perceived site A and site B to

R2 � 50.8%. This map entailsimplications that are similar to theprevious analysis. That is, site A is theclosest to the ideal vector, and consumersstill perceived the three sites to bedistinct one from another, on the basis ofthe product quality and the risk involvedin purchasing from the e-vendor.

Finally, the positioning map for thecombination of store risk and theshopping process is presented in Figure1C. The result of the regression analysisfor these factors explained 18.4 per centof the variation with ideal factor’scoefficients of y � 1.321F1 � 0.468F3.The results, however, deviate from thepattern of the previous two cases.Namely, for this combination of factorssite B addresses consumer preferencesbetter than site A. Site C is yet again themost distant from the ideal factor.

Perceptual gap analysis at the sitecharacteristic level

The positioning maps helped tease outperceptual differences at the factor level



Figure 1C Positioning map for bookstores — Shopping process vs store risk

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�0.3

�0.2

�0.1

0

0.1

0.2

�0.8 �0.6 �0.4 �0.2 0 0.2 0.4 0.6

Site A

Site C

Site B

Shopping process

Store risk

category was analysed. Figure 2B showsthe snake plot of consumer perceptionsof the characteristics that comprise theproduct quality factor.

Similarly to Figure 2A, consumerperceptions of sites A and B are quitesimilar, while perceptions of both aredifferent from perceptions of site C.Again, ANOVA on the attribute ratingsdetermined the statistical significance ofthe perceptual differences betweene-retailers at the characteristic level ofthe product quality factor. The ANOVAresults are presented in Table 4B.

It can be seen that consumers had noperceptual discrimination between site Aand site B on any of the characteristicsfor Factor 2. They did, however, fullydiscriminate between these two sites andsite C.

To conclude this analysis, a similartype of analysis was conducted for factor3, store risk. The results are presented inFigure 2C and Table 4C.

Analysing the results of this step, it canbe seen that again, sites A and B are

be rather close to one another, whileboth are very different from site C.

Whereas Figure 2A presents adescriptive outline of consumerperceptions, the perceptual differences ofthe various stores can also be statisticallytested for each characteristic of theproduct quality factor. Thus, the resultsof an ANOVA analysis of the attributeratings are presented in Table 4A.

The combination of the ANOVA andthe perceptual map provides acomprehensive picture of differencesbetween the three sites. While sites Aand B are different from site C, site Bhas higher attribute ratings on beingmore beautiful, lower cost of informationand a better display. These analyses canserve e-retailers in making moreinformed decisions regarding whatchanges should be made to their sites inorder to improve how they are perceivedby consumers, to close the perceptualgaps if necessary, and to get closer to theideal vector.

Next the second factor in the book



Figure 2A Perceptual gap analysis for the bookstores, factor 1 — Shopping process

3.5 4 4.5 5 5.5 6 6.5Attribute ratingsSite A Site B Site C

Updated

Display

Fun

Cost information

Service

Beautiful

Easy shopping

Find information

Detailed

the competitive positioning of e-retailersin the computer hardware market. In theinterest of space, the findings of theanalysis are presented in a morecondensed form than the previoussubsection. Assuming that the reader hasby now been acquainted with theprinciples of the analysis tools, the resultsare presented and allowed to speak forthemselves. The reader’s attention is,however, drawn to the differentdeterminant of the competitive field inthe computer hardware industry relativeto the bookstore industry (as mentioned

different from site C on three of the fourcharacteristics, with the sole exceptionbeing that all e-retailers are conceived asequally likely to cause monetary losses tothe users.

Computer hardware results

The previous subsection elaborated onthe use of positioning maps andperceptual gap analysis for identifying thecompetitive positioning of e-retailers inthe bookstore market. In this section thesame analysis tools are used to examine



Table 4A: Mean ratings and ANOVA results, bookstores, factor 1 (shopping process)

Characteristic Site A Site B Site C Significance level

Find informationEasy shoppingBeautifulServiceCost informationFunDetailedDisplayUpdated

5.48a

5.49a

5.32b

5.46a

5.57b

4.97a

5.62a

5.03b

5.41a

5.59a

5.77a

5.94a

5.52a

6.07a

5.34a

5.56a

5.73a

5.56a

3.68b

4.37b

4.59c

4.08b

4.81c

3.53a

3.74b

3.53c

4.18b

0.0000.0000.0000.0000.0000.0000.0000.0000.000

Key: a, b, c — The superscripts in the table cells represent the order of the mean value of the perception ofan attribute for the three sites such that ‘a’ denotes a mean value that is significantly (p � 0.05) greater than‘b’. ‘b,’ in turn, is significantly greater than ‘c’. Whenever two sites are represented by the same letter, there isno significant perceptual difference between those sites on that attribute.

Figure 2B Perceptual gap analysis for the bookstores, factor 2 — Product quality

4 4.5 5 5.5 6Attribute ratings

Site A Site B Site C

Interest

Product range

Price

Relevant

Quality

Expectations

extracted from the data on computerstores. The factors explained more than66 per cent of the variance (see Table 3)and were used to construct thepositioning maps. Again, since theprimary concern here is withdemonstrating the usefulness of theapproach rather than with in-depthanalysis of specific sites, the ensuinganalyses refer only to the first threefactors. Similarly, for expository purposestwo factors at a time are mapped. Figures3A, 3B and 3C present the perceptualpositioning maps of the computer stores.

Figure 3A suggests that each of thethree internet shopping sites has a

in the ‘Method’ section, under‘Database’) and to the differentcompetitive balance between thee-retailers, as these are revealed by theanalysis tools. The marketing implicationsof the two e-retail environments will bediscussed in further detail below under‘Marketing implications’.

Positioning maps at the factor level

As in the previous set of analyses of thebook category, the starting point is threepositioning maps of the three internetshopping sites. As was mentioned in theMethod section, six factors having aneigenvalue greater than one were



Table 4B: Mean ratings and ANOVA results, bookstores, factor 2 (product quality)


InterestProduct rangePriceReliable informationQualityExpectations

5.68a

5.55a

5.08a

5.20a

5.58a

5.31a

5.69a

5.79a

5.24a

5.26a

5.50a

5.46a

5.04b

4.50b

4.64b

4.44b

4.98b

4.57b

0.0000.0000.0010.0000.0000.000

Key: a, b — The superscripts in the table cells represent the order of the mean value of the perception of anattribute for the three sites such that ‘a’ denotes a mean value that is significantly (p �0.05) greater than ‘b’.Whenever two sites are represented by the same letter, there is no significant perceptual difference betweenthose sites on that attribute.

Figure 2C Perceptual gap analysis for the bookstores, factor 3 — Store risk

3.5 4 4.5 5 5.5Attribute ratings


Monetary loss

Reliable

Privacy

Security

for managers in such stores are discussedin the following section.

Perceptual gap analysis at the sitecharacteristic level

As was the case with the bookstores,perceptual gaps between the threecomputer hardware sites were furtheranalysed in an attempt to refine therecommendations regarding the changesthat have to be made in the three stores.The starting point is a gap analysis of thecharacteristics comprising first factor, aspresented in Figure 4A, and it iscomplemented with ANOVA analysis, aspresented in Table 5A.

The analysis of factor 1 in Figure 4Aand Table 5A shows a distinct differencebetween sites B and C. Site A isevaluated as somewhat ‘in between’ theother sites. That is, it is similar to bothsites (A and B) with respect to the easeof finding information, ease of shoppingand the level of service. It is, however,similar to site B in terms of the beautyof its design and appealing display, andsimilar to site C in terms of how fun theshopping experience is.

All three sites are very similar in termsof factor 2, as can be seen in Figure 4Band Table 5B. The exception to this isthe difference between site B and site Con the attribute of information aboutproduct costs. In other words, there isvery little differentiation between thethree sites on this factor.

Similar to the results obtained for

distinct positioning. That is, consumersperceptually discriminate between thethree e-retailers regarding the twodimensions. For the ideal factor, thefollowing significant linear regressionresults were obtained:y � 0.858F � 0.797F2, with R2 � 38.8%.This analysis indicates that none of thestores is very close to the ideal vector.That is, the offerings of the current sitesare not exactly in keeping withconsumer preferences. Nevertheless, siteA is the least distant from the idealvector and, therefore, is closer toaddressing consumer preferences withrespect to the ease of the shoppingprocess and the quality of information inpurchasing from that store.

For the two dimensions in Figure 3B,it seems that site A is much closer to theideal vector (ie y � 0.797F2 � 0.427F3,with R2 � 26%) than the other two sites.

As in Figure 3A, it can be seen inFigure 3C that the three sites do notaddress consumer preferences withrespect to the ease of shopping processand the quality of information theyrequire. The positions of all three sitesare quite distant from the ideal vector (iey � 0.858F1 � 0.427F3 withR2 � 23.1%).

In short, it appears that these threee-retailers in the computer hardwaredomain were far from fully addressingconsumer preferences with respect to theease of shopping process and quality ofinformation. The marketing implications



Table 4C: Mean ratings and ANOVA results, bookstores, factor 3 (store risk)


SecurityPrivacyReliableMonetary loss

4.82a

5.02a

4.79a

4.50a

5.13a

5.03a

4.86a

4.37a

4.00b

4.36b

4.17b

4.03a

0.0000.0000.0000.056

Key: a, b — The superscripts in the table cells represent the order of the mean value of the perception of anattribute for the three sites such that ‘a’ denotes a mean value that is significantly (p � 0.05) greater than ‘b’.Whenever two sites are represented by the same letter, there is no significant perceptual difference betweenthose sites on that attribute.



Figure 3A Positioning map for the computer hardware stores — Shopping process vs information quality

Figure 3B Positioning map for the computer hardware stores — Information quality vs monetary risk

�0.1

�0.05

0

0.05

0.1

0.15

0.2

�0.5 �0.4 �0.3 �0.2 �0.1 0 0.1 0.2 0.3 0.4 0.5 0.6

Site A

Site C

Site B

Information quality

Shopping process

�0.08

�0.06

�0.04

�0.02

0

0.02

0.04

0.06

�0.1 �0.05 0 0.05 0.1 0.15 0.2

Site A

Site B

Site C

Monetary risk

Information quality

MARKETING IMPLICATIONSThe set of analyses suggested anddemonstrated in this study can helpe-retailers understand and improve theirposition in the volatile and competitive

Factor 2, it can be seen that there is verylittle differentiation between the threesites on the characteristics comprisingFactor 3, as depicted by Figure 4C andTable 5C.



Figure 3C Positioning map for the computer hardware stores — Shopping process vs monetary risk

Table 5A: ANOVA results, computer hardware stores, factor 1 (shopping process)

Characteristic Site A Site B Site C Significant level

Find informationEasy shoppingBeautifulServiceFunDisplay

5.14ab

5.11ab

4.57b

4.66ab

4.31a

4.30b

4.89b

4.41b

4.35b

4.223.77b

4.12b

5.44a

5.25a

5.78a

4.85a

4.75a

5.02a

0.0330.0000.0000.0040.0000.000

Key: a, b — The superscripts in the table cells represent the order of the mean value of the perception of anattribute for the three sites such that ‘a’ denotes a mean value that is significantly (p � 0.05) greater than ‘b’.Whenever two sites are represented by the same letter, there is no significant perceptual difference betweenthose sites on that attribute. ‘ab’ indicates that the mean attribute value of a site for a specific attribute is notsignificantly different from the other two sites, ‘a’ and ‘b’, but the latter two are significantly different fromeach other.

�0.08

�0.06

�0.04

�0.02

0

0.02

0.04

0.06

�0.5 �0.4 �0.3 �0.2 �0.1 0 0.1 0.2 0.3 0.4 0.5 0.6

Site A

Site B

Site C

Shopping process

Monetary risk



Figure 4A Perceptual gap analysis for the computer hardware stores, factor 1 — Shopping process

Figure 4B Perceptual gap analysis for the computer hardware stores, factor 2 — Quality of productinformation

3.5 4 4.5 5 5.5 6Attribute ratings


Find information

Easy shopping

Beautiful

Service

Fun

Display

4 4.5 5 5.5Attribute ratings


Updated

Reliable

Detailed

Price

Product range

Cost information

perceptions are likely to lead to morefavourable preferences toward theweb-based store. Changes in perceivedstore characteristics can stem from: (1) a‘real’ change, ie a change in the essenceof the characteristic (eg the e-storeprovides more information about the costof the goods sold on the site); or (2) anapparent change, ie a change that stemsfrom the appearance of the e-retailer’s

environment within which they operate.In this section the type of marketingimplications that e-retailers can drawfrom this set of analyses aredemonstrated. Most importantly, theseimplications relate to changing consumerperceptions about the variouscharacteristics of internet shopping sites.Since consumer perceptions and attitudesform their preferences, improving



Table 5B: ANOVA results, factor 2, computer category (quality of product information)

Characteristic Site A Site B Site C Significant level

Cost informationProduct rangePriceDetailedReliable informationUpdated

5.17a

5.38a

4.37a

4.52a

4.69a

4.72a

4.32b

5.25a

4.24a

4.33a

4.41a

4.57a

5.33a

5.10a

4.49a

4.48a

4.45a

4.77a

0.0000.3600.3980.6750.2250.000

Key: a, b — The superscripts in the table cells represent the order of the mean value of the perception of anattribute for the three sites such that ‘a’ denotes a mean value that is significantly (p � 0.05) greater than ‘b’.Whenever two sites are represented by the same letter, there is no significant perceptual difference betweenthose sites on that attribute.

Figure 4C Perceptual gap analysis for the computer hardware stores, factor 3 — Monetary risks

4 4.1 4.2 4.3 4.4 4.5 4.6 4.7

Attribute ratingsSite A Site B Site C

Reliable

Privacy

Security

general implication is that most of thesimilarities and differences between sitesA and B are rooted in the shoppingprocess (ie factor 1).

Site C differs from the other sites onall factors. Another broad implication isthat site B seems to be ‘overshooting’with respect to the level of thecharacteristics comprising factor 1. Thisconclusion is drawn from the proximityof site A to the ideal vector in Figures1A and 1B, the position of site A plot inFigure 1A, and the values in Table 4A.

The more specific implications foreach e-retailer, based on the perceptualgap analyses, can be described as follows.Retailer A, while leading the pack inthis three-site market, can still improvethe shopping process on its site. Suchimprovements may include, for example,a better display of the products, makingthe shopping experience more fun,improving customer service andenhancing the visual appeal of the site.Similarly, it should also try to improveconsumer perceptions of the security ofthe site. The expected changes inconsumer perceptions regarding thesecharacteristics would result in movementof site A toward the ideal vector withrespect to the shopping process and therisk dimensions.

Retailer B may represent the ‘more isless’ phenomenon. It should considercutting down on some of thecharacteristics that comprise factor 1. Ingeneral, it appears that retailer B hasbeen overloading its site with visualdesign features. It could be that

website. For example, the website maystate ‘we now have a secured paymentprocedure’ even if it used such procedurein the past but its existence was notconveyed to consumers properly before.The new statement might change howpotential customers view the site,although the site has essentially remainedunchanged. Since this study measuredconsumer perceptions about sitecharacteristics (ie ‘apparentcharacteristics’), this analysis points towhich characteristics should be changedand in what direction, rather thanindicating the exact form of how tochange or what degree of a change isneeded. For example, whether animprovement should be made withrespect to the assortment the store carriescan be indicated, but what the exacttype of assortment should be cannot.This issue should be addressed by othermethods that can examine the optimalassortment in an internet shopping sitecarrying books or computers.

Bookstores

The discussion of marketing implicationsfor e-retailers begins with the bookstorecategory. Of the three web stores, site Aseems to be the closest, in general, tothe ideal vector. Site C is the mostdistant. Analysing the results from Figures2A, 2B and 2C and Tables 2, 4A, 4B,and 4C conclusions can be drawn as tothe appropriate actions managers of thethree internet shopping sites can take toimprove their positioning. The first



Table 5C: ANOVA results, factor 3, computer category (monetary risk)

Characteristic Site A1 Site B2 Site C3 Significance level4

SecurityPrivacyReliable

4.443a

4.426a

4.304a

4.287a

4.530a

4.287a

4.078a

4.583a

4.357a

0.0890.6270.225

Key: a — There is no significant perceptual difference between the sites

the lead in terms of being the first toclose the gaps to the ideal vectors. Sucha move could propel the e-retailer tomarket leadership, and thus command ahigher market share as compared to therival sites.

The second general implication thatcan be drawn from the analysis of thecomputer e-retailers is that majordifferences exist predominantly in theshopping process factor (ie factor 1) andto a much lesser degree on theinformation quality dimension (ie factor2). This implies that an e-retailerconcentrating on improving the shoppingprocess along the relevant characteristicswill substantially improve the competitivestanding of that site.

Following these two generalimplications, some specific implicationsfor the three computer hardwaree-retailers can now be drawn. Theposition of e-retailer A with respect tothe quality of information it provides isbetter than that of the other two sites, ascan be seen from Figure 3B. This sitecan still improve its position byproviding a better product range andreliable information. With respect to theshopping process, as in the bookscategory, the site that was rated higheston the characteristics that comprise thisfactor is not positioned closer to theideal vector. It implies that this site (iesite C) is ‘overshooting’ with the level ofsome of the characteristics that comprisethis factor. But specific recommendationsto e-retailer A would probably be that itssite should improve by way of making itmore beautiful, have a better display ofthe offerings and increase the fun ofshopping in the site (see Figure 4A andTable 5A).

E-retailer B suffers from consumerperceptions about the characteristics thatcomprise the two first factors — theshopping process and the quality of theinformation provided by the site. It

consumers view sites that are too densein terms of information displayed as wellas sites that are overly coloured andsophisticated as barriers to a smoothshopping process. This could be a resultof the specific product category at hand— books. That is, when purchasing abook, which is not an experientialproduct, consumers may appreciate aneasy, quick shopping process. Theaforementioned features of an internetshopping site are likely to increase theamount of time spent on the site. Highlevels of these characteristics, therefore,might impede the shopping process.

Of the three bookstores, e-retailer Cstands out negatively. With the exceptionof a few characteristics, this site needsimprovements across the board. Indeed,given the extremely undesirable locationof site C on the positioning maps andthe obvious gaps between its perceivedcharacteristics and those of itscompetitors, its management shouldseriously consider a major overhaul of itswebsite. Thus, it seems that the bestcourse of action in this case would be acomplete re-positioning of the site.

Computer hardware storesThe computer hardware e-retail market(as represented by the three sitesexamined in this study) is quite differentfrom that of the bookstores. This wasevident in the large distance between thethree sites and the ideal vector. Whilesite A and site B were close to the idealvector in the bookstore market (ieFigures 1A and 1C), there is no suchproximity in the computer category. Thefirst general implications that can bedrawn from this finding are that: (1) thee-retailers in the study were not properlyaddressing consumer needs; and (2) eachsite has room to adjust to the preferredcombination of the various factors bychanging its site characteristics and taking



internet retailing environment, a contextwithin which the concept of positioninghas thus far gained only limited attention.The implementation of an analysisframework for the assessment of retailerposition in its competitive environmenthas been suggested. The usefulness ofthis framework has been demonstrated inthe context of internet retailing. Thisusefulness stems from the ability ofmanagers to analyse their competitiveposition both on a high level (ie generalfactors) and on a low level (ie specificcharacteristics of each factor). In addition,the sensitivity of the analysis tools to thevariations in the competitiveenvironment has been shown. Thus, thedifferences in two different competitivee-retail environments such as thebookstore market and the computerhardware market were demonstrated inthis study.

Obviously, the analyses presented inthis paper to illustrate the effectiveness ofthe proposed methodological frameworkare bounded by the nature of theindustries, the websites and the sampleinvolved in the original data set. Yet, theauthors believe that this is precisely themain strength and appeal of the study. Itcombines the offer of a generalisedmethodology, suitable for the entiregamut of e-retail domains, with theability to analyse particular e-retaildomains and to deduce site-specificimplications and guidelines.

Thus, on the operational level, thisstudy delivers new insights into theunderlying dimensions of consumerevaluations of internet retailing sites aswell as the competitive intensity of suchmarkets. At the same time, therelationship between current consumerperceptions of various internet shoppingsites on various dimensions and theirpreferred combination of thosedimensions (ie their relative proximity tothe ideal vector) has been shown.

should, therefore, attempt to improveconsumer perceptions on all of thesecharacteristics. This will include:improving the visual attractiveness of thesite; making the shopping process aneasier and more fun experience; andimproving the service and the display ofthe offerings (see Figure 4A and Table5A). At the same time, site B would besubstantially better off by improvingconsumer perceptions about costinformation, as can be concluded fromFigure 4B and Table 5B. Such animprovement would close the perceptualgap between site B and sites A and Cwith respect to this characteristic. Theabove-mentioned changes in consumerperceptions will eventually shift thepositioning of site B from the lower leftquadrant of Figure 3A toward the upperright quadrant.

As mentioned earlier, site C seems tobe overshooting the preferences of itspotential customers with the design ofshopping process implemented on itswebsite. It can be inferred from theanalyses that in order to improve itscompetitive standing, it should undertakesome major adjustments. Themanagement of this site should reducesuperfluous design elements among thecharacteristics that comprise factor 1.Apparently, scaling down the levels ofornamental design could help bring thesite to a better position as a computerhardware retailer. This will shift thepositioning of this site closer to the idealvector (ie Figure 3A).

CONCLUSIONExploring the competitive positioning ofinternet shopping sites is of greatimportance for managers. As intraditional retailing, positioning entailshow consumers view the competitiveintensity between rival retail outlets. Thisstudy investigates positioning in the



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11 Li et al. (2001) op. cit.12 Lohse, G. L. and Spiller, P. (1999) ‘Internet retail

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13 Jarvenpaa and Tractinsky (1999) op. cit.14 Jarvenpaa, S. L., Tractinsky, N., and Vitale, M.

(2000) ‘Consumer trust in an internet store’,Information Technology and Management, Vol. 1, Nos.1–2, pp. 45–71.

15 Lowengart, O. and Tractinsky, N. (2001)‘Differential effect of product category on shoppersselection of web-based store: Probabilistic modellingapproach’, Journal of Electronic Commerce Research, Vol.2, No. 4, pp. 12–26, available online athttp://www.csulb.edu/web/journals/jecr/issues/20014/paper2.pdf.

16 Jarvenpaa et al. (2000) op. cit.17 Gefen, D. (2000) ‘E-commerce: The role of

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18 Lowengart and Tractinsky (2001) op. cit.19 Jarvenpaa et al. (2000) op. cit.20 Zhang and von Dran (2001) op. cit.21 Zhang et al. (2001) op. cit.22 Lowengart and Tractinsky (2001) op. cit.23 Kotler, P. (2000) ‘Marketing management’, 10th edn,

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26 Ibid.27 Lowengart, O. and Menipaz, E. (2001) ‘Positioning

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28 Kotler, P., Jatusripitak, S. and Maesincee, S. (1997)‘The marketing of nations’, The Free Press, NewYork.

29 Wind, Y. and Mahajan, V. (2002) ‘Convergencemarketing’, Journal of Interactive Marketing, Vol. 16,No. 2, pp. 64–79.

30 Iacobucci, D., Arabie, P. and Bodapati, A. (2000)‘Recommendation agents on the internet’, Journal ofInteractive Marketing, Vol. 14, No. 3, pp. 2–11.

31 Berthon et al. (2000) op. cit.32 Lowengart and Tractinsky (2001) op. cit.33 Ibid.34 Grazioli, S. and Wang, A. (2001) ‘Looking

without seeing: Understanding unsophisticatedconsumers’ success and failure to detect internetdeception’, Proceedings of the 22nd International

The framework proposed in this studycan be augmented in various ways. Forexample, the analyses can be expanded toadditional product categories andmarkets. This can create a continuum ofproduct types and then assist ininvestigating the relationship betweenconsumer perceptions and preferencesalong this continuum. Anotherendeavour can focus on exploring thedynamics of consumer perceptions ofinternet shopping sites. In a young andrapidly changing environment such ase-retailing, a ‘standard’ or ‘acceptable’shopping site format has not beenestablished yet. A longitudinal study thatexplores such changes in perceptions canshed more light on the reactions ofe-retailers to changes in the environmentand the corresponding changes inconsumer perceptions and purchasingpreferences.

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2 Ibid.3 Patton, S. (2001) ‘Staying power’, CIO Magazine,

1st December,http://www.cio.com/archive/120101/power.html.

4 Jarvenpaa, S. L. and Tractinsky, N. (1999)‘Consumer trust in an internet store: A cross-culturalvalidation’, Journal of Computer-MediatedCommunication, Vol. 5, No. 2, available online:http://www.ascusc.org/jcmc/vol5/issue2/.

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8 Emmanouilides, C. and Hammond, K. (2000)‘Internet usage: Predictors of active users andfrequency of use’, Journal of Interactive Marketing, Vol.14, No. 2, pp. 17–32.



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39 Bellizzi, J. A., Krueckeberg, H. R., Hamilton, J. R.and Martin, W. S. (1981) ‘Consumer perception ofnational, private, and generic brands’, Journal ofRetailing, Vol. 57, pp. 56–70.

40 Lowengart and Tractinsky (2001) op. cit.41 Urban, G. and Hauser, J. R. (1993) ‘Design and

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42 Ibid.

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36 Lowengart and Tractinsky (2001) op. cit.37 Ibid.38 Wilkes, R. E. and Valencia, H. (1985) ‘A note on



e-retailers’ competitive intensity: a positioning mapping ... · of why consumers prefer one...

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