relationships among brand identity, brand image and brand preference: differences between cyber and...

Relationships among Brand Identity, Brand Image and Brand Preference:Differences between Cyber and Extension Retail Brands over Time!

Maria Sääksjärvi a & Saeed Samiee b,!

a Department of Product Innovation Management, Delft University of Technology, Delft, The Netherlandsb Collins College of Business, The University of Tulsa, OK, USA

Available online 24 May 2011

Abstract

This study examines the relationships among brand identity, brand image, and brand preference in the context of cyber (pure online) andoffline-based extension (traditional brick-and-mortar to online) retail brands over time. We test a conceptual model with survey data gathered overthree time periods. Our results show that offline-based extension brands have an advantage over cyber brands when it comes to translating a brandidentity into a successful brand image, especially in the early Internet stages (i.e., introduction and elaboration stages). Offline-based extensionbrands gain positive spillover effects from their offline-based counterparts, but such effects take time, and are not evident in the early Internetstage. Both types of brands have to work hard in the introductory stage to create a successful brand image that manifests into consumer preferencefor the brand. With regards to Internet use, we found that cyber brands have a slight disadvantage when moving from the elaboration stage to thefortification stage.© 2011 Direct Marketing Educational Foundation, Inc. Published by Elsevier Inc. All rights reserved.

Keywords: Cyber brand; Extension brand; Brand identity; Brand image; Brand preference; Internet stage; Over time

Introduction

Brands are among the most important intangible assetspossessed by firms, contributing to greater value and marketsuccess (De Asis 2001; Shankar, Azar, and Fuller 2008). Thebrand premium is based on the equity that brands gain frombeing familiar, credible, and trustworthy, which lowersinformation costs and perceived risk involved with a purchase(Shankar, Azar, and Fuller 2008). A brand's worth is intimatelytied to consumer reactions to product(s) or service(s) carrying aparticular brand name. Recognizing this issue, much scholarlyinterest in the branding literature has centered on identifyingways of obtaining favorable consumer perceptions towardbrands. For example, Shankar, Azar, and Fuller (2008)demonstrated that consumers' perceptions of a brand contribute

to the brand's relative strength in a market, which drives abrand's value in the marketplace.

The Internet has provided a particularly fruitful arena forbranding (Bart et al. 2005). Online firms such as Amazon.cominvest a great deal of resources in building a brand that elicitsfavorable reactions from consumers, as such reactions drivefuture purchase behavior. Of particular importance is elicitingfavorable evaluations toward Internet brands that differ in theirinitial dispositions: cyber brands that only exist online (i.e., pureonline brands) and have to build a brand from inception, andoffline-based extension brands with marketplace counterparts(i.e., traditional brick-and-mortar brands) that serve as valuablebases for branding activities.

Prior studies have shown that cyber and offline-basedextension brands are likely to evoke different responses fromconsumers (Bart et al. 2005; Degeratu, Rangaswamy, and Wu2000). Several studies also suggest that offline-based counter-parts have an advantage over pure online brands. For example,Sääksjärvi and Samiee (2007) demonstrate that offline-basedbrands are likely to have an advantage over pure online brandssince they can gain positive spillover effects from their offline-

! The first author gratefully acknowledges funding from the Academy ofFinland.! Corresponding author at: Collins College of Business, The University of

Tulsa, 800 South Tucker Drive, Tulsa, OK 74104-9700, USA.E-mail address: [email protected] (S. Samiee).

1094-9968/$ - see front matter © 2011 Direct Marketing Educational Foundation, Inc. Published by Elsevier Inc. All rights reserved.doi:10.1016/j.intmar.2011.04.002

Available online at www.sciencedirect.com

Journal of Interactive Marketing 25 (2011) 169–177www.elsevier.com/locate/intmar

mailto:[email protected]

http://dx.doi.org/10.1016/j.intmar.2011.04.002




based counterparts. However, the extent to which suchadvantages are sustainable over time has not been examined.Although offline-based extension brands may have advantagesin the beginning of their “Internet stage,” such effects maydisappear after pure online brands have established themselvesas viable competitors. A study of brand perceptions over timeaffords us the opportunity to uncover whether such patternsexist with respect to cyber and offline-based extension brands.Accordingly, the objective of this study is to explore therelationships among brand identity, brand image, Internet use,and brand preference for cyber and extension retail brands overtime. The focus of the investigation is on the shift in consumerperceptions along the four constructs of interest to identify theadvantages of each brand type over time. This investigationmakes a contribution to the literature by examining howchanges in core branding components affect consumerpreferences for cyber and extension retail brands over time.

Conceptual Model

Degeratu, Rangaswamy, and Wu (2000) relied on informa-tion integration theory for examining how consumers integrateinformation available to them online as well as offline whenevaluating Internet brands. According to their findings, the typeof information that is important online and offline varies.Consumers often lack sensory information about online brandsand, thereby, to a much greater extent rely on other kinds ofinformation. Since consumers tend to lack any brand-relatedinformation regarding cyber retail brands at inception, othertypes of information such as price or product attributes becomemore important than the brand. This situation is exacerbatedwhen the brand is just establishing itself online. In contrast,consumers have experience with marketplace brands and,therefore, tend to rely more on the brand and their priorevaluations of the brand instead of searching for newinformation (e.g., pertaining to price or product attributes). Inthis case, the brand name becomes a surrogate for other types ofinformation that may be difficult to acquire. This suggests thatconsumers are likely to evaluate online and offline brandsdifferently, and that such evaluations may vary over time.

Aaker and Keller (1996), Aaker, Keller, and Joachimstaler(2000), and Keller (1993, 2003) identify two main sources ofconsumer preference for a particular brand: brand identity andbrand image. Brand identity is defined as a unique set of brandassociations that firms aim to create or maintain, whereas brandimage is defined as consumer perceptions regarding a brand(Keller 2003, p. 66). Brand preferences over time mainly shiftdue to changes in these two components (McEnally and deChernatony 1999). In line with Shankar, Azar, and Fuller(2008), we conceptualize brand identity and brand image ascontaining several subcomponents, with brand identity consist-ing of brand awareness, purpose, differentiation, and offerings(de Chernatony 1999), and brand image pertaining to brandcredibility, brand character, consumers' overall attitude towardsthe brand, and consumers' feelings for the brand (de Chernatony1999). In our conceptualization, brand image serves as amediator between brand identity and preference. Brand identity

represents how firms aspire to be perceived, whereas brandimage refers to how they are perceived. Brand identity does notdirectly influence consumer preferences. Rather, consumersinterpret the firm's identity and translate it into an image (Keller2003), and the image in turn influences consumer preferences(Keller 1993; Martínez and de Chernatony 2004).

We also include Internet use in our model as a component ofpreference to convey the fact that consumers' experiences with aparticular shopping channel may influence their preferencestowards brands in that channel (Bart et al. 2005; Eastlick and Lotz1999). Studies addressing Internet use show that consumers whoextensively use a particular channel (such as the Internet) aremorelikely to differentiate the brands sold through that channel thanshoppers who rarely use the channel (e.g., Eastlick and Lotz1999). The marketing literature suggests four main componentsthat drive the use of a given shopping channel: usage history, timespent (use duration), number of purchases (familiarity withchannel), and spending (Bart et al. 2005; Eastlick 1996; Shimet al. 2001). The proposed conceptual framework modelingconsumer perceptions of cyber and offline-based extensionbrands over time is shown in Fig. 1.

Hypotheses

On the Internet, a brand can be said to evolve in three stages:introduction, elaboration, and fortification, analogous to Park,Jaworski, and MacInnis' (1986) model of brand lifecycles. In theintroductory stage, firms need to develop a set of activities toestablish a brand's positioning in the market. As the externalattributes are more important than brand-related aspects in thisphase, it is important to develop a strong identity that can leveragepositive associationswith the brand (Degeratu, Rangaswamy, andWu 2000; Keller 2003). In the elaboration stage, when the brandis becoming more important, the strategy should shift towardenhancing the value of the brand. In this stage, the key sources forbrand equity should be preserved and amplified (Shankar, Azar,and Fuller 2008) and the positive and unique brand associationscreated in the introductory stage should be fortified (Keller 1993).In the fortification stage, the brand is linked with the firm's other

Brand identity Brand image

Brand preference

Internet use

Brand type

Internet stage

H1

H1

H2

H3

H2

H3

Fig. 1. Measurement model.

170 M. Sääksjärvi, S. Samiee / Journal of Interactive Marketing 25 (2011) 169–177

brands to reinforce all products in the brand's portfolio. In eachInternet stage, consumer preference for a particular brand is likelyto be sensitive to the amount of information available about thebrand, potentially triggering changes in consumer preferencesover time (Degeratu, Rangaswamy, and Wu 2000).

The first hypothesis of our model concerns changes in brandidentity over the stages of the Internet. Degeratu, Rangaswamy,andWu (2000) suggest that establishment of an identity is likelyto be more important in the introductory stage, when consumersare less familiar with a brand and its offering (Park, Jaworski,and Maclnnis, 1986). The purpose of brand identity is toestablish a relationship between the customer and a brandwhich, in turn, expresses the brand's aspirations (Aaker andKeller 1996). As such, firms need to create a unique positioningof the brand by communicating the brand's benefits and itsattributes to consumers (McEnally and de Chernatony 1999).

In an online environment, the effort required for cyber andoffline-based extension brands to create a meaningful brandidentity is likely to be unequal. Cyber brands have to work muchharder than offline-based extension brands to attain a positivepreference because they have to create an identity from scratch.In contrast, an extension brand's connection to a brick-and-mortar parent allows customers to transfer existing familiarityand attitudes regarding the offline-based brand to the firm'sonline extension brand at inception. Offline-based extensionbrands have parent brands with existing brand equity availablefor creating an identity for their new Internet-based brand, whichresults in positive spillover effects from the parent to theextension brand (Aaker and Keller 1990). In a seminal study,Shankar, Azar, and Fuller (2008) demonstrate the importance ofspillover effects of brands from other categories than the focalbrand. Focusing on Allstate Insurance, they showed thatspillover effects occurred in many product categories (fromauto to property and life insurance, and from property to autoinsurance), and that such effects were positively and signifi-cantly related to brand strength. As such, offline-based brandsbenefit from leveraging their reputation in their online channel,thereby alleviating information asymmetries across markets. Ascyber brands do not have spillover effects, they have to investmore in the creation of an identity than offline-based extensionbrands. This effect is likely to decrease over time, once cyberbrands have created an online identity that can successfullyconvey their image. More formally stated:

H1. The decrease in the effect of brand identity on brand imagebetween the stages of the Internet is more pronounced for cyberthan for offline-based extension brands.

We further propose that brand image is likely to demonstratea stronger relationship with consumer preferences in the earlyInternet stages. Following Degeratu, Rangaswamy, and Wu(2000), within the context of a new brand and a new shoppingchannel, consumers have to establish attitudes based on theavailable information. Creating a memorable brand imagerequires more investment from a cyber brand than from anextension brand. Offline-based extension brands leverage theiroffline-based counterparts to create associations in consumers'minds (Aaker and Keller 1990); in forming an image for an

extension brand, consumers only need to update their existingimage of the brand's offline-based counterpart. For example, increating an image for Barnesandnoble.com, consumers canaccess their image of Barnes and Noble's physical stores andupdate it with the online association (cf. Boush and Loken 1991).In contrast, when a cyber brand is introduced, consumers have tocreate an entirely new image for it. No previous categoryassociation exists to which consumers can relate and, therefore,they have to create a new category for evaluating the brand(Boush and Loken 1991). In an online environment, informationis relatively easy to obtain and, as more information aboutrelevant attributes becomes available, the relative importanceweights of the attributes already available should decrease(Degeratu, Rangaswamy, and Wu 2000). Hence, we propose:

H2. The decrease in the effect of brand image on brandpreference between the stages of the Internet is morepronounced for cyber than for offline-based extension brands.

As consumers become accustomed to a particular retailchannel and feel comfortable in using it, the impact of theshopping channel is likely to grow stronger over time (Eastlick1996). For example, Bart et al. (2005) found a positiverelationship between online shopping experience and consumerintentions to buy from a site. Retail channel use is likely to havea larger impact on offline-based extension brands than on cyberbrands. Once consumers start using a cyber brand, they willautomatically associate the brand with the Internet and theonline environment. Brands such as Amazon.com do not existin other contexts and marketplaces and their only shoppingchannel association is with the Internet. In contrast, consumersare already accustomed to using the offline-based counterpart ofextension brands. In this case, Internet use is likely to have asignificant impact on whether they switch from using thephysical outlet to using the Internet channel. Consumers whoare already comfortable using the Internet for shopping are alsomore likely to start using the Internet extension of offline-basedbrands. Thus, we posit:

H3. The decrease in the effect of Internet use on brand preferencebetween the stages of the Internet is more pronounced for cyberthan for offline-based extension brands.

Methodology

The centrality of Internet brand preference in this studyrequired data collection for multiple cyber and offline-basedextension brands at three different points in time. The data forcyber and offline-based extension brands were gathered sepa-rately. The first set of data for this study was collected in 2000,when Internet brands were in an early stage of development. Thesecond and third waves of data were collected in 2005 and 2009,when the use of the Internet had proliferated and online purchaseshad become routine for a significant portion of the population.All three sets of data were gathered from respondents with accessto a computer and the Internet in a Midwestern city which isfrequently targeted as a test market.

171M. Sääksjärvi, S. Samiee / Journal of Interactive Marketing 25 (2011) 169–177

Data pertaining to cyber and offline-based extension brandsideally should be gathered from individuals who have had theopportunity to be exposed to these brands. Thus, as recom-mended by Churchill (1999), this study relied on a non-probability judgment or purposive sampling method in whichrespondents were screened for computer literacy, Internetaccess, and Internet shopping experience. All respondentswere working adult businesspeople from local firms who wereasked to complete two sequential questionnaires administeredabout three weeks apart. Thus, each respondent completed onequestionnaire concerning cyber brands and approximately threeweeks later a second one regarding offline-based extensionbrands. With the exception of brand names pertaining to cyberand offline-based extension brands, the two research forms wereidentical. We measured respondents' familiarity with theparents of offline-based extension brands on a seven-pointscale. The mean for brand familiarity was 5.9, indicating that theuse of the extended brands in this study was appropriate. For thefirst data collection effort in 2000, 114 respondents completedboth forms. In 2005, two completed forms were received from99 consumers. The third data collection in 2009 consisted of150 respondents. Considerable turnover in employment oc-curred between the first and second data collection. As a result,and consistent with other longitudinal studies that rely onprimary data, our consumer samples differed somewhat over thethree data collection periods.

Questionnaire Development

All fourteen Internet retail brands used in the study – bothcyber brands and offline-based extension brands – were amongthe top 100 retail eCommerce sites according to the rankings bytheNationalRetail Federation for 1999 (Reda 1999). Even thoughall Internet brands were in their relative infancy in 1999, the top100 brands were among the most recognized and widely usedcyber and offline-based extension brands. All of the brands usedin the study were launched between the years 1995 to 1999. Thus,in 1999, all of the brands targeted in this investigation were intheir introductory stage.

Tominimize product category bias or retail brand salience andinterest among respondents, for each cyber brand included in thestudy, a closely matched counterpart extension brand was alsotargeted for investigation. The seven retail cyber brands selectedwere matched as closely as possible with a retail extension brand:CDNow.com with TowerRecords.com, eToys.com with Toys-R-Us.com, Amazon.com with BarnesandNoble.com, Egghead.comwithCompUSA.com, Shopping.comwithWalMart.com, eTrade.com with CharlesSchwab.com, and Expedia.com with Citibank.com. Some cyber brands included in the first wave of datacollection were acquired or forced out of business and liquidated.These brands were dropped from our model.

Cyber and offline-based extension brands were matched alongseveral characteristics. First, all brands selected were in the samebroad industry, i.e., retail business. Second, the retail focus foreach pair of brands (that for which they are known) was alsocloselymatched. Third, we broadlymatched each brand's focusedoffering (e.g., the breadth of book titles offered for both cyber and

extension booksellers was extensive). Fourth, cyber and theirmatching offline-based extension brandswere introduced at aboutthe same time. Fifth, cyber brands and their extensioncounterparts selected for the study were at the same stage ofdevelopment on the Internet (that is, had existed for approxi-mately the same length of time during which they were activelyengaged in brand building programs). Two points regarding thematching of cyber and offline-based extension brands arenoteworthy. First, Amazon.com had a slight advantage in termsof its inception over BarnesandNoble.com, and second, Expedia.com and Citibank.com were matched as purveyors of services.

The cyber and extension brand questionnaires were pretestedfor content validity and relevance on a pretest sample of male andfemale subjects 18 to 50 years old. The pretest subjects did notreport any problems in completing the research instruments and,hence, no major change to the forms was deemed necessary.

Measures

For each retail brand type (cyber and extension), summatedscales across all brands were used to develop the indicators for thefour main constructs of this study: brand identity, brand image,brand preference, and Internet use. These measures are shown inAppendix 1. Each item of each construct was measured using aseven-point rating scale, ranging from strongly disagree tostrongly agree. The only exception was brand attitude, which wasmeasured along five dimensions (good-bad, like-dislike, pleasant-unpleasant, high quality-low quality, and good value-poor value)adapted fromMitchell and Olson (1981) and Holbrook and Batra(1987), using seven-point scales to represent brand image.Overall, the database for this study includes 140 separatemeasures across fourteen Web brands across three time periods.

Data Analysis

Partial least squares (PLS) modeling was used to test thehypotheses. PLS is a structural equation modeling technique withseveral key advantages over LISREL for the present study. PLSmodeling accommodates the use of categorical variables and, incontrast to LISREL, does not demand a large sample size (Chin1998). Further, PLS avoids factor indeterminacy and inadmissiblesolutions (Fornell and Bookstein 1982; Gopal, Bostrom, andChin1992). It is well suited for marketing data which often do notsatisfy the requirements of multinormality, interval scaling, or thesample size for maximum likelihood estimation (Fornell andBookstein 1982). PLS results are also easier to interpret, as pathcoefficients are like regression weights (equivalent to standard-ized beta weights in regression) and indicator loadings asprincipal component loadings within the context of the model(Falk and Miller 1992; Gopal, Bostrom, and Chin 1992). Despiteits advantages, PLS modeling has certain limitations. PLS is notas robust as LISREL and does not focus on estimating causalpaths. Instead, a key purpose of PLS is to test portions of atheoretical model, making it better suited for exploratory ratherthan confirmatory research (Chin 1998).

In conducting PLS analysis, constructs can be defined aseither reflective or formative. Reflective constructs are typical


of classical test theory and factor analysis models and areinvoked in an attempt to account for observed variances orcovariances (Fornell and Bookstein 1982). Preference wasmodeled as a reflective construct, as its indicators are assumedto covary. In contrast, formative constructs are not designed toaccount for observed variables; that is, they are used tominimize residuals in the structural relationship (Chin 1998).Here, the constructs are conceived as consisting of separatecomponents that need not covary, as they measure differentparts of the construct (Chin 1998). In the present study, brandidentity, brand image, and Internet use were measured asformative constructs. All of these variables consist ofindependent components that jointly form the constructs. Themodel fulfills the criteria for the validation of formativeconstructs, as the main dependent variable is represented by areflective construct (Diamantopoulos and Winklhofer 2001).

Results

The results for the measurement model are shown in Table 1.To validate the reflective indicators, factor loadings, averagevariance extracted (AVE), and composite reliability areexamined. The results pertaining to preference, a reflectivemeasure in our model, are also shown in Table 1. All valuesexceed their cut-off points; that is, .60 for loadings [!], .70 forcomposite reliability ["c], and .55 for AVE. For formativeindicators, only the weights can be interpreted in the context ofthe model. The weights obtained in time 1 (t1), time 2 (t2), andtime 3 (t3) are shown in Table 2.

To test our hypotheses, we coded brand type (cyber vs.extension) and Internet stage (introduction vs. elaboration stage,elaboration vs. fortification stage, and introduction vs. fortifi-cation stage) as moderating variables. The moderating variableswere estimated for each of the paths in the model, as shown inFig. 1. The results are shown in Table 3.

Hypothesis 1 posits that the decrease in the effect of brandidentity on brand image between Internet stages is morepronounced for cyber than for offline-based extension brands.The brand type!brand identity interaction on brand image wasnot significant between t1 and t2 (pN .10), but marginallysignificant between t2 and t3 (!=.353, t=1.20, pb .10), as wellas between t1 and t3 (!=.353, t=1.20, pb .10). These results

show that cyber brands have to work harder than offline-basedextension brands to translate their identity into a successfulbrand image, especially when the market has developed. Thiseffect is likely due to the positive synergistic effects offline-based extension brands gain over time as spillover from theiroffline-based counterpart. The Internet stage!brand identityinteraction on brand image was significant and negativebetween t1 and t2 (!=!.052, t=2.58, pb .01), and marginallysignificant between t2 and t3 (!=!.479, t=1.37, pb .10) as wellas between t1 and t3 (!=!.353, t=1.39, pb .10). These findingsdemonstrate that brands have to work less on translating anidentity into a brand image as they develop on the Internet.Taken together, these results show that although cyber brandshave to work harder than offline-based extension brands intranslating their brand identity into a brand image, theadvantage held by offline-based extension brands diminishesover time. The main effect of brand type was not significantbetween t1 and t2 (pN .10), but gained significance over time andbecame marginally significant between t2 and t3 (!=!.519,t=1.32, pb .10), as well as between t1 and t3 (!=!.410, t=1.32,pb .10). The main effect of Internet stage was significantbetween t1 and t2 (!=.121, t=2.78, pb .01), and marginallysignificant between t2 and t3 ( !=.513, t=1.44, pb .10) as wellas between t1 and t3 (!=.491, t=1.57, pb .10). The main effectof brand identity was significant between t1 and t2 (!=.624,t=9.90, pb .01), t2 and t3 (!=.263, t=2.68, pb .01), and t1 andt3 (!=.543, t=8.59, pb .01). These results provide support for H1.

Hypothesis 2 posits that the decrease in the effect of brandimage between the Internet stages is more pronounced for cyberthan for offline-based extension brands. The brand type!brandimage interaction on brand preference was not significant at anytime (pN .10). The Internet stage!brand image interaction onbrand preference was significant between t1 and t2 (!=!.017,t=1.64, pb .05) and between t1 and t3 (!=!.611, t=3.35, pb .01),but not between t2 and t3 (pN .10), showing that brands, in general,have to work harder on building a brand image in their earlystages on the Internet. The main effects of brand type were notsignificant at any time (pN .10),whereas themain effect of Internetstage was marginally significant between t1 and t2 (!=.117,t=1.50, pb .10), significant between t1 and t3 (!=.468, t=3.07,pb .01), and non-significant between t2 and t3 (!=.190, t=.27,pN .10). These results provide partial support for H2.

Table 1Loadings, composite reliability, and AVE for preference.

Brand type Construct Indicators t1 t2 t3

! "c AVE ! "c AVE ! "c AVE

Cyber brands Preference Visits .76 .89 .62 .72 .87 .57 .75 .86 .55Overall preference .78 .67 .67Likelihood of buying .85 .72 .80Certainty of buying .86 .87 .72Previous purchases .69 .78 .74

Extension brands Preference Visits .82 .90 .63 .84 .88 .59 .67 .87 .57Overall preference .65 .84 .74Likelihood of buying .89 .68 .79Certainty of buying .84 .69 .87Previous purchases .75 .78 .70


Hypothesis 3 proposes that the decrease in the effect ofInternet use between the Internet stages is more pronounced forcyber than for offline-based extension brands. We did not findsupport for this hypothesis. The only significant interactioneffect was between t2 and t3 for the brand type! Internet use,which was marginally significant (!=!.311, t=1.32, pb .10).The main effect of Internet use was non-significant between t1and t2 (pN .10), significant between t2 and t3 (!=.561, t=2.56,pb .01), and marginally significant between t1 and t3 (!=.086,t=1.27, pb .10). H3 is thus not supported.

Validation

As our database includes two purveyors of services fromdifferent industries (i.e., Citibank and Expedia), we re-examined our findings by excluding these two brands. Theindustries to which the remaining pure online and offline-basedbrands belong were matched and, therefore, consistent. Fromamong the leading brands reported by Stores (Reda 1999),

Citibank and Expedia were broadly matched as service firms,although the industries in which they operate (travel andbanking) may give rise to differences in brand assessment. Weconducted a robustness check to determine whether our resultsfor the proposed model would remain consistent if these twobrands were excluded from analysis. The results in Table 4show that there are slight differences in the beta coefficientswithout these brands. These differences improved the proposedmodel. The proposed effects for H1 are strengthened (seeTable 4) by increasing the significance level of the brandtype!brand identity interaction effect as well as the Internetstage!brand identity interaction effect on brand image frompb .10 to pb .05. The main effect of brand type wasstrengthened in the same manner (i.e., the significance levelincreased from pb .10 to pb .05). These results do not changethe findings reported earlier with Expedia and Citibank includedin the data; however, their exclusion improved the proposedmodel. Therefore, our proposed model can be considered robustacross brands.

Table 2Changes in measurement model over time.

Cyber brands Offline-based extension brands

Construct Indicators Weightt1

Weightt2

Weightt3

Weightt1

Weightt2

Weightt3

Brand identity Brand awareness .314 .496 .422 .356 .093 .146Brand purpose .461 .724 .884 .685 .724 .798Brand differentiation .344 .192 .176 !.029 .118 .183Brand offerings .273 .124 !.272 .111 .302 .229

Brand image Brand credibility .160 .311 .736 .712 !.229 .488Brand character !.094 .200 .062 !.149 !.376 .157Overall attitude toward the brand .005 .130 .367 .064 .362 .386Brand feelings .790 .637 .093 .264 .991 .365

Internet use Usage history !.142 .562 .092 .172 .231 !.548Time spent (use duration) .210 .454 .108 !.531 !.852 .884Number of purchases (familiarity with channel) !1.169 !.409 .185 .916 !.166 .314Spending 1.717 .770 .824 .225 .581 !.487

Preference Visits .192 .169 .292 .233 .303 .128Overall preference .346 .328 .335 .267 .256 .321Likelihood of buying .289 .194 .282 .294 .316 .328Certainty of buying .286 .373 .174 .286 .216 .319Previous purchases .136 .254 .270 .177 .216 .203

Table 3Changes in model across all timelines.

t1 vs t2 t2 vs t3 t1 vs t3 Hypothesis

! t p ! T p ! t p

Brand type!brand identity ! brand image .336 .73 Ns .504 1.22 b.10 .405 1.26 b.10 H1Internet stage!brand identity ! brand image !.052 2.58 b.01 !.479 1.37 b.10 !.434 1.39 b.10 H1Brand type !.292 .66 Ns !.519 1.32 b.10 !.410 1.32 b.10 Main effectInternet stage .121 2.78 b.01 .513 1.44 b.10 .491 1.57 b.10 Main effectBrand identity .624 9.90 b.01 .263 2.68 b.01 .543 8.59 b.01 Main effectBrand type!brand image ! brand preference !.369 .58 Ns !.069 .12 Ns !.302 .59 Ns H2Internet stage!brand image ! brand preference !.017 1.64 b.05 !.302 .45 Ns !.611 3.35 b.01 H2Internet stage! Internet use ! brand preference .054 .29 Ns .035 .15 Ns .010 .05 Ns H3Brand type! Internet use ! brand preference .175 1.17 Ns !.311 1.32 b.10 .134 .84 Ns H3Brand type .293 .46 Ns .515 .89 Ns .245 .48 Ns Main effectInternet stage .117 1.50 b.10 .190 .27 Ns .468 3.07 b.01 Main effectBrand image .518 5.74 b.01 .297 2.44 b.01 .594 6.30 b.01 Main effectInternet use .078 1.06 b.10 .561 2.56 b.01 .086 1.27 b.10 Main effect


Discussion and Conclusions

Our goal in this study was to investigate consumer perceptionsof online retail brands by focusing on the shift in perceptions overthree main variables (brand identity, brand image, and Internetuse) as brands transition to different stages on the Internet. Weexamined these changes across two different types of brands:cyber brands and offline-based extension brands. The presentresearch contributes to the extant literature on branding byshowing that offline-based brands have an advantage over cyberbrands when it comes to translating a brand identity into asuccessful brand image. Traditional offline-based brands gainpositive spillover effects from their offline-based counterparts;however, such effects take time and are not evident in the earlyInternet stage. Both types of brands have to work hard in the earlyInternet stage to create a successful brand image that manifestsinto consumer preference for the brand. With regards to Internetuse,we found that cyber brands had a slight disadvantage betweent2 and t3. (i.e., when moving from the elaboration stage to thefortification stage), probably due to the umbrella brandingstrategies used by offline-based brands.

The results show that for cyber brands, the largest effortinvolves building brand identity, especially in the introductoryInternet stage (i.e., when the market is developing). Cyber brandsneed to create market awareness and brand equity from inceptionand, for these brands, a focus on brand identity is more importantthan a focus on other brand components. In contrast, marketingstrategies for offline-based extension brands should encourageconsumers to shop online in addition to the physical retaillocations, with the ultimate goal of using the extension brand as ameans of gaining additional market penetration and share.

Theoretical and Managerial Implications

Our study makes a theoretical contribution by demonstratingthat offline-based extension brands gain positive spillover effectsfrom their brick-and-mortar parents, but that such effects take

time tomaterialize. Further, our results indicate that the advantageheld by the offline-based brick-and-mortar brands does not extenditself into brand image; both types of brands have to make aneffort to create a successful brand image, especially in the earlyInternet stages. Our results also show that when moving from theelaboration stage to the fortification stage, cyber brands have aslight disadvantage with respect to Internet use, as this may be thetimewhen offline-based brands engage in expansion via umbrellastrategies. These results bring novel insight into the literature onbranding, and suggest insights for researchers who wish to take alongitudinal perspective on branding efforts.

For managers, this study shows that consumers may reactdifferently to brand-building efforts across Internet stages. In theearly stages, much effort needs to be expended on establishing aviable identity (especially in the case of cyber brands) and afavorable image for the brand. In later stages, the focus should shifttoward cross-channel strategies to gain favorable spillover effects.Cyber brands, by virtue of lacking offline-based counterparts, canimitate this strategy by cross-advertising their brands andcooperating with offline-based entities, e.g., by engaging in co-branding or ingredient branding strategies. Such strategies provideconsumers with opportunities to associate cyber brands with themore tangible offline-based entities. In general, brand managementover time poses several managerial problems. Awareness regardingthe manner in which consumer preferences for online brandschange over time affords firms the opportunity to more effectivelymanage brand-building efforts, for example, by allocatingmarketing resources and brand-building budgets to the brandingcomponents that will have the greatest influence.

Limitations

Our results reflect several shortcomings in the design andimplementation of this research. First, as with any cyclicalconcept, the necessary time period to transition from theintroductory period to later stages is undefined. Although ourestimates of the necessary time lapse between stages are

Table 4Model robustness check: Expedia/Citibank excluded.

t1 vs t2 t2 vs t3 t1 vs t3 Hypothesis

! t p " T p ! t p

Brand type!brand identity ! brand image .328 .71 Ns .553 1.64 b.05 .522 1.65 b.05 H1Internet stage!brand identity ! brand image !.055 2.60 b.01 !.552 1.66 b.05 !.423 1.70 b.05 H1Brand type !.288 .70 Ns !.523 1.72 b.05 !.511 1.82 b.05 Main effectInternet stage .124 2.76 b.01 .511 1.44 b.10 .488 1.55 b.10 Main effectBrand identity .637 9.92 b.01 .290 2.68 b.01 .555 8.62 b.01 Main effectBrand type!brand image ! brand preference !.352 .77 Ns !.122 .84 Ns !.332 .84 Ns H2Internet stage!brand image ! brand preference !.022 1.65 b.05 !.289 .63 Ns !.590 3.01 b.01 H2Internet stage! Internet use ! brand preference .084 .36 Ns !.046 .30 Ns .009 .05 Ns H3Brand type! Internet use ! brand preference .168 1.13 Ns !.310 1.40 b.10 .145 .90 Ns H3Brand type .287 .53 Ns .510 .85 Ns .257 .53 Ns Main effectInternet stage .125 1.60 b.10 .189 .26 Ns .452 3.00 b.01 Main effectBrand image .527 5.78 b.01 .286 2.32 b.01 .599 6.35 b.01 Main effectInternet use .090 1.10 b.10 .546 2.49 b.01 .088 1.28 b.10 Main effect


adequately long and reasonable, the newness of the Internetmakes such estimates particularly vulnerable to error. Thus, wecannot be certain that longer (shorter) periods between stages ofdata collection would not result in somewhat different outcomes.Second, despite our efforts to closely match cyber and offline-based extension brands, the retail brands comprising cyber andoffline-based extension brands are not identical. In the case ofExpedia and Citibank, which operate in different industries, weset these firms aside and successfully retested and validated ourmodel. Still, a comparison of non-identical sets of entities overtime may in part explain the differences in effects across brand

types and Internet stages. That is, we cannot rule out alternativereasons for differences in our results. Third, our three samples ofrespondents were matched along several key dimensionsimportant to this investigation; however, the subjects andresponse rates differed somewhat over time. Ideally, data shouldbe gathered from identical samples across time periods. Fourth,alternative paths and explanations for differences across Internetstages cannot be ruled out. As such, non-causality is also alimitation in our study. Finally, single-item measures, even whenused in formative constructs, are prone to measurement error andfuture research should make greater use of multi-item measures.

Appendix 1. Variables Used in the PLS Models

Respondents were asked to indicate the extent to which they agree/disagree with the following statements on 7-point scales foreach of the seven cyber brands and seven offline-based extension brands (i.e., 140 individual measures). Separate questionnaires forcyber and offline-based extension brands were administered three weeks apart. The same questions and brands were used in 2000,2005 and 2009. The constructs and their corresponding question(s) are shown below:

Operationalization of constructs.

Construct Items(summated scales across brand types)

Question(identical questions for each of the 14 brands for t1 and t2)

Brand identity Brand awareness I am familiar with this web brandBrand purpose This web brand has a well-defined use/purposeBrand differentiation This web brand is uniquely different from its competitors

(stores, catalogs, or web sites)Brand offerings This web brand offers a broad range of products

Brand image Brand credibility This web brand is credibleBrand character This web brand lacks character (reverse coded)Overall attitude toward the brand 5-item measure (7-point scale), summated for each brand

Good-BadLike–dislikePleasant–unpleasantHigh Quality–low qualityGood Value–poor value

Brand feeling I have a good feeling about this web brandInternet use Usage history How long have you been using the Internet?

Time spent (use duration) Approximately how much time do you spend on the Internet each week?Number of purchases (familiarity with channel) During 1999/2004, how many times have you purchased something for

personal or household use using the Internet?Spending How much have you spent on personal or household purchases on the

Internet in 1999/2004?Brand preference Visits I visit this web site frequently

Overall preference I prefer this web brand over others like itLikelihood of buying It is likely that I will purchase this brand/do business with this web brandCertainty of buying I definitely will buy from this web brandPrevious purchases I have previously purchased from this web brand

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