Image Based Fit

8/8/2019 Image Based Fit

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The impact of image-based brandcountry fit on global branding

Pascal Bruno

1

Doctoral Student, University of Cologne

Kristina Klein2

Doctoral Student, University of Cologne

Henrik Sattler3

Professor of Marketing and Management, University of Hamburg

Franziska Vlckner4

Professor of Marketing and Management, University of Cologne

The authors thank Young & Rubicam for providing access to the Brand Asset Valuator dataused in this study.

1 University of Cologne, Department of Marketing and Brand Management, Albertus-Magnus-Platz 1, 50923Kln, Germany, phone: +49 (0)221-470-1220, fax: +49 (0)221-470-5648e-mail: [email protected]

2 University of Cologne, Department of Marketing and Brand Management, Albertus-Magnus-Platz 1, D-50923 Cologne, Germany, Phone: + 49 (221) 470-2036, Fax: +49 (221) 470-5648,e-mail: [email protected]

3 University of Hamburg, Institute of Marketing and Media; Von-Melle-Park 5, 20146 Hamburg, Germany,

phone: +49 40 42838-6401, fax: +49 40 42838-3650, e-mail: [email protected] University of Cologne, Department of Marketing and Brand Management, Albertus-Magnus-Platz 1, 50923

Kln, Germany, phone: +49 (0)221-470-7886, fax: +49 (0)221-470-5648e-mail: [email protected]


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The impact of image-based brandcountry fit on global branding

Abstract

This research introduces a new diagnostic metric to evaluate global branding strategies:image-based brandcountry fit. The metric measures the extent to which consumersperceive a brand image as consistent with a country image. The authors conceptualize andempirically analyze the brandcountry fit for more than 1000 brands and three countries usingYoung & Rubicams Brand Asset Valuator database and additional consumer survey data.Furthermore, they examine the impact of the proposed new metric on brand performance andfind a significantly positive association between both constructs. This research thus providesmanagers with a new metric that helps them identify appropriate geographic markets for their

brands and offers key information about whether a global branding strategy is appropriate orif a brands positioning needs to be adapted to new target markets using a regional or local

branding strategy.

Keywords: Global brand positioning; Competition between global, regional, and localbrands; Global brand management and performance; Brandcountry fit


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1. Introduction

The globalization of markets is an indisputably powerful trend (Alden, Steenkamp, &Batra, 2003; Holt, Quelch, & Taylor, 2004) that has prompted many multinationalcorporations to shift their brand portfolios in favor of global brands (e.g., Schuiling &Kapferer, 2004). A global branding strategy is characterized by a standardized and often

uniform positioning and marketing strategy across all countries (Aaker & Joachimsthaler,1999). In contrast, local and regional brands adapt their positioning and marketing activities tospecific national (or smaller) markets (Douglas, Craig, & Nijssen, 2001; zsomer & Simonin,2004; Schuiling & Kapferer, 2004).

A particular challenge for global branding strategies is whether the standardizedpositioning fits well with consumers expectations in each country in which the global brandappears. International marketing theory generally proposes that companies should enter newmarkets that appear similar to their home countries (e.g., Stttinger & Schlegelmilch, 2000),

because they will enjoy the benefits of minimal perceptions of psychological distance in termsof cultural distance (Banerjee, 2008; Evanschitzky & von Wangenheim, 2006). This conceptalso transfers to the context of international branding strategies: The more similar the brand

and its values are to the country and the countrys values, the more readily consumers willaccept it, and the more likely the international branding strategy is to succeed.

This research introduces and empirically tests a new diagnostic metric to evaluate global branding strategies, which we refer to as image-based brandcountry fit, to measure theextent to which consumers perceive consistency between the brand and the country image.Perceived similarity or fit between two entities is a crucial concept in branding literature (e.g.,Aaker & Keller, 1990; Gupta & Pirsch, 2006; Gwinner & Eaton, 1999; Simonin & Ruth,1998; Vlckner & Sattler, 2006). For example, in the context of brand extensions (i.e., usingan existing brand to introduce a new product in a new category), empirical studies show thatconsumers assess the fit of the brand with the new category, taking into account their ownimages of both the brand and the category (Batra, Lehmann, & Singh, 1993; Park, Milberg, &Lawson, 1991). Image-based fit drives brand (extension) success (Batra, Lenk, & Wedel,2009); because the internationalization of a brand involves extending it into new geographicmarkets (i.e., geographic brand extension), we posit that the image-based fit between the

brand and a new geographic market similarly affect the brands performance in that market.We contribute to international branding literature by introducing our new diagnostic

metric, image-based brandcountry fit. Specifically, we conceptualize and empiricallyanalyze the fit between a brands and a countrys image for more than 1000 brands and threecountries (a Germanic (Germany), a Romanic (France), and an Asian (China) country) usingYoung & Rubicams Brand Asset Valuator (BAV) database, along with additional consumersurvey data. Furthermore, we examine the impact of the proposed new metric on brand

performance, which we assess using recently suggested survey-based measures based on thefive brand asset pillars of the BAV (e.g., Lehmann, Keller, & Farley, 2008). Such ananalysis of the image-based fit between existing brands and potential countries for entry canhelp managers identify appropriate geographic markets for their brands and offer keyinformation about whether a global branding strategy is appropriate or if the brands

positioning should adapt to new target markets using regional or local branding strategies.Our empirical analyses show that image-based brandcountry fit is significantly and

positively associated with brand performance. This result holds even when we control for theperceived country of origin of the brand. We thus provide managers with a new diagnosticmetric to help them predict the international target markets they should enter and make betterdecisions about appropriate international brand positioning strategies (i.e., standardization

versus adaptation). Building a global, standardized brand can be promising if the brandsimage fits reasonably well with various country images, but in low brandcountry fit


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situations, international brand adaptation or repositioning strategies in the specific countriesoffer stronger brand performance.

2. The brandcountry fit concept

We define brand image as perceptions of the brand, as reflected by the brand associations

held in consumer memory (Keller, 1993). Likewise, consumers attach certain ideas andimages to countries, according to their experiences and knowledge about the country. Theresulting country associations, held in the consumers memory, constitute the country image.We thus define country image as the total of all descriptive, inferential, and informational

beliefs [that a consumer] has about a particular country (Martin & Eroglu, 1993, p. 193).This definition implies that consumers also associate certain values with a country, such thatresidents of one country should share a common idea about the values epitomized by theirculture and country (e.g., Derr & Laurent, 1989; Hofstede 1980, 1991; Schwartz 1997, 1999;Steenkamp, 2001).

With these construct definitions, it is not surprising that existing brand image and countryimage measurement scales overlap. In her seminal work, Aaker (1997) identifies brand traits,

such as down-to-earth or original, that also describe cultural values (so-calledinstrumental values, Rokeach, 1973) and specific attributes, such as hard-working, whichoften appears in country image measurement scales (e.g., Laroche, Papadopoulos, Heslop, &Mourali, 2005).

In line with this reasoning, the BAV is based on the premise that both brand image andcountry image (as perceived by residents of that country) are multi-attribute constructs thatcan be measured through customer assessments of a set ofkimage items. We can characterizeeach brand and each country by computing overall item scores across respondents for all kimage items.1 For brand b (country c), bk (ck) is the vector of overall item scores for the kimage items that identifies the brands (countrys) location in a k-dimensional image space.We then can conceptualize image-based brandcountry fit as the measure of distance fromeach brand to each country location in the k-dimensional image space. Specifically, weemploy the Euclidean distance (Hair, Black, Babin, & Anderson, 2009), which in this case isthe root of the sum of the squared distances between the item score for the brand and the itemscore of the country across all k image items. The image-based brandcountry fit measureincreases as distance decreases.

We do not ask customers directly about their (dis)similarity judgments of specific brandcountry pairs on a set of image items. Rather, our image-based brandcountry fit metric is anindirect, multi-attribute measure (e.g., Sirgy et al., 1997). This multi-attribute approach offersinsights into the underlying reasons for high (versus low) image-based fit between brand band country c.

3. Method

3.1. Data

We use Young & Rubicams BAV, which monitors 41 brand image attributes (e.g.,trustworthy, down to earth, traditional). The BAV image attributes are not category specific

but rather measure universal brand characteristics (Mizik & Jacobson, 2008). Furthermore,the BAV data cover image assessments with respect to not only brands but also countries,such that respondents from a specific country indicate whether they perceive a brand asauthentic, traditional, fun, and so on, as well as whether they perceive their country asauthentic, traditional, fun, and so on, on binary (yes/no) scales (Mizik & Jacobson,

1 The survey scale for the image items is binary (yes/no). The overall score for image item kfor brand b (countryc) is the percentage of respondents who indicate yes in response to that item with respect to brand b (countryc).


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2008). The image items are identical for brands and countries. In addition, Young & Rubicammonitors five brand asset pillars to assess a brands performance in the market (Table 1; forapplications in other fields, see Lehmann, Keller, & Farley, 2008; Mizik & Jacobson, 2008):differentiation (i.e., perceived distinctiveness of the brand), relevance (i.e., personal relevanceand importance of the brand), esteem (i.e., level of regard consumers hold for the brand),

knowledge (i.e., overall familiarity), and energy (i.e., brand dynamism).

Brand Asset Pillar Underlying Metric Survey Scale BAV Data

Differentiation1. Unique2. Distinctive Yes/noYes/no % responding yes% responding yes

Relevance 1. Relevant to me 1-7 scale Average score

Esteem

1. Personal regard2. Leader3. High quality4. Reliable

1-7 scaleYes/noYes/noYes/no

Average score% responding yes% responding yes% responding yes

Knowledge 2. Familiarity with the brand 1-7 scale Average scoreEnergy

1. Innovative2. Dynamic Yes/noYes/no % responding yes% responding yes

Table 1: Young & Rubicams brand asset pillarsSource: Adapted from Mizik & Jacobson, 2008.

For our study, we use illustrative data and findings about brand and country image todemonstrate how image-based brandcountry fit can offer strategic guidance for internationaltarget market selection and insights into the degree of potential for a brands global

positioning. First, we compute the fit between brands and countries. Second, we examine theimpact of the proposed fit metric on brand performance, which we assess with the five brandasset pillars (e.g., Lehmann, Keller, & Farley, 2008). We analyze data about more than 1000

brands and three countries, a Germanic (Germany), a Romanic (France), and an Asian (China)country. The analyses for the non-German countries are still in progress; therefore, we focusin this extended abstract on the results for the German data set, which includes Young &Rubicams 41 country image items for Germany, 41 brand image items, and five brand asset

pillars for 408 food and beverage brands.Brands can be more or less strongly associated with a specific country of origin (e.g.,

Johansson, Douglas, & Nonaka, 1985; Verlegh & Steenkamp, 1999), and the perceivedcountry of origin of a brand might influence customers perceptions of its fit with a specific

country. For example, German brands may fit particularly well with Germany or Frenchbrands with France (Hubl & Elrod, 1999). We therefore complement our BAV data withconsumer survey data regarding perceived countries of origin. Specifically, we aim to showthat the proposed image-based brandcountry fit metric influences brand performance, evenwhen we control for perceptions about the brands country of origin. With the help of a

professional online market research firm, we collected country-of-origin data through a Web- based survey of 600 respondents (305 male, 295 female), selected according to a quotasampling procedure to represent German consumers in terms of age, gender, and region. Therespondents were randomly assigned to one of 14 questionnaire versions; we also randomizedthe order of the brands in all questionnaire versions. Each questionnaire version covered 32 ofthe 408 brands. We measured the perceived country of origin on two seven-point scales, on

which participants indicated their agreement or disagreement with the following statements(1 = totally disagree; 7 = totally agree): I clearly associate this brand with Germany and Iassociate typical German characteristics such as down-to-earthness, tradition, and reliability


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with the brand. The Cronbachs alpha is .947, and the exploratory factor analysis reveals that97.503% of total variance can be explained. Therefore, we average the two items to build amean index, which measures how strongly each of the 408 brands in the German BAV dataset is associated with Germany as the perceived country of origin.

3.2. AnalysisWe proceed through the following steps to derive the fit metric for each brandcountry

pair:(1) Compute for each brand b and image item kan overall item scorexbk across respondents.

Because Young & Rubicams survey scale for the image items is binary (yes/no), theoverall item scorexbk is the percentage of respondents who answered yes to item kwithrespect to brand b. The respondent-level data thus are accumulated to the brand level,which is our unit of interest.

(2) Compute for each country c and image item k an overall item score xck acrossrespondents. This overall item score is the percentage of respondents who answered yesto item kwith respect to country c.

(3) Derive the measure of distance between each brand and each country; image-basedbrandcountry fit increases as distance decreases. For the Euclidean distance,2 the fit ofbrand b with country c is the root of the sum of the squared distances between the itemscore for the brand and the item score for the country across all kimage items:

d(xb, xc) = (xbk xck)2

k=1

K

where:

d(xb, xc) = distance from brand b to country c.

xbk, xck= overall item score for image item kfor brand b andcountry c, (k = 1, 2, , 41).

(1)

To assess the impact of the proposed fit metric on brand performance, we regress thebrand asset pillars on image-based brandcountry fit, or the Euclidean distance, controllingfor the perceived country of origin of the brands. Because the five brand asset pillars usedifferent scales (e.g., seven-point versus percentage of respondents, see Table 1), we z-standardize the ten measures and compute an overall brand asset index as a measure of brand

performance, which is the equally weighted sum of the z-standardized items (Mizik &Jacobson, 2005, 2008). Our regression equation for the German BAV data therefore becomes:

BrandAssetIndexb = Constant + 1 BrandCountryFitb + 2 Country-of-Originb + b. (2)

4. Model estimation results

4.1. Fit measurementApplying Equation 1 to the German BAV data produces the fit values for each of the 408

brands (Fitmin = .444; Fitmax = .747; Fitmean = .590; SD = .060). Among the brands with thehighest fit values (i.e., lowest distance measures) are several German brands, such as Dr.Oetker (FitDr.Oetker = .457), Schwartau (FitSchwartau = .468), and Deutschlnder (FitDeutschlnder =.471). However, there are also non-German brands that achieve high perceived fit withGermany, such as the French brand Bonne Maman (FitBonneMaman = .444). This result providesthe first indication that our fit measure is not driven solely by country-of-origin effects.Among the brands with the lowest fit values (i.e., greatest distance measure), we identifyBacardi (FitBacardi = .707), Dom Perignon (FitDom Perignon = .742), and Punica (FitPunica = .747).

2 In contrast with correlational measures, which represent patterns, distance measures best indicate the conceptof proximity (Hair et al., 2009). We applied additional distance measures to the data (e.g., city-block distance)and find very similar results, so we focus on the Euclidean distance measure.


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4.2. Brand performance regression analysisTable 2 shows the results of the regression analysis for the German data. As expected, we

find a highly significant and substantial effect of image-based brandcountry fit on brandperformance. Because brandcountry fit decreases as the Euclidean distance increases, theeffect also is in the expected direction. The smaller the distance between the country image

and the brand image (i.e., the higher the fit of the brand image with the country image), thestronger is the brands performance (according to its asset index) in that country.The effect of the brandcountry fit metric also is highly significant, regardless of whether

we control for the extent to which each brand is associated with Germany. Not surprisingly,the regression coefficient of the fit metric decreases from -40.747 (SD = 4.950) to -31.365(SD = 4.627), when we include the country-of-origin measure in the regression analysis.However, we still find a substantial impact of the image-based brandcountry fit on brand

performance when we control for the extent to which each brand is associated with Germany.

CoefficientStandard

Deviation

Standardized

Coefficientt-Value

Constant 10.846 3.037 3.571 ***Image-based brandcountryfit (Euclidean distance)

- 31.365 4.627 - .291 - 6.779 ***

Country of origin(association with Germany)

2.023 .221 .393 9.151 ***

***Significant atp < .01.Notes: R = .290; Adjusted R = .286; variance inflation factor = 1.052.

Table 2: Results of regression analysis on brand performance

To test the robustness of our results, we also conducted a factor analysis on the 41 imageitems, thereby condensing similar image items into their underlying factors. The Euclidean

distance, based on factor scores, results in a comparable fit ranking and a similarly significantand substantial effect of the brandcountry fit metric on brand performance.

5. Discussion and implications

We introduce, with our image-based brandcountry fit measure, a new diagnostic ofglobal branding strategies. We analyze the brandcountry fit for more than 1000 brands andthree countries and find that the proposed new metric is significantly and positively associatedwith brand performance, even if we control for the perceived country of origin of the brand.Image-based brandcountry fit can help managers improve their decisions regarding theappropriate international positioning strategy for their brand. Building a global, standardized

brand offers a promising strategy if the brands image fits reasonably well with the respective

country images, whereas in low brandcountry fit situations, international brand adaptation orrepositioning strategies in the specific countries appear more appropriate. For example, abrand such as Bonne Maman fits well with both France and Germany, whereas a brand likeDr. Oetker indicates a very high image-based brandcountry fit score for Germany and

probably needs to be adapted to appeal to consumers in other countries.


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