gains and losses from the brand origin misinterpretation

23
Brand Strength and Country-of-Origin Image 95 R ecent country-of-origin (COO) research has shown that consumers often do not know the true origin of many (even well-known) brands and that they frequently categorize a brand to the wrong COO. For example, in the United States, Samiee, Shimp, and Sharma (2005) report average correct identification rates of only 49% for 40 domestic brands and 22% for 44 brands from seven other countries. Another study by Hennebichler (2007) in Australia reveals correct brand origin identification rates between 17% and 54% depending on the product category involved; notably, the brands examined were those actually owned by con- sumers in the various product categories examined. 1 Similar findings indicating the limited ability of con- sumers to classify brands according to their true origins have been reported by Paswan and Sharma (2004) in India, Anderson Analytics (2007) in the United States, and Balabanis and Diamantopoulos (2008) in the United Kingdom. Although some of these studies (notably, Balabanis and Diamantopoulos 2008; Samiee, Shimp, and Sharma 2005) attempted to identify antecedent variables (e.g., ethnocentrism, sociodemographic characteristics) that potentially affect consumers’ COO classification ability , little is known about the consequences of brand origin misclassification in terms of outcome variables such as brand image evaluations or buying intentions. However , this is an important issue because “if consumers associ- ate a brand with the wrong COO, their brand evalua- tions (and subsequent buying decisions) could differ from what they would have been if the correct COO had been identified” (Balabanis and Diamantopoulos Gains and Losses from the  Misperception of Brand Origin:  The Role of Brand Strength and Country-of-Origin Image George Balabanis and Adamantios Diamantopoulos ABSTRACT Mounting empirical evidence shows that consumers often associate brands with the wrong country of origin (COO) or are unable to classify a brand to any COO. In this study, the authors investigate the consequences of brand origin mis- classification and nonclassification on consumers’ brand image evaluations and associated purchase intentions. Draw- ing from categorization theory, the authors test hypotheses regarding the impact of brand strength and COO image on misclassification gains and losses on a sample of U.K. consumers. The results show that both misclassification and non- classificat ion have mostly adverse consequences on both brand evaluations and purchase intentions. The findings fur- ther show that strong brands are not immune to misclassification and stress the importance of ensuring that consumers are aware of a brand’s true COO. Keywords: country of origin, brand origin, recognition, branding  Journal of International Marketing ©2011, American Marketing Association Vol. 19, No. 2, 2011, pp. 95–116 ISSN 1069-0031X (print) 1547-7215 (electronic) George Balabanis is Professor of Marketing, Cass Business School, City University of London (e-mail: g.balabanis@ city.ac.uk). Adamantios Diamantopoulos is Professor and Chair of International Marketing, University of Vienna (e-mail: [email protected]).

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Brand Strength and Country-of-Origin Image 95

R

ecent country-of-origin (COO) research hasshown that consumers often do not know the true

origin of many (even well-known) brands and thatthey frequently categorize a brand to the wrong COO.For example, in the United States, Samiee, Shimp, andSharma (2005) report average correct identificationrates of only 49% for 40 domestic brands and 22% for44 brands from seven other countries. Another study byHennebichler (2007) in Australia reveals correct brandorigin identification rates between 17% and 54%depending on the product category involved; notably,the brands examined were those actually owned by con-

sumers in the various product categories examined.1

Similar findings indicating the limited ability of con-

sumers to classify brands according to their true originshave been reported by Paswan and Sharma (2004) in

India, Anderson Analytics (2007) in the United States,and Balabanis and Diamantopoulos (2008) in the

United Kingdom.

Although some of these studies (notably, Balabanis andDiamantopoulos 2008; Samiee, Shimp, and Sharma2005) attempted to identify antecedent variables (e.g.,ethnocentrism, sociodemographic characteristics) thatpotentially affect consumers’ COO classification ability,little is known about the consequences of brand originmisclassification in terms of outcome variables such asbrand image evaluations or buying intentions. However,

this is an important issue because “if consumers associ-ate a brand with the wrong COO, their brand evalua-

tions (and subsequent buying decisions) could differfrom what they would have been if the correct COOhad been identified” (Balabanis and Diamantopoulos

Gains and Losses from the Misperception of Brand Origin:

 The Role of Brand Strength andCountry-of-Origin Image

George Balabanis and Adamantios Diamantopoulos

ABSTRACT

Mounting empirical evidence shows that consumers often associate brands with the wrong country of origin (COO) or

are unable to classify a brand to any COO. In this study, the authors investigate the consequences of brand origin mis-

classification and nonclassification on consumers’ brand image evaluations and associated purchase intentions. Draw-

ing from categorization theory, the authors test hypotheses regarding the impact of brand strength and COO image onmisclassification gains and losses on a sample of U.K. consumers. The results show that both misclassification and non-

classification have mostly adverse consequences on both brand evaluations and purchase intentions. The findings fur-

ther show that strong brands are not immune to misclassification and stress the importance of ensuring that consumers

are aware of a brand’s true COO.

Keywords: country of origin, brand origin, recognition, branding

 Journal of International Marketing 

©2011, American Marketing Association

Vol. 19, No. 2, 2011, pp. 95–116

ISSN 1069-0031X (print) 1547-7215 (electronic)

George Balabanis is Professor of Marketing, Cass BusinessSchool, City University of London (e-mail: [email protected]). Adamantios Diamantopoulos is Professor andChair of International Marketing, University of Vienna(e-mail: [email protected]).

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96  Journal of International Marketing

2008, p. 40). Practically, it is important because manybrand owners use foreign branding to deliberately asso-ciate their brand with a country that has a strong image(Leclerc, Schmitt, and Dubé 1994) or disguise the brandorigin if the country image is weak (Okechuku andOnyemah 1999). As Zhou, Yang, and Hui (2010, p.

204) observe, “The origin information for most brandsmay not be readily accessible either because global mar-keters have the desire to mask the origins of their brandsor the globalization of firms and the cross-border acqui-sition of brands complicate the nature of brand origin.”

The purpose of the current study is to investigate empiri-cally the potential consequences of brand origin misclas-sification and consumers’ inability to classify a brand to

a COO on (1) brand image evaluations and (2) purchaseintentions. We examine these issues within a frameworkof combined dependent effect sizes (see Gleser andOlkin 1994) and test them on empirical data from a sur-

vey of U.K. consumers. Consistent with prior research(e.g., Thakor 1996), we define a brand’s COO as thecountry in which the “corporate headquarters of thecompany marketing the ... brand is located” (Johansson,

Douglas, and Nonaka 1985, p. 389), regardless of where the brand is manufactured (e.g., Nike is a U.S.brand, although all its production is outsourced outsidethe United States).

CONCEPTUAL BACKGROUND AND

HYPOTHESES

We used categorization theory to conceptually underpinour study because categorization plays a central role inconsumer behavior (see, e.g., Ratneshwar et al. 2001),and COO constitutes an important category for con-sumers (Gregan-Paxton and John 1997). To simplify,“categorization is the mental act of coming to think of some object as an instance of the category” (Smith1995, p. 27). According to categorization theory, con-sumers tend to structure their knowledge of specificproduct alternatives in categories (Gutman 1982; Punj

and Moon 2002); specifically, they use category struc-tures to organize and differentiate brands (Johnson and

Lehmann 1997).

As a cognitive process, categorization “expresses thecharacteristic manner in which individuals organize andstructure perceptual inputs deriving from the externalenvironment” (Block et al. 1981, p. 770) and serves two

functions: coding of experience and licensing of infer-ences (Smith 1995). Coding reduces the “demands forperceptual processes, storage space, and reasoning pro-

cesses, all of which are known to be limited” (Smith1995, p. 5); therefore, by categorizing brands into dif-ferent origins, consumers can economize cognitiveresources. In this context, categorization research shows“that people do not deliberately and individually evalu-ate each new stimulus to which they are exposed, but

often evaluate a stimulus in terms of whether or not itcan be classified as a member of a previously definedcategory” (Keller 2003, p. 609).

The other function of categorization is the licensing of inferences, whereby “the category label and/or values of some of the category dimensions are presented and thevalue of another category dimension must be predicted”(Markman and Ross 2003, p. 597). Thus, categoriza-

tion is the means of inferring properties of an object(i.e., brand) from the category to which it belongs (i.e.,the COO). In marketing, several studies have examinedconsumer categorization of brands (Cohen and Basu

1987; Loken 2006; Loken and Ward 1990; see Sujanand Bettman 1989 for a review) and, more specifically,inferences that take place from the COO to the brand(Agarwal and Sikri 1996; Lee and Ganesh 1999; Mah-

eswaran 1994). Specifically, consumers make inferencesabout a brand’s attributes (e.g., value for money dura-bility, reliability) from their knowledge of the brand’sorigin (i.e., the category label); an example is to infer thereliability of a refrigerator knowing that it comes fromPoland. Thus, the category label (i.e., the COO) servesas a signal that all members of a category (i.e., differentbrands) are likely to share some similar attributes.

Han (1989) explains the functions of categorization inthe COO context, whereby the COO can serve either asa stereotype measure (coding function or COO as a“summary construct”) or as a proxy for other productattributes (inference function or “halo effect”). The spe-cific mechanisms through which the category-basedinformation processing of COO can influence brandevaluations and purchase intentions is classified into(1) cognitive, (2) affective, and (3) normative processes.

Cognitive Processes

According to Obermiller and Spangenberg (1989), theCOO’s cognitive influence on brand evaluations or atti-tudes is indirect. From the COO, consumers infer attri-butes of the brand (e.g., durability and reliability) thatare then used to form attitudes or assessments of quality.

Multiple cognitive processes could explain COO effects,which are contingent on the available information about

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Brand Strength and Country-of-Origin Image 97

the brand and the consumer’s familiarity with thebrand (Li and Wyer 1994). According to Li and Wyer(1994), the COO (1) can be a signal to infer otherattributes of the brand when available information islimited, (2) can serve as a comparison standard for thebrand and result in a contrast effect on evaluations

when sufficient information about the product cate-gory and the brand is available, or (3) can be one of several attributes of a brand used in the evaluationprocess. Similarly, Hong and Wyer (1990) show thatprior categorization of brands to COOs leads to theformation of an “initial evaluative concept” of thebrand on the basis of the COO. This “evaluative con-cept” is then used as a basis for overall brand evalua-tions and influences the assessment of information

about the brand’s (other) attributes.

In a comprehensive review of the literature, Bloemer,Brijs, and Kasper (2009) summarize and classify the

identified COO cognitive process mechanisms into fourtypes:

1. The “halo effect,” which occurs when con-

sumers rely on COO cues to infer and formsalient beliefs about the attributes of a prod-uct; in this case, “additional product informa-tion is disregarded” (p. 68) in the final productevaluations.

2. The “summary construct effect,” which occurswhen “additional product information is not

explicitly taken into consideration anymorebecause it is already summarized by the COO-cue” (p. 68).

3. The “default heuristic effect,” which occurswhen the “processing of the COO-cue is donetogether with the processing of additional prod-uct information” and “there is a reciprocalinteraction between the two” (p. 68).

4. The “product attribute effect,” which occurswhen “both the COO-cue and additional prod-

uct information are being processed” (p. 68).

Bloemer, Brijs, and Kasper (2009) apply the elaborationlikelihood model to explain which of the four mecha-nisms underlies observed COO effects. Specifically, theyindicate that the level of prior knowledge of the COO

stored in the memory of a consumer (which ranges fromlow to extended) regulates the cognitive mechanismsthat are activated each time and, as a result, the likely

size of the effect. Extended prior knowledge about theCOO is likely to activate the summary construct effectprocessing mode, whereas moderate COO knowledge islikely to activate the default heuristic effect mode, andlimited COO knowledge is likely to activate the haloeffect processing mode. The use of different processing

modes is expected to influence the strength of the COOeffect on product evaluations. Specifically, in the sum-mary construct effect processing mode, the COO isexpected to have a direct and strong COO influence onproduct evaluations, whereas in the default heuristiceffect processing mode, the COO effect is likely to bemoderate. In the halo effect and product attribute effectprocessing modes, the resultant COO effects areexpected to be weak.

Similarly, Manrai, Lascu, and Manrai (1998) theorizeand empirically establish that prior COO knowledgevaries with the level of economic development of the

COO, and consequently the COO cognitive processingmode and the strength of the COO effect also vary.Highly developed countries record higher levels of COOknowledge, and developing countries record the lowest

levels. According to Manrai, Lascu, and Manrai (1998)the summary construct effect processing mode is morelikely to be linked with products from developed coun-tries, whereas the default heuristic mode is likely to beactivated for products from newly industrialized coun-tries or newly marketizing countries; the halo effect ismost likely to be observed with products from develop-ing countries.

Affective and Normative Processes

Country-of-origin membership can also activate emo-tional responses that might override cognitive inferentialevaluations of the brand (Obermiller and Spangenberg1989). Affect toward and attachment (or lack thereof)to a particular country usually form the basis of theseresponses, as captured by such constructs as consumeranimosity (Klein, Ettenson, and Morris 1998) and con-

sumer affinity (Oberecker, Riefler, and Diamantopoulos2008). For example, several studies have shown that

consumers’ animosity toward a COO due to historicalevents results in their unwillingness to buy productsfrom that country despite the products’ perceived supe-riority (for a review, see Riefler and Diamantopoulos2007). The well-known “home country bias” phenome-non reflects a similar result of affective processing,

whereby domestic products are evaluated more posi-tively than products from other COOs that are objec-tively superior (Balabanis and Diamantopoulos 2004b).

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98  Journal of International Marketing

Normative pressures that moderate the impact of brandattitudes on preferences form the basis of anotherprocess underlying COO effects (Obermiller and Span-genberg 1989). Social norms that prescribe acceptablerules of consumer conduct, such as “buy local” or “donot buy from the enemy,” might explain inconsistencies

and irrationalities in purchasing behavior patterns(which involve disregarding positive evaluations of brands from certain COOs). For example, Wall andHeslop (1986) find that Canadians are willing to paymore for domestic (Canadian) products than for foreignimports. More recently, Drozdenko and Jensen (2009)report that U.S. consumers are willing to pay as much as105% more for an American product. Their study of 11consumer product categories shows that price premiums

are significantly higher for domestic products than forproducts from developed countries.

Affective and normative processes might be partly

responsible for inconsistencies in COO influence onbrand evaluations and purchase intentions, respectively.Peterson and Jolibert’s (1995) and Verlegh andSteenkamp’s (1999) meta-analytical evidence indicates

that COO influence on brand evaluations is substan-tially stronger than that on purchase intentions. Peter-son and Jolibert attribute this discrepancy to the higherdegree of “personal commitment” involved in purchaseintentions compared with perceptual evaluations, whichcauses consumers to respond less enthusiastically.Another explanation for the weaker effect of COO onpurchase intentions is that the purchase intentions are

mediated by product evaluations, which represent amore direct response to the COO; moreover, purchaseintentions have more antecedents than brand evalua-tions, which might also explain the weaker effectsobserved.

Although these studies provide theoretical explanationsfor the categorical information processing of the COOcue, they are all based on the implicit assumption thatconsumers are able to categorize the brands to COOs

and, moreover, that they are able to categorize them cor-rectly. To ensure that this occurs, the COO is disclosed

or revealed to the respondents at some point in theresearch process. However, in their daily encounters,consumers incidentally categorize brands to COOsusing processes that are not flawless (Hutchinson andAlba 1991; Markman and Ross 2003). The categoriesstored in consumers’ memory, not the “true” COOs,

determine consumers’ attitudes and preferences in themarketplace. As Thakor (1996, p. 28) points out, “theactual place that the brand originates from is almost

irrelevant.... Consumers’ perceptions might differ fromreality because of ignorance, lack of salience of origininformation for a particular brand, or deliberate obfus-cation by companies concerned about consumer reac-tions to an unfavorable origin.” Considering this, wedeveloped hypotheses regarding the expected effects on

brand evaluations and purchase intentions when con-sumers (1) categorize a brand to the wrong COO and(2) are unable to categorize a brand to any COO.

Categorizing Brands to the Wrong COO

What happens to behavioral outcomes such as brandevaluations or purchase intentions if (for whatever rea-son) a consumer mistakenly perceives a brand as coming

from country Y instead of associating it with its correctCOO, country X? Assuming that consumers are notindifferent to the COO cue, as most empirical researchsuggests (for literature reviews, see, e.g., Al Sulaiti and

Baker 1998; Liefeld 1993; Papadopoulos and Heslop2003; Peterson and Jolibert 1995; Pharr 2005; Verleghand Steenkamp 1999), the reaction is likely to dependon (1) the extent to which country Y enjoys a more posi-

tive (negative) image than country X according to con-sumers (Papadopoulos and Heslop 2003; Peterson and

  Jolibert 1995; Pharr 2005; Verlegh and Steenkamp1999) and (2) the extent to which the brand name is astronger driver of consumer preference and choice thanthe COO cue (Cordell 1993; Han and Terpstra 1988;Tse and Gorn 1993; Tse and Lee 1993).

There are two types of brand origin misclassifications(i.e., categorization to the wrong COO category):

1. Adverse misclassification, which occurs whenthe brand is mistakenly perceived as being froma country with a weaker image than its true ori-gin, and

2. Favorable misclassification, which occurs whenthe brand is mistakenly perceived as being from

a country with a stronger image than its trueorigin.

Because different consumers might misclassify the samebrand to different COOs and because consumers mightassociate different images with these (wrong) COOs, itis impossible to predict a priori what the net effect of misclassification will be for a brand.2 However, drawing

from the extant COO research, it is possible to predictthe effects of adverse and favorable misclassificationseparately. Meta-analytical evidence based on research

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Brand Strength and Country-of-Origin Image 99

in which the COO was explicitly identified to surveyparticipants (Liefeld 1993; Papadopoulos and Heslop2003; Peterson and Jolibert 1995; Pharr 2005; Verleghand Steenkamp 1999) indicates that brands fromweaker COOs experience negative effects on brandimage evaluations and purchase intentions. For exam-

ple, Manrai, Lascu, and Manrai’s (1998) empirical find-ings for 18 products from 21 countries show that con-sumer evaluations are higher for products from highlydeveloped countries, followed by those for productsfrom newly industrialized countries, newly marketizingcountries, and finally developing countries (in thatorder).

Without the explicit identification of the COO of a

brand, consumers base their attitudes, evaluations,intentions, and behaviors on the COO from which theybelieve the brand comes (Josiassen and Harzing 2008).Erickson, Johansson, and Chao’s (1984) early study

shows that COO affects product evaluations through itseffects on beliefs regarding such attributes as workman-ship, durability, and reliability. A cognitive inferentialprocess takes place in which consumers use information

about the COO to assess the brand along several attri-butes, and the effects of this process are more pro-nounced when the consumer’s knowledge about thebrand or the product category is limited (Hong andWyer 1990; Obermiller and Spangenberg 1989). It isreasonable to assume that COO misclassification of abrand’s origin is an indicator of such limited knowledge(Samiee, Shimp, and Sharma 2005). Thus, when con-

sumers associate a brand with a weaker COO than itstrue origin, brand origin misclassification is expected tolead to losses (i.e., more negative brand perceptions andweaker purchase intentions). The opposite also appliesfor the same reason, namely, that favorable misclassifi-cation (i.e., classification to a COO that is stronger thanthe true COO) is expected to lead to gains. Thus:

H1: Adverse (favorable) COO misclassification of any brand leads to misclassification losses

(gains) in terms of (a) brand image perceptionsand (b) purchase intentions.

Note that H1a and H1b focus on the effects of misclassi-fying the COO of a brand in relation to its true COOand do not state that favorable COOs lead to gains andunfavorable COOs to losses. More specifically, adversemisclassification does not necessarily mean that the

brand will be associated with a COO that has an unfav-orable image; rather, it might be associated with a coun-try that has a less favorable image than the brand’s real

COO. For example, both the United States and Japanhave positive images, although the latter’s is strongerthan the former’s (e.g., Papadopoulos, Heslop, andIKON Research Group 2000). Classifying a Japanesebrand as originating in the United States would be anadverse misclassification, whereas classifying a U.S.

brand as Japanese would indicate a favorable misclassi-fication. Note also that misclassification (regardless of direction) might indicate limited knowledge of thebrand and lead to different evaluations than those of brands correctly associated with the same country.These effects are not obvious and have not been exam-ined in prior research.

Hoeffler and Keller (2003) document the many benefits

of brand strength, which are linked to stronger positiveeffects on consumer evaluations and behavior. Strongbrands (i.e., brands with an established image and sub-stantial brand equity) might intuitively be expected to be

more “resistant” to COO misclassification because con-sumer choice is more likely to be driven by the brandname itself rather than the associated COO (Cordell1992, 1993; Tse and Lee 1993). However, considerable

empirical evidence shows that a brand’s COO influencesbrand equity (Pappu, Quester, and Cooksey 2006;Yasin, Noor, and Mohamad 2007). Therefore, part of the “strength” of a brand might be attributable to itsCOO (Shocker, Srivastava, and Ruckert 1994; Thakorand Katsanis 1997). Moreover, evidence indicates thatalthough a favorable COO can compensate for a weakbrand, the reverse is not true; that is, a strong brand

does not necessarily counterbalance a negatively per-ceived COO (Ahmed et al. 2002; Nebenzahl and Jaffe1996; Tse and Gorn 1993).

Applied to a misclassification context, these findingsindicate that weak brands have more to gain fromfavorable COO misclassification, whereas strongbrands have more to lose from adverse COO misclassi-fication. The reason is that weak brands are likely todraw more of their equity from the country with which

they are associated than strong brands, and therefore alink to a stronger COO (i.e., a favorable misclassifica-

tion) “can compensate for a weak brand as far as con-sumers’ evaluations of product quality and attitudetowards the product are concerned” (Ahmed et al.2002, p. 295). Conversely, this “leverage effect” is likelyto be less pronounced for strong brands because most of them already come from countries with strong images.

As a result, the scope for benefiting from favorable mis-classification is more limited, and association with evenstronger COOs is unlikely to result in major gains in

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100  Journal of International Marketing

equity. However, these “diminishing returns” do notapply in the case of adverse misclassification; linking astrong brand to a COO with an image that is consider-ably inferior to the brand’s true COO is both possibleand bound to tarnish the brand name (Thakor and Kat-sanis 1997). In contrast, such “tarnishing” is likely to be

less critical to weak brands because their names haveless to lose from adverse misclassification.

On this basis of the preceding discussion, we offer thefollowing hypotheses:

H2: Adverse COO misclassification of strongbrands results in (a) greater brand image per-ception losses and (b) greater losses in terms of 

purchase intention compared with weakbrands.

H3: Favorable COO misclassification of weak

brands results in (a) greater brand image gainsand (b) greater gains in terms of purchaseintention compared with strong brands.

Note that H2 and H3 offer an opportunity to provideinsights into the extent to which the strength of a brandis linked to its perceived COO. Although empirical evi-dence shows that brand equity is affected by the brand’sCOO (see, e.g., Pappu, Quester, and Cooksey 2006),such evidence is based on survey designs in which thetrue COO was explicitly communicated to respondents.Whether this finding is consistent under “ecologically

valid conditions, where brand origin information haseither to be acquired at the point of purchase throughactive search or accessed from memory via inter-national, goal-driven efforts” (Samiee, Shimp, andSharma 2005, p. 391), remains to be seen. In this con-text, the COO is a secondary association and can onlybe partly responsible for a brand’s strength; the effectsof misclassification not  differing between strong andweak brands, as H2 and H3 predict, would indicate thatCOO and brand strength are independent cues affecting

consumer behavior.

Inability to Categorize a Brand to a COO

A heavily criticized practice of mainstream COOresearch is making consumers aware of the origin of abrand (Samiee, Shimp, and Sharma 2005; Usunier2006). As Samiee (2009, p. 3) points out, “with minor

exceptions, COO research invariably discloses themanufacturing (design, assembly, etc.) location of prod-ucts being evaluated for the project.”

In a unique study, Hong and Wyer (1989, p. 184)examine what happens when consumers are not awareof the COO of a brand. Their results confirm the cog-nitive elaboration hypothesis of the COO, wherebyawareness of a brand’s origin stimulates interest inother information about the brand and its evaluative

implications. When consumers were not aware of theCOO, they showed less interest in the brand. Hong andWyer conclude that consumers try to confirm thevalidity of hypotheses they might have developedregarding the ability of different COOs to producehigh- (low-) quality products. However, Hong andWyer experimentally manipulate COO categorizationin their study, and it is important to understand theeffects of people’s inability to categorize brands to

COOs in a natural setting.

Some notable results from neuroscience research havepotential implications for our study. This body of 

research reveals that people’s inability to categorizeobjects is linked to certain medical conditions such astemporal lobe damage and fronto-parietal lesions, aswell as disorders such as dyslexia, semantic dementia,

and Alzheimer’s disease (Warrington and Shallice 1984).Although an examination of these medical conditions isbeyond the scope of the current study, a key messagefrom neuroscience research is that a person’s inability tocategorize objects is associated with memory dysfunc-tions that inhibit categorical information processing.For the purposes of our study, the most important find-ing is that “perceptual experiences” or accrued knowl-

edge by a person before memory dysfunction influencesthe person’s ability to categorize objects (Moss et al.1998). This indicates that the absence of knowledge orperceptual experiences with the focal brands—not nec-essarily due to a disorder or memory dysfunction—caninhibit consumers’ ability to categorize brands intoCOOs. In contrast, when some prior knowledge isavailable, consumers might use different categorizationstrategies (explained later) to classify brands accordingto their (perceived) origins.

Categorization of an object is typically based on simi-

larity or rule-based mechanisms (Smith 1995); indeed, an“important Gestalt principle of perceptual organizationis that similar things will tend to be grouped together”(Medin, Goldstone, and Gentner 1993, p. 254). Thus,apart from a lack of explicit knowledge of a brand’sCOO, lack of information about the brand itself might

make it difficult to identify similarities or apply catego-rization rules to assign the focal brand to a COO.Specifically, consumers might not be able to identify the

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Brand Strength and Country-of-Origin Image 101

necessary similarities (e.g., group similar-sounding brandnames) to link brands to a COO category. In addition,they might not be able to apply learned rules to do so. Anexample of a rule that consumers might develop on thebasis of experience is “low-priced brands come fromChina”; consumers might use such a categorization rule

to compensate for the absence of similarities between thecategory (the COO) and the brand. In summary, con-sumers might not have the necessary information to dis-cern the prototypical or other diagnostic features of thebrands that would enable their categorization into differ-ent COOs.

Another strategy used in categorization is reliance onsemantic elements of the information related to the

object (i.e., the brand) to compensate for the absence of diagnostic information. For example, the linguistic orphonologic properties of the brand, or “brand namesuggestiveness” (Hennessey, Bell, and Kwortnik 2004;

Lee and Soon Ang 2003), might not provide sufficientcues to link the brand to a COO. Under such circum-stances, the absence of categorization makes inductiveinference from the COO to the brand impossible, and

therefore brands would be evaluated independently of their COO category membership.

Thus, we expect that the inability to categorize brandsinto COOs will relieve brands of the positive (negative)associations transferred from a country with a strong(weak) image. This would have harmful effects onbrands from countries with strong images but would be

beneficial for brands from countries with a weak image.Thus:

H4: Nonclassification of brands results in losses(gains) for brands from a strong (weak) COOin terms of (a) brand image perceptions and(b) purchase intentions.

ANALYTICAL FRAMEWORK

In light of the foregoing hypotheses, how can the poten-

tial misclassification gains/losses (in terms of brandimage perceptions and purchase intentions) be quantifiedand subsequently compared across brands? In otherwords, if N consumers are exposed to n brands (varyingin brand strength) and are asked to assign them to m ori-gins (varying in image), how would the overall misclassi-

fication (or nonclassification) gain/loss of brand i becomputed and compared with the gain/loss of brand j(i ≠ j)? Because all N consumers are asked to rate all n

brands in terms of outcome variables (e.g., brand imageperceptions), misclassification (or nonclassification)gains/losses are not independent across brands becauseof the within-subject nature of the research design.Moreover, because different consumers might misclas-sify the same brand to different COOs, all possible (i.e.,

up to a maximum m – 1)3 incorrect categorizations needto be considered and subsequently combined to arrive atan overall gain (loss).

We approached this problem by adopting a procedureoriginally proposed by Gleser and Olkin (1994) that hasbeen successfully applied for estimating and subse-quently combining multiple effects identified within astudy. We were specifically interested in the effects of 

misclassifying a brand to the wrong COO (i.e., the mis-classification gain/loss). Such effects can be estimatedfor each brand separately by comparing the group of consumers that classifies the brand to its correct COO

with the group that classifies the brand to the wrongCOO. However, if measurements are taken for severalbrands, the calculation of a composite effect cannot bebased on traditional methods and should take into

account the interdependencies among brands (becauseall consumers are exposed to all brands, and thus theeffects are not independent). Gleser and Olkin’s proce-dure allows the estimation of a composite effect fromone study under these conditions.

We defined the misclassification gain/loss resulting fromincorrectly categorizing brand i (i = 1, 2, ..., n) to origin

k (k = 1, 2, m – 1) as follows:

(1)

where

X–

ik =mean of the outcome variable under consideration(e.g., purchase intentions) based on those con-sumers who allocated brand i to wrong origin k,

X–

ic = mean of the outcome variable under considerationbased on those consumers who correctly allocated

brand i to its true origin c, and

Sic = standard deviation of the outcome variable underconsideration based on those consumers who cor-rectly allocated brand i to its true origin c.

Similarly, we estimated the nonclassification gains/losseson the basis of the same formula, where X

–in replaced

X–

ik, the former being the mean of the outcome variable

dX X

Sik

ik ic

ic

= ,

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102  Journal of International Marketing

under consideration based on those consumers whowere unable to classify brand i to any COO.

We apply Equation 1 separately to each incorrectly ornonassigned COO for all n brands and both outcomevariables (brand image evaluations and purchase inten-

tions). In essence, Equation 1 “normalizes” the misclas-sification gain/loss around the standard deviation of con-sumers who identified the brand’s true origin.4 Note thatpositive values in Equation 1 indicate misclassificationgains, while negative values indicate misclassificationlosses; a value of (or close to) 0 indicates that COO mis-classification does not materially affect the outcomevariable under investigation.

To compute composite effects (e.g., aggregated misclas-sification gains/losses for strong vs. weak brands), weapply Gleser and Olkin’s (1994) formula 22–35. To testthe homogeneity of the constituent effects, we use the Q-

statistic (see Gleser and Olkin 1994, formula 22–38).5 Thelatter is distributed as a chi-square statistic, and a non-significant result indicates that the individual effects arehomogeneous and can therefore be legitimately combined.

DATA AND MEASURES

For data collection purposes, we used the “drop andcollect” method (Brown 1987) to administer a pretestedquestionnaire to a random sample of households in amajor city in the United Kingdom. This approach has

been used successfully in prior COO research (e.g.,Ahmed and d’Astous 2003; Balabanis and Diaman-topoulos 2004b; Papadopoulos, Heslop, and IKONResearch Group 2000). We collected a total of 193 fullycompleted questionnaires (approximately a 70% partic-ipation rate).

We asked respondents to categorize 12 brands of microwave ovens to one of eight COOs; we alsoallowed respondents to specify a country of their own

choice or respond with “don’t know.” We focused on asingle product category “to minimize confounding

effects in the consumer classification process emanatingfrom differences in marketing practices, involvement,and other extraneous factors” (Balabanis and Diaman-topoulos 2008, p. 42). We chose microwave ovensbecause of their high penetration rate (82.7%) in theUnited Kingdom (Mintel 2007), which makes them a

highly familiar product category.6 Another reason wechose microwave ovens is that brands from severalcountries are offered for sale in the United Kingdom

(including brands with linguistically “misleading”brand names, such as Hinari and Matsui, both of whichare U.K. brands).

We focused on two key outcome variables to study theconsequences of brand COO misclassification and non-

classification. We measured brand image evaluations ona four-item, ten-point bipolar scale (1 = “low,” and 10 =

“high”), asking respondents to rate each brand in termsof value for money, reliability, performance, and quality;we established the unidimensionality of the scalethrough confirmatory factor analysis, and its reliabilitywas highly satisfactory for all brands (alpha valuesbetween .82 and .91). We measured purchase intentionon a single-item, ten-point bipolar scale (1 = “not at

all,” and 10 = “extremely likely”); we conceptually jus-tified the use of a single item on the basis that purchaseintention is a concrete, singular construct, and “as prac-titioners have long demonstrated,... for measuring con-

crete attributes such as AD LIKING or PURCHASEINTENT, single-item measures are definitely valid”(Rossiter 2002, p. 314).

To measure country image, we used Roth and Romeo’s(1992) well-established scale that captures innovation,design, prestige, and workmanship; we verified unidi-mensionality using confirmatory factor analysis for allorigins, and scale reliabilities were very satisfactory(alpha values ranging from .79 to .95). For each brand,we allocated each wrong COO to one of two groupsdepending on whether its average image score was

higher or lower than that of the correct COO. We thenused the two groups thus generated to operationalize thefavorable versus adverse misclassification conditionsnecessary for testing H1–H3. We also created a thirdnonclassification group to test H4 based on respondentswho were unable to categorize the brand in question toany COO.

Finally, to classify brands according to brand strength,we consulted the Mintel (2007) report. This report

provides comprehensive information on microwavebrands sold in the United Kingdom. Using this infor-

mation, we designated five brands as strong brandsand the rest as weak. As a manipulation check on thebrand strength classification, we also computed theaverage brand image scores across all respondents inour sample followed by a median split (supported bytwo-step cluster analysis). The resulting two groups

were identical to those generated from the Mintelreport, thus confirming the validity of the brandstrength classification.

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Brand Strength and Country-of-Origin Image 103

To eliminate possible confounding effects arising fromindividual consumer differences on the misclassificationand nonclassification gains/losses, we partialed out theeffects of demographic variables (gender, age, andincome) (Johansson, Douglas, and Nonaka 1985), levelsof purchase involvement (Gürhan-Canli and Mah-

eswaran 2000), perceived importance of the COO (Tseand Gorn 1993), familiarity with the COO (Bloemer,Brijs, and Kasper 2009), and consumer ethnocentrism(Balabanis and Diamantopoulos 2004a) from the twooutcome variables through a preliminary regressionanalysis. We then used the unstandardized residuals of the outcome variables in place of the observed scores inthe calculations of misclassification (or nonclassifica-tion) gains/losses according to Equation 1.

ASSESSMENT OF COMMON METHOD

VARIANCE

Because all construct measures came from the same sur-vey and we used the responses to measure both the inde-pendent and dependent variables, it was important toinvestigate whether common method variance (CMV)

had “a substantial impact on the observed relationshipsbetween predictor and criterion variables” (Podsakoff etal. 2003, p. 897). In addition to using several proceduraltechniques suggested by Podsakoff et al. (2003)—such asusing different response formats, assuring respondents’anonymity, stressing that there are no right or wronganswers, and using pretests to avoid item ambiguity—westatistically investigated the potential impact of CMV by

following the marker variable technique proposed byLindell and Whitney (2001; see also Rindfleisch, Bur-roughs, and Wong 2009).7 We selected a country knowl-edge item—namely, “knowledge of the Netherlands”(which related to a COO not associated with any of thebrands investigated in our study)—as the marker variablebecause it was conceptually unrelated to both the inde-pendent and dependent variables. Of the 105 significantbivariate correlations between our predictors (i.e., coun-try image scores of the eight COOs) and outcomes (i.e.,

brand image evaluations and purchase intentions for the12 brands), 98 remained statistically at p < .05 or better

after partialing out the marker variable. The rest (7 cor-relations) remained significant at  p < .1. These resultsindicate no major threat of CMV in our study.8

DESCRIPTIVE RESULTS

Tables 1 and 2 show the detailed results related to brandimage evaluations and purchase intentions, respectively,

based on Gleser and Olkin’s (1994) procedures we out-lined previously. To aid interpretation, we order thebrands according to their strength (with strong brandsplaced first) and the COOs according to their averagecountry image scores. The last column (labeled UNCL) isthe unclassified category. Each cell in the tables contains

three values: (1) the size of misclassification gain/loss asestablished in Equation 1 for the outcome variable underconsideration (i.e., brand image evaluations in Table 1and purchase intentions in Table 2), (2) the associatedstandard error, and (3) the percentage of consumers whoincorrectly categorized the brand in question to a par-ticular origin. For example, in the first cell of Table 1(Panasonic and Germany), the value –1.020 indicatesthat misclassification resulted in a loss for brand image

perceptions. The standard error associated with that lossis .417, and 5.7% of the respondents reported that Pana-sonic was a German brand.

The shaded cells in each table indicate correct alloca-tions and show the percentage of respondents that cate-gorized the focal brands to their true COOs; for exam-ple, 58.5% of the respondents correctly classified

Panasonic as a Japanese brand. The last row of eachtable shows the composite effects (i.e., the combinedmisclassification and nonclassification gains/lossesacross all brands) following tests for homogeneity.

To illustrate the interpretation of Tables 1 and 2, considerthe Panasonic brand in the first row of Table 1. Panasonicsuffers losses in brand image regardless of the COO into

which it is (incorrectly) categorized. This is indicated bythe negative values, as established by applying Equation1, in the cells in which Panasonic is associated with Ger-many, the United Kingdom, the United States, Korea, andChina. Panasonic also suffers losses when it cannot beclassified into any COO (see the UNCL cell in Table 1).In contrast, Daewoo (see the sixth row of Table 1) enjoysimage gains if it is misclassified as a U.K. or Japanesebrand, but it suffers losses if it is misclassified as a Tai-wanese, Chinese, or (albeit it less so) German brand; it

also suffers losses if it cannot be allocated to any COO.

Notably, no single brand always gains from COO mis-classification; in contrast, all five strong brands consis-tently suffer losses in image from COO misclassificationregardless of which COO they might be (incorrectly)associated with. They also suffer losses if they cannot beassociated with any COO. Across all brands, misclassifi-

cation almost always results in losses in brand image (seethe last row in Table 1). The entries in Table 2 related topurchase intentions can be interpreted similarly.

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104  Journal of International Marketing

 Table 1. Misclassification Gains/Losses: Brand Image Perceptions

Perceived COO

Strong Brands GER JAP UK USA FRA KOR TAIW CHIN UNCL

Panasonic (JAP) –1.020 –.204 –.473 –1.283 –.847 –.583.417 58.5% .379 .314 .260 .581 .416

5.7% 10.4% 11.4% 2.6% 1.0% 9.3%

Sharp (JAP) –.137 –.015 –.234 –.834 –1.113 –.227

.432 31.6% .221 .378 .239 .714 .262

5.2% 28.5% 14.0% 2.1% 3.6% 12.4%

Sanyo (JAP) –.140 –.322 –.117 –.150

76.2% .327 .544 .364 .240

6.2% 3.1% 6.2% 6.7%

Samsung (KOR) –.436 –.119 –.520 –.085 –.632

1.219 .191 28% .304 .208 .281

2.1% 43.5% 5.7% 8.8% 10.4%

Whirlpool (USA) –.390 .5% –.205 –.653 –.810.299 .250 19.7% .365 .336

11.9% 39.4% 2.6% 18.1%

Weak Brands

Daewoo (KOR) –.266 .029 .349 –.656 –.376 –.281

.254 .219 .384 45.1% .195 .424 .362

7.8% 16.6% 3.6% 7.8% 4.1% 10.9%

Matsui (UK) .460 .176 .028 –.318 .013 –.380

.773 .595 3.1% .600 .657 .723 .677

1.6% 50.8% 16.1% 8.3% 7.3% 11.9%

Belling (UK) .660 .266 .134 –.088 .072

.443 .239 53.4% .189 .619 .1683.1% 4.1% 14% 2.1% 19.2%

Delonghi (ITA) .075 –.048 –.335 –.234 .182 –.463 –.609 –.559

.583 .515 .413 .286 .392 .298 .521 .268

4.2% 2.6% 5.7% 20.8% 4.2% 8.3% 4.7% 33.3%

Hinari (UK) –.221 –.348 .282 –.180 –.587

.723 2.1% .737 .746 .734 .786

38.3% 24.4% 11.4% 6.2% 17.1%

Proline (UK) –.151 –.159 .066 –.014 –.360 –1.361 –.941

.522 13.5% .361 .420 .543 .495 .559 .276

2.6% 14.0% 5.7% 2.6% 2.6% 2.6% 52.8%

LG (KOR) .909 .411 .525 .573 –.078 .634 .031

.504 .592 .541 .470 .385 4.2% .538 .3938.9% 4.7% 5.8% 5.2% 2.1% 2.6% 64.9%

Composite effect of all brands –.149 –.037a –.052a –.071 –.375a –.903 –.633a –.133 –.062

.172 .148 .190 .175 .258 .209 .176 .174 .124

aHomogeneous composite effect.Notes: The bold number in the first row of a cell is the misclassification gain/loss effect. The number in the second row of a cell is the associated standard error. Thenumber in the third row of a cell is the percentage of consumers who categorized the brand to that COO (the shaded cells indicate correct allocations).

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Brand Strength and Country-of-Origin Image 105

 Table 2. Misclassification Gains/Losses: Purchase Intentions

Perceived COO

Strong Brands GER JAP UK USA FRA KOR TAIW CHIN UNCL

Panasonic (JAP) –.193 –.175 –.454 –1.117 –.702 –.197.260 58.5% .295 .324 .406 .889 .248

5.7% 10.4% 11.4% 2.6% 1.0% 9.3%

Sharp (JAP) –.587 –.481 –.209 –.796 –.596 –.512

.380 31.6% .214 .272 .522 .459 1.0% .236

5.2% 28.5% 14.0% 2.1% 3.6% 12.4%

Sanyo (JAP) .146 –.712 –.271 .055

76.2% .269 .430 .308 .164

6.2% 3.1% 6.2% 6.7%

Samsung (KOR) .066 –.212 –.011 –.074 –.362

.383 .189 28.0% .312 .236 .224

2.1% 43.5% 5.7% 8.8% 10.4%

Whirlpool (USA) –.303 –.195 –.263 –.183.253 .219 19.7% .396 .248

11.9% 39.4% 2.6% 18.1%

Weak Brands

Daewoo (KOR) –.060 .038 –.031 .054 –.525 –.329

.266 .196 .305 45.1% .324 .254 .259

7.8% 16.6% 3.6% 7.8% 4.1% 10.9%

Matsui (UK) –.735 –.358 –.259 –.553 –.624 –.290

.812 .603 3.1% .607 .640 .659 .617

1.6% 50.8% 16.1% 8.3% 7.3% 11.9%

Belling (UK) .365 .444 –.230 .162 –.160

.582 .479 53.4% .170 .853 .1733.1% 4.1% 14.0% 2.1% 19.2%

Delonghi (ITA) .008 .013 .063 .588 .068 .087 .707 –.217

.653 .511 .447 .334 .374 .451 .715 .286

4.2% 2.6% 5.7% 20.8% 4.2% 8.3% 4.7% 33.3%

Hinari (UK) –.563 –.832 –.910 –.776 –.680

.769 2.1% .828 .861 .822 .796

38.3% 24.4% 11.4% 6.2% 17.1%

Proline (UK) –.430 –.396 –.375 .350 .365 .213 –.293

.313 13.5% .286 .468 .575 .435 .569 .230

2.6% 14% 5.7% 2.6% 2.6% 2.6% 52.8%

LG (KOR) .053 .264 –.117 –.540 –1.160 –.015 –.445

.431 .433 .480 .547 .778 4.2% .542 .405

8.9% 4.7% 5.8% 5.2% 2.1% 2.6% 64.9%

Composite effect of all brands –.153a –.069 –.391 –.276a .914 .357 –.294a –.188a .050

.186 .165 .178 .155 .274 .227 .260 .179 .106

aHomogeneous composite effect.Notes: The bold number in the first row of a cell is the misclassification gain/loss effect. The number in the second row of a cell is the associated standard error. Thenumber in the third row of a cell is the percentage of consumers who categorized the brand to that COO (the shaded cells indicate correct allocations).

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106  Journal of International Marketing

HYPOTHESIS-TESTING RESULTS

Although the results in Tables 1 and 2 illustrate mis-classification and nonclassification gains/losses for thefocal outcome variables, they do not constitute a for-mal test of our research hypotheses. To test H1a and

H1b, we compared favorable misclassifications (Group1) with adverse misclassifications (Group 2)9 and withnonclassification (Group 3) using subgroup analysisvia the analog to the analysis of variance (ANOVA).Table 3 summarizes the results for both outcomevariables.

Regarding brand image perceptions, any misclassifica-tion leads to losses of brand image (mean effect size for

the three groups = –.219,  p < .001). However, as thesubgroup analysis indicates (Q = 6.580,  p < .05), thethree groups differ significantly, with both adverse mis-classifications and nonclassifications resulting in signifi-

cantly greater losses compared with favorable misclassi-fications (mean effect size for the adverse misclassificationgroup = –.316, p < .001; for the nonclassification group,mean effect size = –.288, p < .05). In general, these results

support H1a, with the proviso that favorable misclassifi-cation does not actually lead to (significant) gains inbrand image.

Regarding purchase intentions, the results supportH1b but only for adverse misclassifications (meaneffect size = –.161, p < .05). Although we also observenegative effects for the favorable misclassification and

nonclassification groups, these effects are not statisti-cally significant.

We also tested H2a and H2b, relating to differencesbetween weak (Group 1) and strong (Group 2) brandsunder conditions of adverse misclassification, using theanalog to ANOVA. Table 4 summarizes the results.

Focusing initially on the impact of adverse misclassifica-tion on brand image perceptions, both the overall mean

effect size (–.328,  p < .001) and the effect sizes for theweak (–.221, p < .01) and strong (–.429, p < .01) brands

are significant, indicating that adverse misclassificationresults in losses regardless of the type of brand involved.The group differences are also significant (Q = 3.017,

 p < .1), indicating that strong brands suffer greaterlosses from adverse COO misclassification than weakbrands. These findings support H2a.

Regarding purchase intentions, the results indicate thatthe strong brands suffer from adverse misclassification

(mean effect size = –.299, p < .001) rather than the weakbrands; however, the difference between the two groupsis not significant (Q = 1.676,  p = .195). Note that themean effect size for weak brands is not significant (–.14,

 p = .114), indicating that adverse COO misclassificationdoes not affect purchase intentions of such brands.

Regarding favorable misclassifications (see Table 5), theresults provide no support for H3a, because we observeno actual gains for either weak or strong brands.Specifically, we observed a significant difference inbrand image perceptions between weak and strongbrands (Q = 4.603, p < .05); however, this indicates thatthe strong brands suffer significant losses in their imageperception from favorable misclassification (mean effect

size –.268, p < .05). Furthermore, favorable misclassifi-cation for the weak brands results in no statistically sig-nificant gains in image perception (mean effect size =

.654, p = .493).

Regarding purchase intentions (H3b), favorable misclassi-fication leads to similar results. Strong brands again sufferfrom favorable misclassifications (mean effect size = –.228,

 p < .05), whereas weak brands do not sustain any gainsfrom favorable misclassification (mean effect size = –.115,

 p = .236). These findings do not support H3b.

In general, the results in Tables 4 and 5 indicate negativeeffects even when brands are misclassified to COOswith a stronger image than their true origins; in otherwords, favorable misclassification does not seem to gen-

erate a positive “leverage” effect, and in some instancesit even harms (strong) brands. Finally, focusing oninstances in which consumers were unable to link abrand to any COO, Table 6 shows the results associatedwith testing H4a and H4b, relating to the consequencesof nonclassification.

Regarding brand image perceptions, nonclassificationleads to losses (mean effect size = –.328,  p < .001)regardless of whether the brand originates from a

strong or a weak COO. This provides only partial sup-port for H4a because although nonclassification adversely

affects brands associated with strong COOs (mean effectsize = –.283, p < .01), as we expected, the same applies tobrands from weak COOs; instead of benefiting from non-classification, brands from weak COOs are also harmed(mean effect size = –.436, p < .01).

The results for purchase intentions are identical: non-classification results in lower purchase intentions bothfor brands from strong COOs (mean effect size = –.181,

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Brand Strength and Country-of-Origin Image 107

 p < .05) and brands from weak COOs (mean effectsize = –.329, p < .05). Thus, again, the results provideonly partial support for H4b.

In summary, consumers’ inability to classify a brand toa COO is detrimental in terms of both brand image per-ceptions and purchase intentions. This applies regard-

 Table 3. Effect Size Comparison: Favorable Versus Adverse Versus Nonclassification

A: Analog of ANOVA (Homogeneity Q)

Q d.f. p

H1a: Brand Image PerceptionsBetween 6.5806 2 .0372

Within 52.8553 32 .0116

Total 59.4359 34 .0045

H1b: Purchase Intentions

Between .2505 2 .8823

Within 24.0681 32 .8419

Total 24.3186 34 .8898

B: Q by Group

Group Qw d.f. p

H1a: Brand Image Perceptions

Group 1 17.1268 13 .1936

Group 2 30.0432 15 .0118

Group 3 5.6853 4 .2239

H1b: Purchase Intentions 2

Group 1 8.3693 13 .8187

Group 2 12.8803 15 .6115

Group 3 2.8185 4 .5887

C: Effect Size Results

Mean ES SE –95% CI +95% CI Z P k

H1a: Brand Image Perceptions

Total –.2193 .0469 –.3112 –.1275 –4.6803 .0000 35

Group 1 –.0551 .0795 –.2110 .1008 –.6928 .4884 14

Group 2 –.3160 .0713 –.4557 –.1763 –4.4342 .0000 16

Group 3 –.2883 .0998 –.4839 –.0927 –2.8884 .0039 5

H1b: Purchase Intentions

Total –.1325 .0460 –.2226 –.0423 –2.8797 .0040 35

Group 1 –.1130 .0807 –.2712 .0452 –1.3996 .1616 14

Group 2 –.1618 .0746 –.3080 –.0156 –2.1696 .0300 16

Group 3 –.1161 .0847 –.2821 –.0500 –1.3702 .1706 5

Notes: Group 1 = favorable misclassification. Group 2 = adverse misclassification. Group 3 = no classification. ES = effect size, and CI = confidence interval.

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108  Journal of International Marketing

less of whether the brand’s true origin is associated witha strong or weak image.

DISCUSSION AND CONCLUSIONS

The current study provides empirical insights into theconsequences of brand COO misclassification and non-classification by consumers and thus complements

extant research on the extent and antecedents of brandorigin recognition (e.g., Hennebichler 2007; Paswanand Sharma 2004; Samiee, Shimp, and Sharma 2005).Focusing on two important outcomes (brand image per-ceptions and purchase intentions),10 our study intro-duces to the literature a measure for quantifying the

effects of COO misclassification and nonclassification.It also proposes a novel methodological approach thatallows the combining of evidence from multiple brands

 Table 4. Adverse Misclassification: Weak Versus Strong Brands

A: Analog of ANOVA (Homogeneity Q)

Q d.f. p

H2a: Brand Image PerceptionsBetween 3.0177 1 .0824

Within 43.8776 32 .0787

Total 46.8954 33 .0553

H2b: Purchase Intentions

Between 1.6765 1 .1954

Within 28.6999 32 .6344

Total 30.3764 33 .5984

B: Q by Group

Group Qw d.f. p

H2a: Brand Image Perceptions

Group 1 19.3517 19 .4345

Group 2 24.5260 13 .0266

H2b: Purchase Intentions

Group 1 16.4350 19 .6281

Group 2 12.2650 13 .5060

C: Effect Size Results

Mean ES SE –95% CI +95% CI Z P k

H2a: Brand Image Perceptions

Total –.3280 .0598 –.4452 –.2108 –5.4849 .0000 34

Group 1 –.2213 .0857 –.3893 –.0533 –2.5822 .0098 20

Group 2 –.4292 .0835 –.5928 –.2656 –5.1414 .0000 14

H2b: Purchase Intentions

Total –.2226 .0616 –.3432 –.1019 –3.6150 .0003 34

Group 1 –.1400 .0886 –.3137 .0337 –1.5801 .1141 20

Group 2 –.2996 .0856 –.4673 –.1318 –3.4997 .0005 14

Notes: Group 1 = weak brand. Group 2 = strong brand. ES = effect size, and CI = confidence interval.

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Brand Strength and Country-of-Origin Image 109

and countries to study this underresearched phenome-non. The adopted research design enables researchers tounveil the link between a brand’s COO as it exists in theconsumer’s mind and its likely effects on behavioral out-comes; it also traces the consequences of consumers’inability to assign a brand to a COO.

Controlling for the product category as well as for sev-eral potentially confounding factors (e.g., consumer

demographics, ethnocentrism, level of involvement), wefind that both COO misclassification and nonclassifica-tion have mostly undesirable consequences. Specifically,association with a weaker country image than thebrand’s true COO (i.e., adverse misclassification) isalways detrimental. Both weak and strong brands suffer

losses in brand image, with strong brands being more(adversely) affected; in addition, strong brands are asso-ciated with reduced purchase intentions.

 Table 5. Favorable Misclassification: Weak Versus Strong Brands

A: Analog of ANOVA (Homogeneity Q)

Q d.f. p

H3a: Brand Image PerceptionsBetween 4.6037 1 .0319

Within 20.7681 21 .4732

Total 25.3717 22 .2796

H3b: Purchase Intentions

Between .6285 1 .4279

Within 8.8254 21 .9905

Total 9.4540 22 .9906

B: Q by Group

Group Qw d.f. p

H3a: Brand Image Perceptions

Group 1 16.5665 16 .4142

Group 2 4.2016 5 .5208

H3b: Purchase Intentions

Group 1 7.2078 16 .9690

Group 2 1.6176 5 .8991

C: Effect Size Results

Mean ES SE –95% CI +95% CI Z P k

H3a: Brand Image Perceptions

Total –.0602 .0753 –.2078 –.0874 –.7991 .4242 23

Group 1 –.0654 .0954 –.1216 .2524 .6858 .4928 17

Group 2 –.2681 .1227 –.5087 –.0276 –2.1845 .0289 6

H3b: Purchase Intentions

Total –.1681 .0710 –.3071 –.0290 –2.3684 .0179 23

Group 1 –.1153 .0973 –.3060 .0754 –1.1846 .2362 17

Group 2 –.2280 .1037 –.4312 –.0248 –2.1988 .0279 6

Notes: Group 1 = weak brand. Group 2 = strong brand. ES = effect size, and CI = confidence interval.

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110  Journal of International Marketing

From a theoretical perspective, our findings highlightthat consumers’ categorization of brands to differentCOOs directly affects the inferences they make regard-ing the brands’ attributes (as captured by brand imageperceptions) as well as the associated behavioral inten-tions. This emphasizes the relevance of categorization

theory in COO research and demonstrates that licens-ing of inferences is based on consumers’ perceivedCOO categories rather than the actual origins of 

brands. Moreover, the findings indicate that con-sumers’ inability to categorize brands results in infer-ences that are generally less positive than if (any) cate-gorization had been possible. These patterns indicatethat the perceived origin of a brand has diagnosticvalue for consumers, and they strongly support the

view that “for most research questions, COO researchfocusing on the COA [country of association] wouldbe more appropriate than focusing on an increasingly

 Table 6. Nonclassification: Weak Versus Strong COOs

A: Analog of ANOVA (Homogeneity Q)

Q d.f. p

H4a: Brand Image PerceptionsBetween .6688 1 .4135

Within 15.9444 10 .1012

Total 16.6133 11 .1198

H4b: Purchase Intentions

Between .8537 1 .3555

Within 4.9753 10 .8928

Total 5.8290 11 .8845

B: Q by Group

Group Qw d.f. p

H4a: Brand Image Perceptions

Group 1 13.6519 7 .0577

Group 2 2.2925 3 .5140

H4b: Purchase Intentions

Group 1 4.7182 7 .6943

Group 2 .2571 3 .9679

C: Effect Size Results

Mean ES SE –95% CI +95% CI Z P k

H4a: Brand Image Perceptions

Total –.3278 .0846 –.4937 –.1619 –3.8733 .0001 12

Group 1 –.2834 .1005 –.4805 –.0863 –2.8188 .0048 8

Group 2 –.4357 .1568 –.7430 –.1285 –2.7795 .0054 4

H4b: Purchase Intentions

Total –.2206 .0712 –.3601 –.0811 –3.0988 .0019 12

Group 1 –.1807 .0833 –.3439 –.0174 –2.1692 .0301 8

Group 2 –.3289 .1372 –.5977 –.0601 –2.3982 .0165 4

Notes: Group 1 =unclassified brands from strong COOs. Group 2 = unclassified brands from weak COOs. ES = effect size, and CI = confidence interval.

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Brand Strength and Country-of-Origin Image 111

irrelevant country of manufacture” (Josiassen andHarzing 2008, p. 266).

From a managerial perspective, the key implication of our findings is that COO misclassification is not onlywidespread but also clearly undesirable because its

behavioral consequences are negative. Therefore, com-panies should make conscious efforts to educate and/orremind consumers of their brand’s true COO and avoidassociations with a weaker COO (see Ahmed et al.2002).

As Tables 1 and 2 show and as other research has doc-umented (e.g., Samiee, Shimp, and Sharma 2005), somebrands are more prone to misclassification than others,

and some countries are more likely to be targets of mis-classification than others. On average, strong brands areless likely to be misclassified than weak brands (see theshaded cells in Tables 1 and 2). Although strong brands

have a relatively low average correct identification rate(42.8%), the same rate is much lower for the weakbrands (19.3%). Indeed, a nonparametric correlationtest (Spearman’s rho) between average brand image

scores and correct identification rates revealed a positiveand statistically significant relationship (rho = .715, p <

.01). In light of the results showing that misclassificationto weak COOs is particularly damaging for strongbrands, the lower misclassification rates of strongbrands might “cushion” the adverse effects on brandimage evaluations and purchase intentions. However,this is no cause for complacency because, on average, it

is more likely for a brand’s origin to be misclassifiedthan to be correctly identified even in the case of strongbrands. Therefore, ensuring that consumers can associ-ate the brand with its correct COO (thus attaining ahigh correct identification rate) is still important even if a strong brand is involved.

Surprisingly, favorable misclassification (i.e., to COOswith a better image than the true COO) does not resultin actual gains in either brand image perceptions or pur-

chase intentions. Indeed, strong brands suffer losses forboth outcome variables. This indicates that the conse-

quences of COO misclassification are asymmetrical—that is, brands have (considerably) more to lose than togain if their true origins are misclassified. Managerially,this finding reemphasizes that, in general, COO misclas-sification should be considered a threat to companiesand their brands and should be dealt with as such. This

finding also casts doubt on the effectiveness of “foreignbranding” strategies (Leclerc, Schmitt, and Dubé 1994),whereby a company intentionally chooses a brand name

that is incongruent with the brand’s true origin to asso-ciate the brand with another origin. A good example isthe U.K. brand Hinari, which, as might be expected, isoften erroneously perceived to be an Eastern brand (i.e.,it is typically associated with Japan, South Korea,China, or Taiwan). However, in all instances, misclassi-

fication results in a less favorable brand image andlower purchase intentions for Hinari compared with itstrue (U.K.) origin (see Tables 1 and 2). Nonetheless, thebrands examined in our study all belong to a functionalproduct category, and it remains to be seen whethersimilar findings also apply to hedonic brands for whichforeign branding strategies are more common.

Tables 1 and 2 also show that misclassification of a weak

brand to the United Kingdom leads to gains in brandimage evaluations. Although the perceived COO image of the United Kingdom (the home country) is not thestrongest in the marketplace, it still generates positive

effects. A domestic country bias effect (Balabanis andDiamantopoulos 2004b) appears to be in place, and thekey beneficiaries of erroneous brand domestication arethe weak brands. In contrast, a domestic country bias

effect for the strong brands is not evident in our study.

Finally, consumers’ inability to classify a brand to a COO(i.e., nonclassification) also has adverse consequences forboth brand image evaluations and purchase intentions;this occurs regardless of whether the focal brand’s trueorigin has a strong or weak country image. Thus, itappears that classification to any COO is preferable to

nonclassification. From a managerial perspective, thisimportant finding indicates that brand names that pro-vide no clues as to their likely origin might negativelyaffect consumers’ brand image perceptions and purchaseintentions. A good example in our study is the LG brand;almost two-thirds of respondents were unable to link LGto any COO, and only a small minority (less than 5%)was able to classify it correctly (see Tables 1 and 2). Insummary, the consequences of nonclassification are simi-lar to those of adverse COO misclassification; therefore,

from a company point of view, nonclassification shouldbe seen as a threat to branding.

LIMITATIONS AND FURTHER RESEARCH

In this study, we examined the consequences of theunderstudied phenomenon of COO misclassification

and nonclassification on behavioral outcomes (brandimage evaluations and purchase intentions). We con-ducted the study in a naturalistic context (i.e., using real

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112  Journal of International Marketing

brands and their natural, unmanipulated occurrence/ distribution in the marketplace) and in one of the mostglobalized industries (consumer electronics). Ourresearch provides a fresh approach to the study of COOin a more realistic setting in which people act on theirperceptions of the origin of the brand as (correctly or

erroneously) stored or classified in their memory andnot in an impromptu manner or on the basis of artifi-cially manipulated information that prespecifies the cor-rect origin. This is consistent with advice in the recentliterature that “the important COO cue with regard toorigin management is the COO that consumers associ-ate with the product or the brand” (Josiassen and Harz-ing 2008, p. 268, emphasis added). Our approach alsoavoids making the “dubious assumption that consumers

actually know or seek the origins of brands when form-ing judgments and making purchase decisions” (Samiee,Shimp, and Sharma 2005, p. 392), which has been thesubject of much criticism in COO research (see, e.g.,

Balabanis and Diamantopoulos 2008; Usunier 2006).

Despite the strengths of our study’s design, however,there are issues of generalizability because the results are

from a single country (albeit one with a significant pres-ence in the sector) and a single product category.Although a variety of other electronic products (e.g.,DVD players, television sets) are also the brands inves-tigated herein, thus offering some scope for generaliza-tion beyond the specific product category, future studiesshould examine the phenomena of COO misclassifica-tion and nonclassification in different contexts (e.g., less

globalized industries) and other countries with a loweror higher presence of local brands in the focal productcategory. Furthermore, additional variables (e.g., brandequity dimensions, perceived risk, and product owner-ship) should be used as outcomes.

A related issue is the extent to which the observed insen-sitivity of consumers to favorable COO misclassifica-tion (i.e., the lack of gains) is outcome specific or a moregeneralizable phenomenon. More refined research into

additional factors that might affect the consequences of COO misclassification, such as differences between

novice versus expert consumers and whether the(wrong) origin perceived by the consumer relates to anaffinity or animosity country (see Oberecker, Riefler,and Diamantopoulos 2008; Riefler and Diamantopou-los 2007, respectively), should complement the insightsprovided in our study.

Finally, an interesting question is what happens to brandimage perceptions and purchase intentions if consumers

are alerted to the fact that they have misclassified theCOO and are subsequently informed of the brand’s cor-rect origin? Experimental research aimed at establishingthe extent to which consumers are willing to updatetheir brand evaluations and buying intentions and themagnitude of any adjustments undertaken would be

most welcome to shed light on this important researchquestion.11

In conclusion, our findings support the view that theCOO that is relevant for consumer decision making is“that country which consumers typically associate witha product or brand” (Usunier 2006, p. 62). However,we do not share the view that “the actual place that thebrand originates from is almost irrelevant” (Thakor

1996, p. 27), because discrepancies between the true ori-gin and consumers’ perceived origin of a brand almostinvariably have negative consequences in terms of brandimage perceptions and purchase intentions. Similarly,

when consumers are unable to associate a brand withany origin, both COO misclassification and nonclassifi-cation are likely to hurt the brand.

NOTES

1. Hennebichler (2007) examines brand ownership ineight product categories, namely, televisions, DVDplayers, computers, cell phones, hi-fi systems, refrig-erators, microwave ovens, and digital cameras.

2. As Balabanis and Diamantopoulos (2008, p. 58)point out, “consumers tend to assign many differentincorrect origins” (see also Samiee, Shimp, andSharma 2005).

3. Given m origins, only one of which is correct, themaximum number of wrong COOs to which a brandcan be classified is m – 1.

4. The equation is also known as Glass’s Δ (Glass,

McGaw, and Smith 1981) and is routinely used inmeta-analytical research for similar purposes.

5. Space limitations prevent us from discussing thesestatistics in depth; for full details, see Gleser andOlkin (1994).

6. The choice of product category is consistent with advice

offered in the literature that “high-quality researchdesigns in the study of COO knowledge should onlyask consumers for information on products that they

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Brand Strength and Country-of-Origin Image 113

would actually consider using” (Josiassen and Harz-ing 2008, p. 266).

7. We purposely did not use Harman’s one-factor test toassess CMV in our study, because although widelyused, “it is an insensitive test” (Podsakoff et al. 2003,

p. 889).

8. The absence of a CMV biasing effect in our studymust be interpreted from the perspective of the disci-pline to which the research is affiliated. In this con-text, research evidence indicates that method variancediffers across disciplines, with marketing belonging tothose areas affected least (see Cote and Buckley1987).

9. Recall that adverse (favorable) misclassifications cap-ture allocations to a COO with a weaker (stronger)image than the brand’s true COO.

10. Note that the average correlation coefficient (for all12 brands) between brand image evaluations andpurchase intentions comes to .28. This confirms the

distinct nature (discriminant validity) of the focaloutcome variables; the latter thus provide comple-mentary insights into the consequences of COO mis-classification.

11. The authors thank an anonymous reviewer for rais-ing this important research question.

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ACKNOWLEDGMENTS

The authors thank two anonymous reviewers for con-structive feedback on previous versions of this article.

THE AUTHORS

George Balabanis is Professor of Marketing in the CassBusiness School at the City University of London and

holds a doctoral degree from Strathclyde University.His research focuses on the areas of international mar-keting strategy, cross-cultural consumer behavior, andonline relationships. His work has been published inoutlets such as Journal of International Business Stud-

ies, Journal of the Academy of Marketing Science, Jour-nal of International Marketing , Long Range Planning ,

 Journal of Business Research, British Journal of Man-agement , International Business Review, International Marketing Review,   Journal of Global Marketing , andEuropean Journal of Marketing , as well as many pres-tigious conference proceedings.

Adamantios Diamantopoulos holds the Chair of Inter-national Marketing at the University of Vienna, Austria,as well as visiting professorships at Loughborough Uni-versity and Ljubljana University. His main research

interests include international marketing and researchmethodology, and his work has appeared in, among oth-ers,   Journal of Marketing Research,   Journal of Inter-

national Business Studies,   Journal of the Academy of Marketing Science, International Journal of Research inMarketing ,   Journal of Service Research,   Journal of International Marketing , Journal of Retailing , and Jour-nal of Business Research.

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