Niraj Dawar & Philip Parker

Marketing Universals: Consumers'Use of Brand Name, Price, Physicai

Appearance, and Retailer Reputationas Signais of Product Quality

Marketing universals are defined as consumer behaviors within a segment and toward a particular product cate-gory that are invariant across cultures. Using several definitions of culture and three different criteria for universal-ity, the authors evaluate whether the use of brand, price, retailer reputation, and physical product appearance assignals of quality are marketing universals for consumer electronics products. Using a sample representing 38 na-tionalities, they find that there are few differences in the use of quality signals across cultures for a high priority seg-ment of consumers. They draw conclusions for the adaptation versus standardization debate and argue that cer-tain behaviors are likely to be universal, whereas others are not. Understanding such differences is essential to de-signing international marketing strategies.

E fforts to internationalize products present mailceting man-agers with important ciiallenges. Hair care products,

home/office equipment, medical products, motion pictures,consumer electronics, and various industrial products arenow often launched simultaneously on two or three conti-nents, making the "localize versus standardize" debate anurgent and important one in terms of the stakes involved(Levitt 1983). Managers must balance using culture or na-tional borders as a segmentation criterion with identifyingone or two key segments based on individual (or noncul-tural) consumer characteristics across countries (Price1992). The former criterion is appropriate when consumerbehavior is "culture bound," as it is for certain food prod-ucts, leading to iocal adaptation strategies. The latter ismore pertinent for ' 'culture-independent'' behavior, as it isfor medical products, leading to globalized strategies. De-spite increased managerial and academic interest in thisarea, Jain (1989, p. 70), in an extensive review of the litera-ture, notes that ' 'empirical studies in the area of interna-tional marketing are limited," especially those that focuson "gaining insights into the standardization issue." Onthe basis of a comprehensive review of research in interna-tional marketing, Douglas and Craig (1992, p. 299) call for' 'further research to identify potential cross-national seg-ments and their characteristics." Farley and Lehmann(1992), in a review of international studies of marketing re-

Niraj Dawar is an Assistant Professor and Philip Parker is an Associate Pro-fessor of Marketing, INSEAD, Fontainebleau, France. The authors arelisted In alphabetical order. They thank V. Padmanabhan, Lydia Price,three anonymous reviewers, and both the previous and current editors forhelpful comments. They also thank Pamela McNeil for her excellent re-search support.

sponse, also note the lack of research emphasizing cross-national similarities, or "laws," in marketing. We respondto these calls by proposing a research approach to detect"marketing universals," defined as segment- and product-specific consumer behaviors that are invariant across cul-tures or countries. We illustrate this approach using psy-chographic, demographic, and economic data from amatched sample representing over 30 nationalities. We dem-onstrate how international marketing managers can choosebetter between emphasizing cultural differences and play-ing up differences at the individual consumer level when for-mulating international segmentation and marketing mixstrategies.

We begin our discussion by outlining the critical meth-odological issues involved in testing for similarities in be-havior across cultures. From a managerial perspective, thechoice between adaptation and standardization across mar-kets must explicitly consider the differences and similaritiesin consumer behavior for a particular setting. For example,a seller of medical diagnostic equipment and supplies ismost interested in evaluating the impact of culture on salesto narrow segments across countries (e.g., doctors, hospitaladministrators, health officials). For most managerial situa-tions, general tests of cultural differences across total popu-lations would be irrelevant, given that most products are tar-geted to specific segments (even within a given culture/country). In our study, we consider marketing universalsfor consumer electronics products; the relevant (high-prior-ity) segment across countries for this category are relativelyyoung, mobile, affiuent, and educated consumers. In partic-ular, we examine an area of consumer behavior that re-cently has received great attention: consumer's use of prod-uct quality signals. In setting prices, for example, managers

Joumai of MarketingVol. 58 (April 1994), 81-95 Marketing Universals / 81

make assumptions about the extent to which, say, Britishconsumers use price as a signal of product quality versusFrench or Japanese consumers. Should the degree of signaluse differ substantially across countries, segmentation andadaptation of the marketing mix based on culture may be op-timal; if not, we might conclude that signal use levels areuniversal, and standardization may be warranted for similarsegments across cultures.

Our study focuses on the four product quality signalsthat have received the greatest attention in the marketingand economics literature: branding, pricing, physical fea-tures, and retailer reputation (often called "store name")(see, for example, Brucks and Zeithaml 1991; Enis and Staf-ford 1969; Milgrom and Roberts 1986; Olson 1977; Raoand Monroe 1989; Wheatley and Chiu 1977; Wolinsky1983). As stated in a review by Rao and Monroe (1989, p.351), the results from over two decades of research on thistopic have yielded "little consensus on the magnitude, gen-eralizability, or statistical significance" of signal use. Fur-thermore, the literature has yet to consider whether these sig-nals, which are fundamental to marketing, are used to thesame degree by consumers in all cultures. Naturally, the de-gree of universality in consumer behavior is at least partlydue to the product category chosen for study. There may beless universality for products such as food, for which con-sumer behavior is more culture bound. Although our studyis limited to one broad product category (consumer electron-ics), it illustrates a procedure for testing marketing univer-sals while providing substantive findings with respect to sig-nal use. We conclude with a discussion of our findings andsuggestions for further research in this area.

Investigating iVIariceting UniversalsTo explore the cultural versus individual segmentationcontroversy, it is important that we consider two method-ological concerns: (1) What are appropriate definitions ofculture and their implications for sample characteristics?and (2) How can one substantively test the universality of aconsumer behavior?

Defining Culture and Sample Requirements

Many cross-cultural studies take random samples in severalcountries and perform various mean or variance tests to dem-onstrate similarities or differences in behavior (see Greenand Langeard 1975; Hempel 1974; Lorimer and Dunn1968). This approach is not appropriate for studies of mar-keting universals that focus on a particular product beinglaunched in several markets and targeted to specific seg-ments. One serious drawback of randomly selecting respon-dents is that factors other than culture that differ across coun-tries may drive the observed differences in behavior; inother words, noncultural factors may be confounded withcultural factors (see Clark 1990; Farley and Lehmann 1992;Katona, Strumpel, and Zahn 1973). For example, it may bethat one country is poorer than another, and therefore wewould find differences; if we accept per capita income as anappropriate definition of culture, then the rejection of simi-larity is justified. However, if we were to compare personswith similar incomes across cultures (even across countries

with very different average incomes), we may no longerfind statistical differences—the rejection of similarity maynot be warranted (i.e., noneconomic differences might haveno effect, given that similar consumer segments are com-pared in both countries). Matching samples on relevant seg-mentation criteria is proposed by researchers who find thatmarketers should identify segments (in, for example, bothrich and poor countries) that represent the same target mar-ket to understand actual cultural differences (Kale and Su-dharshan 1987; Levitt 1983; Sheth 1986; Simmonds 1985).Which matching criteria are used will depend on the cate-gory studied but will most likely include economic criteria(wealth, professional status) and/or demographic criteria(age, marital status, lifestyle, family size) that characterizespecific segments (Anderson and Engledow 1977; Engle-dow, Thorelli, and Becker 1975; Katona, Strumpel, andZahn 1973). For example, we would sample doctors for med-ical products, engineers for technical industrial products,and farmers for agricultural products; samples drawnshould be representative of the segments targeted by the mar-keter, and not of the overall population of each culture orcountry.

Once relevant segments and matching criteria havebeen chosen, a prerequisite to the study of multiple culturesis a clear definition of cultural groups, with boundaries thatare believed to yield some commonality within and diver-sity between groups. Operationally, Hall (1966) identifiesthe most important characteristic of culture for our study:Culture is common to members of "cultural groups," orclusters with well-defined boundaries. For example, lan-guage might be seen as a cultural dimension that delineateswell-defined groups (anglophone versus francophone) be-cause all members of one group have a common bond thatis not shared by other groups. Often, nationality is used asa surrogate for culture because all members of a given na-tional group typically share a similar history, language, andpolitical and educational environment. As discussed previ-ously, a person's demographic status (e.g., profession, mar-ital status), economic status (e.g., income, wealth), and psy-chographic profile (e.g., extrovert versus introvert) are nottypically considered cultural dimensions, unless all mem-bers of each culture generally share in these dimensions(e.g., not all Germans have the same standard of living orprofession). Ultimately, it is internal commonality withinsamples that determines the operational definition of cul-ture in most cross-cultural studies in marketing.

Most international marketing studies evaluate two orthree cultures whose boundaries are defined by nationalities(e.g., Japan and the United States). Clark (1990) distin-guishes "cross-cultural" studies as those that compare twoor more national groups and "national character" studiesas those that define cultures on the basis of underlying be-havioral (or other) variables; the former method enables usto extrapolate to populations within each national group,the latter to groups with common traits. In evaluating mar-keting universals, the use of multiple definitions of cultureis appropriate. As described subsequently, the definitionsadopted for our study include two cross-cultural compari-sons made on the basis of (1) nationality and (2) ethno-

82 / Journal of Marketing, April 1994

geographic trade area; and two national character compari-sons made on the basis of (3) national behavioral traits and(4) state of the retail marketing infrastructure. If marketinguniversals exist, they should be independent of the defini-tion of culture adopted. For the present study, four defini-tions (discussed in the next section) were chosen for their in-terpretability and managerial relevance. Typical cross-cultural studies use a single definition of culture, potentiallylimiting their conclusions to that particular definition.

Testing for Marketing Universais

Researchers can investigate marketing universality on threelevels: (1) the existence of specific consumer behaviors, (2)the relative order of importance of behaviors across cul-tures, and (3) the absolute level of behavior across cultures,or the independence of behavior from culture. According tothis hierarchy, at the first level the behavior of interest ex-ists in all cultures. Some anthropological studies of culturaluniversals have used existence tests (Murdock 1945). For ex-ample, to find whether humor is universal, anthropologistsobserving the behavior of humans in various cultures foundthat a universal exists if all cultures studied demonstratesome existence (though not all people in the culture use orparticipate in the activity to the same degree). Although wemay be interested in the universal existence of certain con-sumer behaviors, a simple existence test would not be suffi-ciently informative in the marketing context. The secondlevel of universality requires the more rigorous criterion ofsimilar rank order of use/importance. For example, the Brit-ish may be more responsive to brand advertising than thereputation of the retailer; the opposite may be true for theFrench. Budgets may be equal (proportional) across coun-tries, though the relative weight accorded to each elementvaries. If the rank order of importance for a set of consumerbehaviors is identical across cultures, then managers shouldconsistently allocate efforts in a similar order across market-ing activities (e.g., always focus on brand image first, retailreputation second, etc.).

Relative universals, however, do not entail an identicalallocation of resources across cultures or countries. For ex-ample, brand image may be ranked most important in all cul-tures, but may be extremely important in Britain and lessconsequential in Germany, relative to Britain. The most strin-gent criterion of universality requires that there be no mean-ingful difference between the absolute levels of behavioracross cultures. It is likely in such cases that individual dif-ferences dominate cultural differences in explaining behav-ior. Universals in levels require that the absolute level of be-havior be the same across cultures. Clark (1990) formulatesa general hypothesis, which is commonly used in cross-cultural studies and appropriate for testing differences inlevels:

HQ: Consumers in different cultures exhibit the same levels of(a specific) behavior.

H,: Consumers in different cultures exhibit differing levels of(a specific) behavior.

Although this general formulation may be appropriatewhen testing for differences across cultures or countries, the

strongest possible statistical test for universality using sucha formulation is limited to the nonacceptance of the alterna-tive. Clearly, if we were to perform a census across two cul-tures (or if sample sizes were very large), we necessarilywould find some statistical differences in the level of signaluse and therefore would have to reject the notion of strictuniversality in levels (the null hypothesis is no difference inlevels). The impossibility of accepting the null hypothesison "identical behavior" prevents a strict statistical test ofuniversality in levels. Given the nature of classical hypoth-esis testing, therefore, we would classify consumer behav-ior as a marketing universal of absolute importance if we ob-serve no statistical difference in mean behavior from one cul-ture to another (given appropriately large samples and a pre-determined rejection criterion) and if the variance in individ-ual behaviors is statistically independent of one's culture(given the various definitions of culture). The logic behindthis is akin to that of a pooling test. Two samples can bepooled if no statistical differences are observed, given appro-priately large sample sizes and predetermined rejectioncriteria.

This test rationale places emphasis on managerially rel-evant differences, as opposed to statistical differences. Wemust be formal in stating that this "test" does not imply astatistical acceptance of the null hypothesis, but rather a"substantive" acceptance of the null hypothesis (differ-ences will certainly exist across any two populations, butthese will be small or managerially inconsequential; see Saw-yer and Peter 1982).

StudyIn the following discussion, we consider marketing univer-sals in a cross-cultural study of the use of signals of qualityby consumers. After briefiy reviewing the theoretical litera-ture in this area, we describe our study and summarize ourfindings.

Theoretical Background: Consumers' Use ofProduct Quaiity Signals

In choosing among competing brands, consumers are facedwith uncertainty of product performance and, more gener-ally, quality. With neither infinite time horizons nor the in-centive to perform thorough comparative studies prior to pur-chase, consumers are likely to rely on heuristics to gaugequality across competitive products. Both the economicsand marketing literature have found that signals mostlyserve as heuristics in assessing product quality when (I)there is a need to reduce the perceived risk of purchase(Jacoby, Olson, and Haddock 1971; Olson 1977), (2) theconsumer lacks expertise and consequently the ability to as-sess quality (Rao and Monroe 1988), (3) consumer involve-ment is low (Celci and Olson 1988), (4) objective quality istoo complex to assess or the consumer is not in the habit ofspending time objectively assessing quality (Allison andUhl 1964; Hoch and Ha 1986), or (5) there is an informa-tion search preference and need for information (Nelson1970, 1974, 1978). The most prevalent signals studied in-clude brand names (Akerlof 1970; Darby and Kami 1973;Olson 1977; Ross 1988) or brand advertising (Milgrom and

Universais / 83

Roberts 1986), product features or appearance (Nelson1970; Olson 1977), price (Leavitt 1954; Milgrom andRoberts 1986; Olson 1972, 1977; Rao and Monroe 1989;Scitovsky 1945; Wolinsky 1983), and product/retail reputa-tion, store names, warranties, or guarantees (Cooper andRoss 1985; Emons 1988; Olson 1977; Rao and Monroe1989).

Although few generalizable results have emerged,brand names have been found to be more important thanprice, which is in turn more important than physical appear-ance; retail reputation or store name has been found to beleast consequential in signaling product quality (Jacoby,Szybillo, and Busato-Schach 1977; Rao and Monroe 1989).The relative importance of these signals generally followtheir specificity, or the extent to which a particular signal isnot shared across competitive products. A brand name, forexample, typically is shared by only a few products withina competitive line of products and is therefore a very spe-cific signal. Prices and physical features, on the other hand,can be shared to a greater extent across competing productsand are therefore less specific. Retailer reputation is evenless specific, because a typical retail function is to sell sev-eral competing product lines having a relatively broadrange of quality. The more specific a signal, all else beingequal, the more likely it will provide information that is use-ful in an assessment of product quality. This distinction isconsistent with the belief that signals are relied on as a func-tion of their predictive value (the extent to which the con-sumer believes that the signal is predictive of a product char-acteristic such as product quality; see Cox 1962; Olson1972, 1977). As noted by Rao and Monroe (1989, p. 351),however, the generalizability of these findings is limited.To our knowledge, the overall relative rank order across thefour signals we consider have been observed only in studiesof consumers from the United States.

Culture and ttie Use of Signals

Are the empirical results on signal use reported in the liter-ature specific to the country in which they were conducted(the United States)? Unless signal use is universal in all re-spects (existence, relative importance, and absolute impor-tance), one might expect to find substantial differences inthe use of signals to the extent that the factors contributingto signal use also vary from one culture to another. Al-though not all the factors reviewed previously have beenstudied on an international basis, some studies have arguedor empirically demonstrated international variation for fac-tors that could contribute to signal use: risk aversion (Hof-stede 1980; Hoover, Green, and Seagert 1978; Mitchell andGreatorex 1990) and expertise, refiecting life cycle effects(Jain 1984; Kirpalani and Macintosh 1980; Parameswaranand Yaprak 1987).

On the basis of the theoretical arguments presented pre-viously, one can hypothesize that signal use should be cor-related positively to a culture's risk aversion level. Signaluse may be correlated negatively to a culture's level of ex-pertise (or product familiarity), provided that expertise im-plies less reliance on signal use for the category studied; asRao and Monroe (1988) note, however, experts may be-

come more dependent on signals, given their better under-standing of the relationship between, say, brand names andquality.

Some authors have argued that existing differencesacross cultures have minimal or no impact on certain con-sumer behaviors (Elinder 1961; Fatt 1964; Levitt 1983;Ohmae 1985; Roostal 1963). In an extensive review,Douglas and Craig (1992, p. 298) find that studies of organ-izational buying behavior, consumer cognitive processesand information seeking patterns, consumer attitudes rela-tive to complaining, consumers' response to advertising,and levels of involvement with products ' 'often find strongsimilarities among consumers across countries." This con-vergence often is argued to be the result of the high penetra-tion of mass media advertising in all societies, increasedcompetition among products and media, increased globaliza-tion of products, and increased international mobility of con-sumers. The effects of languages, educational institutions,customs, and other culture-specific factors are swamped bythese converging forces (Levitt 1983). Some authors argue,however, that such convergence has not occurred or that thedifferences actually are increasing (Boddewyn 1981; Fisher1984; Foumis 1962). This debate highlights the importanceof empirical studies of consumer behavior across cultures.The next section describes our empirical contribution tothis debate with respect to signal use.

The DataSample Characteristics

Testing for marketing universals requires a matched sampleof respondents (i.e., identifying a relevant segment for a par-ticular product category) across countries to avoid attribut-ing differences to unrecognized definitions of culture. Weconsider consumer electronics products, for which the tar-get segment has been identified as young, mobile, affluent,and educated consumers; this product-segment combinationwas identified by companies that motivated aspects of thisstudy and previous academic studies on such products (seereview in Gatignon and Robertson 1985). The question-naire was administered to 691 masters of business admini-stration students representing 38 mostly Western industrial-ized countries and Japan (640 responses were useable/complete). An OECD-oriented sample is managerially desir-able, given that some 60-70% of consumer electronics aresold in those countries. Had we considered agriculturalequipment, or certain other categories, our sample wouldunderrepresent countries of managerial interest.

The MBA admissions criteria provide an effective andexpedient matching procedure for our study, given thatsuch consumers represent a primary target segment for thecategory considered. Respondents are matched, in part, bythe admissions procedure on the basis of age, education, pro-fessional aspiration, academic potential, and, indirectly, gen-eral income levels. For culture clusters as defined by Hofst-ede (1980), Table 1 shows the relative sample sizes, propor-tions, means, and standard deviations for various individualand country/culture measures. As the statistics reported inTable 1 indicate, the sample is well-matched on several in-

84 / Journal of Mariteting, April 1994

TABLE 1Panel A

Sample Characteristics Across Hofstede's Culture Clusters^

Characteristics

Age

Years at university

% male% married% with children% owning house% owning car

Latin(N=202)

28.7(2.7)

6.5(1.2)

92.144.530.536.089.6

Germanic(N=85)

28.3(2.4)

6.1(1.2)

89.437.614.131.895.2

Angio(N=238)

27.8(2.2)

5.2(1.3)

73.122.3

8.046.282.4

Nordic(N=62)

28.4(2.4)

6.4(1.3)

87.125.8

8.245.287.1

Other(N=53)

28.6(2.2)

5.9(1.8)

88.737.718.446.075.0

Total(N=640)

28.3(2.4)

5.9(1.4)

83.932.916.740.986.2

standard deviations are in parantheses.

Panel BPercentage of Respondents Reporting Use of Information Sources, by Cuiture

informationSource

Personal friendsConsumer magazinesSalespeopleNewspaperTV commercialsRadio commercials

Latin(N=198)

97.4767.8662.6942.4221.24

5.18

Germanic(N=83)

97.5965.4868.2961.4517.073.57

Anglo(N=235)

96.1778.7275.0043.5325.88

7.93

Nordic(N=62)

98.3950.0080.3354.8425.81

4.84

Other(N=53)

96.2383.0250.0048.0830.0010.00

X2

1.32<:26.0a19.3a11.28''4.5c3.80':

ap < .001*>p < .05<=p not significant

dividual-level criteria such that the subsample from any clus-ter is comparable to the subsample from any other clusteron several demographic variables (age, marital status, edu-cation). The most important question in the marketing con-text is whether these respondents are likely to represent theyoung, affluent, mobile, and educated consumers from theircountries/cultures of origin. The sample generally consistsof young (mean = 28 years of age) professionals who are atsimilar stages in the family life cycle (mostly single, withmany owning one home/apartment and a car—standard-ized, cross-cultural proxies for wealth). This group of con-sumers, therefore, is likely to be representative of the pri-mary target market identified for the consumer electronicsindustry.

The questionnaire was administered in English (all re-spondents were fluent in English). The use of a common lan-guage for all respondents helps overcome potential lan-guage-based response biases common to multicultural re-search (Douglas and Craig 1983). Questionnaires were com-pleted during the first weeks of the MBA program to avoidpossible "MBA group think" biases. Concern may arise,however, over whether persons with such backgrounds orprofiles have shed some of their basic culture, or share a"cosmopolitan" culture. Furthermore, because the entiresample spoke English, the potential dampening of culturaleffects may be a concern. However, for some countries, es-pecially European ones, respondents having English as a sec-ond language is almost unavoidable given national educa-tion systems' emphasis on English. This factor refiects a nat-

urally occuring characteristic of the segment, which is alsolikely to exist for those not considered in our sample butwith similar profiles from the countries represented. Hall(1966) notes that individuals, even after great effort, are un-able to devoid themselves of their culture of origin. In addi-tion, we can examine previous cross-cultural studies relyingon similar (matched) samples to determine if differenceswere detected. Hofstede's (1980) study often is cited as themost comprehensive study of cross-cultural differences atthe individual level. Hofstede's respondents were alsomatched across cultures: all had English as a first or secondlanguage, were mid- or senior-level managers in compa-nies, attended business school or executive training semi-nars, and actually worked for the same firm (IBM). In spiteof this matching, Hofstede nevertheless finds substantialdifferences in self-reported behaviors across cultures (e.g.,uncertainty avoidance). Similar consumer research also hasfound cultural differences among business students (Dur-vasula et al. 1993).

To evaluate possible biases further, three statistical testswere performed on the sample. The first involved evaluat-ing possible contamination effects due to the local campusenvironment. Questionnaire pretests reveaied that responsesare not affected by whether the questionnaire is admini-stered during their first days or early weeks on campus. Thesecond tested for differences among persons having livedin, worked in, or traveled to multiple countries prior to ar-riving on campus and those who had not. Likewise, no sig-nificant differences in responses to the dependent variable

Mari(eting Universals / 85

(signal use) were detected. Finally, should respondentshave shed their cultural origins, one would expect little dif-ference in most behaviors that may be affected by culture.In Table 1, Panel B, we report the third test, which exam-ines differences across cultures for frequency of media usein gathering information on consumer electronics products.If our sample is culturally representative, we would expectmedia usage to vary on the basis of structural differencesacross the nationalities sampled. For example, televisionand radio advertising is restricted in Germany, whereas in an-glophone (the United States, Canada, and the United King-dom) and Latin (France, and Italy) countries, it is more com-mon. Similar structural or local business climate differencesexist for newspaper and consumer magazines across coun-tries. We find that these differences are detected in respon-dent's self-reported reliance on various media for gatheringinformation on consumer electronics; that is, media reliancefor this category and segment is not universal in relative orabsolute levels. In the case of the Nordic countries, for ex-ample, we observe that consumers rely on salespersons to amuch greater extent than consumer magazines, the use ofwhich is not widespread (unlike in other countries). Thesetests enhance our confidence in concluding that any lack ofstatistical differences in behavior is likely to indicate a mar-keting universal for this category-segment combination.Likewise, observed differences in behavior can be ascribedconfidently to differences in culture or country-of-origineffects.

Individual Measures

A three-part questionnaire was used to gather data on eachrespondent's use of infonnation signals, country of origin,and basic demographics. Part 1 of the questionnaire askedfor purchase intentions and ownership ieveis for various con-sumer electronic products (e.g., video cameras, video cas-sette recorder, digital audio tape recorders, high-definitiontelevision) and served to frame respondent's thinking tothis category. Marketing universals, should they exist, willat a minimum be category- and segment-specific (a univer-sal for one product, however, may not be so for another). Inaddition to being relevant to the sample considered, con-sumer electronics products were chosen for several reasons:(1) respondents fi'om most countries will have at least a min-imum level of awareness because this category typically rep-resents 5-10% of annual household purchases when pur-chases are made; (2) the category has high levels of qualityuncertainty, which would imply the use of signals; (3) house-hold penetration (Gatignon, Robertson, and Eliashberg1989), retailing methods, pricing, and promotion levelsvary substantially across the countries represented in oursample; (4) despite general physical standardization, somephysical adaptation is necessary for many products due tocross-country infrastructure differences (e.g., voltage, roomsize), and taste differences (e.g., the extensive use of woodpaneling on consumer electronics in the United States ver-sus the preference for matte finishes in European markets);and (5) the existence of both national and internationalbranding. A pretest questionnaire indicated that the broadcategory of consumer electronics allowed variation on such

aspects as individual expertise, involvement, and ownershipwithin the category. Part 2 of the questionnaire asked forself-report measures of use for each of the four signals ofquality, information search, category interest/familiarity, aswell as a set of risk-related measures.' Measures were ob-tained on a 7-point Likert scale.

Part 3 of the questionnaire collected basic demographicand shopping habit data on individual respondents (age, na-tionality, measures of asset wealth, number/type of informa-tion sources used during search). Self-report measures werechosen over alternatives (projection, field observation, orthe indirect measures of experimental research) given thatpretest results were consistent with the experimental litera-ture on signal use. Although self-reports ' 'can provide sat-isfactory results" if undertaken with care (Nunnally 1978,p. 586), possible biases in responses were minimized byquestion order sorting; verification of responses with admis-sion data, where possible, revealed a high level of self-report accuracy (e.g., demographic or other individualmeasures).

Identification and Measurement of Cuiture

We represent each respondent's culture using the fourclustering procedures mentioned previously: (1) nationality,(2) ethno-geographic trade area, (3) national behavioral clus-ters, and (4) level of engagement in the retail sector. Thefirst clustering method involves defining a person's nation-ality or country of origin as his or her culture (for examplesof this approach in marketing, see Campbell et al. 1988; Far-ley and Sexten 1982; Hoover, Green and Seagert 1978; Jo-hansen, Douglas, and Nonaka 1985; Lehmann andO'Shaughnessy 1974; Lindberg 1982; Weinberger andSpotts 1989). Though descriptive and convenient to imple-ment in multivariate studies (e.g., a country can be repre-sented as a dummy variable, comparisons can consist of t-tests), this approach provides little insight into the factorsthat contribute to cross-national differences, and conclu-sions may be limited to the form "the French are differentfrom the British because the French are French and the Brit-ish are British." This method is nevertheless useful in iden-tifying cross-national differences, should they exist. Four na-tionalities have large representation in our sample: theUnited Kingdom (20%), France (18%), the United States(9%), and Germany (7%). Of the 38 nationalities, thosewith fewer than 15 respondents are clustered in an "other"category; the remaining 533 respondents belong to 12 nation-ality clusters representing industrialized countries fromNorth America, Europe, and Asia. The "other" categorymostly represents economically lesser developed, and non-EEC (European Economic Community) Europeancountries.

The second cross-cultural clustering approach is man-agerially motivated. Many intemational marketing texts dis-

'Each respondent was asked "In general how likely are you to person-ally use brand names [or other signals] as a sign of quality for purchasingelectronics products?" Because consumer electronics are high in experi-ence and credence qualities, respondents were queried on physical "appear-ance" rather than "features" to emphasize extrinsic signal use, which isconsistent with the three other signals studied.

86 / Journai of Marketing, Aprii 1994

cuss global segmentation strategies based on regional prox-imity and/or regional trade affiliation (see, for example,Czinkota and Ronkainen 1988; Keegan 1989). We com-monly see trade press assertions that Americans, Europe-ans, and Asians represent different cultures. In the aca-demic marketing literature, countries are frequently clus-tered by trade area, continent, or other ethno-geographicgrouping (see Takada and Jain 1991, who compare Pacificrim countries with the United States; and Assmus, Farley,and Lehmann 1984; Lilien and Weinstein 1984; Sultan, Far-ley, and Lehmann 1990; and Tellis 1988, who compare theUnited States with Europe). Here, we define cultural clus-ters along the following lines: (1) North American coun-tries (United States and Canada), (2) member countries ofthe EEC, (3) European countries that are not EEC mem-bers, and (4) others. Seventy respondents from countriesnot belonging to any of these trade areas were placed in the"other" category. The advantage of this definition is thatthese groupings are used widely in intemational trade; onedisadvantage, which is similar to using nationalities as cul-tures, is that one cannot attribute cross-cluster differences.Nevertheless, in testing for marketing universals, this clus-tering approach may detect differences in signal use not cap-tured in the previous clustering procedure (e.g.. North Amer-icans may have higher signal use than Europeans).

The next two clustering approaches are based on "na-tional characteristics" and potentially enable us to (1) attrib-ute differences to causal factors, and (2) extrapolate beyondthe countries included in the sample (provided those coun-tries share similar characteristics). The national characteris-tics approach requires samples drawn from a large numberof countries for which characteristic data exist. For exam-ple, see Gatignon, Robertson, and Eliashberg (1989), whoconsider cosmopolitanism, mobility, and women in thelabor force (or gender roles) as national characteristics in astudy of innovation diffusion in Europe. Other studies haveused such measures as income per capita, number of na-tional languages, television penetration, and death rates perthousand either to cluster countries into various groups oras independent measures to explain cross-cultural or na-tional differences (see, for example, Douglas 1979; Greenand Langeard 1975; Sethi and Curry 1973). The drawbackof using national characteristics, however, is that it mayforce researchers to exclude some 100 countries around theworld for which little data exist, other than basic economicstatistics, or for which data collection would be prohibi-tively expensive.

The first national characteristic approach employed inour study involves the use of national behavioral measuresto define a country's culture (see, for example, Eysenck andEysenck 1969; Inkeles and Levinson 1969; Kluckhohn andStodtbeck 1961; Peabody 1985). As noted by Clark (1990,p. 73) a reasonable approach to selecting from among themyriad of cultural dimensions "is to begin with the na-tional character literature per se, and then to establish back-linkages to justify dimensions selected." From among the di-mensions previously explored in the literature, we rely onHofstede's (1980) study, which defines a country's cultureon the the basis of responses to a battery of work-reiated

questions; other cultural measures were not available acrossenough countries to be useful. Hofstede finds that four gen-eral factors are useful in defining a country's culture: powerdistance (i.e., how far people perceive themselves to befrom ultimate decision makers), individualism, masculinity,and uncertainty avoidance (or "relation to risk"). On thebasis of these four factors, countries have been clusteredinto 11 different groups in terms of their relative scores oneach. Given the degrees of freedom by cluster of the sampleanalyzed, we have used 4 of Hofstede's culture clusters: Nor-dic, Latin, Germanic, and Anglo. We also consider an"other" cluster for those countries not belonging to the 4 de-fined clusters.

The second national characteristic approach defines cul-tures on the basis of the level of a culture's engagement inthe retail sector, which shows variance across countries.The percentage of the labor force engaged in the retail sec-tor is used as a surrogate for this characteristic. Although,like any surrogate measure, this may not capture the entireconstruct, it does refiect variances in direct contact, via em-ployment, that a population has with retailing activities.This measure may be confounded with other stmctural dif-ferences, such as those inherent to different economic sys-tems or that refiect the infrastucture of retailing. The lattermight include differences in the concentration of shoppingmalls, hypermarkets, and warehouse distribution or locallaws. However, it also should be noted that the measure isnot a surrogate for the levei of ' 'development of the market-ing infrastructure," because countries such as Denmark, Fin-land, France, Germany, and Sweden are categorized as"low," and Australia, Austria, Belgium, Canada, Japan,and other are categorized as "high." The measure is validto the extent that the proportion of the work force employedin a tertiary sector such as retail services is a reasonableapproximation of the level of consumer marketing activity.It has the advantage of capturing an aspect of culture not ad-dressed by the other clustering methods. The sample wassplit into two clusters on the basis of this measure (usingthe FASCLUS routine in SAS, the F-statistic value on clus-ter means is significant atp-value < .01). Because data onthe state of the retail labor-force variable were not availablefor all countries in our sample, the effective sample size forthe t-tests in this analysis is reduced to 583. This clusteringscheme is appealing in that managers often make contrastsacross such groups (because the problems faced by market-ers in economically developed countries may be similaracross these countries, but dissimilar to those encounteredin developing economies). Table 2 contains the samplesizes for clusters representing the level of engagement inthe retail sector.

The use of multiple definitions of culture ensures thatour test for universals is not culture-definition dependent. Asummary of the country membership in the culture clustersis provided in Appendix A.

Analysis and Results

Universals in Existence

Table 2 reports mean responses to the four signal questions

iViariceting Universais / 87

TABLE 2Means and Standard Deviations of Signai Use by Clusters Using Three Ciustering iVIethods^

Brand

Price

Physicalappearance

Retaiierreputation

Across signaisby ciusters

NorthAmerica(N=88)

5.72(.13)

4.37(.15)

4.06(.17)

3.73(.18)

FP<

Trade Area

EEC(N=431)

5.49(06)

4.32(.07)

4.00(.08)

3.30(.08)

Non-EEC(Europe)(N=51)

5.73(.17)

4.57(.21)

4.57(.22)

3.39(.23)

Non-Aligned(N=70)

5.51(.14)

4.53(.18)

3.83(.19)

3.60(.20)

FP<

1.29.28

.72

.54

2.45.07

1.97.12

152.001

Latin(N=202)

5.41(08)

4.30(.10)

4.00(.11)

3.27(.12)

Hofstede'sCulture Clusters

Germanic(N=85)

5.55(.13)

4.11(.16)

4.09(.17)

3.11(.18)

Angio(N=238)

5.61(08)

4.38(.09)

3.93(.10)

3.59(.11)

Nordic(N=62)

5.58(.15)

4.56(.19)

4.52(.20)

3.24(.21)

Other(N=53)

5.72(.16)

4.77(.20)

3.96(.22)

3.66(.23)

FP<

1.20.31

2.01.09

1.780.13

2.170.07

268.001

RetailDevelopment

Low(N=253)

5.45(.13)

4.25(.16)

4.05(.17)

3.25(.18)

High(N=330)

5.58(.10)

4.52(.13)

3.98(.14)

3.49(.15)

FP<

1.750.19

4.590.33

0.230.64

3.11.08

217.001

^All values are means on 7-point scales. Numbers in parentiieses are standard deviations.

for tiiree culture-clustering schemes. If eacii culture indi-cates some use of signals, tiien we can conclude that the useof signals to judge product quality is a marketing universalin existence fdt this category-segment combination. Aii sig-nals scored significantly greater than 1 on the 7-point Lik-ert scale in the aggregate and across aii definitions of cui-ture (aii p-vaiues < .001). With respect to the category stud-ied, consumers' use of the four signals to judge product qual-ity is, therefore, a marketing universal in existence for thiscategory and segment. The next, more stringent test exam-ines the reiative importance of the various signais acrosscuitures.

Universais in Reiative importance

The next step is to consider whether the rank order of sig-nai use differs across cuitures. We begin by simply observ-ing ranks based on sampie means, to compare our resuitswith the existing literature. Across the entire sampie, brandname scores highest (mean score = 5.6), foiiowed by price(mean score = 4.3), physical appearance (mean score = 4.1),and retaiier reputation (mean score = 3.4). An anaiysis of var-iance (generai iinear modei; GLM) on the entire sample re-veals that the use ieveis of the four signais are significantlydifferent (Fj ,C,Q5 = 211, p < .001). Furthermore, six paired t-tests reveal that each signai is significantly different fromaii others (applying the Tukey correction for family-wiseerror at/7-vaiue < .01). More importantly, the seif-reportedmean signai use measures repiicate the order of importanceattached to each signai reported in experimental consumerresearch conducted in the United States (Rao and Monroe1989). The more specific a signai, the more it wiii be reiiedon by consumers. Brand name is found to be more impor-tant than price or physical appearance as a signai of quaiity.Furthermore, price and physicai appearance were found tobe more important than retaiier reputation as a signai of quai-ity. In spite of the statistically significant differences inmeans, price and physicai feature signais are rated to beused to simiiar degrees.

Is the generai rank order found in U.S. experimental re-search and our aggregate sampie replicated across cuituralgroups? An anaiysis of rank orders across mean values ofbrand name, price, and retailer reputation reveais the foi-iowing for all four definitions of cuiture:

• mean brand name signals are always ranked highest,• mean retailer reputation signals are always ranked lowest,

and• mean price signals are always ranked between brand name

and retailer reputation.

The rank order of physicai appearance is generally beiowprice and above retaiier reputation, yet there are some excep-tions to this. For non-EEC European countries, price andphysical appearance are given equal importance (score =4.57). For some of the 38 individuai nationaiities, physicaiappearance is ranked above price: the United States, Den-mark, Germany, and Belgium. For one nationaiity, Canada,physicai appearance is rated iower than retail reputation.^Again, because differences are seen in nationai ciusters, it isdifficuit to attribute differences in ranks to some underlyingfactor, such as risk avoidance (in which no differences inrank are detected); differences in rank order that do exist,for this study, are generaiiy the result of smaii differencesin means. For the nationai characteristic ciusters, the rankorder of mean responses is invariant to the definition of cui-ture. In summary, the data indicate universality in reiativeimportance across mean behavior ieveis for the product cat-egory (consumer eiectronics), and segment (young affiuentadults) studied: Brand name is universally used more thanprice or physicai appearance, which are in tum used morethan retaiier reputation as signals of product quality.

Beyond simpie comparisons of rank orders revealed bymeans, a number of nonparametric approaches can be ap-piied to test similarity of ranks across samples (see Hoi-

^Resuits of analysis by nationality are not presented in tabuiar form dueto space limitations. Significant results are reported in the text.

88 / Journal of Marketing, April 1994

TABLE 3Correlations in Signal Use (N = 640)

Price

Brand -

Physicalappearance

Retailreputation

Price

1.00

.37(.001)

.25(.001)

.17(.001)

Brand

1.00

.23(.001)

.15(.001)

PhysicalAppearance

—

1.00

.17(.001)

RetailReputation

—

1.00

lander and Wolfe 1973). One approach involves testing thehomogeneity of means across cultures by signal; homogene-ity implies rank equivalence in average behavior. This ap-proach is proposed for testing the third level of universal-ity—absolute importance.

Universais in Absoiute importance

Universality in rank order does not imply that each signal isused to the same extent across cultures. We now focus onthe most stringent criterion for universality: Does the abso-lute level of use for each signal vary from one culture to an-other? We test mean differences across cultures using fouranalyses, following the operational definitions of culture.As discussed previously, we define universality by lack ofstatistical (meaningful) differences across cultures. Thoughconservatively ignoring family error rates, the considerationof which more readily leads to the rejection of differences,we set the critical rejection criterion of .05 for universality(not rejecting the null hypothesis of equivalence in means).Across all four definitions of culture and all four signals, 16analyses of variance (GLM ANOVAs) revealed no meaning-ful differences in signal use across cultures; all p-values aregreater than .05. Marginally significant differences are ob-tained primarily for price and retailer reputation (see Table2). Brand name, like physical appearance, is accorded prac-tically the same level of importance across cultures. Thesetwo factors are the most standardized across countries, espe-cially in the category of consumer electronics. Price and re-tailer reputation, on the other hand, have limited standardi-zation across countries. These marginal differences, how-ever, are exaggerated themselves because they do not explic-itly consider family error rates across the 16 tests.

The results of this more stringent test for universalitycan be summarized as follows: with respect to the culturesinvestigated and for the category and demographic groupstudied, the use of brand name, price, physical appearance,and retailer reputation as signals of product quality appearsto be universal in absolute importance (at /7-value < .05). Inaddition to culture clusters, represented by dummy varia-bles, a battery of interval- and ratio-scaled variables repre-senting national characteristics also were regressed againstsignal use; these include urban density, income per capita,penetration of electricity, penetration of radio, penetrationof television, penetration of telephone services, literacy lev-els, Hofstede's culture measures, and the percentage of

workforce in various industries (e.g., agriculture, retailing,manufacturing). None were found to be statistically signifi-cant. Similarly, several nonlinear relations were tested andalso yielded nonsignificant results.

Because we also find universality in existence and rela-tive importance across the four definitions of culture (at/7-value = .05 level), it appears that cultural segmentation,on the basis of signal use levels, is not justified. Noncul-tural, individual-level factors seem to provide greater poten-tial for segmentation across populations (within this partic-ular demographic group); this possibility is explored next.

individuai Versus Cuiturai Differences

Segmenting consumers on noncultural dimensions requirestesting for individual differences in signal use. One prob-lem our sample presents in detecting individual differencesis that the sample is matched to gauge differences in culturebetter. Ideally, one would take several matched samples(demographic groups) across a large number of countries totest (1) whether the behavior is universal across segments,(2) for factors driving individual differences within a cul-ture across segments, and (3) whether factors explaining in-dividual differences are universal across cultures. Our sam-ple only allows us to look for individual differences withina given demographic group, having found no meaningful dif-ferences across cultures.

In Table 3, we report the Pearson correlation coeffi-cients across tbe various signais. The positive and statisti-cally significant correlations wouid indicate that there arepeople who are generally more "signal oriented" than oth-ers. The more one uses brand names as a signal, for exam-ple, the more one also will use price, physical appearance,and retailer reputation as signals of product quality. Further-more, correlations are high among the more specific signals(brand and price), which in turn correlate less with the leastspecific signals (retailer reputation and physical features).This fact reinforces the previous finding that specificitymight affect the use of a given signal.

If culture cannot be used to differentiate between highand low signal users, what individual factors might be appro-priate to segment consumers? The literature gives some the-oretical guidance. On the basis of the literature reviewed pre-viously and the work of Rao and Monroe (1988), who con-sidered expertise levels, a battery of individual measureswere considered as possible drivers of signal use. These in-clude purchasing behavior and demographic measures re-flecting (1) differing levels of preference across various in-formation sources (search methods), (2) differing levels ofrisk proneness, (3) differences in willingness to pay forhigh-priced brands, (4) differing levels of product interestand familiarity, and (5) differences in demographics (educa-tion, family status).

Because each theoretical construct is multidimensional,the questionnaire used multiple items across several dimen-sions within each domain. We considered variables thatwould best reflect segmentation schemes used in practice.For example, preferences for information search involvedasking which sources are often used to collect product-related information (e.g., television advertisements, per-

Marketing Universals / 89

TABLE 4Correlation Between Individual Level Factors and Signal

Individual Measures Brand

Signals

PricePhysicai

AppearanceRetaii

Reputation

1. information search preferencesSeek advice from othersLikely to research before buyingRely on personal friends''Reiy on salespersons''Rely on teievision advertisements''Rely on radio advertisements''Subscription to magazines

2. Risk pronenessCreate ciothes fashion trendBuy iatest eiectronic gadgetsLikely to bet on horses, casino, etc.

3. Wiiiingness to pay for highpriced brands'̂Personal computersHigh definition teievision

4. Category interestIntention to purchase:• cabie television• high definition television• satellite teievision• video cassette recorder

Perceived relative advantage• cable teievision• high definition teievision• video cameras• video cassette recorder

5. Demographics and educationMarried''Number of childrenNumber of carsYears at universityKnowledge of personal computer

.21

.09

.10

.11 .16.10

.08

.11

.09

.14

.09

.09

.19

.19

.17

.25

.13-

.10

.15

.15

.10-

-.08-.08-.10-.10-.13

.15

.28

-.12

.10

.20

.10

_—

-.08-

-.11

—.13

.09_

.08

.14——

-.09——-_

.12

.09

.13

.13

.20

.16

.12

.09

.18

.13

^Reported correlations significant at .05 level.''Spearman correlations are reported on median splits of the signal concerned.^Similar results obtained on eight other categories.

sonal friends); risk measures inciude the likelihood of onecreating a clothes fashion trend (social or trend settingrisks), gambling on horses or in casino games (financialrisk), or purchasing the latest electronic gadget (innovation/product adoption risk).

In Table 4, we summarize the correlations betweenthese individual segmentation criteria and the four signalsstudied. Only correlations with p-values < .05 are reported;unreported correlations for a given variable are generally ofthe same sign as those reported but are insignificant. Recail-ing that culture is uncorrelated with signal use, Table 4clearly demonstrates that individual factors suggested in theliterature better explain signal use variation. The correla-tions in Table 4 would suggest the following:

• The more people rely on a particular signal, the more theyalso seek information from other sources; the sources used,however, vary from one signal to another;

• Risk proneness is correlated positively with signal use; peo-ple more likely to take on certain forms of risk are also thosewho use signals the most';

• Willingness to pay for high-priced brands is positively asso-ciated with signal use; for example, "brand-prone" peopleare more price inelastic, reflecting the value of the brand sig-nals; this correlation is higher for the more specific signals,reflecting these signals' relative value in quality judgments;

• The higher the category intention to purchase (reflecting in-terest or involvement) or the higher the perceived benefits ofcertain products (mostly audio/visual), the higher the reli-ance on signals, especially brand and price; and

'in a secondary analysis, risk-prone consumers are found to give rela-tively less importance to specific signals (e.g., brand names) and more im-portance to less specific signals (e.g., physical appearance and retailer rep-utation) than risk-averse consumers; one can hypothesize, therefore, that sig-nals reducing risk most (e.g., specific signals) have a relatively low weightin risk-prone consumers' assessments of product quality.

90 / Journal of Marketing, April 1994

• People who have more formal education or are technologyoriented (knowledge of personal computers) are less likelyto use signals, especially brand names.

The individual factors affecting signal use show somevariance across the signals themselves. For example, brandname signal users are more likely to use personal contacts(salespeople, friends) than those who are retail reputation ori-ented; family status and education levels are found to bemostly significant (negatively correlated) for only brand sig-nals among the four studied. Because the empirical and ex-perimental research in this area has focused primarily on in-dividual expertise. Table 4 would suggest that a number ofadditional factors lead to individual signal use levels. Wemust caution, however, that this analysis is limited to a sin-gle demographic group and must be seen as exploratory.We can conclude, however, that individual factors sug-gested in the literature, not cultural factors, determine sig-nal use levels within this demographic group. Segmentingacross signal users by culture group appears less relevant,therefore, than segmenting across individuals, irrespectiveof culture."*

Discussion

Manageriai impiications

Answering calls for empirical studies (Clark 1990; Douglasand Craig 1992; Farley and Lehmann 1992; Jain 1989), weexplore the existence, relative importance, and absolute mag-nitude of signal use across several cultures. Using a multi-cultural sample, we find that self-report measures of signaluse replicate the findings of experimental research con-ducted in the United States; namely, brand name signals arerelied on more heavily than price or physical appearance,which are in turn relied on more heavily than retailer repu-tation for judging product quality—the more specific a sig-nal, the more likely it is to be relied on in assessing quality.Results of the study reported here indicate that there arefew managerially meaningful differences in the use of thesesignals across cultures (for the segment and category stud-ied). Our analysis indicates that variances in signal use areindependent of culture (as defined using four methods), andare likely to be driven by individual factors. The four sig-nals studied appear to be universal in existence, relative im-portance, and absolute importance for this target market forconsumer electronics.

Differences at each of these levels would have implica-tions for the kinds of decisions that would be optimal. InTable 5, we present an illustrative set of decision rules forthe possible outcomes of this kind of research: differencesin existence, relative importance, or absolute level. For eachtype of difference, an example of the kind of finding is pre-sented, and implications drawn for consumer behavior.Thus, for example, if brand names are found not to be used

•"When culture and individual factors are considered simultaneouslyusing multiple regression, 32 likelihood ratio tests based on 48 regressionsindicate the rejection of cultural factors in favor of individual ones for allfour quality signals and all four definitions of culture (p-value < .05); theadjusted R-squares vary from .00 (models with only culture) to .26 (mod-els with only individual factors).

TABLE 5Illustrative Decision Rules for Marketing

Resource Aiiocation Under Different MarketingUniversality Conditions

Level ofUniversaiity Violations, impiications, and Decisions

Existence Violation: Brand name is found not be used typ-ically as a signal of quaiity, but use dependson culture.implication: Consumers in cultures in whichbrand name is not used may reiy on aiternativesignais.Decision: Do not squander resources buildingthe brand. Instead adapt marketing mix tomore salient signals in markets in which brandname is not a salient signal.

Reiative Violation: Consumers are found to rely on re-tailer reputation nfiore than brand names as asignal of quality in some cuitures, whereas inothers the reverse situation holds.Implication: Differences exist in the weightsthat consumers assign to various signals intheir assessments of quality.Decision: Priority across marketing elementswiil differ from culture to culture: a high priorityfor price setting in one cultural market versus ahigh priority for creating channel presence in an-other.

Absolute Violation: Although consumers are found tohave the same order of priorities of signals inassessments of quality across cultures, theydo differ in the absolute weight accorded tothem.implication: Cultures differ not in the relative im-portance accorded to the signals, but in the ab-solute levels.Decision: Standardized relative resource alloca-tion that may differ in terms of the absolute lev-els of the budgets across cultures. Budgetscan be adapted further on the basis of differ-ences, such as media habits and informationseeking.

universally (i.e., a violation of the existence rule), then con-sumers may rely on alternative signals. Marketers would beill-advised to invest resources in building a brand andmight be better off focusing marketing efforts on develop-ing other elements of the marketing mix. Similarly, implica-tions can be drawn for violations of universality in relativeor absolute importance for cultures in which brand names(price, physical appearance, or retailer reputation) are notused as signals of quality (Table 5).

On the basis of our study, it appears that for the productcategory investigated, cultural segmentation based on sig-nal use levels may not be justified. Does this imply that mar-keting strategies should be uniform across the countries stud-ied? The answer is clearly no. As reported previously, sev-eral culture-specific behaviors were detected in our sample,including the use of various information sources (e.g., con-sumer magazines versus salespeople). Clearly, some behav-iors are likely to be universal, whereas others are not. Themarketing manager must understand the extent to whichkey behaviors vary from one culture to another. Local busi-ness conditions, legal restrictions, and other culture-bound

iUlariceting Universals / 91

structural factors certainly will affect the implementation ofstrategies. Our study would indicate that managers shouldfocus most on reinforcing brand signals to an equal degreeacross countries yet consider using different media to do so,given that local marketing environments affect informationgathering.

The identification of a segment of consumers who donot differ across cultures in their use of signals of quality inthe purchase of consumer electronics is an opportunity forcompanies to apply all the recommendations for a standard-ized marketing program on at least a few dimensions. Thissegment of consumers—young, educated, affluent, and up-wardly mobile, described loosely as ' 'yuppies''—indeed ap-pears in many of the industrialized countries. Managerially,it may be worthwhile to uncover further dimensions of uni-versality and culttiral differences in this segment. The dimen-sions on which such studies would focus would be deter-mined by the requirements of the product category beingmarketed. It also may prove worthwhile to uncover othersegments that could be considered universal, at least on keydimensions.

Cultural universals, as discussed in virtually all market-ing texts, are general human behaviors that have been ob-served to be common across cultures. For example, humoris said to be a cultural universal. Marketing universals, onthe other hand, provide a more pragmatic focus on behav-iors within those segments that are relevant to the productor product class under study. As a result, a study of market-ing universals provides more actionable results than one ofof cultural universals.

Thus, the findings from our study can be interpreted toimply that culture or country boundaries can be a less impor-tant segmentation criterion than individual factors across cul-ture for certain signal use behaviors. Although some sug-gest that managers should "think globally, act locally," uni-versals in marketing imply "think individually and act glob-ally." Global action, however, may best be limited to behav-iors found to be marketing universals. Furthermore, individ-ual criteria that dominate cultural dimensions must be iden-tifiable and actionable across cultures. The recognition ofboth relevant individual segmentation criteria and market-ing universals is a prerequisite to global strategies. In post-survey discussions with respondents, many expressed the be-lief that their particular culture would be found to use, for ex-ample, brand name more than other cultures. Such beliefsare consistent with ethnocentric biases, or what Farley andLehmann (1992, p. 1) call the "myth in international mar-keting that all things are different" when one crossesborders.

The literature has yet to investigate universals in market-ing. We propose that marketing universals, which are prod-uct and segment specific, be detected using a systematic ap-proach that includes (1) the use of multiple definitions andoperationalizations of culture, (2) matching samples fromsegments across a large number of countries or cultures (al-lowing for the attribution of differences), and (3) testing uni-versality on three levels: existence, relative importance, andabsolute importance. The result of such research will helpguide managers in approaching multinational markets. For

example, it might be that a certain behavior exists univer-sally (e.g., consumer reliance on outdoor advertising), butthe behavior's relative importance diverges across cultures.Likewise, relative importance may be universal, yet abso-lute importance may diverge across culture. In our study,we found that price is used as a signal of quality to thesame extent across cultures; should this not be the case, per-ceived-quality pricing certainly should be adapted to localcultures. Although marketing allocation decisions can bedriven by divergences across cultures, this study highlightsthe basis on which managers should explore universals inconsumer behavior.

Limitations and Further Research Directions

For the category and demographic group studied, it wouldappear that the use of signals is culture-independent. Natu-rally, the usual caution in interpreting a null result (no dif-ferences) is to be exercised. Different results may be foundfor culture-bound products such as certain food items orthose adapted on the basis of local needs (Ohmae 1985).This potential difference across products raises the questionof which comes first, the global product or global consumerbehavior? The use of a globally uniform strategy mightelicit similar behaviors from consumers worldwide. In anycase, it would be worthwhile to study other product catego-ries and segments.

Another potential limitation of the study is that the sam-ple consists of a relatively homogeneous group of respon-dents across countries. The homogeneity of the samplecould be a limitation if it were the source of the similaritiesobserved across cultures. However, because we do observedifferences in certain behaviors (Table I, Panel B), theymight not be expected to appear if the matching of the sam-ple had obliterated them. The choice of a sample with homo-geneity in terms of age, income, education level, and othervariables was intentional given the product category studiedand the need to directly attribute differences in behavior toculture. Katona, Strumpel, and Zahn (1973, p. 140) arguethat "the higher the education the less likely it is that peo-ple will be affected by the particular history and culture intheir country." Our study empirically supports this conten-tion and stands in contrast to authors who have observedthat tastes and behavior diverge across cultures as people be-come better educated and more affluent (e.g.. Fisher 1984).Although we argue that it is more appropriate to comparesamples from different countries that are matched on a setof demographic and, if possible, psychographic variables toincrease confidence in ascribing differences to culture, werecognize the need to conduct further research that com-pares samples matched across multiple segments simul-taneously (across multiple cultures).

The methodology used in arriving at conclusions aboutuniversality is that of classical hypothesis testing. The usualcaution in interpreting a null result is advised because thelack of statistical significance may not be an appropriate cri-terion for universality. Confidence in the results is in-creased by the use of a large sample drawn from a large num-ber of countries, and the use of many operationalizations ofculture. The relative use of the signals studied do appear to

92 / Journai of Marketing, April 1994

be valid for this product category. However, we did nottake into account possible interactions between these sig-nals as in the addition of value to a brand if it is channeledthrough a retailer of repute or the potential damage to the re-tailer's brand if it is seen carrying a poor brand name.

The use of signals by consumers is a function of manyfactors, including individual consumer level factors, socialor cultural factors, and product and market context. In addi-tion, these factors can interact to provide an even more com-plex picture. We assume the simple but realistic scenario ofcomparing cultural factors with individual ones for a givenproduct category. The significance of certain individual-level factors and the rejection of differences across culture,using four different definitions of culture and a large sam-

ple, increases our confidence that the use of signals for thisproduct category is a marketing universal.

Finally, as with many cross-cultural studies, we mustqualify our findings to the sample and context studied. Al-though our sample is drawn from 38 nationalities, mostlyfrom industrialized countries, we did not consider some100 nationalities, and an even larger number of cultures. Fur-thermore, the representation of each culture/nationality wasunbalanced. We are limited to maintaining that signal use isa marketing universal, therefore, to one stratum of consum-ers across the countries studied, especially those well repre-sented in the sample; further efforts to explore additionalstrata (segments) in a wider sample of countries/cultures(e.g., based on age, income, professional standing) wouldbe desirable.

APPENDIX AList of Nationaiities by Ciuster

Trade-Area ClustersHofstede'sCulture Clusters

Engagement inRetail Sector^

1. North America (N=88)CanadaUnited States

2. EEC(N=431)BelgiumDenmarkFranceGermanyGreeceIrelandItalyLuxembourgNetherlandsPortugalSpainUnited Kingdom

3. Non-EEC-Europe (N=51)AustriaCzekoslovakiaFinlandHungaryIcelandNorwayPolandRussiaSwedenSwitzerlandUkraine

4. Other (N=70)ArgentinaAustraliaBrazilChileChinaHong KongIndiaJapanLebanonMexicoNew ZealandSingaporeSouth AfricaSyriaVenezuelaZimbabwe

1. Latin (N=202)ArgentinaBelgiumBrazilItalyFrariceSpainPortugal

2. Germanic (N=85)AustriaGermanyIsraelSwitzerland

3. Anglo (N=238)AustraliaCanadaIrelandNew ZealandUnited KingdomUnited States

4. Nordic (N=62)DenmarkFinlandIcelandNetherlandsNorwaySweden

5. Other (N=53)ChileChinaGreeceHong KongHungaryIndiaJapanLebanonLuxembourgMexicoSingaporeSouth AfricaSyriaVenezuelaZimbabwe

1. High(N=330)AustraliaAustriaBelgiumCanadaHong KongIrelandItalyJapanLuxembourgNetherlandsNew ZealandNonwaySingaporeSpainSwitzerlandUnited KingdomVenezuela

2. Low(N=253)ArgentinaBrazilChinaDenmarkFinlandFranceGermanyGreeceHungaryIndiaIsraelLebanonMexicoPortugalSouth AfricaSwedenSyriaZimbabwe

°Data on this variable were available only for 583 subjects.

Marketing Universals / 93

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