Cross-national Laws'' and Differences in Market … due to life experiences of people from different...

Cross-national "Laws'' and Differencesin Market Response

John U. Farley • Donald R. LehmannThe Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania 19104

Columbia Business School, New York, New York 10027

International differences in general, and cultural differences in particular, exert profoundinfluence on what people buy. In modeling market response, highly visible international

differences in purchase behavior seem to lead to an assumption by management scientists thatthere are large parallel international differences in market response to such things as price andadvertising. In an interpretive review of market response models, we do find internationaldifferences in response parameters, but we also find that parameter differences due to cross-national factors tend to be smaller than differences related to technical characteristics of themodel or to product/market specifics. We suggest two new intermediate categories of gener-alizability between the extremes of "everything is the same" and "everything is different." Wealso argue that one promising approach to international generalization is through appropriatestatistical adjustment of parameters from existing models.{Marketing; Meta-analysis; International Differences)

The myth in international marketing is that everythingis different. Managers often cling to the myth for un-derstandable reasons; their lives are dominated by atangle of regulatory, economic, and technical differencesamong countries, coupled with the obvious differencesin languages, cultures, and values.

The myth of international difference seems to carryover to management science, prompted no doubt byhighly visible differences in essentially arithmetic char-acteristics of multinational markets—for example, poorpeople buy less of most things than do rich people andso do poor countries. These visible differences in marketsize distract attention from the major thrust of mostmanagement science work on markets, which is to es-timate sensitivities of demand, particularly to instru-ments under the control of managers working in thesemarkets.

Being able to generalize internationally about quan-tities bought and factors affecting market response couldbe very useful for both market assessment and for de-signing market programs for countries or cultures withwhich the firm has no experience. Relatively straight-

forward regressions using demographic and economicvariables can often predict international differences inaverage per capita consumption quite accurately (Arm-strong 1970). If we find relatively small or predictableintemational differences in market response parameters,forecasting response in a new country or culture is fairlystraightforward.

If interproduct differences in response are much largerthan corresponding international differences, a firm'sproprietary market knowledge may transfer well to anew country, at least as a first approximation. On theother hand, large and unpredictable international dif-ferences in market response represent a significant bar-rier to entering new countries or to the transfer of per-sonnel skills from country to country.

Despite a growing volume of international and cross-national research, the myth that international differ-ences are both large and unpredictable is largely unex-amined. This paper attempts an interpretive examina-tion of a set of 18 studies which make implicit or explicitinternational comparisons of market response. We ap-proach the search for "laws" by attempting to determine

0025-1909/94/4001/011l$01.25Copyright © 1994, The Institute of Management Sciences MANAGEMENTSCIENCE/VOI. 40, No. 1, January 1994 111

FARLEY AND LEHMANNCross-national Laws and Differences in Market Response

the consistencies. Where possible, we attempt to esti-mate or at least make judgements about the "effect size"of international differences in response parameters.

The Universality of ManagementScience Theories and theComplicated Realities of "Laws"Our discussion of generalizability involves theories andmodels which seem to generalize fairly easily, and con-stants and parameters which do not generalize as easily.

Most management science theories and models arenot only transnational but are virtually context free. Intheir abstract form expressed mathematically in Greekand Latin script (as they are even in Russian journals),the country of origin is not discernable. Objective func-tions of resource allocation models are shared in appli-cations and in journals from all over the world—in richcountries and poor, in centrally planned and marketeconomies, etc. In the present case, the general speci-fication of market response models as a function of con-trollable variables (price and advertising, for example)and noncontroUables (population and income, for ex-ample) is virtually universal, as are the econometricprocedures used to assess parameters.

In contrast to theories and models, there are sizeableinternational differences in the values of the myriad ofconstants and response parameters that enter the costand response functions of management science models.The generalizability of management science models ingeneral, and international generalization in particular,depends largely on the empirical scale of these differ-ences. Establishing the existence of differences is notnearly as important to generalization as measuring theirsize and trying to explain their sources.

Discussions of international generalization seem topolarize on "everything is the same" versus "everythingis different." Sameness seems associated with Univer-sality of parameters, perhaps with some inconsequentialrandom measurement error—a situation which seldomoccurs in practice. Absence of Universality often seemsto be interpreted as complete Idiosyncracy, where wecan leam nothing from knowledge development in othercontexts. We think that two less extreme cases provemore useful in many real situations, and they turn our

attention to "laws" as tendencies to have similar re-sponse patterns or to have significant but identifiablecomponents of difference.

• When a basic model generalizes but parameters dif-fer systematically in different settings (e.g., in developedvs. developing countries, for consumer vs. industrialgoods, etc.), we call this Parametric Adjustability. Pa-rameters vary around a mean which is not equal to zero,and there are identifiable systematic components in thisvariability. In practice, we have rarely encountered acase in which idiosyncratic error terms are so large thatsome sort of generalization is hopeless—along the in-ternational dimension or any other. Parametric adjust-ability conceptually underlies meta-analyses in whichstatistical techniques are used to identify systematiccomponents in the observed distribution of estimatesof a given parameter. In a test for cross-national gen-eralization, the null hypothesis of generalizability is thatthere are no systematic international differences in theparameter estimates. This can occur in presence of othersystematic differences in parameters. An estimate of aparameter for a new situation can generally be con-structed from the grand mean and appropriate signifi-cant terms from some sort of ANOVA decompositionof a collection of parameter estimates. An example ofintemational parametric adjustability involves productionfunctions; significant differences in labor and capitalcoefficients were found for developed and developingcountries but there was homogeneity of both labor andcapital coefficient within each of the two country groups(Farley etal. 1985).

• When parameters are the same up to an error termwhich cannot be reduced by parameter adjustment ofthe sort just described, we have a situation of StochasticUniversality. If variability of the error term is also rel-atively small, we are probably as close to Universalityas we can get, in practice. There is no claim that exactlythe same result occurs every time, but there is a claimthat parameters are (a) nonzero, and (b) not system-atically related to a laundry list of interstudy differencesin research technology, measurement, setting, or prob-lem definition. The null hypothesis is that parameterscenter about a nonzero mean; this has the benefit ofdiverting attention from the generally discredited nullhypothesis of zero value underlying the individualstudies that make up the body of knowledge about pa-

112 MANAGEMENT SCIENCE/VOI. 40, No. 1, January 1994


rameters. In some cases, the unexplained variability maybe so large that generalization is not practical, but thisis a case-to-case and not a general problem. Fixed re-sponse parameters may exist even in the presence ofsignificant differences in means of such variables asconsumption or knowledge of substantial measurementerror. An example of Stochastic Universality arose whenno systematic patterns in parameters could be foundwhich related to differences in variable definitions, re-search disciplines, methods, or sampling frame in 37published Fishbein models (Farley et al. 1981). Thevariability in the two model parameters is thus specificto the particular situation rather than common to studycharacteristics. (Unfortunately, there are no interna-tional comparisons available in these studies.) WhenStochastic Universality is established, a sort of averagingof parameters from existing studies (perhaps weightedfor "quality") is an appropriate value for the null hy-pothesis for a new study, and "new" information isadded when a newly-estimated parameter differs fromthis value. A Bayesian procedure is available to combineexisting and new information which becomes availableover time (Sultan et al. 1990).

What Does International LawMean?—Countries and CulturesThe debate on international standardization of marketsand marketing (Levitt 1983, Wind and Douglas 1981)is really a debate on whether differences in consumerresponse patterns allow national boundaries to be usedto define market segments. "International," a proxy fora complex of institutional and environmental differencesthat may affect behavior and/or the ability to behavein certain ways, has at least two meanings, and manydiscussions of the presence or absence of internationaldifferences seem to confuse them.

Differences due to crossing boundaries. We might thinkof these as international differences "in the small." Theyare due primarily to institutional factors which are trulyrelated to the nation state, and differences in behaviorresulting from differences in practices that are approx-imately coincident with nation-state boundaries. Mostof these differences involve restrictions—particularly ofthe flow of information or of goods through distributionsystems—that are breaking down with profound effect

in many parts of the world. Examples are the absenceof broadcast advertising media in Scandinavia and state-owned monopoly distribution systems of various types.

Differences due to culture. We might think of theseas international differences "in the large." Differencesin behavior which are culturally based would exist evenif the world were not organized into nation states. Theyare not due to mechanical or controllable factors butrather due to life experiences of people from differentcultures. The experience of "being Japanese" comesunder this meaning, as does the American frontier folk-lore and the influence of value systems such as Islam,Confucianism, and Christianity. Culture and countryare not synonymous, so cultural factors are only looselyrelated to the nation state, and assuming that a countryvariable is a suitable proxy for capturing culture specificfactors can be dangerous. Few large countries are cul-turally homogeneous, and many are visibly or even le-gally multicultural—a fact which may cause systematicwithin-country measurement differences (Calatone etal. 1985). Models that fit to one culture may not fit wellin others, although we are developing better means ofoperationalizing cultural variables for specification inmodel structures; an example is the inclusion of a Con-fucian scale in a customer behavior model used in Sin-gapore where Confucianism is highly influential (Tanand Farley 1987).

Generalizing through DirectIntrastudy Intercountry ComparisonThe most common approach to international general-ization involves comparative studies which use the sameresearch methodology, usually on a pair of countries.Such studies allow us to make a direct and relativelycontrolled international or at least binational comparisonof parameter values. Many cross-country comparisonsseem to start with a presumption that differences are tobe expected and that similarity will be the surprise. Itis easy to generate a long list of differences, includingcountry and culture, and to conclude (usually withoutany particular evidence) that the available results arenot applicable to a new situation. This tendency to ex-pect to find international difference may result from asort of "availability" bias (e.g., Tversky and Kahneman1973) in that differences are easier to perceive than more

MANAGEMENT SCIENCE/VOI. 40, No. 1, January 1994 113


subtle similarities. For example, intemational differencesin product-level advertising expenditure involving theU.S., Mexico, and Brazil turned out to be chiefly relatedto differences in economic output mix, with advertising/sales ratios (a common basis for setting advertisingbudgets; Lynch and Hooley 1990) for a given productsimilar in all three countries (Leff and Farley 1980).Similarly, organizational climates of large manufactur-ing firms in Australia and the U.S. are practically iden-tical, despite a fairly long list of reasons why they mightnot be (Capon and Farley 1988).

International Differences in 18Comparative StudiesFor examples of comparative studies, we surveyed sixjournals from 1985 to 1991 for cross-national marketresearch: Journal of Consumer Research, Journal of Mar-keting, Journal of Marketing Research, Marketing Science,Management Science, and the Journal of InternationalBusiness Studies. We found only 12 articles which hadobvious cross-national marketing focus—an indicationthat pairwise country comparisons in small numberswill yield only limited quantitative generalization. Wealso include six earlier articles which considered inter-national differences (Lindberg 1982, Lilien and Wein-stein 1984, Lehmann and O'Shaugnessey 1974, Assmus,Farley, and Lehmann 1984, Farley, Lehmann, and Ryan1982, and Farley and Sexton 1982). An interpretivesummary of these appears in Table 1. The 18 papers inTable 1 break down as follows:

• Twelve deal with response parameters either di-rectly or indirectly (papers 2, 3, 5, 6, 7, 9, 11, 12, 13,15,16 and 17). Four of these are meta-analyses (papers2,5, 15 and 17).

• Ten of these report intercountry differences (papers2, 3, 6, 7, 9, 11, 13, 15, 16, 17). In all likelihood thereis publication bias in favor of differences and againstthe null hypothesis of sameness.

• Four of ten studies reporting international differ-ences attempt to explain the sources of the differencesas well (papers 2, 6, 7 and 15) and three (papers 2, 7and 15) succeed at least partially.

• The international differences give the appearancesof being relatively small, but only four of the 12 response

parameter studies provide a basis to calibrate them. Thefour meta-analyses also provide a basis for computingthe relative sizes of the international differences; theseare discussed in detail in the next section.

Examining the 14 studies which are not formal meta-analyses leads to several conclusions. First, there hasbeen remarkably little original work done which ex-plicitly sets out to compare market response acrosscountries. As we observed earlier, most authors seemto expect or at least hope to find differences rather thancommonalities as is indicated by discussions of differ-ences in environment explicitly or implicitly related tohypotheses of anticipated behavior difference. Unsur-prisingly, several of the published comparisons dealwith mean levels rather than response sensitivities (e.g.,Anderson and Coughlan 1988, Gilly 1988, Mansfield1988, Desphande et al. 1986); even if differences inmean consumption are large, differences in responseparameters may be relatively small or nonexistent. Onlyone study (Farley and Sexton 1982) explicitly set outto make a multicountry study of a representative sample,albeit of developing countries, and it found more cross-

. country similarities in parameters than differences. (Thisalso appears to be the only study in the set that wasformally corroborated elsewhere (Black and Farley 1977,Farley and Reddy 1980).

When differences due to country arise, and when theycan be compared within study to other effects, they arerelatively small in relation to other determinants. Forexample, Lilien and Weinstein (1984), using theADVISOR data, found differences in spending levelsbut no differences in the relation between strategic vari-ables and advertising spending between the U.S. andEurope. Lindberg (1982) found models predicting con-sumer durable spending calibrated in the U.S. per-formed well in Europe. Even stereotyping based oncountry of origin seems limited (Johansson et al. 1985).

Moreover, across-country differences are often ex-plainable in terms of variables other than country. Tak-ada and Jain (1991), for example, demonstrate that ap-parent differences due to country in imitation rates fordurables adoption may be explained by the fact thatthe higher Pacific Rim country coefficients were esti-mated in a later time period. Thus the products hadactually been on the market in other countries, making



Table 1 A Sample of Past International Comparisons

Study Domain Method/Data Finding

1. Anderson and Cougian

(1988)

2. Assmus, Farley, and

Lehmann (1984)

3. Campbell, Graham,

Jolibert, and Messner

(1988)

4. Desphande, Hoyer, and

Danthu(1986)

5. Farley, Lehmann, and

Ryan (1982)

6. Fariey and Sexton (1982)

7. Gatignon, Eliashberg, and

Robertson (1989)

8. Gilly (1988)

9. Glisman and Horn (1988)

Choice of channels in

foreign markets

Advertising response

Buyer-seller negotiations

in France, Germany,

U.K., and U.S.

Hispanic consumption

patterns

Buyer behavior

Buyer behavior in family

planning based on

knowledge, attitude,

and practice

Diffusion patterns

Sex roles in TV

advertising in

Australia, Mexico,

and U.S.

Invention performance

in France, Italy,

Japan, U.K., U.S.S.R.,

West Germany, and

U.S.

Survey data; logistic regression

(U.S. firms in semiconductors)

Meta-analysis of econometric

models

Laboratory simulation; Correlations

Survey of Hispanic and non-

Hispanic residents in Texas

Meta-analysis of regression

models

Six-equation regression model fit

to survey data from 8 countries

Comparison of Bass model

parameters for 6 product

categories in 14 European

countries

Content analyses of ads

Patent analysis 1963-1983 for 41

industries; Regression model of

impacts on rate

More likely to be via integrated channels

in Europe than in Japan or Southeast

Asia

Advertising elasticities higher in Europe

than in U.S.

"Marketing negotiations proceed

somewhat differently."

• U.S. driven by profits

• France affected by similarity and

attractiveness of other party

• Germans do best when using a

distributive approach. Similarity

reduces seller's profits

• Status of role important in U.K.

negotiations (buyers do better)

Mean differences among Anglo, weak

Hispanic, and strong Hispanic

identifiers on education, income,

household size, brand loyalty, and

prestige product purchase;

considerable differences between

weak and strong identifiers

No significant differences in response

coefficients over countries

Model generalizes qualitatively over

countries, but differences not related

to socio-demographics.

• 9 of 25 coefficients significant in 6 or

more countries

• 42 of 48 lagged behavior

coefficiencies significant

• All coefficients between 0 and 1, most

on smaller side

• Larger coefficient differences between

variables than between countries

Differences across countries explainable

based on cosmopolitanism, women in

labor force, mobility

"Australian advertisements show

somewhat fewer sex role differences

and Mexican advertisements show

slightly more sex role differences

than U.S. advertisements"

Difference due to "catching up"

process, not to government

intervention

MANAGEMENT SCIENCE/Vol. 40, No. 1, January 1994 115


Table 1 Continued

Study Domain Method/Data Finding

10. Johansson, Dougias, and

Nonaka (1985)

11. Lehmann and

O'Shaughnessy (1974)

12. Liiien and Weinstein

(1984)

13. Lindberg (1982)

14. iVIansfield (1988)

15. Sultan, Farley and

Lehmann (1990)

16. Takada and Jain (1991)

17. Tellis (1988)

18. Weinberger and Spotts

(1989)

Impact of country of

origin on product

evaluations

Purchasing agents in

U.S. and U.K.

Determinants of

marketing and

advertising budgets:

U.S. vs. Europe

Demand for new

consumer durables in

U.S., Denmark,

Finland, France,

Norway, Sweden,

West Germany

Speed and cost of

industrial innovation

Diffusion

Diffusion of durables in

U.S., Japan, South

Korea, and Taiwan

Price elasticity

International content in

TV advertising in U.S.

and U.K.

Simultaneous equation model of

15 attributes from Japan, U.S.,

German cars plus overall rating

data fronn U.S. and Japan.

Comparisons of importance of 17

attributes on 4 purchasing

situations of increasing

complexity

ADVISOR data; regression model

Regressed demand as percent of

consumption expenditures vs.

3rd degree polynomial of past

penetration.

Sample of 200 firms in Japan and

U.S.; percent of cost devoted to

different tasks.

Meta-analysis of diffusion models

Bass model estimated on 8

consumer durables

Meta-analysis of 367 elasticities

from 220 brands/markets

Content analysis

"The results also provide little evidence

of stereotyping based on country of

origin." There is some slight impact

on specific beliefs.

Larger difference in attributes and their

importances over purchase situations

than over countries

"The overall relationship between the

strategic variables and advertising

spending levels is not different

between the U.S. and Europe" (based

on Chow test). Small spending

differences exist as the result of

difference in levels of determinants.

Forecast based on U.S. fits other

countries fairly well. Pooled data

from European countries does better.

Japan has manufacturing advantage in

some industries (e.g., machinery),

none in others (e.g., chemicals). In

general bigger between-industry than

between-country differences.

Differences found in coefficients of

innovation between Europe and the

U.S.

Average coefficient of imitation greater

in Pacific Rim countries, which are

similar, than in U.S. (but time periods

differ so product less new in Pacific

Rim countries).

Elasticities; from Australia more

negative; from Europe less negative.

Larger impacts for product category,

model specification, and data type.

U.S. somewhat more information than

U.K. in 1985, but larger difference in

U.S. from 1977 to 1985; more

significant impacts for involvement

and thinking vs. feeling type products

than for country.

them both better known and understood and less risky.Similarly, apparent differences in diffusion parametersacross countries can be explained based on demographic

differences (Gatignon et al. 1989). Differences in theimpacts of factors on invention rate among France, Italy,Japan, U.K., U.S.S.R., West Germany, and the U.S. can

116 MANAGEMENT SCIENCE/Vol. 40, No. 1, January 1994


be explained as a "catching up" process rather thancountry or its governmental policies (Glisman and Horn1988). Finally, there is some evidence of substantialwithin-country differences (e.g., due to subculture inDesphande et al. 1986 or time period in Weinbergerand Spotts 1989), which may be larger than some trueinternational differences. Taken together, these resultssuggest that the impact of country on response param-eters is in fact relatively small.

Generalizing Internationally AcrossQuite Different StudiesA statistical approach to meta-analysis (Farley andLehmann 1986) allows us to calibrate the magnitudeof cross-national variability in model parameters andcompare it to variability traceable to other sources suchas the research technology used or the product or marketstudied. This approach has the advantage of not re-quiring that each individual study in the meta-analysisto involve an explicit intemational comparison, but onlythat multiple countries are involved in the set of studies.There are four such meta-analyses in Table 1.

Meta-analysis itself is controversial (Wachter 1988).For example, meta-analysis may mix with equalweighting results from research of different quality, al-though experiments with quality weighting (Sultan etal. 1990) have not produced major quantitative changesin results. Further, "publication bias" may be causedby unwillingness to publish papers accepting null hy-potheses (Rust et al. 1990). However, viewed broadly,meta-analysis has particular potential for assessing in-ternational generalizability as an imperfect substitutefor large, controlled international studies. Even in thoserare circumstances when the resources are available,such comparisons are hard to control strictly, and cul-tural differences (not the least of which is language)make strict replication difficult internationally.

Meta-analysis approaches generalization with thepresumption of Parametric Adjustability—that is thatthere are identifiable patterns of differences in param-eters from a broad range of models. Meta-analysis ofresponse parameters uses individual estimates from in-dividual models which share a key output variable butare highly disparate in terms of model specification and

research technology. We decompose these parameterestimates from individual models using ANOVAs ofthe following general form:

= / (B,C, £) where (1)

X represents outcome variables from the individualstudies—for example, measures of response sensitivitiesnormalized into scaleiess elasticities to produce com-parable units of measure.

B represents behavioral variables describing the ex-planatory endogenous or exogenous factors related bythe parameters in X (e.g., attitudes, price, purchase)and their operationalizadons (e.g., weighted summativeform or direct measure).

C represents technical characteristics of the study(such as estimation technique used) and model speci-fication (such as whether a carry-over term is includedin an advertising model or an imitation effect in a dif-fusion model).

E represents characteristics of the research environ-ment, e.g., type of sample, method of data collectionused, type of market studied, and, most importantly forthis paper, country or type of country in which the re-search was done. While our discussion focuses oncountry, it is important to recognize that these otherfactors are important explainers of interstudy parameterdifferences.

Significant differences related to elements of B, C, or£ produce the situation we call Parametric Adjustability.When there are no identifiable patterns relative to ele-ments of B, C, or £, we have a situation of StochasticUniversality. Only if the grand mean is also zero canwe conclude that parameters do not generalize at all,and that we have a real case of Idiosyncracy.

In this spirit, we reinterpret results of ANOVAS fromfour meta-analyses in Table 1 which represent collec-tions of response models which happen to include ap-plications from different nations. The internationalcomparisons do not form any systematic research pro-gram, and there is only one explicit within-study inter-national comparison of the sort discussed in the foursets of studies:

• A study of 128 econometric advertising models(Assmus et al. 1984) which differ in many importantways, but which contain two common response param-



eters—a short-term advertising elasticity and (in manycases) a carry-over effect. These are primarily aggregatetime series models. While most used data from the U.S.market, a number of the studies used data from otherindustrial countries, primarily the U.K. and Australia.

• A study of response parameters in four systemmodels of buyer behavior published in the 1970s andsummarized by Farley et al. (1982). The response pa-rameters are converted into comparable elasticities forthis paper. These underlying models are fit to cross-sections of individual respondents. Included are fourstudies of product introductions: a subcompact car inthe U.S. (Farley et al. 1976), a new contraceptive inKenya (Black and Farley 1977), a new soap categoryin Argentina (Farley et al. 1974), and a new food prod-uct in the U.S. (Farley and Ring 1970). Parameter valueswere not significantly different over either countries orproduct in these four studies.

• A study of two parameters—coefficients of imita-tion and innovation—of 213 diffusion models (Sultanet al. 1990). These are also aggregate time series models.Again, while many of the studies used data from theU.S. market, a number of studies used European data.

• A study of 337 price elasticities (Tellis 1988) whichincluded estimates from the U.S., Europe, and Australia.

The various meta-analyses explain between three-tenths and two-thirds of the variability (Table 2) in themodel parameters—in all cases a significant amount—and indicating that Parametric Adjustability may be auseful approach to international generalizability in thiscase. The grand means from the ANOVAs representthe conditional estimates of values for a new modelingexercise, and the individual ANOVA coefficients pro-vide the basis for adjustment of this mean to correspondmore closely to the specifics of the new situation.

Relative Effect Sizes of International Differencesin the Meta-AnalysesThe key question for intemational generalization iswhether the significant international effects are rela-tively "large," which we interpret to mean as large asor larger than other suspected or established sources ofvariability. For comparison, we have devised a measureof "effect size" for a particular factor on a particularparameter as

Effect size for factor i on parameter;'

Range of significant ANOVA coefficients_ related to levels of factor i for parameter;'

Grand mean estimate of parameter /from the meta-analysis ANOVA

(2)We have compared the relative effect sizes of inter-

national differences in parameters with those of fourother key differences, which constituted the bases fordesign of the ANOVAs as described in Farley and Leh-mann (1986). These are the types of product or marketunder study, technical aspects of measurement, and es-timation and specification characteristics (Table 2).When a factor is not significant in the ANOVA, we as-sume the effect is absent—that is, that the effect size iszero.

The international differences are small relative toother effects such as model specification. In two of thesix cases (buyer behavior model coefficients and carry-over coefficients in advertising models), there was nosignificant international effect. In two cases (price elas-ticities and coefficients of imitation), international wasthe smallest significant effect. In one other case (short-term advertising elasticities), the intemational effect waslarger only than the estimation method, and it was muchsmaller than systematic differences due to model spec-ification or the "obvious" effect of differences relatedto the products being studied. In fact, only in the caseof the coefficient of innovation was the European valuean order of magnitude larger than in the U.S.—and thiswas on a base grand mean value of only 0.02, so thesmall denominator in the ratio in (2) may tend to makethe ratio in the effect size unstable.

It is clearly worrisome that technical matters—par-ticularly the tremendous variation in model specifica-tion, and estimation methods that we observe—shouldhave such large effects on parameter estimates. Multi-method within-study comparison is a low-cost way tohelp us gain deeper insights on these technical issuesand should be encouraged.

There are also some model-specific patterns of inter-est:

For the advertising models: Estimated short-term ad-vertising elasticities are 0.039 greater in Europe than

118 MANAGEMENT SciENCE/Vol. 40, No. 1, January 1994


Table 2 Relative Effect Sizes of ANOVA Estimates of Parameters in Four Types of iVIodeis

Relative Effect Size Measured as Range of ANOVA Coefficient(s) Associated with

that Effect Divided by Estimated Grand Mean

Types of Models and

Parameters Studied

Diffusion Model Coefficients of

innovation

imitation

Buyer Behavior Modei Coefficients

Advertising Modei Coefficients

Short-Term Elasticities

Carry-over

Price Elasticities'

Adjusted

Grand

Mean

0.02

0.35

0.29

0.27

0.39

-1.76

Country or

Internationai

2.00.1NS

0.3NS0.3

Product Type

under Study

0.20.8NS

0.50.30.7

Estimation

Method

0.10.20.8

0.10.10.7

Other Variabies

Specified in

Basic Modei

0.11.84.8'=

0.60.40.7

Modei Specific

Parameters'

Specified

NA1.8NA

1.0NANA

Explanatory Power

of Meta-Analysis

{R')

0.32

0.42

0.39

0.50

0.60

0.29

a—Model Specific Parameters are, respectively, a coefficient of innovation in the diffusion models and a carry-over coefficient in the econometric advertising

models.

NS—Effect is not significant in the original meta-analysis.

NA—Not appiicabie because the model contains no such model-specific parameter.

b—Based on Tabie 2 of Teiiis (1988) to assure negative elasticities; coefficient of determination from Tabie 3.

c—includes range negative price elasticities and iarge positive distribution eiastlcities; removing these controllabie exogenous variabies reduces this effect

size to 12.

the grand mean of 0.27, but average 0.057 smaller thanthe grand mean in the U.S. These intercontinental dif-ferences may be due to a number of factors—mediadifferences or restrictions, copy differences, or culturalpreferences about advertising.

For diffusion models: The coefficient of innovation isfairly stable under a wide variety of conditions. How-ever, models fit to data from European countries havesignificantly larger coefficients of innovation than theU.S. models. While this represents the largest relativeeffect of any of the parameters studied, it may be dueto the very small average parameter. This effect mayalso be due to some factor such as relatively dense pop-ulations and/or communication systems rather thaninternational institutions. Also, the innovations were inmost cases introduced in the U.S. first, and this maymake imitation in the later European introductions ap-pear in the models as innovation.

For buyer behavior models: Elasticities computed fora wide range of endogenous variables in these four ap-plications do not vary systematically over countries. Thisis even more important because this particular set of

models involves two countries outside North Americaand Europe—one clearly a developing country.

For price elasticities: There are significant over-alldifferences related to product category, national setting,data characteristics, and estimation method. Specifica-tions including quality and distribution produce signif-icantly different price elasticities, indicating difficultiesin studying one independent variable at a time.

Implications for advertising budgeting: We can com-bine the significant cross-national advertising and pricesensitivity results to generate advertising budgets for an"average" product in Europe and the U.S.; this analysis

EuropeU.S.All Countries

Averageprice elasticity

from Tellis(1988)

-1.62-1.96-1.76

Averageadvertisingelasticity

from Assmuset al. (1984)

0.310.210.27

Impliedadvertising

to sales ratio

0.190.110.15



also allows us to examine the degree of precision thatmay be available. The analysis uses the Dorfman-Steiner(1954) equilibrium condition that

Price elasticity = (1/Ratio of advertising to sales)

X (advertising elasticity).

The combination of higher European advertisingelasticity and lower price elasticity implies that Europeanbudgets should be higher than U.S. budgets in termsof fraction of expected sales budgets for advertising.Small numeric differences in inputs to optimizationprocedures (e.g., the Dorfman-Steiner conditions) canproduce noticeable differences in optimal solutions, sothese results should be treated with caution. Still theseresults provide a starting point for estimating optimaladvertising, though it should be modified based on spe-cifics under study.

ConclusionThere is no doubt that parameters of the market re-sponse models discussed here have significant com-ponents specifically related to country location, as wellas to market characteristics and to some specific tech-nical factors. We call this "parametric adjustability,"with international differences combining with otherfactors to explain about half of the interstudy variabilityin parameter estimates. We conclude, however, that in-temational differences are not systematically larger thandifferences due to market environments studied or dueto technical characteristics of the models. In several im-portant cases, the international differences are not evensignificant, even though some other contextual factorsare.

Are North America and Europe Good Laboratoriesfor the World?Our conclusions are mixed on this issue although thenumber of data points is small and more comparisonsare needed. We have found some relatively small butstill significant differences between the U.S. and Europein values of price and advertising elasticities and in pa-rameters of diffusion models. We also found significantdifferences in labor and capital coefficients of productionfunctions for the developed and developing worlds, buthomogeneity within each country group (Farley et al.

1985), so we are not yet ready to claim that the indus-trial world is an adequate laboratory. On the other hand,parameters from a disaggregate buyer behavior modeldo not differ over three countries ranging from the U.S.to a developing country, even though average valuesof endogenous and exogenous variables range widelyacross these countries.

How Should We Approach Generalizability?In areas in which we have a reasonable base of knowl-edge, like those discussed here, we should abandon thenull hypothesis of no effect which is long discreditedin most situations, and make comparisons with somesort of historical average or weighted average where abody of results exists. For example, advertising elastic-ities are usually positive and generally between 0 and1; in practice, they average about 0.25, and sizable de-viations from that value may be important. Similarlycarry-over coefficients in distributed lag models averagearound 0.4 (Assumus et al. 1984). Price elasticities are,in practice, negative and generally less than unity, whileelasticities involving distribution are generally positiveand greater than unity. Behavioral response coefficientsin consumer choice models (such as those linking at-titude and behavior, for example) range generally be-tween 0 and 1, meaning that such models tend to con-verge to a new equilibrium after a disturbance; further,elasticities tend to be in the 0.1 to 0.3 range, meaningthat the models are fairly insensitive to change and thatconvergence is rather rapid. These first-cut grand meanvalues can be adjusted for a particular situation usingmore specific information from ANOVAS (Table 2)which correspond more closely to the market understudy.

Constructs and Measurement Issues. Whenstudying different cultures, it is not always possible totransport constructs from one to another. For example,the meaning of family, duty, and even attitude varies,and serious problems exist in translating constructsacross languages. Structured scales prevalent in researchin developed countries (e.g., 5-point Likert scales, 100-point constant sum scales) may not be appropriate inother societies. As a consequence, international gener-alizations probably need to rely on a common constructrather than a particular measurement method.



Response Level vs. Response Sensitivity Gener-alization. Clear differences exist in average consump-tion (e.g., between richer and poorer nations or betweenmeat-eating and vegetarian societies), and they are rel-atively easy to both estimate and observe. These dif-ferences should not dominate our search for responsegeneralizability, which appears to vary much less thandoes consumption.

Nation and Omitted Variable Bias. Just becausenational boundaries are easy to identify does not makethem an appropriate variable for segmenting behavior.Somehow longitude and latitude seem less importantthan climate, culture, the economy, regulations, needs,and possibly even genetics in determining behavior.There may be more profound differences in responsebetween the French Quarter in New Orleans and Chi-natown in New York City, or even between pairs ofboroughs within New York City than between, say,Austria and Germany. Country may be a sometimesuseful proxy for the "true" causes of differential re-sponse, but it is not the theoretically most appropriatevariable. While this paper focuses on country as a po-tential source for systematic difference, future researchshould explore those other, more basic causes.

ImplicationsWhile there is still a paucity of data points involvingexplicit international comparison, those available indi-cate that market response patterns do to a surprisingextent generalize across countries—more so, for ex-ample, than industry-specific knowledge generalizesacross industries. Country boundaries do not necessarilyimply barriers to understanding buyer response. Thissuggests that both managers and researchers should useresults from countries where analysis is available as first-cut estimates of response (perhaps in a Bayesian frame-work) in other countries. By combining identifiable dif-ferences in response parameters in a framework likeDorfman-Steiner equilibrium analysis, we can make atleast initial decisions about such matters as relativeprices and relative advertising budgets.'

' The authors are indebted to Doug Bowman, Jamie Kalamarides andWilfried Vonhonacker for helpful comments on earlier drafts.

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122 MANAGEMENT SciENCE/Vol. 40, No. 1, January 1994

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