to the application of uniform marketing policies. ... standardization/customization of international marketing ... H2: Greater customization of marketing strategy.
Standardization versus Customization in International Marketing: An Investigation Using Bridging Conjoint Analysis Imad B. Baalbaki American University of Beirut
Naresh K. Malhotra Georgia Institute of Technology
We present and illustrate a methodology by which researchers can assess the relative importance and test the significance of various marketing-related factors as they influence the degree of standardization~customization of international marketing strategy. The standardization decision is viewed as a continuum with complete standardization and complete customization as the two extremes. Specific hypotheses related to the impact of marketing mix variables on the degree of standardization are formulated. These hypotheses are empirically investigated through a survey of international marketing managers. This investigation is carried out using conjoint analysis. Bridging methodology is introduced in order to accommodate the large number of variables in the study. The analysis is conducted at the individual level, at the group level, and at the aggregate level. Finally, we discuss the methodological and managerial implications of the findings and potential areas of future research.
International marketing managers periodically face the decision as to how much of their marketing strategy in one market applies to another. Standardization of marketing practices across markets is tempting because of potential economies of scale in production, promotion, distribution, Journal of the Academy of Marketing Science. Volume 23, No. 3, pages 182-194. Copyright 9 1995 by Academy of Marketing Science.
and research and development. Standardization can also contribute to a coherent and consistent global image of the firm and its products. However, there are many obstacles to the application of uniform marketing policies. Variations across markets in consumer attitudes, competitive environments, and marketing management related variables must be adequately assessed to insure the success of the product in a particular market (Baalbaki and Malhotra 1993). The purpose of this study is to recommend a methodology and conduct an empirical investigation to assess the relative importance and significance of a large number of marketing management variables as they influence the standardization/customization of international marketing strategy. The standardization decision is viewed as a continuum with complete standardization and complete customization as the two extremes (Boddewyn, Soehl, and Picard 1986; Jain 1989; Sorenson and Wiechmann 1975; Whitelock and Chung 1989). At one extreme, complete standardization of marketing strategy means the offering of identical product lines and features at identical prices through identical distribution systems supported by identical promotional programs. At the other extreme, complete customization of marketing strategy means the development of distinctive tailor-made products, pricing, promotion, and distribution policies that have no standardized elements. The article presents conjoint analysis as a means to empirically test hypotheses related to international marketing practices. Bridging methodology is introduced in order to accommodate the large number of variables in the study.
Baalbaki,Malhotra/ STANDARDIZATIONVS. CUSTOMIZATION 183 For the purpose of this study, certain marketing-related variables are selected out of a set of variables proposed by Baalbaki and Malhotra (1993), and 18 hypotheses related to the impact of these variables on the degree of standardization are presented. These hypotheses are empirically investigated by conducting a survey of international marketing managers. We conduct statistical analysis at the individual level, at the group level, and at the aggregate level. Methodological and managerial implications of the findings and potential areas of future research are then discussed.
Promotion-related Hypotheses H5:
H6:
H7:
THEORETICAL FRAMEWORK
The theoretical framework adopted is based on the recent work of Baalbaki and Malhotra (1993). They classifted variables that influence the degree of standardization of international marketing strategy as either environmental or marketing management. Environmental variables include geographic, political, economic, and cultural variables. Marketing m a n a g e m e n t variables include product-related, promotion-related, price-related, and distribution-related variables. Baalbaki and Malhotra (1993) explain that "these marketing management variables have a direct impact on the selection of the appropriate marketing mix strategy, and hence should be considered by the international marketing manager together with the environmental variables in formulating international marketing strategies" (p. 22). They also proposed several hypotheses that related these variables to the formulation of international marketing strategies and the level of standardization that is possible across international markets. The empirical investigation of these hypotheses was proposed as an area for future research. This article focuses on a selected number of hypotheses pertaining to the various marketing management variables: product related, promotion related, price related, and distribution related. The rationale behind the development and formulation of these hypotheses was discussed in detail by Baalbaki and Malhotra (1993).
H8:
Across-market variations in the interpretation of an ad's theme, slogan, idiomatic expression, words, symbols, and colors necessitate adaptation or customization in the marketing strategy. Differences in the availability and coverage of promotional infrastructure between markets call for a higher level of strategy customization. More customization of marketing strategy is needed in markets that differ significantly in terms of the relative importance customers assign to various media channels. A high level of strategy standardization is warranted in markets that are similar in terms of their laws and regulations related to promotional practices.
Price-related Hypotheses
Greater customization of marketing strategy is needed in the presence of wide variations across markets in government pricing rules and regulations. H10: The degree of risk associated with the volatility of exchange rates impedes the uniformity of pricing practices across markets and thereby lowers the degree of standardization of marketing strategy. H l l : Differences in consumers' price perceptions across markets hinder the possibility of uniform pricing and consequently lower the level of strategy standardization. H12: Intermarket differences in the consumers' price elasticity of demand require an alteration in the pricing strategy across markets and thus necessitate more customization of marketing strategy. H9:
Distribution-relatedHypotheses
Product-related Hypotheses H13: Markets that are similar in terms of their HI:
H2:
H3:
H4:
A greater degree of standardization of marketing strategy is possible for products that are perceived to be essential. Greater customization of marketing strategy is required for products that are in different life-cycle stages in different markets. Across-market differences in laws related to product standards, features, performance, and safety necessitate a higher level of strategy adaptation or customization. Intermarket differences in support requirements affect the acceptability and adoption of some products, thus increasing the need to adapt or customize a product's marketing strategy.
distribution infrastructure (as reflected in the availability, accessibility, complexity, and effectiveness of the markets' distribution systems) are candidates for a standardized marketing approach. H14: Between-market variations in government regulations and laws affecting distribution, such as those related to foreign ownership, licensing, and franchising, force the adoption of more customized marketing strategies. H15: Differences in the geographic structure and dispersion between markets reduce the level of strategy standardization. H16: Intermarket variations in certain social and cultural norms and preferences, such as shop-
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JOURNALOF THE ACADEMYOF MARKETINGSCIENCE ping habits, location preferences, and inventory policies, increase the need for strategy adaptation.
General Hypotheses In addition to the above product-related, promotionrelated, price-related, and distribution-related hypotheses, we selected two more general hypotheses based on Baalbaki and Malhotra (1993). These hypotheses relate to the effect of consumers' attitudes toward foreign products and the effect of the competitive environment on the level of standardization of international marketing strategy. The reason for the selection of those factors is that they play a unique role in the research design by serving as bridging factors. H17: Because attitudes toward foreign products in
general, and toward the product's country of origin in particular, influence the consumers' perceptions and evaluations, variations in such perceptions between markets call for a greater strategy adaptation or customization. H18: Differences in the level and intensity of industry competition between markets diminish the level of standardization of marketing strategy.
RESEARCH METHODOLOGY Conjoint Analysis Conjoint analysis was used to assess the impact of the marketing management variables on the degree of standardization of international marketing strategy. Despite its increased popularity among practitioners and researchers, conjoint methodology has yet to be used in research related to aspects of international marketing strategy. The current study used an additive main effects conjoint analysis model. It is worth noting that the assumption of an additive compensatory model underlying the respondents' (in this case, managers') decision rule is reasonable. Research has shown that the compensatory model of conjoint analysis is likely to capture most of the predictable variance even when the actual decision rule follows a more complex compensatory or noncompensatory heuristic (Green and Srinivasan 1978, 1990; Messier and Emery 1980). Thus it is a very good approximation of reality (Green and Rao 1971). Moreover, Green and Srinivasan (1978) stated that the advantage of improved part-worth estimates resulting from the main effects design more than outweigh the disadvantage of reduced believability of the profiles. In addition, they argued that if the relative importance of the attributes is of interest (as in this study), then an orthogonal design produces less ambiguous results than a design incorporating interactions. In another study, Green (1984) revealed that incorporating interaction effects often results in a lower predictive validity. The gained realism of the model obtained by incorporating interaction terms is offset
SUMMER1995 by the deterioration in predictive accuracy caused by the inclusion of additional parameters. Given the large number of factors of interest, one of the more important methodological considerations is the appropriate data collection method. In this study we preferred the full-profile method over the tradeoff (or the twofactor-at-a-time) method because it increased the realism of the experimental task and reduced the number of judgments required. Also, it was more flexible in supporting various measurement scales of the dependent variable (a crucial consideration because the dependent variable in this task was intervally scaled) (Green and Srinivasan 1978). However, the traditional full-profile method cannot incorporate a large number of variables without causing information overload on the respondents, even if a fractional factorial design or an orthogonal array is employed (Addelman 1962; Green 1974). Overwhelmed by the load of information, respondents attempt to simplify the experimental task by resorting to various simplifying tactics, thereby affecting the predictive validity of the part-worth estimates (Albaum 1989; Malhotra 1982a; Wright 1975). Green and Srinivasan (1978, 1990) recommended confining the traditional full-profile procedure to a maximum of six factors at any particular sort, and resorting to the use of"bridging" designs to accommodate a larger number of factors. Bridging involves dividing the multitude of factors into several sets and creating a separate design for each set under the condition that some common factors exist in the different designs to enable the integration or bridging of their individual results at a later stage. Of course, bridging results tend to gain reliability with an increase in the number of matching factors. Bretton-Clark's BRIDGER software program can accommodate such a bridging technique by creating integrated design and utility files from the separate design and utility files corresponding to each separate design. Bridging factors should have an equivalent number of levels, carry identical level labels, and be analyzed using the same type of preference model in all designs (Albaum 1989). Moreover, BRIDGER can handle an unlimited number of factors, each having up to 20 levels, and is fully integrated with Bretton-Clark's two other conjoint programs, CONJOINT DESIGNER (Carmone 1986) and CONJOINT ANALYZER (Green 1987). It should be noted here that other approaches for dealing with large numbers of factors in conjoint studies have been suggested (e.g., in a recent article, Oppewal, Louviere, and Timmermann 1994 proposed such an approach). However, these alternative approaches are more complex to administer and apply. In this study, each hypothesis was operationalized in terms of one variable representing a factor in conjoint analysis. Each factor was defined at two levels. Except for product essentialness, the levels were no difference and major difference. The two levels for essentialness were essential and nonessential. Four separate designs (Designs 1-4) were developed, each consisting of six factors as shown in Table 1. Although two factors (namely, attitudes toward foreign products and competitive environment-factors that correspond to the general hypotheses) were common across all four designs, each of the
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TABLE 1 Individual-Level Analysis: Factor Significance Percentage of Respondents with Significant Coefficients Factor Label
Hypothesis p < .01 p < .05 p < .10
Design 1 Essentialness Attitude toward foreign products Media importance Exchange risk Distribution laws Competitive environment Design 2 Attitude toward foreign products Product laws Message interpretation Price perceptions Geographic dispersion Competitive environment Design 3 Attitude toward foreign products Product support requirements Promotion infrastructure Pricing laws Shopping habits Competitive environment Design 4 Attitude toward foreign products Product life cycle Promotion laws Price elasticity Distribution infrastructure Competitive environment
H1
19.74
31.58
42.10
H17 H7 H10 H14 H18
30.26 22,37 30.26 18.42 36.84
44.74 ,32.89 39.47 30,26 56.58
59.21 43.42 51,32 42.11 68.42
H17 H3 H5 Hll H15 H18
21.05 21.05 14,47 23.68 14.47 36.84
36.84 47.37 26.32 38.16 27.63 52,63
43.42 53.95 32.89 47.37 36.84 71,05
H17 H4 H6 H9 H16 H18
30.26 34.21 31.58 30.26 32.89 53.95
53.95 52.63 51.32 47.37 52.63 68.42
60.53 59,21 59.21 53.95 69.74 80.26
H17 H2 H8 H12 H13 H18
27.63 27.63 23.68 23.68 32.89 44.74
42.11 40.79 36.84 39.47 38.16 56.58
53.95 47.37 47,37 53.95 55.26 69.74
remaining four factors addressed a particular aspect of the marketing mix. Thus a product-related variable, a promotion-related variable, a price-related variable, and a distribution-related variable was present in each of the designs. This enhanced the realism of the designs. Having all factors defined on the same number of levels, as in our study, is encouraged in conjoint literature. It is argued that the relative importance of a factor may be positively associated with an increase in the number of levels defining that factor. This association impairs the comparability of the relative importance of factors that are explicated along different numbers of levels (Currim, Weinberg, and Wittink 1981; Wittink, Krishnamurthi, and Nutter 1982).
Population Definition and Sample Selection The population under study was defined as all U.S.based firms with international operation and orientation. Thus any firm having a representative office in the United States and currently involved in international business (whether a truly multinational company with offices, plants, and operations in several countries, or a mere
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exporter of domestic-made products) was included in the population. The following sources and directories were used to define the sampling frame: Directory of American
Firms Operating in Foreign Countries, Principal International Business, and Ward's Business Directory of U.S. Private Companies. The Directory of American Firms Operating in Foreign Countries was the primary source of sample selection. In the sample, we included U.S. manufacturing companies from this directory that were cross-listed with the other two directories. This procedure was employed to ensure the availability of sufficient background information about any selected company. Background information gathered from these sources included number of employees, sales volume, year established, industry affiliation, and primary product category (industrial or consumer). This information was collected in order to assess nonresponse bias. A total of 816 U.S. manufacturing firms were identified. Of the selected companies, 515 dealt mainly in industrial goods whereas 301 dealt mainly in consumer goods. We excluded U.S.-based international firms dealing in services and commodities because of the unique business and marketing environment in which they operate.
Questionnaire Design The questionnaire consisted of two major sections. First, the purpose of the study and the definitions of complete standardization and complete customization were presented. The respondent indicated the product category (industrial/consumer) with which he or she was more familiar in marketing internationally. The respondent was asked to complete the entire questionnaire for that specific product category. In section 1, respondents examined all 18 factors involved in the study and rated each of the factors on a 7-point scale according to its importance in deciding on the degree of standardization/customization of marketing strategy. Throughout section 2, the survey instructed the respondent to assume that his or her firm was considering entering and marketing a specific product in several foreign markets. The respondent then examined four sets of hypothetical market situations or profiles, each set of profiles corresponding to one of the four designs (Table 1). Each set contained 12 hypothetical situations or profiles (8 profiles for estimation of model parameters and 4 for assessing the predictive validity of the model). The 8 estimation profiles were selected according to a fractional factorial design. Each situation, described as a profile, provided a comparison of the markets along six marketing-related conditions or factors. As described in an earlier section, two conditions (namely, customers' attitudes toward foreign products and competitive environment) were common across all four sets. Based on the information presented, the respondent rated each situation using a 7-point scale (1 = complete customization, 7 = complete standardization) according to the degree of standardization of marketing strategy that he or she believed as being most appropriate across markets.
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The selection of a 7-point scale conforms to the broad guidelines for the number of scale categories suggested in the literature (Malhotra 1993). In order to reduce ambiguity on the part of respondents and insure a consistent understanding of the rating scale among all respondents, the scale was supplemented with an explanation of the two anchor points: complete standardization of marketing strategy and complete customization of marketing strategy. Respondents were explicitly advised to regard those conditions that were not included in a particular situation as having exactly the same impact across the markets. To insure that the task in section 2 was clear, an illustrative situation was also presented. This was followed by the four sets of hypothetical situations (profiles). The order of the presentation of the four conjoint designs was randomized across respondents to control for any order effects. In the pretest phase, local business executives with adequate international marketing experience were approached. Their feedback and comments were instrumental in determining the order of appearance of the questionnaire sections and supplying adequate instructions to facilitate the survey, especially in clarifying the rating task in the conjoint section (i.e., section 2).
nies. Of these companies, 128 (a response rate of 18.77%) responded to the survey. However, 48 companies declined to participate in the study for a variety of reasons (against company policy, survey not suitable for company's line of business, firm no longer operated in the international market, firm filed for bankruptcy). In effect, we received 80 questionnaires (46 from industrial firms and 34 from consumer firms) for an 11.73% response rate. One questionnaire came in late and was not included in the analysis, and 5 questionnaires were unusable. Thus 74 questionnaires were retained for data analysis. It is important to note that the response rate is realistic considering the length and complexity of the questionnaire. Moreover, and in order to statistically investigate the presence of nonresponse bias, further analyses were performed. We conducted a series of t tests to assess the extent of nonresponse bias with respect to the following background variables gathered from the secondary data sources: number of employees, sales volume, and year established. No significant difference was detected between firms that responded and those that did not. The next section details the statistical analyses of the responses and discusses the findings in detail.
Survey Implementation The questionnaire was addressed to a specific top-level company executive responsible for the company's international operations. In instances where such information was lacking, the questionnaire was addressed to a top-level executive (president or CEO), and the executive was explicitly asked to forward the questionnaire to a manager with adequate experience in international marketing. We mailed a prenotification letter introducing the survey and highlighting its potential to provide a better understanding of international marketing. The questionnaires were mailed one week later. The 9 in. • 12 in. clasp envelope included the questionnaire along with a cover letter that stated the purpose of the study and its significant managerial and academic contributions and encouraged management's participation. Respondents were assured that their responses would be held in strict confidence, and were offered a summary of the results. A postage prepaid 6 in. • 9 in. return envelope was also enclosed. Three weeks later a follow-up postcard reminder was mailed to those firms whose responses had not been received. A final follow-up questionnaire was mailed after another 3 weeks to those failing to respond. In general, the survey approach followed Dillman's (1978) recommendations.
Response Rate and Nonresponse Bias Of the 816 companies that were targeted in the study, only 724 companies actually received the questionnaires. A total of 92 questionnaires were returned because of "moved/not forwardable" or "forwarding order expired" reasons. An additional 42 questionnaires were returned marked "delete from mailing list/person no longer at company." This reduced the actual sample size to 682 compa-
STATISTICALANALYSIS To conduct a more rigorous examination of the hypotheses, we conducted the analyses at the individual, aggregate, and group levels (Moore 1980). Two different approaches were used to group the respondents. One was an a priori segmentation scheme in which the managers were classified into industrial goods or consumer goods segments based on self-reported data. The consumer segment was further split into consumer durable and nondurable segments. The other approach to grouping was based on clustering respondents based on their individual-level part-worths.
Individual Analysis At the individual level, a manager's ratings on the hypothetical situations in each of the four designs were analyzed using CONJOINT ANALYZER. Each design was analyzed separately, thereby obtaining the part-worths for each of the six factors in the design, and consequently the relative importance of each factor. BRIDGER was then employed to bridge the four separate designs to arrive at an overall master design. Because BRIDGER can only handle two designs at a time, two levels of bridging were necessary. Design 1 and Design 2 were bridged yielding combined design and utility files. Design 3 and Design 4 were bridged in the same fashion. Then, another level of bridging was done to combine the bridged designs and utility files of Designs 1 and 2 with those of 3 and 4. The final utility and design files, which were labeled as master utility and design files, were analyzed using CONJOINT ANALYZER to reveal the part-worth of each factor level and the relative importance of all 18 factors. We conducted
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TABLE 2 Pooled Analysis: Bridged/Master Design Aggregate Factor Label
Hypothesis
Essentialness Attitude toward foreign products Media importance Exchange risk Distribution laws Competitiveenvironment Product laws Message interpretation Price perceptions Geographic dispersion Product support Promotioninfrastructure Pricing laws Shopping habits Product life cycle Promotion laws Price elasticity Distribution infrastructure
H1 H17 H7 H10 H14 HI 8 H3 H5 HI 1 H15 H4 H6 H9 H16 H2 H8 H12 H13
Part-Worth
+0.318 +0.511 +0.382 +0.463 +0.389 +0.707 " +0.567 +0.269 +0.512 +0.433 +0.548 +0.438 +0.369 +0.483 +0.427 +0.350 +0.403 +0.417
this analysis on each of the 74 usable questionnaires, producing a total of 74 individual master utility files. Because C O N J O I N T A N A L Y Z E R falls short of supplying the researcher with the significance of each of the factors, it was necessary to perform dummy variable regression on each of the four designs for each respondent. Because each factor was varied at only two levels, a factor level's part-worth was equal to half that factor's regression coefficient. A t statistic was computed for each factor and the correspondingp value indicated the significance of that factor. Table 1 summarizes the findings of the tests of factor significance at the individual level. Taking each design at a time, the table lists the percentage of respondents having a significant coefficient for each of the factors at the .01, .05, and the. 10 significance levels. These results are encouraging when it is realized that (a) only eight observations were available for estimation of the parameters, and (b) not all of the six factors can be expected to be significant for any given respondent.
Aggregate-Level Analysis Analysis at the overall aggregate level resulted in a single conjoint utility function for all 74 respondents. In order to arrive at such a function, respondents' ratings on each hypothetical situation for each design were pooled to create a single data file for that design. The resulting four data files corresponding to the four designs were then fed into C O N J O I N T ANALYZER one at a time to estimate the relative importance and part-worths of the factors and their levels. Then, a two-stage bridging procedure was employed, first bridging Design 1 with 2 and 3 with 4, and subsequently bridging those intermediate-level designs to arrive at the aggregate master design and utility files. Table 2 lists the relative importance and part-worths of all 18 factors in the master design. The part-worths shown in the
Industrial
Importance
3.98% 6.40% 4.78% 5.80% 4.87% 8.85% 7.10% 3.37% 6.41% 5.43% 6.87% 5.48% 4.62% 6.05% 5.35% 4.38% 5.05% 5.22%
Part-Worth
+0.265 +0.563 +0.352 +0.462 +0.369 +0.802 +0.622 +0.257 +0.603 +0.439 +0.635 +0.435 +0.413 +0.518 +0.507 +0.349 +0.467 +0.509
Importance
3.09% 6.58% 4.11% 5.40% 4.31% 9.36% 7.26% 2.99% 7.04% 5.12% 7.41% 5.08% 4.83% 6.05% 5.92% 4.07% 5.45% 5.94%
Consumer Part-Worth Importance
+0.391 +0.438 +0.423 +0.464 +0.415 +0.575 +0.491 +0.286 +0.384 +0.426 +0.429 +0.441 +0.307 +0.435 +0.317 +0.351 +0.315 +0.290
5.45% 6.10% 5.90% 6.46% 5.79% 8.01% 6.83% 3.99% 5.35% 5.94% 5.98% 6.15% 4.27% 6.06% 4.41% 4.90% 4.39% 4.03%
table are those of the no-difference level of each of the factors (the part-worths for the major-difference level are equal in magnitude but are of opposite sign). We performed dummy variable regression to determine the significance of the factors in each design. Table 3 summarizes the findings. For each design, the model fit was assessed with respect to the significance of its R 2 value. All models were significant at the .001 level. Furthermore, tests of significance were computed for each of the six factors in each design. All factors were significant at the .001 level. The signs of the part-worths were also examined to assess the direction of the impact of a factor on the degree of standardization of international marketing strategy. These results were consistent for all the factors in the four designs: positive part-worths were associated with the no-difference level of a factor, whereas negative partworths were associated with the major-difference level of a factor. This further confirmed the hypotheses because a higher degree of standardization of marketing strategy was hypothesized to be plausible if markets were more similar in terms of any of the depicted factors.
A Priori Segmentation Analysisof IndustrialSegment The industrial segment comprised 43 respondents. We combined the managers' ratings for each design to create a single data file. A similar analysis to the one explained in the preceding section was followed. The relative importance and the utilities of the factors and the factor levels are tabulated in Table 2. The significance of each design and each factor within a design are tabulated in Table 4. As can be inferred from Table 4, all 18 factors were found to have a significant impact on the standardization/customization decision that managers of industrial products have to face, thus supporting all 18 hypotheses.
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TABLE 3 Pooled Analysis: Aggregate Model Factor Significance Part- Worth Factor Label
Design 1 (R2 = .606, R = .778) Essentialness Attitude toward foreign products Media importance Exchange risk Distribution laws Competitiveenvironment Design 2 (R2 = .632, R = .795) Attitude toward foreign products Product laws Message interpretation Price perception Geographic dispersion Competitiveenvironment Design 3 (R2 = .636, R = .797) Attitude toward foreign products Product support Promotion infrastructure Pricing laws Shopping habits Competitiveenvironment Design 4 (R2 = .653, R = .808) Attitude toward foreign products Product life cycle Promotion laws Price elasticity Distributioninfrastructure Competitiveenvironment
Importance
No Difference
Major Difference
p
Hypothesis
Conclusion
11.02% 19.70% 13.25% 16.06% 13.48% 26.49% '
+0.318 +0.568 +0.382 +0.463 +0.389 +0.764
-0.318 -0.568 -0.382 -0.463 -0.389 -0.764
.001 .001 .001 .001 .001 .001
H1 H17 H7 H10 H 14 H 18
Supported Supported Supported Supported Supported Supported
17.26% 18.21% 9.17% 17.02% 12.98% 25.36%
+0.490 +0.517 +0.260 +0.483 +0.368 +0.720
-0.490 -0.517 -0.260 -0.483 -0.368 -0.720
.001 .001 .001 .001 .001 .001
H17 H3 H5 H 11 H15 H 18
Supported Supported Supported Supported Supported Supported
16.02% 16.82% 14.06% 13.03% 15.44% 24.63%
+0.471 +0.495 +0.414 +0.383 +0.454 +0.725
-0.471 -0.495 -0.414 -0.383 -0.454 -0.725
.001 .001 .001 .001 .001 .001
H 17 H4 H6 H9 H16 H 18
Supported Supported Supported Supported Supported Supported
17.12% 13.94% 12.65% 14.41% 15.82% 26.06%
+0.492 +0.400 +0.363 +0.414 +0.454 +0.748
-0.492 -0.400 -0.363 -0.414 -0.454 -0.748
.001 .001 .001 .001 .001 .001
H17 H2 H8 H12 H13 H18
Supported Supported Supported Supported Supported Supported
NOTE: All designs significant atp < .001.
Analysisof ConsumerSegment The c o n s u m e r segment consisted of 31 managers. Analysis o f the consumer segment using the conjoint packages and d u m m y variable regression supported the hypotheses. These results are recorded in Tables 2 and 4. We compared the consumer segment to the industrial segment in terms of the segments' part-worths using twogroup t tests. The two segments were found to be signific a n t l y d i f f e r e n t in the i m p o r t a n c e p l a c e d on three variables. Product's essentialness was more important to the consumer segment than to the industrial segment in deciding on the degree o f standardization o f international marketing strategy between markets (p = . 10). Competitive environment and price elasticity were more important to the industrial segment than to the consumer segment (p = .02 and p = .08, respectively). Note that the consumer segment was further divided into consumer nondurable (20 managers) and consumer durable (11 managers) segments to investigate any inherent differences in the international orientation o f managers belonging to these segments. For both segments, all of the factors were found to be significant when it came to decisions regarding the level o f standardization of marketing strategy.
Clustered Segmentation A n attempt was made to develop clusters homogeneous in terms o f the master design part-worth utilities of the 18 variables. Given the large number o f variables relative to the size of the sample, principal component analysis was applied to reduce the number of variables. The KaiserMeyer-Olkin measure o f sampling adequacy (KMO = .822) and the Bartlett test o f sphericity (p = .000) indicated that factor analysis was appropriate. Four resulting factors were extracted. The factors cumulatively explained 66.4% o f the variance. Respondents' factor scores were then cluster analyzed using Ward's method o f hierarchical clustering. Two clusters emerged with 36 and 37 respondents, respectively, and one respondent was atypical. A series of t tests were then conducted to examine any significant differences between the two clusters on the 18 variables. We found the clusters to have significantly different part-worths on five v a r i a b l e s - - n a m e l y essentialness (p = .000), media importance (p = .000), exchange rate risk (p = .000), product support (p = .064), and promotion laws (19 = .026). Product-related variables o f product essentialness and the availability o f product sup-
Baalbaki,Malhotra/ STANDARDIZATIONVS. CUSTOMIZATION
TABLE 4 Pooled Analysis: A Priori Segments Factor Significance Factor Label
Importance
Part-Worth p < Hypothesis
Industrial Segment Factor Significance
Design 1 (R2 = .620) Essentialness Attitude toward foreign products Media importance Exchange risk Distribution laws Competitiveenvironment Design 2 (R2 = .681) Attitude toward foreign products Product laws Message interpretation Price perception Geographic dispersion Competitiveenvironment Design 3 (R2 = .661) Attitude toward foreign products Product support Promotion infrastructure Pricing laws Shopping habits Competitiveenvironment Design 4 (R2 = .683) Attitude toward foreign products Product life cycle Promotion laws Price elasticity Distribution infrastructure Competitiveenvironment
9.27%
+0.265
.001
HI
20.47% 12.32% 16.19% 12.93% 28.82%
+0.584 +0.352 +0.462 +0.369., +0.823
.001 .001 .001 .001 .001
H17 H7 H10 H14 H18
17.27% 17.47% 8.63% 18.07% 12.25% 26.31%
+0.500 +0.506 +0.250 +0.523 +0.355 +0.762
.001 .001 .001 .001 .001 .001
H17 H3 H5 Hll H15 H18
15.48% 18.17% 13.37% 12.98% 15.67% 24.33%
+0.468 +0.549 +0,404 +0.392 +0.474 +0.735
.001 .001 .001 .001 .001 .001
H17 H4 H6 H9 H16 H18
18.05% 14.60% 11.76% 15.21% 14.20% 26.17%
+0.517 +0.419 +0.337 +0.436 +0.407 +0.750
.001 .001 .001 .001 ,001 .001
HI7 H2 H8 H12 H13 H18
Consumer Segment Factor Significance
Design 1 (R2 = .600) Essentialness Attitude toward foreign products Media importance Exchange risk Distribution laws Competitive environment Design 2 (R2 = .571) Attitude toward foreign products Product laws Message interpretation Price perception Geographic dispersion Competitiveenvironment Design 3 (R2 = .609) Attitude toward foreign products Product support Promotion infrastructure Pricing laws Shopping habits Competitiveenvironment Design 4 (R2 = .626) Attitude toward foreign products Product life cycle Promotion laws Price elasticity Distribution infrastructure Competitiveenvironment
13.40%
+0.391
.001
H1
18.65% 14.50% 15.88% 14.23% 23.34%
+0.544 +0.423 +0.464 +0.415 +0.681
.001 .001 .001 .001 .001
H17 H7 H10 H14 H18
17.25% 19.30% 9.94% 15.50% 14.04% 23.98%
+0.476 +0.532 +0.274 +0.427 +0.387 +0.661
.001 .001 .001 .001 .001 .001
H17 H3 H5 H 11 H15 H18
16.81% 14.81% 15.10% 13.11% 15A0% 25.07%
+0.476 +0.419 +0.427 +0.371 +0.427 +0.710
.001 .001 .001 .001 .001 .001
H17 H4 H6 H9 H16 H18
15.83% 13.03% 13.87% 13.31% 18.07% 25.91%
+0.456 +0.375 +0.399 +0.383 +0.520 +0.746
.001 .001 .001 .001 .001 .001
H17 H2 H8 H12 H13 H18
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port requirements were more important to the second cluster, whereas the price-related variable concerning the volatility of exchange rates and the promotion-related variables related to the markets' relative importance of media channels and promotion laws were more important for the first cluster. Such a finding signals the presence o f two streams when m a n a g i n g international marketing plans. Although both streams acknowledge the importance of all the variables in affecting the international marketing strategy, one stream is found to be relatively more driven by the product it offers and the product-related variables in the foreign market. On the other hand, the second stream keeps a relatively close eye on some price-related and promotion-related variables. Conjoint analysis was run on the two segments. Although these results are not shown due to lack of space, all 18 hypotheses were supported, as in the aggregate and a priori segmentation analysis.
VALIDITY, RELIABILITY, AND CONSISTENCY Validity A s s e s s m e n t We used the following validity tests to assess the validity of the conjoint models developed in the preceding analyses.
Internal Validity, Internal validity tests the goodness o f fit o f the conjoint model based on the estimated part-worths. Internal validity between the respondents' input ratings and the model's estimated ratings could be assessed through Pearson's product-moment correlation, or alternatively through R 2, the model's coefficient of determination. The coefficients of determination for the four designs in the aggregate model (.606, .632, .636, .653), in the industrial segment model (.620, .681, .661, .683), in the c o n s d ~ e r segment model (.600, .571, .609, .626), in the consumer durable segment model (.627, .649, .740, .735), and in the consumer nondurable segment model (.599, .566, .567, .592), were high and all were significant at the .001 level.
Cross or Predictive Validity We conducted a cross-validation test to assess the predictive capabilities of the estimated model. For each model, ratings on a hold-out set o f profiles were calculated based on the model's estimated part-worths. The estimated ratings' correlation to the respondents' input ratings reflected the predictive power o f each model. These correlations and their respective significance levels are arranged in Table 5. All models reflect a high degree o f predictive validity as the correlation between the estimated and the input ratings for the hold-out situations in each design were significant at either the .01 or the .05 levels. 1
Convergent Validity Convergent validity could be evaluated by assessing the degree to which the estimated factor importance conforms
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JOURNAL OF THE ACADEMY OF MARKETING SCIENCE
TABLE 5 Predictive Validity of Conjoint Models Design Aggregate Model (N = 74) Design 1 Design 2 Design 3 Design 4 Industrial Model (N = 43) Design 1 Design 2 Design 3 Design 4 Consumer Model (N = 31) Design 1 Design 2 Design 3 Design 4
Correlation
p
