Cross-National Diffusion Research: What Do We Know and How Certain Are We?

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Cross-National Diffusion Research: What Do We Know and How Certain Are We? V. Kumar, Jaishankar Ganesh, and Raj Echambadi

To compete effectively in the global marketplace, marketing managers require insight into how a product gets adopted in different countries. For example, can international marketers identify specific cultural traits that may help them to forecast how quickly a new product will be adopted in a particular country or in a group of somehow related countries? Similarly, can they identify factors that suggest why the adoption process differs among countries? Although these diffusion-related questions address critical issues for international marketing managers, only a few studies have explored cross-national diffusion. To help fill this gap, V. Kumar, Jaishankar Ganesh, and Raj Echambadi present the results of a study that replicates and extends the findings of three previously published studies of cross-national diffusion. Their research aims to replicate four findings from the previous studies: the role of country-specific effects in explaining differences in diffusion parameters, the presence of a lead-lag effect, the use of cultural variables to explain systematically the diffusion patterns across countries, and the merit of country segmentation schemes based on diffusion parameters. They extend the previous research by integrating cross-sectional and time lag variables into a single framework, and they demonstrate how managers can apply this integrated framework for forecasting the diffusion of new products. They replicate the findings from the previous studies by using annual sales data for five product categories (VCRs, microwave ovens, cellular phones, home computers, and CD players) in the following countries: Austria, Belgium, Denmark, Finland, France, Germany, Italy, The Netherlands, Norway, Portugal, Spain, Sweden, Switzerland, and the UK. The product categories and time periods covered differ from the ones in the previous studies; some overlap exists among the countries in this study and the ones in the previous studies. The findings in this study suggest that country-specific characteristics (for example, cosmopolitanism, mobility, percentage of women in the labor force) are useful for identifying the differences in diffusion patterns across countries and innovations. This study also suggests that the lead-lag effect helps to explain differences in diffusion across countries. Factors that this study identifies as possibly influencing the clustering of countries with similar diffusion patterns include timing of entry, geographical proximity, and cultural or economic similarity. Address correspondence to Dr. V. Kumar, University of Houston, Dept. of Marketing Research Studies, Houston, TX 77204-6283. J PROD INNOV MANAG 1998;15:255–268 © 1998 Elsevier Science Inc. All rights reserved. 655 Avenue of the Americas, New York, NY 10010

0737-6782/98/$19.00 PII S0737-6782(97)00082-9

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Introduction

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ow do consumers in different countries react to a new product/service? What role does culture play in influencing consumers’ reactions to new product introductions? Why does a given product gain rapid acceptance in Country A whereas it takes a substantially longer time to penetrate Country B? Is it possible for a marketer to predict (1) how the consumers in a given country would react to a new product, and (2) the time it would take for the product to achieve a certain level of market penetration in that country, before introducing the product in that country? These are some of the critical questions that international marketing managers, faced with the task of selecting and managing product-market portfolios, need to address in order to compete efficiently and effectively in the global marketplace. A study of the diffusion patterns of new products/ technologies in different countries can provide answers to a few of these questions. An understanding of how a product gets adopted in a culture and why there are differences in the adoption process between countries (in this study, the terms country and culture are used interchangeably) can shed light on this important aspect of international marketing research. Despite the importance of this topic, very few empirical studies exist in the area of cross-national diffusion research, the major reason being a lack of reliable time-series data across multiple countries [7]. Given this, Gatignon, Eliashberg and Robertson [4], Takada and Jain [13] and Helsen, Jedidi, and DeSarbo [8] (henceforth referred to as GER, TJ, and HJD respectively) should be commended for their efforts to empirically study cross-national diffusion patterns. These studies have provided us with a preliminary understanding of the use of cross-national diffusion patterns in international marketing strategy formula-

BIOGRAPHICAL SKETCHES V. Kumar (VK) is Marvin Hurley Professor of Business Administration, Melcher Faculty Scholar, and Director of Marketing Research Studies at University of Houston, Houston, TX, 77204-6283. Phone: (713)-743-4569: Fax: (713)-743-4572: e-mail: [email protected]. Jaishankar Ganesh is Assistant Professor of Marketing, University of Central Florida, Orlando, FL 32816. Raj Echambadi is a Doctoral Student in Marketing at the University of Houston, Houston, Tx 77204-6283.

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tion. The findings of these three studies, collectively, form the basis of the extant knowledge in this area. In essence, what is widely accepted today about crossnational diffusion patterns can be summarized as follows: a) The diffusion of a new product/service is a culturespecific phenomenon (TJ), and the differences in the adoption process can be explained to a great extent by country-specific factors (which is crosssectional data) such as cosmopolitanism, mobility, and women in labor force (GER); b) The later a product is introduced in a country, the faster will be the rate of adoption. In other words, there exists a “lead-lag” effect in cross-national diffusion patterns which results in a faster adoption in the lag country (the country where the product is introduced later) when compared to the adoption rate in the lead country (the country where the product is introduced prior to the lag country) (TJ). This finding illustrates the influence of “time lag.” c) The country segments derived based on diffusion parameters (the coefficients of innovation and imitation) are not stable. In other words, the segments differ to some extent due to the nature of the product. Also, these segments do not correspond with the segments derived from traditional analyses of cross-sectional macro-level data (HJD) across countries. As mentioned earlier, unlike research in other areas, much of the evidence in cross-national diffusion is based on the above mentioned three studies. Further, the knowledge from these three studies has been accumulated using product categories and countries that have little overlap across the studies. Also, some of the findings from these studies are contradictory. For example, TJ hypothesize and find support for their argument that the adoption is faster in high context cultures as compared to low context cultures, whereas HJD find little evidence to support this argument. Also, HJD found a negative relationship between the diffusion parameters and lead-lag time which is contradictory to the findings reported by TJ. One of the objectives of this research is to replicate the three published empirical studies with a common set of product categories and countries in an effort to report some generalizable results that can be used as the basis for future research. Given the paucity of generalizable evidence, this replication is a much needed effort in this area. As noted by Hubbard and

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Armstrong [9], replications are rare in marketing, and replications and extensions play a valuable role in ensuring the integrity of a discipline’s empirical results. Accordingly, the objectives of this research are to empirically verify: (a) the role of country-specific effects in explaining the differences in diffusion parameters, (b) the presence of a lead-lag effect, (c) the use of cultural variables to explain systematically the diffusion patterns across countries, and (d) the merit of country segmentation schemes based on diffusion parameters. We examine the diffusion patterns of different products (different from the ones used in the original studies), across several countries (some of which overlap with the past studies), over different time periods. Specifically, data on five product categories from fourteen countries are used in this replication. Additionally, we extend past research by integrating cross-sectional and time lag variables in a single research framework to evaluate the efficiency of the combined model. The managerial usefulness of this integrated framework is demonstrated through forecasting applications for an existing innovation in a new country and new innovations in any country. The remainder of this article is organized as follows: First, the replicated studies are revisited along with a brief review of the background literature. Next, the research methodology section details the replication parameters and estimation procedures of the three studies. A framework that extends past studies is proposed and the corresponding models are estimated. The results section provides discussion of the findings and, finally, the research findings are summarized and managerial implications are offered.

Literature Review The methodological underpinning of any diffusion model in marketing dates back to the Bass Model [1], which models the adoption of an innovation within a population as being influenced by two means of communication—mass media and word-of-mouth. Among the two groups of adopters of an innovation, the group influenced only by mass media communication is the “innovators” and the group influenced only by wordof-mouth communication is the “imitators.” According to Bass, the density function of time to adoption is given as:

dF 5 f ~ t ! 5 @ p 1 qF ~ t !#@ 1 2 F ~ t !# dt

(1)

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where F(t) denotes the cumulative function of adopters, and p and q are the coefficients of innovation and imitation, respectively. In discrete form,

X ~ i ! 5 p @ m 2 N ~ t i21 !# 1 q

S

z @ m 2 N ~ t i21 !# 1 « i

N ~ t i21 ! m

D (2)

where X(i) 5 Sales at time period i, m 5 market potential, N(ti) 5 Cumulative number of adopters up to and including time period i, and «i 5 Error term Further, equation (2) for X(i) assumes that the time intervals are equal. Many estimation methods have been proposed including ordinary least squares (OLS), maximum likelihood estimation (MLE) and nonlinear least squares (NLS) to obtain diffusion model parameters [12]. The Bass model and its variations have been applied to understand the diffusion process in a variety of contexts (see [10]). One of the areas where the Bass model has been applied recently is in the cross-national diffusion area. Three empirical studies (GER, TJ, and HJD) apply the diffusion model to sales data across countries to understand the nature of the diffusion process. GER present a methodology which models the heterogeneity among different countries (using cross-sectional data) in terms of their propensity to innovate and imitate. Their study uses country characteristics (associated with patterns of social communication that affect rate and level of diffusion) to explain the differences in the diffusion parameters. The major application of their methodology is to predict the diffusion of an innovation in a country even before the innovation is introduced, or in the absence of sales data. Specifically, GER use constructs such as cosmopolitanism, mobility, and sex roles to explain the diffusion patterns across countries. According to GER, the motivation behind using these constructs are: The cosmopolitanism variable has been studied extensively in diffusion research and found to be useful in the rural sociology, medical, geography and marketing traditions of research. The mobility variable underlies much of diffusion theory within geography and is a key variable in enhancing or limiting the spread of innovation. The sex roles variable has not been studied, as such, in diffusion research, although it is related to the transmission of influence in terms of

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heterophily (information transfer across dissimilar individuals) within a social system and, therefore, to diffusion rates.

GER offered propositions relating the diffusion parameters (p and q) to these three constructs. They are: P1: Countries with a higher degree of cosmopolitanism (cosmo) show a greater propensity to innovate and a smaller propensity to imitate P2: Mobility (mobil) will be positively associated with propensity to imitate, since it increases the opportunity for social interaction. P3: The percentage of women in the labor force (women) is negatively related to the propensities to innovate for time-consuming innovations and positively related to the propensity to imitate when the work context provided a level of heterophilous influence. Their study indicates the existence of systematic patterns of diffusion across products and countries. Using data on six consumer durable goods— dishwashers, deep freezers, lawnmowers, pocket calculators, car radios, and color televisions—for 14 European countries, they find support for their propositions. Subsequently, TJ (based on past research studies by Hall [5], [6] and Rogers [11] argue that the culture context of a country (high context vs low context) and the time difference in the introduction of the innovation between a pair of countries (termed as lead-lag effect) influence the adoption process. They argue that communication in high context cultures (such as Japan, South Korea and Taiwan) can be regarded as homophilous, and hence is conducive for transfer of ideas and information. In contrast, in low context cultures (such as the US and some European countries) the population is more heterogeneous and the communication more heterophilous, which may cause cognitive dissonance and hence the population is not conducive to transfer of ideas. Based on this argument they propose that: H1: The rate of adoption characterized by high context culture and homophilous communication is faster (higher value for the imitation coefficient, i.e., word-of-mouth effect) than that in countries characterized by low context culture and heterophilous communication. Also based on Rogers’ framework, TJ argue that the adoption rate is likely to be accelerated if the product

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is introduced in the market with a time lag. The basic premise underlying the lead-lag time effect is that the extra time available for potential adopters in the lag country will help them to understand the relative advantage of the product, judge whether the product is compatible with their needs, try the product readily because of possible lower price and hence lower perceived risk, and observe the product more through increased availability in other markets or mass communication. Based on these arguments, they propose that: H2: The later a product is introduced in a market, the faster will be the rate of adoption. Consequently, the imitation coefficient will have a larger value for a country in which the product is introduced later than for the country in which the product is first introduced. TJ find support for both H1 and H2. More recently, HJD attempt to understand the merits of country segmentation schemes based on multinational diffusion parameters. More precisely, they analyze the extent to which the countries belonging to the same (or different) segments based on macroeconomic variables reveal similar (or dissimilar) diffusion patterns. HJD sought to answer the following questions: Q1: To what extent do country segments derived from traditional analyses of macro-level data correspond to segments derived from multinational, product-class specific diffusion parameters? Q2: How well do variables that are typically used in macro-level country segmentation studies perform when used to profile diffusion-based country groups? Q3: How stable are diffusion-based country segmentation schemes estimated across different innovations? The constructs representing the array of macroeconomic variables are identified as mobil, cosmo (these two constructs, though not identical, are similar to GER’s), health, trade, and lifestyle. HJD cluster twelve countries including the US, Japan, and ten European countries on these five constructs and arrive at both two and three-cluster solutions. In the threecluster solution, one of the clusters was the US by itself. Each of the other two segments had four and seven countries, respectively. HJD apply latent structure methodology to the Bass model to determine the

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introduction of the product in each of the countries through the time period for which the most recent data were available. In general, the data covers the time period 1970s, 1980s, and up to 1990. The number of countries for which yearly sales data are available differ between product categories. Also, the number of years of data for each country depends on the product category. For example, in Germany VCRs were first introduced in 1970 while microwave ovens were introduced in 1974. Data on at least ten countries are available for each product category. The data are primarily obtained from several Euromonitor publications and leading manufacturers of the products in Europe.

segments and the segment level estimates simultaneously. The results indicate the presence of three segments for color TVs and VCRs, and two segments for CD players. The segment composition based on macroeconomic variables does not correspond to the segment composition based on the diffusion parameters of any of the three product categories. Further, there was little agreement on the segment grouping across product categories. HJD compare the diffusion-based country groupings to the high/low context cultural classification (similar to TJ) and find little evidence of linkage (unlike TJ) between the diffusion based country clustering and Hall’s cultural classification. Also, unlike TJ, HJD find a negative relationship between the coefficient of imitation and time (lead-lag relationship) for all three product categories. There are other differences observed in the findings across the three studies. For example, HJD conclude that countries rated high on cosmo appear to exhibit stronger tendencies to imitate. However, GER show a negative impact of this variable on the imitation coefficient. Further, country characteristics show a systematic association with the diffusion model parameters in the GER study, while sign reversals or lack of significance occur for the variables in the HJD study. Given that no other studies have attempted to corroborate any of the findings, it leaves us with little evidence regarding the reliability and generalizability of the findings. Therefore, it is useful not only to replicate the three published studies with a common set of product categories but also to extend the knowledge in this area. Findings from past research studies will be integrated in a single modeling framework so that managers interested in exploring foreign markets can use the proposed framework to make more effective decisions.

Replication of GER. The econometric model formulation of GER is used to estimate the diffusion model parameters and assess the influence of the country characteristics associated with patterns of social communication— cosmo, mobil, and women. GER’s values for these three variables (reported in Table 1 of GER) are used in this study for the following reasons. First, the variable values used by GER cover a time span of 16 years (1965– 80) and GER state that the variable values represent a stable pattern. Second, for two of the three variables (cosmo and mobil), GER create the variable indices from six and three items, respectively. Our attempt to recreate these indices relevant to this study was futile since data for some of the original items were not available. Third, the product categories used in this study were introduced in most of the countries between 1970 and 1982, thus rendering feasible the use of GER data on these variables. In discrete form, the model proposed by GER is:

Research Methodology

x ~ i,t ! 2 x ~ i,t 2 1 ! 5 @ p ~ i ! 1 q ~ i ! x ~ i,t 2 1 !#

Data The data used for replicating the three studies are annual sales data of five product categories across multiple countries. The product categories include consumer durables—VCRs, microwave ovens, cellular phones, home computers, and CD players. The countries used are: Austria, Belgium, Denmark, Finland, France, Germany, Italy, The Netherlands, Norway, Portugal, Spain, Sweden, Switzerland, and the UK. The data were collected from the first year of

Model Formulation and Estimation Procedures

3 @ 1 2 x ~ i,t 2 1 !# 1 u ~ i,t ! (3) x(i, t) 5 cumulative penetration of product in country i at time t( y(i, t)/m(i)), y(i, t) 5 cumulative sales of product in country i at time t, p(i) 5 propensity to innovate in country i, q(i) 5 propensity to imitate in country i, m(i) 5 market potential in country i, u(i, t) 5 disturbance term

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As postulated by GER, the fluctuations of the diffusion model parameters across countries can be partially explained by the social system characteristics as

p ~ i ! 5 Z9 ~ i ! g p 1 e p ~ i !

(4)

q ~ i ! 5 Z9 ~ i ! g q 1 e q ~ i !

(5)

Z(i) 5 vector of country i characteristics, including an element 1 corresponding to a constant gp 5 vector of coefficients representing the impact of the country characteristics on the propensity to innovate gq 5 vector of coefficients representing the impact of the country characteristics on the propensity to imitate ep(i), eq(i) 5 disturbance terms The equations (4) and (5) are substituted into equation (3) and GLS procedure is used to estimate the model for each product innovation. Replication of TJ. Similar to TJ, the Bass diffusion model is estimated by NLS procedure to obtain the diffusion model parameters. The country-specific effect hypothesis is tested with a “Least Significant Difference” t-test between countries. The time effect is tested with a regression model

Y ijk 5 a 1 b X ijk 1 u i

(6)

where Yijk and Xijk are the differences in the values of the imitation coefficients and introduction years for a pair of countries i and j for product k, a and b are the intercept and slope coefficients, and u is the error term. Replication of HJD. The two and three country segments obtained by HJD from macroeconomic variables are used to compare the country segments formed on the basis of similarity of diffusion patterns in our study. Country segments are derived for each product category based on the similarity of the diffusion model parameters “p” and “q,” using cluster analysis.

Replication Results Replication of GER The results of the GLS estimation of model (3) [with equations (4) and (5) substituted in (3)] for all the five product categories are provided in Table 1. The values

in Table 1 correspond to the vectors of coefficients gp and gq. Similar to GER, the coefficients of mobil on the propensity to innovate is constrained to zero. As shown in Table 1, for each variable, the number of parameters with correct signs in this study correspond to GER’s findings with the exception of one variable—women in labor force. Unlike GER’s results, women in labor force has four parameters with negative signs in this study. GER perform a joint test [2] to develop a general supportive statement about the propositions using each innovation as a replicate in the analysis. According to Fisher [3], When a number of quite independent tests of significance have been made, it sometimes happens that although few or none of them can be claimed individually as significant, yet the aggregate gives an impression that the probabilities are on the whole lower than would often have been obtained by chance. It is sometimes desired, taking account of only these probabilities, and not of the detailed composition of the data from which they are derived, which may be of different kinds, to obtain a single test of the significance of the aggregate, based on the product of the probabilities individually observed.

If the coefficients for a variable across all product innovations (replicates) are of the same sign, then there is no need for a joint test. Upon investigating the purpose and the use of this joint test, we believe that the joint test may not be applicable to GER’s study. Thus, we refrain from using the joint test. The signs for cosmo on propensity to innovate are positive and significant for four products, while not significant for cellular phones. The signs for cosmo on propensity to imitate show a mixed pattern, with three negative and significant and two positive (with only one of them significant). The effect of mobil on the imitation coefficient shows a mixed pattern. Out of three significant coefficients, two are negative. In the majority of the cases, the effect of cosmo is as hypothesized. Even in the GER study, the effect of mobil on the imitation coefficient is mixed. Out of five significant coefficients, only three are positive. However, GER find support for an overall positive effect for mobil using the joint test. Given that the joint test is used incorrectly, the findings of this study indicate that the effect of mobility can vary depending upon the innovation. However, a comparison of our results and GER’s results reveals that mobility is significant and positive in a majority of the cases indicating support for the hypothesized positive effect.

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Table 1. Simultaneous GLS Estimation (gp, gq) Results of Cross-Sectional Variables Summary of Findings Variables with hypothesized effects

VCR

Cellular Phones

Home Computers

Microwave Ovens

CD Players

0.0699 (16.32) 20.0049NS 2(0.79) 0.0388 (12.6)

0.0045 (3.24) 0.0366 (19.50) 20.0156 2(15.7)

20.0009 2(9.93) .0058 (29.41) 20.0014 2(15.97)

0.0134 (6.24) 0.0206 (9.95) 20.0042 (21.97)

0.4148 (5.13) 20.6527 2(4.39)

0.4452 (63.94) 20.2751 2(20.16)

0.0897 (4.22) 0.1944 (7.3)

0.6308 (40.57) 20.4484 (25.76)

1.7733 (23.45)

0.733 (4.34)

This study

GER

Propensity to Innovate Constant Cosmopolitanism (1) Women in Labor (2) Force

0.0001NS (1.17) 0.0137 (14.25) 20.0030 2(15.66)

41 1 NS 42 11

61 32 21 1 NS

Propensity to Imitate Constant Cosmopolitanism (2)

0.4066 (131.62) 0.0157NS (1.84)

Mobility (1)

20.1189 2(12.17)

20.7047 2(4.00)

0.0039NS (0.19)

Women in Labor Force (1)

20.0774 2(16.24)

20.2641 2(6.29)

0.0318 (4.07)

20.3126 2(25.46)

20.1960 (26.46)

32 11 1 NS 22 21 1 NS 42 11

32 21 1 NS 22 31 1 NS 31 32

t statistics are represented in parentheses. All coefficients are significant at the 0.05 level. NS not significant.

The expected negative effect for women on the innovation coefficient is supported for four of the five product categories. However, the expected positive effect for women on the imitation coefficient is not supported. Actually, GER suggest that the influence of women could depend on the type of innovation (timesaving vs time-consuming). The constants, in general, are positive and significant given that these are the values for innovation and imitation coefficients for a country characterized by average social characteristics (where the characteristics are normalized with zero mean). The consistency in the signs of the coefficients for the construct variables across products in this study indicate that social communication operates somewhat similarly across innovations. Thus, the directional impact of the individual variables are similar between this replication and GER findings. Given that the product categories used in this replication are different from those used by GER, the cumulative evidence for the impact of the hypothesized variables is noteworthy.

Replication of TJ The diffusion model parameters are estimated using the NLS procedure in the SAS/STAT package. The country-specific effects are tested using the least significant difference t-test for pairwise differences in the imitation coefficients between countries. Fourteen European countries are used in the analysis. Out of the 91 pairwise differences, only seven are found to be significant. Although these findings contradict TJ’s findings of culture-specific effects, our results are consistent with the expectations that the European countries may not exhibit significant differences in the imitation coefficients (since according to Hall’s classification they belong to the low-medium context culture). Similar results are also observed by HJD. Regarding time (lead-lag) effects across all products, the estimation of regression model (6) by OLS yields a significant and positive coefficient for both the intercept and the time variable. As shown in Table 2, the estimated model (6) is: Yijk 5 0.018 1 0.048 Xijk.

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Table 2. Capturing Lead-Lag Effect with the Imitation Coefficient Aggregation Pooled across all product categories VCRs Cellular Phones Home Computers Microwave Ovens CD Players TJ Study

Intercept

deltime

0.0181NS (1.49) 0.024NS (1.53) 0.001NS (0.04) 20.048* (22.3) 0.049* (2.32) 0.029NS (1.61) 0.054NS

0.048* (10.29) 0.025* (6.65) 0.12* (11.24) 0.034NS (1.43) 0.062* (8.91) 0.36* (13.67) .005*

t statistics are represented in parentheses. * significant at 0.05 level. NS not significant.

This result is similar to that of TJ (Yijk 5 0.054 1 0.005 Xijk). While TJ obtained a value of 0.005 for the time lag effect, our study yields a much higher value (0.048). One plausible reason for this increase is the fact that the innovations modeled in this study correspond to later time periods (1970s and 80s) compared to the time periods of innovations (1930s– 50s) modeled by TJ. Given the availability of better infrastructure facilities and economic climate in later time periods, the benefits of the innovations are known to consumers in a shorter period of time. Therefore, the influence of time lag is higher in the current study. The model is also estimated for each product category to see if the lead-lag effect is dependent on the type of innovation. Table 2 provides complete details on the estimation results for the time effect. As the results indicate, at the aggregate level the results reveal a positive time effect on the coefficient of imitation (as suggested by TJ). However, at the individual product level this finding holds true for four of the five innovations. Replication of HJD The country segments formed on the similarities of diffusion model parameters for each product category are given in Table 3. An examination of the table reveals some interesting observations. Spain and Portugal group together if they introduce the innovation at

the same time (e.g., Home Computers) indicating that two countries which are closer together and if they exhibit similarity with respect to cultural and economic measures group together. Similarly, Belgium and Netherlands group together in all of the three cases for which data were available jointly for both countries. If two countries, similar in terms of economic/ cultural measures and geographically closer to each other, have innovations introduced at different points in time (e.g., Spain and Portugal for VCRs) then they cluster in different groups indicating the presence of a time lag effect. Among the three countries, France, Germany, and the U.K., at least two of three countries cluster together across product categories. Similarly, most of the Scandinavian countries group together. These two examples lead us to believe that these groups of countries cluster based on either cultural and/or economic similarity. In summary, countries seem to group together in terms of the time of introduction. Second, given similar time periods of introduction, geographical proximity appears to influence the formation of clusters. Third, cultural and economic similarity also seem to influence the formation of clusters. While these observations are based on limited evidence, at least our research study offers some benchmarking for future research. The country segments solution from the diffusion model parameters in this study is compared to HJD’s two-country segments solution obtained using macroeconomic factors, and no correspondence is found between the two clustering schemes. There are several reasons for this discrepancy. First, HJD’s clustering is based on countries from different continents, while our study is based on European countries. Given cluster analysis can be sensitive to addition/deletion of countries, it is not possible to compare HJD’s clustering solution based on macro-economic variables to the clustering solution obtained in our study. Since HJD do not provide the year of introduction for the innovations they studied, it is not possible to draw inferences on the patterns of clusters across product innovations. However, our study offers some reasons as to the groupings of countries. To test the face validity of the segments obtained in this study, we examine the means and standard deviations of the coefficients of innovation and imitation for each product category (Table 3). The examination reveals a clear distinction among the segments obtained using the diffusion parameters

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Table 3. Country Segments Based on Diffusion Parameters Product Categories Segment

VCRs

Cellular Phones

Home Computers

Microwave Ovens

CD Players

1

2

3

standard deviations are denoted in parentheses.

for each product category. For example, three-country segments for VCRs yield mean “p” values of 0.0044, 0.0023, and 0.0036 with corresponding standard deviations of 0.005, 0.001, and 0.005. Likewise, the mean “q” values are 0.38, 0.47, and 0.64 with corresponding standard deviations of 0.014, 0.026, and 0.021 respectively. These values indicate that the segments obtained are distinct. Similar observations are made for the other four product categories. An interesting difference across all three published studies relates to how the diffusion parameters of each product innovation are used for testing of hypotheses. TJ pool the observations across product categories to illustrate the time effect and country effect. GER estimate their model for each product and generally find consistent results across products for their propositions on social communication factors. HJD form countrysegments for each product and find differences in the

composition of country-segments across products. Does this mean that product-specific effects exist or not? To answer this question, we perform a Least Significant Difference t-test for pairwise differences in both innovation and imitation coefficients between products. Tables 4a and 4b present the results of the statistical tests. Out of ten possible differences, 2 are significant (a 5 0.05) for the coefficients of imitation and 6 are significant (a 5 0.05) for the coefficients of innovation. Since multiple comparisons are made, for the experimentwise error rate (a1) to be 0.05, each pairwise comparison should be tested at a 5 0.005. This is because a1 5 1 2 (1 2 a)k where k 5 number of comparisons. If an a of 0.005 is used for the tests of significance, then two pairwise differences are significant for the coefficients of innovation and none are significant for the imitation coefficients. Thus, the results indicate that different types of innovations, on the average, have similar diffusion parameters.

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Table 4(a). Least Significant Difference T-Test for Pairwise Differences in the Coefficients of Imitation Across Time Innovation

VCR

Cellular Phones

Home Computers

Microwave Ovens

CD Players

VCRs Cellular Phones Home Computers Microwave Ovens CD Players

— 0.11 0.07 0.07 0.11

— 0.19* 0.04 0.01

— 0.14* 0.18

— 0.04

—

* indicates significance at 0.05 level.

Table 4(b). Least Significant Difference T-Test for Pairwise Differences in the Coefficients of Innovation Across Time Innovation

VCR

Cellular Phones

Home Computers

Microwave Ovens

CD Players

VCRs Cellular Phones Home Computers Microwave Ovens CD Players

— 0.02* 0.03* 0.001 0.03*

— 0.01 0.02* 0.02

— 0.03* 0.01

— 0.03*

—

* indicates significance at 0.05 level.

Replication Overview

Extension Study

In summary, this replication study has demonstrated that diffusion parameters across countries are influenced by certain country-specific characteristics (cross-sectional data) and lead-lag (time lag) effects. The impact of cross-sectional and time lag variables have been shown to be consistent across innovations. However, the relative influence of these variables may vary across innovations. Some interesting thoughts emerge from this replication. First, given that the effects of cross-sectional and time lag variables on the diffusion process have been established individually, is it useful to combine these two dimensions in a single modeling framework? This integrated model would enhance the richness of the framework in that it combines countryspecific effects as well as the time of introduction of the innovation. Second, is it possible to forecast the diffusion of one of the innovations modeled in this study for a new country? If so, can we assess the relative performance of a cross-sectional model with the proposed cross-sectional time-series model? Third, is it also possible to forecast the diffusion of a new innovation in a country with the existing knowledge about the variables shown to influence the diffusion patterns? Instead of leaving these questions unanswered, we decided to undertake another study to extend the knowledge in this area.

As mentioned earlier, the purpose of this extension study is (a) to evaluate the performance of a crosssectional time-series model for modeling the diffusion of innovations in several countries, (b) to illustrate the ability of the proposed model to forecast the diffusion of an existing innovation in a new country, and (c) to demonstrate the usefulness of the proposed framework for forecasting the diffusion of a new innovation in any country.

Data and Model Formulation As with the replication, data on all five consumer innovations are used in the extension study. The model formulation for estimating the diffusion model parameters is the same as equation (5). The effect of crosssectional and time-series variables are captured by the following model formulations:

p ~ i ! 5 Z9 ~ i ! g p 1 e p ~ i !

(7)

q ~ i ! 5 Z9 ~ i ! g q 1 t i 1 e q ~ i !

(8)

where ti 5 denotes the time lag of the innovation in country i relative to the lead country and the remaining notations are the same as before.

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Results

Forecasting

Equations (7) and (8) are substituted into equation (3) and GLS procedure is used to estimate the model for each innovation. The results of this estimation are provided in Table 5. The values in Table 5 correspond to the vectors of coefficients gp and gq, and the t parameter. As the results indicate, the influence of cosmopolitanism on propensity to innovate is positive and significant in four out of five cases providing support for the hypothesized positive effect. Similarly, the influence of women in labor force on propensity to innovate is negative and significant for four product categories. With respect to the influence of the variables on the propensity to imitate, the coefficients for cosmopolitanism are negative and significant in four cases; the coefficients for mobility and women in labor force are positive and significant for three product categories; and the coefficient for time lag is positive and significant in all cases. Thus, support is found for all the hypothesized effects. Based on the findings of this study, it can be said that differences in rates of diffusion of innovations can be explained by certain country-specific variables and lead-lag influences. For example, if country A has higher mobility value than country B, then one can expect the rate of diffusion to be faster in country A than country B.

Can managers interested in exploring foreign markets use these findings? To answer these questions, we develop various scenarios. Table 6 shows a matrix representation of various scenarios that demonstrate the usefulness of the integrated model. Regarding the extension of existing innovations for existing countries, the fit statistics speak for themselves for the cross-sectional time series model. The values of adjusted R squares ranges from 0.80 to 0.97 across innovations which indicates an excellent fit of the model to the data. In terms of forecasting applications, data on home computers for Denmark are used (because Denmark was not used for model estimation) for illustrating the usefulness of modeling an existing innovation for a new country. For the case of a new innovation in any country, we chose CD players for Sweden and Germany. Although, CD players were used as one of the categories in the model estimation process, for forecasting purposes we use information only from the other four product categories. The sales forecasts for home computers in Denmark is generated by using the coefficients in Table 5 for the cross-sectional time-series model (CSTS model) and values in Table 1 for the cross-sectional model (CS model) for that specific category. Information on cosmopolitanism, mobility, women in labor force, and

Table 5. Simultaneous Estimation of Cross-Sectional and Time-Series Factors Influencing the Cross-National Diffusion Parameters

Variables Propensity to Innovate Cosmopolitanism

Past Research Hypothesis

CD Player

Microwave Oven

VCR

Cellular Phone

Home Computer

Support for Hypothesis

Positive

0.0202 (12.56)

0.0069 (42.02)

0.0174 (34.13)

20.0091 (22.06)

0.0357 (11.64)

yes

Women in Labor Force

Negative

20.0047 (26.63)

20.0018 (229.45)

20.0034 (218.57)

0.0364 (21.11)

20.0169 (214.11)

yes

Propensity to Imitate Cosmopolitanism

Negative

20.1409 (210.93) 0.0779 (6.53)

0.1205 (6.35) 1.1660 (18.00)

20.1044 (224.60) 20.1923 (225.65)

20.2823 (22.26) 20.9200 (25.90)

20.2658 (212.34) 0.2585 (5.13)

yes

0.0500 (6.43) 0.3678 (46.91)

20.1410 (210.89) 0.0534 (15.20)

0.0135 (3.99) 0.0226 (38.57)

20.2901 (28.41) 0.0499 (6.11)

0.0319 (2.11) 0.0366 (4.77)

Mobility

Positive

Women in Labor Force

Positive

Time Lag

Positive

t statistics represented in parentheses. all coefficients are significant at the 0.05 level.

yes

yes yes

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Table 6. A Matrix Representation of Current Study

Existing Innovation

New Innovation

Existing Country

New Country

Used for Model Estimation

Home Computers for Denmark

CD Players for Sweden and Germany

time lag is also obtained. Equations (7) and (8) substituted in (3) are used to generate the sales forecasts. The forecasting results are presented in Figure 1 which indicates that forecasts generated by the CSTS model is better compared to that of the CS model. The mean absolute percentage error (MAPE) values are 17.8% and 27% for the CSTS and CS model respectively. Thus, the utility of combining cross-sectional and time-series influence is demonstrated through this application. For generating sales forecasts of CD players for Sweden and Germany, the values for all the variables shown in Table 5 are obtained. The coefficients for these variables are generated by computing the aver-

Figure 2. A Comparison of Forecast and Actual Sales for CD Players.

Figure 1. Diffusion of Home Computers in Denmark: A Comparison of Forecasts using Cross-Sectional and Time-Series Model and Cross-Sectional Model.

Authors

Product Categories

Influences on Diffusion Parameters

Estimation Method

Countries Studied

Findings

Dishwashers, Deep Freezers, Lawnmowers, Pocket Calculators, Car Radios, Color TVs

Cross-sectional

Simultaneous estimation of diffusion model parameters with covariates using GLS

Austria, Belgium, Denmark, Finland, France, Germany (formerly West Germany), Italy, Netherlands, Norway, Portugal, Spain, Sweden, Switzerland, U.K.

Differences in adoption rates between countries can be explained using country-specific variables— cosmopolitanism, mobility, and women in work force; The countryspecific factors (cross-sectional variables) can be used for forecasting sales in existing and new markets.

Takada and Jain [13]

Black and White TV sets, Washing Machines, Room A/Cs, Passenger Cars, Refrigerators, Calculators, Vacuum Cleaners, Radios

Time-series

Estimate diffusion model parameters and then regress them against time lag

Japan, South Korea, Taiwan, U.S.

Diffusion of products is culture-specific; A positive effect is observed between time-lag and adoption rate in the lag countries (lead-lag effect).

Helsen, Jedidi, and DeSarbo [8]

Color TV, VCRs, CD Players

Cross-sectional and time-series

Estimate diffusion model parameters using latent class regression and then regress them against hypothesized influences

Austria, Belgium, Denmark, Finland, France, Japan, Netherlands, Norway, Sweden, Switzerland, U.K., U.S.

Country segments based on diffusion parameters are product specific and do not correspond with traditional segmentation based on macroeconomic factors; A negative effect is observed between time-lag and adoption rate in the lag countries.

Present Study

VCRs, Cellular Phones, Microwave Ovens, Home Computers, CD Players

Cross-sectional and time-series

Simultaneous estimation of diffusion model parameters with covariates using GLS

Austria, Belgium, Denmark, Finland, France, Germany (formerly West Germany), Italy, Netherlands, Norway, Portugal, Spain, Sweden, Switzerland, U.K.

Country-specific variables and time lag are useful in explaining differences in adoption rates between countries; A positive effect is observed between time-lag and adoption rate in the lag countries; Forecasts for existing and new innovations into existing and new markets using cross-sectional and time-series influences perform better than forecasts generated using only cross-sectional influences; Country segments composition is not consistent across innovations.

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Gatignon, Robertson, and Eliashberg [4]

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Table 7. Empirical Studies in Cross-National Diffusion: A Comparison of Findings

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age of the coefficient values for the other innovations, whose values are in the hypothesized direction. For example, the coefficient for cosmopolitanism (on propensity to innovate) would be the average of 0.0069, 0.0174, and 0.0357 which is 0.02. Similarly, other coefficients are obtained. Using equations (7), (8), and (3), sales forecasts are generated. The resulting forecasts and actual sales are shown in Figure 2 for Sweden and Germany. As the graph indicates, the sales forecasts are similar to that of actual values. In terms of how consumers would react to a new product in these countries, the sales curves provide the answers. The product sales steadily increases indicating that these are no barriers at the early stages. With respect to the time it would take for the product to achieve a certain level of market penetration in a country, it is possible to divide each period sales with an estimate of the market potential for that country. Thus, our integrated framework allows a manager to develop valuable insights before an innovation is introduced in a country. Thus, it is possible to generate forecasts (at least to serve as a benchmark) for a new innovation in any country with good accuracy.

4. Forecasts for existing innovation in a new country can also be generated with the cross-sectional time series framework.

Discussion, Implications, and Limitations

References

The objectives of this research are to (1) replicate and extend the findings of the three empirical studies in the area of cross-national diffusion that collectively form the basis for our knowledge in this area, and (2) draw implications for managers to make effective productmarket decisions. Table 7 provides a comparison of findings of the major empirical studies in cross-national diffusion research including the present study. The utility of the proposed framework which combines the cross-sectional and time-series influences to model the diffusion parameters is illustrated with different scenarios. The implications of the findings of this study are:

1. Bass, Frank, M. A New Product Growth Model for Consumer Durables. Management Science 15:215–227 (January 1969). 2. Dutka, Solomon. Combining Test of Significance in Marketing Research Experiments. Journal of Marketing Research 21(1):118–119 (February 1984). 3. Fisher, R. Statistical Methods for Research Workers, 13th ed. New York: Hafner Publishing Company, 1958. 4. Gatignon, Hubert, Eliashberg, Jehoshua and Robertson, Thomas S. Modeling Multinational Diffusion Patterns: An Efficient Methodology. Marketing Science 8(3):231–247 (Summer 1989). 5. Hall, Edward T. Beyond Culture. New York: Doubleday, 1976. 6. Hall, Edward, T. Hidden Differences. New York: Doubleday, 1987. 7. Heeler, Roger M. and Hustad, Thomas P. Problems in Predicting New Product Growth for Consumer Durables. Management Science 26: 1007–1020 (October 1980). 8. Helsen, Kristiaan, Jedidi, Kamel and DeSarbo, Wayne S. A New Approach to Country Segmentation Utilizing Multinational Diffusion Patterns. Journal of Marketing 57:60–71 (October 1993). 9. Hubbard, Raymond and Armstrong, Scott J. Replications and Extensions in Marketing: Rarely Published but Quite Contrary. International Journal of Research in Marketing 11:233–248 (1994). 10. Mahajan, Vijay, Muller, Eitan and Bass, Frank. Innovation Diffusion and New Product Growth Models in Marketing. Journal of Marketing 54:1–26 (January 1990). 11. Rogers, Everett M. Diffusion of Innovations. New York: The Free Press, 1983. 12. Srinivasan, V. and Mason, Charlotte H. Nonlinear Least Squares Estimation of New Product Diffusion Models. Marketing Science 5:169–178 (1986). 13. Takada, Hirokazu and Jain, Dipak. Cross-National Analysis of Diffusion of Consumer Durable Goods in Pacific Rim Countries. Journal of Marketing 55:48–54 (April 1991).

1. Country-specific variables such as cosmo, mobil, women in labor force are useful for capturing the differences in diffusion patterns across countries and innovations. 2. Time lag effect is influential in explaining differences in diffusion patterns across countries. 3. Although the relative influences of country-specific and time lag variables differ across innovations, the average of the coefficient values of these variables across the existing innovations can be used to generate forecasts for a new innovation; and

Additionally, the study offers some guidelines identifying factors that may influence the clustering of countries. These factors include timing of entry, geographical proximity, and cultural/economic similarity. While the available evidence is limited, this observation serves as a springboard for future research in the area of crossnational diffusion. A conceptual framework should be developed to identify all the factors that can potentially influence the diffusion process and then be subjected to empirical tests. Future research can model the diffusion patterns of industrial innovations across countries and compare the findings to that of consumer innovations with respect to lead-lag effect and clustering of countries based on the similarity of diffusion patterns.

The authors contributed equally to the article. The authors thank the editor and the reviewers for their comments on the earlier version of the manuscript.