Learning effect in multinational diffusion of consumer durables: An exploratory investigation

Learning Effect in Multinational Diffusion of Consumer Durables: An Exploratory Investigation

Jaishankar Ganesh University of Central Florida

V, Kumar Velavan Subramaniam University of Houston

Literature reflects that a product~technological innovation introduced later in a country results in faster diffusion as the consumers in the lag market have an opportunity to learn about the new product from the consumers in the lead market. A systematic understanding of the learning that takes place between consumers in two countries--a pair of lead and lag countries--can provide insights for a firm's international market entry decisions. To provide a richer understanding of the underlying structure and patterns that govern this process, propositions linking factors (country characteristics, product/innovation characteristics, and time lag) to the learning process are drawn. Subsequently, these propositions are tested through an empirical investigation of the diffusion patterns of four consumer innovations in multiple European countries. The findings help provide some preliminary guidelines for manufacturers regarding selection of foreign markets and the timing and order-of-entry decisions.

The introduction of new product innovations in foreign markets involves two major decisions--foreign market selection, and timing and order of entry. Douglas and Craig (1992) argue that there is a need for more research identifying new procedures for country selection and methodologies for sales forecasting and demand estimation in a

Journal of the Academy of Marketing Science. Volume 25, No. 3, pages 214-228. Copyright �9 1997 by Academy of Marketing Science.

multicountry context. One approach toward addressing these critical needs would be to study the multinational diffusion patterns of products and technologies to derive insights on how new products diffuse in different cultures and why diffusion rates and market penetration differ between countries. Since different cultures react differ- ently to product innovations, an analysis of the diffusion patterns across cultures can provide insights into the choice of countries (and the timing and order-of-entry decisions) that are best suited for a firm, given its resource constraints and the nature of its product. Also, diffusion studies can help forecast sales potential in countries where the product is yet to be introduced.

Interestingly, the need for newer methods for predicting diffusion rates in different cultures has long been identified as an important research priority in international business (Nehrt, Truitt, and Wright 1970; Wright and Ricks 1994). Despite this fact, there are very few published studies on this topic (Ganesh and Kumar 1996; Gatignon, Eliashberg, and Robertson 1989; Helsen, Jedidi, and DeSarbo 1993; Kalish, Mahajan, and Muller 1995; Takada and Jain 1991). The major research hurdle is a lack of reliable time-series data of product sales across multiple countries (Heeler and Hustad 1980). These few studies collectively form the knowledge pool in this area.

Early studies (Gatignon et al. 1989; Takada and Jain 1991) examined the diffusion patterns of consumer durables across multiple countries and showed that the diffusion of a new product is a culture-specific phenomenon and that the differences in the diffusion patterns across countries can be explained by certain country-specific factors. Further, Takada and Jain (1991) also observed that a lagged introduction of a product in a country leads to an

Ganesh et al. / LEARNING EFFECT 215

accelerated diffusion. In other words, they observed a positive relationship between the time of introduction of a product in a country and the diffusion rate. Interestingly, a subsequent study by Helsen et al. (1993), also examining the multinational diffusion patterns of consumer goods, reported a negative relationship between the time lag and the diffusion rate.

In an attempt to resolve this contradiction and further explore this phenomenon, Ganesh and Kumar (1996) argue that the lead-lag time effect may well be a proxy for other systematic influences, whereby potenual consumers in the lag markets learn from the experience of the adopters in the lead market, thus resulting in a faster diffusion rate in the lag market. This argument is in line with Kalish et al.'s (1995) hypothesis that the potential adopters in the lag countries observe the introduction and the diffusion of a new product in the lead country. If the product is success- ful in the lead country, then the risk associated with the innovation is reduced, thus contributing to an accelerated diffusion of the product in the lag countries. Ganesh and Kumar refer to this phenomenon as the learning effect and attempt to capture it through an empirical examination of the diffusion process of retail point-of-sale scanners across multiple countries. While the current study adopts this definition, it is critical to stress that the learning effect examined in this study is the behavioral response of potential adopters in the lag markets toward the new product based on their observation of the experiences of the adopters in the lead market. This study does not examine the influence of organizational learning (changes to the marketing mix decisions initiated by manufacturers and retail- ers based on their prior experience in the lead market) on the diffusion process in the lag country markets.

Past studies, in general, call for more research examining the learning effect to better understand its implications for the timing and order-of-entry decisions in international markets. Researchers (Ohmae 1985) have long debated the use of a "sprinkler" strategy (introducing a product in multiple markets simultaneously) over the "waterfall" strategy (introducing products to global markets in a phased manner). The learning effect, if found to exist systematically across product categories, can guide managers toward the appropriate strategy and help in formu- lating the sequence of entry into various country markets. Strong positive effects will not only assist in entry timing and sequencing decisions but may also suggest a cutback in advertising expenditures. Likewise, a weak or negative learning effect might indicate delaying entry into the lag market or intensifying advertising to educate the adopters regarding the benefits of the innovation (Eliashberg and Helsen 1994).

Given the importance of the learning effect in international market entry decisions, this study attempts to extend our knowledge by addressing the critical issues raised in past research. While Gatignon et al. (1989) suggested a methodology to explain variations in the diffusion patterns, Eliashberg and Helsen (1994), like Ganesh and Kumar (1996), suggest a method to capture the learning effect. However, these studies do not discuss the factors

that influence learning. Hence, this study extends the knowledge in this area by empirically capturing the learning effect and systematically explaining the factors affect- ing the learning process. The main objectives of the current study are to

(a) investigate the existence of a systematic learning effect between a pair of lead and lag countries in the case of consumer durables,

(b) propose a theoretical framework that identifies the factors that influence the learning process,

(c) empirically examine the relationship between these factors and the learning effect so as to better understand the dynamics of the learning phenomenon, and

(d) estimate the sales of these products in countries (not included in the estimation) to evaluate the robustness of the proposed model as a forecasting tool.

In particular, the current study will examine the diffusion of four consumer technological innovations (VCRs, home computers, microwave ovens, and cellular phones) across an average of 15 countries in Europe to better understand the dynamics of the learning process.

First, based on a review of past research studies in international marketing and multinational diffusion, we identify the factors that influence the learning process and develop research propositions. Subsequently, in the Re- search Methodology section, we describe the data, the measures, the model specification, and the estimation procedure used in this study. Next, we analyze the data and discuss the research findings. Finally, we address the limitations of the research and provide guidelines for future studies.

RESEARCH FRAMEWORK AND PROPOSITIONS

Traditionally, the diffusion of an innovation has been defined as the process by which an innovation is commu- nicated through certain channels over time among the members of a social system (Rogers 1983). Over the last three decades, numerous research studies have critically examined the diffusion process of an innovation in the domestic context (for comprehensive reviews of the literature, see Gatignon and Robertson 1985; Mahajan, Muller, and Bass 1990; Parker 1994).

As reflected in its definition, the diffusion theory assumes that diffusion occurs within the boundaries of a social system, and the diffusion literature predominantly assumes the identification of the social system. Gatignon and Robertson (1985) argue that this assumption may be reasonable within some fields of study, such as ruralTor medical sociology, but it is questionable in consumer diffusion, in which mass media marketing programs cross social system boundaries. Consequently, they argue that any study of a social system should consider not only the characteristics of the social system but also its interactions

216 JOURNAL OF THE ACADEMY OF MARKETING SCIENCE SUMMER 1997

with other systems. This line of argument, in turn, indicates the need for cross-national diffusion research. ~

As mentioned earlier, very few published studies have examined the cross-national diffusion of products. One set of studies uses the basic diffusion model (focusing only on the characteristics of a social system), while the other uses an extended formulation (focusing not only on the characteristics of a social system but also on its interaction with other social systems). The findings of past research studies can be summarized as follows:

Basic Diffusion Model Studies

1. The diffusion of a new product/technology is a culture-specific phenomenon (Takada and Jain 1991), and the differences in the diffusion rates are a function of country-specific factors such as cosmopolitanism, consumer mobility, and role of women in the labor force (Gatignon et al. 1989).

2. Segmenting foreign markets based on diffusion parameters (the coefficients of innovation and imitation) do not result in stable segments, and the derived segments vary depending on the nature of the product (Helsen et al. 1993).

3. The effect of time lag (the difference in the time of introduction of a product in the first country and in subsequent countries) on the diffusion rate in the lag markets is inconclusive. While Takada and Jain (1991) observe a positive relationship, Helsen et al. (1993) report a negative relationship.

Extended Diffusion Model Studies

. Consumers in a lag country can potentially learn about the benefits of a product from the experience of adopters in the lead country, and this learning can result in a faster diffusion rate in the lag markets. Further, the learning effect can be systematically captured by reformulating the basic diffusion model (Ganesh and Kumar 1996; Kalish et al. 1995).

The current study, conforming to the definition of the learning effect proposed by past research, attempts to examine the dynamics of the interaction between social systems (in this context, learning that occurs between a pair of lead-lag countries) using the extended diffusion model. As stated earlier, a faster rate of diffusion (if observed) in the lag markets could be explained from two perspectives: (a) the influence of the consumers in the lead market on the potential adopters in the lag markets and (b) improvements made by the manufacturers to the product and their marketing strategies (such as change in price, etc.) that result in better product positioning in the lag markets.

The present study focuses on the first perspective, which allows us to identify the factors that influence consumer learning and their impact on the adoption pro-

cess in the lag markets. Identification and empirical veri- fication of these factors can assist managers in market selection and in timing and order-of-entry decisions. The current study does not focus on the second perspective explicitly due to a lack of multicountry time-series data on product improvements and manufacturers' strategies. 2

Factors Influencing the Learning Process

Past research studies on international marketing and multinational diffusion suggest that several factors could potentially influence the learning that takes place between a pair of lead and lag markets. Overall, these factors can be categorized as (a) country-specific factors, (b) the time lag between product introduction in the lead and lag markets (lag time), and (c) product-/innovation-specific factors. The country-specific factors include geographical proximity (or neighborhood effect), cultural similarity (or psychic distance), and economic similarity; the product/ innovation-specific factors include type of innovation (continuous or discontinuous) and existence of an accepted technical standard. In effect, six factors are proposed to influence the learning process that takes place between the consumers in the lead and lag countries. Figure 1 provides a schematic representation of the conceptual model. Al- though the influence of manufacturers' action in the lag markets is represented in the figure for the sake of com- pleteness, as mentioned earlier, the influence of this factor is not empirically investigated in this study.

Geographical proximity. The diffusion theory, by its very definition, implies that the diffusion of any innovation occurs simultaneously in space and time. Mahajan and Peterson (1979) observe that although the time dimension has been investigated by researchers representing a wide variety of disciplines, spatial diffusion has, for the most part, been investigated only by geographers (Brown 1981). Most geographical-based research on diffusion has been directed toward the development and refinement of Monte Carlo simulation models to implement the diffusion process as initially conceptualized by Hagerstrand (1967).

The basic tenet of Hagerstrand's (1967) conceptualiza- tion is that the adoption of an innovation is primarily the outcome of a learning or communication process. This implies that factors related to the effective flow of information are most critical; therefore, a fundamental step in examining the process of diffusion is the identification of the spatial characteristics of information flows. The information flow is primarily dependent on social and terrestrial barriers (particularly the physical distance separating two communicants), which impede, divert, and channel com- munications. Subsequently, Mahajan and Peterson (1979) suggest that assessing how the innovation is diffusing in different geographical regions would enable the innovator to compare adoption rates and assess the feasibility of introducing the innovation in other areas.

Craig, Douglas, and Grein (1992) observe that given the advances in communication technology and consumer mobility, one would expect physical proximity between

G a n e s h e t al. / L E A R N I N G E F F E C T 217

FIGURE 1 Factors Influencing the Learning Process

GEOGRAP.,CAL "~ PROXIMITY /,.P,

CULTURAL SIMILARITY

Pa = = ' / LEARNING EFFECT

.A BETWEEN LEAD & LAG MARKETS

FASTER ADOPTION IN LAG MARKETS

TIME LAG

TYPE OF INNOVATION

TECHNICAL STANDARD

I I

/ \

/ MANUFACTURERS' " / I t ACTIONS IN LAG I

MARKETS I \ /

countries to decline in importance as a determinant of similarity (which, in turn, stimulates transfer of ideas and experience) between nations. On the contrary, they report that despite all the technological advancements, geographical proximity remains a potent force underlying similarity between countries. The preceding discussion leads us to propose the following:

PI : The smaller the geographical distance is between the lead and lag markets, the stronger will be the learning effect.

Cultural similarity. Cultural differences have long been identified as a primary factor that explains differences in consumer behavior and firm strategy across countries. If two countries are culturally similar, they share similar religious beliefs, language, customs, and lifestyles. There- fore, the behavior of consumers in two culturally similar countries is more likely to follow similar patterns which can then be used by firms in selecting foreign markets to enter, particularly in the early stages of the internationalization process.

Cultural similarity also influences the diffusion process. One of the factors identified by Hagerstrand (1967) as impeding the learning process among potential adopters is the sociocultural factor. Rogers (1983), while propound-

ing the diffusion theory, argues that the transfer of information occurs more frequently between individuals who are alike, or homophilous. In contrast, heterophilous communication may cause cognitive dissonance because an individual is exposed to messages that are inconsistent with his or her beliefs, which causes an uncomfortable psychological state. Wiedersheim-Paul, Olson, and Welch (1976) call this factor the psychic distance. They define psychic distance as the sum of factors preventing the flow of information from and to a country. These factors include language, culture, and business practices (Johanson and Vahlne 1977; Johanson and Wiedersheim-Paul 1975). In- sofar as the psychic distance is low, then similarity is likely in patterns of diffusion and consumer adoption. The implication of this perspective is that countries with similar cultures (low psychic distance) are more likely to communicate with each other and share similar behavior patterns and lifestyle attributes. Hence, the following is proposed:

I)2: The more similar the lead and lag markets are culturally, the stronger will be the learning effect.

Economic similarity. Identification of markets similar to the home country in terms of economic growth is expected to reduce uncertainties associated in entering


foreign markets and thus assist in market-selection and mode-of-entry decisions (Goodnow and Hansz 1972). In- cidentaUy, economic similarity to the U.S. market was one of the underlying rationale behind the international product life cycle theory (Vernon 1966; Wells 1968), which provided an early explanation of how the exporting, im- porting, and manufacturing locations of a product change over time (Mullor-Sebastian 1983).

Further, research studies (Lee 1991; Robertson 1971) have argued that economic characteristics of a country have an impact on the adoption and diffusion of new products in that country. These studies argue that most consumer durable goods are expensive, and hence, consumers in economically advanced countries are most llkely to be receptive to these innovations. Economically advanced countries initially adopt these innovations and take the risk of coping with the uncertainties involved with any new innovation. However, consumers in economically less prosperous countries would probably wait for the innovation to prove itself (even though an innovation may have been introduced in an economically prosperous country) before any money is invested in the product. Thus, economic dissimilarity can cause slower growth in the lag country. In other words, the influence of the adopters in the lead country on the potential adopters in the economically less prosperous lag country (learning effect) is minimal. The preceding discussion leads us to propose the following:

P3: The more similar the lead and lag markets are economically, the stronger will be the learning effect.

Time lag. Rogers (1983) identified four attributes of an innovation that can potentially accelerate the rate of adoption. They are (a) the relative advantage of the new product, (b) compatibility with the needs of potential adopters, (c) observability, and (d) trialability. The basic premise underlying the influence of time lag on the diffusion process is that the additional time available for potential adopters in the lag markets helps them to understand the relative advantage of the product, to judge whether the product is compatible with their needs, and to observe the usage of the product from the experience of the lead-country adopters (Takada and Jain 1991). These represent three of the four attributes of an innovation that help accelerate the rate of adoption due to the opportunity to learn from the experience of the adopters in the lead market. Ganesh and Kumar (1996) observe this in their analysis of the diffusion patterns of an industrial innovation in the Euro- pean Union, the United States, and Japan.

Past studies note two components of risk a consumer faces in purchasing a new product. They are uncertainty about how a product will perform and the consequences of poor performance. Roselius (1971) observed that consumers reduce the risk of buying a new product by choosing a product that prior adopters are satisfied with. This satisfaction is the result of the product performance exceeding consumer expectations. In the context of cross-national diffusion, the potential buyers in the lag market have the

opportunity to observe the benefits of a new product introduced in the lead country, and therefore, the perceived risk of buying the new product is reduced. Information on new product performance can be acquired by potential adopters over a period of time. Thus, as time elapses, the potential adopters learn more about the benefits of the innovation. The preceding arguments lead us to propose the following:

P4: The time lag between the introduction of an innovation in the lead and lag markets is positively related to the learning effect.

Type of innovation. Robertson (1971) proposed a continuum that arranged innovations from continuous to radi- cally discontinuous, based on their effects on established consumption patterns. A continuous innovation, according to Robertson, is a minor innovation possessing a majority of features in common with earlier products in addition to some new features that improve performance or add some value to the product. In contrast, the innovations that fall in the other end of the spectrum differ, rather drastically, from earlier forms in several relevant features or attributes or could be altogether new. Research studies have shown that the diffusion process for a continuous innovation is very different from that of a discontinuous innovation (Dickerson and Gentry 1983).

In the case of continuous innovations, such as home computers, the introduction of a successive generation will influence not only its own diffusion but also the diffusion of the earlier generations (Norton and Bass 1987; Wilson and Norton 1989). In such cases, the speed of diffusion will be faster since consumers already have some related knowledge or experience (Dickerson and Gentry 1983). Hence, when a new generation of the product is introduced in the lead market while the lag markets are still adopting the existing (older) generation, information on the added benefits of the new generation travels faster from the lead market to potential adopters in the lag markets. The users in the lag markets will be familiar with the innovation and can easily absorb the benefits of the next generation. This leads us to propose the following:

PS: In the case of continuous innovations, the learning effect will be stronger when compared with discontinuous innovations.

Existence of a technical standard. Consumer technological innovations are generally complex and expensive, requiring higher levels of consumer learning before adoption. Further, once a technological innovation is adopted, consumers may face high switching costs should another competing technology become attractive. Robertson and Gatignon (1986) developed a rich model that explains the adoption process for a technological innovation. Among other things, they argue that the standardization of the technology plays an important role in the adoption process. They hypothesize that customers' resistance to the innova-


tion might be a function of the perceived risk associated with buying a product that may not turn out to be the predominant standard in the industry. Hence, the sooner the consumer perceives standardization on a dominant design, the more rapid the diffusion process.

Technical standards can be either de facto standards set by consumers or voluntary standards adopted by organiza- tions. Irrespective of the source of the standards, learning is influenced by the existence of a technical standard. For example, in the United States, the adoption of retail scanners accelerated with the institution of atl industry-wide bar-coding standard, the Universal Product Code (UPC; Pommer, Berkowitz, and Walton 1980). The effect of the adoption of the UPC on the diffusion of retail scanners was modeled by Bucklin and Sengupta (1993), who show that the UPC standard had a positive impact on the diffusion of scanners in the United States. A similar relationship has been observed for other country markets in Europe and Japan, where scanner diffusion was influenced by the establishment of a worldwide product code (Ganesh and Kumar 1996).

Interestingly, Eliashberg and Helsen (1994), in their analysis of the diffusion patterns of VCRs, observe a negative learning effect of the lead country on the lag markets. They offer a rationale for this surprising result based on the nature of the product. When VCRs were first introduced, there were three competing technologies--the Philips 2000 system introduced by Philips, Sony's Be- tamax system, and the VHS system developed by JVC. Although the VHS system ultimately became the industry standard, lack of a standardized system in the initial phase of the innovation must have sent conflicting signals to the consumers in the lag countries and led to an increase in perceived risk among prospective adopters. While the UPC is a voluntary standard adopted by the UPC organization, the VHS format is a de facto standard set by consumers due to their preferences for VHS over other competing formats. Despite the differences in the forma- tion of the standards, we argue that the presence or absence of a standard affects the risk perceptions of the consumers, and hence we propose the following:

P6: The existence of an industry standard for the technology enhances the learning effect; conversely, a lack of a technical standard weakens the learning effect.

RESEARCH METHODOLOGY

The diffusion patterns of four consumer durables the VCR, microwave oven, home computer, and cellular phone--were analyzed for 11 to 16 European countries (the actual number of countries used varied with product category and availability of reliable data). The countries include Belgium, Germany, the United Kingdom, The Neth- erlands, Italy, France, Spain, Denmark, Austria, Finland, Switzerland, Greece, Portugal, Ireland, Sweden, and Norway.

Data

The data required for modeling the diffusion process in this study were yearly sales data for each product in all the countries studied. The data were collected from the first year of introduction of the product in each of the countries through the time period for which the most recent data were available (European Marketing Data and Statistics 1984-1993). For example, in the case of microwave ovens, data were collected for Germany from 1974 through 1990 (1974 being the year the product was introduced in Ger- many). Likewise for Norway, data on microwave sales were collected from 1982 to 1990.

Yearly sales data were available for all 16 countries for all products except for cellular phones. In the case of cellular phones, data were available for only 11 countries. Further, data on some countries were set aside to form the holdout sample. 3 The holdout sample was used to test the usefulness of the proposed model (the learning model) as a forecasting tool. Also, the country in which a product was first introduced in the region (Europe) was categorized as the lead country for that product category.

Reliable sales data on VCRs were available for all 16 countries. The innovation was first introduced in Germany in 1970 and subsequently in other countries. Thus, Ger- many was categorized as the lead country for VCRs. Of the 16 countries, 5 (Ireland, Netherlands, Norway, Portu- gal, and Switzerland) were selected to form the holdout sample. On an average, annual sales data over 17 years for 10 countries (excluding the lead country) were used to estimate the model, and sales data over 15 years for 5 countries were used for the forecasting application.

The diffusion of microwave ovens was studied for 16 countries, with Germany again being the lead country where the innovation was introduced in 1974. Here, 6 countries (Finland, Greece, Ireland, Portugal, Sweden, and Switzerland) were selected to form the holdout sample. In effect, annual sales data for 13 years for 9 countries formed the estimation sample, and 9 years sales data for 6 countries formed the holdout sample.

In the case of home computers, 4 of the 16 countries (Finland, Ireland, Portugal, and Switzerland) for which data were available were selected for the holdout sample. Also, for this category, the United Kingdom was the country where the product was introduced first in 1980 and hence was categorized as the lead country. In this category, the estimation sample was composed of annual sales data for 9 countries over 9 years, and the holdout sample was composed of sales data for 4 countries over 9 years each. Finally, for the cellular phone category, data were available for only 11 countries, with Norway being the lead country where the innovation was first introduced in 1981. Since the data were available for only 11 countries, only 2 countries (Belgium and Sweden) were picked for the holdout sample. For cellular phones, annual sales data over 10 years for 8 countries were used to estimate the model, and the holdout sample was composed of sales data for 2 countries over 11 years each.


Yearly sales data on each of the product categories for the different countries analyzed in this study and the data on the independent variable---economic similarity--were obtained primarily from European Marketing Data and Statistics (1984-1993). For cellular phones, the unit sales data were also made available by a leading manufacturer of the product in Europe and were used in the estimation of the models.

Measures

Geographical proximity. This factor was operationalized as the distance between capital cities of the lag countries and the lead country. For example, for the product categories VCRs and microwave ovens, Germany was the lead country (the country where the innovations were first introduced). The geographical proximity was measured as the distance between the capital cities for the lag countries and Berlin, the capital of the lead country, Germany. For home computers, the distances were computed between London (United Kingdom was the lead country) and the capital cities for lag countries, and for cellular phones, the distance between Oslo and the capital cities for lag countries were computed. Given this definition, if geographical proximity values are small (indicating neighboring countries), the learning effect will be stronger.

Cultural similarity. There have been numerous attempts to cluster countries based on cultural factors. Prob- ably the most comprehensive of these studies was that conducted by Hofstede (1980, 1983). Based on cultural similarity (obtained through examination of work-related goals and value patterns), Hofstede (1980) grouped countries on four dimensions: (a) power distance, (b) masculinity/femininity, (c) uncertainty avoidance, and (d) individualism. These four dimensions are postulated to constitute fundamental value orientations that underlie national differences in managerial practices, organizational patterns, and decision making. They are viewed as key elements driving patterns of development and are widely acknowledged as key indicators of national organizational cultures (Craig et al. 1992). Although Hofstede's country clusters are based on the cultural characteristics identified in industrial workers, Hofstede (1980) argues that the respondents in his cross-cultural study comprise a well- matched sample, differing systematically only in national- ity, and hence, his findings can be extended to represent national cultures in general. Subsequent research has used Hofstede's dimensions as measures of cultural distance (Kogut and Singh 1988).

It is proposed that the learning will be enhanced when both the lead and the lag countries are culturally similar (measured as a negative index of the sum of absolute differences in each of the four Hofstede dimensions between the corresponding lead and lag countries). Thus, smaller differences in Hofstede's measures indicate that the cultures of the lead and lag countries are more similar, and therefore, the learning effect will be stronger. 4

Economic similarity. Several measures of economic development have been identified and used in past studies (e.g., Craig et al. 1992; Liander, Terpstra, Yoshino, and Sherbini 1967). These include gross domestic product (GDP) per capita, literacy rate, urbanization level, unemployment rate, level of industrialization, and so forth. Secondary data on most of these measures were collected for the 16 countries examined in this study. Some of these measures, such as literacy rate, showed very little variation between the 16 countries. Others, including level of industrialization, have missing values for some countries. Therefore, economic similarity was operationalized as a negative index of the sum of absolute differences in the standardized values (given the differences in the unit of measurement) of GDP per capita, level of urbanization, and unemployment rate between the corresponding lead and lag countries.

Time lag. The influence of the time lag between product introduction in the lead and lag countries on the learning process was tested by operationalizing time lag as the difference in the years of introduction of the product between the lead and lag countries. It was proposed that time lag will be positively related to the learning effect.

Type of innovation. The proposition relating type of an innovation to the learning effect suggests that in the case of continuous innovations, the learning effect will be stronger when compared with discontinuous innovations. The distinguishing characteristic of a continuous innovation over a discontinuous innovation is that consumers already have a certain amount of knowledge regarding the core benefit of the continuous innovation, while the consumer has very little knowledge regarding the core benefit of a discontinuous innovation. Among the products that were used in the study, home computers fall more to the continuous part of the innovation spectrum because most consumers knew the core benefit of a computer (easier information processing) from their experiences with main- frame computers. The other products, such as microwave ovens, VCRs, and cellular phones, fall more toward the discontinuous part of the innovation continuum because when they were introduced, consumers did not know about their core benefits and the manufacturer had to spend considerable resources to educate the consumers about the core benefits of these products. Therefore, we classify home computers (on a relative scale) as a continuous innovation.

Further, successive generations of home computers were introduced in regular time intervals (computers based on 8086 in 1978, 8088 in 1980/1981, 286 in 1982, 386 in 1985, 486 in 1989, and the Pentium in 1993). Each generation improved the overall performance of the product but did not require any major change in consumption of the product. Consumers replaced the product from the previous generation with a current product not only to take advantage of the improvements in technology but also to be compatible with the changes in software. Hence, the type of innovation was operationalized as a dummy vari-


able, with a value of 1 for home computers and 0 for the other three innovations.

Existence of a technical standard. Again, among the four consumer durable innovations studied here, the VCR was the only innovation that had conflicting technologies

5 when first introduced in the marketplace. The existence of three competing standards, each supported by well- known multinational firms, can cause confusion and hence delays in adoption. Therefore, VCR was categorized as an innovation that lacked a technical standard during its introduction. The existence of a technical standard was operationalized as a dummy variable with a value of 1 for VCR and 0 for other innovations.

Model Specification and Estimation

Initially, the classical diffusion model proposed by B ass (1969) was used to capture the diffusion process of each of the innovations in the individual countries. The model formulation is as follows:

s,= + q * m *(m-N,) , (1)

where

s t = sales at time period t, p = coefficient of external influence (i.e., propensity to

innovate), q = coefficient of internal influence (i.e., propensity to

imitate), and N, = cumulative sales till time t, m = market potential.

Subsequently, to capture the learning effect between the lead and lag markets, the learning model was used. The learning model is akin to the independent product model developed by Peterson and Mahajan (1978). The independent product model allows for a one-way interaction between a pair of products. In other words, the influence of the diffusion of one product on the diffusion of another product can be captured. In the current context, this represents the influence of the adopters in the lead country on the potential adopters in the lag countries, and its formulation is as follows:

dFl(O dt - [p + q * Fx(t)

rnl-~2 ) + c * * F2(t)] * [1 - Fl(t)],

(2)

where

Fl(t) = the cumulative penetration ratio till time t for the lag country,

F2( 0 = the cumulative penetration ratio till time t for the lead country,

p = the coefficient of external influence for the lag country,

q = the coefficient of internal influence for the lag country,

c = the learning coefficient for the lag country, m 1 = market potential of the lag country, and m 2 = market potential of the lead country.

As stated earlier, the potential adopters in the lag countries have the opportunity to observe the benefits of the innovation introduced in the lead country. This can result in an accelerated diffusion in the lag country, and this effect is captured by the learning coefficient c. Similar models have been proposed by Eliashberg and Helsen (1994), Ganesh and Kumar (1996), and Kalish et al. (1995). Con- sequently, Equation 2 captures the dynamic influence of the cumulative penetration (at any given time t) of the lead country on the diffusion process in the lag country, as measured by the c parameter.

Further, it is proposed in this study that the difference in the learning effect (captured by the c parameter) across different lag countries can be partially explained by three set of factors: (a) country-specific factors, (b) time lag, and (c) product/innovation-specific factors: The relationships of the first two factors--country-specific factors and time lag (Propositions 1 to 4)-- to the learning effect can be expressed as follows:

ci = o~ + 61 * GEOGi + 6z * CULTi

+ 63 * ECONi + 64 * TLAGi + ~i, (3)

where GEOGi, CULT~, ECONi, and TLAGi represent measures of geographical proximity, cultural similarity, economic similarity, and the time lag, respectively, for each country. The error term represents nonsystematic differences in the perceptions of the innovation characteristics across countries. When Equation 3 is substituted back into Equation 2, the model becomes a pooled cross-sectional time-series model. The heterogeneity across cross sections (here, countries) is modeled with country-specific variables shown in Equation 3. For each country, at every time period, data on the country-specific factors and the time lag variable are multiplied by time-series data of Fdt) and [1 - F~(t)]. The resulting data matrix is used for testing the proposed hypotheses.

The basic model (Equation 1) was estimated using the nonlinear least squares (NLS) procedure recommended by Srinivasan and Mason (1986). Equations 2 and 3 were estimated using a simultaneous generalized least squares procedure as recommended by Gatignon et al. (1989). It is the simultaneous estimation of Equations 2 and 3 that enable us to test our Propositions 1 to 4. If the parameters


TABLE 1 Basic Diffusion Model Parameters (Equation 1)

Innovation

Home Computers Microwave Ovens Cellular Phones VCRs

Country p q m (in O00s) p q m (in O00s) p q m (in O00s) p q m (in O00s)

United Kingdom 0.025 0.28 15,937 0.0026 0.58 14,429 0.026 0.76 1,461 0.012 0.38 25,059 Germany 0.009 0.39 21,085 0.0003 0.43 29,514 0.001 0.73 2,318 0.003 0.36 35,699 Netherlands 0.038 0.24 2 , 2 5 3 0.0016 0.55 2,805 NA HO Belgium 0.04 0.27 1,444 0.002 0.60 1,326 HO 0.003 0.45 2,646 France 0.018 0.35 9 , 2 0 5 ~0.0003 0.70 8,515 0.033 0.64 517 0.0017 0.39 26,347 Sweden 0.04 0.35 957 HO HO 0.0009 0.39 3,840 Austria 0.037 0.39 827 0.0023 0.41 1,892 NA 0.0013 0.49 2,255 Denmark 0.047 0.4 463 0.0023 0.51 418 0.018 0.25 415 0.0004 0.47 2,948 Norway 0.05 0.4 432 0.005 0.91 519 0.023 0.23 500 HO Italy 0.019 0.42 8,242 0.001 0.46 6,079 0.004 0.74 2,581 0.0002 0.65 11,000 Greece 0.023 0.51 225 HO NA 0.005 0.65 1,180 Portugal HO HO NA HO Spain 0.014 0.61 2 , 4 6 2 0.0002 0.57 5,749 NA 0.004 0.49 10,635 Ireland HO HO 0.03 0.54 60 HO Finland HO HO 0.008 0.46 594 0.003 0.43 1,854 Switzerland HO HO 0.037 0.83 245 HO

NOTE: p = coefficient of extemal influence; q = coefficient of internal influence; m = market potential; NA = data not available; HO = holdout sample.

[~1 to [34 are positive and significant, that would provide empirical support to our Propositions 1 to 4. The equations were estimated individually for each product category. Propositions 5 and 6, representing the influence of systematic differences among innovations (product/innovation- specific factors) on the learning effect, were tested using a dummy variable regression model. The values of the learning coefficient c were regressed with the dummy variables. The dummy variable representing the type of innovation (D1) was assigned a value of 1 if the innovation (home computer) was categorized as continuous and 0 otherwise. Similarly, the dummy variable representing the existence of a technical standard (/92) was assigned a value 1 if the innovation (VCR) had competing standards and 0 otherwise. Thus, a significant and positive coefficient for D1 and a significant and negative coefficient for De would offer support for Propositions 5 and 6.

RESEARCH RESULTS

The NLS procedure was used to estimate the diffusion model (Equation 1) parameters (p, q, and m) for each of the four product categories and for all countries for which data were available. The results suggest that, overall, the basic diffusion model explains much of the variance in yearly sales (R 2 varying from 80% to 99%) for all countries across the four product categories. Table 1 presents the results of the parameter estimates obtained using the basic model (Equation 1).

To keep the discussion succinct, we focus on the general patterns of coefficients across product categories and countries for all the parameters of the estimated model. In

general, the coefficient of internal influence (q) increases with time, an observation made by past research studies (Takada and Jain 1991). In the case o f VCRs, the values of q range from 0.36 for the first country, Germany, where the product was introduced in 1970, to 0.62 for Portugal, where the product was introduced in 1982. For cellular phones, q ranges from 0.23 for Norway (1981) to 0.83 for Switzerland (1986). For home computers, the value of q for the United Kingdom (1980) is 0.28 compared with 0.61 for Spain (1982). In the case of microwave ovens, q ranges from 0.43 for Germany (1974) to 0.91 for Norway (1982).

The values of market potential (m) for all countries in each product category appear plausible given the population of each country. For example, Germany has a market potential of about 36 million VCRs (given its population of 60 million) compared with 25 million VCRs for the United Kingdom (with a population of 50 million in 1990). Similar trends can be observed for the other product categories also. Overall, the data seem to fit the basic diffusion model (Equation 1) reasonably well.

The learning model specified in Equation 2 was estimated using the NLS procedure for all the countries and product categories. The values of the learning coefficient (c) were 0.36, 0.30, 0.27, and 0.09 for home computers, microwave ovens, cellular phones, and VCRs, respectively. In general, the R 2 for the models ranged from 0.80 to 0.99. The learning model parameters were found to be significant for all the cases at t~ = .01. This lends evidence to the existence of the learning effect and potentially argues for identifying the factors influencing this learning. Therefore, the simultaneous estimation of Equations 2 and 3 was performed for all product categories.


TABLE 2 Simultaneous Generalized Least Squares

Estimation Results of Learning Model (Country-Related Propositions)

Variable

Innova~on

Home Microwave Cellular Computers Ovens Phones VCRs (n = 101) (n = 101) (n = 81) (n = 166)

Innovation (p) 0.012 0.008 0.005 0.004 (32.18) (63.81) " (9.31) (11.5)

Imitation (q) 0.425 0.351 0.531 0.333 (52.19) (90.81) (49.42) (85.15)

Constant (~) 0.486 0.316 0.158 0.098 (33.68) (13.2) (9.8) (2.78)

Cultural similarity (~2) 0.001 0.011 0.003 0.004 (6.43) (7.4) (6.51) (4.93)

Economic similarity (~3) 0.028 0.105 -0.020 0.055 (5.64) (6.3) (-2.92) (5.33)

Time lag ([~4) 0.479 0.214 0.110 0.080 (15.15) (15.28) (7.27) (3.78)

Fit statistic (Adjusted R 2) 0.9698 0.9645 0.8734 0.9301

NOTE: t-values are in parentheses.

The simultaneous estimation of Equations 2 and 3 revealed that geographical proximity was not significant in explaining the variance in the learning effect for the lag markets for three of the four product categories. However, for cellular phones, the sign of the coefficient for this variable was contrary to our expectations and significant. A close examination of the correlation matrix revealed a high correlation of. 82 between the geographical proximity variable and cultural similarity measure. Hence, this variable was removed, and Equations 2 and 3 were reestimated simultaneously using the generalized least squares procedure. Table 2 presents the reestimated results. The results are presented independently for each product category. As can be seen, all estimated coefficients are statistically significant at the .01 level. 7

It can be seen from this table that the learning effect exists systematically across all product categories and that the learning model explains most of the variations in the sales data (adjusted R 2 ranging from 87% to 97%). This is in line with the theoretical arguments of Kalish et al. (1995) and the empirical findings of Ganesh and Kumar (1996) for an industrial technological innovation. Also, the independent variables explaining the variance in the learning effect between the lag countries are all positive and significant across all product categories (the exception being economic similarity for cellular phones).

Although the propensities to innovate and imitate are not the main focus of this study, it is important to note that the values of these diffusion parameters are positive, significant, and within the typical range of values reported in the literature (Sultan, Farley, and Lehmann 1990). Further, as Gatignon et al. (1989) observe, these values for the respective product categories are indeed the parameters for a country characterized by average socioeconomic characteristics.

Factors Governing the Learning Process

Geographical proximity. The results of the simultaneous analysis revealed that the geographical proximity variable (GEOG) was not significant for three of the four product categories. Hence, the findings do not support our Proposition 1, which states that the smaller the geographical distance between the lead and lag markets, the stronger the learning effect. This finding, which seems contrary to accepted wisdom, can be due to several factors. First, since the countries examined in this study are all located in Europe, the physical distance between countries may not be large enough to justify Hagerstrand's (1967) theory of neighborhood effect. Also, with the increase in international travel and media spillover, the effect of geographical proximity on the learning process might be swamped by other factors. The lack of support for this proposition does not mean that geographical distance is unimportant in influencing the learning process but, rather, that the other factors probably play a greater role.

Cultural similarity. The results presented in Table 2 reveal that the influence of the cultural similarity variable (0.001, 0.011, 0.003, and 0.004 for home computers, microwave ovens, cellular phones, and VCRs, respectively) is positive and significant (o~ = .01) across all four product categories. Hence, the findings support our Propo- sition 2--the more similar the lead and lag markets are culturally, the stronger will be the learning effect. The results can be interpreted as follows. In the case of home computers, for example, if there are two lag countries having a cultural similarity index of 100 and 200, then based on our results, we can claim that the each period penetration (dF/dt) of home computers in the second lag country will be 0.1 [(200- 100) x 0.001] times greater than in the first lag countrybthat is, diffusion of an innovation is faster in lag countries that are culturally similar to the lead country.

Economic similarity. The findings (presented in Table 2) reveal that economic similarity is positive (0.028, 0.105, and 0.055 for home computers, microwave ovens, and VCRs, respectively) and significant (o~ = .01) for three of the four product categories. In the case of cellular phones, economic similarity was found to be negative (-0.02) and significant (tx = .01). A possible explanation for the negative sign of this parameter could be that the other variables (e.g., cultural similarity and time lag) act as stronger influencing factors. Therefore, the residual variation may be correlated with economic similarity contrary to expecta- tion to cause this anomaly. Since the signs of the parameter for three of the four product categories are consistent and significant as hypothesized, we find support for our Propo- sition 3, which states that the more similar the lead and lag markets are economically, the stronger will be the learning effect.

71me lag. The findings of the study presented in Table 2 show time lag to be positive (0.479, 0.214, 0.110, and 0.08 for home computers, microwave ovens, cellular phones,


TABLE 3 Test of Product-Related Propositions

Parameter Support Variable Estimate (t-Values) for Propositions

"13Jpe of innovation (191) 0.85* (6.74) Supported Technical standard (/92) -0.30** (-2.37) Supported

NOTE: The R 2 value for this model is 0.87. *Significance at ~ = .01. **Significance at oc = .05.

and VCRs, respectively) and significant (ct = .01) across all product categories, providing support for Proposition 4. This finding is in line with Takada and Jain's (1991) observation that the diffusion rate is accelerated in the lag markets.

As mentioned earlier, the relationship between product/ innovation-specific factors and the learning effect was tested using a dummy variable regression model. The results of the test of propositions concerning the product/ innovation-specific factors are presented in Table 3.

Type of innovation. The results (see Table 3) indicate that the parameter estimate for the dummy variable representing this factor (D1) is 0.85 and significant at t~ = .0t. As hypothesized, the sign of this coefficient is positive and significant. Thus, we find support for Proposition 5.

Existence of a technical standard. The estimation re- suits shown in Table 3 indicate that the parameter estimate for the dummy variable representing this factor (De) is -0.30 and significant at ct = .05. As expected, the sign of this coefficient is negative and significant. This supports our Proposition 6, which states that the existence of an accepted standard for the technology enhances the learning effect and, conversely, that a lack of an industry-wide standard lessens the learning effect.

Overall, the empirical findings suggest that five of the six factors examined in this study---cultural similarity, economic similarity, time lag, type of innovation, and existence of a technical standard--are strongly related to the learning process. The findings of this study provide a preliminary understanding of the factors that govern the learning phenomenon and offer interesting implications to international marketing managers.

Forecasting Performance

To evaluate the forecasting performance of the learning model, several countries were used as holdout samples for each product category. As stated earlier, for each product category, the number of countries included in the holdout sample depended on a number of factors including the total number of countries for which the data were available. The forecasting performance of the learning model was evalu- ated in comparison with the basic diffusion model across all four product categories. The mean absolute deviation (MAD) and the mean squared error (MSE) were computed to assess the relative performance of the two models for all the countries and for each product category.

To forecast each period penetration rate for the four products using the basic diffusion model, the parameters p, q, and m need to be estimated for each country in the holdout sample. The p and q values were estimated by taking an average of the values of p and q obtained for countries in the estimation sample in which the product was introduced prior to its introduction in the countries in the holdout sample. For example, in Portugal, microwave ovens were introduced in 1984. An estimate of thep and q values for Portugal was obtained by taking an average of the values ofp and q for the United Kingdom, the Nether- lands, Belgium, France, Austria, Denmark, Italy, and Spain (countries in the estimation group where microwave ovens were introduced prior to Portugal). Similarly, an estimate of the values for p and q for all countries in the holdout sample was obtained across product categories.

An estimate of the value of market potential (m) for Portugal was obtained by first computing an average of the population penetration rate (market potential divided by population) for countries in the estimation group where the product was introduced prior to its introduction in Portu- gal. The population data were chosen for the most recent year, 1990, for which the data were available for all countries across all product categories. Next, the population of Portugal was multiplied by the average population penetration rate to obtain an estimate of the market potential for Portugal. The value of market potential thus obtained was used for forecasting with the basic model as well as the learning model.

To forecast each period penetration rate using the learning model, along with the value of market potential m (obtained as described above), an estimate of the values of the learning model parameters p, q, and c were required for the countries in the holdout sample. The values ofp and q generated by the learning model (Equation 2) and presented in Table 2 were used as estimates for the countries in the holdout sample. An estimate of c, the learning parameter, was obtained using Equation 3 and the results of Table 2. Data on economic similarity, cultural similarity, and time lag (with the lead market) were computed for all countries in the holdout sample, for each product category. The values of c were then computed using Equation 3 and the parameter estimates in Table 2. Using the p, q, c, and m values thus estimated, each period penetration data (dF/dt) were generated for all the countries in the holdout sample.

Table 4 presents the results of the forecasting efficiency of the learning model as compared with the basic diffusion model. For the learning model, the MADs range (across product categories and lead countries) from 0.003 to 0.0234 in comparison with the basic model, in which the MADs range from 0.002 to 0.0363. While the average MAD for the learning model was 0.015, the corresponding figure was 0.025 for the basic model. With respect to the MSE, a similar trend was observed for both models. In general, the performance of the learning model was better than the performance of the basic diffusion model in 12 of the 17 cases. For example, in the case of home computers, the MAD for the learning model for Ireland is 0.0135


TABLE 4 Forecasting Performance of Diffusion Models

Country Model

Innovation

Criteria Home Computers Microwave Ovens Cellular Phones VCRs

Switzerland

Portugal

Greece

Norway

Sweden

Ireland

Netherlands

Belgium

Finland

Basic

Learning

Basic

Learning

Basic

Learning

Basic

Learning

Basic

Learning

Basic

Leaming

Basic

Learning

Basic

Leaming

Basic

Leaming

MSE 0.0008 (9) 0.0004 (8) MAD 0.0228 0.0064 MSE 0.0003 0.0005 MAD 0.0135 0.0062 MSE 0.0007 (9) 0.0002 (7) MAD 0.0277 0.0130 MSE 0.0003 0.0001 MAD 0.0167 0.0072 MSE 0.0002 (8) MAD 0.0080 MSE 0.0001 MAD 0.0022 MSE MAD MSE MAD MSE 0.0006 (15) MAD 0.0101 MSE 0.0002 MAD 0.0044 MSE 0.0008 (9) 0.0003 (9) MAD 0.0249 0.0047 MSE 0.0003 0.0003 MAD 0.0135 0.0036 MSE MAD MSE MAD MSE MAD MSE MAD MSE 0.0015 (9) 0.0059 (9) MAD 0.0288 0.0324 MSE 0.0005 0.0042 MAD 0.0166 0.0230

0.0018 (13) 0.0296 0.0010 0.0234

0.0010 (10) 0.0363 0.0005 0.0205

0.0003 (17) 0.0055 0.0002 0.0042 0.0009 (9) 0.0042 0.0006 0.0030

0.0002(17) 0.0095 0.0003 0.0111

0.0005 (9) 0.0186 0.0007 0.0226 0.0007 (20) 0.0084 0.0001 0.0031

NOTE: The forecasting measures are the mean squared error (MSE) and mean absolute deviation (MAD) in each period penetration rate averaged across the time periods. The number in parentheses represents the sample size and corresponds to the particular innovation and the country.

compared with 0.0249 for the basic model. Similarly, in the cellular phones category for Belgium, the MADs are 0.0205 and 0.0363 for the learning model and the basic model, respectively. The basic diffusion model outper- formed the learning model in 3 cases (e.g., Norway for VCR) and performed similar to the learning model in the remaining 2 cases (e.g., Ireland for microwave ovens).

The results of the forecasting models further underscore the robustness of the learning model and the role of the learning coefficient in explaining the diffusion process in the lag markets.

Managerial Implications and Research Limitations

This study, focusing on the multinational diffusion patterns of consumer durables, attempts to examine the pres-

ence of systematic learning effects and identifies factors that govern this phenomenon. Though the study is at the aggregate level, the findings of this study offer several interesting implications for managers.

Is there a learning effect in consumer durables diffusion? Past research has shown that in the case of an industrial technological innovation, the consumers in lag markets learn from the experience of the adopters in the lead market, and this learning helps accelerate the diffusion process in the lag markets (Ganesh and Kumar 1996). The current study, for the first time, has concluded that such an effect exists sys temat ica l ly across consumer durable goods. The findings not only validate past research but also build on existing knowledge by explaining the variation in the learning effect between the lag markets as a function of country-specif ic , t ime lag, and product / innovat ion-


specific factors. By identifying and empirically investigating these factors, the current study offers valuable insights to market entry decisions.

Can learning affect foreign market entry strategies? Past research studies on foreign market selection have been based on an analysis of macro-level country/market characteristics such as (a) market size, (b) growth rate, (c) attractiveness based on environmental similarity to home country and perceived risks, and so forth (for a comprehensive review of the literature, see Papadopoulos and Denis 1988). The findings of this study add one more dimension to market-selection, timing, and sequencing-of- entry decisions. The framework presented here allows researchers and managers to group lag countries on the degree of learning that they consistently (across product innovations) exhibit for a given lead country. Accounting for other market factors, lag countries that exhibit strong learning ties are potential candidates for immediate entry (using a waterfall approach). Entering such markets following product introduction in the lead country can enable the firm to rapidly increase worldwide sales and product acceptance. Also, these markets may potentially require less investment in initial advertisements aimed at creating product awareness and lowering perceived risk among consumers.

Entry into lag countries that exhibit weak learning or take a longer time to learn may be delayed so as to provide the consumers in these countries ample time and opportunity to learn about the new product benefits. Further, early entry into these countries may prove disastrous since the perceived risks associated with product uncertainties are likely to be higher, and hence, the diffusion of the product is likely to be slower when compared with the former group of lag countries. However, the findings of this study do not provide clear evidence to rule out the possibility of adopting a sprinkler approach in these country markets. Additional research is needed before any concrete strategies can be suggested regarding the entry strategies of firms into foreign markets.

Are there any implications from product/innovation- specific factors? The findings of this study relating product-specific factors to the learning effect suggest the following:

1. If a firm is introducing a new product that can be constituted as a continuous innovation, the firm might benefit from adopting a sprinkler approach to market entry rather than a waterfall approach (provided all other factors such as resource com- mitments and competitive pressures also favor adopting a sprinkler strategy). On the other hand, if the new product falls under the category of a discontinuous innovation, it might be more pru- dent to adopt a waterfall approach to entry.

2. It is imperative for a firm introducing a new product based on a new technology to succeed in quickly transforming the technology as a world-

wide standard. One approach toward achieving faster acceptance of the new technology as a worldwide standard is to proactively license the technology to other manufacturers, which results in a faster diffusion and acceptance of the technical standard.

Is the learning model a useful forecasting tool? The theoretical framework and the model proposed in this study offer a powerful methodology to forecast sales and diffusion rates in countries where the product is not yet introduced or where sales data are not available. More often, international marketing managers are faced with the dilemma of experimenting with new foreign markets, not knowing how the market would react to their products and strategies. The proposed methodology allows managers to get some insights into the future performance of their products in new foreign markets. Based on the expected penetration rates, managers can decide whether or not to enter a country and, if a decision is made to enter, then formulate the marketing strategies for the country.

Limitations and Future Research

Since this study is exploratory in nature, more research involving other product categories and countries is required to provide conclusive and generalizable evidence regarding the factors governing the learning process. The findings of this study are based on consumer durables and, therefore, may or may not generalize to industrial technological innovations. Also, the scope of the present study is limited due to the problems associated with the reliability and availability of time-series data across multiple countries. Also, the definitions of products (e.g., home computers) may not be consistent across countries. This study examines diffusion patterns only in European countries (predominantly developed economies), and as such, the findings cannot be generalized to other developing and less developed economies. Also, the data set containing only European countries could also be partly responsible for the lack of significance of the geographical proximity variable in explaining the variation in the learning effect across lag countries. Additional ways (e.g., traffic or media patterns) of operationalizing the geographic proximity variable may be explored.

Future studies could include more product categories (both industrial and consumer technological innovations) and more countries (outside Europe and in various stages of economic development) and attempt to explain the differences in the magnitude of the learning coefficients. With additional data, countries can then be clustered on the basis of the similarity of their learning coefficients. Con- clusive and generalizable implications regarding the timing and order of entry can then be effectively drawn. Further, data (if available) on manufacturers' actions (product and marketing strategies) can be used to test the influence of these critical factors on the adoption process in the lag markets.


ACKNOWLEDGMENTS

The authors thank David Cravens, Robert E Lusch, Michael Harvey, V. (Seenu) Srinivasan, and three anony- mous reviewers for their comments on a previous version of this article. The study benefited greatly from the comments offered during the presentations at Stanford Univer- sity, University of Oklahoma, and the 1996 Marketing Science Conference. The authors contributed equally to the article.

NOTES

1. For example, the adoption behavior of a farmer in Argentina is likely to be more similar to a farmer in France (and hence influenced by the French farmer's experience) than to a doctor in Argentina. Gatignon and Robertson's (1985) argument is that past diffusion studies do not take into account this aspect. Hence, by incorporating the learning coefficient in our model, we explicitly capture the influence of the French farmer on his/her Argentinean counterpart. Thus, interaction between social systems in the context of diffusion studies implies interaction between the adopters in the lead country on the potential adopters in the lag country.

2. It certainly would be interesting to evaluate the effects of manufacturers' strategy on the diffusion process in the lag countries. However, the data for manufacturers' actions in multiple categories were not available for every time period. Therefore, this influence is not modeled in this study.

3. Our rationale for selecting the holdout countries is as follows: (a) for a given product category, the number of holdout countries depended on the total number of countries for which we had data; (b) for each product category, we selected countries that have long enough time-series data to evaluate the robustness of the model; and (c) to reduce variance due to extraneous influences, certain countries (e.g., France, Spain) were retained as part of the estimation sample in all product categories, and certain countries (e.g., Portugal) were included in the holdout sample in all four product categories.

4. An index based on the differences among the four Hofstede measures between the lead and lag country is used for the following reasons: (a) the use of individual dimensions creates multicollinearity problems when estimating the model parameters; (b) while Hofstede's dimensions themselves are uncorrelated, the differences among those dimensions for the countries are strongly correlated; and (c) the use of such index measures is well documented in the literature (Kogut and Singh 1988).

5. In the case of computers, Apple's Mac was positioned as a user- friendly alternative to IBM-based PCs and was targeted toward segments that valued this feature. So in the case of the home computer, it was a question of product differentiation and not an issue of acceptance of a technical standard. There were no realistic expectations, among both the manufacturers and the customers, that one of the formats would fade out as a consequence.

6. Our attempts to explain the variations in all the three parameters (p, q, and c) resulted in a severe multicollinearity problem. Also, the correlations between the covariates o fc were not above .3, and therefore, collinearity was not a problem here.

7. The proposed models have been estimated with the entire sample as well as for the estimation sample. The results are stable across both samples. The fact that the magnitude of the forecasting errors is quite small in the holdout sample is also a testimony to the reliability of the results.

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ABOUTTHE AUTHORS

J a i s h a n k a r Ganesh is an assistant professor of marketing at the University of Central Florida, Orlando. He received his Ph.D. from the University of Houston in 1995. His research interests include global competition and marketing strategy, brand equity and brand extensions, customer satisfaction and brand loyalty, and issues pertaining to product development and introduction.

V. K u m a r is the Marvin Hurley Professor of Business Adminis- tration, Melcher Faculty Scholar, and the director of Marketing Research Studies at the University of Houston. He has been recognized with numerous teaching and research excellence awards and has published numerous articles in many scholarly journals in marketing and forecasting. He has coauthored the textbook Marketing Research and is currently working on a book titled International Marketing Research, which is based on his marketing research experience across the globe. He is on the editorial review board of many journals and has lectured on marketing-related topics in various universities worldwide. His research interests include developing forecasting models, international marketing strategy and research issues, models for sales promotions, and new methodologies for product positioning and market segmentation.

V e l a v a n S u b r a m a n i a m is a final-year doctoral student in marketing at the University of Houston. His research interests focus on marketing strategy and international marketing. He has published articles and presented at conferences on research in his area of interest.

Learning effect in multinational diffusion of consumer durables: An exploratory investigation

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