Sales takeoff is vitally important for the management of new products. Limited priorresearch on this phenomenon covers only the United States. This study addresses the
following questions about takeoff in Europe:1) Does takeoff occur as distinctly in other countries, as it does in the United States?2) Do different categories and countries have consistently different times-to-takeoff?3) What economic and cultural factors explain the intercountry differences?4) Should managers use a sprinkler or waterfall strategy for the introduction of new prod-
ucts across countries?We gathered data on 137 new products across 10 categories and 16 European countries.We adapted the threshold rule for identifying takeoff (Golder and Tellis 1997) to this
multinational context. We specify a parametric hazard model to answer the questions above.The major results are as follows:1) Sales of most new products display a distinct takeoff in various European countries, at
an average of six years after introduction.2) The time-to-takeoff varies substantially across countries and categories. It is four times
shorter for entertainment products than for kitchen and laundry appliances. It is almost halfas long in Scandinavian countries as in Mediterranean countries.3) While culture partially explains intercountry differences in time-to-takeoff, economic
factors are neither strong nor robust explanatory factors.4) These results suggest distinct advantages to a waterfall strategy for introducing prod-
ucts in international markets.(International New Product Growth; New Product Takeoff; New Product Growth; InternationalDiffusion; Diffusion of Innovations)
IntroductionNew products do not grow into maturity at a steadyrate. Rather, their sales pattern is marked by a longintroduction period when sales linger at low levels.At a certain point in time, the new product breaks into
rapid growth, associated with a huge jump in sales.Academic literature and the business press refer tothis point as the takeoff in sales. It is the point of tran-sition between the introduction and growth stage of anew product. The time-to-takeoff is the duration of the
introductory stage or the period from the introductionto the takeoff.The takeoff of new products is a vitally important
phenomenon in the management of new products forseveral reasons. First, growth rate in sales at takeoffmay exceed 400% (Golder and Tellis 1997). Such rapidgrowth requires extensive resources in terms of adver-tising, sales staff, manufacturing, distribution, andinventory support. Second, many new product man-agers are under extensive pressure to pull the plugon new products that show sluggish sales. However,research shows that the introduction period may bequite long and variable. Knowing the likelihood andtiming of the takeoff of a new product helps to with-stand the pressure to pull the plug and preempt pre-mature cancellation of promising new products. Third,takeoff is often a signal of the mass adoption of a prod-uct and its ultimate commercial success. Knowing theimpact of company decisions on the likelihood andtiming of takeoff is important for effectively manag-ing such success. Fourth, the introduction and takeoffof new products across various countries are criti-cal events in international marketing strategies. Thisissue has gained importance with increasing global-ization and unification among countries within tradezones (EU, NAFTA, GATT, etc.). Knowing how take-off varies across countries is important for designingeffective international strategies.Recently, Agarwal and Bayus (2002) and Golder
and Tellis (1997) modeled the takeoff of consumerdurables in the United States. These studies raise anumber of questions about takeoff in the internationalrealm, which we attempt to address in the context ofEurope:• Does the phenomenon of takeoff occur as dis-
tinctly in other countries, especially in Europeancountries, as it does in the United States?• Do different categories and countries have con-
sistently different times-of-takeoff?• What economic and cultural factors explain the
intercountry differences?• Should managers use a sprinkler or waterfall
strategy for the introduction of new products acrosscountries?The only relevant literature on this topic occurs
in the related field of diffusion modeling. Although
this literature is extensive, with more than a hundredstudies (Mahajan et al. 1990), only a few of these stud-ies address the international diffusion of new prod-ucts. Most of them examine how the parameters ofthe Bass diffusion model vary by country (Ganeshet al. 1997, Gatignon et al. 1989, Heeler and Hustad1980, Helsen et al. 1993, Putsis and Sen 2001, Takadaand Jain 1991, and Talukadar et al. 2001). Putsis et al.(1997) model the influence of “mixing” between coun-tries on cross-country diffusion patterns. Dekimpeet al. (2000) examine the sequential adoption of a tech-nological innovation by various countries. None ofthese studies addresses the takeoff of new productsacross multiple countries.The present study focuses on the variation in time-
to-takeoff of 10 new consumer durables across 16European countries (see Figure 1 for examples). Assuch, this is the most extensive study ever conductedon the growth of new products in Europe. Besidescountry-specific differences, the study also examineshow underlying economic and cultural characteris-tics explain the variation in time-to-takeoff acrosscountries.We chose Europe as a domain of analysis for two
reasons. First, European countries have a relativelylong history of developed capitalist markets. As such,data on new product introductions are available inthese countries, though neither complete nor easy toobtain. Second, Europe has a long post-WW II his-tory of unification toward a single market, throughinstitutes such as the European Council, the Benelux,the West-European Union, the North American TreatyOrganization, the Organization for Economic Coop-eration and Development, the European Free TradeAssociation, The European Union, and the EuropeanMonetary Union.1 With the introduction of a singleEuropean currency (the EURO), the culmination ofthe unification movement, there is a growing percep-tion among some economists and non-Europeans thatthe western half of the continent constitutes a singleEuropean market. Yet at the same time, these coun-tries differ substantially in economic strength and
1 These institutions have changed names over the course of theirexistence; we use the most recent ones.
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TELLIS, STREMERSCH, AND YINThe International Takeoff of New Products
Figure 1 Illustration of Takeoff in Selected Countries
cultural identity. Thus there is an equally strong per-ception among nationalists and Europeans that thesecountries have distinct market identities. The takeoffof new products is an important context in which totest some of these rival perceptions.This paper is organized as follows. The second sec-
tion presents the model and research hypotheses. Thethird section explains the data. The fourth sectionpresents the results. The final section discusses thefindings, implications, and limitations.
Modeling International TakeoffAs Golder and Tellis (1997) explain, takeoff is a time-dependent binary event that is best modeled by the
hazard model. However, unlike Golder and Tellis(1997), we use a parametric hazard model ratherthan the proportional hazard model that they use.We modify the threshold rule they use and alsouse raters to identify the takeoff. We explain ourapproach below in two parts: model development,and conceptual framework and hypotheses.
Model DevelopmentThe hazard model is often used in medicine and biol-ogy, where the focal event, failure, typically corre-sponds to accident, death, or recurrence of disease.For us the focal event, takeoff, although a success,corresponds to the “failure” in the hazard literature.
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Similarly, being in the introductory stage correspondsto “survival” or “success” in the hazard literature.Because the model for international takeoff we pro-
pose includes both time-varying (such as market pen-etration and GDP) and time-invariant covariates (suchas cultural characteristics), a fully parametric hazardmodel is most appropriate (Helsen and Schmittlein1993, Kalbfleisch and Prentice 1980). In our treatmentof the parametric hazard model and the derivationof the likelihood, we draw heavily upon Petersen(1986a, 1986b). Let T be a nonnegative continuousrandom variable that denotes the duration in a state.In the presence of time-varying covariates—which wedenote as X�t�—the hazard function or the rate oftransition from one state to another (e.g., introductionto growth) is
h�t �X�t��= lim�t→0
Pt≤T <t+�t �T ≥ t�X�t��/�t� (1)
Let tk be the duration in a state at the time wheneither the state is left or censoring occurs. We divide tkinto k nonoverlapping but adjacent segments of time,which need not be of the same length. Let t0 = 0 andt0 < t1 < · · ·< tj−1 < tj < · · ·< tk. We obtain the follow-ing probability of surviving beyond duration tj givensurvival at duration tj−1, conditional upon the pathtaken by X�t� up to duration tj :
PT ≥ tj � T ≥ tj−1�X�tj ��
= exp[−∫ tj
tj−1h�s � X�tj�� ds
]� (2)
The survival function for duration of tk can then bewritten as
Stk � X�tk�� =k∏j=1
PT ≥ tj � T ≥ tj−1�X�tj ��
= exp[−∫ t1
0h�s � X0�� ds
]
∗ · · · ∗exp[−∫ tk
tk−1h�s � X�tk−1�� ds
]
= exp[−
k∑j=1
∫ tj
tj−1h�s � X�tj−1�� ds
]� (3)
The probability density function becomes
f �tk � X�tk��= h�tk � X�tk��Stk � X�tk��� (4)
We propose to test a log-logistic function becauseit allows for nonmonotonic baseline hazards. Thehazard function (h�t�) and survival function (S�t�)for a logistic distribution can, in a general form, bewritten as
h�t�= �p��t�p−1
1+ ��t�p� (5)
S�t�= 11+ ��t�p
� (6)
where:
�= e−�′xi � p = 1
�� (7)
The log-likelihood is
lnLi = �i lnh�tk � xk�−k∑j=1
∫ tj
tj−1h�s � xj� ds� (8)
An interpretation of this model is that the haz-ard function is adjusted by the independent vari-ables of each individual category in each country ateach time period. This adjustment occurs by the haz-ard ratio, which is defined as e−�. Positive � coeffi-cients decrease the probability of takeoff and negative� coefficients increase the hazard of takeoff. Theparameter � characterizes the distribution of thehazard rate. �i is a right-censoring indicator.Similar to Helsen and Schmittlein (1993), but unlike
Jain and Vilcassim (1991), we do not include a termfor unobserved heterogeneity. We capture hetero-geneity across categories and countries by includingtheory-based category and country variables in ourmodel.
Conceptual Framework and HypothesesWe develop a theory to explain takeoff aroundtwo broad groups of factors, country characteristicsand category characteristics. We can further classifycountry characteristics into economic, cultural, andinformation access variables. The subsequent discus-sion develops specific hypotheses for each of thesevariables.
Economic Variables. Four economic constructs arelikely to play an important role in the takeoff of new
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products: a country’s wealth, economic progressive-ness, economic roles in the household, and opennessof the economic system.
Wealth: Prior research posits that wealth stronglyinfluences the speed with which inhabitants of a spe-cific country adopt a new product (Helsen et al. 1993).So we expect that wealth will have a strong effecton time-to-takeoff. A well-known conclusion of thediffusion literature is that innovators generally arewealthier than later adopters (Rogers 1995). Wealthierpeople attach a lower utility to money, which is whateconomists call the “wealth” effect. The lower util-ity of money has two consequences. First, wealthierpeople can better afford the risks of adopting a newproduct early (Dickerson and Gentry 1983). Risk is animportant determinant of timing of adoption (Sheth1968). Second, because prices of new products tend tostart high and drop steadily (Golder and Tellis 1998),wealthier people will be able to better afford newproducts early when prices are high. In sum, wealth-ier people are expected to adopt a new product earlierthan less-wealthy people. So, we hypothesize:
Hypothesis 1 (H1). New products take off faster incountries with higher average wealth than in those withless average wealth.
Economic Progressiveness: One meaning of economicprogressiveness is the extent to which wealth is dis-tributed within countries. Even when a population atlarge in a country has high average wealth, it maybe concentrated in a few homes. In this case, thevast majority of people may still be poor and may beunable to afford the new product. Thus, after control-ling for average wealth per capita, high disparity inwealth may mean that many people cannot afford anew product, causing it to take off later than whenincome disparity is low.Economic progressiveness also refers to the extent
to which countries participate in economic unions.Country unions facilitate the movement of capi-tal, labor, suppliers, and goods between countries.Unions reduce economic disparity among countriesand encourage the formation of a common market.This economic atmosphere is more likely to promotethe dispersion and growth of new products in coun-tries that belong to the union. Thus, sales of new
products may grow more rapidly within countriesthat take part in such unions. This argument is alsoin line with the finding of Mahajan and Muller (1994),that a borderless Europe leads to faster diffusion. Wehypothesize:
Hypothesis 2 (H2). New products take off faster incountries with greater economic progressiveness than incountries with lower economic progressiveness.
Economic Roles in the Household: Over the secondhalf of the century there has been a steady changein work roles in the family. Families have movedfrom having predominantly one income earner to twoincome earners (with the woman, in addition to theman, working outside the home). Such householdsexperience intense pressure on time, and the familyhas less time for housework, relaxation, and entertain-ment. Such families put a high value on any appli-ances that help them save time. All new productshave at least some time-saving features. For example,dryers and washers free a great deal of time com-pared to manual washing and hanging clothes to dry.VCRs enable convenient watching of movies at homeinstead of time-consuming trips to movie theaters.Home computers save time in word processing andhome accounts.Thus, we expect that countries in which more
women work outside the home are likely to adoptdurables faster than those in which fewer women doso. This position is consistent with that of Gatignonet al. (1989). We hypothesize:
Hypothesis 3 (H3). New products take off faster incountries with a high activity rate of women than in coun-tries with a low activity rate of women.
Openness of the Economic System: The openness ofthe economic system of a country refers to the extentto which the country is involved in internationaltrade. Because of increasing international free tradebetween countries, this may be an important andincreasingly relevant factor in the international take-off of new products. Open economic systems mayspeed the takeoff of new products for two reasons.First, economic openness encourages the develop-ment or opening of unified infrastructure betweencountries, such as freeways, phone lines, railways,
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TV broadcasting, etc. Such infrastructure may facili-tate the faster spread of new products through obser-vation or word of mouth. Economic openness alsofosters greater competition, which increases produc-tion and distribution efficiency (Talukdar et al. 2001).Savings from these efficiencies should make newdurables more affordable to consumers, with a fastertakeoff as a consequence. So, we hypothesize:
Hypothesis 4 (H4). New products take off faster incountries that have a more open economic system than incountries that have a less open economic system.
Cultural Variables. Prior research suggests thata country’s culture strongly affects the speed atwhich its citizens adopt a new product (Dekimpeet al. 2000, Gatignon et al. 1989, Takada and Jain1991). We identify four cultural variables that canaffect time-to-takeoff across countries: uncertaintyavoidance, masculinity,2 need for achievement, andindustriousness.
Uncertainty Avoidance: Uncertainty avoidance refersto the level of anxiety about the future (Hofstede 1980,2001). Societies that are high in uncertainty avoidancecontinuously feel the inherent uncertainty in life asa threat that must be fought, while societies low inuncertainty avoidance more easily accept uncertaintyand take “each day as it comes” (Hofstede 1980).We expect countries that are high in uncertainty
avoidance to show later takeoffs than those that arelow in uncertainty avoidance, for two reasons. First,societies that are low in uncertainty avoidance aremore willing to take risks. Therefore, they will morereadily accept new products (Rogers 1995). Second,societies high in uncertainty avoidance consider novelideas as dangerous and are more intolerant towardchange than societies low in uncertainty avoidance(Hofstede 1980). Thus, countries that are low in uncer-tainty avoidance will embrace a new product moreeasily than countries that are higher in uncertaintyavoidance.Our expectation is also consistent with prior
research. For example, Lynn and Gelb (1996) find
2 We found neither theoretical support nor empirical evidence inour data to include the two other Hofstede dimensions, power dis-parity and individualism, as explanatory variables.
a negative correlation between a country’s uncer-tainty avoidance and the penetration of six con-sumer durables. Steenkamp et al. (1999) find thatconsumers in countries high in uncertainty avoidanceare less innovative than consumers in countries lowin uncertainty avoidance.
Hypothesis 5 (H5). New products take off faster incountries low in uncertainty avoidance than in countrieshigh in uncertainty avoidance.
Masculinity: In most cultures men tend to be moreassertive, while women tend to be more nurturing.Male behavior is associated with autonomy, aggres-sion, exhibition, and dominance, while female behav-ior is associated with nurturance, affiliation, andhumility (Hofstede 1980, 2001). Masculinity and fem-ininity refer to the sex role pattern in society at large,to the extent it is characterized by male or femalecharacteristics. We expect masculinity to affect thespeed of takeoff for two reasons.First, masculine societies attach more value to
recognition and wealth, while feminine societiesattach more value to human contacts and living envi-ronment (Hofstede 1980). The adoption of new prod-ucts allows consumers to exhibit their wealth andsuccess, which may be more compatible with mascu-line societies. Consumers in masculine societies maythus show higher innovativeness, as compared to con-sumers in more feminine societies (Steenkamp et al.1999).Second, in masculine societies people tend to make
decisions independently and admire the strong andthe independent (Hofstede 1980). When a new prod-uct first emerges, adoptions are few and require inde-pendent decisions by innovators. This trait of mas-culine societies may also lead to better acceptance ofnew products. In contrast, in feminine societies thetendency to make group decisions may lead to lessacceptance of a product. For all these reasons, weexpect masculine countries to show faster takeoff thanfeminine countries.So, we hypothesize the following.
Hypothesis 6 (H6). New products take off faster incountries high in masculinity than in countries low inmasculinity.
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Need for Achievement: In one of the earliest discus-sions of need for achievement, Murray (1938) describesit as the tendency or desire to do things as rapidlyand/or as well as possible. Need for achievementincludes successfully and independently overcom-ing obstacles, competing with and surpassing others,and high self-regard. Veroff et al. (1962) associate ahigh need for achievement particularly with workingharder, being less satisfied with current success, andbeing more oriented to the future fruits of work. Allthese traits may lend themselves to greater eagernessfor adopting new products and greater willingness toexperiment with new products as soon as they areavailable. Thus, we expect:
Hypothesis 7 (H7). New products take off faster incountries in which the inhabitants have a high need forachievement than in countries in which the inhabitantshave a low need for achievement.
Industriousness: Industrious people are inclined towork and tend to value the fruits of work more thanless industrious people. The industriousness of a pop-ulation can affect the speed of takeoff for supply anddemand reasons. Such people realize that innovationscan make work more productive as well as make theirrest from work more productive, so industrious peo-ple tend to be more receptive to innovations as well asto work harder to develop innovations. Thus, when anew product is available, industrious entrepreneurs,retailers, and distributors are likely to work harderto make this product available to the general popu-lation. At the same time, the people themselves aremore likely to search for, try out, and adopt the newproduct. Thus, the new product is likely to take offfaster in an industrious culture than in one that is notindustrious. So we expect the following.
Hypothesis 8 (H8). New products take off faster incountries with a more industrious culture than in one thatis less industrious.
Information Access. Prior research suggests thatpeople’s access to information strongly affects thespeed at which they adopt a new product (Rogers1995). Therefore, we may expect that a new producttakes off faster in countries in which inhabitants haveeasy access to information than in other countries.
We identify three factors that capture different dimen-sions of information access: media intensity, mobility,and education.
Media Intensity: Mass media such as newspapers,radio, and television play an important role in cre-ating awareness of a new product among poten-tial adopters (Beal and Rogers 1960) and influencingacceptance of a new product (Katz and Lazarsfeld1955). Mass media may also lead to greater abilityof consumers to detect superior new products, andthus increase the rate at which, and the likelihoodthat, consumers will adopt them. Mass media alsocontribute to the cosmopolitanism of consumers of acountry, especially if it concerns “cosmopolite chan-nels” (Rogers 1995). Through cosmopolite channels,consumers in a target country can access informationabout innovations that have been introduced in othercountries, even before the innovation is introduced inthe target country (Gatignon et al. 1989). Such infor-mation can hasten the takeoff of the innovation in thetarget country. For all these reasons, we hypothesize:
Hypothesis 9 (H9). New products take off faster incountries high in media intensity than in countries low inmedia intensity.
Mobility: Interpersonal communication affects therate at which consumers learn about new products.An important facilitator of such communication ismobility. Gatignon et al. (1989) have shown thatthe higher the mobility of a country’s inhabitants,the more rapidly new products penetrate the socialsystem. So, we hypothesize the following.
Hypothesis 10 (H10). New products take off faster incountries in which the inhabitants have high mobility thanin countries in which the inhabitants have low mobility.
Education: Education involves the exposure of peo-ple to a constant stream of new ideas, which makesthem more receptive to innovations. Education alsosensitizes people to the importance of technologyin human progress. That again makes them morereceptive to innovations. Indeed, a general findingin diffusion research is that educated people tend toadopt new products earlier than noneducated people(Rogers 1995). So, we hypothesize the following.
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Hypothesis 11 (H11). New products take off faster incountries in which the inhabitants have enjoyed higher edu-cation than in countries in which the inhabitants have notenjoyed higher education.
Category CharacteristicsWe identify four category characteristics that mayaffect the probability of takeoff: product class, mar-ket penetration, number of prior takeoffs, and yearof introduction. (Although these variables are intrinsi-cally category characteristics, some, like market pen-etration, number of prior takeoffs, and introductionyear, could also vary by countries.)
Product Class: Product class may affect the probabil-ity of a new product’s takeoff (Gatignon et al. 1989,Golder and Tellis 1997). In particular, we distinguishbetween white goods, such as kitchen and laundryappliances, and brown goods, such as entertainmentand information products. We expect brown goods totake off earlier than white goods because they appealto all members of a household, provide more instantgratification, and are more visible to guests. Thus, wehypothesize:
Hypothesis 12 (H12). Brown goods take off fasterthan white goods.
Market Penetration: We define market penetration asthe percentage of households that have purchased thenew product. Prior research posits that product cate-gories reach takeoff at an average market penetrationof 2.5–3% (Golder and Tellis 1997). The diffusion liter-ature also suggests that market penetration may be animportant correlate of the event that we call takeoff(Sultan et al. 1990). Thus, an increase in market pene-tration increases the likelihood of a takeoff. To avoidproblems of simultaneity, we include the lagged valueof market penetration as an independent variable inthe model. Thus, we hypothesize:
Hypothesis 13 (H13). The higher the market penetra-tion, the higher the probability of takeoff.
Prior Takeoffs: A new product’s prior takeoff in othercountries can stimulate takeoff in a target country forat least four reasons. First, as the product takes off inother countries, the media are more likely to report itsuse or popularity, increasing its attractiveness in the
target country. Second, on seeing a takeoff of a newproduct in other countries, manufacturers or retailersare more likely to promote sales in the target coun-try, triggering a takeoff. Third, takeoff in other coun-tries implies more adopters, so that a consumer in atarget country has a higher probability of contactingan adopter from the other countries. Such a contactcan increase acceptance of the new product and thustakeoff in the target country. Fourth, when a prod-uct takes off in other countries, potential adopters ina target country are more likely to perceive the newproduct to be a success. This perception is likely toreduce the perceived risk associated with adoptingthe new product, increasing acceptance of the newproduct and takeoff in the target country. Thus, wehypothesize:
Hypothesis 14 (H14). The higher the prior takeoffs inother countries, the higher the probability of takeoff in atarget country.
Year of Introduction: The literature is ambiguousabout the effect of the year of introduction. Golderand Tellis (1997) argue that due to the faster speedof technological innovation in more recent years,new products improve faster. Thus they are likely toappeal to consumers and takeoff off sooner than prod-ucts introduced in prior decades. On the other hand,Bayus (1992, 1994) argues that technological change isnot occurring any faster in more recent decades than itdid in earlier decades. By this logic, takeoff should notoccur any faster in more recent decades than in earlierdecades. Recently, Van den Bulte (2000) showed thatalthough on average there has been an increase in dif-fusion speed, this effect disappears when one controlsfor economic and demographic evolutions as well asthe nature of products studied. Therefore, we will notposit a hypothesis for this effect, but merely includeit as a control variable.
DataThis section describes our data collection andmeasures.
Data CollectionThe data collection was difficult and slow. It tookthe periodic efforts of several research assistants and
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authors over four years to assemble, prepare, and ana-lyze all the data. In searching for data, the authorshad to pursue numerous leads (many of them futile),including contacting dozens of sources through hun-dreds of e-mails and phone calls, and traveling tovarious international cities.We hoped to gather data for 10 consumer dur-
ables (refrigerator, washing machine, freezer, dish-washer, color TV, dryer, VCR, computer, CD player,and microwave oven) across 16 European countries(Austria, Belgium, Denmark, Finland, France, Ger-many, Greece, Ireland, Italy, the Netherlands, Norway,Portugal, Spain, Sweden, Switzerland, and the UnitedKingdom). In effect, we succeeded in obtaining dataon 137 of these 160 country-categories, for about an86% achievement rate.For data on product sales, the following sources
proved most valuable:• Euromonitor.• GFK.• The Economist Intelligence Unit (EIU).• TableBase of Responsive Database Services, Inc.• Archives and publications of associations of app-
liance manufacturers in various European countries.• William P. Putsis Jr., who generously sent us data
on a few categories.Some of the data series from these sources over-
lapped. When sources overlapped, sometimes the fig-ures were identical and sometimes they differed. Evenwhen figures from two sources did not match, theywere highly correlated. When figures did not match,we used the figures from the source that provided(i) all observations for a series and (ii) the greateramount of overall data across series. These rulesensured that (i) a particular series was unlikely tohave a sudden increase or decrease in sales, justbecause of a change in the source of data, and (ii) amaximum consistency existed in the underlying pro-cess that generated various series of data, given thatwe had to tap multiple sources. Note that the use ofmultiple sources increases the noise in the data, andthus would increase the probability of not finding thepatterns that we expect.To gather data on our explanatory variables, we pri-
marily used publicly available sources because theseare credited to be more reliable (Golder 2000). Our
key sources of data are:• The Statistical Yearbook of the United Nations;• The Penn State World Table Database;• World Bank Statistics;• Eurostat Review;• Individual sources, such as Parker (1997) and
Hofstede (1980, 2001).
MeasuresThis subsection explains the measures for year ofintroduction, takeoff, and the independent variablesin our model.
Year of Introduction. For most country-categorieswe have data from the year of introduction, withsales as low as 1,000 units per year. However, fora few country-categories we were unable to getthe early years of data, especially during or beforeWorld War II. To avoid left-truncation bias we dropsuch country-categories. For most country-categories,we found that a consistent rule to include country-categories is if first-year penetration is less than 0.5%.(In addition, we dropped four countries in VCRsbecause sales for those countries started quite highrelative to the others). By using this rule, we have yearof introduction for 120 of the 137 country-categories.The average first-year sales per country (across cate-gories) ranges from 1,000 units for smaller countriesto 20,000 units for the largest one.
Takeoff. Recall that takeoff is the start of thegrowth stage of the life-cycle characterized by a rapidgrowth in sales. A heuristic to identify takeoff is veryimportant because it provides a basis for empiricalanalysis as well as a simple rule that practicing man-agers can use. However, when the base sales aresmall, a relatively large percentage of growth in salesmay occur without signaling takeoff. On the otherhand, when the base sales are large, takeoff may occurat a relatively small percentage of growth in sales.Therefore, Golder and Tellis (1997) defined a thresh-
old for takeoff, which is a standard plot of growth rateversus sales by year. They defined takeoff as the firstyear growth crosses the threshold. They inferred theshape of the threshold so that it would give the bestprediction of takeoff identified visually. The advan-tages of the threshold rule are that it is simple to use,
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Figure 2 Threshold for Takeoff
0
100
200
300
400
500
600
700
0.1
0.3
0.5
0.7
0.9
1.1
1.3
1.5
1.7
1.9
2.1
2.3
2.5
2.7
2.9
3.1
3.3
3.5
Market Penetration
Gro
wth
in
Sa
les
(in
%)
is interpersonally certifiable, and has predictive valid-ity. That is, the rule enables one to predict the yearof takeoff as it occurs, without need for all the data.However, the Golder and Tellis (1997) threshold didnot easily fit our multinational application because ofthe great diversity of base sales across the 16 countriesin our sample.As a result, we modified the rule, using market
penetration rather than sales as a base with whichto evaluate growth. We define the threshold for take-off as a standard plot of growth in sales for variouslevels of market penetration, as shown in Figure 2.We operationalize takeoff as the first year a product’sgrowth in sales crosses the threshold in Figure 2. LikeGolder and Tellis (1997), we inferred the shape of thethreshold so that it gives the best prediction of takeoffidentified visually.This simple rule for international takeoff conforms
very well with a visual inspection of the sales curvein 132 of 137 country-categories. In two country-categories growth failed to cross the threshold albeit atakeoff occurred. In three other country-categories, wedid not have enough data to determine the takeoff.We also developed an entirely different method for
defining takeoff, based on a multirater visual identifi-cation of takeoff without raters having any knowledgeof the threshold rule (see Tellis et al. 2002). To ascer-tain the robustness of this rule, we repeated the entiredescriptive and hazard analysis using the alternativemethod. The main results are robust to the alternatemeasure of takeoff.
From our identification of the introduction andtakeoff, we calculated “time-to-takeoff” as the numberof years between introduction and takeoff. By usingthis rule, we have time-to-takeoff for 117 of the 137country-categories.
Independent Variables. We measured economicwealth by GDP per capita in thousands of U.S. dol-lars. We also included real GDP per capita in constantdollars, adjusted for changes in the terms of trade(we used the 1985 international prices for domes-tic absorption and current prices for exports andimports). This measure gave similar results.Recall that we operationalized a country’s economic
progressiveness as income inequality and participa-tion in economic unification. A well-established mea-sure of income inequality is the GINI index. Weextracted our measure of the GINI index from thedatabase of Deininger and Squire (1996), which can beaccessed on the website of the World Bank. To max-imize consistency across countries, we selected theGINI coefficient based on net income (not expenditureor gross income), number of households (not popu-lation), and national coverage. We selected the GINIcoefficient that was closest to 1980, to be consistentwith the time of the Hofstede measures on culture.For participation in economic unions we used a
dummy, which indicates if the country was a mem-ber of the European Union (or one of its predeces-sors, such as the European Coal and Steel Community(ECSC) and the European Economic Community(EEC)) in a given year. The EU (then called ECSC)was founded in 1951 by Belgium, France, Germany,Italy, Luxemburg and the Netherlands. Later, Austria(1995), Denmark (1973), Finland (1995), Greece (1981),Ireland (1973), Portugal (1986), Spain (1986), Sweden(1995), and the United Kingdom (1973) all joined.We measured a country’s economic openness by its
level of international trade, which encompasses theexports (fob) and imports (cif) per capita in thousandsof U.S. dollars. As a composite measure for economicopenness or international trade, we use the sum ofexports and imports per capita in thousands of U.S.dollars.We measured economic roles in the household
through the activity rate of women, which refersto the percentage of women that are economically
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TELLIS, STREMERSCH, AND YINThe International Takeoff of New Products
active. People that are economically active are peoplewho have a job plus those who are unemployed butactively seeking work.For the cultural variables of uncertainty avoidance
and masculinity, we used Hofstede’s measures. Read-ers may refer to the original work of Hofstede (1980)or its most recent edition (Hofstede 2001). Since Weber(1958), need for achievement is closely tied to reli-gion. The major religious difference among nations inEurope is the ratio of Protestants to Catholics. There isstrong evidence in sociology that Protestant religionsare more supportive of a high need for achievement thanis the Catholic faith (McClelland 1961, Weber 1958).Therefore, we will operationalize need for achieve-ment by the percentage of Protestants (see Parker1997). We use climate as a proxy for industrious-ness (reverse scaled). This operationalization is basedon the argument that climate plays a strong role inhuman mood, work ethic, and productivity. A hot cli-mate discourages hard work and leads to lethargy.On the other hand, in cold climates the need to keepwarm stimulates individuals to undertake physicalactivity. Over time, these influences could lead towork ethics and attitude, which involve more indus-try and enterprise in cool climates than in warm cli-mates. We measure climate by monthly high temper-atures (max C), as inventoried by Parker (1997).We measured media intensity through several mea-
sures. A first measure is the total circulation of news-papers—the number of newspaper copies printed—inthe country, per 100 inhabitants of the country. A sec-ond measure is the number of radios (receivers) in thecountry, per 100 inhabitants of the country. Note thatthis series has the disadvantage that the UN switcheddefinition in the 80s from inventorying the numberof licenses to an estimated number of receivers inuse. A third measure is the number of TVs (receivers)in the country, per 100 inhabitants of the country. Afourth measure is the number of telephones in thecountry, per 100 inhabitants of the country. As a com-posite measure for media intensity, we use the sumof these four measures. Because in post-WWII Europethe most important mobility factor is car possession,we measure mobility by the number of cars in thecountry, per 100 inhabitants. For education, we use
the number of third-degree (university) students as apercentage of total population.To account for differences between brown and
white goods we included the product class as adummy variable, coded 1 for white goods and 0for brown goods. For market penetration, we usedthe average possession of the product by house-holds in the country. One of our sources provided uswith the market penetration for the white goods. Forbrown goods, we calculated the market penetration asfollows:
penetrationt=penetrationt−1
+{�salest−salest−r �/�number of householdst�
}� (9)
where “r” is the average repurchase time for productin a particular category. Note that because takeoff typ-ically occurs at a low level of penetration, adjustingthe penetration to the ceiling values (Dekimpe et al.1998) in each category is not critical.For the number of prior takeoffs, we used two
measures. First, a generic measure that counts thetotal prior takeoffs across all countries of Europe.The second is a region-specific measure that counts thetotal takeoffs in the cultural-geographic region to whicha country belongs (Scandinavian, mid-European, orMediterranean; see below). Both measures gave simi-lar results and we report those for the second measure.Because the variables in our model include both
time-varying and time-invariant measures, we needto point out clearly which measures are of whichtype.• Time-varying measures are: market penetration,
number of prior takeoffs, GDP, EU membership,exports, imports, number of TVs, cars, radios, andtelephones, circulation of newspapers, and education.• Time invariant measures are: product class, intro-
duction year, income inequality (GINI), activity rate ofwomen, uncertainty avoidance, masculinity, percent-age of Protestants, and maximum temperature.
Empirical ResultsWe first present some descriptive statistics, then theestimates of the model, and finally, an assessment ofthe model’s performance.
198 Marketing Science/Vol. 22, No. 2, Spring 2003
TELLIS, STREMERSCH, AND YINThe International Takeoff of New Products
Descriptive StatisticsRecall that the first two goals of this study are todetermine if the phenomenon of takeoff generalizesto Europe and whether there are country-specific dif-ferences in takeoff across European countries. Thedescriptive statistics help to answer these questions.They cover the time-to-takeoff, and the mean leadsand lags in takeoff across countries.
Time-to-Takeoff. On average, new products inEurope take 6.0 years to takeoff (see Table 1A). Thisaverage is statistically different from 0, having a stan-dard deviation of 3.3 years. At the same time, somesignificant differences in this average exist across keycountries and categories.Each category has a particularly distinct time-to-
takeoff, which is often significantly different fromother categories (Table 1A). A more dramatic, albeitrelated, result is the difference in time-to-takeoffbetween product classes (see Table 1B). As weexpected, white goods (kitchen and laundry appli-ances) generally have a longer time-to-takeoff than
brown goods (entertainment or information prod-ucts). The mean time-to-takeoff is eight years forthe former, and two years for the latter—four timeslonger! The reason could be the greater visibility of,prestige associated with, and immediate satisfactionfor, all members of a family derived from browngoods than that derived from white goods. However,other variables also affect time-to-takeoff, so we needto evaluate this result in the multivariate analysisusing the hazard model.Another categorical variable that affects take-
off is the country. Each country seems to havea distinct time-to-takeoff (see Table 2A). One ofthe most striking results is the dramatic differencebetween geographic regions of Europe (see Table 2B).Scandinavian countries (Denmark, Sweden, Norway,Finland) have the shortest time-to-takeoff. It is a
Table 2A Time-to-Takeoff by Countries Sorted by Increasing Time-to-Takeoff
TELLIS, STREMERSCH, AND YINThe International Takeoff of New Products
mere 4 years (even shorter excluding Finland). Thisnumber is almost half that for Mediterranean coun-tries (France, Greece, Italy, Portugal, and Spain),which have a mean time-to-takeoff of 7.4 years.The time for the rest of Europe (United Kingdom,Ireland, Germany, Austria, Belgium, Netherlands, andSwitzerland) is in between, at 6 years. These differ-ences are based on such a large number of differentproducts and time periods that the results are unlikelyto be due to chance.
Leads and Lags in Takeoffs. Because of the dif-ferences across categories and because categories arenot evenly distributed across countries, we computethe intercountry differences in calendar year of takeoffafter correcting for category differences. To do so, wefirst compute the mean year of takeoff for each cate-gory across all countries. We then subtract the meanof a category from each country’s year of takeoff forthat category. The result gives us the lead or lag timeby which a product takes off in a country relative tothe average in Europe. If the takeoff occurs in a coun-try before the average, that country leads. Alterna-tively, that country lags. The means of these leads andlags across categories for each country are in Table 3A.Note that positive numbers imply overall leads, whilenegative numbers imply overall lags. The countriesare listed in decreasing order of lead times.Here again, after controlling for category differ-
ences, there is a very clear difference in leads andlags in year of takeoff across countries. Although weexpected some difference across European countries,the variation, displayed in Table 3A, is huge. Noteespecially the dramatic difference in net lead/lag of10 years between Denmark, which tops the list, andGreece, which is at the bottom. The difference in meanleads and lags in year of takeoff carries over to thedifferences among the country groups in Table 3B.
Estimates of the Hazard ModelAre the differences in the countries and these coun-try groups due to economics, culture, informationaccess or the category characteristics? Our priors werethat economic variables would be the prime factorthat explained intercountry differences. Cultural vari-ables would be the next important factor. Most peo-ple with whom we discussed the study and most
Table 3A Lead and Lags in Year of Takeoff by Country Sorted by LeadTime
Number of Mean Lead (+) or Lag (−)Country Categories in Takeoff (years)
Table 3B Lead and Lags in Year of Takeoff by Country Groups
Country Number of Mean Lead (+) or Lags (−)Innovativeness Country-Categories in Year of Takeoff
Scandinavian 34 2.0Mid-West Europe 56 1.5Mediterranean 42 −3�4
audiences before whom we presented also felt like-wise. Subsequent empirical analysis with the hazardmodel enables us to test these expectations. The haz-ard analysis allows for multiple explanatory variablesand thus is a convenient framework for such a test.We present our results in two subsections: parameterestimates and model performance.
Parameter Estimates. Table 4 contains the param-eter estimates. Note that positive � coefficientsdecrease the probability of takeoff and negative �
coefficients increase the probability of takeoff (asspecified in Equations (5)–(7)). Because of the largenumber of variables and the potential for multi-collinearity, we ran the model with each independentvariable alone, as well as with all the independentvariables together.
200 Marketing Science/Vol. 22, No. 2, Spring 2003
TELLIS, STREMERSCH, AND YINThe International Takeoff of New Products
Table4A
Estim
ateof
theHa
zard
Mod
el
Variables
Individu
alMod
els
Intercept
1�00
9∗∗∗
2�05
2∗∗∗
3�26
6∗∗∗
2�22
6∗∗∗
2�05
8∗∗∗
0�63
41�74
2∗∗∗
1�95
6∗∗∗
2�00
8∗∗∗
�0�125
��0�176
��0�422
��0�115
��0�106
��0�900
��0�099
��0�251
��0�097
�
Prod
uctcateg
ory
Prod
uctclass
(1:w
hitego
ods,
1�03
4∗∗∗
0:brow
ngo
ods)
�0�143
�
Marketp
enetratio
n−1
�044
�0�637
�
Intro
ductionyear
−0�023
∗∗∗
�0�006
�
Numbero
fprio
rtakeoffs
−0�439
∗∗∗
�0�084
�
Econ
omicvaria
bles
GDP
−0�079
∗∗∗
�0�020
�
Incomeinequality(GINI)
0�03
4�0�028
�
European
unificatio
n−0
�046
�0�145
�
Rolesintheho
usehold
−0�008
�0�008
�
Internationaltrade
−0�126
∗∗∗
�0�033
�
Cultu
ralvariables
Uncerta
intyavoidance
Masculinity
Need
fora
chievement
Indu
strio
usness
Inform
ationaccess
Mediaintensity
Mob
ility
Education
LL−2
99�321
−326
�388
−326
�023
−305
�507
−321
�108
−330
�554
−331
�361
−330
�945
−319
�884
N=
117(produ
ct-cou
ntry
series)
and82
6(observatio
ns)
Notes:Sign
ificancelevels(one-sided):
∗ :p<
0�10
;∗∗ :p<
0�05
;∗∗∗:p
<0�01
.Stand
arderrors
areinparentheses.
Marketing Science/Vol. 22, No. 2, Spring 2003 201
TELLIS, STREMERSCH, AND YINThe International Takeoff of New Products
Table4B
Hazard
Mod
elRe
sults
Variables
Individu
alMod
els
FullMod
el
Intercept
1�30
6∗∗∗
1�52
6∗∗∗
1�84
7∗∗∗
−0�107
2�30
1∗∗∗
2�09
4∗∗∗
2�10
3∗∗∗
−1�446
�0�194
��0�147
��0�096
��0�834
��0�157
��0�132
��0�137
��2�056
�
Prod
uctcateg
ory
Catego
ry(1:w
hitego
ods,
1�71
2∗∗∗
0:brow
ngo
ods)
�0�405
�
Marketp
enetratio
n−0
�688
∗
�0�414
�
Intro
ductionyear
0�02
2�0�027
�
Numbero
fprio
rtakeoffs
−0�263
∗∗∗
�0�102
�
Econ
omicvaria
bles
GDP
0�04
2�0�085
�
Incomeinequality(GINI)
0�00
3�0�044
�
European
unificatio
n−0
�083
�0�246
�
Rolesintheho
usehold
0�00
9�0�016
�
Internationaltrade
−0�049
�0�077
�
Cultu
ralvariables
Uncerta
intyAvoidance
0�00
6∗∗
0�00
7�0�003
��0�007
�
Masculinity
0�00
50�00
5�0�003
��0�005
�
Need
fora
chievement
−0�357
∗0�28
6�0�191
��0�533
�
Indu
strio
usness
−0�050
∗∗0�00
4�0�023
��0�047
�
Inform
ationaccess
Mediaintensity
−0�052
∗∗∗
−0�037
�0�013
��0�040
�
Mob
ility
−0�020
∗∗∗
0�02
3�0�006
��0�022
�
Education
−0�313
∗∗∗
0�00
5�0�096
��0�154
�
LL−3
28�674
−330
�169
−329
�631
−328
�885
−322
�848
−326
�194
−325
�474
−268
�176
N=
117(produ
ct-cou
ntry
series)
and82
6(observatio
ns)
Notes:Sign
ificancelevels(one-sided):
∗ :p<
0�10
;∗∗ :p<
0�05
;∗∗∗:p
<0�01
.Stand
arderrors
areinparentheses.
202 Marketing Science/Vol. 22, No. 2, Spring 2003
TELLIS, STREMERSCH, AND YINThe International Takeoff of New Products
The analyses with individual independent variablesshow that 10 of the 14 hypothesized effects are signif-icantly different from 0 and in the expected direction.The hypotheses not confirmed are progressiveness(H2), roles in the household (H3), masculinity (H6),and market penetration (H13). In particular, we findthat:• Products take off faster in wealthier countries
than in poorer countries (H1).• Products take off faster in countries with more
open economies, as compared to countries with less-open economies (H4).• Products take off faster in countries low in uncer-
tainty avoidance, as compared to countries high inuncertainty avoidance (H5).• Products take off faster in countries high in need
for achievement, as compared to countries low inneed for achievement (H7).• Products take off faster in industrious countries,
as compared to less-industrious countries (H8).• Products take off faster in countries high in
media intensity, as compared to countries low inmedia intensity (H9).• Products take off faster in countries high in
mobility, as compared to countries low in mobility(H10).• Products take off faster in countries high in edu-
cation, as compared to countries low in education(H11).• The higher the prior takeoffs in other countries,
the higher the probability of takeoff in a target coun-try (H14).• Brown goods take off faster than white goods
(H12).We also find that products that were introduced
more recently took off faster. However, when weinclude all these variables in the full model, many ofthe effects are not significantly different from 0. Onereason for this result is multicollinearity. The condi-tion index takes on a value of more than 2,200, whichby far crosses the threshold of 30 (Belsley et al. 1980).Nonetheless, while all country characteristics seemsusceptible to this problem, the effects of three cat-egory variables are strong, have the right sign, andare very robust to model specification: product class
(H12), lagged market penetration (H13), and priortakeoffs (H14).To deal with the multicollinearity problem, we con-
ducted a factor analysis to extract orthogonal, higher-order constructs. We extracted factors using principalcomponents, which we rotated using Equamax, anorthogonal rotation method (to avoid collinearityamong factors). On the basis of a scree plot, weretained three factors (explaining 71% of the variance)which we named, in order of importance:(1) Economic wealth (43% of variance): GDP (0.909),
number of telephones (0.915), number of cars (0.885),number of TVs (0.908), exports (0.860), imports (0.854),and education (0.647) load heavily on this factor.(2) Venturesome culture (19% of variance): uncer-
tainty avoidance (−0.777), need for achievement(0.839), and industriousness (0.799) load heavily onthis factor.(3) Economic progressiveness (9% of variance):
income inequality (−0�734) and EU membership(0.679) load heavily on this factor.Note that the factor analysis was unable to dis-
criminate between economic wealth and informationaccess as separate factors. This result may not be sur-prising when one considers that information accessis largely dependent on the wealth of a country. Wethen included these factors together with the prod-uct category characteristics as independent variablesin the hazard model. Table 5 presents the results of
Table 5 Results of Hazard Model with Factors
Standard Change in HazardVariables Coefficient Error Ratio (in %)
TELLIS, STREMERSCH, AND YINThe International Takeoff of New Products
this estimation. In the last column of Table 5, wealso present the percentage change in the hazard ratiogiven a one-unit change in the independent variable.This value is equal to 100 ∗ �e−�−1� (see Equation (7)).Note that this hazard ratio cannot be straightfor-wardly compared across variables, because it is notunit-free.Here, again, we find that the product characteristics
are highly significant. Consistent with our hypothe-ses, takeoff occurs earlier for brown goods, as laggedmarket penetration increases, and with more priortakeoffs in neighboring countries. Counterintuitively,we now find that takeoff occurs later for more recentlyintroduced categories. The reason for this result maybe the omission of old categories from our data. Wedo not have data prior to 1950. As a result, we do nothave information on old categories that were intro-duced very early and which we suspect took a longtime to takeoff. If we had these data, the effect mayhave been insignificant as Golder and Tellis (1997)found, or may have had a sign opposite to what weobtained.Of the country factors we extracted, we find that
only the venturesome culture (p = 0�054) has a signif-icant effect on time-to-takeoff. The sign is consistentwith our hypothesis, in that takeoff occurs faster incountries with a more venturesome culture. Economicwealth and economic progressiveness do not have asignificant effect on time-to-takeoff. This is a surpris-ing result, because many observers credit economicsas having a predominant influence on time-to-takeoff.
Performance of Hazard ModelThis section discusses the stability of the parameters,the baseline hazard, and the model’s fit.
Robustness of Results: To assess the robustness ofour results, we checked the sensitivity of our resultsto alternative distributional assumptions, unobservedheterogeneity, and the method for determining take-off. First, to check if our distributional assumptiontowards the baseline hazard (logistic) affected ourresults, we estimated a Weibull specification of thebaseline hazard. We found that it did not signifi-cantly affect our point estimates, nor did it affect thestandard errors of these estimates. A Weibull hazardmodel is a flexible form that allows for monotonically
increasing or decreasing hazards, and also nests anexponential hazard model, which has a constant haz-ard. The logistic specification we adopted also allowsfor nonmonotonic hazards.Second, we checked if unobserved heterogeneity
was a problem. We modeled unobserved heterogene-ity through the often-used gamma mixing distribu-tion. We found unobserved heterogeneity to be veryweak. Also, the point estimates and standard errorswe obtained in the model without heterogeneity werevery close to the ones we found in a model withgamma mixing. We conclude that our model performssatisfactorily as compared to a more complex para-metric hazard model with gamma mixing, and thusretain our more parsimonious specification.Third, we stated before that we also calculated take-
off years using visual inspection to check for thevalidity of our findings. Using the takeoff years ofthis visual inspection method resulted in findings thatare not significantly different from the findings weobtained through the use of our threshold rule.In sum, we find our estimates to be very sta-
ble towards the assumed distribution of the haz-ard, unobserved heterogeneity, and alternative takeoffrules.
Baseline Hazard: The distribution of the hazard func-tion is modeled through the parameter � . For themodel with the factor scores of which the resultsare presented in Table 5, the � parameter is equalto 0.37 and is significant at the 0.01 level. We dis-play the baseline hazard function of this model inFigure 3. Note that the X-axis represents time in
Figure 3 Hazard of Takeoff Using Log-Logistic Function
Duration in years
.2
.4
.6
.8
.0
5 11 16 22 27.0
HazardFn
204 Marketing Science/Vol. 22, No. 2, Spring 2003
TELLIS, STREMERSCH, AND YINThe International Takeoff of New Products
years and the Y -axis represents the hazard functionof Equation (7). This figure shows that the likelihoodthat takeoff will occur, given that it has not occurredyet, initially increases very rapidly to a maximum ataround 3 years. After about 3 years the hazard grad-ually declines. In our data set, there were no productsthat took longer than 27 years to take off in a specificcountry.
Measures of Fit: To assess the model’s performance,we use the likelihood ratio index (LRI, McFadden 1974).This index is a measure of how much the model red-uces uncertainty and is similar to Hauser’s (1978) U 2.It is analogous to the R2 of multiple regression,although low values of LRI relative to R2 still repre-sent relatively good fits. A first model against whichto evaluate the fit of our model is a proportional haz-ard model, which only includes a constant. Evaluatedagainst this model, the model with the three factors(Table 6) has an LRI of 0.55. This value is excellent,as compared to the model proposed by Golder andTellis (1997), who obtained an LRI of 0.31 as eval-uated against this base model. We also calculatedthe reduction in uncertainty as compared to a log-logistic model that includes only a constant. The LRIwe obtained compared to this model is 0.18. Tak-ing into account that our null model to calculate LRIincludes a constant and, most importantly, already fitsa log-logistic distribution to the hazard function, wecan assess this LRI as satisfactory compared to priortakeoff research in marketing (Golder and Tellis 1997).
DiscussionWhile the takeoff of new products is an importantphenomenon, it has received limited attention, all ofwhich focuses on the U.S. market. Because Europehas had 55 years of successful evolution of free con-sumer markets, that arena constitutes a good con-text in which to test the generalizability of takeoff.The post-World War II economic boom in Europe,together with its growing economic unification, hasled to a perception among some that all of Europehas become, or is steadily becoming, one market.The takeoff of consumer durables across Europe is asuitable context in which to test the validity of thatperception.
We analyzed the takeoff of 10 consumer durablesacross 16 Western European countries. While still lim-ited in scope, this is one of the largest studies on inter-national new product growth. Our analysis leads tosome clear conclusions:• Sales of most new products display a distinct
takeoff in various European countries, at an averageof 6 years after introduction.• Time-to-takeoff differs dramatically across prod-
uct classes. The mean time-to-takeoff is 8 years forwhite goods (kitchen and laundry appliances) and2 years for brown goods (entertainment and informa-tion products).• Time-to-takeoff differs dramatically between
countries (e.g., 3.3 years for Denmark and 9.3 yearsfor Portugal). On average, time-to-takeoff is almosthalf as long in Scandinavian countries (4 years) versusMediterranean countries (7.4 years).• Culture partly explains these differences. In par-
ticular, the probability of takeoff increases with higherneed for achievement and industriousness and loweruncertainty avoidance. While economic factors indi-vidually affect the probability of takeoff in theexpected direction, their effects are neither strong norrobust to model specification.• The probability of takeoff of a new product in a
target country increases with prior takeoffs in othercountries.These results have important implications for inter-
national entry and marketing of new products. Themost important implication is that specific regionsof Europe have distinct commonalities in terms oftime-to-takeoff of new products, with sharp differ-ences across regions. While we expected some dif-ferences, we were surprised by the size of thedifferences. We were also surprised by the fact thatScandinavian countries tend to have the shortest time-to-takeoff of all European countries. In contrast, thelarge economies of Europe, France, Germany, Italy,Spain, and the United Kingdom turned out to be less“innovative” than the Scandinavian countries. Whenwe presented these results to audiences of managersand researchers in the United States and Europe, theywere also surprised, yet found the results believable.They suspected that Scandinavian countries are rel-atively innovative, while Latin countries are muchless so.
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TELLIS, STREMERSCH, AND YINThe International Takeoff of New Products
A second important implication that follows fromour results is the distinct advantages to a waterfallstrategy over a sprinkler strategy. A waterfall strat-egy implies sequential introduction, while a sprin-kler strategy implies simultaneous introduction ofnew products across countries. Our results support awaterfall strategy for three reasons. First, managersare under great pressure to pull the plug on a prod-uct that has not taken off. Thus, introducing in a fewcountries that are likely to show early takeoff canwin internal support for continued marketing of thenew product. It can help to convince critics withinthe company of the new product’s potential and pre-vent the premature withdrawal of a promising newproduct. Past studies on diffusion have typically notemphasized this perspective, which is unique to afocus on takeoff. For example, Putsis et al. (1997,p. 354) even suggest introducing first in Germany,France, Italy, and Spain, for “seeding the diffusionprocess” (p. 354).Second, takeoff in one country increases the like-
lihood or pressure of takeoff in other countries, asindicated by our empirical results and past research(Gatignon et al. 1989, Kalish et al. 1995). Moreover,quick takeoffs in some countries can convince dis-tribution channels in other countries with slowertakeoffs to carry the new product and support it ade-quately. Third, an early takeoff generates revenuesand profits for the company, which it can use toimprove the product, market it more aggressively, andintroduce it in other international markets.Have managers of new products adopted such a
strategy in Europe? Our empirical analysis suggestsnot. Apparently firms do not introduce their prod-ucts in different countries along their expected time-to-takeoff. This is a third important implication of ourstudy. We arrived at this conclusion from an analysisof intercountry leads and lags in year of introductionfor each category. A lead or lag in year of introduc-tion for a target country is the difference between themean year of introduction for a category across allcountries minus the year that it was introduced inthe target country. This analysis is based on only 120country-categories because for 17 country-categorieswe were unable to determine the precise introductiondate. We can compare the average leads or lags in
Table 6 Variation in Year of Introduction Across European Countries
Number of Mean Lead (+) or Lags (−)Country Categories in Introduction (years)
year of introduction (see Table 6) to those in year oftakeoff in Table 3A.Note first that there are considerable differences in
mean leads and lags in year of introduction acrosscountries. These leads and lags do not correspondexactly to the leads and lags in year of takeoff. Inparticular, large, developed economies, such as theUnited Kingdom, Germany, and France, show earlyproduct introductions but late product takeoffs, whileScandinavian countries, such as Sweden and Norway,show relatively late product introductions and earlytakeoff. (Note that this effect for the Scandinaviancountries occurs even after the inclusion of prior take-offs that accounts for potential spillover due to take-off in other countries.) This difference could occur forseveral reasons.First, managers of new products may not be aware
of these results and may not have analyzed the suc-cess of past introductions along these lines. Second,managers of new products may be introducing prod-ucts in economically advanced countries, assumingthat these countries are likely to see quick producttakeoffs. If so, our results are the opposite. Third,managers of new products may be focusing purely onsales, and not on the profits or signaling value thataccrue from an early takeoff.
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A fourth implication of our study is that managerscan use the takeoff of products in one country to pre-dict takeoff in other countries. For this purpose, thehazard model is useful and convenient. Even with-out using the model, managers can use the consis-tent intercountry differences in takeoff as a heuristicto gauge the likelihood and timing of takeoff in othercountries. Similarly, public policy makers can use theresults of the study to ascertain the time and likeli-hood of takeoff of a new product in their own coun-try, given its performance in other countries. Such adetermination may play a role in encouraging or sup-porting investments in production and marketing soas to encourage timely local manufacturing of newproducts.This study has many obvious limitations, some of
which we need to mention. First, we were unable toobtain data before 1950. Thus, we had to drop cat-egories if they were introduced in a country before1950. This problem especially affected refrigerators.The mean time-to-takeoff may have been longer forrefrigerators (2.9 years) if data for this category inall countries were included. Fortunately, this omis-sion biases one of our results in the direction oppositeto our major findings and hypotheses. If we had allthe data, we would probably find that takeoff time islonger for white goods than we presently find.Second, we were unable to get consistent measures
of price and distribution in all the categories andcountries. We were thus unable to assess the roleof important variables that managers can control totrigger takeoff.Third, we did not include U.S. data. In many cases,
the products in our sample may have taken off inthe United States before they did so in any Europeancountry. It would be interesting to compare and relatethe takeoff in Europe with that in the United States orother North American countries.Fourth, our measures of cultural differences may
not directly assess peoples’ readiness to adopt newproducts (e.g., Parsuraman 2000). However, untilsuch measures are available, the intercountry time-to-takeoff itself could serve as a surrogate of theinnovativeness of countries.Despite these limitations, we hope that researchers
and academics will find value in these findings.
We trust our effort will stimulate further research toovercome these limitations.
AcknowledgmentsThe authors gratefully acknowledge the comments of DelaneBotelho, Peter Golder, Geert Hofstede, Joseph Johnson, AshishSood, Jan-Benedict Steenkamp, and participants at seminars in themarketing departments of the University of Washington, Univer-sity of Iowa, University of Miami, University of Texas, Universityof Southern California, and Erasmus University Rotterdam. Theauthors also thank William Putsis, Jr. for sharing some of his data.Finally, they thank CIBEAR (USC) and the Marketing Science Insti-tute for financial support.
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