Market Share Rewards to Pioneering Brands: An Empirical Analysis and Strategic
`Market Share Rewards to Pioneering Brands: An Empirical Analysis and Strategic
`Implications
`Implications
`
`
`
`Glen L. Urban; Theresa Carter; Steven Gaskin; Zofia Mucha
`Glen L. Urban; Theresa Carter; Steven Gaskin; Zofia Mucha
`
`Management Science, Vol. 32, No. 6. (Jun., 1986), pp. 645-659.
`Management Science, Vol. 32, No. 6. (Jun., 1986), pp. 645-659.
`
`Stable URL:
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`IMMUNOGEN 2248, pg. 1
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`MANAGEMENT SCIENCE
`
`Vol. 32, No. 6, June 1986
`
`Prrnted in U.S.A.
`
`
`MARKET SHARE REWARDS TO PIONEERING BRANDS:
`
`AN EMPIRICAL ANALYSIS
`
`AND STRATEGIC IMPLICATIONS*
`
`
`GLEN L. URBAN, THERESA CARTER, STEVEN GASKIN
`
`AND ZOFIA MUCHA
`
`Alfred P. Sloan School of Management, Massachusetts Institute of Technology,
`
`Cambridge, Massachusetts 02139
`
`International Business Machines, Greensboro, North Carolina 27407
`
`Information Resources Inc., Waltham, Massachusetts 02254
`
`McKinsey & Co., New York, New York 10022
`
`
`An empirical analysis indicates that the order of entry of a brand into a consumer product
`category is inversely related to its market share. Market share is modeled as a log linear
`function of order of entry, time between entries, advertising, and positioning effectiveness. The
`coefficients of the entry, advertising, and positioning variables are significant in a regression
`analysis on an initial sample of 82 brands across 24 categories. These findings are confirmed
`by predictions on 47 not previously analyzed brands in 12 categories. Managerial implications
`for pioneers and later entrants are identified.
`(MARKETING; COMPETITION; NEW PRODUCTS)
`
`Introduction
`
`One strategy for new product development is based on innovation and the creation
`of new markets. It is expensive and risky to be a pioneering brand (Urban and Hauser
`1980). The costs of development are often large and the first firm in a market must
`allocate funds to make consumers aware of its product and convenience them to buy
`it. The risk of failure is high because the potential demand is not known with certainty.
`An alternative strategy is based on being the second (or later) entrant into the market.
`The costs may be lower since the innovator has created the primary demand and the
`basic product design exists; the risk also may be less because a proven demand exists.
`If an equal market share can be gained, this strategy could be more profitable. If, on
`the other hand, as a result of being the first entrant in a market, a dominant market
`share is achieved and maintained, the innovation strategy may be superior. The
`purpose of this paper is to investigate the market share effects of being a pioneering
`brand.
`If the market grants a long-run market share reward to early entrants, this would
`encourage innovation. From a public policy point of view, this would serve a similar
`function to that of patents by providing an additional reward to innovators. Although
`patents sometimes provide protection, in many cases they are ineffective because of
`difficulties of establishing and protecting the rights and the ability of other firms to
`"invent around" the patent as technology advance (von Hippel 1982). This difficulty
`of protecting an innovation is compounded by the fact that imitators generally take
`less time and require fewer funds to copy the innovation (Mansfield, Schwartz, and
`Wagner 1981). If pioneering brands earn a long-run market share advantage, the
`effectiveness of patent protection may be less critical in providing incentives for
`innovation and firms may be more willing to innovate without patent protection.
`
`*Accepted by John R. Hauser; received February 8, 1984. This paper has been with the authors 3 months
`for 1 revision.
`
`645
`
`0025-1909/86/3206/0645$01.25
`Copyright 0 1986, The Institute of Management Sciences
`
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`G. L. URBAN, T. CARTER, S. GASKIN AND Z.MUCHA
`
`Several authors have argued on theoretical grounds that such long lived advantages
`can exist. Early ideas by Bain (1956) indicated that existing products can have an
`advantage accruing from fundamental consumer traits that lead to stable preference
`patterns. If an experience curve is present, production costs for the pioneer may be
`lower because its cumulative production is likely to be greater than later entrants
`(Abell and Hammond 1979). If the pioneer can not only gain a cost advantage but
`also erect barriers to entry (Porter 1980), sales advantages may be even greater.
`Recent theoretical work by Schmalensee (1982) is based on the fundamental notion
`that once buyers use the first entrant's product, they will be willing to pay more for it,
`if it works, because they are not certain the second product will work. Based on a
`number of assumptions (e.g., products either work or do not work, second entrant
`objectively equal to first, no response by pioneer to new entrant, and no advertising
`effects) he shows that a long-run price advantage can persist for the pioneering brand.
`In this model, the second entrant must offer a price reduction to persuade consumers
`to try and learn about the product. This can imply higher profits for the pioneer. Lane
`and Wiggins (1981) also assume that consumers only know the exact quality of the
`products they have used. Their model is similar to Schmalensee's but includes
`advertising and some response by the pioneer to later entrants. After examining profit
`maximizing strategies they find "even with entry, the first entrant's advantage persist
`in the form of higher demand and profitability" (p. 3).
`Hauser and Shugan (1983) have formulated a defensive strategy model which uses
`the product positioning of the new entrant to determine share. In this model, the
`persistence of the sales levels of pioneering brands depends on how well the pioneer
`designed the product attributes to meet heterogeneous consumer preferences. If the
`"best" positioning was chosen by the first firm, later entrants may have lower market
`shares because, if they want to differentiate, they must adopt an inferior position.
`However, if the first brand to enter did not fully understand consumer preferences, the
`second entrant could get a preferential positioning advantage and earn a greater share.
`These theoretical models show the possibility of long-run market share rewards for
`pioneering brands and indicate these rewards also will be a function of the product
`positioning and pricing strategies of the new and old products.
`A limited amount of empirical analysis on the benefits of early entry has been
`reported. Biggadike (1976) studied 40 industrial product entries into new markets
`represented by large firms in the PIMS project. He found that after four years the
`average share of these entrants was 15 percent while the share of the largest existing
`competitor in each of the 40 businesses decreased from 47 percent to 28 percent when
`new entrants came on the market. These data suggest that although the share of the
`pioneering brand decreases as a result of subsequent entry, shares may not equalize.
`Robinson and Fornell (1985) studied the PIMS data for 371 consumer goods
`business units that were in the mature phase of their life cycle. In this sample firms
`designated themselves as "pioneers, early followers, or later entrants." "Pioneers" had
`an average share of 29 percent while "early followers" had 16 percent share and "later
`entrants" had 11 percent market share. The authors conducted an econometric
`analysis to uncover the mechanisms underlying the share differences. They found that
`pioneers tended to have higher quality products and a broader product line. In
`convenience goods, market pioneers gained additional advantages due to distribution
`effects. Pioneers also benefited in markets with low price and low purchase frequency.
`This cross-sectional study provides evidence of order of entry effects at the business
`unit level.
`Two longitudinal industry studies have been conducted which have information
`relevant to entry effects. The first is by Bond and Lean (1977) and reflects a study of
`two related prescription drugs (diuretics and antiaginals). A historical review and time
`
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`
`series regression analysis of the sales, entry and promotion in each of these markets led
`the authors to conclude for these prescription drugs that "the first firm to offer and
`promote a new type of product received a substantial and enduring sales advantage"
`(p. vi). Neither heavy promotional outlays nor low price dislodged the pioneers.
`However, later entrants that offered therapeutic novelty did achieve substantial sales
`volumes when backed by heavy promotional expenditures. They found that "large
`scale promotion of brands that offer nothing new is likely to go unrewarded" (p. vi).
`Another interpretative study of trends in seven cigarette submarkets by Whitten
`(1979) led to the finding that the "first entry brand received a substantial and enduring
`sales advantage" in six of the seven cigarette market segments (p. 41). She found,
`however, that later entry brands which were early in a growing market or which were
`significantly differentiated could gain a substantial share in the market or even
`dislodge the first entry brand from its dominant position.
`These theoretical and empirical analyses suggest order of entry may affect the
`market share potential of later entries and that this effect may be modified by the
`entrant's positioning, quality, pricing, and marketing strategy. This paper enlarges the
`body of empirical knowledge by a cross product analysis over many categories of
`frequently purchased brands of consumer goods. It includes effects of order of entry as
`well as advertising and product positioning. We begin by describing the data base and
`specifying the statistical model. Then we describe its fit to an initial data base of 82
`brands, assess its predictive ability on a new sample of 47 brands, and present a
`re-estimation of the model parameters based on the pooled data. We consider the
`strategic implications of our findings and close with a discussion of future research
`needs.
`
`Data
`Pre-test market assessment procedures have been widely used in the markets for
`frequently purchased brands of consumer products. One such system, called ASSES-
`SOR (Silk and Urban 1978), provides a rich data base for the study of order of entry
`effects. In this procedure, data on existing products are collected first and then new
`product response is measured. We are concerned here with only the data on existing
`products. Studies were carried out in the 1979-82 period. In each category studied, 300
`(or more) respondents were interviewed to determine their evoked set of brands, their
`preferences for these brands (constant sum paired comparisons across each consumer's
`evoked set), the last brand they purchased, and ratings of selected evoked brands on
`product attribute scales.' These data allow market shares to be estimated by the
`fraction of the sample which last purchased the brand. The preference and ratings data
`supply a basis of determining product positioning and differentiation. An initial
`sample of 24 categories was selected for exploratory analysis. 82 major brands existed
`across these categories. After the collection and analysis of the initial sample, data for
`47 different brands were made available. This second sample became the data for
`predictive testing. The products in these samples represented tightly defined categories
`of frequently purchased goods (e.g., liquid detergent, instant freeze dried coffee, fabric
`softener, anti-dandruff shampoo). The categories were well established. The average
`time in the market for second entrants was 25.9 years, third entrants 20.5 years, fourth
`entrants 15.2 years, fifth entrants 8.9 years, and sixth entrants 6.2 years. These data
`
`'The respondents were intercepted at a shopping mall, screened for category usage, and interviewed if
`they were within the age and demographic quotas established in the stratified sampling plan for each study.
`The evoking is based on positive unaided response to one of the following conditions: now using, ever used,
`on hand, would consider using, or would not consider using. Approximately 90% of evoking is associated
`with use experience.
`
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`G. L. URBAN, T. CARTER, S. GASKIN AND Z. MUCHA
`
`were supplemented by advertising expenditures obtained from the Leading National
`Advertisers published media audits. Although these audits may not report 100 percent
`of each brand's spending, they are useful in comparing advertising expenditures if we
`assume no biases in relative advertising. Since the brands considered had been on the
`market at least two years, these spending levels represent post-introductory expendi-
`tures.
`The order of entry was determined by identifying the time of national introduction
`for each brand. This was done by personally calling the firms which market each of
`these products and determining when it was introduced. In the few cases where the
`firms were not willing to provide this data, at least two competitors were asked to
`provide an estimate of the entry time and their average response was utilized.
`These data provided a cross sectional data base for the investigation of order effects.
`At the time of each study, the shares for the existing brands, the year of each product's
`entry into the market, the brand's recent advertising spending, and the relative product
`preferences are known.
`
`Statistical Model
`The dependent variable in this study is the ratio of the market share of the nth
`(second, third, fourth . . . ) brand to enter the market to that of the first product to
`enter. Since the number of brands in each category varies, the absolute shares also
`vary; the ratio allows a meaningful comparison of relative relationships of brands
`within and across categories. Brands are included in the analysis if
`they were
`advertised at a significant level (greater than one million dollars per year) and a
`reasonable share estimate could be obtained (at least 30 respondents reporting a
`specific brand as last brand purchased).
`The order of entry (first, second, third . . . ) is used as an independent variable. This
`variable can empirically reflect the theoretical long lived share advantages of pioneer-
`ing brands argued by Schmalensee (1982) and Lane and Wiggins (1981). If, as
`theorized, the early entrant becomes the standard of comparison and subsequent
`brands require consumers to make additional investments in learning, the order of
`entry variable will be negatively correlated to the share index. This variable is
`supplemented by another which is defined as the number of years between the nth
`entry and the one which immediately preceded it. Being the second brand in the
`category may have a different share effect if the lag between the pioneer is one year
`rather than two, three, or four years, Whitten (1979) stressed the importance of a firm
`being early after a new trend is established. Advertising is represented by the total
`advertising expenditure over the last three years by the nth brand to enter the category
`divided by that of the pioneering brand. This variable reflects the sustaining level of
`advertising spending and allows the order of entry effect to be modified by the
`application of marketing resources.
`Differential product positioning has been identified as another moderator of the
`effect of order of entry. The Bond and Lean (1977) and Whitten (1979) studies stress
`its significance. Robinson and Fornell (1985) and Hauser and Shugan (1983) also
`argue for its importance. One method of constructing a positioning variable is by
`combining the product attribute ratings to estimate the utility for a brand. (See Urban
`and Hauser (1980) or Shocker and Srinivasan (1979) for a review.) Many procedures
`exist and they usually reproduce stated preferences or choices well. Another method is
`to use stated preferences directly. This has the advantage of avoiding variance due to
`lack of fits between the attributes and preferences, but has the disadvantage of not
`linking the attributes to preferences. Because our primary purpose is to use the
`positioning variable as a covariate of order of entry in explaining share rather than
`
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`649
`
`supporting the design of new products, we choose to use preference to construct the
`positioning variable. The constant sum preferences supplied by respondents over their
`evoked set reflect their overall evaluations of the brand's price and features. After
`scaling the preferences by least square procedures (see Silk and Urban 1978), we
`obtain a preference value for each evoked brand j, respondent i and category c (y?,).
`We define a relative preference for a brand for each consumer and average over all
`individuals who evoke the brand:
`
`yj, = preference value for respondent i and brand j in category c,
`1;, = number of respondents in category c who evoke brand j,
`p, = scale parameter for category c,
`R,, = relative preference of brand j in category c.
`The value of R,,
`is a measure of the consumers' evaluation of the product given that it
`is evoked. It reflects consumers' preferences that result from a specific multiattribute
`positioning. In most cases evoking occurs by use of the brand. If it performs well and
`price is low, R,, will be high; if it does not perform well and price is high, R,, will be
`low. The scale parameter
`is estimated by logit procedures (see Silk and Urban 1978,
`for details) and it empirically has values in the range of 1 to 3 with a median of about
`2. This scaling of preferences results in R,, approximating the probability of purchase
`of the brand given that it is evoked. The driving forces behind R,, are the measured
`preferences across the evoked set, but this scaling must be remembered when the
`statistical analysis is interpreted (see below).
`Another aspect to emphasize is that R,,
`is conditioned by evoking. The same market
`share (e.g., 10%) for a brand could be due to high preference conditioned on evoking
`and low evoking (e.g., 50% preference given evoking and 20% evoking), low condi-
`tioned preference and high evoking (e.g., 20% preference and 50% evoking) or
`moderate levels of both (e.g., 33% preference and 33% evoking). The variable RJc is not
`necessarily correlated to share. Before 1974, Tylenol had a low share, but pre-test
`market evaluations indicated high preference by those who had used it. After Tylenol
`advertised and promoted its product, its share increased dramatically as the fraction of
`the population evoking it increased.
`In our model we are interested in the positioning quality of later entrants relative to
`the pioneer, so we define the ratio of R,, for the nth brand to RJ, for the first brand to
`enter as the variable to represent the relative preference given evoking. If the later
`entrant is superior, the ratio is greater than one, and if less desirable, the ratio is less
`than one.
`The form of the model is nonlinear to reflect the hypothesis that the impact of the
`second brand to enter on the pioneer will be greater than the third or fourth brand.
`Considerable precedent exists for modelifig a nonlinear response to advertising (Little
`1979). Bond and Lean (1977) indicate an interaction between order, position, and
`marketing promotion and this can be captured in an elasticity function. Formally for
`brand n in category c:
`
`S,,, = EZ'P,"c"An",'LZ,
`
`S,,, = ratio of the market shares of the nth brand to enter category c to the market
`share of the first brand to enter the category,
`En, = order of entry of nth brand in category c (n = 1,2,3,4 . . . ),
`P,, = ratio of preference given evoking for nth brand to preference for first brand
`
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`G. L. URBAN, T. CARTER, S. GASKIN AND Z.MUCHA
`
`given evoking,
`Pnc= R,,/R,, where
`Rjc = preference for jth brand in category c conditioned by evoking (see Equation
`11,
`A,, = ratio of the last 3 years advertising for nth brand to enter to the last three
`years advertising for first brand,
`L,, = number of years between n and n - 1 brand entry plus one (L,, = 1 if entry is
`in the same year).
`This model captures some major theoretical phenomena. If a , is negative and
`significant, it supports the notion of an enduring share advantage for early entrants. If
`a, is positive and significant, it confirms the notion that the order of entry effect can
`be moderated by a product which is superior in price and features as reflected in the
`preferences of those who have it in their evoked set. If a, is positive and significant, it
`suggests advertising may modify the effect of later entry. If a, is negative and
`significant it would indicate a larger penalty for the nth entrant the later it arrives in
`the market. If the positioning (P,,) and advertising (A,,) indices have a value of one to
`reflect parity and entry is in the same year (L,, = I), equation (2) predicts the share
`ratio to be only a function of the order of entry. If En, is equal to two to reflect the
`second product in the market, in this case the ratio of its share to the first (S,,)
`is 2"3.
`Note that in equation (2) the share ratio takes a value of one when the first brand is
`considered (n = 1).
`Statistical analysis is based on a log transform of equation (2):
`
`where the primes denote the logs of the respective variables defined in equation two.
`Note that this is a linear regression with no additive constant term (a,). The constant
`would confound the interpretation of the magnitude of a's because with an additive
`constant in equation (3), the share index would not equal one for the first brand in the
`market as is required for logical consistency.
`
`Fitting
`
`The first application of the model is to the initial sample of 82 brands across 24
`categories. Regression is used to estimate the parameters in equation (3). These
`regression procedures are based on 58 data points because the first brand is not
`appropriate for inclusion in relative share formulation given in Equation (3) (i.e., all
`first brand variables would have values of o).' The resulting F(4,54) is 58.0 and it is
`significant at the one percent level. The t values also are significant at the one percent
`level (see Table 1) for order, positioning, and advertising. The order coefficient (a,) is
`negative as hypothesized indicating that subsequent entrants are associated with
`reduced shares relative to the pioneering brand. The positioning effect (a,) is positive,
`indicating good positioning is associated with larger shares. In this log-linear model the
`positioning effect increases share proportionately at each entry point. Therefore share
`for the nth entrant is reduced by the order effect (a,) and modified by the positioning
`effect (a,). It is possible for the nth entrant to earn a dominant share when its
`positioning is sufficiently superior to overcome the order effect penalty. The relative
`advertising coefficient (a,) is also positive and reflects another correlate to increased
`
`2 ~ nalternate approach is to include the additive constant in equation (3) and regress over the first and
`later entrants. This is not as theoretically attractive a procedure but the a, is driven toward zero by the
`number of first brands. Empirical application of this procedure led to an a, that was not significantly
`different from zero at the 10 percent level (a,= 0.12, t = 1.2).
`
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`MARKET SHARE REWARDS TO PIONEERING BRANDS
`
`
`TABLE 1
`
`Statistical Fitting Results
`
`
`Variable
`
`Parameter
`
`Value
`
`t Statistic
`
`Order of Entry (E)
`Position (P)
`Advertising ( A )
`Lag Between Entry (L)
`
`at
`02
`
`a3
`
`a4
`
`- 0.48
`1.14
`0.27
`0.04
`
`-4.5*
`6.8*
`5.5*
`0.6
`
`*Values significant at 1% level. Critical value with 55 degrees of freedom
`and two tail test is t = 2.7.
`
`share when a brand is a late entrant. Superior positioning and aggressive advertising
`spending would be the most likely correlates of dominance in a category by a later
`entrant. The parameter reflecting the time between entry (a,) is not significantly
`different from zero.
`Appendix 1 shows the actual and predicted values for the share indexes plotted
`versus the order of entry variable for three representative categories along with the
`unadjusted order effect E Z . Recall the predictions are obtained from our multivariate
`model so any deviation from the declining effect of order of entry ( a , ) reflects
`positioning and/or advertising effects. For example the third entry in the antacid
`market (Rolaids) achieved a predicted share higher than the second entrant due to
`higher advertising and positioning values (A,,, of 1.6 and positioning value P,,, of 2.1).
`This more than compensated for the order of entry decline and the resulting predicted
`share is greater than the share of Digel or Tums.
`In assessing these fits, we calculate R 2 at 76 percent.3 Another measure of goodness
`of fit is to determine the proportion of the cases where the model prediction corre-
`sponds to the turns in the actual data exemplified in Appendix 1 . There are 58 turns
`and the direction of actual and fitted values agrees for 45 turns or 78 percent of them.
`Multicollinearity among the independent variables is low; five out of six of the
`pairwise correlations are less than 0.25 in absolute value. The sixth is the correlation
`between order of entry and the time between subsequent entries. In this data there is a
`moderate negative correlation of -0.37 indicating some tendency for shorter intervals
`between entrants as more brands enter the market. The parameter estimates are quite
`stable as variables are added to the regression. The order effect parameter is -0.61
`( t = -5.1) when it is the only independent variable, -0.53
`( t = -5.9) when the
`positioning variable (P) is added, -0.43 ( t = -5.7) when the advertising variable ( A )
`also is appended, and -0.48 ( t = -4.5) with all the variables.
`Examination of the residuals indicate that they are not significantly different from a
`normal di~tribution.~ Heteroscedasticity was not evident. The standard deviation in the
`residuals was not significantly different for second and third or later entrank5
`
` ecalculate R'
`in this case of regression with no constant by following the procedure suggested by
`
`
`~
`3
`Judge et al. (1980 p. 253):
`
`where Y, = actual values of dependent variable and P, = predicted value.
`4The residuals were rank ordered and divided into six approximately equally sized classes. The chi-
`squared statistic was calculated based on the actual frequency and the frequency expected from a normal
`distribution of the same mean and variance. X2 = 0.455, df = 3. This implies a 99 percent chance of
`observing a value this high or higher from a normal distribution.
`'The standard deviation of the residuals for brands of order of entry two was 0.537 and for order three
`0.54. These are not significantly different at the 10 percent level F(24, 15) = 1.01. Similarly, for second
`versus fourth or greater F(24,32) = 1.5.
`
`IMMUNOGEN 2248, pg. 8
`Phigenix v. Immunogen
`IPR2014-00676
`
`

`
`652
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`G. L. URBAN, T. CARTER, S. GASKIN AND 2. MUCHA
`
`The estimates have been reviewed for adverse effect from the leverage of outlying
`data points (Belsley, Kuh and Welsh 1980). Three data points were identified as
`having high leverage (Tegrin, Datril and Ocean Blend Cat Food), but when they were
`removed, the significant parameters ( a , , a,, a,) changed less than 15 percent from
`their original values and the t's remained significant at the one percent level.
`A number of alternative forms (e.g., linear and exponential) and variable specifica-
`tions (e.g., advertising as a percentage of category spending and order of entry and lag
`time combined as one variable to reflect years from first to nth entry) were evaluated
`in the statistical analysis, but none were theoretically or empirically superior to the
`results reported here.
`In reviewing the regression results (see Table l), the positioning variable is most
`significant followed by the advertising and order of entry parameters. A stepwise
`regression shows the relative explanatory power of the order of entry variable. If the
`order of entry variable is the first to be included, 32 percent of the variation is
`explained. Adding positioning increased the R 2 to 62 percent and including the
`advertising variable raised it to 76 percent. In each case the incremental variance
`explained was significant at the 10 percent level.
`Some care must be exercised in interpretation of the advertising and positioning
`coefficients. Although the advertising index ( A ) correlates highly with the share index,
`this may not be due to advertising causing share changes. In fact if advertising budgets
`were set by a rule such as "advertising equals X % of sales," the causal relationship is
`one of advertising being dependent on sales. Although the interpretation of the
`advertising coefficient must be cautious, we assume our procedure removes a compo-
`nent of covariance and does not affect the interpretation of the order of entry
`coefficient (a,). We observe that the variables have relatively small intercorrelations
`and one may consider order as a significant explanatory v