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Article Excerpt
Empirical research suggests that, on average, the well-established market share advantage accruing to a pioneering firm is more than offset by a cost disadvantage (Boulding and Christen 2003). (1) The offsetting market share and cost findings raise questions about the overall pioneering profit advantage/disadvantage and suggest the need for evaluation of specific entry strategies. One such strategy is for a pioneering business to bolster its market share advantage via preemption of followers with a broad product line (Tellis and Golder 1996). The effectiveness of this strategy in building a strong market share advantage is well established in both theoretical and empirical research (e.g., Robinson and Fornell 1985, Schmalensee 1978). However, neither cost nor profit implications of this strategy for pioneers have been examined in detail. As a result, we do not know whether, or when, a broad product line is a way for pioneers, on average, to overcome the cost disadvantage of pioneering and build a long-term pioneering profit advantage. To give a firm advice on how to best leverage its first-mover status into a profit advantage, it is therefore important to understand the relationship between demand advantages and cost disadvantages of a strategy. Presumably, firms do not want to execute a strategy of buying a market share advantage paid for by an even higher cost disadvantage.
In this paper, we examine the moderating effect of a broad product line strategy on the overall pioneering cost disadvantage and test whether a pioneering firm can build a sustainable profit advantage over followers with a broad product line strategy. We do so in the repeating "ETE" (empirics-theory-empirics) tradition articulated by Bass (1995). We first empirically examine our entry strategy of interest in two different product environments--consumer goods and industrial goods. We then use the contrasting findings from these two settings to generate an emerging theoretical perspective, which we subject to empirical testing. We conclude with substantive insights about when a broad product line is more likely to lead to a pioneering profit advantage.
1. Empirical Model and Estimation Method
To evaluate the moderating effect of a broad product line on pioneering effects, we use a model specification similar to what Boulding and Christen (2003, 2008) used in their analysis of the overall profitability of pioneering, except that we specify the three functions for inverse demand, average cost and profit for business unit i at the level of a product line strategy 1:
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (1)
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (2)
[[PI].sub.lit] = exp[[[beta].sub.03l] + [[gamma].sub.3li]*[Pion.sub.li] + [X.sub.3lit][[beta].sub.3l] + [[epsilon].sub.3lit]], with [[gamma].sub.3li] = [[gamma].sub.Pli] + [[gamma].sub.3l]. (3)
The elements of the three different equations are defined as follows: [P.sub.lit], [AC.sub.lit], and [[phi].sub.lit] are price, average cost, and profit, respectively, of business unit i with product line strategy l in year t; [Q.sub.lit] is the quantity sold in units; [Pion.sub.li] is an indicator variable ([Pion.sub.li] = 1 if i is a pioneer, otherwise [Pion.sub.li] = 0); [X.sub.jlit] is a vector of other market and firm factors in equation j = 1 ... 3; and [[beta].sub.jl], [[gamma].sub.jli], [[gamma].sub.Dli], [[gamma].sub.Cli], [[gamma].sub.Pli], [[gamma].sub.jl], and [[delta].sub.jl] are model parameters for equation j and product line strategy l. To examine the effect of pioneering for different cost components, we use the corresponding cost components as the dependent variable in Equation (2). Like previous research (e.g., Boulding and Staelin 1995, Jacobson 1990), we assume three different sources of error: (i) unobserved fixed factors, [[alpha].sub.jli]; (ii) unobserved random factors, [[eta].sub.jlit], that consist of a first-order autoregressive component with parameter [p.sub.ij] (to capture dissipating returns); and (iii) a random component, [[omega].sub.ljit]. In other words, we have [[epsilon].sub.jlit] = [[alpha].sub.jli] + [[eta].sub.jlit] and [[eta].sub.jlit] = [[rho].sub.jl]*[[eta].sub.jlit-1] + [[omega].sub.jlit]. (2)
Results from the first two equations yield indirect evidence about a pioneering profitability effect for different product line strategies l, whereas the third equation allows us to directly assess the moderating effect of a product line strategy l on the profitability of pioneering relative to following. Estimating these three equations for each product line strategy l allows us to assess the impact of a broad product line strategy on building a pioneering advantage by comparing the parameter estimates for pioneering from these three equations across differing product line strategies 1. Because previous empirical work examining pioneering effects compares results across the domains of consumer and industrial goods (Robinson and Fornell 1985, Robinson 1988), we begin by estimating Equations (l)-(3) in both of these settings to see if interesting differences emerge.
Our research interest concerns differences between pioneers and followers solely attributable to the order of market entry and the product line strategy, and not to differences due to other characteristics of pioneers and followers (e.g., resources). To control for such other potential differences, we use the instrumental variable (IV) estimation procedure developed by Hausman and Taylor (1981)--HT-IV estimation hereafter. The details of the elaborate estimation procedure that allows for consistent estimation of time-fixed variables like pioneering while controlling for unobserved fixed factors is described in detail in previous work (e.g., Boulding and Christen 2003,2008). The following summary highlights key aspects.
To apply the HT-IV estimation, we first control for omitted contemporaneous factors and first-order autoregressive effects using standard methods (Jacobson 1990). Instruments for the final estimation stage are derived from the model variables themselves by making additional exogeneity assumptions. (3) For identification, one needs at least as many instruments as there are time-fixed variables included in the model. Importantly, these assumptions are all tested with a specification test that Hausman and Taylor (1981) developed based on the standard Hausman specification test (Hausman 1978). (4) To control for movements along the inverse demand curve that may occur due to (1) underlying changes in supply (i.e., cost) and movements along the average cost curve and (2) underlying changes in demand, quantity is considered endogenous in estimation. Moreover, we include a number of unique demand and supply shifters in the respective equations to ensure identification. (5)
2. Data
We estimate the effect of a product line breadth on pioneering effects using data from the profit impact of marketing strategies (PIMS) database. The advantages and disadvantages of this database are discussed elsewhere...
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