SUSTAINABLE PIONEERING ADVANTAGE? PROFIT IMPLICATIONS OF THE ENTRY TIMING DECISION

Transcription

1 Working Papers R & D SUSTAINABLE PIONEERING ADVANTAGE? PROFIT IMPLICATIONS OF THE ENTRY TIMING DECISION by W. BOULDING* and M. CHRISTEN** 001/03/MKT (Revised Version of 99/31/MKT) * Professor of Business Administration at the Fuqua School of Business, Duke Universy, Durham, NC 7708, USA. ** Assistant Professor of Marketing at INSEAD, Boulevard de Constance, Fontainebleau Cedex, France. A working paper in the INSEAD Working Paper Series is intended as a means whereby a faculty researcher s thoughts and findings may be communicated to interested readers. The paper should be considered preliminary in nature and may require revision. Printed at INSEAD, Fontainebleau, France.

2 Sustainable Pioneering Advantage? Prof Implications of the Entry Timing Decision William Boulding and Markus Christen* December 000 * William Boulding is Professor of Business Administration, the Fuqua School of Business, Duke Universy, Durham, NC 7708 (ph: ; bb1@mail.duke.edu). Markus Christen is Assistant Professor of Marketing, INSEAD, Boulevard de Constance, Fontainebleau, France (ph: ; markus.christen@insead.fr). We give our thanks to and acknowledge the support of the Strategic Planning Instute, but note that any errors are our own. We thank Mike Moore, Rick Staelin, John Lynch, seminar participants at the Marketing Science Conference, INSEAD, Ohio State, and the Marketing Science Edor, Area Edor, and reviewers for helpful comments.

3 Sustainable Pioneering Advantage? Prof Implications of the Entry Timing Decision Abstract The question whether firms gain a sustainable advantage from being first to market has captured the attention of academic researchers and managers alike. A long list of theoretical arguments has emerged that indicate the existence of a sustainable consumer-based pioneering advantage. The veracy of such an advantage is supported by numerous empirical studies, which show a significant and enduring effect of entry timing on market share or sales. Yet, is unclear whether a market share effect is sufficient to support the existence of a first-mover prof advantage. In fact, reviews of the entry timing lerature have repeatedly pointed to the prof implications as one of the key unanswered questions in this area of research. This profabily question is not only of theoretical interest. There is at least anecdotal evidence that managers hold strong beliefs that being first pays off and that they act upon those beliefs. Many recent Internet-related investment decisions have been justified wh exactly this argument. The main objective of this paper is to empirically examine the effect of entry timing on profabily. More specifically, we examine prof differences between pioneers and followers that are strictly attributable to the entry timing decision, and control for differences due to other characteristics (e.g., resources). To do so, we need to control for unobserved differences between pioneers and followers that influence performance. Specifically, we test and control for the potential endogeney of the entry timing decision. Since entry timing is self a fixed effect, existing methods in the marketing lerature to control for unobserved factors cannot be applied. Thus, we utilize the instrumental variable approach developed by Hausman and Taylor (1981), which allows us to control for unobserved fixed effects while providing a consistent estimate of the entry timing effect. Surprisingly, our results for a broad sample of consumer goods business uns from the PIMS database indicate that pioneers have a long-term prof disadvantage relative to followers. In a more detailed analysis we replicate the typical demand-side pioneering advantage. Hence, in this respect our results are consistent wh the existing lerature. However, we find that pioneering leads to an even greater average cost disadvantage. We confirm the robustness of the profabily result by varying the profabily measure and the functional form of our empirical model and extending to a sample of firms selling industrial goods. In addion to the profabily results we find that in all cases the assumption that entry timing is exogenous is rejected, thereby supporting the theoretical argument that this decision should be treated as endogenous. These results hold for the average pioneering firm. To gain further insights about the effect of the entry timing decision on firm profabily, we examine how the prof implications change over time. This analysis shows that pioneers obtain an inial prof advantage relative to later entrants that declines over time and turns into a disadvantage after about 10 years for firms in our

4 sample of consumer businesses and about1 years for firms in our sample of industrial businesses. We then use these parameter estimates to approximate the lifetime prof stream accruing to pioneers relative to followers and calculate the break-even discount rate that sets the present value of the prof stream to zero. Though only an approximation, these results are compatible wh a null effect of entry timing, per se, on lifetime profabily. We also explore three different factors likelihood of consumer learning, market share posion and patent protection that we hypothesize should moderate the average entry timing result. This condional analysis shows that pioneering leads to a sustained prof advantage in the consumer goods sample when (i) consumer learning is limed, (ii) the pioneering firm maintains a dominant market share posion, and (iii) the pioneering firm continues to be protected by product patents. In the industrial goods sample, we find that pioneering leads to a sustained prof advantage when the pioneering firm continues to hold a process patent. Moreover, when customer learning is limed, the disadvantage disappears. We conclude that firms should not rely on entry timing, per se, as a source of a sustainable prof advantage. Rather, they need to articulate and evaluate why and how being first to market will lead to a sustainable advantage. Key words: Pioneering Advantage, Profabily, IV-Estimation, PIMS.

5 1. Introduction PepsiCo Inc. Chairman Roger Enrico learned a humbling lesson in 1983 about consumer marketing: Being first matters. [ ] Mr. Enrico balked at demands from the owners of NutraSweet to buy their newly approved low-calorie sweetener, aspartame, to use in Diet Pepsi. Coca-Cola Co. beat Pepsi to the punch. I vowed then that I would never hang back like that again, Mr. Enrico said [ ]. Sure enough, there has been no hanging back this time around. On June 30, barely one hour after the U.S. government gave s long-awaed approval for another sweetener, acesulfame potassium, Pepsi announced was introducing a new one-calorie drink, called Pepsi One, that would use a blend of the new sweetener and aspartame. Wall Street Journal, October 6, 1998 As this example suggests, both in belief and action, first to market can be a strongly held strategic principle by practioners. From entry into emerging markets such as China to the rush into e-business, this principle is often high on firms list of arguments to justify their strategic moves. In addion, the potentially high stakes accruing to the entry timing decision has attracted the interest of researchers in marketing, strategy and economics. Much of this academic research supports the veracy of the practioners principle. For example, at the business un and brand level, a strong inverse relationship between order of entry and long-run market share has been found (e.g., Robinson and Fornell 1985; Urban et al. 1986). In fact, evidence supporting this inverse relationship is so extensive that now exists as an empirical generalization in the marketing lerature (Kalyanaram et al. 1995). 1 At the consumer level, experiments have shown that the order of entry can have a significant impact on customer 1 The Kalyanaram et al. (1995) paper even suggests that the one significant piece of evidence against a pioneering market share advantage (Golder and Tellis 1993) can be reinterpreted to show that surviving pioneers obtain a market share advantage. 1

6 preferences, memory and judgment (e.g., Carpenter and Nakamoto 1989; Kardes and Kalyanaram 199). The bulk of the empirical work in this area focuses on demand-side consequences of the entry timing decision, e.g., sales or market share. Interestingly, most cricism of existing empirical research centers around methodological issues, i.e., including only surviving firms or using self-reports to measure entry timing (Golder and Tellis 1993). An addional issue to consider is whether a market share advantage is sufficient to support the existence of a firstmover advantage given the uncertainties in both the market share-prof relationship (e.g., Jacobson 1988; Boulding and Staelin 1993) and the entry timing-cost relationship. In fact, reviews of the entry timing lerature have repeatedly pointed to prof implications as one of the key unanswered questions in this area of research (e.g., Lieberman and Montgomery 1988 and 1998; Kerin et al. 199; Robinson et al. 1994). The main objective of this paper is to address this gap in the lerature and empirically examine the prof implications of the entry timing decision. More specifically, we want to know whether pioneering, per se, provides a long-term, sustainable prof advantage. For example, a pioneering prof advantage would suggest that part of today s profabily difference between Coca-Cola and Pepsi-Cola could be attributed to the fact that Coca-Cola entered the cola market ten years earlier than Pepsi-Cola. Thus, we are looking at long-term prof differences between pioneers and followers attributable to the entry timing decision and not differences due to other characteristics (e.g., resources) of pioneers and followers. We believe this distinction is important in the event that managers believe that choice of entry timing, in and of self, can lead to sustained prof differences in a market. After addressing this fundamental question, we then ask whether the time since entry and different firm/market condions moderate the relationship between pioneering and profabily. We call particular attention to our analysis that examines the moderating effects of time since entry. This analysis allows us to determine the time path of Unpublished work (Boulding and Moore 1987; Srinivasan 1988) suggests that pioneering does not provide a prof advantage.

7 profs that accrue to the entry timing decision and some insight into the question of lifetime profabily of pioneers relative to followers. Finally, in addion to the overall prof impact we determine the effect of pioneering on s underlying economic components, i.e., demand and supply. We note that to determine the performance difference between pioneers and followers strictly attributable to the order of entry decision, is necessary to rule out other explanations. Thus, we test and control for the potential endogeney of the entry timing decision, i.e., that entry timing is a function of factors that also influence firm performance. We note that the endogeney of entry timing has been ced as one of the key unresolved issues in empirical entry timing research (Lieberman and Montgomery 1998). As first noted by Lieberman and Montgomery (1988), firms resources and capabilies in all likelihood affect their choice of entry timing. For example, Sony generally tries to enter markets first, while Matsusha pursues a strategy of following Sony. Therefore, Sony and Matsusha likely differ in ways that reflect the difference in their entry timing strategies. However, these differences may also affect their overall profabily. In other words, if these same resources and capabilies that influence the decision to enter first or later have a subsequent direct effect on other results like market share, average cost, or prof, the obtained estimates of pioneering effects will be biased. More formally, the resource-based-view lerature (e.g., Wernerfelt 1984; Barney 1986; Dierickx and Cool 1989) suggests that an enduring competive advantage must be due to differences in underlying resources. This lerature is explic in stating that there can be no first-mover advantage whout heterogeney in resources across firms (Barney 1991). Rather, the resourcebased view of the firm suggests that there should be no effect on profabily due to entry timing after controlling for resource differences across firms. Since our goal is to focus on the financial returns to the entry timing decision, per se, the conceptual issue of endogenous entry timing becomes the classic empirical issue of controlling for unobserved firm differences (e.g., see Jacobson 1990). Unfortunately, existing methods in the marketing lerature that control for unobserved fixed effects (e.g., Boulding and Staelin 3

8 1995) do not work when the effect of theoretical interest is self fixed over time, e.g., the entry timing decision. Thus, we utilize the instrumental variable (IV) approach due to Hausman and Taylor (1981) that enables one to control for unobserved fixed effects while obtaining consistent estimates of fixed effects of theoretical interest (i.e., entry timing). Moreover, this procedure allows us to explicly test both whether entry timing is endogenous and whether the obtained IV estimates are themselves known to be free from bias due to omted fixed factors. In sum, this paper makes several addions to the entry timing lerature. First, we determine whether pioneering leads to a sustainable prof advantage for the average business un in our sample. Second, in addion to the well-known demand-side (e.g., market share) effects of entry timing, we also explore the supply-side (average cost) effects. Third, we examine how the prof impact of the entry timing choice changes over time and what this implies wh respect to the lifetime profabily of the entry timing decision. Fourth, we explore market condions that we believe will alter the average effect of pioneering. Finally, we use an estimation procedure that simultaneously addresses the endogeney of the entry timing decision and provides assurance that the obtained estimates are consistent. The remainder of the paper is organized as follows. First, we briefly summarize the extensive lerature on entry timing and develop three scenarios for the prof implications of the entry timing decision. We then formally specify equations that allow us to examine these implications as well as the effect of entry timing on demand and cost. Next, we describe our data and the particular measures that enable estimation of our model. This is followed by a discussion of different estimation issues, wh particular focus on the Hausman-Taylor (hereafter referred to as HT) estimation procedure. In the results section we first present results for the average pioneering firms, including analysis that checks the robustness of our findings across various model specifications. We then extend the analysis to different industry settings and analyze condions that moderate the observed average effect of entry timing on profabily, including the effect of time. We conclude by discussing the implications of our findings for managers and academics. 4

9 . Pioneering Advantages and Disadvantages Are there reasons to believe that pioneering leads to a sustainable prof advantage? In accordance wh economic theory any prof advantage must be reflected in the demand function and/or supply function of a pioneering business. There is an extensive list of theoretical arguments suggesting that demand-side advantages accrue to the pioneering firm. For example, arguments for an advantageous effect of pioneering include the risk associated wh swching (Schmalensee 198); the influence on consumer preference structures (Carpenter and Nakamoto 1989) and consumer learning (Kardes and Kalyanaram 199); the probabily of inclusion in consumer s consideration set (Hauser and Wernerfelt 1990); status quo bias that favors the incumbent (Samuelson and Zeckhauser 1988); and the abily to get more out of a product due to consumer knowledge from past usage (Ratchford 000). In addion to these arguments, there are tradional entry-barrier explanations, which are summarized by Lieberman and Montgomery (1988), e.g., buyer swching costs. Empirical evidence generally supports these theoretical arguments and suggests that the average pioneering firm obtains a demand advantage, typically in the form of higher market share or sales (e.g., Robinson and Fornell 1985; Robinson 1988; Robinson et al. 1994; Urban et al. 1986). In contrast, Golder and Tellis (1993) question this finding and argue that this demand advantage is upward biased due to exclusion of failing pioneers and misclassification of surviving firms as pioneers after the true pioneer has failed. However, a meta-analysis by VanderWerf and Mahon (1997) did not find statistically significant evidence for a survivor bias. There have been other cricisms of the pioneering demand advantage finding on methodological grounds. For example, Moore et al. (1991) questions the demand-side findings because most existing studies fail to control for the possibily of endogeney in the entry timing decision. In support of this claim, Robinson et al. (199) show that pioneers and followers exhib systematically different skills and resources. Still, is important to note that some of the first 5

10 mover advantage findings are based on experimental procedures free from these methodological problems (e.g., Carpenter and Nakamoto 1989). Thus, at a minimum, available evidence indicates that surviving pioneers obtain a demand-premium that is directly due to the timing of entry. This conclusion is the basis for the conventional wisdom that being first matters. In contrast, theoretical arguments about the effect of order of entry on cost do not lead to unambiguous conclusions. Arguments in favor of a pioneering cost advantage must be weighed against potential disadvantages. On the cost advantage side, is typically suggested that pioneering firms gain a cost advantage via learning and experience effects, preemption of scarce inputs, and technology leadership, e.g., patents. For an overview of these arguments we refer the reader to the review articles by Kerin et al. (199) and Lieberman and Montgomery (1988). In addion, buyer swching costs, i.e., a demand-side advantage, could imply that, at the margin, marketing activies would be less effective for the following firms. Thus, for example, advertising costs per customer should be lower for the pioneering firm than later entrants. A fair amount of empirical evidence is consistent wh the notion of a pioneering advertising advantage (e.g., Bowman and Gatignon 1996; Buzzell and Farris 1977; Comanor and Wilson 1974; Fornell et al. 1985). In the short run, the size of this advantage could be migated if followers are able to free ride on the pioneer s buyer education activies. However, in the long run, if buyer education is no longer an issue in conjunction wh a persistent pioneering demand advantage, we may observe a pioneering advertising cost advantage. 3 Against these advantages for pioneering firms work a number of forces that provide an advantage to followers. Lieberman and Montgomery (1988) articulate four sources of pioneering disadvantage: (1) abily to free-ride on first-mover investments, () resolution of technological and market uncertainty, (3) technological discontinuies that provide gateways for new entry, and (4) various types of incumbent inertia that make difficult for the incumbent to adapt to environmental change (p. 47). In particular, followers abily to free ride on the information produced by pioneers may more than offset possible cost advantages through 3 In fact, though not reported herein, when we examine advertising costs we find a pioneering cost advantage. 6

11 patents, accumulation of experience, preemption of scarce resources or buyer swching costs. Theoretical work shows that when demand is uncertain and later entrants can learn the private information of incumbents from observed actions, they obtain higher profs (Gal-Or 1987; Shinkai 000). In addion, changes in the market place offer potential cost advantages to followers. For example, empirical work by Bevan (1974) and Yip (198) suggests that technological discontinuies allow gateways to entry for followers. A particular gateway to entry is that more recent production technology has greater efficiency. Whin the economics lerature these production technology discontinuies are called vintage effects (Intrilligator 199; McLean and Riordan 1989). They result in lower costs for the later entrant. Recent theoretical work in marketing (Bohlmann 1997) suggests that under certain condions the cost discontinuy associated wh vintage effects can lead to a follower advantage. Why would change favor a potential new entrant over an incumbent? One powerful reason is incumbent inertia. Incumbent inertia can be both rational and irrational. The case for rational inertia is as follows. Firms make investments in specific production and delivery assets. Thus, swching costs faced by the pioneer (e.g., disposal of existing processes) may make adoption of new practices less attractive than such practices would be for a later entrant. In particular, under condions wh fixed assets in place (i.e., the pioneer) incremental changes often look more attractive than wholesale changes. Therefore, the firm s existing production assets may make optimal for a firm to continue wh less efficient manufacturing and delivery systems. Tang (1988) presents a formal model of rational inertia in the setting of the U.S. steel industry, where steel producers continued to invest in a demonstrably less efficient furnace technology. McMillan (1983) makes similar arguments in the setting of the health care industry. In addion to rational inertia, firms can fail to adapt when, from an economic perspective, adaptation is appropriate. In particular, we point to the likelihood of stickiness in organizational practices and structures. Hannan and Freeman (1984) formally outline factors that lead to incumbent inertia. These factors include development of standard operating procedures and 7

12 internal polical considerations that may cause firms to fail to recognize the need for adaptation. Moreover, even if firms recognize the need for change, inertia can interfere wh implementation of change. For example, is well known that organizations and their employees are highly resistant to change (e.g., Strebel 1996). In support of this statement, Cooper and Schendel (1976) examined 15 incumbents commted to adoption of new technologies. Of these, only two were successful in implementing the intended change, the other 13 were derailed by organizational inertia. Overall, theoretical arguments regarding the effects of entry timing on cost are mixed, and empirical work on these effects is limed. In an illustrative study consisting of 48 innovative chemical, electrical, and pharmaceutical products, Mansfield et al. (1981) found that imating firms were able to match patented innovations, at a rate of 65% of the innovator s cost. This study did not differentiate between early and late entrants but suggests that even patents provide ltle protection that can yield a cost advantage. While one cannot make definive theoretical or empirical claims about the effect of entry timing on cost, the existing lerature certainly allows for the possibily of a pioneering cost disadvantage. Given this ambiguy in the effects of entry timing on cost, the effect of entry timing on overall profabily is also unclear. However, we offer three different scenarios that yield different predictions about the overall prof implications of the entry timing decision. In the first scenario we adopt an economic perspective, which argues for no prof differences due to the entry timing decision. The premise under this scenario is that pioneering advantages are dissipated due to imabily. As argued elsewhere in the strategy lerature (e.g., Wensley 198; Erickson and Jacobson 199), if knowledge about a strategic relationship exists, for example, first entrants are more profable, then whout what Wensley refers to as isolating mechanisms, firms will compete away the returns implied by this relationship. Thus, for example, knowledge about a demand-side pioneering advantage could lead to a race to entry that competes away this advantage through cost disadvantages. We note that this overall prediction is que similar in spir to the resource-based view. Unless firms have inimable resources, this 8

13 theory suggests that there can be no sustainable returns to the entry strategy, per se (Barney 1991). Thus, after controlling for resource differences across firms (as we do in our empirical analysis), both perspectives predict a null effect of entry timing on overall firm profabily. In the second scenario, one would combine a pioneering demand advantage wh a supply side (cost) advantage, or simply a demand advantage that outweighs any cost disadvantage. This combination leads to an unambiguous prediction of a pioneering prof advantage. This scenario may occur if entry timing is due to random events rather than deliberate planning, thus precluding competing away the advantage. Such a scenario is consistent wh Alchian s (1950) argument that innovation is due to luck. Importantly, however, for this scenario to hold, part of the luck would have to be inimable. Further, we note that the luck argument also has strong implications for whether entry timing is endogenous. If innovation results from luck, then the entry decision would be a random event and therefore exogenously determined--in contrast to theoretical arguments that support the view that entry timing is endogenous. Of course, whether the entry decision is endogenous or exogenous is a testable proposion. In the third scenario, one would combine a pioneering demand advantage wh a cost disadvantage of greater magnude. Under this scenario pioneering leads to an overall prof disadvantage. For this to happen, firms would have to consistently over-estimate the demand advantage, or under-estimate the cost disadvantage, accruing to a first-mover strategy. In sum, from the existing lerature follow two strong predictions, (1) the entry timing decision is endogenous and () there exists a pioneering demand advantage. In contrast, there are valid arguments both for and against a pioneering cost and prof advantage. 3. Model We now specify a model that allows us to test the effects of entry timing on firm performance. We start wh the following prof function: 9

14 (1) NI exp[ β + γ Pion + X β + α + η ] =, where 01 i1 i (1a) γ i1 = γ Pi + γ1 and η 1 = ρ 1 η ω 1. Estimation of this equation allows us to address the overall prof implications of entry timing and whether entry timing is endogenous. In addion, we decompose the entry timing effects into demand and supply side components. Because prices, costs and quanties are jointly determined in equilibrium, we specify a system of two simultaneous equations consisting of the following inverse demand function: () P Q exp[ β + γ Pion + X β + α + η ] = δ, where (a) γ i = γ Di + γ and η = ρ η -1 + ω, and the following average cost function: 0 3 (3) AC Q exp[ β + γ Pion + X β + α + η ] 03 i i3 i = δ, where (3a) γ i3 = γ Ci + γ 3 and η 3 = ρ 3 η ω 3. The elements of the different equations are defined as follows: P, AC, NI = firm i s average price, average cost and net income, respectively, in year t, Q = quanty sold in uns by firm i in year t, Pion i = pioneering indicator variable, where Pion i = 1 if a pioneer, otherwise Pion i = 0, X j = a vector of other factors in equation j, β j, γ ij, γ Di, γ Ci, γ Pi, γ j, δ j = model parameters, α ji = unobserved fixed factors, and η j = unobserved random factors, which consists of a first-order autoregressive component wh parameter ρ j and a random component, ω j. There are several points worth noting about equations (1) to (3). First, the parameter γ 1 is the parameter of interest wh respect to the prof implications of entry timing. This parameter captures the average, i.e., generalizable, effect of pioneering on prof. The parameter of interest wh respect to demand considerations is γ, which captures the average firm effect of pioneering i i i 3i

15 on price (demand). The parameter γ 3 is the parameter of interest wh respect to the cost implications of entry timing. Second, this specification acknowledges heterogeney in the effects of pioneering across firms, i.e., we include the firm specific parameter γ Pi, γ Di, and γ Ci, respectively. As we later discuss (and provided technical details in Appendix B), we do not estimate these firm specific effects of pioneering, but control for their possible biasing effects on our estimate of the average effect. Related to this point, to obtain a consistent estimate of the average effect in our empirical analysis we want to ensure that the pioneering variables are independent of any empirical error term. Third, simultaneous estimation of the demand and cost equations allows us to control for movement along a firm s demand curve that may occur due to underlying changes in supply (i.e., cost) and movements along a firm s average cost curve that may occur due to underlying changes in demand. Thus, quanty is considered an endogenous variable in this specification and the factors X and X 3 are such that equations () and (3) are uniquely identified. Fourth, the functional forms of the equations are que specific. Although they are consistent wh earlier empirical work (e.g., Boulding and Staelin 1993), we later relax the assumed functional form. Moreover, we examine whether the prof results are robust to the prof measure employed in equation (1) by exploring whether the use of other measures of profabily changes the substantive insights. 4. Data In this research we use PIMS data on business uns to examine the prof implications of entry timing. The PIMS database has received much cricism (e.g., Anderson and Paine 1978; Ramanujam and Venkatraman 1984). However, work suggests that many of s perceived limations can be eliminated when taking advantage of the panel nature of PIMS data (e.g., Boulding and Staelin 1995). Moreover, PIMS provides the most diverse sample of industry data 11

16 available in the public domain, wh detailed descriptions of company, customer, competor, and market factors. Since we are interested in the long-term effects of pioneering, we lim our attention to business uns that compete in mature and declining markets. Also, because previous research indicates that the effect of pioneering differs in consumer and industrial markets (Robinson 1988), we inially restrict our analyses to durable and non-durable consumer products. The final estimation sample contains data for 370 different business uns wh 1,118 observations that can be used for estimation after data transformations. Later, we broaden our analysis to firms selling industrial goods. In choosing this sample we note an addional benef and a potential drawback from our use of PIMS data. Specifically, our data are consistent, other than the longudinal aspect, wh data used in previous empirical work (e.g., Robinson and Fornell 1985; Moore et al. 1991) that examines the effects of pioneering on firm performance. On the other hand, Golder and Tellis (1993) show that PIMS analyses yield results substantially biased in support of a pioneering advantage. This is because PIMS only includes data on surviving pioneers and because firms classified as pioneers in PIMS could in fact be misclassified early followers. Thus, we caution the reader that the results obtained herein are potentially biased in favor of a pioneering performance advantage. Several variables relevant to our specified models are measured and reported in PIMS, but are disguised. This requires first transforming the disguised data to obtain consistent estimates. The log-first difference transformation to make these data usable suggested in Moore and Boulding (1987) and employed by Boulding and Staelin (1993) does not work, since pioneering self is a fixed effect and would drop out in the differencing transformation. In the following paragraphs we describe the dependent and independent PIMS measures used and discuss the potential impact of the disguised variables on the estimation results. 1

17 Dependent Variables All of the dependent measures in our equations (1) to (3) are disguised. First, the dependent measure for profs is derived from the net income index, NI, available in PIMS. After linearizing equation (1) by taking logs the net income measure becomes (4) log NI log NI + log K i where the nuisance term logk i (the firm specific, time invariant disguise factor) becomes a part of the fixed error term. However, equation (1) presents another estimation issue because net income can take on negative values. This requires us to transform the net income measure before we can linearize equation (1) by taking logs. We do this via the ζ-transformation introduced by Cooper and Nakanishi (1983). This transformation consists of two steps. We first standardize the net income measure whin each business un and then apply the ζ-transformation to these standardized z-values, i.e., NI NI i K i ( NI NIi ) (5) z = = and σ K σ NIIi i NI i 1 1. (5a) ζ ( z ) ζ ( z ) = 1+ if z 0 and = 1+ if z 0 This transformation eliminates all negative net income values by mapping them into the 0-1 range. It also eliminates the disguise factor, K i, as can be seen in (5). Thus, we substute ζ for net income and take logs to linearize equation (1). Second, PIMS does not contain a direct measure for price, P. However, a price index, P, is available that satisfies the identy P P /P Bi, where P Bi is a firm-specific, time invariant, unknown base price. After linearizing by taking the log, the actual dependent price measure used to estimate () is (6) log P log P log PBi, implying that the nuisance term, logp Bi, is added to the fixed error term. 13

18 Finally, PIMS reports no direct information on average cost AC. However, in a manner similar to the derivation of the price index measure, one can calculate an average cost index, AC (see Appendix A for s derivation). After linearizing equation (3) by taking logs, the dependent average cost measure used in estimation is (7) log AC log AC log PBi again implying that the nuisance term, logp Bi, is added to the fixed error term. Fortunately, the potentially biasing effect of this nuisance term logp Bi in (6) and (7) is controlled for by the Hausman and Taylor (1981) estimation procedure. Therefore, we can estimate the inverse demand and average cost functions whout bias due to measurement error in the dependent variable. Independent Variables Pioneering is the independent variable of central interest. PIMS provides a measure that defines pioneers as those business uns that were one of the pioneers in their categories at the time of entry. 4 For business uns that fall into this category, we set the pioneering variable, Pion i, to 1, for all other business uns to 0. We have no theoretical interest in the quanty variable, but is important because controls for movements along the demand and average cost curves. Quanty is not directly available in the PIMS database, which necessates use of a quanty index measure for estimation purposes (see Appendix A). Again, the potentially biasing effect of the nuisance term that is added to the fixed error component is controlled for by the HT-estimation procedure. The other independent variables, described as X j, j = 1...3, in equations (1) to (3), consist of variables included for control purposes and those included mainly for identification purposes. The former set of variables contains a business un s market posion (MP), competive environment (CE), product qualy (PQ), and the log of years since entry (LYSE). The last variable is of particular interest since controls for possible learning curve effects due to time in 4 The actual PIMS measure is described more fully in Robinson and Fornell (1985). 14

19 market rather than pioneering, per se. Market posion, MP, represents internal firm effects and is measured by a business un s market share relative to s three largest competors. This variable is transformed such that the standard deviation equals 1 and the minimum (worst possible market posion) equals zero. The measure of competive environment, CE, represents firm external effects and builds on Porter s (1980) framework for assessing the competive structure of an industry. For components of this construct and a description of the exact definion we refer the reader to Boulding and Staelin (1993, 1995). The scaling is such that zero represents the most intense competive environment found in the PIMS database. For identification purposes, we also utilize the following four relative cost (intensy) variables, measured as a percent of revenues: purchase (supply) intensy (SI), production intensy (PI), R&D intensy (RI) and marketing intensy (MI). 5 These variables help us ensure that the models are over-identified to apply the HT-specification test. These ratio variables are likely independent of any fixed firm factors, i.e., the fixed firm components cancel out in the numerator and denominator. Importantly, this is a testable conjecture. In addion, we include Market Growth (MG) and the fraction of New Product Sales (NP) only in X in the demand equation () and Capacy Utilization (CU) and Employee Productivy (EP) only in X 3 in the average cost equation (3) to ensure that the two equations are uniquely identified. The exact specification of each model, including the final choice of instruments, is shown in Appendix C. Finally, all nominal dollar amounts are transformed to constant dollar amounts by use of a GNP deflator. 5. Estimation Testing the effects of entry timing on firm performance requires an estimation method that controls for different kinds of error and still yields estimates of all model parameters. As noted, of particular importance is to ensure that estimates are free from bias due to potential endogeney of the entry timing decision. Specifically we need to control for unobserved factors, 5 We do not include these variables in the average cost equation (3). 15

20 e.g., differing skill and resource profiles of pioneers and later entrants that correlate wh the entry timing decision. Existing estimates of the effect of entry timing on performance that control for firm differences in resources and skills yield results that can only be considered unbiased by assumption. For example, the estimation strategy to measure firm resources and skills and include them in the entry-performance equation (e.g., Robinson et al. 199; Murthi et al. 1996) must assume that all relevant firm skill and resource variables are included. Similarly, the instrumental variable approach by Moore et al. (1991) is based on cross-sectional data and thus must make an assumption that the instruments themselves are free of the unobserved fixed effects. Because of such limations, Hausman and Taylor (1981) developed an instrumental variable (IV) procedure that enables consistent estimation of the effects of time-fixed variables while controlling for unobserved fixed factors. A key feature of this procedure is that allows the researcher to test the validy of the chosen instruments. The estimation procedure relies on the assumption that a subset of time varying variables, X j, is uncorrelated wh the fixed, unobserved factor, α ji, in equation j = 1 3. These variables can serve two functions because of their variation across firms and over time: (i) the difference from individual firm means provides instruments to obtain consistent estimates of β j and δ j ; and (ii) the individual firm means provide instruments for the pioneering variable (and other fixed variables). The former use of these variables is always valid and is equivalent to fixed-effect estimation. In other words, consistent estimates for β j and δ j are always available, which provides a benchmark for the HT-specification test. Large differences from these benchmark estimates indicate that the set of included firm means is not independent of the fixed error term. By adding the pioneering variable to the set of firm means, is treated as exogenous and the specification test allows for a direct test of the exogeney of the entry timing decision. Before the HT-estimation can be applied, is necessary to remove the potentially biasing effects of contemporaneous shocks and serial correlation even though these unobserved factors cannot, or are unlikely to, influence the entry decision made some years in the past in addion to 16

21 current performance. However, the HT-specification test requires consistent estimates of all effects. 6 This is achieved by first using instruments for the time-varying variables that are lagged two periods to control for omted contemporaneous effects; and then rho-differencing the data to control for omted autoregressive effects. This results in an elaborate estimation procedure, which is described in detail in Appendix B. This estimation procedure is applied to all our models. Although the resulting estimation procedure is highly calculation intensive, the cost of estimation is far outweighed by the benef of being able to test whether the selected instruments are themselves free of the fixed effect. That is, all assumptions about candidates for instruments are tested. Moreover, the HT-IV procedure has a secondary benef. Measurement error in the quanty and pioneering variables could potentially lead to bias in their estimates. Instrumental variable (IV) estimation is a classic solution to such an errors-in-variables problem. 6. Profabily Implications of Entry Timing Table 1 presents the summary results of interest for our sample of consumer goods business uns for the three equations specified above. It also includes the results from the model in which ROI is used instead of net income as the profabily measure. Complete estimation results for these four equations including the selected instruments for pioneering are provided in Appendix C. [Insert Table 1 Here] Before turning to the estimates of substantive interest, we begin by assessing the appropriate estimation treatment of the pioneering effect. The results of the HT-specification tests used to test the exogeney of the entry timing decision are shown in columns and 4 of Table 1. Column shows the test results for the endogenous specification of the entry variable. These results indicate that we cannot reject the suabily of the instruments used for these estimates, i.e., the test indicates that the instruments selected are themselves free of the fixed effect (see Appendix C for instruments). Thus, we can conclude that none of the presented results in column 1, i.e., where entry is considered endogenous, are biased due to omted 6 We would like to thank the reviewers for alerting us to this problem. 17

22 unobserved factors. Column 3 presents the estimates based on the assumption of exogenous (wh respect to fixed factors) entry timing, and column 4 presents the direct test of this assumption. The column 4 results indicate that the assumption of exogeney in entry timing is rejected wh a high level of significance in all four equations. This implies that all the column 3 estimates are known to suffer from bias. Therefore, we do not discuss these estimates any further, other than to note that they deviate from the consistent estimates in column 1. In addion, since our results strongly support the endogeney of entry timing, we reject the conjecture presented in scenario two that entry timing is largely due to luck and therefore a random event. To examine our pioneering effects of substantive interest, we now turn attention to the results reported in column 1 of Table 1. The estimated pioneering effect on net income is negative and significant ( ˆγ 1 = -46.5, p = 0.048). In other words, we find a significant long-term pioneering disadvantage. Because of the importance of our profabily finding, we checked the sensivy of this result to the particular choice of prof variable. Since ROI is frequently used as a measure of firm performance in marketing studies we report the effect of entry timing on ROI as well. As shown in Table 1, we find that the effect of pioneering on ROI is also negative and marginally significant ( ˆγ 1 = -44., p = 0.088). Finally, though not reported in Table 1, we examine whether our results are an artifact of prof measures that include fixed costs. In particular, one could argue that pioneering effects are more appropriately examined by testing the influence of entry timing on margins. Thus, we examine yet a third measure of profabily, value added. This measure is formed by taking the ratio of price minus un costs divided by price. When using this measure we again find a significant negative effect of pioneering ( ˆγ 1 = , p =. 07). 7 Given this provocative prof result, we next examine the underlying demand and supply effects of pioneering for our sample of consumer goods firms. The effect of pioneering in the 7 Significance levels for the effects of entry timing in this section are based on two-tailed tests. 18

23 inverse demand function () is posive and highly significant ( ˆγ = 6.5, p = 0.00). Therefore, our results replicate, whin the framework of a formal demand model, the well-established finding that pioneers obtain a sustainable consumer-based demand advantage. Consistent wh this demand result and the overall profabily results, the effect of pioneering in the average cost equation is also posive and highly significant, and exceeds the demand-side advantage by a significant margin ( ˆγ 3 = 3.1, p = 0.00). The estimates of all other variables included in our empirical model (see Appendix C) are not of substantive interest. However, we note that these parameter estimates typically have the expected sign and are consistent wh previously reported results. For example, we note that our inverse demand function is downward-sloping and time (i.e., experience) has a negative effect on average cost. Both a stronger market posion and a softer competive environment tend to increase a firm s profabily. Given the extensive controls in the estimation procedure, the relatively high number of insignificant estimates is not surprising. It is also worth noting from Appendix C that the estimate of the AR(1) coefficient, ρ, is small and not significant in the demand equation (ρˆ = 0.03) and average cost equation ( ρˆ 3 = 0.06). This is consistent wh previous findings in similar studies (Boulding and Staelin 1993; 1995). In the net income and ROI equations, the estimate of ρ 1 varies between 0.3 and 0.4 and is always highly significant. These estimates indicate a significant dynamic effect in the profabily data series. Finally, we note that the set of instruments is fairly consistent across equations. In particular, production intensy (PI), R&D intensy (RI) and the competive environment (CE) are generally found to be independent of the empirical fixed error term. Given these baseline estimates we further examine the sensivy of the findings to our assumptions by using different functional forms for equations (1) to (3). Specifically, we estimated an exponential model wh quanty in the exponent, a linear model, and a multiplicative model for all four equations reported in Table 1. This provided 1 addional estimates of the effects of pioneering. The results, which we do not report in detail, indicate that the findings are robust to the selection of functional form. In particular, the 1 addional 19

24 estimates of the effects of pioneering all exhib the same sign as the original four effects reported in Table 1. Moreover, 10 of these 1 estimates reach significance at the 0.1 level or better. In addion, we find that pioneering is endogenous in all 1 addional models. We also investigated the sensivy of the pioneering estimates to the exact model specification (included variables) along wh differing functional forms. We again find the same pattern of average effects for our sample of consumer goods business uns: (1) pioneering leads to a long-term demand-side advantage and a cost-disadvantage, () pioneering leads to a long-term prof disadvantage and (3) pioneering is endogenous. We interpret these results as providing strong evidence for the robustness of our findings about the average effect of entry timing on firm performance. 7. Extended Analysis Given the somewhat surprising, and robust, result that, on average, pioneering has an overall negative impact on long-term profabily for the firms in our sample of consumer goods businesses, we now extend our analysis in three ways. First, we examine whether the basic profabily result holds for a different industry setting, i.e., firms selling industrial goods. Second, for both consumer and industrial goods firms we examine how the prof implications of the entry timing decision evolve over time. Third, we explore three different firm and market condions that may moderate the prof implications of the entry timing decision. Entry Timing Profabily for Industrial Goods. To examine whether the prof implications of entry timing generalize to other industry settings, we use a sample of PIMS firms selling industrial goods consisting of,788 observations from 874 business uns. The results, reported in Table, suggest that the effect of pioneering on firm prof for the industrial goods sample is identical to those for the consumer goods sample. Column 1 in Table again provides the consistent estimates of the pioneering effect on prof (detailed estimation results are reported in Appendix C). Whether we use net income ( ˆγ 1 = -67.7, p = 0.038) or ROI ( ˆγ 1 = -57.8, p = 0.055), the effect of pioneering on long-term profabily is negative and significant. 0