Here Today But Gone Tomorrow: The Dissipating Advantage of Pre-Entry Experience in a Technologically Dynamic Industry∗ Rajshree Agarwal College of Business University of Illinois at Urbana-Champaign 350 Wohlers Hall, 1206 S. Sixth Street Champaign, IL 61822 Voice: (217) 265-5513 Fax: (217) 244-7969
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Barry L. Bayus Kenan-Flagler Business School University of North Carolina CB 3490 Chapel Hill, NC 27599 Voice: (919) 962-3210 Fax: (919) 962-7186
[email protected]
Abstract We investigate the role that entrepreneurial flexibility vs. pre-entry experience plays in determining the post-entry performance of firms that enter a new market. We hypothesize that in markets characterized by intense technological change, the organizational flexibility possessed by entrepreneurial denovo entrants helps them overcome their initial experience based disadvantage as the industry evolves over time. Using a comprehensive database for the personal computer industry, we find support for dissipating effects of pre-entry experience over time. This indicates that over the industry life cycle, organizations that possess entrepreneurial flexibility have higher survival rates and market share than firms with prior experience. Consistent with the notion that denovo firms are better able to keep pace with technological change, we find that the denovo firms that enter later in the industry life cycle are able to overcome their initial disadvantage by introducing more new products than their experienced competitors. Key words: entrepreneurial flexibility, diversifying entrants, industry evolution, technological change
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We appreciate comments by Raj Echambadi, Glenn Hoetker, Steven Klepper, MB Sarkar, Anju Seth, Charles Williams and participants at the seminars given at Purdue University and University of Illinois at Urbana Champaign. All remaining errors are ours.
Entrepreneurial entry by firms that follows immediately after the first commercialization of a new technology is credited with forces of “creative destruction” in the classic Schumpeterian (1934) tradition. These entrants vary in their resources and capabilities, and in particular, are either denovo start-ups or firms diversifying from other industries (Helfat and Lieberman, 2002). Entrepreneurial start-ups possess organizational flexibility (Hannan and Freeman, 1984), and one research stream has noted its importance for success in a turbulent environment associated with radical innovation and technological change (Christensen, 1993; Henderson and Clark, 1990; Tripsas, 1997; Tushman and Anderson, 1986). At the same time, another research stream has found prior experience in other industries and access to related resources and knowledge to be crucial for sustained performance advantages (Carroll, Bigelow, Siedal and Tsai, 1996; Klepper and Simons, 2000; Mitchell, 1991). Given the importance of both the organizational flexibility possessed by entrepreneurial start-ups and the prior experience and access to resources enjoyed by firms currently in existence, are there conditioning factors that determine which of the two is more relevant as a criterion for successful performance? We focus on industry evolution to be one such conditioning factor. The environment in which firms compete evolves after the initial introduction of a new product and is an important determinant of success. Rich bodies of literature in technology management (Tushman and Anderson, 1986; Utterback and Abernathy, 1975), organizational ecology (Carroll and Hannan, 1989) and industrial economics (Gort and Klepper, 1982; Nelson and Winter, 1982) have focused on the ensuing product life-cycles. Not only do industry life cycles have a direct effect on firm performance, the impact of key environmental, organizational and strategic variables of firm performance may change over different stages of the industry life cycle (Agarwal, Sarkar and Echambadi, 2002).
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In this paper, we investigate the conditioning role of industry evolution on the pre-entry experience—performance relationship. By linking strategic implications of heterogeneous entrant capabilities to the evolutionary models of competition, we build and test a dynamic model of inter-firm competition. We first develops hypotheses relating the impact of different types of pre-entry experience on key measures of firm performance, namely firm survival and market share, and how this relationship is affected by the industry evolution. Moreover, by focusing on a third performance measure—new product offerings—we are able to directly test if the attributes of entrepreneurial start-ups lend them to adapt more quickly to technological shocks that underlie the industry life cycle. We test our hypotheses using data on the personal computer industry, which is a particularly appropriate setting for our study, given both the diversity in the nature of entrants, and the degree of technological change it has experienced. Our theoretical framework integrates three distinct literature streams— industry evolution, entrepreneurship and pre-entry experience, to address research gaps in each. Our study addresses the important criticism of a-historicism that typically plagues studies of technology life cycles. While industry evolution studies recognize the direct effects of temporal transformations on firm performance, the effects of important variables are considered to be largely time invariant. Agarwal et al. (2002) show how time conditions the relationship of firm age, size, and entry timing with survival, and call for further research to investigate alternative contingency conditions. We do so by focusing on the time variant effect of pre-entry experience of firms in explaining the two key performance measures of firm survival and market share. Further, in reviewing the literature on pre-entry experience, Helfat and Lieberman (2002) question the standard assertion of population ecology studies relating to organizational legitimacy effects, and call for further research on the mechanisms through which pre-entry experience has an effect.
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Our integrative model allows us to discern between situations wherein diversifying entrants and entrepreneurial start-ups may each enjoy a relative advantage, thereby reconciling the findings in both the pre-entry experience and entrepreneurship literatures that extol the advantages of each type of firm. THEORETICAL FRAMEWORK Background Studies that focus on whether entrepreneurial entrants or industry incumbents are the primary vehicle on innovations and technological change date back to Schumpeter (1934). Indeed, while the early Schumpeterian work focused on entrants being “agents of change” that resulted in “creative destruction,” later on Schumpeter (1950) emphasized the importance of resource base and infrastructure possessed by existing firms. The controversy on whether entrants or incumbents within the focal industry are better positioned to compete in technologically dynamic industries is based to a large extent on whether the organizational flexibility possessed by new entrants is more important than experience and access to resources enjoyed by industry incumbents, and whether the innovation is radical or incremental (Christensen, 1993; Hannan and Freeman, 1984; Henderson and Clark, 1990; Tripsas, 1997; Tushman and Anderson, 1986). Denovo entrants, or entrepreneurial start-ups, are credited with organizational flexibility that is often lacking in established firms for several reasons. Denovo firms are unconstrained by existing organizational routines that are slow to change, either due to myopic learning and competency traps (March, 1991; Levinthal and March, 1993; Levitt and March, 1988) or due to path dependency (Nelson and Winter, 1982). On the other hand, experience and access to both core and complementary resources gives incumbent firms an advantage in the form of superior
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endowments. Since technologically dynamic industries often require both organizational flexibility and endowments, not surprisingly, there is not much consensus regarding the relative advantage of new entrants vs. incumbents (Anderson and Tushman, 1990; Christensen and Bower, 1996; Methe, Swaminathan and Mitchell, 1996). The Impact of Prior Experience Recent work has emphasized that in addition to the distinction between entrants and incumbents, entrant heterogeneity is an important factor affecting subsequent firm performance. Pre-entry experience has been identified as an important source of heterogeneity, since founding conditions imprint on an organization to have long-lasting effects (Stinchcombe, 1965). Diversifying entrants are advantaged relative to denovo entrants since they can access existing organizational resources and capabilities while venturing into new product markets. However, to the extent that diversifying entrants are similar to industry incumbents and also inherit organizational inertial tendencies, established routines may constrain their flexibility and adaptability to change (Hannan and Freeman, 1984; 1989). While there exists the same tension as above between access to resources and organizational flexibility when comparing diversifying entrants and denovo entrants, the existing literature is less equivocal on the net effect, pointing to a pre-entry experience advantage (see the review by Helfat and Lieberman, 2002). Relying on arguments of legitimacy (Carroll et al. 1996), distribution networks (Mitchell, 1991), learning by doing (Lane, 1989) or relatedness of products (Klepper and Simons, 2000), these studies find that diversifying entrants have access to relevant resources that bestows an advantage over denovo firms. The results from studies that explicitly examine the effect of pre-entry experience on performance have also been corroborated by other research. For instance, in a study that examined entry into market sub-
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fields, Mitchell (1991) found that incumbents with complementary assets were advantaged relative to denovo entrants. Further, diversifying firms are shown to have an advantage over denovo entrants in studies that used this variable as a control variable (e.g. Agarwal et. al., 2002). Accordingly, we have the following replication hypothesis. H1: Diversifying entrants have higher performance than denovo firms. Alternatively, it may be argued that the degree of competitive advantage rendered by preentry experience is related to the degree to which firm resources are similar to the required resources in the industry (Helfat and Lieberman, 2002), i.e., rather than the existence of pre-entry experience per se, it is important to distinguish between the different types of diversifying entrants and ascertain the match between the firm and industry resources and capabilities. In technologically dynamic industries that experience several disruptive innovations over their lifetime, important core capabilities relate to both technological expertise and knowledge of the potential market (Cohen and Levinthal, 1990; Helfat and Raubitschek, 2000; Teece, 1986). As opposed to arguments of institutional legitimacy or availability of general resources for pre-entry experience advantages (Carroll et. al 1996; Lane, 1989), we posit that firms with relevant skills relating to these two attributes are better positioned for success. The importance of technological capabilities in determining competitive advantage has been well documented in the literature (Cohen and Levinthal, 1990; Christensen, 1993; Teece, 1986). Diversifying firms that have technological experience in industries that are closely related will possess resources and capabilities that are a closer match to what is needed in the new industry. Technological knowledge in closely related areas give these diversifying entrants important advantages pertaining to production and product quality. In fact, studies that find advantages for pre-entry experience often relate these to technological expertise. For example,
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Klepper and Simons (2000) found that radio manufacturers that entered the television receiver industry benefited due to their ability to leverage their technological skills in the new industry. In their review article, Helfat and Lieberman (2002) cite core technological knowledge as the reason for superior performance of diversifying firms such as Canon, and Nikon in the photolithographic industry, and Sharp in electronic calculators. Klepper (2002b) attributes the success of diversifying entrants into the automobile industry to their technological prowess. Thus, we have the following hypothesis: H2: Diversifying entrants with prior technological experience have higher performance than denovo firms. Knowledge of customer needs within the industry is an additional core capability, and firms that possess either this knowledge (Helfat and Raubitschek, 2000) or established distributional infrastructure and prior history with the customer base (Mitchell, 1991) may also benefit from a closer match of their resources and capabilities with those required by the industry. Diversifying firms that have market experience in industries that draw from the same customer base will have the advantage of a close knowledge of customer needs and potential demand for the product. This is particularly applicable to technologically dynamic industries that are characterized by uncertainty regarding market conditions. Further, firms possessing preentry market experience can leverage their established distribution channels effectively, and rely on already existing complementary resources (Teece, 1986). Integrated marketing decisions and actions by these types of diversifying entrants enable them to meet the value requirements of their customers (Slater & Olson, 2001). Thus, we propose the following hypothesis. H3: Diversifying entrants with prior market experience have higher performance than denovo firms.
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The Impact of Technological Change and Industry Evolution The dynamic changes that characterize industry evolution have been documented across rich bodies of literature in technology management (Tushman and Anderson, 1986; Utterback and Abernathy, 1975), organizational ecology (Carroll and Hannan, 1989; Hannan and Freeman, 1989), and evolutionary economics (Gort and Klepper, 1982; Klepper, 1996). Evolution introduces a dynamic element into selection processes, since the competitive environment facing the firms is transformed due to the evolutionary changes. The premise underlying these models is that entering firms initially act as agents of change, bringing in new information and knowledge and helping legitimize the organizational population, but as the technology matures, competitive pressures and changes in the sources of knowledge create an advantage for incumbent firms (Suarez and Utterback, 1995). Consistent with the literature on first mover advantages, studies of industry evolution have shown that firm cohorts that enter early in the industry life cycle have higher survival probabilities over firms that enter later (Agarwal et. al., 2002; Carroll and Hannan, 1989; Klepper, 1996; Suarez and Utterback, 1995). More importantly, a recent study by Agarwal et. al. (2002) adopts a time variant approach, finding that important relationships between various organizational and environmental characteristics and firm performance relationships are conditioned by industry evolution. This raises the question of whether the pre-entry experience—performance relationship holds universally, or is it affected by the changing competitive environment? While the standard industry evolution model typically consists of phases of initial ferment, growth, maturity and decline, more recent research has shown that industries often undergo disruptive shocks that lead to new growth associated with the establishment of new market segments (Christensen, 1993; King and Tucci, 2002; Mitchell, 1989). Although there is some discussion on what constitutes
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new industries or markets, the consensus in the literature is that shifts in technology, customer needs or state of business practices lead to continual changes in the competitive landscape (Helfat and Lieberman, 2002). Thus, in industries that undergo rapid technological change, established routines and learning curves may be less important sources of competitive advantage. In summary, adopting the evolutionary approach to studying the pre-entry experience— performance relationship implies that the match between firm resources and capabilities and those required in the industry itself undergoes dynamic changes. To some extent, the literature on pre-entry experience has incorporated the time varying aspect of the pre-entry experience and performance relationship by focusing on how timing of entry and age of the firm conditions the impact of pre-entry experience on performance. For instance, Teagarden, Echols and Hatfield (2000) and Klepper (2002a) hypothesize and find that the relative advantage over denovo firms is greatest for diversifying firms that enter early. In addition, Klepper (2002a) finds that denovo firms that survive to a later age are no worse off than their diversifying entrant age cohorts. While these studies address the question of how timing of entry interacts with pre-entry experience, they leave unanswered the question of whether experience continues to matter as the industry evolves. Do the experienced early entrants remain strong and invincible incumbents later in the industry life cycle, with early denovo and later entrants relegated to being peripheral players? Or, does the advantage of pre-entry experience enjoyed by the early diversifying entrants attenuate as the industry ages? In the presence of constant technological change that requires organizational flexibility, we argue for the latter effect. A nascent industry has very little stock of industry specific knowledge (Gort and Klepper, 1982), and malleable institutional environments characterize the market. Entering firms that
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have core technological capabilities in related industries bring in relevant information, and are thus advantaged during the early years of an industry’s evolution (Klepper and Simons, 2000; Klepper, 2002a). Moreover, the resource endowments of diversifying entrants enable them to leverage or develop collateral assets that help build market infrastructure (Teece, 1986) and create customer demand in the nascent market. In the absence of an industry specific knowledge base and legitimacy of the industry among consumers, the endowment and reputation effects of diversifying entrants during the early years acts as a surrogate mechanism. This additional legitimating role tips the balance in favor of diversifying entrants. While organizational flexibility remains an important consideration, its effects on firm performance may be overshadowed by the importance of access to resources and pre-entry experience of diversifying entrants. Thus, among the early entrants in an industry, we expect an advantage bestowed on diversifying entrants on account of their initial conditions (Klepper, 2002a; Teagarden, Echols and Hatfield, 2000). However, as the industry develops and undergoes technological transformations, the stock of industry specific knowledge, rules and routines increases (Gort and Klepper, 1982). Experience outside the product market boundaries, even in related industries, and distributional infrastructure and consumer ties become less and less relevant, as the industry develops its own knowledge base and institutional structure. While initial endowments and pre-entry experience give diversifying entrants an initial advantage over denovo start-ups, their relative lack of flexibility (Hannan and Freeman, 1984), reliance on outdated practice and knowledge (Levinthal and March, 1993; Levitt and March, 1988), potential incompatibility of complementary assets (Teece, 1986; Tripsas, 1997), or internal politics (Pfeffer and Salancik, 1978) may render them incapable of changing as quickly as their environment. Thus, as the industry evolves, if the pace
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of change in the industry is faster than the pace of change within the organization, the initial match of resources and capabilities between the diversifying entrants and the industry will attenuate. Baum, Korn and Kotha (1995) argue that inertia often makes it difficult for existing firms to fully exploit enhanced technologies, thus creating opportunities for denovo firms to develop specialized assets, market knowledge and reputation. Relying on the above arguments, we posit that the resources and organization structure possessed by denovo firms allow them to be more dynamically efficient than diversifying entrants. Relying on their entrepreneurial origin, denovo firms may emphasize qualities of originality over compliance in their human resources, and target development of new product models rather than emphasize incremental product and process improvements. Their organizational structure may be more decentralized than that of diversifying entrants, allowing them to use a bottom-up in addition to top-down decision making process, and emphasize being first with creative ideas rather than meeting established goals in terms of costs and delivery. By tying the reward structure to innovation rather than planning, and rewarding risk takers rather than followers, denovo firms may be able to outpace diversifying entrants in their ability to keep up with environmental changes. For diversifying entrants, then, what were previously assets now become bottlenecks, and resulting in a shift of the advantage from firms that have pre-entry experience to the flexible denovo firms. Thus, we have the following hypothesis. H4: Any positive effect of prior experience on firm performance only exists in the initial years of industry formation, and dissipates as the industry ages. Importantly, the mechanism through which denovo firms can overcome their initial disadvantage lies in their ability to target new customer needs and proactively pursue new market niches. As an industry experiences new generations of technological disruptions, the
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organizational flexibility of denovo firms allows them to both see new opportunities, and seize them (Anderson and Tushman, 1990; Christensen and Bower, 1996; Hannan and Freeman, 1984; Teece, 2000). While denovo firms may start off with lower levels of resources and capabilities, they are able to keep up and even create the pace of technological change in the industry. Their organizational flexibility plays an important role in developing their dynamic capabilities, thus allowing them to out-pace inertial diversifying entrants in terms of new model offerings that incorporate disruptive technologies. Accordingly, we propose the following hypothesis. H5: As an industry evolves over time, denovo entrants offer more new products than diversifying entrants. THE PERSONAL COMPUTER INDUSTRY In this section, we provide an overview of the industry setting and available data for our empirical analysis. Being one of the most dynamic sectors of the US economy, the personal computer industry is an excellent context for our study. A personal computer can be defined as a general-purpose, single-user machine that is microprocessor based and can be programmed in a high-level language. The evolution of the personal computer industry has involved substantial improvements in technology as the microprocessors used in one product generation were rapidly superceded by more advanced technology generations. Complete historical reviews of this industry are given in Langlois (1992) and Steffens (1994). Data Sources For our study, information for the US market from International Data Corporation’s (IDC) Processor Installation Census is used to develop a panel data set of firm entry and exit times as well as market share and several industry and firm-level control variables. As noted by Lawless and Anderson (1996), IDC is the oldest among the various companies that tracks the
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computer industry and is widely respected as having an accurate picture of the activity in this industry. Annual firm-level data for the personal computer industry for the period 1974 to 1994 were constructed from detailed product-level information in the IDC database1. The electronic data were then manually checked and cleaned for discrepancies. In particular, as noted by Swanson (2002), the IDC electronic data file suffers from some survivor bias, and additionally lists some companies that had merged or been acquired as a single entry throughout its life span. We rectified such errors by referring to the source material, Electronic Data Processing/Industrial Reports (EDP/IR) from which the electronic data had been compiled, and relied on searches in Lexis/Nexis. In addition, the product offerings and models listed for a manufacturer in the IDC database gave us corroborating evidence that allowed us to identify the merger/acquisition date of the companies. The resulting data set includes 3,068 firm-year observations for 638 personal computer manufacturers. The annual number of firm entries and exits are given in Figure 1 and the annual sales of personal computers are given in Figure 2. As is clear from the two figures, the industry has experienced dramatic growth in the number of competitors for much of this time period, and continues to have increases in unit sales. [Insert Figure1 and Figure 2 about here] For data on pre-entry experience, we referred to the annual volumes of the Thomas Register of American Manufacturers. The Thomas Register, which dates back to 1906, is a national buying guide widely used to study firm activity in the evolution of markets (e.g., Gort and Klepper 1982; Agarwal and Gort 1996; Klepper and Simons 2000). In describing various sources of business information, Lavin (1992, p.129) states that “the Thomas Register is a 1
This information is only available through 1994 since IDC changed its data collection procedure to a more aggregate format in 1995. Also, products with CPU’s that could not be identified due to proprietary technology are excluded from our analysis.
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comprehensive, detailed guide to the full range of products manufactured in the United States. Covering only manufacturing companies, it strives for a complete representation within that scope.” The type of pre-entry experience (if any) was determined by manually matching each firm in the IDC database with its corresponding information in the Thomas Register. More specifically, if the firm was listed in the firm index volumes of the Thomas Register the year preceding its entry into personal computers, it was classified as a diversifying entrant. Back issues of the Thomas Register were then checked to determine the firm’s age at the time of its entry into the personal computer industry. These data were also corroborated using other data sources such as Lexis/Nexis and the International Directory of Company Histories. Variable Definitions The rationale and empirical operationalization of all the variables used in the study are provided in Table 1. The descriptive statistics and correlation matrix are presented in Table 2. The dependent variables in the study and our primary variables of interest—pre-entry experience and industry age—are discussed in greater detail below. Dependent Variables Performance may be defined either in terms of firm survival (a threshold indicator) or market share (a continuous measure conditional on survival). For firm survival, we use a dummy variable to indicate whether the firm was still in existence in the following period or had exited the market. Acquisitions are considered censored observations. Market share is measured as the percent of industry sales that is produced by a firm. Finally, new model offerings are measured in a two step procedure. Since several new generations of personal computers (based on technological improvements in central processing units) were produced over the industry life
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cycle, we first compute the fraction of product offerings of a firm that are of the newest generation. Second, we compute the industry average of this fraction for the year, and subtract it from the firm level measure of the fraction of new products offered, to control for time varying differences. Pre-entry Experience Based on the discussion in Steffens (1994), we use the Thomas Register information on the primary line of business to classify the pre-entry experience of firms in the IDC database into direct technical experience, indirect technical experience, market experience, and other experience. Firms with prior direct technical experience include those in directly related product-markets (e.g., mainframe or minicomputers) and/or technology-markets (e.g., microprocessors or semiconductors).
Examples of firms in our data set with prior direct
technical experience include IBM, Hewlett Packard, and Digital Computer. Firms with prior indirect technical experience include those in tangentially related industries (e.g., video games, typewriters, business machines). Notable examples include Commodore International, Wang Laboratories, Xerox, and Sony. Firms with prior market experience include those with knowledge of the potential customers for personal computers (e.g., retailers, consultants, manufacturers of peripherals). Prominent examples include Tandy/Radio Shack, Wyse Technology, Lexmark, and Everex Systems. If the firm’s prior experience could not be categorized in any of the above categories, they were classified in the “other pre-entry experience” category. Other diversifying personal computer firms include Leading Edge and Diebold. Personal computer firms that did not appear in the Thomas Register before their inclusion in the IDC database were classified as denovo (e.g., Apple, Compaq, Dell, Acer). As is typically the case (Carroll et.al, 1996; Helfat and Lieberman, 2002; Klepper, 2002) the majority
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of entrants in the industry were denovo entrants (77 percent of the observations in the data relate to denovo firms). Within the diversifying entrant category, eight percent had prior direct technical experience, seven percent had prior indirect technical experience, five percent had prior market experience, and three percent had unrelated experience. Industry Age Industry age is measured as the number of years since the inception of the industry. We adopt this continuous measure of industry evolution, rather than discontinuous period dummies for several reasons. First, as noted by Helfat and Lieberman (2002) there is evidence that industries experience several technological shocks leading to new growth opportunities. Particularly for the personal computer industry, this has resulted in the industry being in an overall growth mode for most of the observed lifespan, with only some indication that it may be transitioning into a “mature phase.” Thus, there are not enough data points to conduct a twophase analysis as in Agarwal, Sarkar and Echambadi (2002)2. For the same reason, we found that the squared term of industry age (e.g. Baum, 1995), when included, to be insignificant. Second, the continuous industry age variable allows us to better represent the industry’s experience of a continuous series of transformations and metamorphosis, rather than discontinuous breaks that are widely spaced over time. In the Anderson and Tushman (1986) paradigm, the discontinuities were lower in both number and frequency. Given that each technological shock has long lasting consequences, it is not easy to ascribe the changes to particular phenomena, or divide them into different time periods. However, the net result of all of the technological transformations is the creation of increasing turbulence. In this context, a continuous measure of time seems most appropriate.
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Even though the last few years of data do show a dramatic decline in gross entry rates, there has not been enough time to conclude the emergence of a mature period in the industry life cycle.
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ESTIMATION METHODOLOGY To test the hypotheses related to firm survival, we use hazard rate analysis. A firm's hazard rate, h(t) is defined as the probability that it will die in a particular time interval (t+ ∆t), given that it has survived until t, i.e. , (1)
h(t) =
lim [Pr (t, t + ∆ | t ] / ∆t
∆t → 0
This gives the probability of failure conditional on age. Our model of the firm's hazard rate hf(t) is: (2)
h (t ) = h(t ; x ) = exp( x ′i (t ) β ) i i
where xi is the vector of explanatory variables for the ith firm and β is a vector of regression parameters to be estimated. Several discrete and continuous time approaches are available to estimate this model (Allison 1995). Consistent with earlier studies (Baum & Oliver, 1991; Henderson, 1999), we use a multiple spells formulation with a complementary log-log specification that allows for time varying covariates3. Further, we adopt a random effects model to control for unobserved heterogeneity among firms. To ensure the robustness of our results, we also estimated probit, logistic, and Cox proportional hazards models. Although not reported here, the results are very similar across the different model specifications. To test the hypotheses on market share and new product offerings, we use a standard panel data methodology to control for unobservable heterogeneity among firms. Thus, we estimate the following model: (3)
yit = α + X it β + vi + ε it
3
Although a firm may fail at any point within a given year, the data on failure are updated only annually. A multiple spells, complementary log-log formulation allows continuous-time hazard rates to be obtained from discrete time failure data. The instantaneous probability of failure of a firm at time t in this model is given by h(t ) = 1 − exp[− exp( βx t )] . See Allison (1995).
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where vi + ε it is the residual which includes the regular error term as well as a firm specific component vi. We use random effects panel regression since the alternative fixed effects methodology cannot be used due to the time invariant nature of the main variables of interest. (e.g., indicators of pre-entry experience). The random effects model imposes the assumption that the firm specific error component vi not be correlated with the explanatory variables. We conducted the Hausman test for model specification to ensure that the error term is not correlated with the time varying covariates in our data. This indicates that the coefficients of the time varying variables do not statistically differ between fixed and random effects model.
RESULTS Our estimation results are reported in Tables 3, 4, and 5. In each table, the unconditioned effects of pre-entry experience are presented in Models 1 (dummy for pre-entry experience) and 2 (dummies for type of pre-entry experience), while Models 3 and 4 report the interactions with the dummies for presence and type pre-entry experience, respectively.
Firm Survival The results presented in Model 1 of Table 3 indicate that the replication hypothesis H1 regarding higher firm survival for diversifying entrants relative to denovo firms is not supported. However, the coefficient for direct technical experience in Models 2 and 4 is positive and significant. This provides support for H2 when firm survival is the performance measure. Since the coefficient for market experience is not significant, H3 is not supported for firm survival. Industry age, consistent with models of industry evolution, decreases the probability of survival. More importantly, the effect of industry age on diversifying and denovo firms is significantly different. In Model 3, the coefficient of pre-entry experience is positive and significant, while the interaction term is negative and significant. Also, from Model 4, we can see that this is
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attributable to the firms with direct technological experience, since only the main and interaction terms relating to this variable are significant in the hypothesized direction. Firms with prior direct technical experience have higher survival rates relative to denovo firms, but this effect attenuates as the industry ages. The marginal effects indicate that the advantage bestowed by direct technological experience reverses itself by late 1991, even before the industry shakeout that occurs a few years later. Thus, we find support for H4 when firm survival is used as the performance measure. Among the firm level control variables, significant coefficients relate to the squared term of the experienced firm’s age at time of entry, firm age in industry and firm sales. It is important to note that firm age within the industry decreases the probability of survival for a substantial segment of the firm’s life. The marginal effects for firm age indicate that the increased mortality for a firm goes well beyond what may be expected under either the liability of newness or adolescence—age has a negative effect on survival till age thirteen. Also, we note that while the year of firm entry is not explicitly in the model, due to the linear relationship between time of entry, firm age and industry age, we find results consistent with the order of entry literature, i.e., earlier entrants have higher survival rates4. As expected, larger sized firms (as measured by firm sales) have higher probabilities of survival. Among the industry level control variables, the coefficient of growth in industry sales is significant and negative, and the non-monotonic effects of competitive density have the opposite effect than has been predicted by the organizational ecology literature.
4
More specifically, entry year = industry age – firm age. Models in which entry year is included in the analysis, but industry age or firm age is suppressed, yield consistent results.
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Market Share Turning to the estimation results of market share5 as a measure of firm performance in Table 4, the replication hypothesis is once again not supported. None of the pre-entry experience dummies in Models 1 and 2 are significant. However, from Model 4 we find firms with prior market experience benefiting early in the industry life cycle, thus providing partial support for H3. We note that the reversal of fortunes for firms with prior market experience occurs around 1989. More importantly, we find strong evidence that denovo firms have an increasing market share relative to all diversifying firms, since the coefficients of the interaction terms involving industry age in Models 3 and 4 are negative and significant. Further, the cumulative importance of denovo entrants is depicted in Figure 3, which charts the total market share by type of experience. At the time of sales take off in the industry, diversifying firms collectively controlled more than 80 percent of the total industry sales. However, the share of denovo startups increased rapidly over time, and by the end of the sample period, denovo firms accounted for more than three quarters of total industry sales. Thus, H4 is strongly supported for market share as the performance variable. Among the control variables in the market share regression, the linear terms for age of experienced firms at time of entry and firm age for all firms subsequent to entry, are positive and significant, while the squared term is negative and significant. While industry age itself decreases market share, none of the industry control variables appear significant in affecting the firm market share.
5
We restrict the number of observations in the regressions for market share to the years after sales take-off in the industry, since early market share values do not have much meaning. Including these years only affects the coefficient for year of entry though, and the coefficients of the main variables of interest remain largely unchanged.
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New Product Offerings In Table 5, we report the explanatory factors related to the proportion of product offerings by firms that are “new models”. Models 1 and 2 reveal that only firms with market experience offer many new products. Model 3 shows that while diversifying firms initially offer more new products, they subsequently fall behind in their new product offerings, since the interaction term with industry age is negative and significant. In Model 4, the interaction coefficients for industry age with prior direct technological and market experience is negative and significant. Thus, we find support for H5. Among the control variables for the analysis of new product offerings, as expected, firm size and number of models offered is positively related to the fraction of new models offered by the firm. It is interesting to note that as firms age, they are less likely to have a portfolio of product offerings that contain more new models. As the number of firms increases, more new products are offered, but other industry level variables have no effect on the proportion of new product offerings.
DISCUSSION AND CONCLUSIONS Models of industry evolution explicitly incorporate dynamic changes in the competitive environment as the industry evolves through life cycle stages. Whether these models assume a single initial disruptive technological shock (Gort and Klepper, 1982, Utterback and Abernathy, 1975) or a series of disruptive technological shocks (Tushman and Anderson, 1986; Henderson and Clark, 1990), the underlying technological changes are associated with changing competitive conditions. Importantly, these models imply that industry evolution conditions key relationships between firm characteristics and performance (Agarwal et. al. 2002) or that firm resources and capabilities match those needed in the industry (Helfat and Lieberman, 2002).
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In this paper, we empirically examine the pre-entry experience—performance relationship within the lens of industry evolution. For the personal computer industry, we find that pre-entry experience matters not because of previously attributed reasons such as established decision structures (Carroll et al., 1996) or learning by doing (Lane, 1989), but because of the relevance of direct technological experience (Klepper and Simons, 2000) and marketing capabilities and access to distribution channels (Mitchell, 1991). The advantage bestowed by related pre-entry experience dissipates over time, however. We find evidence that the organizational flexibility possessed by denovo firms is an important capability, and entrepreneurial firms are better able to keep up with dynamic changes in an industry. As the industry ages, denovo firms have higher performance levels, and additionally, are seen to offer more new product models. Thus, their ability to target new consumer needs and develop a reputation overshadows their initial disadvantage. We now discuss the mechanisms through which the dissipation of each of the related experience advantage occurs, while also tying our results with those received from prior literature. Innovative ability matters, and so firms with direct technical experience were able to initially leverage their abilities for competitive advantage. This is consistent with the emphasis on innovative capabilities of pre-entry experienced firms (Klepper and Simons, 2000; Klepper, 2002). However, inertial tendencies may have caused these firms to lag behind the denovo entrants in terms of later technological changes. We find evidence that denovo firms have a higher rate of new product model offerings relative to direct technically experienced firms over time, thus enabling them to overcome their initial technological disadvantage. The difference in results regarding the attenuation of pre-entry technological experience advantage may be due to the empirical context of our study. For instance, in the studies by Klepper quoted above, the
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industries studied (e.g., television receiver industry) went through relatively few technological disruptions as opposed to the personal computer industry that went through several technological upheavals that affected the initial match of firm and industry resources for the advantaged firms. Thus, as reported by Klepper and Simons (2000), the process innovation that radio producers could apply to the TV industry was possible because the industry did not experience any major disruptions in the technology for most of the industry life cycle. However, when the TV industry did experience a disruption from solid state electronics, Japanese and other foreign producers could use this to successfully challenge the dominance of the US radio producers turned TV producers. In contrast, the PC industry went through several disruptive changes, each one of which rendered the pre-entry experience of the direct technology firms more and more obsolete. These firms were unable to keep up with the industry pace of technological change, as is evidenced by their lower rate of new product offerings, resulting in their elevated mortality rates, and lower market share when compared to other entrants. In the early years of the PC industry, entrants that integrated backward (e.g., Tandy) changed the cost structure of marketing and distribution activities (Steffens, 1994). Marketing costs were lower for these firms since they could use established channels for regional and national advertising and existing cooperative advertising with dealers. Further, extensive distribution channels, such as those of Tandy and Commodore, represented an enormous capital investment which none of the other firms could rapidly imitate. Linked to effective promotional activity, such outlets provided a distinctive competitive advantage as was demonstrated by their rapid success after entry (p 96, Steffens, 1994). However, as industry growth exploded and new customer bases were created by subsequent technological change, innovative firms such as Dell created direct marketing and distribution channels, and developed superior marketing
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capabilities. The increased market share of denovo entrants is an indication of their organizational flexibility since it allowed them to overcome their initial disadvantage.
Theoretical Implications Contrary to existing studies that extol the advantages enjoyed by diversifying firms over entrepreneurial denovos, our study finds conditioning factors to the pre-entry experienceperformance relationship. In doing so, our study contributes to the literature in three important ways. First, we find that it is not just pre-entry status, but the type of experience that matters in explaining post entry performance, since only firms that possess core capabilities related to technological and marketing capabilities benefit from their prior experience. Second, by integrating the literature on pre-entry experience and industry evolution, we find a dissipating advantage for firms with pre-entry experience. Third, we find important evidence on the mechanisms through which the pre-entry experience may or may not render superior firm performance. In particular, we address the trade-off that has been recognized in the literature between organizational flexibility and access to resources and experience. We find that entrepreneurial entrants that retain their flexibility can overcome their initial disadvantage by proactively keeping up with the technological change. By investigating what type of experience matters in an industry that offered opportunities for entry for all kinds of diversifying firms, we provide support for Helfat and Lieberman’s (2002) conjecture that it is the match between firm and industry resources that is most relevant. In addition, our results attest to the need for incorporating dynamic elements in empirical analyses. Contrary to the established literature on the liability of newness, we find that new firms are advantaged over older ones for several years after entry. This is consistent with the notion that in markets faced with a continuous onslaught of technological change, vintage effects
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associated with older technology offset the benefits of experience, and that inertial tendencies of older firms overshadow any learning by doing advantages (Bahk and Gort, 1993; Jovanovic and Nyarko, 1996).
Managerial Implications Our empirical results for the personal computer industry paint a picture of industry evolution and firm competition in a technologically dynamic industry. We find that as a new industry evolves over time, increasing competitive pressures mean that firm survival, as well as market share, generally decreases over the industry life cycle. Firms also face a selection process, since the relationship between survival and firm age is U-shaped). At the same time however, established firms are able to garner higher market share. Interesting, the older, established firms compete with fewer product offerings than their younger competitors. It is well known that there is a high level of technology and market uncertainty in new, technologically dynamic industries. In the early stages of the industry life cycle, we find that prior experience matters. Specifically, prior technical experience is positively related to firm survival and prior market experience is positively associated with market share and new product offerings. In addition, early entrants have a higher probability of survival over later entrants. As the industry matures over time however, we find that any initial advantage of prior experience vanishes. Our results suggest that the denovo firms that compete later in the industry life cycle do so by offering more new products than the established firms in the market. As we argue previously, these denovo firms are better able to keep pace with technological change due to their entrepreneurial flexibility that better matches the dynamic market conditions. These results have some interesting managerial implications. During the early stages of a new market, denovo entrants should take their cues from the entrants with prior experience. The
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firms with prior technical and market experience seem to be better able to navigate the initially uncertain environment associated with a new industry. As the industry matures however, the firms with prior experience should carefully monitor the entrepreneurial actions of the denovo entrants. Later in the industry life cycle when the technical and market uncertainties have been resolved, denovo firms bring new product insights that may not have been fully considered by the entrenched competitors who are focusing on process improvements and cost reductions.
Limitations and Future Research As with all empirical research, our study has several limitations that suggest directions for future research. For example, future research studies might consider whether our findings generalize to other industry settings. Particularly of interest would be other studies that explore the role of prior technological and market experience. In addition, future research can expand the set of variables related to competition between both the denovo and diversifying entrants. This would allow us to ascertain the impact of firm decisions concerning pricing, advertising, and distribution channels on their performance, and whether this varies over the industry life cycle.
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Table 1: Definition of Variables and Rationale Variable Name Variable Description Dependent Variables Firm Survival Dummy = 1 if firm survived to the following period (acquisitions treated as censored observations) Market Share Firm sales as a percent of total industry sales New Model Number of new models (of the latest generation) offered by the Offerings firm – average number new models offered by all firms in that year. Results robust to operationalization as a proportion Key Variables in Study Direct Technical Firms that enter with pre-entry experience in directly related Experience technological fields (e.g. mainframe computers, forward integrators, etc) Indirect Technical Firms that enter with pre-entry experience in technological fields Experience not directly related to computer manufacturing (e.g. video games, typewriters, calculators, etc.) Market Experience Firms that enter with pre-entry experience in market segments targeted by PCs (e.g. consultants, VARs, retailers, etc.) Other Experience Firms that enter with pre-entry experience in industries that do not fall in the above categories Industry Age The year of current operations. Control Variables Diversifying Firm’s Age at Entry Firm Age in Industry Firm Sales Industry Sales Industry Growth Number of Firms
The age of the firm at time of entry in the computer industry. A squared term is included to model non monotonic effects Chronological age of firm since entry into the computer industry. A squared term is included to model non monotonic effects Log of firm sales in any year Log of sales of the industry per year Growth in log of sales of the industry per year Number of firms in the industry per year. A squared term is included to model non-monotonic effects
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Rationale Dependent variable for H1, H3 Dependent variable for H2, H4 Dependent variable for H5. Measures firm innovativeness and flexibility of operations relative to the mean innovativeness and flexibility of operations in the industry Technological experience in directly related industries can be leveraged into the new industry Technological experience in other industries not directly related to computers can be leveraged into the new industry Market experience and distribution networks can be leveraged into the new industry Resources and assets of diversifying firms can be leveraged into the new industry . Industry age has a main effect on the dependent variables, and moderates the experience—performance relationship Captures age and tenure related advantages of pre-entry experience Captures the effect of operating in the computer industry
Measures the size effect in models of survival and new model offerings Represents resource munificence Represents growth opportunities for firms Captures competitive density effects
Table 2: Descriptive Statistics and Correlation Matrix 1 2 3 4 5 6 7 8 9 10 11 12 13
Variable Survival Market Share Direct Technical Experience Indirect Technical Experience Market Experience Other Experience Industry Age Diversifying Firm’s Age at Entry Firm Age in Industry Firm Sales Industry Sales Industry Growth Number of Firms
Mean 0.84 0.69
Std. Deviation 0.37 4.08
1
2
3
4
5
-0.07
1.82
3.99
-0.03
0.04
0.08 0.07 0.05 0.03
0.28 0.25 0.22 0.17
-0.05 0.02 -0.004 0.16
15.76 4.90 4.20 35912.28 8500448.63 221.93
3.98 14.67 3.12 160633.68 4060551.79 69.33
-0.05 0.02 -0.22 0.12 -0.04 0.13
0.07 0.07 -0.004 -0.19
-0.08 -0.07 -0.05 0.04
-0.06 -0.05 -0.04
-0.04 0.02
0.01
0.09 0.07 0.22 -0.38 0.43 -0.21
0.33 0.08 0.10 0.05 -0.02 0.03
0.44 0.14 0.10 -0.01 0.01 -0.05
0.06 -0.01 -0.02 0.02 -0.001 -0.01
0.25 -0.002 0.03 0.01 0.004 -0.01
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6
7
8
9
10
11
12
-0.03 0.38 0.34 0.81 -0.41 0.64
0.16 0.12 -0.004 0.01 -0.05
0.41 0.27 -0.11 0.15
0.28 -0.13 0.21
-0.53 0.71
-0.52
Table 3: Random Effects Complementary Log-Log Model for Probability of Survival Explanatory Variable Model 1 Model 2 Model 3 Model 4 Intercept 2.02*** 1.81*** 2.12*** 1.83*** (0.12) (0.12) (0.22) (0.12) Pre-Entry Experience 0.28 0.44** (0.22) (0.22) ----Direct Technical Experience --0.47* --0.96*** (0.29) (0.37) Indirect Technical Experience --0.39 --0.51 (0.34) (0.36) Market Experience --0.26 --0.42 (0.26) (0.30) Other Experience ---0.09 ---0.11 (0.37) (0.37) Industry Age -0.20*** -0.20*** -0.20*** -0.20*** (0.03) (0.03) (0.03) (0.03) Pre-Entry Experience x Industry Age -0.07** ----(0.03) --Direct Technical Experience x Industry Age ---0.20*** ----(0.08) Indirect Technical Experience x Industry Age ---0.04 ----(0.07) Market Experience x Industry Age ---0.07 ----(0.07) Other Experience x Industry Age --0.08 ----(0.08) Pre-entry Experienced Firm’s Age at Entry -0.02 -0.02 -0.02 -0.03 (0.02) (0.02) (0.02) (0.02) Pre-entry Experienced Firm’s Age at Entry 0.0004* 0.0004* 0.0004* 0.0004** Squared (0.0002) (0.0002) (0.0002) (0.0002) Firm Age in Industry -0.13*** -0.13*** -0.13*** -0.13*** (0.04) (0.04) (0.04) (0.04) Firm Age in Industry Squared 0.01*** 0.01*** 0.01*** 0.02*** (0.01) (0.01) (0.01) (0.01) Firm Sales 0.40*** 0.40*** --0.40*** (0.04) (0.04) (0.04) Industry Sales 0.19 0.19 0.17 0.15 (0.21) (0.21) (0.21) (0.21) Industry Growth -0.22** -0.22** -0.22** -0.22** (0.09) (0.09) (0.09) (0.09) Number of Firms -0.003** -0.003** -0.003** -0.003** (0.001) (0.001) (0.001) (0.001) Number of Firms Squared 0.00004* 0.00004* 0.00004* 0.00004* (0.000) (0.000) (0.000) (0.000) Number of Observations 3068 3068 3068 3068 Wald Chi square 193.42*** 191.13*** 195.29*** 195.89*** Panel level variance component 0.22*** 0.22*** 0.21*** 0.21*** Standard errors in parentheses. Significance (two-tailed) at 0.10 level denoted by *, at 0.05 level by **, and at 0.01 level by ***.
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Table 4: Random Effects Panel Regression of Market Share Explanatory Variable Intercept Pre-entry Experience Direct Technical Experience
Model 1 0.52*** (0.14) 0.02 (0.14) ---
Model 3 0.61*** (0.13) 0.18 (0.14) ---
-0.05** (0.02)
0.05 (0.19) -0.06 (0.21) 0.06 (0.19) -0.04 (0.25) -0.05** (0.02)
-----
-----
-0.17*** (0.02)--0.16*** (0.02) ---
Indirect Technical Experience
---
Market Experience
---
Other Experience
---
Industry Age
Model 2 0.51*** (0.08) ---
Pre-entry Experience x Industry Age
-------
Direct Technical Experience x Industry Age Indirect Technical Experience x Industry Age Market Experience x Industry Age
---
---
---
---
---
---
Other Experience x Industry Age
---
---
---
0.05*** (0.01) -0.0004*** (0.0001) 0.14*** (0.01) -0.01*** (0.002) -0.05 (0.13) 0.01 (0.15) -0.001 (0.001) 0.00001 (0.00002) 2917 183.10*** 0.08
0.05*** (0.01) -0.0004*** (0.0001) 0.09*** (0.02) -0.01*** (0.00) -0.05 (0.13) 0.01 (0.15) -0.001 (0.001) 0.00001 (0.00002) 2917 183.43*** 0.08
0.04*** (0.01) -0.0004*** (0.0001) 0.13*** (0.01) -0.01*** (0.00) -0.07 (0.13) -0.01 (0.14) -0.001 (0.001) 0.00002 (0.00002) 2917 246.31*** 0.10
Pre-entry Experienced Firm’s Age at Entry Pre-entry Experienced Firm’s Age at Entry Squared Firm Age in Industry Firm Age in Industry Squared Industry Sales Industry Growth Number of Firms Number of Firms Squared Number of Observations Wald Chi square R2
Standard errors in parentheses. Significance (two-tailed) at 0.10 level denoted by *, at 0.05 level by **, and at 0.01 level by ***.
29
Model 4 0.46*** (0.08) --0.16 (0.20) -0.03 (0.21) 0.32* (0.19) 0.11 (0.25) -0.04** (0.02) ---0.07** (0.03) -0.22*** (0.03) -0.24*** (0.04) -0.16*** (0.05) 0.04*** (0.01) -0.0004*** (0.0001) 0.09*** (0.02) -0.01*** (0.00) -0.06 (0.13) -0.01 (0.14) -0.001 (0.001) 0.00002 (0.00002) 2917 265.29*** 0.10
Table 5: Random Effects Panel Regression of New Model Offerings Explanatory Variable Intercept
Model 1 ---
Pre-Entry Experience
0.31 (0.20) ---
Direct Technical Experience
Model 2 0.11 (0.12) ---
Model 3 --0.37** (0.18) ---
-0.29*** (0.03) ---
0.01 (0.25) 0.25 (0.26) 0.53** (0.24) 0.48 (0.32) -0.29*** (0.03) ---
Direct Technical Experience x Industry Age
---
---
-0.33*** (0.03) -0.06** (0.03) ---
Indirect Technical Experience x Industry Age
---
---
---
Market Experience x Industry Age
---
---
---
Other Experience x Industry Age
---
---
---
Total Number of Model Offerings
0.29*** (0.01) -0.05*** (0.01) 0.0004** (0.001) -0.27*** (0.03) -0.01 (0.0001) 0.07*** (0.02) 0.04 (0.15) -0.02 (0.12) 0.01*** (0.0001) -0.00001 (0.00002) 3062 1739.4*** 0.36
0.29*** (0.01) -0.05*** (0.01) 0.0004** (0.001) -0.27*** (0.03) -0.01 (0.0001) 0.07*** (0.02) 0.04 (0.15) -0.02 (0.12) 0.01*** (0.0001) -0.00001 (0.00002) 3062 1743.9*** 0.36
0.29*** (0.01) -0.05*** (0.01) 0.0004** (0.001) -0.27*** (0.03) -0.01 (0.0001) 0.07*** (0.02) 0.04 (0.15) -0.02 (0.12) 0.01*** (0.0001) -0.00001 (0.00002) 3062 1743.9*** 0.36
Indirect Technical Experience
---
Market Experience
---
Other Experience
---
Industry Age Pre-Entry Experience x Industry Age
Pre-entry Experienced Firm’s Age at Entry Pre-entry Experienced Firm’s Age at Entry Squared Firm Age in Industry Firm Age in Industry Squared Firm Sales Industry Sales Industry Growth Number of Firms Number of Firms Squared
-------
Number of Observations Wald Chi square R2 Standard errors in parentheses. Significance (two-tailed) at 0.10 level denoted by *, at 0.05 level by **, and at 0.01 level by ***.
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Model 4 0.10 (0.12) --0.20 (0.25) 0.32 (0.26) 0.63** (0.24) 0.57 (0.42) -0.29*** (0.03) ---0.20*** (0.05) -0.06 (0.05) -0.09* (0.06) -0.04 (0.07) 0.29*** (0.01) -0.06*** (0.01) 0.0005*** (0.001) -0.27*** (0.03) -0.01 (0.0001) 0.06*** (0.02) 0.02 (0.15) -0.02 (0.12) 0.01*** (0.0001) -0.00001 (0.00002) 3062 1774.8*** 0.0.37
350 300
Firms
250 200 150 100 50 0 73
79
85
91
97
Year Gross Entry
Gross Exit
Number of Firms
Units sold (in millions)
Figure 1: Entry, Exit and Number of Firms in Personal Computers
18 15 12 9 6 3 0 73
79
85
91
Ye ar Unit Sales
Figure 2: Unit Sales of Personal Computers
31
97
Market Share by Experience 90 80
Market Share
70 60 50 40 30 20 10 0 80
82
84
86
88
90
92
94
96
Year
Direct Tech
Indirect Tech
Market
Other
Figure 3: Market Share by Experience
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Denovo
DeAlio
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