Incremental and Radical Innovation by U.S. Automobile Manufacturers, : An Ecological Perspective

Transcription

1 Incremental and Radical Innovation by U.S. Automobile Manufacturers, : An Ecological Perspective Tai-Young Kim SKK Graduate School of Business Sungkyunkwan University Tel: Fax: Anand Swaminathan Goizueta Business School Emory University Tel: Fax: Albert C Y Teo Department of Management and Organization National University of Singapore albertteo@nus.edu.sg Tel: Fax: October 13, 2015 * This research was supported by Stanford Graduate School of Business, and Department of Management of Organizations, Hong Kong University of Science and Technology. The research reported in this paper draws from a larger collaborative project on the worldwide automobile industry by Michael Hannan and Glenn Carroll, whose advice we have benefited from and appreciate. We also appreciate the helpful comments of Glenn Carroll, Stanislav Dobrev, Martin Ruef, and Olav Sorenson on earlier drafts of this paper. All errors, of course, are ours.

2 Incremental and Radical Innovation by U.S. Automobile Manufacturers, : An Ecological Perspective Abstract Despite growing consensus that different types of technological innovations emerge from different paths and have differing effects on an organization s behavior and performance, there has been little conceptual and empirical work on how (organizational) ecological processes influence the rate of occurrence of different types of innovation, such as incremental or radical innovation, and their consequences for organizational performance. We predict that organizational scope and niche crowding increase the rate of incremental innovation, while prior experience in another industry lowers the rate of both incremental and radical innovation. Further, we argue that while innovation itself may improve an organization s survival chances, the simultaneous occurrence of radical innovation with other organizational changes, such as a change in market position, is likely to lower the organization s survival chances. Our results are consistent with the implications of a cascading model of organizational change with incremental innovations providing survival benefits and radical innovations lowering survival chances when they occur jointly with market position change. Using longitudinal data on all American automobile manufacturers from , we find substantial support for our predictions.

3 Ecological research has provided insights into organizational and industry evolution through the development and testing of various theory fragments including theories of age dependence, density dependence, resource-partitioning and niche width, localized competition and scale-based competition. Nevertheless, with a few notable exceptions (see e.g. Wade 1996; Stuart 1999) there has been little work on how ecological processes affect the rate of technological innovation by firms. In particular there has been little theoretical and empirical research on how ecological processes affect the rate of innovations of varying magnitude, such as the rates of incremental and radical innovations (e.g., minor improvements in current technologies or fundamental changes that represent clear departures from exiting ones). Further, little is known about how each type of innovation affects organizational performance (e.g., mortality) in concert with other organizational changes. In this paper, we study the impact on the rate of incremental and radical innovation of three ecological features of organizations that have been shown to influence the behavior and performance of firms: niche width or the scope of a firm, niche overlap or competitive crowding, and a firm s prior experience before entering a new industry. These three ecological features relate to a firm s internal context, a firm s external market position, and a firm s historical context respectively. Furthermore, we examine the conditional effects of these two different types of innovations (i.e. incremental or radical innovations) that follow from a cascading model of organizational change proposed by Hannan, Pólos and Carroll (2003). We specifically examine the effects on organizational mortality of innovation events and changes in market position when they occur simultaneously. We believe that examining how the three ecological features affect different types of innovations and how the innovations jointly affect organizational mortality with other organizational changes contributes significantly to our understanding of evolutionary dynamics in organizational populations. At the organizational level, we examine the evolutionary dynamics of incremental and radical innovation in the context of the American automobile industry during the period We test our hypotheses with organizational- and population-level data on all firms known to have existed in the U.S. automobile manufacturing industry during the study period. By using cross-sectional time series analysis and event history analysis, and with the focal organization as our unit of analysis, we aim to enhance our 1

4 understanding of the interconnections between ecological processes on the one hand and rates of radical and incremental innovation and organizational mortality on the other. Theory and Hypotheses Innovations have taken many forms: old versus new (Cooper and Schendel 1976), evolutionary versus radical (Abernathy and Utterback 1978), competence-enhancing versus competence-destroying (Tushman and Anderson 1986; Anderson and Tushman 1990), incremental versus radical (Abernathy 1978, Utterback 1994), and sustaining versus disruptive (Christensen and Bower 1996). While the particulars of these typologies based on contrasting names may vary, they have in common the idea that different types of technological innovations emerge from different paths and have differing effects on an organization s behavior and performance. In this study, we distinguish between incremental and radical innovations because they are directly related to the degree of technical differences from previous innovations. While an incremental innovation indicates minor improvements or small adjustments to existing technologies, a radical innovation represents a major technical breakthrough (Abernathy and Utterback 1978, Dewar and Dutton 1986). We first develop hypotheses about how three ecological features, niche width or the scope of a firm, niche overlap or competitive crowding and a firm s prior experience in an industry affects the rate of incremental and radical innovation and then develop hypotheses about the contingent effects of the two types of innovation on organizational mortality, particularly when accompanied by a change in market position. Niche Width and Innovation By definition, firms with broad niches or scope operate across multiple domains. To operate multiple business units successfully, such firms rely on a wide range of resources as organizational inputs. The greater the variance in resources utilized by an organization, the more likely it is to expand its niche through exploration (Dobrev, Kim, and Hannan 2001). Firms with broad niches often are organized unto multiple units and possess slack resources that allow them to tolerate undesirable outcomes when some 2

5 units are less profitable than others. In such firms internal structures and routines are likely designed to support exploration, allowing them to select and retain desired outcomes from multiple units. As Stinchcombe (1990: 136) noted, socially organized market segments carry different information. Accordingly, a technologically broad firm gathers heterogeneous information on different markets, which provides fresh insights into the activities of various units within the firm and stimulates the growth of creative and innovative ideas. For instance, Greve (1996) found that radio stations were more likely to adopt a new radio format if such a format were already adopted by other units within the organization, suggesting that a broad niche is beneficial for the adoption of innovations. Multi-unit organizations are also more likely to benefit from the vicarious information released by market exits (Kalnin, Swaminathan, and Mitchell 2006). Firms with broad niches are likely exposed to clients in multiple industries, thus allowing them to transfer innovative ideas from one context to another (Hargadon and Sutton 1997). We therefore argue that the presence of multiple units in a firm facilitates the innovative process (Sorenson, McEvily, Ren, and Roy 2006). That is, the notion that technologies or innovations emerge as novel combinations of antecedent technologies or innovations (Schumpeter 1975) likely describes the experience of generalist firms with broad niche widths. Generalists with multiple business units tend to establish routines that allow discussion of varied inputs for heterogeneous markets and feasible solutions for constantly changing environments. Such routines increase organizational adaptability through learning processes among multiple units and selection processes for innovations. For the above reasons, generalists should experience a higher overall rate of innovation. But does the influence of niche width apply equally to rates of incremental or radical innovation? We do not think this is the case. We contend that the difference between generalists and specialists in terms of their likelihood to innovate is only limited to incremental innovations. Specifically, generalists are more likely than specialists to introduce incremental innovations because such innovations would lower the probability of disrupting their external relationships with investors, suppliers or customers in these domains than radical innovations. Generalists must address the diverse interests of different groups of investors, suppliers and customers. The introduction of radical innovations would be problematic as the 3

6 balance among these diverse interests is disrupted. Generalists also often occupy lucrative markets or the market center (Dobrev et al. 2001). It would be safer for them to introduce incremental innovations that do not require them to abandon their main product lines and move into new untested markets. This argument is consistent with the claim that incremental innovations come mostly from large, established firms that have large R&D budgets and occupy the market center (Nelson and Winter 1982). As an organization widens its niche width to cover more product domains, the number of organizational structures and routines required for coordination and control also increases. That is, firms with broad niche widths face increasing internal complexity, which in turn generates organizational inertia. Such inertia from internal complexity impedes generalists ability to engage in radical innovations. Consistent with this expectation, Dowell and Swaminathan (2007) found that bicycle firms that introduced a larger number of new products, were much less likely to make the transition to the dominant design. The new products of these firms reflected incremental innovations rather than the radical innovation represented by the dominant design. Based on this reasoning, we argue that a generalist is only more likely than a specialist to introduce incremental innovations, and we offer the following hypothesis: Hypothesis 1: The greater the niche width of an organization, the higher its rate of incremental innovation. Niche Overlap and Innovation While a firm s scope or niche width indicates its internal constitution, its market position or niche overlap indicates its external context. A longstanding assertion in the innovation literature is that technological innovations are shaped by the social and organizational contexts in which organizations operate (Hughes 1983; Tushman and Anderson 1986). One particular context that has gained much attention is the crowding of an organization s niche, which is typically captured by an increased niche overlap with other organizations in a market. Generally, an increase in the number of organizations in a market leads to intensified competition. Researchers have found that such competition increases the 4

7 organizational mortality rate (Baum and Singh 1994; Dobrev, Kim and Carroll 2002), retards the organizational growth rate (Ranger-Moore, Breckenridge and Jones 1995), elevates the rate of moving to different markets (Dobrev et al. 2001; Dobrev et al. 2002) and increases the rate of forming interorganizational alliances (Stuart and Podolny 1999). But how does crowding affect the rate of innovation? Organizations in competitively crowded areas are likely to threaten one another s positions because they share similar technologies and target similar types of consumers, which are ideal conditions for the occurrence of Red Queen competition (Barnett and Hansen 1996). To dodge intensified competition, organizations push one another to search for alternatives that allow them to develop novel competencies. Organizations in crowded areas find it difficult to survive such fierce competition, unless they have structures, routines and cultures that nurture innovative manufacturing and marketing skills. We subscribe to the view that technological development is a cumulative endeavor whereby organizations in a particular domain try to introduce innovations in their R&D, manufacturing, and marketing activities (Nelson and Winter 1982, Podolny and Stuart 1995). Organizations in crowded domains often identify problems with current technologies and set future innovation agendas collectively. In technology-driven industries, such as the computer, semiconductor, and biotechnology industries, organizations in similar product domains generally agree upon what innovations should come next. Once a set of challenges are overcome, the organizations then move on to the next set of more advanced challenges. In his study of the semiconductor industry, for example, Stuart (1999) confirmed that semiconductor manufacturers in crowded areas often reach a consensus about what their critical problems are and what the next innovations should be. Such consensus led each individual semiconductor manufacturer to increase its R&D expenditure and achieve a higher innovation rate compared to its competitors in order to win the innovation race. As Schumpeter (1975) argued, innovation involves amendments or improvements to, or novel combinations of, antecedent technologies. A fresh insight or perspective on a firm s own activities often comes from competing in a market. Organizations in crowded areas are stimulated to innovate as they are 5

8 presented with fresh perspectives on their own products or services. Such stimulation occurs when all organizations in a domain constantly monitor each other s actions closely, so that each organization learns from its own and its competitors antecedent and current innovations (Miner and Mezias 1996). Thus, organizations in crowded areas are more likely to improve on previous innovations or to generate new innovations by combining old ones. Therefore, we expect that crowding in the technological space greatly affects the rate of innovation. To avoid the risk of lagging behind their competitors, organizations in a competitive market attempt to learn, accumulate experience and knowledge, and innovate, by observing their competitors behaviors and experience. Competitive crowding thus has a positive effect on the overall rate of innovation. We believe, however, that crowding exerts differential effects on rates of incremental and radical innovation. We argue that organizational crowding increases the rate of incremental innovation but does not have an impact on the rate of radical innovation. Organizations in a crowded area tend to observe continuously what their competitors do and attempt to imitate what their competitors do or improve on what they are doing. The vicarious learning process stimulates new ideas at various stages of the product design and production process. We expect that these ideas lead to incremental innovations, rather than radical breakthroughs in technologies, due to the immediate need for generating profits. As Tushman and Anderson (1986) have suggested, incremental technological progress, unlike the initial breakthrough, occurs though the interaction of many organizations stimulated by the prospect of economic returns (p. 441). Organizations in a crowded area have to be more concerned about short-term profits than those in a less crowded area. Many of the organizations subject to this intense competition are likely to experience performance below aspiration levels triggering a local search process that leads to incremental innovations (Greve, 1998). Moreover, organizations in a crowded area have a tendency to conform to industry standards by watching and imitating one another. They are aware of what the industry standards are and often have a good sense of where the industry is headed. This tendency towards conformity results in organizations not deviating too much from previous and existing technologies and innovations. Learning from one another 6

9 may be a good strategy for organizational success. However, this also constrains the possibility of organizations carrying out radically or fundamentally different R&D projects, which occur when organizations learn from firms in other industries. Radical ideas often come from outsiders who do not conform to existing practices and experiences in an industry. Organizations in a crowded area often lack resources to carry out fundamental, radical innovations as well due to competitive interactions with competitors that drive organizations to focus on shorter-term R&D projects (Stuart 1999). That is, implementing a long-term R&D project might make an organization s competitive position fragile because it takes great effort, resources and a long time for the project to be successful, especially when its outcome is unpredictable. Instead of allocating resources to the pursuit of fundamental, radical innovations organizations engage in local search based on exploitation of their current knowledge with similar technologies (cf. March 1991). 1 Therefore, we predict that when an organization is under high competitive pressure, it tends to generate incremental innovations at the expense of fundamental, radical innovations (Stuart 1999). Thus, we posit the following hypothesis: Hypothesis 2: The greater the crowding within an organization s niche, the higher its rate of incremental innovation. Prior Experience and Innovation Much of the organizational literature agrees that an organization s prior experience affects its subsequent survival chances. We distinguish de alio firms that have prior experience in another industry from de novo ones that start anew without any prior experience. De alio entrants into an industry from other industries experience low failure rates because they possess slack resources and operational experience (Mitchell 1989; Mitchell 1994; Carroll, Bigelow, Seidel and Tsai 1996; Klepper 2002). They also benefit from previously established connections to customers or affiliation networks from their 1 This imbalance of resource allocation generates more incremental innovations than radical innovations. Our argument differs from the density delay hypothesis that predicts higher mortality rates of organizations founded in crowded markets due to resource scarcity (Carroll and Hannan 1989). We argue that organizations do not allocate resources to fundamental, radical innovations, and not that they are impoverished. 7

10 industries of origin (Aldrich, Staber, Zimmer and Beggs 1990). This piggyback strategy of latching onto existing expertise and networks reduces the cost of mobilizing resources and establishing connections to suppliers and buyers. And when environments are turbulent, such resources, experience and connections can buffer an organization from adverse environmental effects. Further, such diversification is likely more persistent when it is related to the organizations core capabilities (Pennings, Barkema, and Douma 1994, Carroll et al. 1996). If prior experience enhances organizational survival chances, how does it affect the rate of innovation? In terms of structural inertia theory (Hannan and Freeman 1984), de novo organizations do not face the organizational inertia that arises from sunk costs and prior experience. De novo organizations do not have any prior industry experience that might constrain their innovative activities. Especially in rapidly changing industries, de novo organizations can establish new structures and routines that allow them to experiment with new technological ideas. By contrast, de alio organizations are slower to adapt to such environments because of inertial pressures to retain existing routines, structures, and cultures. Since de novo organizations are less constrained by inertia, they are also more likely to learn vicariously from the experiences of their competitors. Evolutionary economists such as Dosi (1982; 1988) conceptualize technological innovation in similar ways with firms moving along technological trajectories constrained by their initial choices. Klepper and Simons (2000) show that de alio firms from the radio industry had higher rates of innovation, greater market shares and higher survival chances than did de novo firms. However, they acknowledge that the TV industry may be atypical of most technology-intensive industries because of the low degree of technological uncertainty during the industry s formative period (Klepper and Simons 2000). We argue that in industries that are characterized by technological uncertainty, de alio firms with prior experience are less likely to produce innovations. Khessina and Carroll s (2008) study of new production introductions in the optical disk drive industry provides evidence in support of this claim. They find that de novo firms introduce products that are technologically superior to those introduced by de alio firms. However, products introduced by de alio firms are available for longer periods of time, not only because of inertial pressures but also because of greater slack resources available to de alio firms. 8

11 De alio firms often grow through mergers and acquisitions, corporate strategies that likely work against organizational innovation (Hitt, Hoskisson, Johnson, and Moesel, 1996). Organizations pursuing such strategies tend to have managers who pay more attention to finance-oriented strategies and less attention to innovation. Given that such finance-oriented strategies are prominent entry modes for existing organizations into other industries, de alio organizations are likely constrained in their innovation activities. By contrast, de novo organizations can pay more attention to innovation activities as they are less likely to face resource competition between finance-related strategies and innovation-related activities. Due to these characteristics of de novo firms, we think de novo firms are more likely than are de alio firms to introduce not only incremental innovations but also radical innovations. With their dedication to an industry, they are better able (compared to de alio firms) to accumulate experience quickly and develop expertise efficiently without being constrained by their past experience. As they are also not constrained by prior organizational and technological legacies, they are flexible enough to catch up with changing technological standards in an industry. As a result, they are the ones who challenge existing market leaders by generating radical innovations that might make old technologies obsolete and that alter the industry standards. Therefore, we propose that de novo firms are more likely than are de alio firms to engage in both incremental and radical innovations. Thus, we offer the following hypothesis: Hypothesis 3: A de novo organization has a higher rate of incremental innovation, as well as a higher rate of radical innovation, when compared to a de alio organization. Innovation, Position Change and Organizational Mortality Punctuated equilibrium models of organizational change describe organizations as evolving through long periods of stability interspersed with short periods of revolutionary change (see e.g., Tushman and Romanelli 1985; Gersick 1991). Revolutionary change or fundamental organizational transformations involve simultaneous change along multiple dimensions of an organization. For instance, Romanelli and Tushman (1994) found that major changes in environmental conditions and the accession of a new CEO increased the likelihood of changes in strategy, structure and power distribution in a 9

12 sample of 25 minicomputer producers. There is some evidence that multi-dimensional organizational changes lead to improved organizational performance in turbulent environments (Virany, Tushman, and Romanelli 1992; Tushman and Rosenkopf 1996). With great prescience, however, Tushman and Romanelli (1985) cautioned that revolutionary changes that involve multiple dimensions of organizations heighten the risk of short-term failure for two reasons. First, revolutionary change disrupts established activities and cultural understandings within an organization. Second, it is possible that the new organizational configuration represents a state that is less munificent than the original configuration. Tushman and Romanelli s cautionary note has received full attention in research in organizational ecology that suggests the utility of separating out the consequences of any organizational change, including innovation, on performance into two distinct effects related to the content and the process of change (Barnett and Carroll 1995). The view that innovations invariably benefit organizations is typically based on the content effects of such innovations. The content effect of a change is the net impact on performance that a firm realizes from moving from one position to another. The process effect of the change is the disruption to the firm's routines and relationships because of the change efforts. In the case of an innovation, the content effect of change, therefore, varies according to the quality of the strategic choice made by a firm. If the innovation reduces the cost of a product or adds value to a product, the content effect is positive. However, if it imposes additional costs or reduces the value of a product, perhaps due to a misconception of customer needs, the content effect of the innovation will be negative. While the content effect of change can be positive or negative, the process effect is always disruptive and is expected to manifest itself in short-term deterioration in firm performance (Barnett and Carroll 1995). Evidence in support of the content-process model of organizational change has been found in the context of various organizational changes in the Finnish newspaper industry (Amburgey, Kelly, and Barnett 1993), new product introductions in the computer industry (Barnett and Freeman 2001) and the transition to a dominant technological design in the bicycle industry (Dowell and Swaminathan 2006). In an earlier study of the effects on innovations on organizational mortality in the US automobile industry, Carroll and Teo (1996) found weak evidence for disruptive process effects. Incremental innovations by a 10

13 focal firm reduced the firm s mortality rate whereas radical innovations did not increase the mortality rate as predicted. In this study, we explore the consequences of simultaneous organizational changes, specifically innovation and market position, on organizational performance. Changes in technology and market position are two of the four types of core changes described by Hannan and Freeman (1984), the others being changes in goals and internal structure and systems. However, as Barnett and Carroll (1995) note, empirical research on the consequences of organizational change has attributed core properties to a wide variety of organizational features. Hannan, Pólos, and Carroll (2003) propose an alternative conceptualization of organizational change. Using the tools of logical formalization, they model organizational change in the form of modifications to an organization s architecture and the subsequent cascade of changes. They show that changes made to organizational units with greater centrality result in longer cascades. Longer cascades, in turn, increase the time spent in reorganization and reduce an organization s growth rate due to missed opportunities, thus increasing its probability of failure. We consider the implications of simultaneous changes in two organizational features, technology and market position, on organizational mortality. Simultaneous changes in technology and market position should result in longer cascades of change within an organization, as other organizational features have to adjust independently to these two changes. This adjustment should take longer than in the case when only one type of change is involved. Further the disruptive effect of these simultaneous changes should be greater in the case of radical technological innovation. Therefore we propose the following hypotheses: Hypothesis 4: The simultaneous occurrence of technological innovation and a change in market position will increase the mortality rate of an organization in the short run. Hypothesis 5: Radical innovation will increase an organization s mortality rate to a greater extent than will incremental innovation when it occurs simultaneously with a change in market position. 11

14 Data and Methods We used data on all the American automobile producers known to have operated during the period to conduct our analyses. These data were derived from a large collection effort that coded the life histories of automobile manufacturers worldwide, using published reports of automobile historians and collectors (Carroll and Hannan 1995; Hannan, Carroll, Dundon and Torres 1995). Most of the information on American automobile manufacturers came from a multi-volume encyclopedic source that provides thorough, authoritative coverage called the Standard Catalog of American Cars (Flammang 1989; Gunnell, Schrimpf and Buttolph 1987; Kimes and Clark 1989; Kowalke 1997). The New Encyclopedia of Motor Cars (Georgano 1982), The World Guide to Automobile Manufacturers (Baldwin, Georgano, Sedgwick and Laban 1987), The Complete Guide to Kit Cars, Auto Parts, and Accessories (Kutner 1979), and America at the Wheel: 100 Years of the Automobile in America (Automotive News 1993) provided supplementary information. Since the information sources do not report the histories of automobile producers but of marques, aggregation of the information to the firm level was required (Carroll and Teo 1996; Dobrev et al. 2002). For example, the data sources do not list a single entry for General Motors, but separate entries for Chevrolet, Cadillac, Buick, Oldsmobile, Pontiac and other marques produced at various times by General Motors. The aggregated firm-level data file that was created thus captured the life histories of all American automobile manufacturing firms known to have existed at some point in time between 1885 and Information on innovations in the American automobile industry came from Abernathy, Clark, and Kantrow s (1983) Industrial Renaissance: Producing a Competitive Future for America. Abernathy, Clark and Kantrow (1983) used various industry and expert sources to generate a chronological, firmspecific listing of innovations introduced by American automobile producers. As they tapped a diverse range of data sources (from published works by scholars to trade industry journals and publications to various company histories), Abernathy et al. s (1983) listing captured both innovations that were commercial successes as well as innovations that hardly made an impact on the market. Therefore, we believe that Abernathy et al. s (1983) list of innovations does not suffer from a retrospective success bias 12

15 and does not conflate innovativeness with the success of the innovation. The comprehensive list identified a total of 641 innovations (i.e., both product and process innovations) for the period While most of the innovations listed were linked to specific automobile manufacturing firms, there were 10 innovations attributed to all producers or most producers, and another 26 innovations attributed to component suppliers. We excluded these 36 innovations from our dataset, as our research interest lies primarily in examining the competitive dynamics among automobile producers. Measures For our analyses, we considered each firm i's tenure in the automobile manufacturing industry rather than its organizational age (Dobrev et al. 2002). We calculated the tenure in automobile production in a straightforward fashion when the firm s dates of entry and exit were exact or nearly exact. However, the archival sources contained varying degrees of precision in dating events. Sometimes, the sources gave the exact date of an event; at other times, they gave only the month of a year, the season of a year, or just the year itself. To make our analyses tractable, we converted all of the information on entry and exit dates to decimal years. Dates given to only the year were coded as occurring at the midpoint of that year. These coding rules are consistent with Petersen s (1991) recommendations for dealing with the problem of time aggregation. Number of Innovations, Number of Incremental Innovations, and Number of Radical Innovations. Abernathy et al. (1983) defined an innovation as the first significant commercial introduction of a new product or process idea. For each innovation that they identified, Abernathy et al. (1983) rated it on a seven-point transilience scale. Their concept of transilience indicates the degree of overall impact an innovation had on the production process. This conception reflects Abernathy et al. s (1983: 109) assertion that what is truly important about innovation, whatever its technical novelty, is the extent to which it changes an industry s basis of competition at the same time that it disrupts established production competence, marketing, and distribution systems, capital equipment, organizational structures, and the skills of both managers and workers. 13

16 An innovation that measures 1 on the transilience scale is one that has no or very little impact on the production process. In contrast, an innovation that scores a 7 on the transilience scale is one that is very disruptive to the production process. Examples of innovations with the scores of 1 are: Elmore s introduction of electric sidelights and taillights in 1901; Chrysler s introduction of front seats that move up and down as well as back and forth in 1938; and Packard s introduction of power-operated windows in And examples of innovations with transilience scores of 7 are: General Motors large-scale production of V-8 engines for its Cadillac marque in 1914; Hudson s introduction of inexpensive closed cars built of wood and steel in 1922; and Nash s Budd design of an all-steel, single unit body (i.e., integral body and frame) in Following Carroll and Teo (1996) in their earlier study of innovation and organizational failure in the American automobile industry, we also collapsed the seven-point transilience scale into two categories. 2 One category consists of innovations with transilience scores of 1 to 3, and the other category consists of innovations with scores of 4 to 7. The former category constitutes incremental innovations (81% of all innovations), while the latter constitutes radical innovations (19%). Almost one in five innovations is considered a radical innovation. On the basis of this categorization, we then constructed three variables: the number of incremental innovations, the number of radical innovations, and the number of innovations (i.e., the sum of both incremental and radical innovations) introduced by firm i in year t. Tables 1a and 1b provide data on the distribution of incremental and radical innovations over time. They show that the more radical the innovations are, the fewer they are, providing some face validity for our measure. [Tables 1a and 1b about here] out of 327 firms yearly spells have multiple innovations. Therefore, using continuous innovation scores leads to two possible coding schemes: 1) the highest innovation score among innovations or 2) the average innovation score among innovations. Suppose a firm has three innovation scores, 1, 5 and 7. We get two numbers, 7 (highest) and 4.33 (average). However, neither of them captures the fact that it has one incremental (1) and two radical innovations (5 and 7). Our chosen coding scheme allows us to measure numbers of increment and radical innovations separately. We note that our models also include numbers of incremental and radical innovations at time t-1 respectively as control variables. 14

17 It is noteworthy that this study takes the focal organization as the unit of analysis. The unit of analysis in prior innovation research has often been the product/process innovation itself (Utterback and Abernathy 1978), technological characteristics (Abernathy and Utterback 1978; Anderson and Tushman 1990; Burgelman 1994; Cooper and Schendel 1976; Henderson and Clark 1990; Tushman and Anderson 1986; Utterback 1994), or the industry including its subfields (Mitchell 1989; Mitchell 1994; Tushman and Anderson 1986), rather than the focal innovating organization (but see Stuart 1999; Wade 1996). Innovation Clock, Incremental Innovation Clock, and Radical Innovation Clock. These three variables indicate the time (in years) that have elapsed since firm i last introduced an incremental innovation, a radical innovation, and an innovation (either incremental or radical), respectively. Niche Width and Niche Overlap. Niche width indicates the range of engine capacities in terms of horsepower across all models produced by firm i in any given year t. Niche overlap is a count of all the firms whose niches overlapped with the niche of the focal firm i in a given year t. It should be noted that although using a single dimension to define organizational niches has its limitations, this definition allows us to draw meaningful comparisons among firms that have existed in remote historical periods and thus makes it possible to analyze the industry in its entirety. Our choice of engine capacity made by automobile producers over the years reveals not only the ranges of their technological offerings but also of these firms strategies in product marketing and competitive pricing (cf. Dobrev et al. 2001). Prior experience. Some firms records indicated that they engaged in other activities prior to entering the automobile industry. These de alio firms likely entered the automobile industry with a greater resource base and more experience than did de novo firms. The dummy variable, prior experience is coded as 0 if firm i was a de novo firm without any other industry experience before entering the automobile industry. The dummy is coded as 1 if firm i was a de alio firm, i.e., a lateral diversifier that operated in another industry before entering the automobile industry (Carroll et al. 1996). Position Change (PC). We measured the magnitude of the changes in the niche width position of firm i as the difference in the distance from the market center at which the firm s niche midpoint is between two consecutive years only if it voluntarily changed its niche width position in the two years. 15

18 This means that this variable measures a firm s relative movement from its original position compared with changes in the market center at a certain point in time. Control Variables. We measured organizational size as firm i s scale of operations, specifically its annual production of automobiles (cf. Dobrev et al. 2001). We control for the main effects of organizational size on the rate of innovation and the rate of disbanding/exiting using the metric measure of size, ln(size). We specify the effects of organizational density (N) in nonmonotonic fashion, consistent with established theory and findings in organization ecology (Carroll and Hannan 2000). This specification includes a linear and second-order term (N 2 ) of annual counts of the number of producer organizations. Following Hannan (1997), we interact the effects of the contemporaneous density variables with a set of variables measuring the age of the population (Ind. Age). This specification allows the effects of density to vary as a function of the population age. We also include a fixed covariate for each firm measuring density at the time of its entry into the industry (Density at entry). We also control for the effects of market center stability, which equals unity if the midpoint of the technological niches of the four largest firms in the market does not shift between two consecutive years. The market center is defined by the range of the niches of the four largest firms in the industry each year (Dobrev, Kim, and Carroll 2003). Distance away from the market center is measured as the difference between the midpoint of the focal firm s niche and the midpoint of the market center. We estimate the effects of the distances of firms both above the market center (Position: DAMC), meaning a niche width that contains a larger engine capacity than the center, and below the market center (Position: DBMC). We measure change in relative position as the difference in the distance from the market center at which the firm s niche midpoint stands between two consecutive years and niche center change as the difference in each firm s niche midpoint between two consecutive years. Cumulative niche center change (CNCC) sums the number of prior changes in a firm s niche center and time since last change is a clock variable that counts the years elapsed since the last niche center shift. On the basis of Altshuler, Anderson, Jones, Roos, and Womack s (1984) assessment of three distinct technological or organizational regimes in the world s automobile manufacturing industry, we 16

19 coded three dummy variables corresponding to the dates associated with the regimes: (a) mass production, which took the value of 1 from ; (b) product differentiation, which took the value of 1 from ; and (c) JIT/TQC (i.e., just-in-time/total quality control), which took the value of 1 from The gross national product variable, GNP, was adjusted for inflation, and the annual values were obtained from Maddison (1991). We excluded the years of the Second World War from our analyses because the production of motor vehicles in the U.S. for private use was minimized during the duration of the war. Model Specification and Estimation Rates of Innovation We considered two innovation rates as dependent variables: the rate of incremental innovation and the rate of radical innovation. Because we needed to analyze both between-firm and within-firm variations, we did not use fixed effect models that capture only within-firm variation. Instead, we used random-effects models with generalized method of moments (GMM) and maximum likelihood (ML) estimators. 3 It was important to control for unobserved heterogeneity between firms since innovation is a repeated event. We included a variable that measures a firm s number of innovations in a previous year and four period dummy variables in all models. For random effect models, we found that there is negligible autocorrelation in our data, which does not affect our results significantly. To deal with problems associated with heteroskedasticity or misspecification of the error structure, we estimated socalled robust standard errors, using the sandwich estimator developed by Huber (1967) and White (1982) to obtain consistent standard errors. Tables 2 and 3, respectively, present the descriptive statistics and correlations of the variables we specified in the innovation rate models. [Tables 2 and 3 about here] 3 To confirm the results from random-effects models, we also used the method of generalized estimating equations (GEE) developed by Liang and Zeger (1986; see also Zeger and Liang 1986). We found that our results remained unchanged. 17

20 Rates of disbanding/exit To estimate the rates of disbanding/exit, we used a stochastic piecewise-exponential function with organizational tenure as the key time clock. Upon examining the life histories of firms and exploring the estimates of various durational breakpoints for organizational tenure, we determined that an appropriate set of tenure breakpoints would be at 1.0, 3.0, 7.0 and We estimated models using the method of maximum likelihood as implemented in TDA 5.7 (Rohwer 1994; Blossfeld and Rohwer 1995). As per convention with time-varying covariates, this is a split-spell file with spells artificially censored each year and the values of the covariates updated. Tables 4 and 5, respectively, show the descriptive statistics and correlations of the variables we specified in the organizational mortality models. [Tables 4 and 5 about here] Results Rates of Innovation Tables 6 and 7 present the models for the rate of incremental and radical innovation. In addition to basic control variables, such as organizational age, organizational size, GNP, period dummies, population density, distance from the market center, as well as variables related to past innovation activities, model 1 in Table 6 and model 5 in Table 7 includes the variable, niche width, to test hypothesis 1. [Tables 6 and 7 about here] Model 1 in Table 6 indicates that niche width has a positive impact on the rate of incremental innovation. By contrast, model 5 in Table 7 does not yield a significant niche width effect on the rate of radical innovation. These results provide empirical support for hypothesis 1. Model 2 in Table 6 and Model 6 in Table 7 include an additional niche overlap variable to test hypothesis 2, which suggests a positive effect of crowding on the rate of incremental innovation, but no effect on the rate of radical innovation. However, the results of the test of hypothesis 2, which posits a positive effect of niche overlap on the rate of incremental innovation, are mixed. Model 2 yields an 18

21 unexpected negative effect of niche overlap on the rate of incremental innovation. Therefore, in model 3, we included its squared term and found a non-monotonic, U-shaped relationship between niche crowding and the rate of incremental innovation. Models 6 and 7 in Table 7 show that niche overlap does not have a significant effect on the rate of radical innovation. Our interpretation of this result is that a firm does not feel unduly threatened as soon as a small number of entrants target the firm s market position. Often, its immediate reaction is to fight back, perhaps by engaging in a price war. In fact, the firm may limit its innovation activities and channel its resources to compete more fiercely with the new entrants. However, as the niche overlap increases further and the market becomes even more crowded, the firm realizes that it is losing the competitive battle and begins exploring alternatives along its existing technological trajectory. Accordingly, we observe that the rate of incremental innovation starts to increase as a consequence of more intensified competition. Model 4 in Table 6 and model 8 in Table 7 incorporate an additional dummy variable, an organization s prior experience. Model 4 offers strong empirical support for hypothesis 3, which predicts that the rate of both incremental and radical innovation is higher for a de novo firm than for a de alio firm. Newcomers without any prior experience from other industries (i.e., de novo firms) are more likely to innovate than are firms with prior experience from other industries (i.e., de alio firms). Rates of Disbanding/Exit Table 8 presents the models for testing the effects of innovations and position change on the rate of disbanding/exit. All of the models shown in table 8 include the effects of other variables shown to have influenced the disbanding rate of automobile firms in earlier studies. 4 [Table 8 about here] Model 1 presents the effects of the focal organization s own innovations on the rates of disbanding/exit. We find that innovating organizations are more likely to experience a higher survival rate. 4 The full model indicating the effects of all the covariates on the mortality rate is presented in Appendix A. Table 8 extracts from Appendix A the estimates of covariates that correspond to our hypotheses. 19

22 Model 2 presents the joint effects of a firm s innovations and position change. We find that the joint effects predicted by hypothesis 4 are not statistically significant. However, when incremental innovations and radical innovations are separated in model 2, we find that only incremental innovations lower the rate of disbanding/exit; radical innovations do not affect the rate of disbanding/exit. Model 4 presents interaction terms between incremental or radical innovations and position change. We find that while the interaction variable between a firm s incremental innovations and position change does not affect the rate of disbanding/exit, the interaction variable between a firm s radical innovations and position change positively affects the rates of disbanding/exit. This finding lends empirical support to hypothesis 5 that radical changes in both technology and market position resulting in longer cascades of change within an organization generate more disruptive effects on organizations than do changes in either technology or market position. 5 Discussion We examine how ecological processes affect the rate of radical and incremental innovation and how each type of innovation affects organizational performance (e.g., mortality) when it occurs with a change in market position. Our results suggest that the way the three ecological features (scope, crowding, and prior experience) affect the rate of innovation depends on the specific type of innovation considered. We find that while scope and crowding affect only the rate of incremental innovation, prior experience in another industry decreases both the rate of incremental innovation and the rate of radical innovation. These results are consistent with previous assertions that different types of innovations emerge through different paths and interactions (Tushman and Anderson 1986) and have different consequences for organizational action and performance. 5 Appendix B presents simpler versions of the models included in table 8. These models only include the effects of organizational age, size, innovations and position changes on the mortality. We find that the results are similar to those reported in the more complete models in table 8. The only difference is that the negative effect of position change on the mortality rate becomes statistically significant. The results of these simpler models increases our confidence in the pattern of results reported in table 8. 20