Incremental and Radical Innovation by
U.S. Automobile Manufacturers, 1885-1981:
An Ecological Perspective
Tai-Young Kim
SKK Graduate School of Business
Sungkyunkwan University
Email: mnkim@skku.edu
Tel: 82-2740-1514
Fax: 82-2-740-1513
Anand Swaminathan
Goizueta Business School
Emory University
Email: anand.swaminathan@emory.edu
Tel: 1-404-727-2306
Fax: 1-404-727-6663
Albert C Y Teo
Department of Management and Organization
National University of Singapore
Email: albertteo@nus.edu.sg
Tel: 65-6874-6444
Fax: 65-6775-5571
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.
Incremental and Radical Innovation by
U.S. Automobile Manufacturers, 1885-1981:
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 1885-1981, we find substantial
support for our predictions.
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 1885-1981. 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
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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
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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
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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
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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
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
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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.
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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.
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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
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
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.
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Data and Methods
We used data on all the American automobile producers known to have operated during the
period 1885-1981 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 1981.
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
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 1885-1981. 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.”
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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
1948. 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 1941.
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]
2
126 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.
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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
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 (N2 ) 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
coded three dummy variables corresponding to the dates associated with the regimes: (a) mass
production, which took the value of 1 from 1902-1981; (b) product differentiation, which took the value
of 1 from 1950-1981; and (c) JIT/TQC (i.e., just-in-time/total quality control), which took the value of 1
from 1968-1981. 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
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 15.00
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
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
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
In evaluating the consequences of innovation and market position change, we find that while
innovation alone improves an organization’s survival chances, innovation in concert with market position
change lowers its survival chances. Further analysis revealed that the improvement in survival chances is
entirely due to incremental innovations while the disruptive effects of innovation and position change can
be attributed to radical innovations. These results are consistent both with the intuition of Tushman and
Romanelli (1985) on the risks of implementing simultaneous organizational changes and with the
performance consequences of a cascading model of organizational change (Hannan, Pólos, and Carroll
2003). Changes in routines required by radical innovations are likely to lead to longer cascades of
changes in other parts of an organization’s architecture, thus leading to deterioration in performance.
While our results are consistent with such an explanation, future research could track the cascades set off
by specific incremental and radical innovations and model their impact directly on organizational
performance.
Technological innovation by a focal organization not only represents an attempt to adapt to
environmental changes, but also has implications for organizational survival (Klepper and Simons 1997).
Therefore, research on the causes and consequences of technological innovation has the potential to
inform both adaptation and selection processes in organizational populations. This paper attempts to
bridge the literature on selection and adaptation processes (see e.g., Levinthal 1991) with the innovation
literature by addressing two main arguments: first, that innovation is a critical mechanism through which
scope, crowding and prior experience influence adaptation and selection processes in an organizational
population; and second, that the workings of this mechanism depends on the type of innovation. These
two main arguments are tested in two ways: first, by examining how each of the ecological features
affects the rates of two different types of innovations: incremental and radical innovations (hypotheses 1
to 3); and, second, by showing the joint effects of innovations and position change on rates of
organizational disbanding/exit (hypothesis 4) and the joint effects of different types of innovations and
position change on rates of organizational disbanding/exit (hypothesis 5).
21
Previous research in organizational ecology has explained industrial evolution in terms of the density
dependence theory of legitimation and competition (Hannan and Carroll 1992; Carroll and Hannan 2000),
resource partitioning theory (Carroll 1985), localized competition (Hannan et al. 1995; Bigelow, Carroll,
Seidel and Tsai 1997), prior experience versus focused identity (Carroll et al. 1996), population ageing
process (Hannan 1997), scale-based competition (Dobrev and Carroll 2003), and moves between market
segments (Dobrev and Kim 2006). Our results suggest that future research could address how different
types of innovations are intertwined with these processes to better explain industrial evolution. For
example, it would be useful to see how innovative activities are intertwined with resource partitioning in
organizational populations (Carroll 1985). Given our findings that de novo firms are more likely to
innovate, these firms might be creating new market segments, while the large generalists tend to expand
through a strategy of brand proliferation and image differentiation (Swaminathan 2001). Our paper
suggests that a consideration of technological innovation along with other ecological processes can
provide us with a comprehensive account of industry evolution.
22
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28
Table 1a. Technological Innovations among U.S. automobile manufacturers, 1885-1981
Number of firms observed in production
2149
Number of firm-year spells
8892
Number of technical innovations
642
Number of technical innovations with:
Low extensiveness score
520
High extensiveness score
122
Table 1b. Number of incremental and radical innovations by firm i in year t.
Innovation score
1
2
3
4
5
6
7
Total
Frequency
272
169
79
66
37
10
9
642
Percent
42.2
26.2
12.3
10.2
5.7
1.6
1.4
100
Valid Percent
42.4
26.3
12.3
10.3
5.8
1.6
1.4
100.0
Cumulative Percent
42.4
68.7
81.0
91.3
97.0
98.6
100.0
100
Table 2. Descriptive Statistics for Variables used in the Innovation Analyses
No. of innovations
No. of radical innovations
No. of incremental innovations
Organizational age
Organizational size
No. of incremental innovations in year t-1
No. of radical innovations in year t-1
No. of innovations in year t-1
Clock for all innovations
Clock for incremental innovations
Clock for radical innovations
Distance from market center
Density
Niche width
Niche overlap
Prior Experience
Min.
0
0
0
.5
-2.3
0
0
0
0
0
0
0
1
.01
0
0
29
Max.
12
4
11
78
15.48
11
4
12
42
42
29
364.25
345
552.01
344
1
Mean
.088
.017
.072
6.43
3.70
.07
.02
.09
.67
.72
.64
21.33
203.05
14.93
83.97
.41
Std. Dev.
.53
.15
.46
10.05
3.69
.46
.16
.54
2.81
3.01
2.99
32.82
110.31
35.45
83.21
.49
Table 3. Bivariate Correlations among Variables used in the Innovation Analyses
#
V1
V2
V3
V4
V5
V6
V7
V8
V9
v10
v11
V12
V13
V14
V15
v16
Variable
No of innovations
No of radical innovations
No of incremental innovations
Organizational age
Organizational size
No of innovations (t-1)
No of radical innovations (t-1)
No of incremental innovations (t-1)
Clock for all innovations
Clock for radical innovations
Clock for incremental innovations
Distance from market center
Density
Niche width
Niche overlap
Prior Experience
1
1
.58
.96
.45
.36
.63
.33
.61
-.04
.07
-.03
-.03
-.01
.53
-07
.04
2
3
1
.34
.20
.22
.31
.26
.27
-.03
-.02
.01
-.03
-.03
.19
-02
.03
1
.46
.35
.62
.30
.62
-.04
.09
-.04
-.03
-.16
.54
-07
.04
4
5
6
1
.64
1
.45 .35
.18 .20
.46 .34
.32 .30
.38 .33
.34 .31
.07 .07
-.37 -.28
.59 .39
-.24 -.23
-.08 -.10
1
.59
.95
-.02
.08
-.01
.01
-.08
.51
-.07
.04
7
8
9
10
11
1
.34
1
-.01 -.02
1
.01 .09 .44
.003 -.02 .95
-.03 .001 -.04
-.03 -.08 .001
.18 .54 .03
-.04 -.07 -.08
.02 .03 .07
1
.42
-.04
-.03
.11
-.09
-.09
1
-.05
.002
.03
-.08
-.07
12
1
-.54
1
.20 -.36
-.40 .54
.18 -.18
Table 4. Descriptive Statistics for the Variables used in the Mortality Analyses
#
V1
V2
V3
V4
V5
V6
V7
V8
V9
V10
V11
V12
V13
V14
V15
V16
V17
V18
V19
V20
V21
V22
V23
V24
V25
Label
Mass Production
Production Differentiation
JIT / TQC
Depression year
Density
Density at entry
Ln (Size+.1)
Relative size (x 10 –3)
Size ≤ 50 (Dummy)
Prior experience (Dummy)
GDP
Niche width (NW)
Niche overlap (NO)
Position: distance above market center (DAMC)
Position: distance below market center (DBMC)
Change in relative position
Niche center change (Dummy)
Cumulative niche center change (CNCC)
Time since last change
Market center stability (Dummy)
Industry concentration (C4)
Scale competition x Size > 50
Position Change (PC)
No. of radical innovations
No. of incremental innovations
30
Min
0.00
0.00
0.00
0.00
1.00
1.00
-2.30
0.00
0.00
0.00
42.40
0.01
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.31
0.00
0
0
0
Max
1.00
1.00
1.00
1.00
345.00
345.00
15.48
5284.50
1.00
1.00
977.10
552.01
362.00
206.50
364.25
275.02
1.00
53.00
9.00
1.00
1.00
59.92
275.02
4
11
Mean
0.90
0.14
0.08
0.17
204.90
224.87
3.12
35.55
0.68
0.57
200.75
12.93
86.12
3.95
16.95
5.18
0.52
3.83
0.17
0.45
0.65
1.06
4.38
.017
.072
13
Std. Dev.
0.30
0.35
0.27
0.38
109.55
106.44
3.48
284.97
0.47
0.50
221.07
32.09
82.75
9.89
33.02
11.79
0.50
6.57
0.66
0.50
0.21
3.43
11.43
.15
.46
14
15 16
1
-.07
1
.08 -.10
1
Table 5. Bivariate Correlations among Variables used in the Mortality Analyses
V1
V2
V3
V4
V5
V6
V7
V8
V9
V10
V11
V12
V13
V14
V15
V16
V17
V18
V19
V20
V21
V22
V23
V24
V25
Label
V1
V2
V3
V4
V5
V6
V7
Mass Production
Production Differentiation
JIT / TQC
Depression year
Density
Density at entry
Ln (Size+.1)
Relative size (x 10 –3)
Size ≤ 50 (Dummy)
Prior experience (Dummy)
GDP
Niche width (NW)
Niche overlap (NO)
Position: distance above market center (DAMC)
Position: distance below market center (DBMC)
Change in relative position
Niche center change (Dummy)
Cumulative niche center change (CNCC)
Time since last change
Market center stability (Dummy)
Industry concentration (C4)
Scale competition x Size > 50
Position Change (PC)
No. of radical innovations
No. of incremental innovations
1
0.13
0.09
0.05
0.28
0.24
0.21
0.04
-0.17
-0.01
0.19
0.08
-0.13
-0.05
0.14
0.11
0.06
0.14
0.07
-0.07
0.02
0.09
0.05
0.02
-0.01
1
0.72
-0.18
-0.60
-0.60
0.14
0.20
0.01
-0.28
0.90
0.49
-0.33
-0.03
0.65
0.34
0.16
0.23
0.04
0.04
0.65
-0.09
0.09
0.13
0.03
1
-0.13
-0.41
-0.45
0.09
0.12
0.04
-0.25
0.90
0.19
-0.25
-0.02
0.41
0.27
0.15
0.16
0.05
0.01
0.50
-0.06
-0.01
0.06
0.003
1
-0.07
0.04
0.05
-0.02
-0.06
0.03
-0.15
-0.06
-0.04
0.08
-0.06
-0.02
-0.02
0.03
0.01
0.06
-0.06
0.03
0.02
0.01
0.02
1
0.70
-0.24
-0.17
0.09
0.18
-0.57
-0.34
0.50
-0.02
-0.51
-0.25
-0.12
-0.30
0.01
-0.18
-0.77
0.07
-0.08
-0.14
-0.07
1
0.03
0.08
-0.09
0.21
-0.55
-0.14
0.35
-0.01
-0.53
-0.20
-0.14
0.00
0.07
-0.10
-0.56
0.08
-0.07
0.06
0.03
1
0.39
1
-0.86 -0.18
0.10 -0.05
0.18
0.18
0.37
0.64
-0.22 -0.08
0.02 -0.01
0.07 -0.00
0.19
0.11
0.19
0.06
0.59
0.49
0.19
0.03
0.08
0.02
0.34
0.17
0.19 -0.03
0.05 -0.001
0.03
0.65
0.20
0.27
V11
V11
V12
V13
V14
V15
V16
V17
V18
V19
V20
V21
V22
V23
V24
V25
1
0.34
-0.37
-0.04
0.63
0.31
0.18
0.25
0.03
0.05
0.68
-0.06
0.06
0.09
0.02
V12
1
-0.06
0.03
0.20
0.27
0.16
0.52
0.01
0.001
0.34
-0.05
0.10
0.52
0.18
V13
1
-0.07
-0.38
-0.17
0.13
-0.16
0.11
-0.08
-0.59
-0.04
-0.03
-0.07
-0.02
V14
1
-0.20
0.06
-0.05
0.03
0.07
0.02
-0.00
0.08
0.12
0.01
-0.01
V15
1
0.26
0.05
0.04
0.03
0.07
0.56
-0.06
0.04
-0.03
-0.02
V16
1
0.28
0.25
0.001
-0.14
0.30
0.00
-0.02
0.08
0.03
V17
1
0.37
0.27
0.02
0.14
0.02
0.05
0.33
0.20
31
V18
1
0.10
0.06
0.35
0.04
-0.01
0.65
0.27
V19
1
0.02
0.07
0.11
-0.04
-0.17
-0.11
V20
1
0.01
0.01
-0.01
0.03
-0.01
V21
1
0.01
0.10
0.12
0.05
V22
1
-0.04
-0.10
-0.07
V8
V23
1
0.004
0.005
V9
V10
1
-0.16
1
-0.01 -0.29
-0.19 -0.08
0.12
0.10
-0.04
0.02
-0.01 -0.19
-0.13 -0.05
-0.17 -0.003
-0.45
0.05
0.20
0.09
-0.06
0.00
-0.18 -0.18
-0.45
0.05
-0.03 -0.02
-0.17 -0.03
-0.11 -0.02
V24
1
0.35
V25
1
Table 6. Models of Rate of Incremental Innovation of U.S. Automobile Manufacturers, 1885-1981
Organizational age
Organizational size
GNP x 10-2
Mass production
Product differentiation
JIT/TQC
No. of RIs
No. of IIs
Clock for IIs
Distance x 10-2
(Distance)2 x 10-4
Density x 10-2
Niche width
Niche overlap x 10-2
(Niche overlap)2 x 10-4
De Novo
Constant
Wald X2
Model 1
0.01 ●●●
(0.001)
0.01 ●●●
(0.002)
0.01
(0.01)
-0.06 ●●●
(0.02)
-0.14 ●●●
(0.04)
0.02
(0.05)
0.26 ●●●
(0.03)
0.37 ●●●
(0.01)
-0.02 ●●●
(0.002)
-0.08 ●●●
(0.02)
0.01
(0.02)
0.01
(0.01)
0.004 ●●●
(0.0002)
Model 2
0.01 ●●●
(0.001)
0.01 ●●●
(0.001)
0.01
(0.01)
-0.08 ●●●
(0.02)
-0.13 ●●●
(0.04)
0.03
(0.05)
0.25 ●●●
(0.03)
0.36 ●●●
(0.01)
-0.02 ●●●
(0.002)
-0.10 ●●●
(0.04)
0.03 ●
(0.02)
0.02 ●●●
(0.01)
0.004 ●●● (0.0002)
-0.03 ●●●
(0.01)
Model 3
0.01 ●●●
(0.001)
0.01 ●●●
(0.002)
0.01
(0.01)
-0.08 ●●●
(0.02)
-0.12 ●●●
(0.04)
0.04
(0.05)
0.25 ●●●
(0.03)
0.36 ●●●
(0.01)
-0.02 ●●●
(0.002)
-0.10 ●●●
(0.04)
0.03 ●●
(0.02)
0.02 ●●●
(0.01)
0.004 ●●●
(0.0002)
-0.07 ●●●
(0.02)
0.02 ●●
(0.01)
-0.02
0.01
0.02
(0.02)
7717.76
(0.02)
7778.68
(0.02)
7783.51
Model 4
0.01 ●●●
(0.001)
0.01 ●●●
(0.002)
0.01
(0.01)
-0.08 ●●●
(0.02)
-0.12 ●●●
(0.04)
0.03
(0.05)
0.25 ●●●
(0.03)
0.37 ●●●
(0.01)
-0.02 ●●●
(0.002)
-0.10 ●●●
(0.04)
0.03 ●●
(0.02)
0.02 ●●●
(0.01)
0.004 ●●● (0.0002)
-0.07 ●●●
(0.02)
0.02 ●●
(0.01)
0.03 ●●●
(0.01)
0.01
(0.02)
7809.93
Notes: Numbers in parentheses are standard errors; Number of spells: 8,892; Number of firms: 2,197 ● p < .1
●● p < .05 ●●● p < .01; IIs and RIs indicate incremental and radical innovations, respectively.
Table 7. Models of Rate of Radical Innovation of U.S. Automobile Manufacturers, 1885-1981
Model 5
Model 6
Model 7
Model 8
Organizational age
0.001 ●● (0.0003)
0.001 ●●
(0.0003)
0.001 ●●
(0.0003)
0.001●●●
(0.0003)
Organizational size
0.01 ●●● (0.001)
0.01 ●●●
(0.001)
0.01 ●●●
(0.001)
0.01 ●●●
(0.001)
GNP x 10-2
-0.002
(0.004)
-0.002
(0.004)
-0.002
(0.004)
-0.003
(0.004)
Mass production
-0.02 ●●
(0.009)
-0.01 ●
(0.01)
-0.02 ●
(0.009)
-0.02 ●
(0.01)
Product differentiation
0.01
(0.02)
0.01
(0.02)
0.01
(0.02)
0.01
(0.02)
JIT/TQC
-0.07
(0.02)
-0.01
(0.02)
-0.01
(0.02)
-0.01
(0.02)
No. of IIs
0.05 ●●● (0.005)
0.05 ●●●
(0.01)
0.05 ●●●
(0.005)
0.05 ●●●
(0.01)
No. of RIs
0.16 ●●● (0.01)
0.16 ●●●
(0.01)
0.16 ●●●
(0.01)
0.16 ●●●
(0.01)
Clock for RIs
-0.01 ●●● (0.001)
-0.01 ●●●
(0.001)
-0.01 ●●●
(0.001)
-0.01 ●●●
(0.001)
Distance x 10-2
-0.02 ●●
(0.02)
-0.02 ●
(0.02)
-0.03
(0.02)
-0.02 ●
(0.01)
(Distance)2 x 10-4
0.01
(0.01)
0.003
(0.01)
0.01
(0.01)
0.01
(0.01)
Density x 10-2
-0.002
(0.003)
-0.004
(0.003)
-0.003
(0.003)
-0.003
(0.003)
Niche width
0.0001
(0.0001)
0.0001
(0.0001)
0.0001
(0.0001)
0.0001
(0.0001)
Niche overlap x 10-2
0.004
(0.003)
-0.01
(0.01)
-0.01
(0.01)
(Niche overlap)2 x 10-4
0.004
(0.003)
0.004
(0.003)
De Novo
0.01 ●●●
(0.004)
Constant
0.02●
(0.008)
0.01
(0.009)
0.02●
(0.009)
0.01
(0.01)
Wald X2
1171.10
1177.48
1180.46
1192.32
Notes: Numbers in parentheses are standard errors; Number of spells: 8,892; Number of firms: 2,197;
● p < .1 ●● p < .05 ●●● p < .01; IIs and RIs indicate incremental and radical innovations, respectively.
32
Table 8. Estimated effects of innovations and position change variables on the disbanding/exit
hazard of U.S. automobile manufacturers, 1885–1981 (see Appendix B for the full model)
Model 1
Model 2
Model 3
Model 4
Tenure in the industry
U < 0.5
0.5 ≤ u < 1
1≤u<3
3≤u<7
U≥7
Innovations and Position Change
-1.57●●
-1.51●●
-1.96●●●
-2.07●●●
-1.53●●
(0.62)
(0.63)
(0.63)
(0.63)
(0.73)
-1.57●●
-1.51●●
-1.96●●●
-2.07●●●
-1.53●●
(0.62)
(0.63)
(0.63)
(0.63)
(0.73)
-1.56●●
-1.50●●
-1.95●●●
-2.06●●●
-1.52●●
(0.62)
(0.63)
(0.63)
(0.63)
(0.73)
-1.55●●
-1.49●●
-1.94●●●
-2.06●●●
-1.51●●
(0.62)
(0.63)
(0.63)
(0.63)
(0.73)
Position change (PC)
-0.003
(0.01)
-0.003
(0.01)
-0.003
(0.01)
-0.003
(0.01)
All innovations
All innovations x PC
-0.84●●
(0.34)
-1.06●●
0.04
(0.42)
(0.03)
Incremental innovations
-1.23●● (0.48) -1.19●● (0.55)
Radical innovations
-0.08
(0.52) -0.79
(0.79)
Incremental innovations x PC
-0.01
(0.06)
Radical innovations x PC
0.11●●● (0.04)
Notes: ● p < .1 ●● p < .05 ●●● p < .01 Note: T-statistics are in parentheses. u denotes tenure in the industry.
All other variables described in the paper are controlled for.
33
Appendix A. Estimated effects of innovations and position change variables on the disbanding/exit
hazard of U.S. automobile manufacturers, 1885–1981 (Full Model)
Model 1
Tenure in the industry
U < 0.5
-1.57●●
-1.51●●
0.5 ≤ u < 1
-1.96●●●
1≤u<3
-2.07●●●
3≤u<7
-1.53●●
U≥7
Prior existence
-0.10●●
Socio-Econ-Industrial Environment
Mass Production
0.73●●●
Production Differentiation
0.55●●
JIT / TQC
0.39
Depression year
-0.22●●●
GDP
-0.003●●
Organizational Size-Based Measures
Ln (Size)
u<7
-0.18●●●
u≥7
-0.22●●●
Size ≤ 50
Model 2
Model 3
Model 4
(0.62)
(0.63)
(0.63)
(0.63)
(0.73)
(0.05)
-1.57●●
-1.51●●
-1.96●●●
-2.07●●●
-1.53●●
-0.10●●
(0.62)
(0.63)
(0.63)
(0.63)
(0.73)
(0.05)
-1.56●●
-1.50●●
-1.95●●●
-2.06●●●
-1.52●●
-0.10●●
(0.62)
(0.63)
(0.63)
(0.63)
(0.73)
(0.05)
-1.55●●
-1.49●●
-1.94●●●
-2.06●●●
-1.51●●
-0.10●●
(0.62)
(0.63)
(0.63)
(0.63)
(0.73)
(0.05)
(0.18)
(0.25)
(0.38)
(0.07)
(0.001)
0.73●●●
0.55●●
0.39
-0.22●●●
-0.003●●
(0.18)
(0.25)
(0.38)
(0.07)
(0.001)
0.73●●●
0.55●●
0.39
-0.22●●●
-0.003●●
(0.18)
(0.25)
(0.38)
(0.07)
(0.001)
0.73●●●
0.54●●
0.39
-0.22●●●
-0.003●●
(0.18)
(0.25)
(0.38)
(0.07)
(0.001)
(0.03)
(0.06)
-0.18●●● (0.03)
-0.22●●● (0.06)
-0.18●●● (0.03)
-0.22●●● (0.06)
-0.18●●● (0.03)
-0.22●●● (0.06)
1.39●●●
0.80
-0.01
1.28●
0.03●●●
-1.04●
(0.50)
(0.59)
(0.01)
(0.73)
(0.01)
(0.61)
1.39●●●
0.81
-0.01
1.28●
0.03●●●
-1.05●
(0.50)
(0.59)
(0.01)
(0.73)
(0.01)
(0.61)
1.37●●●
0.79
-0.01
1.26●
0.03●●●
-1.03●
(0.50)
(0.59)
(0.01)
(0.73)
(0.01)
(0.61)
1.36●●●
0.77
-0.02●
1.27●
0.03●●●
-1.04●
(0.50)
(0.59)
(0.01)
(0.73)
(0.01)
(0.61)
Position change (PC)
-0.003
(0.01)
-0.003
(0.01)
-0.003
(0.01)
-0.003
(0.01)
All innovations
-0.84●●
(0.34)
-1.06●●
(0.42)
0.04
(0.03)
u<7
u≥7
Relative size (x 10 –3)
Industry concentration (C4)
Scale competition x Size > 50
C4 x Size ≤ 50
Innovations and Position Change
All innovations x PC
Incremental innovations
-1.23●● (0.48) -1.19●● (0.55)
Radical innovations
-0.08
(0.52) -0.79
(0.79)
Incremental innovations x PC
-0.01
(0.06)
Radical innovations x PC
0.11●●● (0.04)
Notes: ● p < .1 ●● p < .05 ●●● p < .01 Note: T-statistics are in parentheses. u denotes tenure in the industry.
34
Appendix A (Continued). Estimated effects of other control variables on the disbanding/exit hazard
of U.S. automobile manufacturers, 1885–1981 (Full Model)
Model 1
Model 2
(0.01)
(0.24)
(0.30)
(0.05)
(0.16)
(0.03)
(0.001)
(0.01)
(0.01)
-0.02●●●
0.58●●
0.65●●
-0.11●●
-0.42●●●
0.09●●●
0.001●
-0.05●●●
0.01
Distance below market center (DBMC)
0.04●●●
(0.01)
0.04●●● (0.01)
0.04●●● (0.01)
0.04●●● (0.01)
Change in relative position
0.003
(0.01)
0.003
0.003
0.003
C4 x NW
0.06●●●
(0.01)
0.06●●● (0.01)
0.06●●● (0.01)
0.06●●● (0.01)
C4 x Position: DAMC
-0.01
-0.04●●●
-0.01
(0.01)
(0.01)
(0.01)
-0.01
(0.01)
-0.04●●● (0.01)
-0.01
(0.01)
-0.01
(0.01)
-0.04●●● (0.01)
-0.01
(0.01)
-0.01
(0.01)
-0.04●●● (0.01)
-0.01
(0.01)
0.004●●
(0.002) 0.004●●
(0.002) 0.004●●
(0.002) 0.004●●
(0.002)
-0.01●●
0.01●●
(0.004) -0.01●●
(0.003) 0.01●●
(0.004) -0.01●●
(0.003) 0.01●●
(0.004) -0.01●●
(0.003) 0.01●●
(0.004)
(0.003)
-0.03●●
(0.01)
-0.03●●
(0.01)
-0.03●●
(0.01)
-0.03●●
(0.01)
(0.07)
-0.03
(0.07)
-0.03
(0.07)
-0.03
(0.07)
(0.09)
-0.02
(0.09)
-0.01
(0.09)
-0.01
(0.09)
Niche overlap (NO)
C4 x NO
C4 x NO x Size ≤ 50
Niche center change (NCC)
Cumulative niche center change (CNCC) -0.03
Time since last change
-0.02
Market center stability
NCC x Ln (Size)
NCC x Ln (Size)2 (x 10 –1)
NCC x NW
Log-likelihood
0.22●●●
-0.14●●
-0.01●●●
(0.01)
-0.02●●●
0.59●●
0.65●●
-0.11●●
-0.42●●●
0.09●●●
0.001●
-0.05●●●
0.01
(0.01)
(0.24)
(0.30)
(0.05)
(0.16)
(0.03)
(0.001)
(0.01)
(0.01)
Model 4
-0.02●●●
0.58●●
0.65●●
-0.11●●
-0.42●●●
0.09●●●
0.001●
-0.05●●●
0.01
C4 x Position: DBMC
C4 x NW x Size ≤ 50
(0.01)
(0.24)
(0.30)
(0.05)
(0.16)
(0.03)
(0.001)
(0.01)
(0.01)
Model 3
N
N2 (x 10 –4)
N x Ind. Age (x 10 –3)
N2 x Ind. Age (x 10 –5)
N x Ind. Age2 (x 10 –4)
N2 x Ind. Age2 (x 10 –6)
Density at entry
Niche width (NW)
Distance above market center (DAMC)
(0.01)
-0.02●●●
0.59●●
0.66●●
-0.11●●
-0.43●●●
0.09●●●
0.001●●
-0.05●●●
0.01
(0.01)
(0.24)
(0.30)
(0.05)
(0.16)
(0.03)
(0.001)
(0.01)
(0.01)
(0.01)
(0.06) 0.22●●● (0.06) 0.22●●● (0.06) 0.22●●● (0.06)
(0.07) -0.14●● (0.07) -0.14●● (0.07) -0.15●● (0.07)
(0.003) -0.01●●● (0.003) -0.01●●● (0.003) -0.01●●● (0.003)
-0.32●●●
(0.11)
-3,613.36
35
-0.32●●● (0.11)
-3,612.59
-0.32●●● (0.11)
-3,612.31
-0.32●●● (0.11)
-3,609.64
Appendix B. Estimated effects of innovations and position change variables on the disbanding/exit
hazard of U.S. automobile manufacturers, 1885–1981 (Simple model)
Model 1
Model 2
Model 3
Tenure in the industry
U < 0.5
0.5 ≤ u < 1
1≤u<3
3≤u<7
U≥7
Ln (Size)
Innovations and Position Change
-0.17●●●
-0.23●●●
-0.81●●●
-1.01●●●
-1.07●●●
-.021●●●
(0.03)
(0.05)
(0.04)
(0.06)
(0.09)
(0.01)
Position change (PC)
-0.01●●
(0.003) -0.01●●
(0.003) -0.01●●
All innovations
-1.02●●
(0.34)
-1.42●●
(0.36)
-0.01
(0.09)
All innovations x PC
-0.17●●●
-0.23●●●
-0.81●●●
-1.01●●●
-1.07●●●
-0.21●●●
(0.03)
(0.05)
(0.04)
(0.06)
(0.09)
(0.01)
-0.17●●●
-0.23●●●
-0.81●●●
-1.01●●●
-1.07●●●
-0.21●●●
(0.03)
(0.05)
(0.04)
(0.06)
(0.09)
(0.01)
Model 4
-0.17●●●
-0.22●●●
-0.81●●●
-1.01●●●
-1.06●●●
-0.21●●●
(0.003) -0.01●●
(0.03)
(0.05)
(0.04)
(0.06)
(0.09)
(0.01)
(0.003)
-1.43●●● (0.48) -1.39●●● (0.52)
Incremental innovations
Radical innovations
-0.20
(0.53) -0.76
(0.73)
Incremental innovations x PC
0.002
(0.03)
Radical innovations x PC
0.07 ●● (0.03)
Notes: ● p < .1 ●● p < .05 ●●● p < .01 Note: T-statistics are in parentheses. u denotes tenure in the industry.
These simple models include age, size, innovations and position changes only.
36