Evolutionary Perspectives on Firms’ Internal and External Portfolios of New... Gurneeta Vasudeva

advertisement
Evolutionary Perspectives on Firms’ Internal and External Portfolios of New Capabilities
Gurneeta Vasudeva
University of Minnesota
3-365 Carlson School of Management
321 19th Avenue South
Minneapolis, MN 55455
Phone: (612) 625-5940
Email: [email protected]
Jaideep Anand
Fisher College of Business
Ohio State University
2100 Neil Avenue
Columbus, OH 43210-1144
Phone: (614) 247-6851
Email: [email protected]
Version: November 1, 2015
(Submitted to Strategy Science Special Issue)
1
Evolutionary Perspectives on Firms’ Internal and External Portfolios of New Capabilities
Abstract
Received wisdom from evolutionary theory and behavioral approaches yields potentially heterogeneous
search pathways for firms under uncertain conditions. On one hand, the need for experimentation under
uncertainty propels firms to initiate a broad-based search, followed by the selection and retention of a
narrow set of capabilities as learning occurs. We label this search pathway as ‘outside-in’ wherein variety
is followed by focus. On the other hand, behavioral assumptions of cognitive constraints and routines
point to a focused set of capabilities, which broaden as firms build absorptive capacity over time.
Moreover, path dependence can render the search process inflexible making selective retention more
difficult. We label this alternative search pathway as ‘inside-out’ wherein focus is followed by variety.
We find support for these two alternative search pathways in a radical technological context: ‘inside-out’
and ‘outside-in’ characterizing firms’ internal and external portfolios of capabilities, respectively. We
reason that as learning occurs, firms’ internal portfolios are reconfigured less easily relative to external
portfolios constituting more loosely coupled arrangements such as alliances. Our theory and empirical
findings hold important implications for understanding firms’ technology strategy and innovation
performance in an evolving technological context.
2
Introduction
How do firms configure, adapt, and modify their capabilities in new and emergent contexts? To address
such issues, evolutionary theory based reasoning has pointed to the importance of a learning orientation
aimed at innovation and improvements upon the steady state (Nelson and Winter 1982). As Nelson
(1994:111) observed, for firms facing uncertainty in any given knowledge domain, “good responses…are
still to be learned.” Consequently, evolutionary theory suggests that confronted with alternative
approaches and solutions, firms initialize their search through variation-seeking and experimentation
because such an approach allows for flexibility for reconfiguration as firms monitor the developments in
the industry. Over time, as firms learn and gain experience, a winnowing process occurs resulting in the
retention of only the most relevant knowledge. Based on these mechanisms of variation, selection, and
retention, evolutionary theory predicts an ‘outside-in’ pathway for firms’ search characterized by
increasing diversity followed by more focused approaches.
Although the evolutionary model of variation-selection-retention offers important insights for
understanding the pattern of search, behavioral continuity manifested in cognitive constraints and
organizational routines (Cyert and March 1963, Levitt and March 1988, Levinthal and March 1993),
yields a different pattern of search. According to this reasoning, firms’ search is initialized by focused
investments, and any increase in breadth becomes possible only to the extent that firms build the
commensurate absorptive capacity. Moreover, contrary to the winnowing mechanism, this behavioral
approach emphasizes path dependencies that make it difficult for firms to reconfigure and get rid of
competencies once these are added to a firm’s repertoire. In other words as Nelson and Winter (1982:
134) observed, “firms may be expected to behave in the future, according to the routines they have
employed in the past.” Based on this logic, firms’ capabilities are likely to grow ‘inside-out’.
Thus, while evolutionary theory based reasoning is useful in offering solutions and identifying
constraints to the problems of uncertain and emergent contexts, incorporating the role of cognitive
constraints and path dependence could generate alternative pathways, suggesting therefore, that
3
evolutionary theory may be underspecified. Based on these dual perspectives, we ask the question: do
alternative pathways for building new knowledge and competencies coexist or is there a unique path?
Our goal in this study is to develop and empirically test the implications of evolutionary theory
applied to firms’ internal and external portfolios in an emergent technological setting. Given the
important consequences of new capabilities, especially in the context of radical and uncertain
technological shifts (Henderson and Clark 1990, Nagarajan and Mitchell 1998, Tripsas and Gavetti 2000),
understanding their evolutionary path over time becomes a crucial research question. We propose two
distinct evolutionary processes and argue that they apply differentially to the configuration and
reconfiguration of internal and external portfolios of technological capabilities. We test our hypotheses
using data on the technological diversity of firms’ internal portfolios comprising fuel cell technology
patents produced by firms’ R&D units, and their external portfolios characterized by the fuel cell
technology patents held by their alliance partners, over the period 1981-2004. Fuel cell patents can be
categorized into technological areas that represent the various types and components of fuel cells. The
diversity of firms’ patents therefore, conveys important information about whether firms are
simultaneously developing multiple capabilities or focusing in a few areas.
We find that the evolution of internal portfolios is characterized by an approach which we label
‘inside-out’ whereby focused competencies expand into a broader set of capabilities as the firm builds
absorptive capacity over time. At the same time, internal portfolios are slower to reconfigure implying
that embedded routines and path dependence limit the winnowing process. In contrast to internal
portfolios, the external portfolios evolve ‘outside-in’ which conforms to the more classical prediction of
greater variation in the initial stages, and subsequent reconfiguration through the selection and retention
mechanisms. We suggest that such a path is enabled by loosely coupled inter-organizational arrangements
that lend themselves to experimentation and selective retention as learning occurs. Our findings reveal
that firms may use their internal and external portfolios strategically to counterbalance each other, such
that a greater degree of variation in one is offset by a greater degree of focus in the other, and one is
subject to faster reconfiguration than the other.
4
Theory and Hypotheses
There exists an array of theoretical perspectives on firms’ ability to reconfigure or modify their resources
and capabilities to sustain competitive advantage. On one end of the spectrum, a resource-based
perspective views firms as bundles of capabilities which create value to the extent that the underlying
resources tend to be durable and are not easily replicated or redistributed (e.g., Wernerfelt 1984, Barney
1991, Priem and Butler 2001), leaving firms with few alternatives other than governance mechanisms
rooted in transaction cost economics (Williamson, 1991), to protect and coordinate existing resources and
capabilities. At the other extreme, population ecology argues that firms are fundamentally incapable of
engineering a resource reconfiguration. Instead, in this ecological perspective, change is accomplished at
the population level by organizational birth and death (e.g., Hannan and Freeman 1984, Haveman 1992).
Our approach takes an evolutionary view which resides somewhere in between these contrasting
perspectives concerning firms’ adaptation to a changing environment (Nelson and Winter, 1982). It
emphasizes the ability of firms to reconfigure their portfolios of capabilities, albeit, in a ‘Lamarckian’
sense, limited by cognitive constraints, organizational routines and path dependence. Such a skill
constitutes a dynamic capability which refers to “the firm’s ability to integrate, build, and reconfigure
internal and external competences to address rapidly changing environments” (Teece et al. 1997: 516) and
sustain competitive advantage (Helfat and Peteraf 2003, Helfat et al. 2007).
Alternative evolutionary pathways: ‘outside-in’ and ‘inside-out’
When competing in a new and emergent context, firms are usually faced with a considerable amount of
uncertainty stemming from various unknowns. For example, in the context of technological change, often
players do not know which technologies will emerge as the dominant designs, what kinds of skills and
resources they need to or can develop (Abernathy and Utterback 1978, Tushman and Anderson 1986,
Henderson and Clark 1990), or which firms or industries will ultimately become their competitors or
collaborators. Under such uncertain conditions, evolutionary theory based reasoning calls for learning
through trial and error entailing experimentation with alternatives (Nelson and Winter 1982, Nelson 1994,
Zollo and Winter 2002). As firms receive feedback from their trials and the external environment, they
5
learn about the capabilities most suited to successful competition. Such learning leads to an increase in
focus (Levitt and March 1988). This logic yields the classic evolutionary trajectory of capabilities
characterized by variation-selection-retention, whereby firms initiate search through broad-based
approaches, and then based on the ensuing learning, make their bets and winnow out certain capabilities
while retaining others, resulting in the selection and retention of a narrower set of capabilities. We label
this approach as ‘outside-in’ because in this evolutionary model, firms’ initial response is to broaden their
scope and then narrow it down.
An alternative view which also draws on behavioral theory (Cyert and March 1963) predicts that
firms search cumulatively in the neighborhood of their current knowledge or tend to “climb local peaks”
(Levinthal 1997). Nelson and Winter (1994) similarly observed that “organizations are much better at
changing in the direction of “more of the same than they are at any other kind of change.” Entrenched in
competency traps (Leonard-Barton 1992) that perpetuate search in the neighborhood of their existing
technologies and products, firms are likely to demonstrate less variation at the outset. Levinthal and
March (1993) referred to this short-sighted behavior of firms as “the myopia of learning.” Even as
organizations learn to expand their capabilities, they tend to become entrenched in existing routines and
encounter organizational inertia that inhibits drastic changes or inflexion from the existing trajectory
(Levitt and March 1988, Tripsas and Gavetti 2000). Indeed, as Henderson and Clark (1990) observed,
change becomes especially problematic because it disturbs the firms’ knowledge architecture and requires
redeployment of capabilities. These insights yield a different evolutionary trajectory which is constrained
by absorptive capacity and existing routines such that firms start with a narrow set of capabilities. As
firms learn from their environment, new capabilities are added, but these capabilities persist due to path
dependence and organizational inertia. We label this approach ‘inside-out’ because it starts with a narrow
set of capabilities, continuing to expand in scope.
We suggest that firms’ responses to environmental uncertainty are likely to embody both these
approaches, but apply to different dimensions of their search. Whereas a firm’s internal portfolio of
capabilities will demonstrate the ‘inside-out’ approach, its external portfolio will demonstrate the
6
‘outside-in’ approach. By considering the totality of firms’ capabilities that reside either within the firm or
are accessed through external relationships, our approach is consistent with an ecological view that
accounts for organizational interdependence. According to this view, a firm’s search and adaptation to the
changing technological landscape depends not only on its own capabilities, but also those of others in its
ecosystem thereby perpetuating cooperation and mutualism (Barnett 1990, Barnett and Carroll 1987).
The importance of external portfolios of inter-organizational alliances, in particular, stems from
their ability to help firms obtain resources from the environment, learn and develop capabilities, and
improve performance (Lavie 2006, Anand et al. 2010, Vasudeva and Anand 2011). Alliances also allow
firms to cope with discontinuities by continuously scanning the environment and gathering market
intelligence to gain response time. Moreover, an external orientation helps firms to break out of their
routines and escape path dependency.
In sum, while studies have noted that both internal and external portfolios of capabilities have
important implications for firms’ competitive advantage and innovation performance (Mitchell and Singh
1996, Nagarajan and Mitchell 1998, Aggarwal and Hsu 2009, Vassolo et al. 2004, Vasudeva and Anand
2011), the evolutionary path of these portfolios remains unknown. The following hypotheses take a step
towards addressing this issue.
External portfolio: ‘Outside-in’ evolution
Before theorizing about the evolutionary path of the firm’s external portfolio comprising interorganizational alliances, it is useful to define the key components of the evolutionary model characterized
by variation, selection and retention. The element that varies, is selected, and ultimately retained is
technological capabilities, which in the case of external portfolios resides in the alliance partner as
opposed to the firm. The focal firm is the agent that induces variation and makes selection and retention
choices by periodically reconfiguring the mix of partners (and by extension their technological
capabilities) with which it maintains alliances. The external portfolio of alliances changes as the firm
establishes or renews alliances with certain partners, or as existing relationships are no longer
contributing to learning, either because alliances are terminated or have persisted beyond their productive
7
lifespan. The portfolio’s technological capabilities may also change as partners themselves develop new
competences during the alliance lifetime. As Nelson (1994) suggests, the selection mechanism—the
criteria driving “fitness”—functions as the key to explaining the direction of the evolutionary process.
Applied to this study, it is worth clarifying what drives the firm to reconfigure its external portfolio, and
in what direction such reconfiguration will occur. We argue that the focal firm’s technological age
characterizing its lifespan and the learning that occurs from the alliance relationships function as the
primary selection mechanism underlying the process by which portfolio capabilities are reconfigured.
More variation in the early stages. When faced with novel and uncertain technological contexts,
firms seek alliances with other firms to access new capabilities and engage in complementary
development (Nagarajan and Mitchell 1998, Anand et al. 2010). Importantly, however, in the early stages
of an emergent technological context there is uncertainty regarding which partners the firm should ally
with, which have the best capabilities, and which will provide the best relational and technological fit
with the focal firm (Vasudeva and Anand 2011). For these reasons, in the nascent stages, firms will likely
follow a strategy of seeking variation in their search for partners’ capabilities. The purpose is to broaden
the technological scope of the external portfolio to increase exposure and postpone decisions regarding
focused investments. Such a logic for configuring the external portfolio resonates with extant work
suggesting that firms cope with uncertainty by establishing relationships with new partners bringing novel
skills and information rather than redundant resources (Beckman et al. 2004, Goerzen 2007). Moreover,
maintaining a more diverse external portfolio insures against the risk of not finding the appropriate
partners in the event of a discontinuous change, when competition for external resources--that can
potentially enhance the value of a firm’s existing capabilities—is likely to intensify. Thus, in the early
stages of a firm’s entry into an uncertain domain, a broad range of technological capabilities will be
represented in a firm’s external portfolio.
Faster reconfiguration towards selection and retention. In subsequent years, as firms gain
experience, the quest for diversity in partner capabilities will recede for three reasons. First, in the process
of experimentation firms are likely to learn about which types of partner capabilities are complementary
8
with their internal capabilities. Second, as firms assimilate their partners’ diverse capabilities, they no
longer need to maintain broad-based external portfolios, and can focus on retaining partners that are most
pivotal for their technological goals. Finally, beyond a certain threshold, having ties to a highly diverse
set of partners is likely to impose constraints on firms’ absorptive capacity (Vasudeva and Anand 2011).
These factors will perpetuate the transition into a phase of selection and retention. Armed with
greater knowledge about what kinds of capabilities it needs (and does not need) to compete, in this later
stage the firm will systematically rationalize the mix of partners in its external portfolio. For example,
some firms will see more promise in competing within a particular technological segment of the broader
domain, and thus seek partnerships that strengthen its position within that segment. Other firms may seek
competitors or complementors, and thus limit alliances with firms from other domains. Firms thus
transition from a phase of variation-seeking to selection and retention (Rothaermel and Deeds 2004).
Such a transition is consistent with the work of Lavie and Rosenkopf (2006), who observed that firms
tend to balance broad-based and focused activities through strategic alliances over time—so that periods
of learning primarily through one type are followed by periods of learning primarily by another type.
In proposing that the process of external portfolio reconfiguration will be characterized by a
stage of increasing variation followed by one of decreasing variation, we do not suggest that a specific
level of variation should be regarded as the optimum for all firms within a technological domain.
However, we do suggest that consistent with evolutionary theory (Nelson and Winter 1982, Nelson 1994),
firms will first expand and then reduce their external portfolio’s scope of technological capabilities.
Hypothesis 1: In an uncertain environment, a firm’s external portfolio of alliance capabilities will
follow an evolutionary path characterized by more technological diversity in the early stages of
the firm’s technological lifespan, but faster reconfiguration towards selection and retention in the
later stages.
Internal portfolio: ‘Inside-out’ evolution
Just as a firm’s external portfolio evolves with time, its internal stock of knowledge and capabilities also
change. Although internal capabilities and external opportunities are inter-connected (Mowery et al. 1996,
Stuart 2000, Rosenkopf and Nerkar 2001), their evolutionary paths are likely to remain distinct.
9
Less variation in the early stages. In the early stages of a firm’s entry into a new and uncertain
arena, the scope of its internal portfolio will be quite narrow. Given the inherent costs and organizational
complexity of growing organically, expansions in internal capabilities by way of adding R&D labs, hiring
inventors, or acquiring firms will be limited. Consistent with this idea, Tripsas and Gavetti (2000) showed
that even when firms make new technological discoveries, they often find it difficult to make
technological commitments, which reduces their internal scope in the early stages of their entry into a
new domain.
Slower reconfiguration towards selection and retention. Over time, however, firms broaden their
internal capabilities by building on their existing knowledge and assimilating external knowledge from
their external portfolios (Vasudeva and Anand 2011), for instance. Learning has the cumulative effect of
increasing the internal scope. Moreover, once investments in R&D, for instance, are made and
organizational units are established, discontinuing, downsizing or reversing these commitments can
become difficult due to organizational inertia or simply the lack of adequate information about which
technologies to divest (Sirmon et al. 2007).
For these reasons, internal portfolios tend to demonstrate more path-dependence (Karim and
Mitchell 2000) and are more difficult to reconfigure (Karim 2006). As an example, although only a small
proportion of a firm’s total patents are ever utilized for commercial application, yet, these patents provide
the firm with a platform for its subsequent technological development. Internal shakeouts that are pathbreaking are likely less frequent. Based on these observations, we suggest that as internal portfolios
expand in scope, they become less flexible, and are therefore, not easily reconfigured.
Hypothesis 2: In an uncertain environment, a firm’s internal portfolio of capabilities will follow
an evolutionary path characterized by less technological diversity in the early stages of a firm’s
technological lifespan, continuing to expand in the later stages, but with slower reconfiguration
towards selection and retention in the later stages.
10
Data and Methodology
We test our hypotheses in the context of firms’ internal and external portfolios in the emergent fuel cell
technology domain. Fuel cells convert the chemical energy stored in hydrogen into electrical energy in
multiple applications such as automobiles, portable devices and stationary power generation. The two oil
shocks in 1973 and 1979 that stemmed from the political developments in the Middle East (Hamilton
1983) triggered exploration in a number of alternative energy technologies such as fuel cells. Not
surprisingly, 1981 marks the year of the first publicly reported fuel cell alliances between Hitachi and
Toshiba, and Hitachi and New Energy Development Organization (NEDO). Among the various
technological alternatives, industry experts regard fuel cells as a radical technology that could usher in the
new hydrogen economy (Avadikyan et al. 2003). Triggered by environmental considerations, oil price
spikes, and energy shortages, firms have used the external capabilities of their alliance partners to explore
the promise of fuel cell technologies. Fuel cell technology integrates know-how from a number of
scientific and engineering fields, yielding a broad range of products and designs and necessitating
collaboration through alliances by the multiple organizations interested in its development. Given that
most fuel cell technologies were still in the pre-commercial stages in the period covered by this study,
developers faced considerable uncertainty about which technological designs and applications will gain
market acceptance. Consequently, fuel cell technology development provides an appropriate empirical
context to study the evolution of firms’ internal and external portfolios.
Sample. We identified firms belonging to the fuel cell industry from patents granted by the U.S.
Patents and Trademarks Office (USPTO) to innovators in this domain. Based on consultation with an
expert patent examiner at the USPTO, we learned that fuel cell patents were assigned to patent class 429
(sub-classes 12–46). Firms that filed at least one patent in any of these 35 sub-classes were included in
our sample. The earliest patents granted in these sub-classes date back to 1971, and since then patenting
activity has risen sharply. The firms in the initial sampling frame represent innovators who were granted
patents in the period 1971–2004. Since our interest resides in comparing the evolutionary pathways of
firms’ internal and external portfolios, we retained only those firms that had formed at least one fuel cell
11
technology development alliance until the year 2004. Thus, our sample includes the earliest cohort of
innovating firms and captures all entrants since then, providing us with an ideal setting to observe the
evolution of firms’ portfolios as the industry grew from a nascent to a pre-commercial stage. For each
firm, we constructed yearly internal and external portfolios based on alliances spanning the period 1981–
2004. Our final sample contains 109 firms from 11 countries.
External portfolios. We collected fuel cell alliance data based on publicly available archival
sources such as news reports, industry journals and trade magazines compiled in the Lexis-Nexis
databases, which includes more than 670 international titles (Ahuja 2000, Hagedoorn and Narula 1996,
Rosenkopf and Almeida 2003). Since alliance termination dates are difficult to determine and usually not
reported, following prior research we assumed that the productive lifespan of alliances lasts five years
(Gulati and Gargiulo 1999, Kogut 1988, Schilling and Phelps 2007, Stuart 2000). Thus, each individual
alliance entered a firms’ portfolio in the year it was formed and was dropped from the portfolio after five
years. Despite the errors of omission associated with this approach (some alliances may have prolonged
utility), it provided the best way to capture the dynamic aspects of external portfolios. Moreover, such
errors of omission, if any, would apply consistently across the stages of a firm’s lifecycle, and therefore,
not alter our results with regards to our predictions concerning the pattern of external portfolio evolution.
A firm’s external portfolio was recorded for each year regardless of whether the portfolio
changed in its composition, until it no longer had any alliances in the portfolio. As an illustration, for firm
X that formed its first alliance in 1990 and its second and final alliance in 1994, the external portfolio
would include one alliance in the period 1990-1993, two alliances in the year 1994, and again one alliance
in the period 1995–1998. Since we include the firm’s earliest alliances, there is no left censoring in the
data. In this manner we capture how a firm’s portfolio evolves since its first alliance. As new alliances are
formed and old alliances are phased out, the external portfolio’s characteristics change over time. Our
sample includes 655 firm external portfolios observed during the period 1981–2004. The number of
alliances in external portfolios range from one to 22, with a mean of 2.77 and standard deviation of 3.07.
12
Dependent variables
External portfolio technological diversity. We measure the technological scope of the external
portfolio based on the technological diversity of the firms’ partners in the alliance portfolio. Greater
technological diversity suggests greater exploration because it exposes the firm to a range of
technological capabilities. Such diversity also gives the firm the option to choose the technologies that
best suit its requirements depending on how the industry evolves (Powell et al. 1996, Wuyts et al. 2004).
Conversely, less technological diversity suggests greater focus in the alliance portfolio. Fuel cell patents
are assigned to 35 different patent sub-classes (429/12-46) that represent distinct aspects of fuel cell
technology development. These patent sub-classes are also illustrative of considerable intra-technology
competition driven by component costs, fuel conversion efficiency, fuel reformation and storage systems,
modularity and miniaturization (Avadikyan et al. 2003).
The technological diversity measure was calculated by first computing a Herfindahl index (which
measures concentration and ranges from zero to one) and then subtracting this measure from one.
Technological diversity was calculated using the formula: 1-(ni /N)2 where ni represents the cumulative
number of partners’ patents belonging to patent sub-class i (where i ranges from 1-35) and N represents
the cumulative number of fuel cell patents issued to the partners up to the observation year. A minimum
value of zero indicates that all partners concentrated their patents in one patent sub-class, and a maximum
value of one indicates equal distribution across sub-classes in the partners’ technological stock.
Internal portfolio technological diversity. The internal portfolio’s technological diversity was also
calculated using a Herfindahl index (as described earlier) where ni is the number of the focal firm’s
patents in the ith patent sub-class, and N is the total number of patents. This measure ranges from zero to
one, and accounts for the breadth of the firm’s internal technological portfolio.
Explanatory variable
Firm technological age. The focal firm’s technological age or technological lifespan is measured by the
number of years since its entry into the fuel cell technology domain, as indicated by the firm’s first fuel
cell patent application. This operationalization captures the time that a firm has spent in the fuel cell
13
industry and serves as a proxy for its familiarity and experience. A zero value for technological age
indicates initialization or the founding year in which a firm entered the industry. The longer a firm has
been in the industry, the more advanced its technological age. Firms’ technological age in the sample
ranges from 0 to 32 years, with a mean of 9.98 years and a standard deviation of 8.69 years.
Control variables
Firm characteristics. The focal firm’s technological base, which reflects its stock of technological
resources was calculated as the patent count up to the observation year (Cohen et al. 2002, Griliches
1990). The cumulative number of inventors obtained from a firm’s patent records accounts for the human
resources that could impact the technological scope of the internal and external portfolio. The number of
prior citations to others captures learning effects and may influence the firm’s technological scope and its
reconfiguration. We included Freeman's (1979) degree centrality, which captures the prominence of each
firm in the overall network of fuel cell innovators which could influence alliance formation. We used
Burt's (1992) network efficiency variable to capture the extent of non-redundancy and novelty of
technological knowledge in a firm’s alliance network, where higher values of efficiency (which ranges
from 0 to 1) signify a network high in structural holes. To ascertain which players were active in the
industry in a given year, we identified their entry and exit years. We assumed that a player entered the
industry three years preceding the year of its first patent application, and exited three years following the
year of its last patent application. The technological distance between the focal firm and those in its
external portfolio captured the degree of familiarity and complementarity between the firm and its
partners which could influence the diversity of the portfolio capabilities. Jaffe's (1986) measure of
technological distance1 is calculated based on the patent sub-classes of the firm’s patents and the pooled
sub-classes of its partners’ patents. The distance measure ranges from zero (perfectly similar
technological profiles) to one (the firm and its partners’ patents are in non-overlapping sub-classes). Prior
1
Technological distance between the firm i and portfolio j was calculated as 1- Pij where Pij is the measure of
technological proximity. Pij is calculated using the formula: FiF′j/[(FiFj)(Fji)] 1/2 where Fi is the vector of
technological positions for firm i, and Fj is the corresponding vector for firm i ’s portfolio j. The vector F is
represented by (F1, F2, F3…Fk) where Fk is the cumulative proportion of patents assigned to patent sub-class k.
14
industry experience could lead to firm-specific capabilities and path dependencies (e.g. Benner and
Tripsas 2012) leading to an evolutionary pathway that is distinct from new entrants in an industry. This
observation is germane to settings such as fuel cell technology development that involve both new
entrants and incumbents from related industries such as automobiles and power generation. We controlled
for the number of innovators engaged in fuel cell technology development in a given year to account for
the potential for alliance formation.
External portfolio characteristics. The average duration of alliances in the external portfolio
accounts for the number of years since the alliances were formed which could influence the learning and
associated reconfiguration. The proportion of equity alliances in the external portfolio indicates the
governance mechanism in the portfolio. External portfolios in which the majority of alliances involve
equity investments should require greater commitment and coordination between the firm and the partners
and hence, involve higher asset specificity and task interdependence than external portfolios in which the
alliances are structured as arm’s length type of transactions (Gulati and Singh 1998, Nagarajan and
Mitchell 1998). Similarly, the proportion of repeated ties, calculated as the number of partners with which
the focal firm had established at least one alliance prior to the currently observed portfolio (Gulati 1995),
could influence the commitment and reconfiguration of external portfolios. To account for the possibility
that partners possessing technologically valuable inventions may alter the scope of the portfolio, we
included partners’ technological value as the ratio of citations to partners’ patents relative to all fuel cell
patent citations up to the observation year. Geographical diversity in the external portfolio provides the
firm access to partners located in different countries. Such diversity, allows the firm to tap into the
scientific and human resources offered by various national innovation systems, and access multiple
markets. Moreover, since technologies tend to develop in the context of local demand conditions, labor
supply, and government policies; geographical diversity allows the firm access to idiosyncratic, novel, or
specialized technologies (Lavie and Miller 2008). The geographical diversity of partners was calculated
analogously to technological diversity, except that ni now represented the number of partners located in
country i, and N represented the total number of partners in the portfolio. A partner was assigned to the
15
country in which it conducted the bulk of its fuel cell innovation. In all cases this corresponded to the
partner’s home country.
The firm fixed effects accounts for time-invariant factors at the firm level. The year fixed effects
accounts for unobserved variations across years. These fixed effects are especially helpful in ruling out
sources of endogeneity based on stable but unobserved factors that are likely to explain the technological
diversity of a firm’s internal and external portfolios.
Method
In this study the outcome of interest is the technological diversity of firms’ internal and external
portfolios, which is a continuous variable bounded between the values of zero and one. Our hypotheses
suggest that the external portfolio diversity has a curvilinear relationship with the firm’s technological age
after controlling for firm and portfolio variables. We express this equation below, where the external
portfolio diversity in time t for firm i (EDit), is a function of the firm’s age (FAit), a vector of covariates
(Xit) and an error term εit.
EDit = β1FAit + β2FAit2 + β3 Xit + εit
Similarly, firm’s internal portfolio diversity (IDit), is a function of the firm’s age (FAit), and a vector of
covariates (Xit) and an error term εit.
IDit = β1FAit + β2FAit2 + β3 Xit + εit
Since the contemporaneous error terms associated with the firm’s internal and external portfolio diversity
are likely correlated we use a seemingly unrelated regression (SUR) model using maximum likelihood
estimates (Zellner 1962, Cameron and Trivedi 2005). The SUR regression implemented in STATA 11
provides joint estimates of firm’s internal and external portfolio technological diversity. The explanatory
variables in both equations however differ.
We present the findings based on this model, and conduct a variety of supplementary analysis as
reported below to demonstrate the robustness of our findings to alternative specifications.
--Insert Figures 1 (a) and 1(b) and Figure 2 here---Insert Tables 1(a) and 1(b) and Tables 2 and 3 here--
16
Results
Figures 1 (a) and (b) illustrate the observed evolutionary path of the firms engaged in fuel cell technology
development included in our sample. As can be seen from these graphs, in the early stages of their
technological lifespan, firms’ internal portfolios tend to be less technologically diverse than external
portfolios. At the same time, internal portfolios demonstrate a faster increase in their technological
diversity compared to external portfolios and are less elastic as evidenced from the continuously
increasing technological diversity compared to external portfolios. These observed evolutionary patterns
of internal and external portfolios, suggest preliminary support for Hypotheses 1 and 2.
Tables 1(a) and 1(b) provide the summary statistics and correlations for the model variables. Both
a firm’s internal and external portfolio technological diversity are positively correlated with its
technological age, but the technological diversity of a firm’s internal portfolio has a larger correlation
with age (0.66) compared its external portfolio diversity (0.14). Internal and external portfolio
technological diversity are also positively correlated with one another.
Tables 2 and 3 present the results from the seemingly unrelated regressions that estimate the
firm’s internal and external portfolios’ technological diversity simultaneously. The Breusch-Pagan test
shows significant negatively correlated errors (p<0.001), thereby validating the appropriateness of the
seemingly unrelated regression model. Table 2 which includes only the linear term for technological age
shows that a firm’s technological age has a significant positive effect on its internal technological
diversity, but technological age is not significantly related to the external portfolio’s technological
diversity. Table 3 reports the full model and includes both the linear and quadratic terms for technological
age to account for the non-monotonic relationship between technological age and the portfolio’s
technological diversity. As the results in Table 3 reveal, a firm’s technological age and its squared term
are both significantly related to its internal and external portfolio’s technological diversity. Figure 2
illustrates these results graphically. In particular, at the mean technological age of around 10 years, the
firm’s internal technological diversity is 0.49 which is considerably lower compared to the external
17
portfolio technological diversity of 0.78. A firm’s internal technological diversity however exceeds that of
the external portfolio when the technological age is one standard deviation above the mean value.
The coefficient for technological age suggests a more positive and significant marginal effect on a
firm’s internal technological diversity compared to its external portfolio diversity. This effect points to the
continued expansion of a firm’s internal technological diversity compared to its external portfolio’s
diversity. The coefficient for the squared term for technological age is negative and significant for both
internal and external diversity, but the relative size of the coefficient suggests a greater inflexion in the
external diversity compared to internal diversity. In particular, as a firm’s technological age increases
from the mean value to one standard deviation above it, its internal technological diversity increases from
0.50 to 0.80, but its external portfolio diversity decreases slightly from 0.78 to 0.77. These results point to
a slower reconfiguration or less flexibility of the internal portfolio compared to the external portfolio.
Together, these findings lend support for Hypotheses 1 and 2.
Turning to the control variables in the model, the results show that a firm’s network characterized
by structural holes that enables access to more novel knowledge in its relationships is associated with
greater internal portfolio technological diversity, but lower technological diversity of its external
portfolio. The size of the industry has a small but significantly negative effect on its internal technological
diversity, suggesting that more competition may spur firms to develop focused approaches internally. The
technological distance between the firm’s internal and external technological capabilities has a significant
negative relationship with both the internal and external portfolio’s technological diversity, thus
illustrating the need to balance technological distance and technological diversity. Firms that had
experience in another industry such as transportation or power generation prior to their entry into the fuel
cell technological domain demonstrated more external portfolio diversity. Similarly, a greater proportion
of repeated alliances associated with greater external portfolio technological diversity, possibly due to less
reconfiguration in such instances. Partner’s technological value and geographical diversity also associated
with significantly greater technological diversity of the external portfolio. The effect of other control
variables was non-significant.
18
--Insert Tables 4, 5 and 6 here-Supplementary Tests
Alternative model specifications. To check the robustness of our results, in the seemingly
unrelated regression model reported in Table 3, we estimated models with lagged effects. In the model
estimating the external portfolio’s evolution, we included as predictors a one-year lag of the firm’s
internal portfolio’s technological diversity and a one-year lag of the external portfolio’s technological
diversity. Similarly, in the model estimating the firm’s internal portfolio’s technological evolution we
included a one-year lag of the firm’s internal portfolio’s technological diversity and the external
portfolio’s technological diversity. Including these lagged effects did not change our main findings.
Given that the dependent variable is censored between the values of zero and one, an ordinary
least squares regression may yield inconsistent estimates. Hence, we also estimated the model using a
panel tobit regression with firm and year fixed effects to account for unobserved heterogeneity
(Wooldridge, 2002), which yielded similar results.
Finally, we estimated the firm’s internal and external portfolios’ technological diversity modeled
as independent models with robust standard errors. The results from this model reported in Table 4
remain unchanged from those the seemingly unrelated model reported in Table 3.
Cohort effects. To distinguish between the early and late cohort of firms we divided the firms
based on the year in which they entered the industry as determined by their first patent application to the
USPTO. Firms that entered the industry prior to 1990 were considered as early entrants, and firms that
entered in subsequent years were grouped as late entrants. The order of entry could alter the evolutionary
pathways because the technological uncertainty encountered by the firms in the early cohort is likely
greater than that for firms in the late cohort.
As shown in Table 5, a comparison of firm’s internal and external portfolios for the early cohort
shows that when technological uncertainty is higher internal portfolios demonstrate an increasing and
then decreasing technological diversity, while the external portfolio’s technological diversity is not
significantly affected by the firm’s technological age. For the late cohort, which experiences relatively
19
less technological uncertainty, the external portfolio demonstrates an increasing and then decreasing
technological diversity in the predicted manner, while the internal portfolio demonstrates the reverse
pattern of decreasing and then increasing technological diversity. Late entrants might learn vicariously
from the experience of early entrants and engage in less experimentation. At the same time, late entrants
may be able assemble more diverse portfolios because of a greater range of technological opportunities
created by firms that preceded them. These effects suggest that early and late entrants differ in their
evolutionary pathways.
Discussion
In this study, we applied the basic tenets of evolutionary theory (Nelson and Winter 1982, Nelson 1994)
and combined it with insights from behavioral approaches and organizational learning (Cyert and March
1963, Levitt and March 1988, Cohen and Levinthal 1990) to arrive at two alternative evolutionary
pathways: ‘inside-out’ and ‘outside-in’ characterizing firms’ internal and external portfolios, respectively.
In particular, we find that the extent of technological diversity and the rate at which the transition from
diversity to focus occurs varies across firms’ internal and external portfolios. Our evolutionary approach
recognizes that although firms encounter heterogeneity in their resource endowments and face constraints
in identifying the optimal mix of capabilities under uncertain conditions, firms need not own or control
the requisite resources and capabilities; they can access valuable capabilities externally through alliances.
Moreover, the distribution of resources and capabilities within an industry does not remain fixed or static,
but instead continues to evolve as firms learn and gain experience.
By accounting for the capabilities that reside in firms’ partners, our perspective takes an
ecological view wherein the resources and capabilities that firms need for survival in an uncertain
environment reside not only inside the firm but also in the firms’ portfolio of external relationships
(Barnett 1990, Barnett and Carroll 1987). In this vein, our work extends previous studies in strategic
management that have recognized the role of external capabilities for adapting to a changing
technological and competitive landscape (e.g. Nagarajan and Mitchell 1998, Karim and Mitchell 2000,
Anand et al. 2010). It is worth pointing out that the mechanism that drives fitness in the population and
20
leads to the selection of the requisite capabilities for firms’ survival is their ability to learn from their
internal and external search processes. Such a learning ability is intrinsic to what scholars have previously
labeled ‘dynamic capabilities’ (Teece et al. 1997, Helfat and Peteraf 2003).
Our findings confirm that even though internal and external capabilities are inter-linked through
knowledge flows, differences persist in their starting and ending levels of diversity, and the rate of change
and peaks across firms’ internal and external portfolios. Taking a contingent view of organizational
search, Siggelkow and Levinthal (2005) similarly showed that the pattern of variation, selection, and
retention of alternatives can vary across different organizational structures, such as centralized versus
decentralized arrangements.
These differences have important implications for firms’ innovatory performance. Importantly, a
longer exploratory span for the internal portfolio means that firms must expend considerable time and
effort to continue to sift through opportunities. Conversely, in the case of external portfolios, firms appear
to make their bets early in their lifecycle. Although such an approach has its benefits in terms of
compressing the locus of experimentation (March 1991), early bets can prove costly: as an illustration, the
early models of small cars introduced by the auto makers in response to the oil crises of the 1970s turned
out to be inferior products (Bresnahan and Ramey 1993). Thus, given that the optimal scope of search is
not easily determined, our findings show that firms’ can offset this difficulty by pursuing different levels
of scope and pace of reconfiguration in their internal and external portfolios. This insight corroborates
prior works on how firms can move into new domains by gradually redeploying their resources
strategically (Anand and Singh 1997, Anand et al. 2010, Baumann and Siggelkow 2013).
While our study takes a step towards delineating the evolutionary pathways of firms’ internal and
external portfolios, our findings must be interpreted in light of its limitations and boundary conditions.
The implications we have discussed so far are tempered by the fact that we only focus on the evolutionary
path in the context a radical technological development, but do not assess the performance consequences
of the evolutionary path. Such an endeavor however, becomes challenging because a continuing state of
flux precludes firms from emulating the better performing firms that have survived in the population
21
(March and Simon 1958, Hannan and Freeman 1989), and herd towards a dominant design (Abernathy
and Utterback 1978, Levinthal 1997).
We find that important deviations from the predicted evolutionary pathways can arise when we
consider the early and late cohort of firms that are exposed to different levels of uncertainty characterized
by more radical versus incremental technological changes (Henderson and Clark 1990). Further research
could examine such differences in greater depth, by employing contingency theory for instance, which
suggests that firms’ organizational characteristics correspond to the features of the environment to which
they are exposed (Lawrence and Lorsch 1967). Firms that enter the industry in its nascent stages, are
exposed to greater levels of uncertainty and may, therefore, respond in markedly different ways compared
to the late entrants. A variant of this thinking suggests that the uncertainty may filter through the firm’s
institutional context such that governments may intervene to coordinate and alter resident firms’
technological trajectory (Spencer et al. 2005, Vasudeva 2009).
In conclusion, as firms seek to compete in new markets and technologies globally, a dynamic
perspective on how firms’ capabilities are reconfigured becomes a central issue worthy of further
investigation. Our study contributes to this effort by identifying two alternative evolutionary pathways
and applying this theoretical insight for understanding the joint evolution of firms’ internal and external
portfolios of capabilities.
22
REFERENCES
Abernathy, W.J., J. Utterback. 1978. Patterns of industrial innovation. Technology Review, June-July 4047.
Aggarwal, V.A., D.H. Hsu. 2009. Modes of cooperative R&D commercialization by start-ups. Strategic
Management J., 30(8) 835-864.
Ahuja, G. 2000. Collaboration Networks, Structural Holes, and Innovation: A Longitudinal Study. Admin.
Sci. Quart., 45(3) 425-455.
Anand, J., H. Singh. 1997. Asset redeployment, acquisitions and corporate strategy in declining
industries. Strategic Management Journal. 18(S1) 99-118.
Anand, J., R. Oriani, R.S. Vassolo. 2010. Alliance activity as a dynamic capability in the face of a
discontinuous technological change. Organization Science. 21(6) 1213-1232.
Avadikyan, A., P. Cohendet, J. A. Héraud. 2003. The economic dynamics fuel cell technologies. Springer
Verlag.
Lavie, D., L. Rosenkopf. 2006. Balancing exploration and exploitation in alliance formation. Acad.
Management J. 49(4) 797.
Barnett, W. P. 1990. The organizational ecology of a technological system. Administrative Science
Quarterly 31-60.
Barnett, W. P., G.R. Carroll. 1987. Competition and mutualism among early telephone
companies. Administrative Science Quarterly, 400-421.
Barney, J. B. 1991. Firm resources and sustained competitive advantage. J. Management, 17(1) 99-120.
Baumann, O., N. Siggelkow. 2013. Dealing with complexity: integrated vs. chunky search
processes. Organization Science. 24(1) 116-132.
Beckman, C. M., P. R. Haunschild, D.J. Phillips. 2004. Friends or strangers? Firm-specific uncertainty,
market uncertainty, and network partner selection. Organ. Sci. 15(3) 259-275.
Benner, M. J., M. Tripsas. 2012. The influence of prior industry affiliation on framing in nascent
industries: the evolution of digital cameras. Strategic Management Journal. 33(3) 277-302.
Bresnahan, T. F., V. A. Ramey. 1993. Segment shifts and capacity utilization in the US automobile
industry. The American Economic Review. 213-218.
Burt, R. S. 1992. Structural holes: The social structure of competition. Cambridge, MA: Harvard University
Press.
Cameron, A. C., P.K. Trivedi. 2005. Microeconometrics: methods and applications. Cambridge University
Press.
Cohen, W. M., D.A. Levinthal. 1990. Absorptive Capacity: A new perspective on learning and
innovation. Admin. Sci. Quart. 35(1) 128-152.
23
Cohen, W. M., A. Goto, A. Nagata, R. R. Nelson, J. P. Walsh. 2002. R&D spillovers, patents and the
incentives to innovate in Japan and the United States. Res. Policy. 31(8-9) 1349–1367.
Cyert, R.M., J.G. March. 1963 A Behavioral Theory of the Firm. Englewood Cliffs, NJ: Prentice-Hall.
Freeman, L. C. 1979. Centrality in social networks conceptual clarification. Social Networks. 1(3) 215239.
Goerzen, A. 2007. Alliance networks and firm performance: The impact of repeated partnerships.
Strategic Management J. 28(5) 487-509.
Griliches, Z. 1990. Patent statistics as economic indicators: A survey. J. Econom. Lit. 28(4) 1661–1707.
Gulati, R. 1995. Social structure and alliance formation patterns: A longitudinal analysis. Administrative
Science Quarterly. 619-652.
Hagedoorn, J., R. Narula.1996. Choosing organizational modes of strategic technology partnering:
international and sectoral differences. J. Internat. Bus. Stud. 27(2).
Hamilton, J. D. 1983. Oil and the macroeconomy since World War II. The Journal of Political Economy.
228-248.
Hannan, M. T., J. Freeman. 1984. Structural inertia and organizational change. American Sociological
Review. 149-164.
Hannan, M.T., J. Freeman. 1989. Organization Ecology. Harvard University Press. Cambridge.
Haveman, H. A. 1992. Between a rock and a hard place: Organizational change and performance under
conditions of fundamental environmental transformation. Administrative Science Quarterly 48-75.
Helfat, C. E., M.A. Peteraf, M. A. 2003. The dynamic resource-based view: capability lifecycles.
Strategic Management J. 24(10) 997–1010.
Helfat, C. E., S. Finkelstein, W. Mitchell, M. A. Peteraf, H. Singh, D. J Teece. S.G. Winter. 2007.
Dynamic capabilities: Understanding strategic change in organizations. Malden, MA: Wiley-Blackwell.
Karim, S. 2006. Modularity in organizational structure: The reconfiguration of internally developed and
acquired business units. Strategic Management Journal 27(9) 799.
Karim, S., W. Mitchell. 2000. Path-dependent and path-breaking change: Reconfiguring business
resources following acquisitions in the US medical sector. Strat. Mgmt. J. 21 (10-11) 1061–1081.
Lavie, D. 2006. The competitive advantage of interconnected firms: an extension of the resource-based
view. Acad. Management Rev. 31(3) 638-658.
Lavie, D., S.R. Miller. 2008. Alliance portfolio internationalization and firm performance. Organ. Sci.
19(4) 623-646.
Lawrence, P. R., J.W. Lorsch. 1967. Differentiation and integration in complex
organizations. Administrative Science Quarterly 1-47.
24
Leonard-Barton D. 1992. Core capabilities and core rigidities: a paradox in managing new product
development. Strategic Management Journal. 13 (SI) 111-126.
Levinthal, D. A. 1997. Adaptation on rugged landscapes. Management Science. 43(7) 934-950.
Levinthal, D. A., J. G March. 1993. The myopia of learning. Strategic Management Journal. 14(S2) 95112.
Levitt, B., & March, J. G. 1988. Organizational Learning. Annual Rev. Soc. 14(1) 319-340.
March, J. G. 1991. Exploration and exploitation in organizational learning. Organization Science. 2(1)
71-87.
March, J.G., H.A. Simon. 1958. Organizations. Oxford, England: Wiley Organizations.
Mitchell, W., K. Singh. 1996. Survival of businesses using collaborative relationships to commercialize
complex goods. Strategic Management Journal. 17(3) 169-195.
Mowery, D. C., J. E. Oxley, B. S. Silverman. 1996. Strategic alliances and interfirm knowledge transfer.
Strategic Management J. 17(1) 77-91.
Nagarajan, A., W. Mitchell. 1998. Evolutionary diffusion: Internal and external methods used to acquire
encompassing, complementary, and incremental technological changes in the lithotripsy industry.
Strategic Management Journal. 19 1063–1077.
Nelson, R. R. 1994. Evolutionary theorizing about economic change. In Smelser, N. J. and R. Swedberg
(eds.), Handbook of Economic Sociology (pp. 108–136). Princeton, NJ: Princeton University Press.
Nelson, R. R., S.G. Winter, S. G. An Evolutionary Theory of Economic Change. Cambridge, MA:
Harvard University Press.
Powell, W. W., K. W. Koput, L. Smith-Doerr. 1996. Interorganizational collaboration and the locus of
innovation. Admin. Sci. Quart. 41(1) 116-145.
Priem, R.L, J. E. Butler. 2001. Is the resource-based ‘view’ a useful perspective for strategic management
research? Academy of Management Review 26(1): 22–40.
Rosenkopf, L., P. Almeida. 2003. Overcoming local search through alliances and mobility. Management
Sci. 751–766.
Rosenkopf, L., A. Nerkar. 2001. Beyond local search: Boundary-spanning, exploration, and impact in the
optical disk industry. Strategic Management Journal. 22(4) 287-306.
Rothaermel, F. T., D. L. Deeds. 2004. Exploration and exploitation alliances in biotechnology: A system
of new product development. Strategic Management J. 25(3) 201–221.
Schilling, M. A., C.C. Phelps. 2007. Interfirm collaboration networks: The impact of large-scale network
structure on firm innovation. Management Sci. 53(7) 1113–1126.
Siggelkow, N., D. A. Levinthal. 2005. Escaping real (non-benign) competency traps: Linking the
dynamics of organizational structure to the dynamics of search. Strategic Organization. 3(1) 85-115.
25
Sirmon, D. G., M. A. Hitt, R. D. Ireland. 2007. Managing firm resources in dynamic environments to
create value: Looking inside the black box. Acad. Management Rev. 32(1) 273-292.
Stuart, T. E. 2000. Interorganizational alliances and the performance of firms: A study of growth and
innovation rates in a high-technology industry. Strategic Management J. 791–811.
Teece, D. J., G. Pisano, A. Shuen. 1997. Dynamic capabilities and strategic management. Strategic
Management J. 18(7) 509-533.
Tripsas, M., G. Gavetti. 2000. Capabilities, cognition, and inertia: Evidence from digital
imaging. Strategic Management Journal. 21(10-11) 1147-1161.
Tushman, M. L., P. Anderson. 1986. Technological discontinuities and organizational environments.
Admin. Sci. Quart. 31(3) 439–465.
Vassolo, R. S., J. Anand, T. B. Folta. 2004. Non-additivity in portfolios of exploration activities: A real
options-based analysis of equity alliances in biotechnology. Strategic Management J. 25(11) 1045–1061.
Vasudeva, G., J. Anand. 2011. Unpacking absorptive capacity: A study of knowledge utilization from
alliance portfolios. Academy of Management Journal. 54(3) 611-623.
Wernerfelt, B. 1984. A resource-based view of the firm. Strategic Management J. 5(2) 171-180.
Williamson, O. E. 1991. Comparative economic organization: The analysis of discrete structural
alternatives. Admin. Sci. Quart. 36(2).
Wooldridge, J. 2002. Econometric Analysis of Cross Section and Panel Data. Cambridge: MIT Press.
Wuyts, S., S. Dutta, S. Stremersch. 2004. Portfolios of interfirm agreements in technology-intensive
markets: Consequences for innovation and profitability. J. Marketing, 68(2) 88–100.
Zellner, A. 1962. An efficient method of estimating seemingly unrelated regressions and tests for
aggregation bias. Journal of the American Statistical Association. 57(298) 348-368.
Zollo, M., S. G. Winter. 2002. Deliberate learning and the evolution of dynamic capabilities. Organ. Sci.
339–351.
26
0
.2
.4
.6
.8
1
Figure 1(a). Internal and External Portfolio Technological Diversity Evolution (Observed—All
Firms)
0
10
20
30
Firm Age
Portfolio Technological Diversity
Firm Technological Diversity
Fitted values
Fitted values
0
.2
.4
.6
.8
1
Figure 1(b). Internal and External Portfolio Technological Diversity Evolution (Observed—Ballard
Power Systems)
0
10
Firm Age
Portfolio Technological Diversity
Firm Technological Diversity
20
30
Fitted values
Fitted values
27
Figure 2 Internal and External Portfolio Technological Diversity Evolution (Estimated)
1
Technological Diversity
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
0
4
8
12
16
20
Firm Age
Firm Technological Diversity
24
28
32
Portfolio Technological Diversity
28
Table 1(a) Summary Statistics
Variable
Internal Portfolio Tech. Diversity
Tech. Age
Tech. Base
Degree
Efficiency
Total Inventors
Total Prior Citations
Industry Size
Jaffe Dist.
Pre-Entry Experience
Average Age of Alliances
Prop. Equity Alliances
Prop. Repeated Alliances
External Portfolio Tech. Value
External Portfolio Tech. Diversity
External Portfolio Geog. Diversity
Obs
985.00
985.00
985.00
985.00
985.00
656.00
912.00
985.00
985.00
981.00
985.00
985.00
985.00
985.00
985.00
985.00
Mean
0.44
9.99
12.11
5.21
0.90
15.48
8763.08
266.14
0.76
0.85
2.74
0.26
0.06
0.01
0.77
0.20
Std. Dev.
0.38
8.70
24.71
7.57
0.17
39.56
40379.46
119.88
0.25
0.36
1.26
0.38
0.18
0.01
0.29
0.32
Min
0.00
0.00
0.00
0.82
0.33
0.00
0.00
43.00
0.10
0.00
1.00
0.00
0.00
0.00
0.00
0.00
Max
1
1.00
0.66
0.61
0.13
-0.10
0.42
0.21
0.04
-0.56
-0.02
0.10
-0.15
0.06
0.10
0.21
0.00
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
1.00
0.48
-0.02
-0.10
0.29
0.14
0.16
-0.45
0.15
0.13
-0.15
0.12
0.03
0.14
0.01
1.00
0.04
0.00
0.67
0.47
0.07
-0.52
-0.11
0.08
-0.06
0.04
0.00
0.13
0.00
1.00
-0.14
0.13
0.13
-0.35
-0.18
0.02
-0.06
-0.07
0.03
-0.03
0.04
0.02
1.00
0.02
-0.01
0.01
0.22
-0.04
0.05
0.11
-0.02
-0.03
-0.14
0.10
1.00
0.61
0.08
-0.41
-0.16
0.06
-0.06
0.10
-0.02
0.10
0.07
1.00
0.14
-0.34
-0.21
0.02
0.02
0.11
-0.05
0.07
0.12
1.00
-0.22
-0.19
-0.06
0.05
0.05
-0.04
0.07
-0.08
1.00
0.04
-0.04
0.11
-0.05
-0.18
-0.32
0.15
1.00
0.15
-0.01
0.02
0.09
0.01
-0.09
1.00
0.00
0.05
-0.01
-0.02
-0.04
1.00
0.09
-0.10
0.00
0.08
1.00
-0.02
0.05
0.09
1.00
0.30
-0.28
1.00
0.39
1.00
0.95
32.00
228.00
100.00
1.00
391.00
539320.00
408.00
1.00
1.00
5.00
1.00
1.00
0.09
1.00
1.00
Table 1(b) Correlations
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
Variable
Internal Portfolio Tech. Diversity
Tech. Age
Tech. Base
Degree
Efficiency
Total Inventors
Total Prior Citations
Industry Size
Jaffe Dist.
Pre-Entry Experience
Average Age of Alliances
Prop. Equity Alliances
Prop. Repeated Alliances
External Portfolio Tech. Value
External Portfolio Tech. Diversity
External Portfolio Geog. Diversity
29
Table 2. Seemingly unrelated regression estimates of firm internal and external portfolio
technological diversity (linear term for technological age)
Variables
Firm Characteristics
Technological Age
Technological Base
Degree Centrality
Efficiency
Total Inventor Count
Total Citations Count
Industry Size
Jaffe Technological Distance
Pre-Entry Industry Experience
Alliance Portfolio Characteristics
Average Alliance Duration
Proportion Equity
Proportion Repeated
Technological Value
Geographical Diversity
Firm Fixed Effects
Year Fixed Effects
N
Constant
Breusch Pagan Chi-square test of
Correlated Errors
Internal Portfolio
Technological Diversity
External Portfolio
Technological Diversity
0.03 (0.01)***
-0.002 (0.00)*
0.001 (0.00)
0.15 (0.05)**
0.0009 (0.00)*
-0.00 (0.00)
-0.0001 (0.00)
-0.33 (0.04)***
0.09 (0.14)
-0.004 (0.01)
-0.003 (0.00)*
0.001 (0.00)
-0.25 (0.06)***
0.0004 (0.00)
-0.00 (0.00)
0.0007 (0.00)
-0.53 (0.05)***
1.20 (0.26)***
Yes
Yes
0.001 (0.01)
-0.04 (0.03)
0.05 (0.05)
6.65 (0.71)***
0.60 (0.03)***
Yes
Yes
655
0.01
11.51***
30
Table 3. Seemingly unrelated regression estimates of firm internal and external portfolio
technological diversity (linear and quadratic terms for technological age)
Variables
Firm Characteristics
Technological Age
Technological Age Sq.
Technological Base
Degree Centrality
Efficiency
Total Inventor Count
Total Citations Count
Industry Size
Jaffe Technological Distance
Pre-Entry Industry Experience
Alliance Portfolio Characteristics
Average Alliance Duration
Proportion Equity
Proportion Repeated
Technological Value
Geographical Diversity
Firm Fixed Effects
Year Fixed Effects
N
Constant
Breusch Pagan Chi-square test of
Correlated Errors
Internal Portfolio
Technological Diversity
External Portfolio
Technological Diversity
0.064 (0.01)***
-0.0008 (0.00)***
-0.0008 (0.00)
-0.0007 (0.00)
0.14 (0.05)**
0.0003 (0.00)
0.00 (0.00)
-0.001 (0.00)*
-0.31 (0.04)***
0.16 (0.13)
0.008 (0.01) †
-0.0003 (0.00)*
-0.002 (0.00)*
0.0004 (0.00)
-0.30 (0.06)***
0.0002 (0.00)
-0.00 (0.00)
0.0002 (0.00)
-0.52 (0.05)***
1.34 (0.26)***
Yes
Yes
0.001 (0.01)
-0.03 (0.03)
0.08 (0.05)*
6.80 (0.71)***
0.60 (0.03)***
Yes
Yes
655
0.27
16.00***
β coefficient (standard errors); *** p < 0.001; **p<0.01; *p<0.05; †p<0.10; single-tailed tests
31
Table 4. Fixed effects estimates of firm internal and external portfolio technological diversity as
independent equations
Variables
Firm Characteristics
Technological Age
Technological Age Sq.
Firm Tech. Diversity
Technological Base
Degree Centrality
Efficiency
Total Inventor Count
Total Citations Count
Industry Size
Jaffe Technological Distance
Alliance Portfolio Characteristics
Portfolio Tech. Diversity
Average Alliance Duration
Proportion Equity
Proportion Repeated
Technological Value
Geographical Diversity
Firm Fixed Effects
Year Fixed Effects
Constant
N
R-square
Internal Portfolio
Technological Diversity
External Portfolio
Technological Diversity†
0.06 (0.01)***
-0.008 (0.00)**
0.02 (0.03)
-0.005 (0.00)*
-0.22 (0.08)**
-0.002 (0.00)*
0.0002 (0.00)
-0.23 (0.09)**
0.0002 (0.00)
-0.00 (0.00)
0.0001 (0.00)
-0.59 (0.09)***
-0.0008 (0.00)
-0.0007 (0.00)
0.14 (0.07)*
0.0003 (0.00)
0.00 (0.00)
-0.001 (0.00)
-0.31 (0.08)***
-0.0005 (0.04)
Yes
Yes
0.38*
655
0.52
0.002 (0.01)
-0.03(0.04)
0.09 (0.10)
6.81 (2.08)***
0.60 (0.05)***
Yes
Yes
1.13*
655
0.40
β coefficient (standard errors); *** p < 0.001; **p<0.01; *p<0.05; †p<0.10; single-tailed tests; robust standard errors
32
Table 5. Seemingly unrelated regression estimates of firm internal and external portfolio
technological diversity
Variables
Early Cohort (Firms Entering
Industry before or in 1990)
Internal
External Portfolio
Portfolio
Technological
Technological
Diversity
Diversity
Late Cohort (Firms Entering
Industry after 1990)
Internal Portfolio
External
Technological
Portfolio
Diversity
Technological
Diversity
Firm Characteristics
Technological Age
0.13 (0.02)***
0.004(0.02)
-0.02(0.01)*
0.07(0.01)***
Technological Age Sq.
0.00001(0.0002)
0.004(0.001)***
-0.001(0.001)
Technological Base
Degree Centrality
Efficiency
-0.0009
(0.00)***
-0.003 (0.00)**
-0.001 (0.00)
0.06 (0.05)
-0.003(0.001)*
0.004(0.001)*
-0.30(0.06)***
0.01(0.003)***
0.007(0.003)*
0.17(0.08)*
-0.002(0.004)
-0.007(0.004)*
0.10(0.10)
Total Inventor Count
0.00 (0.00)*
0.0009(0.0005)*
0.008(0.001)***
-0.003(0.002)
Total Citations Count
0.00 (0.00)
-0.00(0.00)*
-0.00(0.00)*
0.00(0.00)
Industry Size
Jaffe Technological Distance
Pre-Entry Industry Experience
-0.01 (0.00)***
-0.23 (0.05)***
0.51(0.23)*
0.0007(0.001)
-0.56(0.05)***
0.78(0.28)**
0.0005(0.0004)
-0.32(0.07)*
0.12(0.12)
-0.0004(0.000)
-0.25(0.09)**
0.34(0.15)*
Alliance Portfolio
Characteristics
Average Alliance Duration
0.004(0.007)
-0.01(0.009)
Proportion Equity
Proportion Repeated
Technological Value
Geographical Diversity
0.06(0.03)*
0.03(0.05)
6.88(0.82)***
0.62(0.03)
-0.21(0.04)***
0.06(0.09)
7.22(1.46)***
0.63(0.04)***
Firm Fixed Effects
Year Fixed Effects
Constant
N
Breusch Pagan Chi-square test
of Correlated Errors
Yes
Yes
Yes
Yes
-0.20
371
2.05†
Yes
Yes
Yes
Yes
0.52*
284
2.64*
β coefficient (standard errors); *** p < 0.001; **p<0.01; *p<0.05; †p<0.10; single-tailed tests
33
Download