r Academy of Management Journal
2022, Vol. 65, No. 5, 1747–1769.
https://doi.org/10.5465/amj.2018.1181
A TROJAN HORSE INSIDE THE GATES? KNOWLEDGE
SPILLOVERS DURING PATENT LITIGATION
KIRAN S. AWATE
Virginia Polytechnic Institute and State University
MONA MAKHIJA
The Ohio State University
While patent litigation is an important appropriability mechanism for protecting firms’
proprietary knowledge, through the litigation process, valuable knowledge may unintentionally spill over from firms defending their patents to those they accuse of patent
infringement. We examine whether such spillover subsequently enhances the innovation
of accused firms by analyzing over 3,000 patent litigation cases from 1998 through 2015
in the U.S. pharmaceutical industry. We find that firms accused of infringement have
higher levels of innovation following litigation relative to other similar firms. Furthermore, litigation of patents that build on recent and heterogeneous knowledge and are
characterized by greater scope more strongly enhance the accused firms’ subsequent
innovation. These findings support the argument that patent litigation can facilitate
knowledge spillovers.
document—insights into its proprietary knowhow
related to the patented technology. In disclosing this
knowledge to the court, it is also made available to
the firm accused of infringement (hereafter, for purposes of brevity, the accused firm). Through such revelations, patent litigation may result in the transfer of
proprietary knowledge to the accused firms, offering
them a unique opportunity to learn. That is, while
firms undertakes litigation in order to protect and
capture value from their innovation, it may also result
in proprietary knowledge unintentionally spilling
over to accused firms that can subsequently be used
in their own innovation activities. In this way, patent
litigation may be a double-edged sword. We examine
whether knowledge spillovers that occur during patent litigation enhance subsequent innovation outcomes of accused firms.
Firms undertake litigation for patents they consider to be highly valuable (Lanjouw & Lerner,
2001). Assuming that litigation creates a breeding
ground for spillovers, accused firms should benefit
more as the value of the spillovers increases. Since the
firm accused of patent violation will likely operate in
the same technological space as the patent-holding
firm, it will have both the intent and the capacity to
learn (Bettis & Prahalad, 1995; Hamel, 1991; Makhija
& Ganesh, 1997). Irrespective of which firm ultimately
wins the case, we expect that the accused firm can use
insights gained during litigation to optimize its subsequent innovation efforts (Pisano, 1994). In order to
For firms to engage in the uncertain activity of innovation, they must be able to extract adequate value
from their efforts. Appropriability regimes create barriers to imitation and protect firms’ knowledge from
spilling over to their rivals (Cohen, Nelson, & Walsh,
2000; Pisano, 1994; Teece, 1986). Strong appropriability regimes include patents, which ensure intellectual
property rights for a certain duration of time (Mahoney & Pandian, 1992), and their enforcement through
litigation (Lanjouw & Schankerman, 2001; Somaya,
2003). In industries where patents are effective intellectual property protection mechanisms, it is the ability to litigate them that defines their efficacy.
An intriguing consequence of patent litigation that
has gone unnoticed is its potential to facilitate knowledge spilling over from firms defending their patents
against infringement to those they accuse of patent
infringement. During the process of patent infringement litigation, the patent-holding firm may need to
reveal—above and beyond what is in the patent
We thank our colleagues, Michael Leiblein, Benjamin
Campbell, Oded Shenkar, Charles Stevens, and Rafael Corredoira, for reading various drafts and providing important
feedback. We also thank Daniel Chow and Bryan Choi
from the Moritz College of Law for their very valuable
insights on patent litigation. We are particularly grateful to
Editor Kevin Steensma and three anonymous reviewers for
their extremely helpful advice and thoughtful suggestions
to improve this research.
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understand this phenomenon and ground our theory development, we interviewed patent litigation
lawyers, scientists and managers who had served
as witnesses in patent litigation, and academics
who specialize in intellectual property laws.
To examine the effects of spillovers during litigation, we developed a database with over 3,000 patent
infringement lawsuits from 1998 to 2015 in the pharmaceutical industry. The pharmaceutical industry’s
reliance on patents makes it an ideal context for this
research. The database includes detailed information on pharmaceutical firms that were involved and
not involved in litigation, the nature of the litigated
patents, and attributes of the litigation cases. Consistent with prior research that has assessed learning
through subsequent performance outcomes (Baum,
Li, & Usher, 2000; Madsen & Desai, 2010), we measure the accused firm’s novel innovation outcomes
subsequent to patent litigation. Our findings support
the argument that knowledge spillovers during litigation enhance accused firms’ subsequent novel
innovations relative to those of other firms. Furthermore, this relationship strengthens with increases in
the recency, heterogeneity, and scope of the knowledge on which the litigated patent is built. These
findings highlight the limitations of a key appropriability mechanism for curtailing spillovers in an otherwise strong appropriability regime.
LITIGATION AS AN
APPROPRIABILITY MECHANISM
When knowledge unintentionally spills over from
originating firms, recipient firms can improve their
products or processes at little or no cost (Jaffe, Trajtenberg, & Fogarty, 2000; Mansfield, 1985; Shaver &
Flyer, 2000). Because others benefit from their innovation activities, the rewards to the originating firm
are greatly reduced, diminishing its incentive to
engage in such costly activities in the first place.
Appropriability regimes assist firms in capturing
value from the innovations they create, by preventing knowledge spillovers (Cohen, Nelson, & Walsh,
2000). An appropriability regime consists of environmental factors that govern the ability to capture
rents generated by an innovation (Teece, 1986), and
include intellectual property rights and legal mechanisms for protecting these rights (Teece, Pisano, &
Shuen, 1997). A strong appropriability regime confers ownership rights over an innovation created by
a firm through a patent and excludes others from
using the innovation for a certain duration of time to
ensure that the patent owner is the primary beneficiary
October
of its efforts (Somaya, 2012; Pisano, 2006). The stronger the regime, the greater the protection of intellectual
property through durable patents and their enforcement (Gulati & Singh, 1998).
Evidence suggests that litigation does play a role
in preventing firms from infringing patents (Lanjouw & Schankerman, 2001; Linden & Somaya,
2003; Teece, 2000). The threat of litigation alone can
be a credible deterrent to potential infringers (Clarkson & Toh, 2010; Nerkar, Parachuri, & Khaire, 2007;
Reitzig, Henkel, & Heath, 2007; Reitzig & Wagner,
2010), due to its high direct and indirect costs (Lanjouw & Lerner, 2001; Somaya, 2012; Tan, 2016). This
may explain why firms with greater resources are
also more likely to initiate lawsuits (Agarwal, Ganco,
& Ziedonis, 2009; Waldfogel, 1995). Even then, firms
do not undertake litigation for just any potentially
infringed patent. They litigate only those patents
most valuable to their competitive position (Allison,
Lemley, Moore, & Trunkey, 2003; Simcoe, Graham,
& Feldman, 2009).
Because patent litigation is such a key element of a
strong appropriability regime, scholars have implicitly assumed that litigation, once undertaken, gives
rise to the outcome for which it is designed—ensuring that patented knowledge is correctly assigned to
the firm that originated it and limiting the unwarranted use of spillovers by others. However, a closer
consideration of litigation processes highlights an
unforeseen consequence of litigation—the necessity
of sharing information to demonstrate infringement
can itself give rise to spillovers. The problem stems
from the intangible nature of information—it is in part
a public good and therefore difficult to contain (Arrow,
1962; Mansfield, 1985). Litigation by a patent-holding
firm to enforce its intellectual property rights can be a
mechanism that increases knowledge spillovers to a
competitor, the very problem it was meant to address.
This is due to a number of intrinsic characteristics of
the patent litigation process.
The first stems from the particularly close interface between the plaintiff and defendant firms during litigation.1 The more geographically proximate
firms are, the more likely it is that there will be spillover of complex and intricate knowledge (Appleyard, 1996; Chung & Alcacer, 2002; Rosenkopf &
1
In this research, the patent-holding firm is the plaintiff
in litigation that is accusing another firm of patent
infringement. The firm accused of infringing the patent is
the defendant. The terms “patent-holding firm” and
“plaintiff” are therefore used interchangeably here, as are
the terms “accused firm” and “defendant.”
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Awate and Makhija
Almeida, 2003). Litigation creates even closer colocation than many previously considered contexts for
spillovers, as well as deeper interaction among participating firms over a particular technological innovation. The rules and procedures of litigation require
litigating parties to share highly firm-specific information and respond directly to very pointed questioning
about the patented innovation. In doing so, the litigation process itself can give rise to highly proprietary
and valuable spillovers to the accused firm, which is
often also a competitor.
A second issue arises from the codified nature of the
litigated patent that often limits the extent to which
the knowledge contained within it is easily understood (Lanjouw & Schankerman, 2001; Somaya, 2003).
The patent may even be intentionally obscure in an
effort to hamper imitability (James, Leiblein, & Lu,
2013). Since patents do not disclose complete information on the inner workings of a new technology or its
underlying science (Devlin, 2010; Ouellette, 2012),
additional disclosures by the patent-holding firm
become necessary during litigation. In particular, the
firm may need to offer new information not documented in the patent to defend its arguments of
infringement. In the process of elaborating this knowledge, it may provide detailed insight into its more
complex aspects that are not readily apparent. These
spillovers can help the accused firm gain a broader
and deeper comprehension of the knowledge than
would otherwise be possible.
Third, since the patents selected for litigation typically involve knowledge considered to be particularly important for a firm’s competitive position
(Allison et al., 2003; Lanjouw & Schankerman, 2001),
additional proprietary information not part of the patent can unintentionally be revealed. That is, while
explaining the nature of its patented knowledge during litigation, a firm can inadvertently shed light on
firm-specific concepts, approaches, and procedures
that are above and beyond the litigated patent. Such
procedural knowledge is typically not protected by
patents (Arora, 1997; Cohen & Klepper, 1996). For
example, information about hypotheses that were considered and then rejected can reveal to the accused
firm approaches that should not be pursued in the
future, thus helping to preserve its resources. The
accused firm can also use insights into best practices
and superior investment approaches for innovation.
Given the uncertainties of the innovation process,
learning about another firm’s innovation-related
choices, processes, and outcomes can inform the
accused firm’s subsequent efforts in a related domain.
In this way, spillovers of knowledge complementary
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to the patent can also be valuable to the recipient
firm.
KNOWLEDGE SPILLOVERS DURING THE
LITIGATION PROCESS
In the United States, patent litigation begins when
a firm files an infringement complaint against another
firm in one of 94 U.S. federal district courts. At this
point, the accused firm can also present counterclaims (Nard & Wagner, 2008), such as noninfringement (its activities are outside the domain of the
patent), invalidity (the patented art is not novel or is
nonobvious) and unenforceability (the patent is misrepresented or misconstrued). The presiding judge
then schedules a meeting or a pre-trial conference
with the plaintiff and defendant firms to lay out the
framework that will govern how the case will go forward. The parties agree to a case schedule—the process by which the parties will exchange data, provide
copies of relevant documents, respond to written
questions called “interrogatories,” and give sworn
testimony in depositions (Roper, Cooper, Gutman, &
Pappas, 2016).
The pre-trial conference is followed by large-scale
exchange of information known as “discovery,” in
which each party can obtain evidence and information relevant to the case from the opposing party or
third parties. This phase offers an important opportunity for each party to provide information that supports its case and learn about the positions and
supporting evidence of the opposing party. Parties
can request research notebooks and electronically
stored information about procedures or experiments
related to the patent, and inspect equipment and
physical locations (Jarvis, Frelinghuysen, Rader, &
Christoff, 2018; Manzo, 2012). It is common for hundreds of thousands, or even millions, of pages of documentation to be exchanged (Moore, Holbrook, &
Murphy, 2018). Discovery also includes deposition
(sworn oral testimony) of the parties’ employees and
third-party expert witnesses to explain the facts of the
case to the judge or jury and provide expert opinions
concerning the issues of infringement. Any information obtained during discovery can be highlighted
and discussed in the courtroom (Manzo, 2012).
In the final phase, the parties can request trial by
jury or judge alone. Given that neither the judge nor
the jurors will have expertise with the patented technology, much of a patent trial involves educating
them about the relevant technology, the patented
invention, the nature of infringement, and any prior
art that allegedly invalidates the patent. Each side
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presents its arguments about the evidence through testimony and exhibits presented during trial. Infringement is proven when “each and every element” in a
claim of the patent is shown to be employed by the
accused firm (Jarvis et al., 2018: 297). At the end of the
trial, a verdict is returned. The entire litigation process
can take up to two and a half years.2
As the above description of the litigation process
indicates, a necessary component of the litigation process is the sharing, explication, and discussion of
firms’ proprietary information. Thus, even though litigation is undertaken for the protection of a firm’s
intellectual property rights, this process will unavoidably give rise to spillovers. While exact patented
knowledge may in fact be prohibited for use by other
parties, limited roadblocks appear to exist in the transfer of other highly related types of knowledge provided during courtroom proceedings. For example,
despite pervasive use of confidentiality agreements
for protecting sensitive disclosures (Chisum, 2019),
several intellectual property rights attorneys we interviewed indicated difficulties in enforcing them. To
understand the nature and implications of the knowledge being discussed in the courtroom and pursue a
particular litigation strategy, attorneys have little
choice but to interface heavily with the scientists and
managers of the firm they are representing. This
makes it difficult to curtail transfers of any proprietary
knowledge to their client firm. One attorney noted,
Both patent attorneys and expert witnesses are usually in-house. There is no way that an attorney has
the knowledge to conduct the case by himself. The
attorney discusses everything with people in the firm,
everything. Otherwise they would not know how to
conduct the case.
In addition, the stakes of litigation are so high that
obtaining a favorable ruling often overshadows concern over knowledge spillovers. As a professor specializing in IP law explained,
People know that leakages happen. But I have found
that it is really hard to prove—confidential information may or may not be [related to] the patent.
Two scientists who served as witnesses in patent lawsuits discussed with us the highly detailed
testimony they provided in court that elaborated
2
According to some studies (e.g., Ansell, Holzwarth,
O’Brien, & Scally, 2017), patent holders’ litigation success
rate is about 33%. Assessment of success may be complicated by court decisions that uphold some but not all patent claims. In addition, three quarters of cases are appealed
after trial, which can also lead to decision reversals.
October
the exact steps of the experimental process undertaken to come up with the patented technology.
Both noted that questioning was often so detailed
that they were unable to withhold proprietary
information. They agreed that such information
could provide useful insights for another firm’s
subsequent innovation efforts in similar domains.
Insights gained through knowledge spillovers can
help a firm go down the learning curve faster than
if it had to replicate the entire innovation process
on its own.
HYPOTHESES
Effect of Knowledge Spillovers During Litigation
on Novel Innovation Outcomes
An innovation comes about through recombining
knowledge components (Ethiraj & Levinthal, 2004;
Fleming, 2001; Katila & Ahuja, 2002). The search for
useful recombinations is, however, difficult due to
the inability to forecast exactly how to achieve a
desired result (Fleming & Sorenson, 2004). Thus, the
innovation process involves not only successes but
also significant failures from which firms learn to
make decisions about next steps. In this way, the
search for an innovation is not only complex but also
path-dependent and reiterative. The issuance of a patent represents a successful outcome of this process.
Differences in firms’ knowledge bases lead to variations in their ability to create novel innovations (Yayavaram & Ahuja, 2008; Yayavaram & Chen, 2015). A
firm will likely have limited depth of understanding
or experience with at least some of the knowledge
components that make up another firm’s innovation
(Katila & Ahuja, 2002; Kaplan & Vakili, 2015; Stuart &
Podolny, 1996), or have little grasp of how the components are integrated so that they work in the correct
way (Henderson & Clark, 1990). The innumerable
ways in which knowledge components can potentially
be recombined reflects a “rugged landscape” that
makes the more superior options difficult to identify
(Fleming & Sorenson, 2004). Even if a patent-holding
firm is able to successfully overcome these difficulties,
information about how it managed to do so is not contained in the patent and is essentially invisible to other
firms. The processes by which the coveted knowledge
is created remains mostly within the firm.
When patent-holding firms undertake litigation
to defend their patents, they have the burden of
both demonstrating infringement and defending
themselves against counterclaims of invalidity or
unenforceability of the patent. There are several ways
in which this process can expose its proprietary
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firm-specific information to the accused firm. To
show that a patented technology has been infringed,
the patent-holding firm will have to explain the
nature of the technology in detail, often with supporting new information that is not explicitly in the patent. Evidence for this can be seen in a case brought by
Purdue against Amneal in 2015 regarding its patent
covering the controlled release oral dosage containing
oxycodone (Purdue Pharma L.P. v. Amneal Pharms.,
LLC, 2015). The court heard testimony from multiple
expert witnesses and admitted hundreds of exhibits
that revealed detailed information about the nature of
the patent claims. According to the court docket, Purdue disclosed important information regarding the
invention not documented in the patent to clarify the
nature of the intellectual property covered in the patent. For example, it indicated the sources of key information used for the invention and elucidated the
exact science underlying the patent, both of which
were previously unknown to Amneal, the firm
accused of infringement. Amneal was able to hear
Purdue explain exactly how to achieve the desired
level of gel viscosity for a proprietary use. While the
exact technology as patented may not be utilized,
insights can still be gained into ways to improve or
extend this technology in new directions, or develop
alternative gel-based technologies that are similar but
not exactly the same. Since neither the patent nor
court rulings include this information, the accused
firm has significant leeway for making use of these
insights.
In the process of demonstrating that the patented
technology is valid (i.e., novel and nonobvious), the
patent-holding firm may need to provide detailed
information about the procedures that led to the
innovation, including assumptions and choices
made, allocation of resources for particular activities, insights from mistakes, and an unforeseen trajectory of decision-making that yielded the successful
innovation. In this respect, the plaintiff firm may unintentionally divulge information that can be extremely
useful to the defendant firm for improving its own
decision choices to create innovations in a related
technological space. It can avoid the mistakes made by
the patent-holding firm while learning from its successes. Consider the case of Amgen Inc. and Genetics
Institute Inc. (GenI) filed in 1989 (Amgen, Inc. v. Chugai Pharm. Co., 1989). Amgen cloned a gene called the
human hormone erythropoietin (EPO), which helps to
treat chronic anemia associated with end-stage renal
disease, and received a patent for the procedure in
1987. GenI utilized a similar technique for cloning the
gene, leading Amgen to file a patent infringement
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lawsuit against GenI. During the discovery phase of
litigation, Amgen provided handwritten notes on
detailed discussions between two of its leading scientists that revealed a sequential plan to systematically
probe and screen genomic libraries. Amgen also
shared memos documenting key decisions undertaken by the team, outcomes of the experiments
conducted, and detailed consideration of their
implications. In their testimonies, scientists discussed some of their assumptions proven wrong and
the insights they gained from their mistakes. In the
end, Amgen explained virtually every step for bringing about a complex and highly innovative outcome—
the cloning of a gene. Given the relatively new science
involved, the accused firm had the opportunity to
learn at length from experts in the patent-holding firm
about best practices in cloning genes. Even if GenI did
not go on to use the exact knowledge for the cloning of
the EPO gene, it could leverage new insights to
improve its own cloning techniques.
The fact that accused firms will likely operate in
similar technological spaces as the patent-holding
firms plays a key role in their ability to learn from spillovers and come up with novel discoveries in a related
domain. Learning through such means assists the firm
in making better choices among alternative courses of
action that not only increase the likelihood of successful innovation outcomes but also make the process
of innovation more efficient (Posen & Chen, 2013).
The case of Endo Pharms v. Actavis Inc., in which
Mallinckrodt LLC was also a plaintiff, exemplifies
such a situation (Endo Pharms. Inc. v. Actavis Inc.,
2017). Although a suit was brought against Actavis
and Teva for violating Mallinckrodt’s patent for reducing the ABUK content of oxymorphone to 5 ppm,
these firms brought a countersuit based on patent
unenforceability, arguing that the plaintiffs did not
actually have a workable invention when they
received the patent. In the process of addressing this
countersuit, Mallinckrodt not only pointed out errors
in the defendants’ approach but also explained in
detail the process by which ABUK can be reduced
from 400 ppm (which the level defendants had knowledge about) to 5 ppm. Actavis and Teva had already
displayed an intent to compete in this space by introducing their own versions of the drug, but they lacked
the ability to reduce ABUK to acceptable levels. Since
the procedural aspects of the knowledge introduced
into court by Mallinckrodt were outside the scope of
the patent, the accused firms were now in a better position to use this knowledge in subsequent innovations.
Note that the knowledge spillovers that occur during litigation are of a more exact and detailed nature
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than are those that occur in many other contexts.
The discovery stage alone gives the accused firm
access to extensive and even proprietary information
about the patent-holding firm’s innovation process.
Court proceedings can result in considerable explanation and clarification of these processes. In this
way, new information to which it otherwise would
not have had access can flow to the accused firm.
Given the difficulties in coming up with novel innovations (Fleming & Sorenson, 2004), insights gained
through knowledge spillovers during litigation can
bolster the accused firm’s ability to subsequently
develop its own novel innovations in a related
technological domain. Firms that do not undergo
litigation will not have access to such spillovers. The
ability to draw from spillovers that occur during litigation will assist accused firms toward higher subsequent novel innovation output than firms without
litigation events.
Hypothesis 1. Accused firms will have higher subsequent novel innovation output than firms not accused
of patent infringement.
Spillovers of More Recent Knowledge During
Litigation
Litigation-related spillovers are not uniform in their
value to the recipient firm; some types will be more
beneficial than others. Spillovers pertaining to more
recent or newer knowledge will be more valuable to
the accused firm than those related to older knowledge. More recent knowledge may reflect an underlying technology or science that is still evolving (Ahuja
& Lampert, 2001; Heeley & Jacobson, 2008), and is
plagued with a higher degree of causal ambiguity and
uncertainty (Arend, Patel, & Park, 2014; Johannessen,
Olsen, & Lumpkin, 2001). The opportunity to receive
first-hand information from a firm that originated
or utilized a new technology successfully will therefore be very instructive. During litigation, the patentholding firm can shed light on how it identified,
tested, and utilized the newer knowledge elements
to create the new invention. In addition, the firm
can provide insights into other knowledge components with which this knowledge recombines well
and the procedures that best correspond with its effective application. In this way, spillovers during litigation of a patent containing more recent knowledge can
provide important learning opportunities for the
accused firm that can eventually help it to develop its
own novel innovations in a similar technological
domain.
October
Patent litigation involving knowledge that is older
and already known in the industry is likely to offer
comparatively less useful learning opportunities to
the accused firm. Older knowledge may be already on
the road to obsolescence (Darr, Argote, & Epple, 1995).
The longer such knowledge has been around, the
more opportunities other firms in the industry will
have had to learn about it (Fleming, 2001; Hargadon &
Sutton, 1997). For example, the patent-holding firm
might have licensed that technology or process, resulting in greater knowledge diffusion. Older knowledge
may have already resulted in rival firms finding ways
to “design around” it. Since technologies continue to
evolve over time, spillovers associated with older
knowledge may not be adequately informative for subsequently bringing about novel outcomes. We therefore expect litigation involving more recent knowledge
to create better learning opportunities than older
knowledge, enhancing accused firms’ subsequent
novel innovations in the same technological domain.
Hypothesis 2. The more recent the knowledge in the
litigated patent, the higher an accused firm’s subsequent novel innovation output over firms not accused
of patent infringement.
Spillovers Involving Heterogeneous Knowledge
During Litigation
The heterogeneity of the knowledge in the litigated patent also influences the value of spillovers to
the accused firm. Homogeneous knowledge involves
components with important similarities and overlap
in their function that jointly create deep understanding or fine-grained intuition about that function
(Kaplan & Vakili, 2015). Because they emanate from
a single discipline or domain and possess obvious
interconnections, a combination of similar knowledge components in an innovation can reduce the
potential for unexpected and unusual insights (Taylor & Greve, 2006). On the other hand, heterogeneous
knowledge components that differ considerably in
their underlying attributes and involve scientific
principles from different disciplines can give rise to
unique and path-breaking innovations (Carnabuci &
Operti, 2013; Phene, Fladmoe-Lindquist, & Marsh,
2006). It is for this reason that more heterogeneous
knowledge is often considered to be particularly
valuable for bringing about more novel innovations
(Hargadon & Sutton, 1997; Rodan & Galunic, 2004).
The innovation process to effectively bring heterogeneous knowledge components together and successfully create an innovation will, however, have
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ex ante uncertainty due to unknown causal associations among them (Rosenkopf & Almeida, 2003).
Because of their underlying dissimilarities, recombination of heterogeneous knowledge components is
not easily accomplished. Even after the firm has figured out which elements to use, it will still have
imprecise knowledge about how to actually combine
them in a way that leads to the desired outcome
(Fleming & Sorenson, 2004). Initially, it will understandably operate with incorrect assumptions that
result in suboptimal outcomes. Ongoing experimentation that rectifies these assumptions is thus
required to improve outcomes. The ability the firm
eventually develops for successfully utilizing heterogeneous knowledge in its innovation and the
understanding it now has about how such knowledge elements interact with one other constitute a
competitive advantage that other firms will have difficulty emulating.
When litigation involves a patent that has recombined such interdisciplinary knowledge, spillovers
can provide intricate and multifaceted insights to
the accused firm about the recombination process
that it would otherwise not have. For example, the
patent-holding firm might disclose pertinent information about how it went about creating connections among knowledge components that come
from disparate disciplines and draw on different
technological principles. Such spillovers offer the
accused firm an opportunity to understand and
unravel complex causal linkages that underlie
the patented technology (Henderson & Clark,
1990; Yayavaram & Ahuja, 2008). These insights
can provide the accused firm with a much better
understanding of how it too can successfully
integrate such heterogeneous knowledge. Merged
with its own knowledge, this new understanding
can improve the accused firm’s ability to create
novel innovations in a similar technological
space.
In contrast, when patent litigation involves less heterogeneous knowledge, spillovers during litigation
will tend to have comparatively less value for the
accused firm. Whereas spillovers related to knowledge spanning dissimilar technological domains can
help a firm broaden its horizons and utilize more creative combinations of knowledge (Hargadon & Sutton, 1997), those pertaining to less heterogeneous
knowledge may result in deeper understanding but
fewer insights of a cutting-edge nature (Fleming,
2001). We therefore expect that spillovers during
patent litigation involving more heterogeneous
knowledge will enhance the accused firm’s ability to
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subsequently create novel innovations in the same
technological space.
Hypothesis 3. The more heterogeneous the litigated
patent knowledge, the higher the subsequent novel
innovation output of accused firms over firms not
accused of patent infringement.
Spillovers Pertaining to Knowledge Scope
During Litigation
Above and beyond the recency and heterogeneity
of a litigated patent’s knowledge components, the
scope of the patent and the knowledge contained
within it also affects the value of spillovers to the
accused firm. The overall patent reflects a unique
contribution to the state of knowledge or “art” in a
particular sphere of technology. It embodies distinct
scientific or technical insights and relationships that
are new, and referred to as new art (Mueller, 2018).
Patents can vary, however, in the extent of new scientific relationships that comprise this art (Jaffe et al.,
2000; Manzo, 2012). While a successfully granted
patent must articulate or claim at least one new technical relationship (uspto.gov), patents can claim
multiple such relationships for characterizing their
innovation (Nard & Wagner, 2008). The number
of claims in a patent, reflecting distinct technical
relationships underlying the innovation, reflects the
scope of knowledge in the patent.
Litigated patents with a larger number of claims create more opportunities for spillovers of unique knowledge during litigation. To demonstrate infringement,
the patent-holding firm will need to explain both how
each claim in its patent differs from prior art and in
what way it has been infringed by the accused firm.
The more claims there are in the patent, the more elucidation will be required by the patent-holding firm.
Since any particular claim is unlikely to be completely
self-explanatory or unambiguous in how it relates to
the new art (Roper et al., 2016), the patent-holding
firm will need to explain in detail to the court the
essential role each claim has in creating the new art
and the manner in which they operate jointly to give
rise to the innovation (Nard & Wagner, 2008). In this
way, a patent characterized by greater scope can result
in more extensive explanation and elaboration during
the litigation process.
After absorbing this information presented during
litigation, the accused firm can gain a much better
understanding of the patented technology than it
possessed prior to litigation. Since the number of
claims is directly tied to the scope of the patent,
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litigation of a patent with more claims will give rise
to a deeper and more enriching debate over the
underlying features of the new art. Only by defending the uniqueness of all the claims encompassed
within the patent will the patent-holding firm be in a
position to demonstrate how these insights have
been infringed by the accused firm. Litigation of a
patent characterized by more claims thus increases
the magnitude of proprietary knowledge spillovers,
in turn enhancing the accused firm’s subsequent
novel innovations within the same technological
domain.
Hypothesis 4. The greater the scope of the litigated
patent, the higher the accused firm’s subsequent
innovation output over firms not accused of patent
infringement.
METHODS
Industry Context and Sample
To test the hypotheses, we utilize the context of the
U.S. pharmaceutical industry, in which firms expend
significant resources for innovation activities (Sorensen & Stuart, 2000). The well-functioning appropriability regime for intellectual property in the United
States incentivizes firms in this industry to seek
patents to protect their proprietary knowledge and
undertake litigation when they believe their patents
are infringed. The U.S. pharmaceutical industry thus
offers a rich context to examine how patent litigation
might affect organizational learning and innovation.
The sample was constructed from the domestic and
foreign pharmaceutical firms operating in the U.S.
market and covered in Pharmaprojects, a comprehensive database on the activities of pharmaceutical firms
that has also been used in prior studies (Adegbesan &
Higgins, 2011; Hess & Rothaermel, 2011; Kapoor &
Klueter, 2015). Financial data for firms were obtained
from COMPUSTAT via Wharton Research Data Services. Patent data were taken from the United States
Patent and Trademark Office, and alliance-related
information from Recombinant Capital, a proprietary
database tracking the life sciences industry. Data on
patent litigation were obtained from Westlaw, a database by Thompson Reuter that tracks litigation cases
in the United States. Finally, drug approval data were
collected from U.S. Food and Drug Administration
(FDA). After combining all these sources, a unique
dataset was created that includes firms that have and
have not undergone litigation (i.e., have not initiated
lawsuits nor been sued). Inclusion of nonlitigated
October
firms ensures that counterfactuals are accounted for
in our estimations.
In the pharmaceutical industry, drugs and their patents span 14 different therapeutic classes (Pharmaprojects, 2015; WHO, 2018). Each therapeutic class refers
to a distinct medical condition, such as oncology, central nervous system, cardiovascular, etc., and treatments specific to that condition. Drug development
projects within a given therapeutic class have strong
complementary relationships and spillover effects.
Hence, we utilize a firm-therapeutic class-year panel
dataset, with independent and dependent variables
measured at the therapeutic class level. A particular
firm-therapeutic class combination is included when
a firm is active in that class. In the original sample
drawn from Pharmaprojects, consisting of 444 public
and private firms, we identified 132 firms that had
undergone a litigation event and 312 firms that had
not. Firms were dropped from this set when financial
and other data were not available for the entire sample
period. After accounting for missing data, the sample
for our main analysis consisted of 22,486 firm-therapeutic-year class observations for 169 firms (60 with a
litigation event and 109 without such an event) for the
period 1998 to 2015, constituting an unbalanced panel
dataset.
Dependent Variables
Firm’s novel innovation output. The dependent
variable (DV) for our hypotheses is assessed by the
number of a firm’s novel drugs in a given therapeutic
class approved by the FDA subsequent to the litigation event.3 For every firm i, we count the number of
drugs in therapeutic class j in year t. This measure
has also been used in prior research to measure a
firm’s novel innovation output (Hoang & Rothaermel, 2005; Rothaermel & Deeds, 2004). While the
FDA gives approval for both novel and generic
drugs, our measure accounts only for novel drugs.
Drugs considered novel by the FDA include those
utilizing new molecular entities, as well as those
containing new combinations of existing molecular
entities that offer significantly better treatment or
3
The approach used here is consistent with that typically used in the learning literature (Argote & Epple, 1990;
Desai, 2008; Haunschild & Sullivan, 2002; Lieberman,
1984), in which learning outcomes are measured through
the following sequence: a prior state, a treatment through
which learning can occur, and an outcome state. The
change in behavior between the prior state and subsequent
state is attributed to learning (holding other influences
constant).
2022
Awate and Makhija
much lower side effects than existing drugs (Hunt,
2002; Sorescu, Chandy, & Prabhu, 2003).
To test the sensitivity of our dependent variable,
we utilize an additional measure to assess spillovers
subsequently utilized by the accused firm: citations
of the litigated patent in the accused firm’s subsequent patents. This robustness test is discussed in
the results section.
Independent Variables
Litigation event. To distinguish between accused
firms and those not involved in litigation, we utilize
a dummy variable. If a focal firm i is accused of
infringing a patent in therapeutic class j in year t 2 5,
this observation is coded as 1, and 0 otherwise. We
utilize a rolling window approach (one to eight
years) to identify the optimal lag between the litigation event and the firm’s novel innovation output. A
five-year lag is identified as yielding the strongest
coefficient for the litigation event dummy (findings
available upon request). As expected, spillovers
greatly reduce the overall time for coming up with
innovations, which is typically eight to 10 years
(Buzzell, 1988; Zemmel & Sheikh, 2010).
Knowledge recency. The measure for knowledge
recency is applied to all patents in therapeutic class
j that are litigated against firm i in year t 2 5. This
builds on prior work suggesting that recency of
knowledge of a patent is reflected in a smaller difference between the grant dates of a focal patent
and the patents it cites (Heeley & Jacobson, 2008;
Katila, 2002; Nerkar, 2003). We utilize the formula
in Heeley and Jacobson (2008) for calculating knowledge recency, as follows:
N
X
Knowledge Recency5
j50
jt 2 f jt
P
pjt
S:D:
where fjt is the average age of patents cited by the litigated patent in a technology class j in year t, with
jt is the average age of citations by all patents
j 2 N; P
in technology class j in year t; S.D. is the standard
jt ; and, pjt is the proportion of the focal
deviation of P
firm i’s patents in class j in year t. To temper the
effect of very old citations, we include only those
cited patents whose recency is in the top 10th percentile of all cited patents. Because we subtract the
firm score from the average technology class score,
litigated patents using relatively newer technological inputs will have more positive scores, while
those with relatively older inputs will have more
negative scores.
1755
Knowledge heterogeneity. Our measure of knowledge heterogeneity is applied to all patents in therapeutic class j that are litigated against firm i in year
t – 5. While a patent is assigned to one primary therapeutic class, it can also have connections to other
secondary therapeutic classes (indicated in the Pharmaprojects database).4 Litigated patents incorporating more secondary therapeutic classes are expected
to yield more heterogeneous knowledge. Heterogeneity is thus assessed through the distribution of secondary therapeutic classes in litigated patents using
an entropy index (Desai, 2008; Haunschild & Sullivan, 2002). For every focal firm i that is active in a
focal therapeutic class j, we assess the knowledge
heterogeneity of the patents litigated in year t 2 5 as
follows:
Knowledge
X heterogeneity5
2 ½Pk ðln Pk Þ for k51, . . . , 13
where P is the proportion of secondary therapeutic
classes (with k representing the 13 possible therapeutic classes). This measure takes higher values
when more therapeutic classes are associated with
litigated patents, and lower values when fewer therapeutic classes are involved.
Knowledge scope. The number of patent claims,
each of which identifies fundamental and distinct
features of the new art, reflects the scope of the patent (Merges & Nelson, 1990; Novelli, 2015). We
therefore measure knowledge scope by taking the log
of the number of claims in the litigated patents for
firm i in therapeutic class j and year t 2 5.
Control Variables
We control for a number of alternative explanations for a firm’s innovation outcomes utilizing a
range of additional variables for firm i in year t. To
isolate the effects of learning from spillovers, we
control for the innovation capabilities of the firm
prior to the litigation event through the accused
firm’s prior novel drug approvals in a particular
therapeutic class in the prior five-year period. This
controls for a firm’s experience with prior successes
that can also influence subsequent innovations. We
include the number of a firm’s patents as well as
its research and development (R&D) intensity as
additional measures of the firm’s prior innovation
4
For example, Pharmaprojects can assign a cancerrelated patented drug to a primary therapeutic class of
oncology, while also indicating relevant secondary therapeutic classes, such as musculoskeletal or pulmonary.
1756
Academy of Management Journal
capabilities and knowledge stock (Baum, Calabrese,
& Silverman, 2000). Firm revenue (logged) serves as
a proxy for firm size (Henderson & Cockburn, 1994;
Yang et al., 2010), since larger firms may have more
resources, complementary assets, and product lines
that can be employed for innovation (Freeman &
Soete, 1997). To address a firm’s accumulation of
knowledge over time, we control for firm age in
years (logged). Since pharmaceutical firms operating
in multiple primary therapeutic classes may be better able to recombine knowledge from different
classes, improving their innovation outcomes, we
control for the active technology areas of a firm by
assigning 1 to a firm that operates in more than one
primary therapeutic class (Gambardella & McGahan,
2010). To control for a firm’s access to diverse
knowledge and resources that might also influence
learning in the innovation process (Powell, Koput, &
Smith-Doerr, 1996), we include the firm’s R&D in
foreign countries. Since learning can also occur
through alliances (Makhija & Ganesh, 1997; Steensma & Corley, 2001), we control for total numbers of
codevelopment and licensing alliances in the last
five years. The effects of mergers and acquisitions
the firm undertakes are also accounted for, as acquisitions are common in the pharmaceutical industry
and can influence knowledge acquisition. Since
firms from developed countries may have better
access to resources than those from developing
countries, we control for this through the number of
patents filed in the firm’s country of origin. Due to
additional opportunities they afford for spillovers,
we also control for co-occurring litigation, or any
instance in which an accused firm is a defendant in
more than one litigation in a year, as well as when
the accused firm is a plaintiff in other litigation, both
through dummy variables.
Estimation Method
Our dependent variable is a count variable, making
a Poisson fixed-effects model well-suited for conducting the analysis (Hausman, Hall, & Griliches, 1984).
Our data include therapeutic classes nested within
firms. It is possible that errors for firm observations
across therapeutic classes are correlated, violating the
assumption of nonindependence of observations.
According to scholars (Cameron, Gelbach, & Miller,
2011; Pepper, 2002), standard errors should be clustered at the highest level of aggregation, which in our
case is the firm. We further test our models using
two-way clustered errors at the firm and therapeutic
class levels, and find similar results. To control for
October
time-invariant heterogeneity across classes, we
include firm fixed effects as well as 14 dummies for
therapeutic class. We also add year dummies to
account for sources of heterogeneity stemming from
factors that vary over time but are generally unvarying among firms, such as changes in economic or regulatory conditions. We conduct our estimation using
two models. The first model examines the effect of
the litigation event variable on number of novel
drugs. The second model assesses the effects of
knowledge recency, heterogeneity, and scope on
number of novel drugs.
RESULTS
The descriptive statistics and correlations among
all variables used in the analysis are displayed in
Table 1. To confirm that our analysis is not unduly
affected by multicollinearity, we estimate variance
inflation factors (VIF) for all variables. VIFs for all
models in the main analysis are below 2, which is
well within the acceptable range. Although VIFs are
neither a necessary nor sufficient indicator of multicollinearity, a large sample such as ours mitigates
the loss of power associated with multicollinearity
(Echambadi, Campbell, & Agarwal, 2006).
In order to understand the value of litigation for an
accused firm’s innovation outcomes, we begin our
analysis by comparing firms with and without litigation events in a given technological class. If the two
sets of firms differ systematically in their underlying
characteristics, it may mean that these characteristics are driving their innovation outcomes, rather
than whether a litigation event occurred. To assess
such possible selection bias among firms that underwent litigation and those that did not, we employ a
coarsened exact matching (CEM) technique (Blackwell, Iacus, King, & Porro, 2009). CEM improves the
estimation of causal effects by reducing imbalance
between “treatment” (firms with a litigation event)
and “control” (firms without a litigation event)
groups. Compared to other matching techniques,
CEM better aligns the distributional characteristics
of these groups to help reduce concerns of statistical
bias and model dependence (Iacus, King, & Porro,
2012), as well as endogeneity concerns. For our purposes, eliminating systematic differences that relate
specifically to the innovation capabilities of the two
sets of firms will improve their comparability. To
create a matched sample, we therefore utilize four
firm-level variables that can affect novel innovation output—revenue, R&D intensity, patent output, and age.
21
19
20
17
18
16
15
14
12
13
11
9
10
8
2
3
4
5
6
7
1
Mean
SD
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
a
Correlations calculated with logged variables. Mean and SD reported as normal; n 5 22,486.
No. of novel drugs
0.05
0.3
1.00
approved
4187 10084
0.15 1.00
Firm sizea
R&D intensity
994.2
4764.8 20.03 20.31 1.00
Firm agea
41.2
42.2
0.11 0.57 20.16 1.00
Prior drug approvals
0.3
1.0
0.30 0.29 20.06 0.27 1.00
Patents
25.2
80.2
0.11 0.33 20.06 0.35 0.23 1.00
Research in
2.0
2.7
0.13 0.36 20.09 0.31 0.28 0.41 1.00
different
a
geography
Codevelopment
8.1
21.6
0.17 0.55 20.14 0.46 0.36 0.43 0.45 1.00
alliancesa
1.8
3.6
0.19 0.50 20.10 0.46 0.39 0.40 0.51 0.73 1.00
Licensing alliancesa
Merger and
0.03
0.2
0.08 0.16 20.04 0.16 0.12 0.11 0.17 0.15 0.16 1.00
acquisition flag
Active technology
1.0
0.2
0.00 0.00 0.03 0.02 0.01 0.02 0.01 20.02 0.03 0.03 1.00
areas
Litigation event
0.07
0.3
0.12 0.27 20.07 0.20 0.24 0.16 0.06 0.24 0.17 0.12 20.00 1.00
Other litigation as
0.03
0.2
0.10 0.21 20.04 0.18 0.27 0.20 0.04 0.26 0.20 0.06 0.01 0.30 1.00
plaintiff
Cooccurring
0.01
0.1
0.02 0.11 20.02 0.06 0.03 0.02 20.04 0.02 20.00 0.06 0.00 0.33 0.08 1.00
litigation
Citations to litigated
1.4
21.6
0.03 0.07 20.01 0.04 0.05 0.10 0.02 0.05 0.03 0.00 20.01 0.17 0.02 0.10 1.00
patent
Prior citation to
1.0
0.2 20.04 20.17 0.05 20.11 20.07 20.02 20.04 20.05 20.04 20.07 20.01 20.46 20.07 20.28 20.05 1.00
litigated patent
Technology overlap
0.05
0.2
0.06 0.16 20.05 0.17 0.14 0.11 0.12 0.19 0.18 0.10 20.00 0.10 0.07 20.00 0.01 20.08 1.00
No. of technology
1.5
2.8
0.04 0.16 20.04 0.15 0.09 0.14 0.09 0.12 0.12 0.11 0.02 0.14 0.05 0.05 0.12 20.19 0.33 1.00
subclasses
Knowledge recency 20.01
0.01 20.02 20.04 0.01 0.01 20.08 20.00 20.07 20.04 20.05 20.06 0.00 0.00 20.02 20.01 0.04 0.04 20.04 20.02 1.00
Knowledge
0.2
0.6
0.08 0.31 20.08 0.20 0.13 0.13 0.04 0.23 0.17 0.09 0.02 0.60 0.20 0.39 0.18 20.45 0.08 0.13 20.04 1.00
heterogeneity
a
Knowledge scope
122
638
0.05 0.20 20.04 0.10 0.10 0.08 20.00 0.11 0.06 0.09 20.02 0.56 0.14 0.62 0.32 20.40 0.01 0.08 0.03 0.58 1.00
Variables
TABLE 1
Summary Statistics
2022
Awate and Makhija
1757
1758
Academy of Management Journal
The “Pre-CEM” and “Post-CEM” columns in Table
2a show the difference in means of the selected variables before and after the CEM balancing process. It is
evident that, prior to CEM balancing, the treatment
and control groups differ in key characteristics,
whereas post-balancing, the lack of statistically significant differences in the means of the matching variables suggests that the groups are more comparable.
Building on previous research using matched sampling (e.g., Sorenson & Stuart, 2008; Zhang & Guler,
2020), we use a conditional fixed effects Poisson
model to control for unobserved attributes of firms
with and without litigation events, producing unbiased and efficient estimates (Armstrong, Gasparrini,
& Tobias, 2014). Together, the CEM sampling
approach and conditional Poisson method enable a
direct comparison between the two sets of firms, and
allow us to assess whether a litigation event has an
impact on a firm’s subsequent innovation outcomes.
Table 2b contains results for the conditional fixed
effects Poisson model using the balanced sample in
which firm-class-year observations with a litigation
event are matched with firm-class-year observations
without such an event. In this way, firms without a litigation event are active in the same therapeutic class
as those with a litigation event. We also control for a
variety of other factors to reduce any further potential
imbalance between the two sets of firms. The crosssectional nature of this analysis allows us to utilize a
larger sample of firms (359) than our main analysis
and match more firms with litigation events (66 firms
with 2,318 observations) to those without litigation
events (293 firms with 2,387 observations), for a
total of 4,705 observations. As Table 2b reports,
TABLE 2A
Pre- and Post-Balancing Using CEM
October
TABLE 2B
CEM Analysis of Matched Firms With and Without
Litigation Events
DV 5 No. of novel drugs approved
Firm size
R&D intensity
Firm age
Prior drug approvals
Patents
Research in different geography
Codevelopment alliances
Licensing alliances
Merger and acquisition flag
Active technology areas
Litigation event
Observations
Number of firms
Firm dummies
Year dummies
Therapeutic class dummies
Adj. R-squared
Model
20.00†
(0.00)
20.66†
(0.40)
0.00
(0.00)
0.18
(0.05)
20.00
(0.00)
20.05
(0.06)
0.34
(0.13)
0.75
(0.18)
0.52
(0.33)
0.49
(0.23)
0.36*
(0.18)
4,705
359
Yes
Yes
Yes
0.27
Note: Included: firms’ country of origin; robust standard errors
in parentheses. The cross-sectional approach used for CEM
estimations allowed us to match more firms with and without
litigation events. The matched set of firms includes 66 with
litigation events and 293 without such events.
† p , 0.1
p , 0.05
p , 0.01
p , 0.001
Comparison of Firms With and
Without Litigation Event
Pre-CEM
Firm size
R&D intensity
Patent output
Firm age
3666.7
(170.0)
908.2
(13.95)
9.95
(0.37)
26.49
(0.54)
Note: Robust standard errors in parentheses.
† p , 0.1
p , 0.05
p , 0.01
p , 0.001
Post-CEM
162.6
(267.4)
0.03
(0.02)
0.14
(0.49)
1.02
(0.81)
after using the matched sample, the treatment variable litigation event has a positive and significant
effect on novel innovation outcomes for accused
firms (b 5 0.369, p , 0.05). The significance of the
treatment variable gives us initial confidence that
a litigation event plays an important role in
influencing a firm’s innovation outcomes.
Table 3 contains the analysis for all hypotheses for
all firms. Model 1 contains only control variables,
with subsequent models adding in the explanatory
variables. Model 6 is the full model. We first discuss
the findings for the hypotheses, and then the findings for controls.
2022
Awate and Makhija
According to our baseline hypothesis (Hypothesis 1),
a patent litigation event will improve accused firms’
subsequent novel innovation output over those without such an event. The rationale for this relationship
stemmed from the notion that knowledge spillovers
from the patent-holding firm during litigation will
assist accused firms with their own subsequent innovations in the same technological class as the litigated
patent. Results for Hypothesis 1 for all firms are seen
in model 2 of Table 3. We find that accused firms’
litigation event is positive and highly significant
1759
(b 5 0.461, p , 0.001), consistent with the logic
that spillovers during litigation assist accused firms’
subsequent novel innovations. The magnitude of the
coefficient suggests that litigation increases the odds
of an accused firm’s novel drug discovery by 18%.
The findings from both the CEM and the main analyses strongly confirm Hypothesis 1.
Hypothesis 2 suggested that when litigation pertains to patents involving more recent knowledge,
the accused firm’s subsequent novel innovation outcomes will be enhanced relative to nonlitigation
TABLE 3
Effect of Litigation on Accused Firms’ Novel Innovation Output
DV: No. of Novel Drugs Approved
Firm size
R&D intensity
Firm age
Prior drug approvals
Patents
Research in different geography
Cooccurring litigation
Other litigation as plaintiff
Codevelopment alliances
Licensing alliances
Merger and acquisition flag
Active technology areas
1
2
3
4
5
6
0.05
(0.05)
20.00†
(0.00)
20.18
(0.36)
0.12
(0.04)
0.00
(0.00)
0.05
(0.08)
0.06
(0.34)
20.07
(0.18)
0.23
(0.14)
0.27
(0.17)
0.18
(0.20)
20.40
(0.14)
0.06
(0.04)
20.00†
(0.00)
20.15
(0.38)
0.11
(0.03)
0.00
(0.00)
0.07
(0.08)
0.02
(0.34)
20.14
(0.18)
0.21
(0.14)
0.31†
(0.17)
0.18
(0.20)
20.41
(0.13)
0.46
(0.13)
0.05
(0.04)
20.00†
(0.00)
20.17
(0.36)
0.13
(0.04)
0.00
(0.00)
0.06
(0.08)
0.05
(0.34)
20.07
(0.18)
0.23†
(0.14)
0.28
(0.17)
0.19
(0.21)
20.40
(0.14)
0.05
(0.04)
20.00†
(0.00)
20.08
(0.34)
0.12
(0.04)
0.00
(0.00)
0.06
(0.08)
20.01
(0.35)
20.10
(0.18)
0.23
(0.14)
0.37
(0.18)
0.24
(0.22)
20.44
(0.14)
0.06
(0.04)
20.00†
(0.00)
20.09
(0.39)
0.12
(0.03)
0.00
(0.00)
0.06
(0.08)
20.03
(0.35)
20.16
(0.18)
0.20
(0.13)
0.30†
(0.17)
0.17
(0.20)
20.41
(0.13)
0.07
(0.04)
20.00†
(0.00)
20.02
(0.37)
0.12
(0.03)
0.00
(0.00)
0.08
(0.08)
20.08
(0.37)
20.18
(0.18)
0.21
(0.14)
0.38
(0.18)
0.23
(0.21)
20.44
(0.142)
Litigation event
Knowledge recency
2.61
(1.15)
Knowledge heterogeneity
0.29
(0.10)
Knowledge scope
Observations
Number of firms
Country of origin
Firm fixed effects
Therapeutic class dummies
Year dummies
22,486
169
Yes
Yes
Yes
Yes
Note: Robust standard errors in parentheses.
† p , 0.1
p , 0.05
p , 0.01
p , 0.001
22,486
169
Yes
Yes
Yes
Yes
22,486
169
Yes
Yes
Yes
Yes
22,486
169
Yes
Yes
Yes
Yes
0.09
(0.02)
22,486
169
Yes
Yes
Yes
Yes
2.00
(0.99)
0.22
(0.09)
0.08
(0.02)
22,486
169
Yes
Yes
Yes
Yes
1760
Academy of Management Journal
firms. Given that more recent knowledge will likely
be more unfamiliar to the accused firm, such spillovers can generate new insights that can be valuable
for its subsequent innovations. As can be seen in
models 3 and 6 in Table 3, the coefficient is positive
and significant in both cases (b 5 2.610, p , 0.05; b 5
2.002, p , 0.05). The magnitude of the coefficient
indicates that a one standard deviation increase in
knowledge recency of a litigated patent increases the
odds of the accused firm’s novel drug discovery by
7%. Hypothesis 2 is thus supported.
Hypothesis 3 predicted that patent litigation involving greater knowledge heterogeneity will enhance
accused firms’ subsequent novel innovation outcomes
over those of accused firms. This causal association
was based on the expectation that such spillovers
shed light on more intricate interrelationships among
diverse knowledge elements. It also offers the accused
firm an opportunity to understand the strategic rationale for combining such heterogeneous knowledge
elements. The coefficients for this variable are significant in both models 4 (b 5 0.291, p , 0.05) and 6 (b 5
0.223, p , 0.05). The magnitude of the coefficient
indicates that a one standard deviation increase in a
litigated patent’s knowledge heterogeneity increases
the odds of accused firms’ novel drug discovery by
14%. These findings are consistent with the argument that litigation involving more heterogeneous
knowledge elements offer richer insights on complex underlying causal mechanisms, allowing the
firm to improve its own ability to subsequently
create novel innovations. Hypothesis 3 is thus
supported.
Hypothesis 4 predicted that litigated patents associated with greater scope will enhance the accused
firm’s subsequent novel innovation output. This
argument was based on the logic that greater patent
scope results in a wider range of knowledge spillovers. The coefficient for knowledge scope is positive and significant in both models 5 (b 5 0.091, p ,
0.001) and 6 (b 5 0.080, p , 0.001). The magnitude
of the coefficient indicates that a one standard deviation increase in the scope of a litigated patent
increases the odds of accused firms’ novel drug discovery by 17%. Hypothesis 4 is thus supported.
Among the control variables, three are seen to be
statistically significant across the models. Prior drug
approvals significantly affect subsequent innovation
outcomes, supporting the role of experiential learning in innovation. Licensing alliances are also seen
to play a crucial role in enhancing innovation outcomes, as predicted. Interestingly, more technology
areas in which the firm is active negatively affects
October
innovation outcomes, suggesting that specialization
is important for the ability to learn from spillovers.
Other control variables are not found to be statistically significant, possibly due to some being highly
correlated with other control variables.
Robustness Checks
To ensure the robustness of our results, we conducted a number of additional tests, with results in
Tables 4 to 6. First, we account for the possibility
that the accused firm’s citations of the litigated patent are a more direct reflection of it benefiting from
spillovers. We therefore rerun the analyses using
number of citations of the litigated patent in the
accused firms’ subsequent patents as an alternate
dependent variable. Results are shown in Table 4.
Even with additional controls becoming significant when using this dependent variable, findings
for the explanatory variables remain consistent
with those in the main analysis, confirming all
hypotheses.
Another possible issue for our analysis is endogeneity biasing our estimates (Certo, Busenbark, Woo,
& Semadeni, 2016). For example, the accused firm
may have been targeted for litigation rather than
some other firm due to the former’s prior work in the
same domain as the litigated patent. Evidence for
nonrandom targeting of the accused firm for litigation may be indicated by overlap in the subclasses of
the litigated patent and the accused firm’s prior patents. Such overlap suggests that the accused firm is
already active in a domain related to the litigated patent and possesses innovation capabilities in this
domain. To address possible endogeneity, we employ
a two-stage Heckman (1979) model, with the first stage
accounting for whether the accused firm had a patent
in the past five years with at least one subclass in common with the litigated patent. Next, we select an
exclusion restriction that influences the first-stage
selection equation but not the second-stage dependent
variable and error term (Shaver, 1998; Wolfolds &
Siegel, 2019). The exclusion restriction selected is
the total number of subclasses in the accused
firm’s patents. Its correlation of 0.36 with the firststage outcome variable, and 0.02 and 0.01 with the
second-stage dependent variable and error term,
respectively, fulfills the exclusion restriction criteria. To correct for selection bias, the inverse Mills
ratio (IMR) obtained from the first-stage Probit
equation is inserted into the second-stage equation. Tables 5 and 6 contain the two-stage Heckman results for both the main and the alternate
2022
Awate and Makhija
1761
TABLE 4
Robustness Check—Effect of Litigation on Accused Firms’ Citations of the Litigated Patent
DV: No. citations of litigated patent
Firm size
R&D intensity
Firm age
Prior drug approvals
Patents
Research in different geography
Cooccurring litigation
Other litigation as plaintiff
Citation before litigation
Codevelopment alliances
Licensing alliances
Merger and acquisition flag
Active technology areas
1
2
3
4
5
6
0.30
(0.07)
28.46
0.26
(0.09)
28.01
0.32
(0.07)
28.77
0.32
(0.12)
24.85
0.16
(0.13)
24.92
0.22†
(0.12)
24.48
(1.43)
20.25
(0.26)
0.22
(2.16)
20.43
(0.31)
0.07
(0.05)
0.00
(1.47)
20.31
(0.26)
0.21
(1.52)
20.31
(0.35)
0.19
(1.71)
20.30
(0.38)
0.10
(0.06)
0.00
(0.05)
0.00
(1.70)
20.24
(0.39)
0.05
(0.04)
0.00
(0.00)
0.28
(0.34)
1.92
(0.30)
20.52
(0.21)
0.29
(0.66)
0.85
(0.24)
21.58
(0.31)
20.85
(0.66)
20.65
(0.24)
3.75
(0.65)
(0.00)
0.17
(0.28)
2.78
(0.34)
20.24
(0.21)
20.52
(0.79)
0.79
(0.24)
21.03
(0.29)
20.91
(0.74)
21.11
(0.25)
(0.00)
0.45
(0.44)
1.17
(0.35)
20.48
(0.30)
20.10
(0.45)
1.53
(0.44)
22.72
(0.63)
20.75
(0.62)
20.18
(0.16)
(0.00)
0.40
(0.37)
1.20
(0.52)
20.69
(0.30)
20.03
(0.43)
1.07
(0.42)
22.07
(0.36)
20.60
(0.45)
20.31
(0.21)
(0.00)
0.40
(0.36)
1.05
(0.50)
20.64†
(0.35)
20.07
(0.40)
1.33
(0.42)
22.29
(0.38)
20.65
(0.44)
20.09
(0.20)
0.88
(0.35)
22,486
169
Yes
Yes
Yes
Yes
10.66
(3.09)
0.87
(0.34)
0.60
(0.29)
22,486
169
Yes
Yes
Yes
Yes
(0.08)
0.00
(0.00)
0.09
(0.26)
2.89
(0.45)
20.07
(0.21)
20.42
(0.94)
0.80
(0.23)
21.00
(0.29)
20.96
(0.79)
21.19
(0.27)
Litigation event
Knowledge recency
34.55
(7.18)
Knowledge heterogeneity
2.69
(0.75)
Knowledge scope
Observations
Number of firms
Country of origin
Firm fixed effects
Therapeutic class dummies
Year dummies
22,486
169
Yes
Yes
Yes
Yes
22,486
169
Yes
Yes
Yes
Yes
22,486
169
Yes
Yes
Yes
Yes
22,486
169
Yes
Yes
Yes
Yes
(0.04)
0.00
Note: Robust standard errors in parentheses.
† p , 0.1
p , 0.05
p , 0.01
p , 0.001
dependent variables. As expected, the exclusion
restriction variable number of subclasses is significantly associated with the first-stage outcome variable
subclass overlap (p , 0.001). The significant and negative IMR in the second-stage models in Table 6
indicates the possibility of endogeneity. Even so,
results for the explanatory variables remain similar
to those of our main analysis, providing additional
support for the four hypotheses.
DISCUSSION
Extant research has highlighted patent litigation
as a key part of a strong appropriability regime that
ensures a firm’s ability to derive value from its own
innovations. Since the overarching purpose of patent litigation is to prevent knowledge spillovers, all
else being equal we should expect that undergoing
litigation will have no real value to accused firms.
This would be particularly true if nondisclosure
1762
Academy of Management Journal
October
TABLE 5
Robustness Check Using Heckman 2-Stage Models: Effect of Litigation on Accused Firms’ Novel Innovation Output
DV: No. of Novel Drugs Approved
Firm size
R&D intensity
Firm age
Prior drug approvals
Patents
Research in different geography
Cooccurring litigation
Other litigation as plaintiff
Codevelopment alliances
Licensing alliances
Merger and acquisition flag
Active technology areas
No. of technology subclasses
First- Stage Model
0.00
(0.02)
20.00
(0.00)
0.10
(0.07)
0.05
(0.01)
20.00
(0.00)
0.12
(0.08)
20.61
(0.24)
20.05
(0.11)
0.08
(0.07)
0.17†
(0.09)
0.29
(0.28)
20.16
(0.22)
0.09
(0.02)
IMR
1
2
3
4
5
6
0.05
(0.05)
20.00
(0.00)
20.18
(0.36)
0.12
0.06
(0.04)
20.00
(0.00)
20.14
(0.38)
0.12
0.05
(0.04)
20.00
(0.00)
20.16
(0.36)
0.13
0.05
(0.04)
20.00
(0.00)
20.09
(0.35)
0.12
0.06
(0.04)
20.00
(0.00)
20.07
(0.39)
0.12
0.07
(0.04)
20.00
(0.00)
20.01
(0.38)
0.12
(0.04)
0.00
(0.00)
0.05
(0.08)
0.04
(0.36)
20.07
(0.18)
0.23
(0.14)
0.28
(0.18)
0.18
(0.22)
20.40
(0.14)
(0.04)
0.00
(0.00)
0.08
(0.08)
20.01
(0.37)
20.15
(0.18)
0.21
(0.14)
0.32†
(0.17)
0.19
(0.21)
20.42
(0.14)
(0.04)
0.00
(0.00)
0.06
(0.08)
0.03
(0.37)
20.07
(0.18)
0.23†
(0.14)
0.28
(0.18)
0.20
(0.22)
20.40
(0.14)
(0.04)
0.00
(0.00)
0.06
(0.08)
0.01
(0.38)
20.10
(0.18)
0.23
(0.14)
0.37
(0.18)
0.24
(0.24)
20.43
(0.14)
(0.03)
0.00
(0.00)
0.07
(0.08)
20.07
(0.39)
20.17
(0.18)
0.20
(0.13)
0.31†
(0.17)
0.19
(0.21)
20.42
(0.14)
(0.03)
0.00
(0.00)
0.08
(0.08)
20.09
(0.40)
20.18
(0.18)
0.21
(0.14)
0.38
(0.18)
0.24
(0.22)
20.44
(0.14)
0.08
(0.21)
0.02
(0.20)
0.09
(0.02)
22,486
169
Yes
Yes
Yes
Yes
1.99
(0.99)
0.22
(0.09)
0.08
(0.01)
22,486
169
Yes
Yes
Yes
Yes
0.03
(0.20)
Litigation event
0.06
(0.20)
0.46
(0.13)
Knowledge recency
0.02
(0.20)
2.60
(1.15)
Knowledge heterogeneity
20.02
(0.20)
0.29
(0.10)
Knowledge scope
Observations
Number of firms
Country of origin
Firm fixed effects
Therapeutic class dummies
Year dummies
22,486
169
Yes
n/a
Yes
Yes
22,486
169
Yes
Yes
Yes
Yes
22,486
169
Yes
Yes
Yes
Yes
22,486
169
Yes
Yes
Yes
Yes
22,486
169
Yes
Yes
Yes
Yes
Note: DV of Heckman first-stage model: Technology overlap between accused and plaintiff firms. Robust standard errors in parentheses.
† p , 0.1
p , 0.05
p , 0.01
p , 0.001
agreements effectively limit meaningful spillovers.
Our findings show, however, that litigation can actually enhance accused firms’ subsequent novel innovation output relative to firms that have not undergone
litigation, even after controlling for innovation-related
capabilities. This suggests not only that knowledge
spillovers occur during the litigation process, but that
they occur in ways that help accused firms bring about
their own novel innovations in a related domain. This
is surprising given the role played by patent litigation
in strong appropriability regimes for limiting spillovers. An alternative measure to directly assess the
use of spillovers (citations of the litigated patent) lends
further credence to this overall finding.
2022
Awate and Makhija
1763
TABLE 6
Robustness Check Using Heckman 2-Stage Models: Effect of Litigation on Accused Firms’ Citations of
the Litigated Patent
DV: No. Citations Of Litigated Patent
Firm size
R&D intensity
Firm age
Prior drug approvals
Patents
Research in different geography
Cooccurring litigation
Other litigation as plaintiff
Citation before litigation
Codevelopment alliances
Licensing alliances
Merger and acquisition flag
Active technology areas
IMR
1
2
3
4
5
6
0.26
(0.07)
28.19
(1.49)
20.27
(0.26)
0.19
(0.09)
0.00
(0.00)
0.17
(0.18)
3.18
(0.43)
0.01
(0.18)
20.35
(0.87)
0.79
(0.23)
21.05
(0.28)
21.12
(0.73)
21.18
(0.26)
20.45
(0.39)
0.20
(0.09)
27.91
(2.41)
20.46
(0.34)
0.01
(0.04)
0.00
(0.00)
0.38
(0.24)
2.51
(0.40)
20.42
(0.17)
0.40
(0.60)
0.83
(0.24)
21.70
(0.31)
21.37
(0.70)
20.67
(0.19)
20.97
(0.33)
4.10
(0.59)
0.29
(0.08)
28.59
(1.51)
20.33
(0.26)
0.18
(0.07)
0.00
(0.00)
0.27
(0.19)
3.09
(0.38)
20.15
(0.20)
20.45
(0.72)
0.76
(0.23)
21.08
(0.28)
21.07
(0.70)
21.10
(0.24)
20.50
(0.39)
0.26
(0.10)
23.58
(1.43)
20.54
(0.39)
0.12†
(0.06)
0.00
(0.00)
0.76
(0.29)
1.95
(0.38)
20.11
(0.23)
20.09
(0.45)
1.43
(0.60)
22.97
(0.84)
22.00
(0.95)
20.08
(0.32)
22.06
(0.68)
0.06
(0.13)
24.41
(1.47)
20.44
(0.44)
20.05
(0.04
0.00
(0.00)
0.64†
(0.38)
2.10
(0.55)
20.50
(0.22)
20.03
(0.39)
1.15
(0.38)
22.51
(0.52)
21.80†
(1.00)
20.20
(0.16)
21.93
(0.63)
0.15
(0.10)
23.37
(1.56)
20.62
(0.49)
20.00
(0.04)
0.00
(0.00)
0.70
(0.36)
2.02
(0.47)
20.31
(0.30)
20.05
(0.40)
1.33
(0.49)
22.68
(0.62)
22.02†
(1.14)
0.11
(0.23)
22.41
(0.80)
1.08
(0.21)
22,486
169
Yes
Yes
Yes
Yes
15.52
(3.61)
1.36
(0.58)
0.71
(0.17)
22,486
169
Yes
Yes
Yes
Yes
Litigation event
Knowledge recency
35.08
(6.65)
Knowledge heterogeneity
3.41
(1.02)
Knowledge scope
Observations
Number of firms
Country of origin
Firm fixed effects
Therapeutic class dummies
Year dummies
22,486
169
Yes
Yes
Yes
Yes
22,486
169
Yes
Yes
Yes
Yes
22,486
169
Yes
Yes
Yes
Yes
22,486
169
Yes
Yes
Yes
Yes
Note: DV of Heckman first-stage model: Technology overlap between accused and plaintiff firms. First-stage results are in Table 5.
Robust standard errors in parentheses.
† p , 0.1
p , 0.05
p , 0.01
p , 0.001
We also observed that some types of spillovers are
more useful than others for enhancing the accused
firm’s subsequent novel innovation output. Spillovers that help to reduce the ambiguity of causal
relationships underpinning another firm’s innovation are most useful to the accused firm. The relative
unfamiliarity of newer and more recently created
knowledge components in the litigated patent renders spillovers more valuable by shedding additional light on this new knowledge and how it can be
effectively used. Similarly, spillovers that clarify
how heterogeneous components in the litigated
1764
Academy of Management Journal
patent are successfully integrated provide insights to
the accused firm about how to use similarly heterogeneous or disparate components for an innovation.
Litigation of patents with greater scope likewise creates spillovers that unravel the patent’s complex and
multidimensional nature, leading to a better understanding of its underlying science. In all, findings
support our argument that learning through knowledge spillovers does operate during courtroom proceedings, an issue not previously considered in the
literature.
This study highlights how, even in an appropriability regime that confers strong ownership rights
over innovations, spillovers are difficult to contain.
In particular, it points to a key trade-off in the patent
litigation process for protecting a firm’s proprietary
knowledge. While litigation helps to protect the
knowledge covered in the patent itself, it also
requires the firm to give up additional proprietary
knowledge in the process, above and beyond the patent. Given that a patent-holding firm is likely to
defend some of its most valuable knowledge through
litigation—that which underpins its competitive
advantage (Somaya, 2003)—the risk of unintended
knowledge spillovers to other firms will have serious
ramifications for its competitive position. Through
spillovers during litigation, the firm may have
strengthened a competitor (Hamel, 1991; Makhija &
Ganesh, 1997). This research thus demonstrates how
an institution whose primary purpose is to protect
intellectual property rights also has the effect of
enabling broader diffusion of this knowledge. These
dual effects underscore the double-edged sword of
patent litigation.
Knowledge spillovers are known to occur through various means, including employee mobility
(Agarwal, Ganco, & Ziedonis, 2009; Corredoira &
Rosenkopf, 2010; Rosenkopf & Almeida, 2003), network ties (Liebeskind, Oliver, Zucker, & Brewer,
1996; Powell, Koput, & Smith-Doerr, 1996), and alliances (Mowery, Oxley, & Silverman, 1996; Steensma
& Corley, 2001). While studies have highlighted
informal means of knowledge transmission, the very
specific rules and procedures that litigating parties
must follow generate the spillovers that occur during
litigation. It is the requirement for disclosures at various stages of litigation that make spillovers more or
less inevitable during the process. This unusual element may also make spillovers more systematic than
in other contexts, as they relate more directly to a
particular patented innovation.
This study also adds important contributions to
our understanding of patent litigation, which has
October
thus far explained firms’ decisions to initiate litigation rather than what happens once they undertake
litigation. This research hones in on the latter, and
underscores the vital importance of spillovers during this process. Since the litigating parties are often
competing in a similar space, their desire to learn
from one another is particularly acute. Spillovers
can facilitate particularly valuable learning, including about better technologies, superior processes,
and more viable innovation strategies.
Griliches (1992) and Jaffe, Trajtenberg, and Fogarty
(2000) noted that the most interesting aspect of spillovers is the impact of the discovered ideas or compounds on the research endeavors of others. Our
research is particularly helpful in shedding light on
this relationship. A litigated patent represents a successful invention, one that has likely undergone prior
failures and engendered a valuable learning process
for the patent-holding firm, giving rise to previously
unknown but important new insights. For this reason,
any new information about the processes by which
the firm came up with the new knowledge, the
paths that lead to failure and should be avoided,
and new directions to take the knowledge, will be
valuable to other firms. Innovation involves a particularly difficult process, and insights from other
firms can not only save resources and time but also
improve their ability to develop their own novel
innovations. This research demonstrates that spillovers during litigation can actually provide learning
opportunities that can enhance the innovativeness
of other firms. This is important in that innovation
represents a capability that is not easily developed
or transferred.
Since patent litigation can present a unique opportunity for the accused firm to learn from knowledge
spillovers during the proceedings and enhance its
own innovation capabilities, a key question arises—
should a firm actively solicit being targeted for litigation as a strategy? Some may suggest that the opportunities to learn may be worth involvement in
litigation, although the evidence does not automatically support such a conclusion. Litigation is not
only accompanied by significant costs in terms of
expenditures and time, it also carries the risk of an
unfavorable ruling that can result in loss of a strategic market, revenues, and reputation. There is also
the possibility of leakage of the accused firm’s own
proprietary knowledge—spillovers can operate as a
two-way street during litigation. Nonetheless, the
findings of this study suggest that the accused firm’s
ability to benefit from knowledge spillovers may
help to balance the costs of litigation.
2022
Awate and Makhija
Future Research Directions
This research can be extended in a number of
directions. Although our focus was on how the
accused firm gains insights from the patent-holding
firm, future research might consider litigation that
involves contested patents of both firms, as this may
reflect conditions under which innovation-related
knowledge spillovers also accrue to the patentholding firm. Such a context may also assist in
developing greater understanding about how litigation dynamics influence spillovers to each party.
Similarly, while this research examined effects of
spillovers while holding technological class constant, it may be that specific disease areas or baseline
technologies have stronger effects on the hypothesized relationships. We believe that a deeper examination of such factors can provide more insights into
the micro-mechanisms at play in this setting.
Another issue worth examining is the use of patent litigation in industries characterized by weaker
appropriability regimes than that of the pharmaceutical industry. While we expect that our arguments
here are largely generalizable to other industries,
those with comparatively weaker regimes will likely
exacerbate spillovers to competitors, potentially
reducing incentives for innovation. It may be that in
such industries, firms adopt alternative isolating
mechanisms and complementary assets to thwart
spillovers and maintain their proprietary knowledge
(Teece, 1986). On the other hand, industries in
which technologies evolve at a slower pace may be
less hampered by weaker appropriability regimes,
given lower innovation-based competition.
We argued that, irrespective of the outcome of litigation—that is, whether a firm wins or loses—the
important phenomenon of unintended knowledge
spillovers during courtroom discussion will still
occur and create conditions for learning. Future
research can, however, consider whether and how
litigation outcomes matter for learning. For example,
it may be that learning from spillovers is even more
impactful when the other firm wins the case, as this
win reflects the proprietary nature of the knowledge
spillovers. On the other hand, the outcome of the
case may have a psychological effect on the use of
spillovers, in that the winning firm chooses to ignore
spillovers due to its belief that its own knowledge is
superior. It may also be useful to examine the trajectory of subsequent innovations for litigated and nonlitigated patents. For example, given the potential for
knowledge spillovers through litigation, subsequent
innovations in a litigated technological domain may
1765
occur more rapidly than for those patents not subjected to litigation.
Even though this research highlighted how knowledge spillovers are generated during litigation, it
remains unclear whether patent-holding firms
appreciate the effect of such spillovers. It may be
that the implications of spillovers escape the notice
of firms that initiate patent litigation only sporadically or infrequently. On the other hand, firms that
more regularly undertake patent litigation may have
learned about their spillover effects. This may be
reflected in a reduction in the use of patent litigation
over time, or through specific strategies by which
such spillovers are reduced. To assess whether
patent-holding firms learn over time about the beneficial effects of knowledge spillovers to their competitors, and, if so, how they respond, it would be
helpful for researchers to take a more longitudinal
perspective of firms’ litigation strategies.
We assumed in this research that the patentholding firm unintentionally spills knowledge during litigation. Given the value of this proprietary
knowledge to the firm’s competitive advantage, it
seems unlikely that the firm reveals this knowledge
intentionally. However, subsequent research may
reexamine this assumption. It may be that firms actually make an effort to provide information sparingly
and only as necessary. For example, if a firm believes
it has a strong case, it may try to withhold information. On the other hand, if the firm thinks it might
lose the case, it may choose to introduce more supporting evidence. Providing information may be
recognized as a necessary evil in the litigation process, without which the patent becomes worthless
to the firm.
CONCLUSION
Scholars have highlighted the importance of
strong appropriability regimes that provide patents,
and have emphasized that patent litigation protect
firms’ proprietary knowledge and reduce spillovers
(James, Leiblein, & Lu, 2013; Mahoney & Pandian,
1992; Somaya, 2003). Past research has paid little
attention, however, to how patent litigation actually
affects spillovers of firms’ proprietary knowledge.
This study demonstrated that litigation can actually
enhance knowledge spillovers from a patent-holding
firm to an accused firm. Proving infringement of a
patent often requires presentation of considerable
information during litigation that had previously
remained secret, including the proprietary processes
that led to the patented innovation, disproved
1766
Academy of Management Journal
assumptions and alternative hypotheses tested, and
deeper appreciation of the nature of the new scientific insight and its further application. With the
intent and capacity to learn (Hamel, 1991; Makhija &
Ganesh, 1997), the accused firm is uniquely positioned to acquire valuable insights about the innovation process that can help it to subsequently create
its own novel innovations in the same technological
arena. This study is the first to demonstrate how
knowledge spillovers during litigation benefit the
accused firm’s subsequent novel innovations, shedding insight into the micro-mechanisms through
which such learning occurs.
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Kiran S. Awate (akiran@vt.edu) is an assistant professor of
management at Pamplin College of Business, Virginia
Polytechnic Institute and State University. He holds a PhD
from The Ohio State University. His research interests
include innovation, strategic alliances, and organizational
learning.
Mona V. Makhija (makhija.2@osu.edu) is Fisher College
of Business Distinguished Professor of international
business at Fisher College of Business, The Ohio State
University. She received her PhD in international business
from the School of Business, University of Wisconsin. Her
research interests include institutional environments,
knowledge management, and organizational learning.
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