Journal of Management Studies 61:5 July 2024
doi:10.1111/joms.12968
Understanding the Link between Post-­Acquisition
Resource Reconfiguration and Technology
Out-­Licensing
Thomas Maximilian Kluetera , Solon Moreirab and
Clinton Ofoedua
a
IESE Business School; bFox School of Business, Temple University
We develop a novel framework to explain how the unique properties of out-­licensing
enable R&D reconfiguration in the context of technology acquisitions. Out-­licensing is an
attractive R&D strategy following acquisitions as it expands opportunities for resource reconfiguration to outside the organization by using external partners while at the same time allowing firms to continue to benefit from the technology, both financially and strategically. We
also propose that the positive relationship between technology acquisitions and out-­licensing is
weaker when firms cannot determine the full value potential of their R&D due to uncertainty or
when they have high availability of short-­term financial slack resources. Using a sample of bio-­
pharmaceutical firms, the result of a 2SLS fixed-­effect regression that accounts for the potential
endogeneity of technology acquisitions provides support for our theoretical framework.
ABSTRACT
Keywords: licensing, resource reconfiguration, slack, technology acquisitions, technological
uncertainty
INTRODUCTION
Acquisitions are an important way for firms to access strategic technology-­based resources that can be used to enhance their R&D and increase the number and quality
of innovations (Chaturvedi and Prescott, 2022). In contrast to other forms of external
knowledge sourcing (e.g., alliances), acquisitions provide access to entire blocks of the
target’s R&D resources and capabilities (Ahuja and Katila, 2001). Therefore, benefits
Address for reprints: Thomas Klueter, IESE Business School, Barcelona 08034, Spain (tmklueter@iese.edu).
All three authors contributed equally.
This is an open access article under the terms of the Creative Commons Attribution-NonCommercialNoDerivs License, which permits use and distribution in any medium, provided the original work is
properly cited, the use is non-commercial and no modifications or adaptations are made.
© 2023 The Authors. Journal of Management Studies published by Society for the Advancement of Management Studies
and John Wiley & Sons Ltd.
T. M. Klueter et al.
accruing from technology acquisitions do not solely come from the transactions, but the
acquirer’s ability to ‘graft’ acquired R&D onto its own resource base, i.e., integrating and
reconfiguring the targets and acquirer’s R&D resources (Colombo and Rabbiosi, 2014;
Puranam et al., 2006; Zaheer et al., 2013).
When examining resource reconfiguration, researchers typically discuss the internalizing of acquired resources to internally recombine them in novel ways or the externalizing and/or deleting of resources through external divestitures (Capron et al., 2001;
Feldman and Sakhartov, 2022; Karim, 2006). However, in the context of technology
acquisitions, there are alternative resource reconfiguration strategies. In particular, out-­
licensing allows an acquirer to externalize the recombination and investments in a set of
technologies without discarding the resources from their internal R&D portfolio. While
maintaining control of the technology, firms also profit from technologies they do not
want to develop internally through upfront fees and save R&D costs by transferring responsibilities in developing a technology to the licensee (Conti et al., 2013; Laursen et
al., 2017; Markman et al., 2008). Thus, out-­licensing has unique properties that make it
an important strategy for firms to adjust their R&D following acquisitions. Yet, despite its
strategic importance, the link between acquisitions and technology out-­licensing has not
been examined and conceptualized. Such a lack of research connecting licensing to resource reorganization following acquisitions is even more surprising, given that licensing
is considered to be one of the mostly commonly used strategies to adjust, rebalance and
reorganize R&D activities (Anand and Khanna, 2000; Conti et al., 2013).
We aim to fill this gap by systematically exploring the relationship between technology
acquisitions and out-­licensing. Building on the literatures of resource reconfiguration
(Chaturvedi and Prescott, 2022; Karim and Capron, 2016; Karim and Mitchell, 2000),
technology licensing (e.g., Fosfuri, 2006; Laursen et al., 2017) and acquisitions (e.g.,
Colombo and Rabbiosi, 2014), we develop a novel framework to explain the ways in
which the properties of out-­licensing allow R&D reconfiguration in the context of technology acquisitions. In particular, we propose that out-­licensing expands opportunities for
resource reconfiguration to outside the organization by using external partners while, at the same
time, allowing firms to continue benefiting from the technology, both financially and strategically. As such, we expect a baseline relationship between the number of technology
acquisitions and the intensity of subsequent out-­licensing.
We further explain the link between acquisitions and licensing by examining two important contingencies for their relationship by explicating how out-­licensing may not only
hold advantages but may also have downsides. In particular, extant work has noted that
out-­licensing can carry the inherent risk of providing strategic technologies to possible rivals (Fosfuri, 2006; Moreira et al., 2019). Moreover, out-­licensing entails leaving the onus
of recombination and advancement predominantly with another firm, which can increase
dependencies and coordination costs when compared to developing and enlarged R&D
base in-­house (Moreira et al., 2018; Moreira et al., 2019). Considering these trade-­offs,
we theorize that there are two conditions under which firms may refrain from utilizing
out-­licensing as a resource reconfiguration strategy. First, when the acquired knowledge increases and is uncertain, the acquirer may find it harder, in the short term, to determine the
potential of its R&D resources (Markman et al., 2009; McDermott and O’Connor, 2002).
This makes it more difficult to determine what technologies to out-­licence. Moreover, such
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and John Wiley & Sons Ltd.
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uncertainty related to the target’s firm technologies may make firms more hesitant to out-­
license as the acquirer could fear giving possibly valuable technologies to a future competitor (Bianchi et al., 2014; Fosfuri, 2006). Second, when firms have substantial deployable
resources post-­acquisition that are available in the short-­term, they are more flexible in
exploring all of the innovation directions the acquired knowledge base offers internally
without coordinating with another firm (Kuusela et al., 2017; Nohria and Gulati, 1996),
ultimately decreasing their need to use out-­licensing following acquisitions.
We test our theoretical framework examining the acquisition and out-­licensing activities of 467 bio-­pharmaceutical firms between 1985 and 2014. The underlying dataset
is compiled from several sources, including ReCap partnering database, Compustat North
America, Pharmaprojects and patent data from the United States Patent and Trademark Office
(UPTSO). The results of a 2SLS fixed-­effect regression support the idea that acquisitions
are associated with a significant subsequent increase in firms’ out-­licensing activities. At
the same time, the level of uncertainty of the acquired R&D base and the short-­term
availability of financial slack can attenuate this tendency.
The paper makes several contributions to the literature on both acquisitions and technology licensing. To the best of our knowledge, this study is the first to anchor technology
out-­licensing as a resource reconfiguration strategy in the context of technology acquisitions. Out-­licensing is significantly different from the resource reconfigurations strategies
found in prior studies, including discontinuations, divestments and in-­house recombination (Capron et al., 2001; Karim, 2006). In particular, most strategies described in prior
studies only separate the internalization or externalization of resources post-­acquisitions.
We show that, in the R&D context, licensing allows for resource reconfiguration to outside the organization while, at the same time, allowing firms to retain residual ownership and control over a technology. Thus, firms can choose a resource reconfiguration
strategy that combines the advantages of adding and moving as well deleting resources,
which are core to the post-­acquisition reconfiguration process (Karim, 2006) and could
be a reason that licensing is such an important R&D strategy post-­acquisition.
We also reveal that heterogeneity in the characteristics of the target firm’s technological
portfolio matters by showing that when firms cannot determine the full value potential of
their R&D due to uncertainty, they are less likely to engage in out-­licensing, even if they
have acquired large blocks of R&D (Markman et al., 2009). In a similar vein, we explore
the role that short-­term available financial slack resources have in terms of the relationship between acquisitions and licensing. We illustrate how the presence of financial slack
resources will ease the process of integrating R&D post-­acquisitions, making firms rely
less on licensing following acquisitions. Overall, the paper conceptualizes out-­licensing as
a key resource reconfiguration strategy that firms can deploy post-­acquisition.
THEORETICAL FRAMEWORK
Technology Acquisitions and Resource Reconfiguration
The R&D resources accessed through acquisitions enlarge the acquirer’s R&D capacity
and lead to important benefits such as increasing the amount and quality of its innovations (Ahuja and Katila, 2001; Karim and Kaul, 2015). Prior research has noted that
© 2023 The Authors. Journal of Management Studies published by Society for the Advancement of Management Studies
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Resource Reconfiguration and Out-­Licensing
T. M. Klueter et al.
benefits from technology acquisitions stem from the reconfiguration of the combined
R&D resources (Colombo and Rabbiosi, 2014; Puranam et al., 2006) and that post-­
acquisition redeployment between a target and acquirer is crucial to realizing strategic
advantages such as improving the acquirer’s R&D (Ahuja and Katila, 2001). It follows
that firms need to adjust their R&D processes (Karim and Mitchell, 2000) in order to
generate value from the acquisition.
Capron et al. (2001) explain that the reshuffling of resources post-­acquisition ‘involve[s] changes that range from allowing the firm to augment existing activities to undertaking
substantial transformation of routines and resources’ (p. 820). The authors note that such
process can entail the use by a target or acquirer of the other business’ resources, for
example, the sharing of manufacturing facilities, distribution systems or the addition
and reorganization of complete business units.1 We closely follow this work and define resource reconfiguration in R&D as the ‘addition, deletion or, movement of technological
resources’ following an acquisition (Capron et al., 1998; Feldman and Sakhartov, 2022;
Karim, 2006:802; Karim and Mitchell, 2004). Thus, in the context of technology
acquisitions, resource reconfiguration pertains to the reorganization processes that
unfold with the intention to manage and adjust the expanded R&D activities (e.g.,
Aghasi et al., 2017; Kapoor and Lim, 2007).
While adding and moving resources internally expands the variety and breadth of
R&D and creates new opportunities for recombination for the acquirer, we also know
that this process is highly complex and uncertain (Feldman and Sakhartov, 2022) as the
acquiring firm must simultaneously reconfigure technological inputs, organizational
assets and ongoing R&D projects (e.g., Kroon et al., 2022; Ranft and Lord, 2000).
Moreover, a firm can only pursue a finite number of R&D initiatives internally (Galunic
and Rodan, 1998). Consequently, prior work has shown that firms often have to discharge and delete resources, which is the reason that eliminating resources is another
critical element of the post-­acquisition reconfiguration process (Capron, 1999; Capron
et al., 2001; Karim, 2006; Vidal and Mitchell, 2015).
There are two main R&D strategies firms use when reconfiguring their resources
through deletion (Capron et al., 2001; Karim and Capron, 2016; Kuusela et al., 2017).
One possibility is to narrow the number of R&D initiatives and discarding those projects holding little value (Arora et al., 2009). Discontinuation of R&D projects also
helps unlock scarce resources in the acquiring firm (Chao et al., 2009). In addition to
simply discarding R&D, firms can also divest assets, which entails the outright sales of
resources to another firm (Anand and Singh, 1997; Capron et al., 2001; Chaturvedi
and Prescott, 2022; Folta et al., 2016). Both strategies allow firms to free up resources
in the short term by shedding internal R&D activities which are currently not needed
or that are redundant (Kuusela et al., 2017). However, there are alternative strategies
for resource reconfiguration following technology acquisitions that the literature has
not yet considered.
R&D Out-­Licensing
We noted that, following technology acquisitions, resource reconfiguration has typically
been conceptualized in terms of internalizing the acquired R&D with the intention of
© 2023 The Authors. Journal of Management Studies published by Society for the Advancement of Management Studies
and John Wiley & Sons Ltd.
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recombining and expanding upon it or externalizing and/or deleting those resources
through divestitures or discontinuations. Alternative strategies for resource reconfiguration, however, allow the acquirer to externalize the recombination and investments
in a set of technologies but without fully discarding the resources from their own R&D
portfolio. In particular, firms can engage in the market for technology using out-­licensing
to commercialize and profit from technologies they do not want to develop internally but
over which they want to maintain ownership and control (Conti et al., 2013; Markman
et al., 2008).
Out-­licensing relies on arm’s-­length transactions in which the technology holder sells
to other firms the rights to exploit and use a technology in their own R&D initiatives in
exchange for upfront fees or/and royalties (Arora et al., 2001). They most commonly
involve technologies which will be used by the licensee to complement on-­going R&D
efforts (Moreira et al., 2018; Moreira et al., 2020). The volume of licensing activities has
grown substantially over the last decades and is currently considered to be one of the
most common means of developing and commercializing new technologies (Moreira et
al., 2019). Next, we systematically introduce out-­licensing as an important R&D strategy
and explain how it connects to resource reconfiguration following technology acquisitions.
Out-­Licensing as Resource Reconfiguration Following Technology
Acquisitions
As acquisition intensity increases, firms need to rely on strategies that allow them to
reconfigure R&D resources. However, as mentioned earlier, acquirers may be unable
to internally exploit an entire new set of technological opportunities (Colombo and
Rabbiosi, 2014; Meyer and Lieb-­Dóczy, 2003; Sirmon et al., 2007) and often must relinquish control of these assets by discontinuing them or divesting. These problems are
compounded as firms often engage in multiple acquisitions within a short window. In
such a context, out-­licensing has several important characteristics that can support the
post-­acquisition reconfiguration process.
Out-­licensing involves the transfer of already existing technologies; therefore, as an element
of the deal, firms can specify which technologies are being licensed (Klueter et al., 2017;
Moreira et al., 2020). This is relevant as it allows the technology holder to evaluate its existing technological portfolio and current R&D initiatives with the goal of defining those that
can be commercialized in upstream technology markets and those to continue to exploit
fully in-­house. Relatedly, prior studies have noted the importance of out-­licensing to swiftly
balance the size of R&D projects and that a growth in R&D initiatives is associated with
more out-­licensing activity (e.g., Nishimura and Okada, 2014). Thus, licensing provides a
flexible strategy for firms that need to adjust their R&D resources.
Furthermore, out-­licensing offers direct upfront fees paid by the licensee, which improves
the technology holder’s financial position in the short term and allowing the additional revenue to be channelled to the firm’s other R&D initiatives (Moreira et al., 2019). This gives
firms the flexibility to deal with resource constraints from an increase in R&D opportunities
post-­acquisitions (Ding and Eliashberg, 2002). It follows that out-­licensing is a resource-­
freeing strategy that helps firms reconfigure their R&D portfolio without requiring the significant resource investments necessary to keeping all technologies in-­house.
© 2023 The Authors. Journal of Management Studies published by Society for the Advancement of Management Studies
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Resource Reconfiguration and Out-­Licensing
T. M. Klueter et al.
Through licensing, a firm also retains the ownership of the technology; in the long run, allowing the licensor to earn returns from the licensee through royalties (Laursen et al., 2017).
This increases options as firms retain the ownership of the technology despite another firm
being involved in its subsequent development and commercialization (Ziedonis, 2007). This
is an important characteristic as divestures and discontinuations imply realization of losses,
given that these two strategies lead to outright termination of R&D initiatives. In contrast,
using licensing to manage post-­acquisition resource reconfiguration does not entail the outright deletion of resources but instead allows firms to generate short-­and long-­term revenue
streams based on upfront payments and royalties (Anand and Khanna, 2000).
Beyond the monetary advantages, there are also strategic gains in granting a firm the
right to build on licensed technologies (Choi, 2002). The technology holder not only retains ownership, but may also benefit from the advancement of the technologies through
the investments of the licensee (Laursen et al., 2017). Compared to simple deletions or
divestitures, which are irreversible, firms do not discard their resources and control but
may participate in the technology’s future value creation. Once more, we expect such
an R&D strategy to be particularly salient as the volume of R&D resources increases
post-­acquisitions and the acquirer would be unable to develop all technologies internally.
Overall, out-­licensing is significantly different from the previously discussed resource
reconfigurations strategies, including discontinuations and divestments. Out-­licensing
expands the opportunities for resource reconfiguration to outside the organization by using another
firm while, at the same time, firms can continue to benefit from the technology, both
financially and strategically. As such, out-­licensing combines the strategic advantages of
adding and moving as well deleting resources (Karim, 2006). Considering these specific
properties, we expect out-­licensing to be an important R&D strategy when firms engage
in technology acquisitions. We propose:
Hypothesis 1: Technology acquisitions will be associated with increasing subsequent out-­
licensing from the acquiring firm.
Retaining Technologies In-­House Post-­Acquisition –­Contingencies
Despite its possible advantages, involving an external firm in resource-­reconfiguration
via out-­licensing also holds risks that could make firms refrain from deploying this strategy when facing an increase in R&D resources following acquisitions. We know that
out-­licensing can carry the inherent risk of providing strategic technologies to possible
rivals, which can lead competitive implications related to rent dissipation (Fosfuri, 2006;
Moreira et al., 2019). Moreover, when out-­licensing, the responsibility of recombination
and advancement of the technology predominantly lies with another firm. This can increase dependencies and coordination costs when compared to developing and enlarged
R&D base in-­house (Arora et al., 2013; Moreira et al., 2019).
Building on these arguments, we next consider two important contingencies that may
tilt firms toward keeping technologies in-­house following an intense period of acquisitions rather than relying on out-­licensing. First, when the acquired knowledge increases
and is uncertain, it is unclear how the expanded R&D base can be effectively combined,
which raises the risk of licensing-­out valuable technologies and thus nurturing a future
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rival (Fosfuri, 2006; Miller, 2002). Second, when firms are endowed with resources sufficient to supporting the post-­acquisition integration and recombination process, they may
initially try to keep their technologies in-­house to avoid having to coordinate resource reconfiguration with another firm (Iyer and Miller, 2008). Both contingencies also connect
to the literature on acquisitions. The technological uncertainty of the target’s R&D has
been directly associated with the need for post-­acquisitions integration (e.g., Puranam
et al., 2009). Relatedly, the availability of resources to fuel the efforts to combine acquirer’s and target’s R&D has already been shown to affect the integration and divestment
approach following acquisitions (e.g., Iyer and Miller, 2008; Kuusela et al., 2017; Larsson
and Finkelstein, 1999). We rely on these contingencies theoretically to explain how, in
some cases, reconfiguration through out-­licensing may not transpire, even after an intense period of acquisitions.
The role of technological uncertainty. Acquisitions differ with respect to the types of
technological knowledge and assets they add to the acquirer’s existing R&D. In
particular, the acquired technologies may have varying levels of uncertainty, which
we define in terms of the extent to which the technological feasibility, as well as
its innovative and commercial potential, are known ex ante (Laursen et al., 2017;
Ziedonis, 2007). Enlarging the acquirer’s R&D base, paired with adding uncertain
technologies, can lead to issues in using out-­licensing as a resource reconfiguration
strategy as firms lack the ability to readily assess the future potential of its technology
pool (Oriani and Sobrero, 2008).
Technology acquisitions provide access to technologies in early stages of development that often possess a significant number of new features, which are difficult to
assess in terms of their potential for future knowledge recombination (Galunic and
Rodan, 1998). For such acquired technologies, it is also unclear what assets and expertise will ultimately be required to exploit them (Ahuja and Lampert, 2001; Markman
et al., 2009; Vinokurova and Kapoor, 2020). Technological uncertainty likely creates difficulties for resource reconfiguration via out-­licensing as the process requires
short-­term decisions as to which projects can be out-­licensed to external partners and
which are more relevant to internal development (Conti et al., 2013; McDermott and
O’Connor, 2002). However, when the acquired knowledge base is uncertain, firms do
not fully understand how the existing and acquired technologies can be integrated
or if the acquired technologies could, ultimately, replace existing internal technologies. This makes firms more careful about prematurely licensing-­out existing internal
technologies as they may need additional time to determine the full potential of a
new knowledge pool. It follows that when firms enlarge the R&D pool through acquisitions and those technologies come with a high level of uncertainty, it is harder
to appropriately assess which technologies are likely to be needed in the future for
recombination in-­house and which can be out-­licensed.
In addition, out-­licensing from a highly uncertain technology pool also increases the
risk that firms make technologies available for commercialization that, ultimately, turn
out to be highly valuable (Fosfuri, 2006; Laursen et al., 2017). This can have detrimental consequences in terms of raising new competitors and losing a strategic advantage
(Moreira et al., 2019). Therefore, when the level of uncertainty underlying the acquired
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Resource Reconfiguration and Out-­Licensing
T. M. Klueter et al.
technologies is too high, firms may refrain from using out-­licensing as resource reconfiguration strategy as they would also consider the associated competitive risks of providing
their technologies to an external firm (Fosfuri, 2006).
Finally, out-­licensing relies on the ability of firms to write contracts that document the
future use of the technology along with a renumeration structure (Choi, 2002). Indeed,
a well-­designed contract (for example, using the grant back clause provision) can be used
to reduce the risk of a firm’s losing its technological edge and experiencing damage to its
competitive position (Laursen et al., 2017). However, when there is too much uncertainty
about the future value of the post-­acquisition R&D pool, it is also much harder for the
technology holders to rely on contractual clauses to cover possible contingencies related
to the use of that technology (Ziedonis, 2007). Therefore, uncertainty reduces a firm’s
ability to anticipate all contingencies related to the future development of a technology
and consequently makes codifying these contingencies much more difficult. We know
from prior research that when the future development of a technology is extremely uncertain, licensing contracts can be ineffective in establishing legal remedies to the misuse
or misappropriation of a technology (Choi, 2002). In those cases, it is also much more
challenging to determine an appropriate remuneration structure for the out-­licensed
technologies, one that requires codifying royalties or upfront payments and other contractual contingencies (Kotha et al., 2018).
Based on all arguments, we suggest that when the technological uncertainty of the
acquired technologies is higher, we will observe a reduction in a firm’s willingness to out-­
license technologies following acquisitions.
Hypothesis 2: The positive association between acquisitions and subsequent out-­
licensing will be weaker the higher the level of uncertainty of the acquired knowledge
base.
The role of financial slack. A key insight from the literature on resource integration
is that firms are differently endowed with available financial resources that can
help support the post-­acquisition R&D process internally (Kuusela et al., 2017). In
particular, firms may have financial slack resources, i.e., resources in excess of the
minimum necessary, which can be allocated toward organizational tasks (Voss et
al., 2008). Therefore, financial slack resources are fundamentally associated with the
trial-­and-­error process and the recombination of new technologies that we expect to
unfold post-­acquisition (George, 2005). Indeed, in the absence of slack, we know that
firms are much more likely to avoid pursuing a large number of R&D tasks as this
will also lead to higher rates of unsuccessful attempts (Nohria and Gulati, 1996). It
follows that, in the presence of financial slack, firms may opt to first explore possible
novel recombinations internally to better learn about the potential of the combined
R&D resources post-­acquisition, and not immediately out-­license their technologies
to external firms to free up resources. This effect will be greatest following a period of
intense technology acquisitions as not only the potential for recombination, but also
short-­term resource constraints, are high.
With the availability of slack, some of the key features of out-­licensing, such as receiving upfront payments in the short run to support ongoing internal R&D projects,
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also become less relevant. The presence of slack provides a ‘buffer’ from immediate resource constraints (Bourgeois III, 1981; Chen and Miller, 2007; Wiseman and
Bromiley, 1996), reducing the need to cross-­finance R&D projects through the immediate proceeds from out-­licensing. Hence, the trade-­off between quickly providing
a technology to an external firm through out-­licensing and keeping a technology
in-­house to explore its potential likely tilts toward the latter. It follows that a higher
availability of financial slack reduces the need to use out-­licensing to free up resources
following a period of intense technology acquisitions, at least in the short run. In a
similar vein, research has shown that financial slack may allow firms to continue operating existing businesses while reducing the number of divestments to address possible
resource constraints (Kuusela et al., 2017; Shimizu, 2007). Conversely, firms without
financial slack would face intense resource constraints following a period of intense
acquisitions. While such firms could benefit from an exhaustive internal exploration
of the innovative potential of their enlarged knowledge base, they would more likely
engage in out-­licensing owing to a lack of resources. Considering all arguments, we
propose:
Hypothesis 3: The positive association between acquisitions and subsequent out-­licensing
will be weaker the higher the level of the acquiring firm’s financial slack.
METHODOLOGY
Context: The Pharmaceutical Industry
We test our hypotheses in the context of the biopharmaceutical industry. Several characteristics make it an ideal setting to study the relationship between technology acquisitions
and out-­licensing. First, the existing competencies of firms in this industry are threatened
by breakthrough technologies such as gene expression and gene sequencing, as well as new
therapeutic approaches such as the use of monoclonal antibodies or stem cells (Kapoor
and Klueter, 2015). These ongoing technological changes require firms in the industry to
regularly use technology acquisitions as a means of quickly upgrading underlying research
capabilities and gaining access to complex, highly specialized skills and knowledge (Higgins
and Rodriguez, 2006; Schuhmacher et al., 2016). Furthermore, firms frequently use licensing with the goals of developing and commercializing technologies with an external partner
(Laursen et al., 2017; Moreira et al., 2019; Reepmeyer et al., 2011). Indeed, the biopharmaceutical industry is considered one in which a well-­functioning market for technologies has
emerged (Moreira et al., 2020).
A second reason for choosing this industry is that firms are continuously investing in
formal appropriability mechanisms (i.e., patents) as a strategy to capture value from their
investments in innovation (Caner et al., 2018). With these investments, firms can better
derive rent from their technologies while minimizing the risks that their technologies
can be invented around, imitated, or commercialized royalty-­or rent-­free (Cohen and
Levinthal, 1989). This is important as it allowed us to determine technology acquisitions
in the first place and to derive several relevant technology-­based measures for our study
using firms’ patenting activities.
© 2023 The Authors. Journal of Management Studies published by Society for the Advancement of Management Studies
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Resource Reconfiguration and Out-­Licensing
T. M. Klueter et al.
Finally, the biopharmaceutical industry is highly competitive in both upstream and downstream markets, as investment in R&D only leads to a few new products being launched
every year. As a result, firms pay attention to their competitive position, choosing strategies
such as those required to acquire and/or out-­license technologies that ensure and safeguard
their competitive advantage and R&D effectiveness (Fosfuri, 2006; Moreira et al., 2020).
Sampling
To create our sample, we first used the Compustat database to identify publicly listed companies operating in the biopharmaceutical industry. Our focus on public firms ensured
that the vital information needed to construct the key control variables, including financial
data, was available. We limited the sample to firms classified under the Standard Industrial
Classification (SIC) system for pharmaceutical firms: 2834 –­Pharmaceutical Preparations. Firms
in the industry are not only active acquirers of technology-­based companies but are also
holders of numerous patented technologies that can be licensed out to acquire monetary
and strategic benefits (Arora et al., 2001). We constructed a panel data of the financial information of each firm in our sample, which covered the years 1985 to 2014.
Next, we used the corresponding firm names from Compustat to link those organizations
to the Recap Deloitte database, which has been utilized extensively in prior studies examining questions related to knowledge sourcing and innovation (e.g., Laursen et al., 2017).
Compiled by Recombinant Capital, Recap Deloitte is a comprehensive database containing
a variety of information about technology acquisitions and licensing deals, including details
about the technology exchanged and transferred. Based on information reported on Recap,
we were able to retrieve detailed data on acquisition and licensing deals.
We also linked the United States Patent and Trademark Office (UPTSO) PatentsView
database to Compustat. In order to connect these two databases, we started from the
company name listed in Compustat and then manually matched its patent data using
PatentsView. Whenever necessary, we used the M&A information to correct changes in
patent ownership to compute our variables. This dataset gave us access to relevant information related to the technological profiles of the firms in our sample. To produce a final
database, we linked the firms with data from Pharmaprojects to capture product development activities.
After dropping 288 firm-­year observations due to missing financial data from Compustat,
the final dataset comprised 467 unique firms and 551 technology mergers and acquisitions
made between 1985 and 2014.2 We structured the dataset as a panel with 5217 firm-­year
observations, i.e., each firm i appears only once in year t but multiple times throughout the
panel. Overall, the sample consists of firms that were very active in technological acquisitions as well as others that conducted few or no acquisitions at all. This addresses methodological problems that arise in evaluating the impact of acquisitions on the post-­acquisition
reconfiguration of acquiring firms as it includes both firms that are active in acquisitions and
those that did not make acquisitions (Ahuja and Katilla, 2001). In the absence of the latter
group of firms, it would be difficult to refute the argument that resource reconfiguration by
acquiring firms was not shared by similar non-­acquiring firms (Fowler and Schmidt, 1988).
In other words, to test the effect of acquisitions on out-­licensing, one needs a counterfactual
based on firms that do not acquire.
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2110
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Dependent Variable
Technology out-­licensing. Using the Recap database to track their annual numbers of out-­licensing
deals, this variable captures the firms’ licensing behaviours. We counted the number of
licensing deals the sample firms made each year. The measure corresponds to the total count
of out-­licensing deals that firm i has engaged in year t.3 We tracked out-­licensing activities
longitudinally for up to 30 years for each firm. To facilitate our econometric approach (see
‘Model Choice’), we used the logarithm of Technology Out-­Licensing +1.
Independent Variables
To test the consistency of our estimations against prior studies, we employ two main
independent variables to test the baseline effect of acquisitions on out-­licensing decisions.
The first variable is based on the count of acquisitions and the second on the number of
patents related to the target firms. We operationalize these variables as follows:
Technology acquisitions. We used the Recap database, which tracks acquisitions under a
specific ‘transaction type’. We aggregated the acquisition deals yearly to obtain the total
number of acquisitions firms made each year. To ensure complete adherence to our
definition of Technology Acquisitions, we restricted our count to those acquisitions of target
firms that had applied for patents or that had out-­licensed technologies within the five
years prior to the year of being acquired. By doing so, we excluded non-­technology
acquisitions such as those aimed at expanding geographically or simply to increase
manufacturing but that may add little or nothing to the technological knowledge base
of the acquirer and are thus less likely to impact the acquiring firms’ subsequent R&D
activities and R&D strategy (Ahuja and Katila, 2001).
Size of acquired knowledge base. We counted the cumulative number of patents that the
target firm had acquired within the five-­years from a focal year. After these patents
were identified, we removed all duplicates to ensure that a patent is counted only once.
Because firms may enter into multiple acquisitions in a given year, we aggregated the
values for the target’s knowledge base at the focal acquirer firm’s i at year t. This
measure follows prior work by Ahuja and Katilla (2001). To reduce skewness, the final
measure is based on its logarithm +1.
Technology uncertainty. A core theoretical argument of our study is that the degree of the
technology uncertainty related to an acquired technology is an important contingency
determining if firms following acquisitions engage in out-­licensing. We followed prior
work by Ziedonis (2007) and measured technology uncertainty based on the number
of backward citations found in the target firm’s patent portfolio.4 The idea is that the
lower the number of backward citations of the acquired technology, the more likely
the technology is at the technological frontier of the industry. For such acquired
technologies, uncertainty is high as they do not build on any previous knowledge (Ahuja
and Katila, 2001). Thus, fewer references to prior technologies will increase uncertainty
regarding their recombination potential. For our measure, Technology uncertainty, we
captured the degree of technology uncertainty by calculating the average number of
© 2023 The Authors. Journal of Management Studies published by Society for the Advancement of Management Studies
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Resource Reconfiguration and Out-­Licensing
T. M. Klueter et al.
backward citations of all the acquired patents from the target firm within the five-­
year period prior to the year of acquisition. We then inverted the values, to reflect an
increasing technology uncertainty, by multiplying by −1.
Financial slack. We followed prior work and captured financial slack through the ratio of
a firm’s equity to debt each year (Bourgeois III, 1981; Iyer and Miller, 2008; Kuusela
et al., 2017). This relationship between equity and debt is also commonly used as the
measure for ‘potential slack’ (Bromiley, 1991), which, for example, prior studies found
most salient in the context of acquisitions. Such potential slack is believed to act as a
short-­term ‘buffer’ to shield the firm from resource constraints (Bourgeois III, 1981;
Chen and Miller, 2007; Wiseman and Bromiley, 1996).
Control Variables
To ensure that the firms in our analysis were comparable in their observable characteristics, we controlled for the firm-­and technology-­level variables that might affect the rate
of technology acquisitions by firms and the decision of a firm to engage in technology
out-­licensing activities. We winsorized any variable that showed extreme values of skewness at the 5th and 95th percentile levels.
R&D productivity. As firms’ internal R&D productivity may provide a strong incentive to
engage in technology acquisitions (Higgins and Rodriguez, 2006), we added the ratio
of patent stock to R&D expenditure for a focal year time t as a control in our model.
Patent stock of acquiring firm. We expected that the number of patents in a firm’s portfolio
prior to an acquisition might influence the willingness of the firm to engage in
technology out-­licensing activities (Arora and Ceccagnoli, 2006). We controlled for this
by calculating the total number of patents the acquiring firm had accumulated in a five-­
year window prior to a focal year time t. The final measure is based on its logarithm +1.
Acquiring firm product pipeline. A robust product pipeline may allow firms to out-­license
technologies. Moreover, more product in development may require firms to free R&D
resources that can be channelled to other areas of the innovation stage (Nishimura and
Okada, 2014). This variable was tracked using data from Pharma-­Projects to calculate
the total number of drugs in development (i.e., in stages prior to market launch) in each
year. The final measure is based on its logarithm +1.
Phase III failures. We also controlled for the fact that later-­stage failures in firms’ research
pipelines may have an impact on those firms’ decisions to acquire other firms with late-­
stage products (Higgins and Rodriguez, 2006) but may also affect out-­licensing choices
(Hu et al., 2015). We counted the number of failures that firms experienced at the phase
III stage using Pharmaprojects.
Strategic alliances. A firm’s openness to seek out external knowledge was a measure of the
number of collaborations in which an acquiring firm engaged during the three years
prior to focal year t. R&D alliances can be defined as strategic partnerships in which the
© 2023 The Authors. Journal of Management Studies published by Society for the Advancement of Management Studies
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2112
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focal firm in our sample pools-­in resources or capabilities with other firms for innovation
purposes (Ghosh and Klueter, 2022). The information used to count the number of
strategic partnerships in a given year was extracted from Recap. The final measure is
based on its logarithm +1.
Out-­licensing experience. The experience garnered from engaging in out-­licensing activities
before the acquisition may shape post-­acquisition decisions to out-­license technologies.
Firms that previously did so are more likely to possess the necessary knowledge to manage
and appreciate the benefits of licensing out technologies (Laursen et al., 2017). The
variable takes the total number of out-­licensed technologies in which a firm has engaged
within three years prior to a focal year t. To reduce skewness, the final measure is based
on its logarithm +1.
In-­licensing experience. To address the possibility of our argued relationship being driven by
open innovation, which involves an ongoing inflow or outflow of licensed technologies,
we controlled for the number of in-­licensing activities of the acquiring firm within three
years prior to a focal year t (Chesbrough et al., 2006). To reduce skewness, the final
measure is based on its logarithm +1.
Organizational myopia. Firms may have general tendencies to experiment with new knowledge.
Thus, we captured the previous exploration activities of a firm as the extent to which a firm
uses their earlier ideas or patents to build upon or develop new knowledge. The measure
captures the backward citations of the patents of the acquiring firm accumulated in the five
years prior to the focal year time t and computes a ratio of self-­citations to total citations.
Industry competition. The greater the competition in the industry, the greater the reluctance
of a firm to out-­license its technologies because of the fear of further depleting its potential
profit downstream (Moreira et al., 2019). We captured this variable using 1 − the Herfindahl–­
Hirschman index (HHI), calculated using firm sales based on the four digits SIC code.
Technological diversity. We controlled for the degree of technology diversity in the firms’
knowledge base by calculating the Herfindahl index based on technology classes for the
number of patents in firm’s patent portfolio, accumulated during the five years from a
focal year. The final diversity measure was calculated by subtracting this value from 1,
reflecting the dispersion of patent classes across different technology domains.
Backward citations. Similar to the measure related to target firms, we also included a
measure capturing the number of backward citations in the acquiring firms’ knowledge
base within the five-­year period to a focal year (Lee and Kim, 2019). To reduce skewness,
the final measure is based on its logarithm +1.
R&D intensity. The commitment to R&D activities of an acquiring firm can affect the rate
of its licensing activities. High R&D intensity could be an indicator that firms possess
valuable technologies that will be attractive to other firms in the industry. Therefore, we
add a control using the ratio between R&D expenditures incurred by a given firm i at
year t and the total sales in that same year.
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Resource Reconfiguration and Out-­Licensing
T. M. Klueter et al.
Firm size. Larger firms could have strong incentives to keep their technologies in-­house
as they are likely in possession of large complementary assets, including marketing and
sales personnel. Hence, we controlled for overall assets volume using the natural log of
total assets of a firm in each year.
Market growth. This variable captures the rate of growth in acquiring firms’ product markets
as market performance can shape out-­licensing (Fosfuri, 2006). We computed the size of the
product market based on total sales reported by firms operating in the same four-­digit SIC
code. Market growth then is defined as (Market Sizet − Market Sizet_1)/Market Sizet_1.
Potential licensees. We controlled for the Number of Firms present in the same industry as the focal
firm, as the level of demand for technologies in the industry also shapes the possible licensing
deals that can be made. To reduce skewness, the final measure is based on its logarithm.
Exogenous sunk cost. Prior research (Moreira et al., 2019) has shown that sunk costs are an
important determinant of a firm’s out-­licensing decisions. Therefore, we controlled for
exogenous sunk costs by using the ratio of firm’s i yearly values for property, plant, and
equipment to total assets, as reported in Compustat.
Model Choice
Given the nature of our variables and our empirical setup, we opted to test our hypotheses using a set of fixed-­effect estimators while employing robust standard error to account
for potential heteroskedasticity. We used year-­and firm-­fixed effects to account for several characteristics related to the acquiring firm and the overall temporal trends in licensing rates that might lead to a biased estimation. Despite using such specifications, it is still
possible that unobserved factors could simultaneously affect our explanatory variables
and a firm’s out-­licensing strategy. This could lead to biased estimations and inaccurate
inferences from our estimations. Therefore, to rule out such alternative explanations, we
required a research design that captured exogenous changes in firms’ out-­licensing decisions. To tackle this issue and make our analysis more robust, we deployed a two-­stage
least squares (2SLS) regressions approach.5
To implement the 2SLS approach, one must use at least one valid instrument that fulfils the conditions of relevance and exogeneity, i.e., the instrument has to be correlated
with the endogenous variables and only affect the dependent variable through their effect on the endogenous variable. First, we built on recent studies examining acquisitions
that used the changes of Statements of Financial Accounting Standard (SFAS) 141 and
142 that happened in 2001 as an instrument (Ali and Kravet, 2016; Shalev et al., 2013;
Zhang and Tong, 2021). Such changes in accounting rules affected the incentives for
chief executive officers (CEOs) to engage in acquisitions because it eliminated the pooling interest method of accounting (SFAS 141) and amortization of goodwill (SFAS 142),
which are important factors in shaping the financing of acquisitions (Shalev et al., 2013).
Despite the possible effect of the new SFAS rules on acquisitions activity, there was no
indication that it would have a direct effect on firms’ technology out-­licensing activity.
Additionally, we used a second instrument called organizational capital (OC), which is
the ratio of selling, general and administrative expenditures6 to total assets. Organizational
© 2023 The Authors. Journal of Management Studies published by Society for the Advancement of Management Studies
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2114
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capital has been defined by prior studies as the ‘knowledge used to combine human skills
and physical capital into systems for producing and delivering want-­satisfying products’
(Evenson and Westphal, 1995, p. 2337). Thus, based on these studies organizational
capital represents the knowledge and routines embodied in employees and companies’
capabilities. We argue that firms with high organizational capital will be less likely to acquire other firms for two reasons. First, it is well documented that acquisitions are one of
the prioritized ways that firms will use to overcome internal deficiencies associated with
downstream or upstream capabilities. Accordingly, firms with high organizational capital
will be less likely to have little need to turn to acquisitions to overcome internal issues.
Second, as firms have increasing levels of organizational capabilities, it becomes harder
for them to find suitable available external targets that add value for them. Therefore,
firms with high levels of organizational capital will have fewer options in terms of viable
targets to acquire in the first place. This suggests that organizational capital will negatively predict the acquisition activities of a focal firm.
Our empirical model of the 2SLS approach for Technology Acquisitions is as follows:
Technology acquisitionsi,t = 𝛿 1 SFAS Changei,t + 𝛿 1 OCi,t + Controlsi,t + 𝛾 i + 𝛾 t + 𝜂 i,t
Technology Out − licensingi,t +1 = Technology Acquisitionsi,t + Controlsi,t + 𝛾 i + 𝛾 t + 𝜀i,t
We also estimate the same setup for Size of Acquired Knowledge Base using the following
approach:
Size of Acquired Knowledge Basei,t = 𝛿 1 SFAS Changei,t + 𝛿 1 OCi,t + Controlsi,t + 𝛾 i + 𝛾 t + 𝜂 i,t
Technology Out − licensingi,t +1 = Size of Acquired Knowledge Basei,t + Controlsi,t + 𝛾 i + 𝛾 t + 𝜀i,t
As described above, we first estimated our models for the variable Technology acquisitions and then Size of Acquired Knowledge Base. In both cases, we used them
as dependent variables estimated with the SFAS Change, captured using a dummy
variable equal to 1 for all the events of stock-­for-­stock mergers and acquisitions that
occurred after 2001; the other observations are zeros, and the OC ratio, while including all controls. Accordingly, our estimation of interest deployed Technology Out-­
Licensing in which a firm i engages at year t as the dependent variable and the values
of exogenous changes in Technology acquisitions and The Size of the Acquired
Knowledge Base. To increase the robustness of our estimations, we included firm-­
and year-­fixed effects in both the stages of the instrument variable regressions and
clustered standard errors at the firm level.
RESULTS
Main Findings
Table I reports the descriptive statistics (standard deviations and means) and simple
pairwise correlations between the dependent and independent variables in the regression analysis. In our sample, the correlations among the independent variables did
© 2023 The Authors. Journal of Management Studies published by Society for the Advancement of Management Studies
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Resource Reconfiguration and Out-­Licensing
T. M. Klueter et al.
not raise significant concerns regarding collinearity between our main independent
variables and the controls. Furthermore, we also noted that the mean–­variance inflation factors (VIF) associated with the variables in our empirical models did not raise
further concerns.
In Table II, we report the estimations to test our hypotheses. Models I and II correspond to the first and second stages of our 2SLS estimations. We use the changes
in SFAS and OC as instruments for predicting the technology acquisitions deals in
Model I (First Stage). From the analysis, we find that there is a strong association
between our instruments and the number of technology acquisition deals in which
the sample firms engaged. We further tested the strength of the instrument in the
first stage and found that F-­statistics was 74.68, which is greater than the required F-­
statistic of 10 in Stock and Watson (2003), suggesting that the instrument is not weak. Our
overidentification test for our instrument yielded an insignificant Hansen J statistic
(Hansen J-­statistic = 0.944, p-­value = 0.331), suggesting that the instruments are likely
uncorrelated with the error.
We observe in Model II (Second Stage) that, after accounting for endogeneity from
unobserved factors, the positive relationship between Technology Acquisitions and
Technology out-­licensing remains highly significant (β = 0.117, p-­value < 0.05). This result offers support for the argument in Hypothesis 1 that an increase in technology acquisition is associated with an increase in firms’ rates of technology out-­licensing. The
magnitude of the effect is also substantial; based on the result, firms on average would
experience a 11.7 per cent increase in out-­licensing for each additional technology acquisition in which they engaged. In addition, these results corroborate previous findings that
have suggested a positive relationship between technology out-­licensing and firm R&D
intensity (β = 0.003, p-­value < 0.05), and negative relationship with firm size (β = −0.016,
p-­value < 0.05) and Organizational Myopia (β = −0.344, p-­value < 0.05) (Moreira et
al., 2019). We also find that firms with prior in-­licensing experience (β = 0.073, p-­value
< 0.001) out-­license more often (Chesbrough et al., 2006).
In Models III and IV, we continue relying on 2SLS estimations to test our argument as to the moderating effect of Technology Uncertainty and Financial Slack. The
results reported in Model III show that the degree of technology uncertainty of the
acquired technologies post-­acquisition weakens the positive relationship between the
acquiring firm’s technology acquisitions and its technology out-­licensing activities.
The co-­efficient for the interaction term, Technology acquisitions and Technology
uncertainty, is negative and statistically significant (β = −0.004, p-­value < 0.05). To give
further credence to this result, we show the interaction effect graphically in Figure 1.
The margin plot demonstrates that the increase in technology out-­licensing due to an
additional technology acquisition will be reduced more for acquired knowledge with
high (above the mean) technology uncertainty than for those with low technology uncertainty. All this suggests broad support for Hypothesis 2, which predicts the effect of
acquisition on out-­licensing to be weaker, the greater the degree of uncertainty of the
acquired technologies.
Next, based on Model IV, we examine the moderating effect of financial slack. The results show that high availability of financial slack weakens the positive relationship between
the acquiring firm’s technology acquisitions and its technology out-­licensing activities. The
© 2023 The Authors. Journal of Management Studies published by Society for the Advancement of Management Studies
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2116
Technology
licensing-­out
Technology
acquisitions
Size of acquired
knowledge
Technology
uncertainty
Financial slack
Productivity
Patent stock of
acquiring firm
Acquiring firm
product pipeline
Phase III failure
Strategic alliance
Out-­licensing
experience
In-­licensing
experience
Organizational
myopia
Industry
competition
Technological
diversity
Backward citations
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
(10)
(11)
(12)
(13)
(14)
(15)
(16)
1.709
0.611
0.937
0.053
0.769
0.673
0.191
0.068
1.570
1.798
0.208
0.191
−10.295
0.161
0.106
0.260
Mean
1.333
0.355
0.007
0.088
0.887
0.695
0.369
0.252
1.328
1.786
0.905
0.501
23.889
0.728
0.408
0.451
S.D
0.182
−0.017
0.000
0.129
0.358
0.690
0.409
0.172
0.397
0.318
0.005
0.014
−0.116
0.188
0.183
1.000
(1)
0.124
0.045
−0.065
0.096
0.345
0.202
0.142
0.229
0.326
0.267
−0.027
−0.030
−0.103
0.768
1.000
(2)
0.110
0.049
−0.028
0.129
0.354
0.189
0.133
0.212
0.338
0.291
−0.027
−0.032
−0.076
1.000
(3)
−0.151
−0.040
−0.003
−0.078
−0.299
−0.135
−0.079
−0.146
−0.302
−0.284
0.020
0.038
1.000
(4)
−0.007
−0.013
0.040
−0.012
0.007
0.036
0.027
−0.021
−0.015
−0.016
0.019
1.000
(5)
0.053
−0.076
0.039
0.041
−0.067
0.012
−0.004
−0.034
−0.038
0.048
1.000
(6)
0.598
−0.171
−0.050
0.630
0.541
0.417
0.318
0.284
0.654
1.000
(7)
0.341
−0.043
−0.055
0.369
0.654
0.533
0.343
0.396
1.000
(8)
0.114
0.009
−0.027
0.124
0.327
0.215
0.136
1.000
(9)
0.204
−0.048
−0.090
0.140
0.293
0.544
1.000
(10)
(Continues)
0.288
−0.076
−0.025
0.185
0.431
1.000
(11)
2117
© 2023 The Authors. Journal of Management Studies published by Society for the Advancement of Management Studies
and John Wiley & Sons Ltd..
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(1)
Variables
Table I. Descriptive statistics and correlations coefficients (N = 5217; Firms = 467)
Resource Reconfiguration and Out-­Licensing
Research intensity
Market growth
Potential licensees
Exogenous sunk
cost
(19)
(20)
(21)
Backward
citations
(16)
Firm size
Technological
diversity
(15)
(17)
Industry
competition
(14)
0.148
5.498
0.071
4.389
2.487
1.709
0.611
0.937
0.053
0.769
5.498
0.071
0.134
0.260
0.076
2.672
5.526
1.333
0.355
0.007
0.088
0.887
S.D
0.134
0.260
0.076
2.672
5.526
S.D
0.053
0.142
−0.025
0.638
−0.098
0.205
0.044
−0.037
0.210
1.000
(12)
0.016
0.091
0.006
0.271
0.015
(1)
0.063
−0.023
−0.014
0.343
−0.030
0.304
−0.147
−0.031
1.000
(13)
0.015
0.089
−0.055
0.352
−0.056
(2)
0.256
−0.447
0.525
−0.045
0.004
−0.237
0.115
1.000
(14)
0.040
0.062
−0.029
0.354
−0.067
(3)
0.166
−0.152
0.088
0.015
−0.101
−0.606
1.000
(15)
−0.061
0.023
−0.010
−0.347
0.081
(4)
0.274
−0.096
0.134
0.056
−0.140
0.073
0.003
0.047
0.130
−0.043
0.636
−0.001
(8)
0.218
0.050
−0.025
1.000
(18)
−0.027
0.661
−0.054
(7)
−0.189
1.000
(17)
−0.012
−0.027
0.019
−0.081
0.021
(6)
−0.151
0.305
0.058
1.000
(16)
−0.059
−0.008
0.050
0.016
0.024
(5)
0.153
−0.350
1.000
(19)
0.050
0.071
−0.023
0.327
−0.048
(9)
−0.257
1.000
(20)
−0.037
0.182
−0.056
0.210
0.052
(10)
1.000
(21)
−0.025
0.171
−0.025
0.320
0.040
(11)
T. M. Klueter et al.
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(18)
Organizational
myopia
Variables
(13)
Exogenous sunk
cost
(21)
In-­licensing
experience
Mean
Potential licensees
(20)
(12)
0.148
Market growth
(19)
4.389
Firm size
2.487
Research intensity
(18)
Mean
(17)
Variables
Table I. (Continued)
2118
(0.020)
(0.014)
(0.033)
0.000
0.014
(0.031)
(0.035)
0.068*
0.016
(0.035)
(0.017)
0.024
0.052
(0.012)
(0.018)
0.016
0.014
(0.012)
(0.006)
0.005
(0.002)
−0.012
−0.003
(0.014)
(0.007)
0.002
−0.003
−0.006
(0.001)
(0.002)
(0.020)
−0.001
(0.033)
0.062
(0.037)
0.016
(0.016)
0.016
(0.018)
0.005
(0.006)
−0.003
(0.095)
(0.005)
(0.020)
−0.002
(0.033)
0.068*
(0.036)
0.024
(0.016)
0.017
(0.018)
0.003
(0.020)
−0.003
(0.033)
0.062
(0.038)
0.016
(0.016)
0.017
(0.018)
0.004
(0.006)
−0.003
(0.070)
−0.003
−0.204*
(0.002)
−0.214**
(0.002)
(0.015)
0.009
(0.001)
0.001
(0.069)
0.071
(Continues)
Model V 2SLS Second
Stage
−0.004*
(0.014)
0.010
(0.001)
0.000
(0.057)
0.138*
Model IV 2SLS
Second Stage
−0.004*
(0.014)
−0.003
(0.001)
0.001
(0.063)
0.000
0.052
(0.052)
Model III 2SLS
Second Stage
0.117*
Model II 2SLS
Second Stage
0.013***
Model I 2SLS First
Stage
2119
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Out-­licensing experience
Strategic alliances
Phase III failures
Acquiring firm product pipeline
Patent stock of acquiring firm
R&D productivity
Technology acquisitions × financial slack
Technology acquisitions × technology
uncertainty
Financial slack
Technology uncertainty
Technology acquisitions
Variables
Table II. 2SLS regression model predicting out-­licensing
Resource Reconfiguration and Out-­Licensing
(0.086)
1.011***
−0.089
(0.071)
−0.088
(0.053)
−0.084
(0.270)
0.043
(1.585)
(1.673)
(0.288)
0.496
−0.348
(0.007)
(0.005)
(0.001)
−0.016*
0.017**
(0.001)
(0.015)
0.003*
−0.000
(0.010)
(0.030)
−0.000
0.005
(0.019)
(31.074)
0.018
0.044*
(33.899)
(0.175)
−19.387
−3.183
(0.071)
−0.089
(0.270)
−0.112
(1.598)
0.403
(0.007)
−0.016*
(0.001)
0.003*
(0.015)
−0.001
(0.030)
0.014
(31.162)
−21.936
(0.171)
−0.332
(0.014)
0.072***
Model III 2SLS
Second Stage
(0.071)
−0.090
(0.269)
−0.095
(1.588)
0.503
(0.007)
−0.016*
(0.001)
0.003*
(0.015)
0.001
(0.030)
0.019
(31.026)
−20.071
(0.175)
−0.323
(0.014)
0.075***
Model IV 2SLS
Second Stage
(0.070)
−0.089
(0.268)
−0.122
(1.600)
0.409
(0.007)
−0.016*
(0.001)
0.003*
(0.014)
0.000
(0.030)
0.015
(31.046)
−22.612
(0.170)
−0.311
(0.014)
0.074***
Model V 2SLS Second
Stage
T. M. Klueter et al.
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SFAS (instrument)
Exogenous sunk cost
Potential licensees
Market growth
Firm size
R&D intensity
Backward citations
Technological diversity
industry competition
(0.136)
(0.014)
−0.344*
−0.152
(0.012)
Organizational myopia
0.073***
0.036**
In-­licensing experience
Model II 2SLS
Second Stage
Model I 2SLS First
Stage
Variables
Table II. (Continued)
2120
Yes
Yes
4737
Firm fixed effects
Cluster at firm
Observations
4737
Yes
Yes
Yes
4737
YES
YES
YES
(30.475)
21.150
Model III 2SLS
Second Stage
4737
YES
YES
YES
(30.339)
19.306
Model IV 2SLS
Second Stage
4737
YES
YES
YES
(30.357)
21.830
Model V 2SLS Second
Stage
2121
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Note: Robust standard errors in parentheses; *p < 0.05; **p < 0.01; ***p < 0.001.
Yes
18.618
(30.388)
2.746
Model II 2SLS
Second Stage
(33.163)
Year fixed effects
Constant
−0.063**
Organization capital (instrument)
(0.024)
Model I 2SLS First
Stage
Variables
Table II. (Continued)
Resource Reconfiguration and Out-­Licensing
T. M. Klueter et al.
0
Technology Out-Licensing (Log)
2
4
6
co-­efficient for the interaction term Technology acquisitions × Financial Slack is negative and
statistically significant (β = −0.214, p-­value < 0.01). We also show the interaction effect
graphically in Figure 2. The margin plot demonstrates that the increase in technology out-­
licensing due to an additional technology acquisition is reduced for firms with high financial
slack. These results strongly support the idea that financial slack plays an important role in
a firm’s post-­acquisition integration, thereby affecting its decision as to whether to commercialize its technologies through out-­licensing or keep them in-­house for further development.
Finally, we placed both interaction terms in one model (Model V), and the results remained
consistent.
We replicated the same empirical setup and estimations using the variable Size of the
Acquired Knowledge Base. As one can see in Table III, the results are similar to those
observed for the effect using the count of acquisitions. One difference is that the interaction term, Size of Acquired Knowledge Base and Technology Uncertainty, continues
to predict a negative effect on our dependent variable, but the two-­tailed test becomes
slightly less significant (p < 0.10) for this alternative acquisition measure. We note that
this can be due to the fact that both variables, the Size of Acquired Knowledge Base and
Technology Uncertainty, are based on the underlying patents associated with the target
firms. This can lead to redundancy, making it harder to disentangle the individual effects
for those two proxies. Still, we consider that the overall results point in the same direction
as predicted in our theory.
1
2
3
4
5
Technology Acquisitions
Low Technology Uncertainty
6
7
8
High Technology Uncertainty
Figure 1. Graphical presentation of moderation: technology acquisitions and technology uncertainty
[Colour figure can be viewed at wileyonlinelibrary.com]
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2122
2123
Additional Analyses
We ran several checks to ensure the robustness of our findings and to rule out alternative
explanations. We detail them within the following sections, testing different lag structures, testing alternative model specifications, and testing alternative variable operationalization and controls.
Testing different lag structures. First, we explored different time windows of the theorized
effects of plus/minus two years and alternated the period chosen for our sample to
ensure that our results were not spurious. Our main results have a one-­year t + 1 lag
structure, and, in results available from the authors, we also provide the estimations for
two additional periods t + 2 and t + 3. We find that the effect of acquisitions on out-­
licensing remains positive and statistically significant with t + 2 period. For t + 3 period,
the effect, while positive, becomes weaker and non-­significant. Overall, the results are
consistent with our main estimations and our theoretical framework how technology
acquisitions shape out-­licensing.
1
.5
0
-.5
Technology Out-Licensing (Log)
1.5
Testing alternative model specifications. Next, we replicated our results in Table II using
the GLM and Poisson count model. We performed this additional test to assess if our
results were sensitive to different model specifications. The GLM estimations follow
the same approach recommended by Rönkkö et al. (2022) as a strategy to avoid taking
the logarithm of the dependent variable and creating potential model misspecification.
1
2
3
4
5
Technology Acquisitions
Low Financial Slack
6
7
8
High Financial Slack
Figure 2. Graphical presentation of moderation: technology acquisitions and financial slack [Colour figure
can be viewed at wileyonlinelibrary.com]
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Resource Reconfiguration and Out-­Licensing
T. M. Klueter et al.
The Poisson approach follow the guidelines proposed by Lin and Wooldridge (2019)
and also avoids taking the logarithm of our dependent variable, with the advantage
of still accounting to endogeneity through the use of two-­stage models. These two
approaches represent an important trade-­off we had to make in the paper as, on
one side, we can use longitudinal estimations with within firm-­fixed effects and the
possibility to correct for endogeneity. On the other side, we can estimate the GLM
model accounting for the distribution of our dependent variable without using log
transformations (Rönkkö et al., 2022). Overall, the results remain comparable to our
main results as we continue to find a baseline effect of Acquisitions on Out-­licensing.
Indeed, in Table IV Models I and III, we demonstrate that the effect of technological
acquisitions on out-­licensing activities is positive and highly significant, which aligns
with our prior results using the 2SLS model. We also examined the moderating
effect of technology uncertainty and financial slack, finding that the results of the
interaction terms in models II and IV (Table IV) still support our arguments as seen
in Hypothesis 2 and 3. These findings provide assurance that our results are robust to
different econometric model specifications and are not sensitive to taking the log of
the dependent variable.
Testing alternative variable operationalization. Next, we assess different operationalization of
our key variables. To ensure that the result for the interaction term testing the argument
in Hypothesis 2 and 3 is not conditional on a particular measurement for technology
uncertainty and financial slack, we repeated our analysis using alternative measures.7 For
technology uncertainty, using the Pharmaprojects database, we tracked the acquisitions of
firms by documenting the new products in development that were acquired. We then split
the analysis by firms which, compared to others in the industry, engaged in low uncertainty
acquisitions (i.e., with drugs further along in development) and high uncertainty acquisitions
(i.e., with drugs in development at more earlier stages). The results show that out-­licensing
occurs more frequently following low uncertainty acquisitions, which syncs well with the
analysis done using technology uncertainty proxied through patents.
Relatedly, the uncertainty associated with the acquirer knowledge base should also be relevant to moderating the relationship between acquisitions and out-­licensing. We tested
this assumption by applying a similar approach to Ziedonis (2007) and measured technology uncertainty based on the number of backward citations found in the acquirer
patent portfolio. In line with our expectations, the moderating effect for the acquirer’s
knowledge base uncertainty is also negative and statistically significant. We also examined the role of Absorptive Capacity on our variables of interest. Interestingly, our proxy
for Absorptive Capacity does not show any significant effect in our sample. We speculate
that this lack of effect is due to the fact that our sample is comprised of firms that actively
invest in the development of new drugs and therapies. That means that, for this sample,
most firms will all have extremely high levels of investment in R&D, making it difficult to
capture this effect empirically.
Next, we examined various alternative slack measures, building on the argument
that an increase in a firm’s liquidity and the existing amount of unabsorbed resources
is likely to result from anticipated needs to meet short-­term obligations (Cheng and
Kesner, 1997). Therefore, for financial slack, we instead used the ratio of working
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2124
0.001
(0.020)
(0.028)
(0.033)
(0.040)
0.004
0.066*
(0.035)
(0.069)
0.029
0.036
(0.017)
(0.023)
−0.046
0.011
(0.019)
(0.025)
0.052*
0.006
(0.006)
(0.005)
−0.020
−0.003
(0.014)
(0.012)
−0.003
−0.002
−0.013
(0.002)
(0.005)
(0.020)
0.000
(0.033)
0.061
(0.036)
0.027
(0.016)
0.010
(0.019)
0.005
(0.006)
−0.003
(0.020)
−0.001
(0.033)
0.066*
(0.035)
0.036
(0.016)
0.015
(0.019)
0.005
(0.006)
(0.020)
−0.001
(0.033)
0.061
(0.036)
0.028
(0.016)
0.014
(0.019)
0.004
(0.006)
−0.003
(0.070)
(0.055)
−0.002
−0.154*
(0.002)
−0.164**
(0.002)
(0.015)
0.009
(0.002)
−0.001
(0.078)
0.084
(Continues)
Model V
2SLS Second Stage
−0.003
(0.014)
0.009
(0.002)
−0.002
(0.060)
0.139*
Model IV
2SLS Second Stage
−0.003
(0.014)
−0.003
(0.002)
−0.001
(0.069)
−0.002
0.063
(0.054)
Model III
2SLS Second Stage
0.120*
Model II
2SLS Second Stage
0.030***
Model I
2SLS First Stage
2125
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Out-­licensing experience
Strategic alliances
Phase III failures
Acquiring firm product pipeline
Patent stock of acquiring firm
R&D productivity
Size of acquired knowledge × financial slack
Size of acquired knowledge × technology
uncertainty
Financial slack
Technology uncertainty
Size of acquired knowledge base
Variables
Table III. 2SLS regression model predicting out-­licensing
Resource Reconfiguration and Out-­Licensing
(0.072)
(0.144)
1.004***
−0.077
(0.078)
(0.277)
−0.182
−0.061
(0.441)
(1.649)
(2.707)
−0.147
0.632
(0.007)
(0.010)
−1.506
−0.016*
(0.001)
(0.001)
0.020*
0.003*
(0.015)
(0.015)
−0.001
0.001
(0.030)
(0.031)
−0.002
0.020
(31.885)
(55.435)
0.031
−16.287
(0.179)
−29.745
(0.295)
(0.072)
−0.082
(0.290)
−0.157
(1.726)
0.061
(0.007)
−0.017*
(0.001)
0.003*
(0.015)
0.001
(0.030)
0.018
(33.484)
−27.108
(0.176)
−0.357*
(0.014)
0.069***
Model III
2SLS Second Stage
(0.072)
−0.074
(0.279)
−0.068
(1.670)
0.672
(0.007)
−0.018
(0.001)
0.003*
(0.015)
0.003
(0.030)
0.021
(32.167)
−15.991
(0.179)
−0.371*
(0.014)
0.072***
Model IV
2SLS Second Stage
(0.072)
−0.079
(0.291)
−0.159
(1.752)
0.118
(0.007)
−0.018**
(0.001)
0.003*
(0.014)
0.003
(0.030)
0.019
(33.807)
−26.445
(0.176)
−0.347*
(0.014)
0.071***
Model V
2SLS Second Stage
T. M. Klueter et al.
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SFAS (Instrument)
Exogenous sunk cost
Potential licensees
Market growth
Firm size
R&D intensity
Backward citations
Technological diversity
Industry competition
−0.383*
(0.014)
0.176
(0.020)
Organizational myopia
0.070***
0.063**
In-­licensing experience
Model II
2SLS Second Stage
Model I
2SLS First Stage
Variables
Table III. (Continued)
2126
Yes
4737
Cluster at firm
Observations
4737
Yes
Yes
4737
YES
YES
YES
(32.754)
26.223
Model III
2SLS Second Stage
4737
YES
YES
YES
(31.462)
15.353
Model IV
2SLS Second Stage
4737
YES
YES
YES
(33.062)
25.608
Model V
2SLS Second Stage
2127
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Note: Robust standard errors in parentheses; *p < 0.05; **p < 0.01; ***p < 0.001.
Yes
Firm fixed effects
Yes
(31.189)
Yes
15.605
(54.101)
Model II
2SLS Second Stage
28.564
Year fixed effects
Constant
−0.116*
Organization capital (instrument)
(0.049)
Model I
2SLS First Stage
Variables
Table III. (Continued)
Resource Reconfiguration and Out-­Licensing
T. M. Klueter et al.
Table IV. GLM and POISSON models predicting out-­licensing (main results)
Variables
Model I
(GLM)
Model II
(GLM)
Model III
Second Stage
(Poisson)
Model IV
Second Stage
(Poisson)
Technology acquisitions
0.121**
0.039
0.257*
0.188
(0.043)
(0.067)
(0.116)
(0.122)
0.003
0.005**
−0.001
0.001
(0.002)
(0.002)
(0.003)
(0.003)
0.022
0.066
0.011
0.052
(0.056)
(0.058)
(0.059)
(0.056)
Technology uncertainty
Financial slack
Technology acquisitions × technology uncertainty
−0.005**
−0.005**
(0.001)
(0.002)
Technology acquisitions × financial slack
−0.464*
−0.458*
(0.201)
(0.233)
Year fixed effects
Yes
Yes
Yes
Yes
Firm fixed effects
Yes
Yes
Yes
Yes
Cluster at firm
Yes
Yes
Yes
Yes
Observations
4737
4737
3949
3949
Robust standard errors in parentheses; *p < 0.05; **p < 0.01; ***p < 0.001.
Note: Controls and instrument included but not shown in table.
capital to sales as well as the current ratio (current assets divided by current liabilities);
our results supporting our theoretical predictions in Hypothesis 3 remains highly comparable to those reported in our main analyses. We also tested the curvilinear effect on
slack as the moderating effect may differ at very high and very low values of financial
slack. The results reveal no additional improvement of the model when adding the
squared term of slack as an additional moderating squared term of slack to the model.
Contrasting Divestitures as Alternative R&D Reconfiguration Strategy
We noted earlier that extant research has broadly discussed divestments in the context
of post-­acquisitions reconfiguration (e.g., Feldman and Sakhartov, 2022; Karim and
Capron, 2016; Mariotti et al., 2023). Accordingly, we wanted to contrast our out-­licensing
results to those examining divestitures. We captured assets divestitures as the sale of physical or organizational assets of the acquiring firm post-­acquisition (Capron et al., 2001).
Such transactions are reported in Recap, and we generated the variable Assets Divestitures
decision by tracking the various divestiture related activities and aggregating them yearly
to get a cumulative number of divestitures a focal firm had engaged divestitures within
one year from the focal year.
We find that divestitures are much rarer as licensing such that, for every divestiture, we observe around 4.5 licensing deals. In addition, licensing transactions always
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involve some underlying technology (in the form of patents, software or simply technological knowledge), but divestitures can be related to non-­R&D transactions. For
example, firms can divest full business units, sometimes those even unrelated to the
biopharmaceutical industry (such as adhesives, chemicals or cosmetics). When excluding those non-­R&D-­related divestitures, we observe 15.4 licensing deals for each
divestiture for firms in our sample, suggesting the broad use of licensing over divestitures in the industry.
Once again, we used a 2SLS regression model predicting the decision of firms to engage
in assets divestiture similar to the analysis for out-­licensing. In Table V, Model I shows the
results, with Asset Divestitures activities as a dependent variable. The result of the coefficient
of technology acquisitions in this model is positive and shows a statistically significant effect.
This finding aligns with the conventional perception that firms tend to engage in the divestiture of assets following an acquisition (Capron et al., 2001). In Models II and III, we probed
the relationship between assets divestiture and technology acquisitions by interacting the
technology acquisition activities with technology uncertainty and financial slack. The result
showed a negative effect, but that effect is only statistically significant for the interaction of
financial slack and technology acquisitions. We also find different effects from controls as, for
example, Phase III Failures predict divestitures but not out-­licensing. These findings, overall,
provide evidence of a possible strategic difference between the two modes, assets divestiture
and out-­licensing. We elaborate on these differences further in the discussion.
DISCUSSION
Examining resource reconfiguration following acquisitions is at the heart of understanding
how firms accrue value when acquiring technology-­based firms (Karim and Capron, 2016;
Karim and Mitchell, 2000). In this paper, we systematically examine out-­licensing as an important post-­acquisitions strategy for firms to engage resource reconfiguration. As a departing point, we show that, following technology acquisitions, resource reconfiguration using
out-­licensing increases. At the same time, we reveal pertinent trade-­offs in using out-­licensing
as a resource reconfiguration strategy post-­acquisition. Namely, the acquirer may consider
future rent dissipating effects and the potential damage of giving away potentially highly
valuable technology to another firm (Fosfuri, 2006). Supporting this idea, we find that, when
acquirers add highly uncertain technologies, firms are more likely to keep R&D projects
in-­house, owing to the difficulties of assessing the true potential of their post-­acquisition
R&D resources. Moreover, we show that the availability of financial slack that can be used
to support the acquisition integration process in the short term may tilt firms toward keeping
technologies in-­house instead of using out-­licensing.
These findings contribute to two literatures on technology acquisitions and resource
reconfiguration in several ways. First, they explain why out-­licensing is a key resource-­
reconfiguration strategy following technology acquisitions. Prior studies have examined
resource reconfiguration predominantly through externalizing resources by divesting or
discontinuing them due to resource constraints (Capron et al., 2001; Karim, 2006). We
show that out-­licensing is an important resource reconfiguration strategy as it allows the
acquirer to externalize the subsequent development of a technology and save costs, while,
© 2023 The Authors. Journal of Management Studies published by Society for the Advancement of Management Studies
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Resource Reconfiguration and Out-­Licensing
T. M. Klueter et al.
Table V. 2SLS regression model predicting assets divestitures
Variables
Model I
Second Stage
Model II
Second Stage
Model III
Second Stage
Model IV
Second Stage
Technology acquisitions
0.161*
0.143
0.178*
0.160
(0.078)
(0.090)
(0.078)
(0.090)
−0.000
−0.000
−0.000
−0.000
(0.001)
(0.001)
(0.001)
(0.001)
0.010
0.010
0.021
0.021
(0.012)
(0.012)
(0.013)
(0.013)
Technology uncertainty
Financial slack
Technology acquisitions ×
technology uncertainty
−0.001
(0.002)
Technology acquisitions ×
financial slack
R&D productivity
−0.001
(0.002)
−0.176*
−0.175*
(0.074)
(0.080)
−0.007
−0.007
−0.007
−0.007
(0.004)
(0.004)
(0.004)
(0.004)
Patent stock of acquiring
firm
0.037*
0.036*
0.035
0.035
(0.018)
(0.018)
(0.018)
(0.018)
Acquiring firm product
pipeline
0.013
0.013
0.014
0.014
(0.014)
(0.014)
(0.014)
(0.014)
Phase III failures
0.073**
0.071**
0.073**
0.071*
(0.028)
(0.029)
(0.028)
(0.029)
0.013
0.010
0.010
0.007
(0.043)
(0.044)
(0.043)
(0.045)
−0.436**
−0.431**
−0.419**
−0.414**
(0.134)
(0.134)
(0.132)
(0.133)
−38.637***
−38.390***
−38.151***
−37.897***
(7.803)
(7.832)
(7.756)
(7.787)
−0.006
−0.007
−0.005
−0.007
(0.025)
(0.025)
(0.025)
(0.025)
−0.026*
−0.026*
−0.025*
−0.025*
(0.012)
(0.012)
(0.012)
(0.012)
−0.000
−0.000
−0.000
0.000
(0.001)
(0.001)
(0.001)
(0.001)
0.002
−0.001
−0.001
0.001
(0.006)
(0.006)
(0.006)
(0.006)
7.275*
7.176
7.177*
7.075
(3.639)
(3.659)
(3.642)
(3.664)
0.029
0.029
0.029
0.029
(0.058)
(0.058)
(0.058)
(0.058)
Previous divestiture
Organizational myopia
Industry competition
Technological diversity
Backward citations
R&D intensity
Firm size
Market growth
Exogenous sunk cost
(Continues)
© 2023 The Authors. Journal of Management Studies published by Society for the Advancement of Management Studies
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Table V. (Continued)
Variables
Model I
Second Stage
Model II
Second Stage
Model III
Second Stage
Model IV
Second Stage
Constant
36.327***
36.099***
35.868***
35.632***
(7.324)
(7.350)
(7.278)
(7.307)
Year fixed effects
Yes
Yes
Yes
Yes
Firm fixed effects
Yes
Yes
Yes
Yes
Cluster at firm
Yes
Yes
Yes
Yes
Observations
4737
4737
4737
4737
Note: Robust standard errors in parentheses; *p < 0.05; **p < 0.01; ***p < 0.001.
at the same time, the acquirer maintains residual ownership of the technology (Moreira
et al., 2019; Moreira et al., 2020). Thus, resource reconfiguration through out-­licensing
entails externalizing resources while simultaneously benefiting from the subsequent recombination efforts of the external licensees (Karim, 2006). This extends theories on
resource reconfiguration and resource redeployment by showing the utility and unique
advantages of out-­licensing in supporting this process, compared to previously discussed
strategies (Capron et al., 2001). As such, insights from our study complement prior work
that has documented resource reconfiguration in commercial units post-­acquisitions to
choices related to technologies which have not yet reached commercial markets (Feldman
and Sakhartov, 2022; Karim and Capron, 2016).
We also provide important contingencies in the form of technology uncertainty and financial slack that increase the understanding of the well-­known trade-­offs firms face when
out-­licensing (Fosfuri, 2006). This explains the reasons that acquirers, despite augmenting
their R&D resources through acquisitions, may continue to opt to develop their technologies in-­house rather than using out-­licensing for reconfiguration (e.g., Choi, 2002; Moreira
et al., 2019). These insights augment discussions in the literature as to when firms resort to
technology commercialization strategies such as out-­licensing in the first place and the role
of internal resources to support the R&D resource reconfiguration process (Chaturvedi and
Prescott, 2022; Karim and Capron, 2016; Kuusela et al., 2017).
Finally, this study systematically links two important strategic transactions: acquisitions as the in-­sourcing of technological solutions and licensing by commercializing
technologies through the market for technology. Why and when firms choose to out-­
licence their technologies has long been a centre stage debate in the licensing literature
(e.g., Arora and Ceccagnoli, 2006; Fosfuri, 2006; Moreira et al., 2023). We show that
technology acquisitions may be an important antecedent for out-­licensing due to the
need to reconfigure resources (Laursen et al., 2017). This complements studies that
have examined the interplay between different in-­sourcing activities such as acquisitions following alliances (Shi et al., 2012; Shi and Prescott, 2011), as well as balancing
in-­sourcing and out-­sourcing activities in open innovation (Cassiman et al., 2005).
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Resource Reconfiguration and Out-­Licensing
T. M. Klueter et al.
The paper also provides insights for practitioners. Given the prevalence of acquisitions
in many technology-­intensive industries (Fosfuri and Giarratana, 2010) managers need
to carefully assess the strategies at their disposal for resource reconfiguration. The paper
notes the frequent use of out-­licensing, in particular when compared to divestitures, which
makes clear that managers consider out-­licensing as an important strategy for resource
reconfiguration following acquisitions. Yet, even though out-­licensing is considered one
of the most pertinent technology commercialization strategies, the paper reveals important trade-­offs of managers when making such decisions. When managers are unable to
assess the ultimate potential of their technologies, they may refrain from out-­licensing
post-­acquisitions. In a similar vein, managers may opt to continue experimenting with
the acquired R&D base in-­house if they have sufficient resources to support the knowledge integration process. Given these trade-­offs, firms may consider establishing a dedicated out-­licensing team that can handle the transfer of knowledge to interested external
parties. Some of the most active firms in acquisitions, such as Merck & Co, AstraZenca,
GE or IBM, have all already established dedicated out-­licensing groups.
The study has limitations that provide several interesting avenues for future research.
Clearly, the bio-­pharmaceutical industry is an important area in which to study in-­
sourcing in the form of technology acquisitions and out-­licensing (Nicholls-­Nixon and
Woo, 2003). While we theorized on a general level about out-­licensing as resource reconfiguration strategy following acquisitions, the particularities of the biopharmaceutical
industry call for further research testing our framework in other contexts.
In its current form, we also focused on counting the number of acquisitions and, using
a 2SLS approach, providing robust results on subsequent out-­licensing. Alternatively, researchers could analyse firms’ strategies, contingent on making acquisitions, to examine
their acquired knowledge bases as well as the structure of those bases. Although we provide tests for different alternative measures, future research should take this a step further.
For example, one could more carefully explore the technological distance of knowledge
from target to acquiror as well as the degree to which technologies complement each
other (Zaheer et al., 2013). Future studies could investigate such factors conditional on
making acquisitions, building on the research which has explored their role in the context
of divestitures following acquisitions (Capron et al., 2001; Karim and Capron, 2016).
Finally, we did not link technology out-­licensing to specific technologies and how that
activity relates to previously made acquisitions. In our context, some out-­licensing cases
can be specific, for example, out-­licensing a distinctly new project in the form of a compound in development. However, in many cases, that also relates to broader technologies,
which cannot be easily attributed to specific projects. We were able to gain some insights
by matching in-­licensed and out-­licensed products for a subset of transactions (see section ‘Additional Analyses’). However, we encourage researchers to find better ways to
more systematically link technology out-­licensing transactions to actual patents or products in development.
Overall, connecting out-­licensing to resource reconfiguration can provide important
insights into how firms systematically manage their post-­acquisition and open innovation
activities in general. We hope our study helps spur further interest by researchers who
can build on and extend our framework with the goal of shedding additional light on
this important topic.
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ACKNOWLEDGMENTS
T. Klueter acknowledges the research support provided by the Mack Institute for Innovation Management
at The Wharton School.
NOTES
[1] The work on post-­acquisition reconfiguration also includes larger conglomerates that include additional
businesses to their product portfolio and engage in subsequent rebalancing.
[2] Estimating our models without the variables leading to missing values provided no evidence of potential
issues with sample attrition.
[3] We also report different lag structures, i.e., t + 2 and t + 3, as part of our additional analyses in
Table IV.
[4] We also report various alternative Uncertainty measures as robustness test in section ‘Contrasting
Divestitures as Alternative R&D Reconfiguration Strategy’.
[5] We report several alternative specifications in the Results section. Additional Analysis, following the
main results. Results are, also, robust using a regular OLS fixed effect regression approach.
[6] We removed the R&D expenses to avoid any indirect correlation with focal firms’ licensing activities.
[7] These results can be provided by the authors upon request.
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