Paper to be presented at the
35th DRUID Celebration Conference 2013, Barcelona, Spain, June 17-19
Internal knowledge accumulation and the acquisition of external
technology: Is there a trade-off?
Vincenzo Palermo
Georgia Institute of Technology
Scheller College of Business
[email protected]
Abstract
By combining the markets for technology framework and research on organizational boundaries, I examine the impact of
internal knowledge accumulation on firm performance. When firms specialize on internal knowledge and adopt an
inward-looking attitude, they can be reluctant to adopt acquired technologies. While recent studies on technology
acquisition have emphasized the importance of combining technologies from different sources, there exists a lack of
attention on the integration of external technologies into innovative production. A potential tension may exist between
external knowledge acquisition and internal knowledge accumulation. I use a unique panel dataset on 92 active
pharmaceutical firms between 1997 and 2008 to examine the impact of internal knowledge exploitation on firm sales and
licensing adoption. The results show that reliance on existing knowledge has a positive effect on performance while it
negatively moderates the marginal effect of licensed technology. Companies can benefit from perceived internal
knowledge superiority but this attitude may conflict with external technology exploitation, thereby limiting the potential
benefits associated with the technology markets.
Jelcodes:L25,-
Internal knowledge accumulation and the acquisition of external
technology: Is there a trade-off?
Abstract
By combining the markets for technology framework and research on organizational boundaries, I
examine the impact of internal knowledge accumulation on firm performance. When firms specialize on
internal knowledge and adopt an inward-looking attitude, they can be reluctant to adopt acquired
technologies. While recent studies on technology acquisition have emphasized the importance of
combining technologies from different sources, there exists a lack of attention on the integration of
external technologies into innovative production. A potential tension may exist between external
knowledge acquisition and internal knowledge accumulation. I use a unique panel dataset on 92 active
pharmaceutical firms between 1997 and 2008 to examine the impact of internal knowledge exploitation
on firm sales and licensing adoption. The results show that reliance on existing knowledge has a positive
effect on performance while it negatively moderates the marginal effect of licensed technology.
Companies can benefit from perceived internal knowledge superiority but this attitude may conflict with
external technology exploitation, thereby limiting the potential benefits associated with the technology
markets.
Keywords: Not invented here syndrome, technology licensing, markets for technology
1
INTRODUCTION
The decision to define firm boundaries is still an open and interesting question in the strategy
literature. Prior work has emphasized the role of internal capabilities and external technologies in
determining how boundaries should be defined (Arora and Gambardella, 1990; Cassiman and Veugelers,
2006; Parmigiani, 2007) . According to this stream of research, firms rely on their existing competencies
and search for external knowledge in order to introduce new products and develop new technologies
(Kogut and Zander, 1993). Recently, greater importance has been placed on the rising markets for
technology, and firms therefore have increasingly exploited licensed technologies (Arora and
Gambardella, 2010). Despite the importance of licensing for technology development and
commercialization, there is still little attention on corporate factors that may assist or limit the markets
for technology. While it has been shown that complementary assets, intellectual property rights
ownership and transaction costs can affect the licensing decision (Ceccagnoli et al., 2010; Ceccagnoli and
Jiang, 2012; Gambardella et al., 2007), it is crucial to understand organizational factors that reduce the
adoption of external technologies and their integration with existing capabilities.
For instance, the benefits of external technology acquisition and its combination with internal
knowledge have been emphasized in previous studies (Arora and Gambardella, 1990; Cassiman and
Veugelers, 2006; Ceccagnoli et al., 2011). Existing literature has highlighted the benefits of external
technology acquisition
innovative output but there exist a gap on understanding how
internal knowledge accumulation affects the adoption of external technologies.
Specifically, companies may extensively rely on their internal knowledge and capabilities, thereby
isolating themselves from external ideas. The acceptance of externally developed technologies imposes
a comparison between existing technological expertise and that of the external technology. This may be
a threat for the perceived expertise of a group. Employees, working teams and communities can
respond to this threat with resistance towards external knowledge. This attitude has been defined as
2
the Not Invented Here (NIH) syndrome (Clagett, 1967; Katz and Allen, 1982); it refers to the negative
attitude towards knowledge developed outside the institution, and this attitude can translate into a
detriment to performance. The concept can be applied to individuals, groups and institutions that face
the competition of external knowledge (Lichtenthaler and Ernst, 2006).
The markets for technology literature often assumes technology adoption by all potential users;
however, organizational factors should affect both the decision to acquire external technologies and to
integrate and adopt them. For example, knowledge externalities can play an important role in the
adoption of external knowledge (Jaffe et al., 1993). This is likely due to the degree of tacitness of the
external knowledge, which increases the cost of adoption. Furthermore, organizations invest significant
resources in the development of their absorptive capacity, in order to enhance their ability to exploit
externally generated knowledge (Cohen and Levinthal, 1990). However, knowledge tacitness and
absorptive capacity alone capture a limited view of an organization s ability to integrate external
knowledge. In fact, factors like internal knowledge availability and internal favoritism can also affect the
assimilation process. Companies can exploit their specialization on a specific technology trajectory to
internalize some of the knowledge spillovers created by its own departments. This mechanism may
favor internal productivity and thus the technology make-or-buy decision. If so, it is crucial to find a
balance between the propensity to use internal knowledge and the acquisition of the external one.
The management literature has identified this trade-off as an important factor in the conversion of
organizations from closed to open models (Chesbrough, 2006). For example, Huston and Sakkab (2006)
report the following about P & G transition from the classic R & D model to the new Connect and
Develop (C&D) model:
W
A
3
how we defined, and perceived, our R&D organization from 7500 people inside to 7500
plus 1.5 million outside, with a permeable boundary between them. (p.61)
More generally, recent work on the economics and management of innovation has shown that
organizational boundaries have important implications for knowledge transfer and organizational
performance. Within this stream of work, the contribution of this study is twofold. First, it fills a gap in
the literature on firm boundaries and knowledge management by analyzing whether companies can
translate the advantages generated by external technology into higher performance in the presence of
an inward orientation toward knowledge accumulation, which may be associated with the NIH
syndrome. As argued by Katz and Allen (1982), in certain inwardly-oriented organizations external
knowledge may be perceived as inferior or as a potential threat when compared to internal knowledge.
It is important to understand the extent to which such organizations are more likely to reject the
adoption of external technologies or whether they are less likely to efficiently combine and integrate
external knowledge with their existing capabilities. A second contribution of this study is to emphasize
the positive benefits associated with an inward orientation in the process of knowledge accumulation
within the firm, once its negative effect on the productivity of investments in external technology is
factored out.
More specifically, combining insights from the markets for technology and knowledge management
literatures, I predict that the propensity to adopt internal knowledge may have two opposite effects on
firm performance. First, it may reduce the marginal effect of external knowledge. The central argument
is that firms are better off when they are able to combine different sources of knowledge, but
specialization in the use of internal knowledge increases the cost of adopting knowledge developed
and when the integration cost is too high the adoption of external
knowledge is reduced. Second, such inward knowledge specialization can also have a positive effect on
4
performance by reducing internal coordination costs and by favoring the creations of efficient internal
knowledge flows.
This paper is organized as follows. The next section discusses and highlights the relevant literature
on the importance of external technology acquisition and it outlines the main hypotheses. The following
sections describe the empirical data and setting, the empirical results, and the robustness checks. The
last two sections discuss the results and conclude.
THEORY AND HYPOTHESES
The recent strategy literature suggests that innovative firms need to exploit external knowledge
when they develop their innovations. Internal research and development (R & D) is no longer sufficient
to guarantee innovative performance. Firms usually need to integrate their existing capabilities with
external knowledge through licensing, outsourcing and acquisition (Arora and Gambardella, 1990;
Cockburn and Henderson, 1998). In such a dynamic environment, firms are forced to adapt their
boundaries by developing new technologies internally or by acquiring them from external sources.
Kogut and Zander (1993) show that firms specialize in internal transfer of tacit knowledge, which implies
that the choice of buying and selling knowledge is determined by the efficiency of internalizing codified
knowledge. However, firms can simultaneously engage in both make and buy decisions, and can exploit
possible complementarities that arise from the combination of different activities (Cassiman and
Veugelers, 2006; Parmigiani, 2007).
Existing literature has mainly
choices: the capability-based view and the transaction cost theory. The former proposes that a
choice is driven by the level of complementary internal capabilities and external knowledge (Kogut and
Zander, 1993), while transaction cost theory advocates that the boundary choice is based on the
comparison of the costs sustained to develop the knowledge either internally or externally (Williamson,
5
1975). The limit of transaction cost theory is that it assumes a substitute relationship between
knowledge sources despite recent evidence that has demonstrated a complementary relationship
between internal and external capabilities (Cassiman and Veugelers, 2006).
Recent developments in the strategy literature suggest that companies should develop skills in both
internal development and in external sourcing to be able to develop dynamic capabilities and survive
over time. Agarwal and Helfat (2009) point out that firms are required to successfully undertake both
internal and external knowledge development. Under this view, acquisitions, alliances and licensing
agreements help reduce the obsolescence of existing capabilities and encourage acquisition of
knowledge to fill existing internal knowledge gaps (Capron and Mitchell, 2009; Rosenkopf and Nerkar,
2001). Similarly, internal development is important because it generates the capabilities required to
evaluate external opportunities (Cohen and Levinthal, 1990). As such, the ability to undertake both
types of knowledge sourcing is fundamental in science-based industries (Pisano, 2010).
Other studies have moved beyond the
questions and focused on how firms can
effectively adopt external sourcing modes to build new capabilities and boost their performance
(Parmigiani, 2007). For example, dependency on external technology may affect a
bargaining position, therefore increasing transaction costs (Adegbesan and Higgins, 2011). Moreover,
there is evidence that firms can benefit from their knowledge acquisition by increasing the size of their
acquired knowledge base (Ahuja and Katila, 2001) or by exploiting information about a target
innovative activity prior to the acquisition (Higgins and Rodriguez, 2006).
Firms that engage in both internal production and external acquisition of knowledge may
experience synergies between different knowledge sources. Complementarity between internal and
external innovative efforts suggests that different sources of knowledge modes are mutually dependent.
In other words, this implies that there exist synergies between knowledge-generating activities. It
follows that when different activities are complementary, firms that acquire external knowledge must
6
also continue to engage in internal R & D to remain competitive (Agarwal and Helfat, 2009; Chesbrough,
2003). It is widely accepted that complementarity influences
external
knowledge (Arora and Gambardella, 1994; Cassiman and Veugelers, 2006) through several modes such
as licensing, alliances and acquisitions (Arora and Gambardella, 1990; Cockburn and Henderson, 1998).
Moreover, the degree of complementarity depends on intellectual property considerations and the
basicness of the R &D base, the type of experience in different learning stages, the firm s absorptive
capacity and the economies of scope (Cassiman and Veugelers, 2006; Ceccagnoli et al., 2011; Cohen and
Levinthal, 1990).
I
mechanisms. For example, it is possible that competition from external ideas may facilitate internal
process and boost in-house effort. Fosfuri and Rønde (2009) suggest that companies can gain advantage
from leveraging their resistance to change. While their setting describes competition between two
different internal departments, a similar approach can be applied when comparing internal R & D
departments and external sources of knowledge. Competition with external sources can increase
internal motivation and effort.
Similarly, when considering the geographic location of knowledge, myopic inventors in large firms
produce patents that are less cited externally but they are highly cited internally, therefore suggesting
potential benefits associated with internal reliance as a mechanism to support efficiency and
coordination costs (Agrawal et al., 2010). For example, resistance to change can be the source of
internal routines that increase firm performance and generate complementarities among internal
activities, thus reducing internal inefficiencies. Research has shown that knowledge transfer is more
likely across units that are part of the same organization (Ingram and Simons, 2002). Similarly, ZellmerBruhn (2003) finds that units adopt best practices from other units internal to the organization than
7
from external business units. In addition, firms in the semiconductor industry learn three times as much
from their internal knowledge as from external one (Irwin and Klenow, 1994).
The inward-looking behavior described by Cohen and Levinthal (1990) is also consistent with the
instability in the comparison between internal and external knowledge. A greater level of identification
with the company implies a stronger bias in favor of internal knowledge and a higher perceived threat of
external knowledge. Although a strong group favoritism may have positive effects on firm activities
(Gioia et al., 2000)
cohesive social
-
I
-
(Burcharth and Fosfuri, 2012). Other scholars have interpreted group affiliations
and actions that reinforce membership as an efficient mechanism by which to support coordination and
develop trust among members, which facilitates within-group transactions and reduces internal
coordination costs (Efferson et al., 2008). For instance, knowledge flows between scientists is favored
when individuals are connected by a dense web of connections (Reagans and McEvily, 2003); in
performance than knowledge adopted from external sources (Darr et al., 1995).
In addition, firms that rely on their internal knowledge generate synergies between internal
activities of the value chain (e.g. R&D, marketing and manufacturing) (Arora and Gambardella, 2010).
Another explanation lies in the costs of accessing external knowledge. For example, pharmaceutical
‘ D
ir
ability to establish links with academic environments. This behavior is necessary to protect internal
innovation and limit disclosure (Cockburn and Henderson, 1998). Companies that exploit internal
knowledge can reduce their costs because there is less need to acquire external technologies, and they
can specialize on a technological trajectory reducing the risk of entry since they control the underlying
8
technology (Hall et al., 2005). Finally, the existence of incomplete contracts within firms can provide
incentives to prefer internal innovations, therefore reducing the adoption of external technology. In
other words, it may be possible that the inward-looking behavior is the outcome of the commitment to
overcome these incomplete contracts. For instance, if internal ideas can be easily implemented and
firms rewards their employees if internal innovations are adopted, then competition among ideas
inhibits the use of external knowledge (Rotemberg and Saloner, 1994).
All these studies suggest that organizations are likely to benefit from the accumulation and
exploitation of internal knowledge. The propensity to use internal knowledge reduces coordination
costs, positively affects internal activities of the value chain and it favors the creation of internal
knowledge flows. Therefore, I suggest that the main effect of internal knowledge accumulation on firm
performance is positive.
H1. The main effect of internal knowledge accumulation on firm performance is positive
One important aspect of the firm boundary decision relates to the ability to identify and exploit
acity (Cohen and
Levinthal, 1990). Cohen and Levinthal (1990) identify two different components of absorptive capacity:
the outward-looking and the inward-looking absorptive capacities. In fact, there may be a trade-off
between the ability to internalize external information and the efficiency of internal knowledge flows.
While some overlap of knowledge within the company favors information sharing and it is necessary for
internal communication, there are benefits associated with the diversity of knowledge sources.
Therefore, excessive inward-looking or outward-looking behaviors may be detrimental for the company.
For example, if internal communication linkages are well defined and established, firms can benefit from
them by reducing their coordination costs and by establishing norms and routines. However, if internal
9
knowledge becomes extremely specialized, it impedes the assimilation of outside knowledge and the
inward-looking attitude may generate the NIH syndrome. Similarly, Hall et al. (2005) suggest that an
excessive reliance on internal knowledge may
thus
. Having high levels of absorptive capacity does not imply that
the NIH syndrome cannot occur, in that firms may fail to understand the potential of external
knowledge acquired based on their absorptive capacity because of their inward-looking attitude. As a
consequence, firms may not effectively integrate and use the acquired knowledge. However, firms with
higher absorptive capacity should be able to reduce the uncertainty and bias towards external
knowledge, therefore, they should not have a strongly negative attitude towards external knowledge
(Lichtenthaler and Ernst, 2006).
It follows that the assimilation of external knowledge requires companies to accept and adapt
existing capabilities, but the inward-looking behavior and it consequences (i.e., the NIH syndrome) can
be a factor influencing this assimilation. Most prior work has referred to the NIH syndrome as a
theoretical concept and has emphasized the negative effects associated with it. A pioneering
contribution to the analysis of the NIH syndrome is represented by the work of Clagett (1967). This
author analyzed four different case studies where several plants adopted process innovations developed
by the R & D unit. Two factors were identified as antecedents of the NIH syndrome: violation of the
norms and routines of the own organizational unit, and resistance to changes in a familiar environment.
Both factors generate a negative attitude towards knowledge and reduce the adoption of external
technologies.
After surveying several R & D professionals in 50 project groups, Katz and Allen (1982) found similar
results to those of Clagett (1967). The NIH syndrome is generated by the insecurity and environmental
instability generated by the external technology. In particular, new knowledge affects and changes
existing routines and roles, creating instability. As a result, project performance diminishes when teams
10
operate in a stable collaborative environment for more than five years. Knowledge creation is a complex
process involving individuals, beliefs and information and the combination of these factors creates an
internal system of routines that support process information and problem solving (Nelson and Winter,
1982). These routines are often tacit and they evolve over time so they are difficult to imitate and to
transform (Teece et al., 1997). External knowledge can threaten the existing internal status quo among
an
y. If individuals feel that their contribution to the knowledge generating
process is being threatened then they may slow the adoption of new technologies and show a hostile
behavior toward external knowledge.
Another antecedent of the NIH syndrome can be identified in the incentive system (de Pay, 1989,
1995). Inappropriate incentive systems can stimulate intolerance against external knowledge. For
example, a reward system favoring an organizational culture based on an individualistic attitude would
increase the occurrence of the NIH syndrome. Based on survey data from the U.S. and Germany, de Pay
(1989, 1995) found that companies experienced longer adoption times for acquired technology when
incentive systems enabled the NIH syndrome. Similarly, members of a unit are less likely to transfer
knowledge among different groups if they are not rewarded in adopting existing knowledge (Menon and
Pfeffer, 2003).
In this paper, I focus on licensing investments as source of external knowledge. In particular, I study
the joint effect of internal knowledge accumulation and licensing investments: when companies adopt
an inward-looking behavior they may suffer from the NIH syndrome and experience a negative bias
against external knowledge. As a consequence, this behavior can lower the effectiveness with which
licensed technologies
. It follows that my first hypothesis is that
internal knowledge accumulation negatively moderates the effect of licensing on firm performance.
11
H2. Internal knowledge accumulation negatively moderates the effect of licensing on firm
performance
EMPIRICAL SETTING AND DATA
The two hypotheses were tested on an unbalanced panel of 92 pharmaceutical companies that
were between 1997 and 2008. The sample was based on a unique dataset built from a variety of
sources. First, I identified all of the companies that had an FDA approved drug and were listed in the FDA
Orange Book database. Next, these companies were matched to the Deloitte-ReCap database to collect
data on licensing investment and external technology acquisition (e.g., royalties, up-front payments and
milestones). Data on R & D productivity and drug development (e.g., the various stages of clinical trials,
FDA approval and project discontinuations) were collected from Pharmaprojects.
Data on total R & D expenditures and the number of firm employees was collected from
Compustat. Sales from new products and advertising expenditures were
IM“ MIDA“
The promotion data includes investments directed towards physicians and hospitals, journal advertising
and direct-mail to promote drugs. Finally, all granted patents to the companies were collected from the
USPTO patent database to build citation measures. To avoid potential bias, I limited my analysis to
therapeutic patents in technology classes A61K and C07D, as described by Graham and Higgins (2007).
All financial variables are presented in year 2000 constant U.S. dollars. Descriptive statistics are provided
in Table 1 and correlations are presented in Table 2.
<Insert Table 1 and Table 2 about here>
12
Main Measures
The dependent variable in this study, firm performance, is measured by the annual Firm Sales
IM“ MIDA“
A
new product revenues represent the success of their
innovative effort. Internal innovative development is an essential driver of revenues because it
transforms innovations into final products. Similarly, knowledge acquired externally can strengthen and
boost the effect of product development (Chesbrough, 2006).
Licensing investment variable is one of the
external knowledge acquisition. To measure licensing investments, I relied on data collected from
Deloitte-Recap database. My measure includes both up-front and milestone payments. Royalties were
not included in this measure because they represent a cost based on future sales, and are thus
unobservable within the scope of this study.
The Internal Knowledge Exploitation variable measures the use and adoption of internal knowledge
by the focal firm. This measure represents the degree to which the focal firm draws from prior
technologies developed internally. Following Agrawal et al., (2010), all patent backward citations and
the number of self-citations were collected. Backward citations may be added by examiners and may
not represent the actual behavior of the company. To avoid potential bias, citations listed by examiners
were excluded. The formula used to measure the internal knowledge accumulation was:
.
For each company, Cit represents the total number of citations of all patents assigned to firm i at
time t, and CitS represents the total number of self-citations (based on assignee) of all patents for firm i
at time t. This measure captures firm i attitude towards reliance on internal knowledge. The greater
the share of self-citations the firm has, the greater the internal knowledge accumulation process.
13
Instrumental Variables
In order to deal with potential endogeneity introduced by licensing investments, I used four
variables as instruments in the GMM estimations. The source of endogeneity, in this case, comes from
unobserved factors that may drive both the firm performance as well as the decision to invest in
licensing agreements. I use investments in co-specialized assets, trademarks, average drug novelty and
number of competitors as instruments for the licensing variable. The test for over-identifying restriction
is reported for all the GMM estimations and it is significant in all my specifications. The xtoverid
command in STATA 12 was used to test for over identification (Schaffer and Stillman, 2006). The test
statistic is distributed as chi-squared with degrees of freedom equal to the difference between the
number of excluded instruments and the number of regressors. A rejection casts doubt on the validity of
the instruments (Schaffer and Stillman, 2006).
Prior research has identified marketing capabilities as important co-specialized assets in the
pharmaceutical industry (Chan et al., 2007). I collected the yearly marketing expenditure for each drug.
My intent was to control for the effect of marketing investments on performance. It is reasonable to
assume that there is a positive correlation between sales and promotion investments. Previous
literature has identified that promotion inhibits entry into the pharmaceutical industry (Hurwitz and
Caves, 1988) ,and therefore it protects the incumbent position.
Trademarks are often used to reinforce the appropriability of innovation returns (Fosfuri et al.,
2008). Therefore, trademarks could be associated with licensing investments. The variable represents
the number of new trademarks assigned to firm i at time t and it was collected from the USPTO
database.
Pharmaproject contains independent ratings about the novelty of pharmaceutical compounds. For
each compound in the pipeline, novelty is ranked on a six point scale where higher values represent
more innovative drugs. I measured firm novelty as the percentage of drugs with the highest novelty rank
14
(i.e., those with six points). On average, 17 % of the compounds in all development stages are highly
innovative.
When a market is highly competitive, firms may have greater incentive to invest in innovative activities
to develop new products and, potentially, may gain a competitive advantage. I used the number of
competitors in the same primary therapeutic area as a proxy for the incentive to be innovative and
productive. The variable was collected from the IMS database and it represents the count of companies
with at least one product in the main therapeutic area of the focal firm.
Controls
Non-patent references can be a source of influence or knowledge to develop a new
technology. It can be argued that the cited references represent the original stock of scientific ideas
and insights that are further developed in the citing articles. The number of scientific citations was
therefore used as a measure of
absorptive capacity.
I gathered the dollar amount of internal R & D investment from Compustat. Since the collected
measure includes both internal R & D investment and external acquisitions1, I subtracted the value of
licensing investments from the Compustat data. I included R & D investment to control for the effect of
internal effort on firm performance.
I included the number of employees to account for the effects related to the company size. The
data were collected from Compustat. The firms in my sample employ a mean of approximately 15000
employees. Next, the percentage of licensed compounds per development stage was collected using
1
For example, ABBOTT 2010 10-K “EC
I
as incurred. Clinical trial costs incurred by third parties are expensed as the contracted work is performed. Where
contingent milestone payments are due to third parties under research and development arrangements, the
milestone payment obligations are expensed when the mi
MERCK 2010 10-K SEC
‘
ent is expensed as incurred. Upfront and milestone payments due
to third parties in connection with research and development collaborations prior to regulatory approval are
15
Pharmaprojects database. Each variable represents the percent of licensed compounds that a firm has
at each phase of the clinical development process (Phase I, Phase II and Phase III).
Finally, I included specifications with time trend dummies and firm-specific dummies to control for
firm heterogeneity. In models without firm-fixed effects I also included geographic location dummies
(North America, Europe, and Other) and the total number of therapeutic areas (ATCs).
Model Specification
In order to test my two hypotheses to examine the effects of the NIH syndrome on performance I
estimated several panel regressions including panel random effects, panel fixed effects and panel GMM.
The different empirical methods are needed to take potential endogeneity problems into
considerations. For all the specification, I estimate the following equation:
where Perfit represents the performance at time t for firm i. Licit and Self_citeit are the licensing
investment and the percentage of self-citations of firm i at time t, respectively. Kit is the vector of
controls variables and
it
T
3
is used to test the first hypothesis; a
negative value confirms the predicted effect of internal knowledge accumulation on external
technology. The main effect of internal knowledge accumulation on performance is described by the
2.
This effect captures the impact of self-citations after controlling for the moderating role
on licensing investments; the second hypothesis predicts a positive coefficient on firm performance.
RESULTS
16
Table 3 reports the benchmark estimation to test my hypotheses. The natural logarithm of sales
was the dependent variable in all the specifications. All models reported include both year dummies and
main therapeutic area (ATC) dummies.
In each of the three empirical methods reported, I included the interaction term between the
Internal Knowledge Exploitation and Licensing investments variables to test my first hypothesis. The
combined effect of the internal knowledge exploitation and licensing on sales is negative and significant
at the 5% significance level. The magnitude of the coefficient represents a standard elasticity, suggesting
that a 10% increase in both licensing investments and patent self-citations would yield a reduction in
sales between 0.3% and 1.6%. These results support the first hypothesis and confirm the negative effect
of internal knowledge exploitation on the adoption of external technologies. In particular, the results in
Table 3 suggest that when companies increase their reliance on internal knowledge, they may not be
able to fully exploit the acquired technologies. If firms consider their internal knowledge superior to
external knowledge, they reject external technologies. There exists a contrast between the advantage
derived from licensing and the rejection of the external knowledge due to the NIH syndrome, which
leads to reduced sales.
To test the second hypothesis, I computed the marginal effect of the Backward Self-Citation
variable and I also ran the regressions without the interaction term to provide further support for my
hypothesis. In all models reported in Table 3, the NIH variable has a positive and significant effect on
firm sales, which is consistent with the second hypothesis. A 10% increase in patent self-citations
generates a growth in sales between 3% and 8%, all else being equal. This result provides support for
the idea that internal knowledge exploitation may lower coordination costs, favor internal knowledge
flows and lead to an increase in internal innovative efforts to compete against external technologies.
Finally, the marginal effect of Internal Knowledge Exploitation suggests that a 10% increase in selfcitations leads to an increase in sales between 2% and 3.7%. The results on the marginal effect are in
17
line with those of Hall et al. (2005). The authors suggest that self-citations can have two opposing effects
on firm value: a positive effect based on the higher private value associated with internal knowledge and
a negative effect due to a firm self-bias. Their results confirm a positive effect of self-citation, thus
supporting the idea that the value associated with internal knowledge reduces the need to acquire
technology from others and it increases the firm market value. However, the results reported in this
paper go beyond the marginal effect of Internal Knowledge Exploitation and they are able to separate
the two effects associated with self-citations described by Hall et al. (2005). On the one hand, by testing
the first hypothesis, the negative moderating role of internal knowledge exploitation reflects the impact
of
self-bias against external knowledge (e.g. the NIH syndrome). On the other hand, through the
second hypothesis, the coefficient of the linear term of Internal Knowledge Exploitation suggests that
firms are able to take advantage of potential spillovers generated by its own departments and they can
reduce their need to acquire knowledge from external sources.
<Insert Table 3 about here>
ROBUSTNESS CHECK
Different estimates were used to verify that the results are robust compared to alternative
estimation methods. All the methods adopted represent different ways to control for unobserved firmspecific differences. The random effects model assumes that firm heterogeneity is random and
uncorrelated with the explanatory variables, while the fixed effects model controls for unobserved
heterogeneity by assuming that a firm fixed component is correlated with the explanatory variables.
Finally, I implemented an instrumental variable approach to help reduce endogeneity in the study by
using instruments that are uncorrelated with the error term.
18
To further validate the results, I estimated the annual market capitalization of each company as a
measure of firm performance. Market Capitalization at time t for firm j is defined as the product of the
average stock prices of the firm and the average number of outstanding shares at time t. The new
regressions are reported as robustness checks, using the natural logarithm value of Market
Capitalization as a dependent variable.
The estimated coefficients in the models all confirm my previous findings (Table 4). The interaction
term between licensing investments and self-citations is significant and negative, supporting the first
hypothesis. In addition, the effect associated with self-citation on firm performance is positive, further
supporting the second hypothesis. Finally, these additional regressions are also robust to different
estimation methods.
<Insert Table 4 about here>
DISCUSSION
The results reported here expand our understanding of internal knowledge exploitation and its
impact on firm performance. First, I find evidence of the negative moderating role that the reliance on
internal knowledge has on the marginal effect of licensing activity. The estimate coefficients suggest
that the effect of licensing is reduced when combined with an inward looking behavior. It is possible that
companies that specialize in the use of their internal knowledge suffer from the NIH syndrome Higher
levels of internal technology reliance may create a systematic bias towards external technologies
adoption. Firms tend to favor their internal knowledge more in comparison to external knowledge, thus
reducing
ability to adopt an open innovation process and to exploit potential synergies with
internal R&D.
19
Second, the regressions support the hypothesis that internal knowledge exploitation has a positive
effect on firm performance. A possible explanation of this positive effect is that companies are able to
reduce their coordination costs and to create more efficient knowledge flow. For it to be effective,
external knowledge needs to be assimilated and adapted to the internal capabilities of the firm, which
leads to increased transaction costs. This process becomes even more evident in the case of tacit
knowledge. An alternative explanation is that there may be a greater incentive to increase the
innovative effort within the firm when internal knowledge faces the threat of competition from external
technologies (Fosfuri and Rønde, 2009).
Literature on knowledge management and firm boundaries has discussed the potential benefits
associated with the use of internal knowledge (Ingram and Simons, 2002; Irwin and Klenow, 1994;
Parmigiani, 2007; Zellmer-Bruhn, 2003); a main contribution of this study is the identification of both
positive and negative effects related to knowledge developed internally and self-citations (Hall et al.,
2005). In other words, my results represent a different aspect of the same phenomenon. On one hand,
companies can exploit social integration among employees to create a unique set of values, needs, and
beliefs and consolidate internal knowledge flows to increase performance (Gioia et al., 2000). In
addition, firms can rely on their own knowledge to reinforce their capabilities, reduce their coordination
costs and better manage their internal innovative effort (Cohen and Levinthal, 1990; Fosfuri and Rønde,
2009). On the other hand, an inward-looking behavior may generate the NIH syndrome and introduce a
negative attitude with respect to external knowledge such that this bias can transform into a rejection of
acquired technologies (Agrawal et al., 2010; Katz and Allen, 1982). This may reduce the
ability to
implement an open innovation strategy and the ability to rely on the markets for technology.
From my results, it can be inferred that in order to increase firm performance, it is crucial to find a
balance between the inward-looking attitude and the reduction of the biases against external
knowledge. Past research identifies knowledge goalkeepers and champion scientists as possible
20
mechanisms that firms can use to manage and reduce biases introduced by internal knowledge
exploitation (Katz and Allen, 1982; Lichtenthaler and Ernst, 2006). Goalkeepers possess high levels of
trust, they are more exposed to scientific and technological literature, and they maintain informal
contact with the scientific community. The ability to bridge across knowledge boundaries is a crucial
capability (Reagans and McEvily, 2003). For instance, Gittelman and Kogut (2003) show that firms can
increase their innovative performance by having scientists who are able to bridge between the logic of
scientific communities and the logic of patenting.
For example, Merck has historically been known as an inward oriented company that relied on
internal development as its main innovative driver. Merck scientists did not consider it worth spending
time on something that was not created inside Merck. While this closed innovative process was
sufficient in past decades, the company has recently changed their attitude towards external knowledge
to fill its pipeline.2 Pharmaceutical companies have recently begun adopting strategies to reduce the
negative effects of the NIH syndrome without reducing the positive effect of internal innovative effort.
For example, when Pfizer downsized their UK establishment (known as Pfizer Sandwich) in 2011, a group
of scientists created a spin-out called The Research Initiative The scope of the project is to support the
integration of external research and to connect project teams with different sources of knowledge.3 This
strategy allows Pfizer to maintain their organizational culture and to reduce potential biases towards
external technologies. This initiative creates a bridge between the employees of the internal innovative
process and the environment outside the firm boundaries. Similarly, Ely Lilly has introduced the
Phenotypic Drug Discovery (PD2) initiative. The program is based on the submission of molecules by
scientists outside the company4, thus creating a competitive environment that may boost internal effort
2
http://www.fastcompany.com/59248/not-invented-here. Last accessed 09/26/2012
http://www.labnews.co.uk/comment/big-ask/dating-agency-scientists-andrew-mcelroy/, Last Accessed
09/26/2012
4
http://www.genengnews.com/insight-and-intelligenceand153/big-pharma-s-open-innovation-initiatives-zoom-inon-discovery/77899468/. Last Accessed 09/26/2012
3
21
and facilitate the acceptance of outside knowledge. A similar approach was adopted by Procter &
Gamble; by implementing their Connect and Develop Strategy the company had to adopt a new
innovative process that included both internal and external scientists. As a result of this new strategy, 35
% of Procter & Gamble new products include elements that originated outside the company and their
R & D productivity has increased by 60 % (Huston and Sakkab, 2006).
CONCLUSION
This paper contributes to a number of theoretical streams. First, it has implications for the markets
for technology and the firm boundaries literature, with a particular emphasis on the effect of internal
knowledge exploitation. It has been shown that companies can use external technologies to lower
product development uncertainty (Danzon et al., 2005), fill capabilities gaps (Agarwal and Helfat, 2009)
and boost innovative production (Arora and Gambardella, 1990). Despite the extensive literature on
licensing transactions and the boundary of the firm, there is still a surprising lack of understanding as to
how firms are able to internalize the acquired knowledge. It may be possible that internal organization
creates a limit in external technology adoption. In particular, firms that develop a dominant inwardlooking behavior may suffer from the NIH syndrome and potential biases towards external knowledge. I
found a negative interaction between licensing investments and self-citations. This result supports the
idea that when companies consider internal knowledge superior to the outside knowledge, they may
have a negative attitude with respect to adopting external technologies. As a consequence, this
behavior may limit the adoption of open innovation strategies and the acquisition of external
technologies.
Second, this paper also answers the call for more results on organizational boundaries and
knowledge management within organizations (Ingram and Simons, 2002; Irwin and Klenow, 1994).
Internal knowledge is important in defining a set of norms and behaviors to which employees should
22
adhere (Zellmer-Bruhn, 2003). It favors the development of knowledge flows and the creation of
routines that increase internal efficiency. The positive effect associated with internal knowledge
exploitation reduces coordination costs and the need to acquire technologies from outside the firm
boundaries (Agrawal et al., 2010; Fosfuri and Rønde, 2009).
Third, previous studies have also shown the importance of external knowledge in the generation of
complementarities among different activities (Cassiman and Veugelers, 2006; Ceccagnoli et al., 2011).
These results suggest that companies should undertake both internal knowledge development and
external acquisition in order to compensate for lacking capability and to experience better performance.
The combination of internal and external technology investments generates positive synergies on
product development and performance, therefore confirming the importance of engaging in technology
acquisition. For instance, firms that successfully undertake both internal development and external
sourcing may be able to renew their capabilities more effectively and experience performance
advantages (Capron and Mitchell, 2009). However, if an inward-looking attitude reduces the adoption of
acquired technologies, companies may not be able to take advantage of the benefits associated with the
combination of knowledge sources. The exploitation of internal knowledge may limit the positive
benefits of external technology acquisition, which include reduced developmental uncertainty, the
creation of spillovers and synergies with internal capabilities, and their exploitability when
complementary assets are high (Arora et al., 2001; Cassiman and Veugelers, 2006).
This study has some limitations and offers potential areas for future research. First, by just using
the pharmaceutical industry in my sample I may limit the generalization of these results to other
industries; therefore, a deeper analysis into other industries is necessary to fully understand this
phenomenon. Finally, it is important to understand the contingent factors that affect the impact of the
internal knowledge reliance on performance. For example, further research is needed on the conditions
that favor an inward-looking behavior.
23
From a practical point of view, this study has important managerial implications. This paper shows
the importance of managing knowledge within organizational boundaries in a way that accounts for the
positive and negative effects associated with the use of internal knowledge. Firms are often in favor of
exploiting internal knowledge to reduce their cost and specialize on technological trajectories; however,
the same attitude may limit the ability to integrate external knowledge. Managers should be aware of
the trade-off between a strong inward oriented behavior that may generate the NIH syndrome and the
ability to exploit internal knowledge.
In conclusion, this study emphasizes a critical question about knowledge exploitation and the
markets for technology. By focusing on internal knowledge exploitation, I provide insights on the
potential trade-offs generated by high levels of inward-looking specialization in that companies can gain
greater performance by exploiting their existing knowledge but they may also increase their negative
bias towards external technologies, thus reducing the benefits of the markets for technology.
24
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27
Table 1 Descriptive Statistics
Variable
Mean
Std. Dev.
Min
Max
Sales
2522706
6329668
0
4.35E+07
Market Capitalization
20709.2
41705.13
1.024
290444
Licensing Investment
3085.222
16096.45
0
331505
Internal Knowledge Exploitation
25.309
92.540
0
1110
Pipeline novelty
0.173
0.304
0
1
Competitors
1262.472
571.965
191
2701
Trademarks
4.14
14.745
0
168
Promotion
71548.57
190661.2
0
1189990
Patents
13.216
31.631
0
242
R&D Investment
661.733
1458.479
0
12183
Firm Size
15.408
28.618
0.003
138
% Licensed Compound (Phase I)
0.150
0.281
0
1
% Licensed Compound (Phase II)
0.244
0.352
0
1
% Licensed Compound (Phase III)
0.257
0.373
0
1
Scientific references
22.022
22.232
0
126.833
North America
0.847
0.360
0
1
Europe
0.12
0.325
0
1
Number of ATC
6.487
5.743
1
16
28
Table 2 Correlation Table
1
1.Sales
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
1
2.Market Capitalization
0.846
3.Licensing Investment
0.615 0.429
1
4.Internal Knowledge Exploitation
0.187 0.339 0.091
5.Pipeline novelty
0.228 0.182 0.126 0.104
6.Competitors
0.222 0.151 0.139 -0.018 -0.005
7.Trademarks
0.491 0.492 0.300 0.082 0.082 0.111
8.Promotion
0.952 0.795 0.622 0.205 0.199 0.226 0.468
9.Patents
0.610 0.700 0.322 0.630 0.204 0.147 0.242 0.639
10.R&D Investment
0.932 0.849 0.569 0.217 0.207 0.204 0.523 0.860 0.578
11.Firm Size
0.782 0.899 0.441 0.317 0.110 0.169 0.526 0.750 0.602 0.815
1
1
1
1
1
1
1
1
1
12.% Licensed Compound (Phase I) 0.109 0.147 0.016 0.060 0.290 0.005 0.031 0.075 0.119 0.123 0.161
1
13.% Licensed Compound (Phase II) 0.067 0.103 0.021 0.241 0.320 0.010 0.026 0.049 0.132 0.084 0.095 0.178
1
14.% Licensed Compound (Phase III) 0.193 0.213 0.069 0.192 0.290 0.038 0.046 0.148 0.209 0.192 0.182 0.214 0.163
1
15.Scientific references
0.372 0.299 0.262 0.139 0.147 0.205 0.311 0.324 0.260 0.410 0.293 0.101 0.058 0.150
16.North America
-0.290 -0.157 -0.213 0.076 -0.160 -0.268 -0.164 -0.301 -0.073 -0.220 -0.199 -0.021 0.059 -0.047 -0.110
17.Europe
0.304 0.202 0.251 -0.064 0.125 0.229 0.204 0.337 0.092 0.257 0.243 0.024 -0.060 0.058 0.112 -0.871
18.Number of ATC
0.593 0.593 0.298 0.331 0.063 0.218 0.308 0.567 0.511 0.545 0.661 0.141 0.091 0.180 0.211 -0.439 0.351
29
1
1
1
1
Table 3 Main regressions. Dependent variable ln(Sales)
Random Effect Fixed Effect
(1)
(2)
0.0410
0.0325
(0.0282)
(0.0282)
0.303**
0.359***
(0.120)
(0.117)
**
-0.0305
-0.0352**
(0.0136)
(0.0139)
0.0300
-0.0420
(0.126)
(0.129)
0.143
0.0457
(0.111)
(0.114)
0.471*
1.083**
(0.271)
(0.419)
-0.168
-0.169
(0.327)
(0.322)
-0.0194
0.0122
(0.227)
(0.249)
-0.101
-0.150
(0.253)
(0.256)
-0.0812
-0.0889
(0.0996)
(0.105)
-0.0667
(0.516)
0.185
(0.466)
0.364***
(0.0697)
7.428***
11.06***
(1.021)
(0.973)
0.208**
0.249***
(0.094)
(0.091)
Licensing
Internal Knowledge Exploitation
Licensing*Internal Knowledge Exploitation
Patent
R&D
Firm size
% Licensed compound (Phase I)
% Licensed compound (Phase II)
% Licensed compound (Phase III)
Scientific references
North America
Europe
Number of ATCs
Constant
Marginal Effect - Internal Knowledge Exploitation
OverIdentification Test (Chi-sq)
p-value
Observations
857
Clustered standard errors in parentheses
*
p < 0.10, ** p < 0.05, *** p < 0.01
30
857
GMM
(3)
0.322
(0.240)
0.883***
(0.296)
-0.160**
(0.0622)
0.0343
(0.137)
0.00190
(0.148)
0.652**
(0.263)
-0.205
(0.223)
-0.124
(0.205)
-0.0631
(0.183)
-0.214*
(0.119)
0.728
(1.784)
0.476
(1.863)
0.393***
(0.0750)
6.916***
(1.925)
0.371***
(0.12)
34.665***
0.0001
815
Table 4 Robustness checks. Dependent variable log(Market Capitalization)
Licensing
Internal Knowledge Exploitation
Licensing*Internal Knowledge Exploitation
Patent
R&D
Firm size
% Licensed compound (Phase I)
% Licensed compound (Phase II)
% Licensed compound (Phase III)
Scientific references
North America
Europe
Number of ATCs
Constant
Marginal Effect - Internal Knowledge Exploitation
OverIdentification Test (Chi-sq)
p-value
Observations
Clustered standard errors in parentheses
*
p < 0.10, ** p < 0.05, *** p < 0.01
31
Random Effect
(1)
0.0390**
(0.0156)
0.187***
(0.0552)
-0.0237***
(0.00528)
-0.0407
(0.0683)
0.414***
(0.0746)
0.833***
(0.0985)
0.0557
(0.141)
0.0473
(0.121)
0.0770
(0.118)
-0.0840
(0.0551)
0.617**
(0.264)
0.308
(0.295)
0.0589**
(0.0287)
2.892***
(0.425)
0.111**
(0.048)
Fixed Effect
(2)
0.0305**
(0.0148)
0.198***
(0.0575)
-0.0208***
(0.00571)
-0.0617
(0.0739)
0.307***
(0.0741)
0.731***
(0.210)
0.0287
(0.149)
0.0312
(0.138)
0.0394
(0.126)
-0.0883
(0.0569)
838
838
4.438***
(0.573)
0.132***
(0.051)
GMM
(3)
-0.0484
(0.120)
0.262*
(0.150)
-0.0427
(0.0311)
0.0306
(0.0665)
0.458***
(0.0759)
0.872***
(0.102)
0.0862
(0.107)
0.0715
(0.0977)
0.0755
(0.0883)
-0.121**
(0.0592)
0.654
(0.537)
0.459
(0.553)
0.0685***
(0.0252)
2.760***
(0.602)
0.122**
(0.059)
34.665***
0.0001
797