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The Impact of Knowledge Worker Mobility through an Acquisition on Breakthrough Knowledge

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Abstract

Acquisitions enable firms to access new knowledge from target firms, along with the scientists who created the knowledge, to enhance their own knowledge creation outcomes. We explore how the retention of target firm scientists and acquired knowledge characteristics affect new knowledge creation outcomes for the acquiring firms. Using a sample of 111,227 patents following 301 high-tech acquisitions in 1990-2000, we find that acquiring firms that avoid the exodus of target firm scientists increase their likelihood of creating highly impactful knowledge. Moreover, the characteristics of acquired knowledge and organizational context of the acquiring firms moderate this relationship. The positive effect of target firm scientist retention on the likelihood of creating highly impactful knowledge during the post-acquisition period is stronger when the acquired knowledge is complex, whereas such a relationship is weaker when the acquired knowledge stock is similar to that of the acquiring firm. This article is protected by copyright. All rights reserved.
The Impact of Knowledge Worker Mobility through
an Acquisition on Breakthrough Knowledge
Haemin Dennis Park, Michael D. Howard and
David M. Gomulya
University of Texas at Dallas; Texas A&M University; Singapore Management University
ABSTRACT Acquisitions enable firms to access new knowledge from target firms, along with
the scientists who created the knowledge, to enhance their own knowledge creation
outcomes. We explore how the retention of target firm scientists and acquired knowledge
characteristics affect new knowledge creation outcomes for the acquiring firms. Using a
sample of 111,227 patents following 301 high-tech acquisitions in 1990–2000, we find that
acquiring firms that avoid the exodus of target firm scientists increase their likelihood of
creating highly impactful knowledge. Moreover, the characteristics of acquired knowledge
and organizational context of the acquiring firms moderate this relationship. The positive
effect of target firm scientist retention on the likelihood of creating highly impactful
knowledge during the post-acquisition period is stronger when the acquired knowledge is
complex, whereas such a relationship is weaker when the acquired knowledge stock is similar
to that of the acquiring firm.
Keywords: acquisition, breakthrough knowledge, human capital specificity, knowledge-based
view
INTRODUCTION
Firms often acquire another firm to gain new knowledge and the human capital from
target firms to improve their odds of creating valuable new knowledge. Prior research
has extensively documented how acquisitions provide an opportunity for acquiring
firms to access new knowledge that can be recombined with their existing knowledge
Address for reprints: Haemin Dennis Park, Organization, Strategy, and International Management, Naveen
Jindal School of Management, University of Texas at Dallas, Richardson, TX 75080, USA (parkhd@
utdallas.edu).
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Journal of Management Studies 55:1 January 2018
doi: 10.1111/joms.12320
stock to create new knowledge (e.g., Ahuja and Katila, 2001; Carayannopoulos and
Auster, 2010; Makri et al., 2010; Puranam and Srikanth, 2007). Yet the effect of
retaining target firm scientists on the odds of acquiring firms to create impactful new
knowledge is not immediately clear. On the one hand, retaining target firm scientists
can facilitate the acquiring firms to transfer and integrate the acquired knowledge
with their existing knowledge stock to create new knowledge. On the other hand, inte-
grating those scientists into the acquiring firms can prove to be challenging due to the
resistance of acquiring firm scientists in utilizing knowledge developed elsewhere (Katz
and Allen, 1982).
In light of these opportunities and challenges for the acquiring firms in utilizing
acquired knowledge and human capital for new knowledge creation, we examine not
only whether retaining target firm scientists benefits the acquiring firms but also how the
characteristics of the acquired knowledge moderate this relationship. Drawing on the
knowledge-based view (KBV) of the firm (Grant, 1996; Nonaka, 1994), we first establish
that avoiding the migration of skilled knowledge workers from the target firm can
enhance acquiring firms’ outcomes in recombining acquired knowledge with their exist-
ing knowledge stock. We argue that this ability to recombine the two knowledge stocks
can create significant value in the form of breakthrough knowledge for the acquiring
firms. We then propose how two relevant characteristics of the acquired knowledge
moderate this relationship. On the one hand, acquiring firms can benefit more from
retaining target firm scientists when the acquired knowledge is more complex, as the
transfer of such knowledge involves more tacit information and becomes more difficult
(Helfat, 1994; Sorenson et al., 2006). On the other hand, they benefit less from retaining
those scientists when the acquired knowledge is more similar to their own, as the com-
bined human capital would involve more overlapping and redundant skillsets. We find
support for these predictions using a sample of 111,227 patents filed after 301 high-tech
firm acquisitions between 1990 and 2000.
This paper contributes to several streams of literature. First, we provide new insights
on the literature concerning the mobility of knowledge workers through acquisition.
Acquisition is an important mean of absorbing highly skilled workers into an existing
organization. In such a process, we not only highlight the importance of human capital
in facilitating the post-acquisition knowledge integration process, but also examine the
significant role that a specific type of human capital (i.e., target firm scientists) may play
in helping the acquiring firms create highly impactful knowledge. Our results show that
the effect of such human capital is highly contingent upon the type of acquired knowl-
edge; i.e., whether the acquired knowledge is complex or similar to the acquiring firm’s
existing knowledge stock. We underline the role of human capital specificity between
knowledge and the scientists who created it (Helfat, 1994). This interdependency has an
important influence on the subsequent recombination of knowledge with other compo-
nents for the eventual creation of highly impactful knowledge. This paper provides novel
insights on the joint effect of knowledge and human capital from target firms have rarely
been studied together.
Second, despite the emphasis of the knowledge-based view (KBV) on valuable and rare
knowledge, most prior KBV studies have considered mere patent counts or citations as
the outcome variable. By focusing on breakthrough knowledge creation, this study gets
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closer to the essence of the KBV by providing insights on the antecedents of rare and
valuable knowledge creation through acquisition. More generally, this study contributes
to the growing stream of knowledge governance literature that focuses on the evolution-
ary process of firm boundaries, knowledge creation, and firm performance (e.g., Foss,
2007; Kapoor and Adner, 2012; Nickerson and Zenger, 2004). As the post-acquisition
period provides an interesting and important setting to study knowledge development as
firms internalize externally-developed knowledge and absorb acquired human capital,
our study offers new insights on the effect of the elimination of firm boundaries on subse-
quent knowledge creation, as well as the different ways firms can navigate this complex
process.
THEORY AND HYPOTHESES
The knowledge-based view (KBV) of the firm (Grant, 1996; Kogut and Zander, 1992;
Nonaka, 1994) extends the resource-based view (RBV) of the firm (e.g., Barney, 1991;
Wernerfelt, 1984) that considers a firm’s unique and valuable resources as the source of
sustained competitive advantage. KBV considers knowledge as the most important asset
of the firm, particularly in high-velocity industries where the dynamics of competition
change rapidly through new knowledge creation (Zander and Kogut, 1995).
KBV suggests that firms exist to provide a coordinating mechanism to integrate spe-
cialized knowledge that resides within individuals of the firm (Grant, 1996; Kogut and
Zander, 1992; Nickerson and Zenger, 2004). The effectiveness of coordinating knowl-
edge creation within or outside the firm depends on the complexity of the knowledge.
Following prior literature, we define knowledge complexity in terms of ‘the level of
interdependence inherent in the subcomponents of a piece of knowledge’ (Sorenson
et al., 2006). When knowledge is complex, changes in its constituent subcomponents
shape the effectiveness of that piece of knowledge. Hierarchies are more effective
when problems are complex and not decomposable, whereas markets are more effec-
tive when problems are less complex and decomposable. This is because reduced con-
flicts of interest through trust and social interface among employees within a firm can
serve as the basis for combining specialized individual knowledge into unique firm
knowledge (Grant, 1996; Kogut and Zander, 1992; Lam, 2000; Nickerson and Zen-
ger, 2004).
KBV distinguishes explicit from tacit knowledge (Kogut and Zander, 1992; Nonaka,
1994).
[1]
The two types of knowledge differ in their ease of transferability. Explicit
knowledge can be relatively easily expressed and transferred from one party to another.
For instance, chemical compounds of a particular drug belong to this category because
any scientist with basic knowledge, equipment, and materials could recreate the drug
with proper documentation about the drug. In contrast, tacit knowledge is more difficult
to be expressed and transferred from one party to another. For instance, although not a
very challenging task, riding a bicycle is tacit because it is not easy for bikers to explain
and transfer their know-how on how to ride a bicycle to another person (Polanyi, 1958).
Indeed, learning and experience play an important role in acquiring tacit knowledge
(Kogut and Zander, 1992).
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The post-acquisition period provides an interesting setting for the study of new knowl-
edge creation because acquiring firms not only gain new knowledge from the target
firms (Ahuja and Katila, 2001), but also expand their human capital by retaining the sci-
entists who created such knowledge (Kapoor and Lim, 2007; Ranft and Lord, 2000).
Thus, the post-acquisition knowledge creation strategy of acquiring firms can lead to a
path-dependent, heterogeneous bundle of knowledge that could lead to competitive
advantage (Dierickx and Cool, 1989).
However, acquiring firms often face challenges in improving their post-acquisition
knowledge creation outcomes for several reasons (Kale and Puranam, 2004; Puranam
and Srikanth, 2007). First, acquiring firms may consider acquisitions as a substitute to
internal knowledge development, thereby reducing post-acquisition R&D investments
which in turn undermine new knowledge creation outcomes (Hitt et al., 1991). Second,
integrating target firm scientists into the existing operations of acquiring firms may be
difficult as it may interrupt acquiring firms’ established internal routines and slow down
their knowledge creation efforts (Ahuja and Katila, 2001). Third, at the more individual
level, acquiring firm scientists may resist using the acquired knowledge for new knowl-
edge creation (Katz and Allen, 1982) due to various reasons, including a lack of mutual
understanding and shared experience (Coff, 2002; Kapoor and Lim, 2007). These
opportunities and challenges in utilizing acquired knowledge for new knowledge crea-
tion provide an opportunity for us to examine not only whether retaining target firm
human capital may benefit the acquiring firms but also how the characteristics of the
acquired knowledge moderate this relationship.
Retention of Target Firm Scientists
We view new knowledge creation as a search problem through the recombination of
existing knowledge components (Ahuja and Lampert, 2001; Fleming and Sorenson,
2004; Galunic and Rodan, 1998; Katila and Ahuja, 2002; Kogut and Zander, 1992;
Schumpeter, 1934).
[2]
Firms can engage in either local search by making relatively small
improvements from their existing knowledge base or distant search by creating new
knowledge distant from their existing knowledge base (Katila and Ahuja, 2002; March,
1991). The former tends to result in secure but relatively low payoffs, whereas the latter
tends to result in uncertain but relatively high payoffs.
Acquisitions provide a unique setting for knowledge recombination because the dif-
ferent knowledge stock of a target firm can be brought within the boundaries of an
acquiring firm, possibly along with the scientists who created it. Acquired knowledge is
typically more distant from the existing knowledge stock of the acquiring firm because it
was externally developed by the target firm, drawing from a different knowledge stock
and different competencies through the firm’s unique prior history of knowledge accu-
mulation (Ahuja and Lampert, 2001; Katila and Ahuja, 2002; Phene et al., 2006). As a
result, the newly acquired knowledge is likely to span new knowledge domains. Although
such a boundary spanning knowledge search could be riskier, it tends to lead to a
greater likelihood of creating highly impactful knowledge (Katila and Ahuja, 2002;
March, 1991).
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However, because knowledge can rarely be perfectly expressed or transferred (Kogut
and Zander, 1992), human capital familiar with a particular knowledge base can play
an important role in its subsequent utilization. Acquiring firms are more likely to create
highly impactful knowledge when they retain target firm scientists who are more familiar
with the acquired knowledge and are thus better positioned to overcome challenges
associated with utilizing such knowledge (Helfat, 1994). Indeed, Nickerson and Zenger
(2004) suggest that firms exist to facilitate the transfer and exchange of specialized
knowledge by individual employees to create a firm-level knowledge stock. As such,
acquiring firm scientists could better duplicate and extend acquired knowledge when
they have greater opportunities to socially interact and exchange ideas with the target
firm scientists who are more familiar with the acquired knowledge. Hence, retaining tar-
get firm scientists will increase the odds of acquiring firms to capture the benefits of the
acquired knowledge that can be recombined with their existing knowledge stock to cre-
ate more impactful new knowledge.
Hypothesis 1: Greater retention of target firm scientists will increase the likelihood of
acquiring firms developing highly impactful knowledge.
Moderating Effect of Acquired Knowledge Complexity
KBV (Grant, 1996; Kogut and Zander, 1992; Nickerson and Zenger, 2004; Nonaka,
1994) suggests that firms exist to provide a coordinating mechanism to integrate special-
ized knowledge that resides in individuals within the firm. The effectiveness of coordi-
nating knowledge creation inside or outside a firm will depend on the complexity of the
knowledge, i.e., the level of interdependence among knowledge subcomponents (Soren-
son et al., 2006).
When knowledge is complex, changes in subcomponents of knowledge shape the
effectiveness of that piece of knowledge. Hence, acquiring firms are likely to face greater
difficulty in internalizing complex knowledge developed externally for several reasons.
First, because complex knowledge tends to be more tacit, and thus more human capital-
specific (i.e., bond between the knowledge and the scientists who developed the knowl-
edge), due to the interdependencies among its subcomponents, acquiring firms will likely
face greater difficulty in comprehending and utilizing such knowledge (Zander and
Kogut, 1995). Second, even if acquiring firms manage to understand complex knowl-
edge developed externally, building upon such complex knowledge remains unpredict-
able because of its potential interaction with other knowledge pieces in unexpected ways
(Sorenson et al., 2006). As a result, acquiring firm scientists who lack direct experience
working with the complex acquired knowledge may benefit from the presence of the
original inventors. Mutual trust and communication is likely to be strengthened when
both acquiring and target firm scientists are employed in the same firm and can interact
with one another (Grant, 1996; Kogut and Zander, 1992; Lam, 2000). We thus posit
that the hierarchy resulting from the retention of target firm scientists will be particularly
effective in enhancing knowledge creation outcomes of the acquiring firms. In contrast,
when acquired knowledge is less complex, acquiring firms will be able to comprehend
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the acquired knowledge with less difficulty even with the absence of the target firm
scientists.
Hypothesis 2: Greater complexity of acquired knowledge will strengthen the positive
relationship between the retention of target firm scientists and the likelihood of
acquiring firms developing highly impactful knowledge.
Moderating Effect of Knowledge Similarity
We define knowledge similarity as the extent to which two knowledge stocks share the
same origin in their development. Firms can generally gain more from acquiring other
firms that possess knowledge more similar to their own due to relationship-specific
absorptive capacity, i.e., the degree to which firms share a common base and under-
standing of knowledge (Lane and Lubatkin, 1998). Greater knowledge similarity
between two firms will enhance their efficiency of exchange and learning through the
acquisition process (Makri et al., 2010). Thus, acquiring firms are more likely to identify
promising combinations of knowledge stocks and subsequently apply them to create val-
uable new knowledge when acquiring and target firm knowledge stocks are similar.
However, the expertise of target firm scientists becomes less critical for understanding
and building on similar knowledge, because knowledge and expertise of target firm sci-
entists may be redundant in that acquiring firm scientists are more likely to have worked
with similar knowledge. Such redundancy will reduce the potential benefits of retaining
target firm scientists. Further, despite the potential efficiency that may be gained from
integrating similar knowledge, acquiring firm scientists may be particularly sceptical of
integrating target firm scientists who possess similar knowledge to their own, as sug-
gested by the not-invented-here (NIH) syndrome (Katz and Allen, 1982). In contrast,
when acquired knowledge is not similar to the acquiring firm’s existing knowledge stock,
acquiring firm scientists are less likely to be familiar with the acquired knowledge and
less likely to be subject to the NIH syndrome. As a result, the presence of target firm sci-
entists for utilizing the acquired knowledge in creating subsequent knowledge is likely to
be more effective.
Hypothesis 3: Greater similarity between acquiring and target firm knowledge stocks
will weaken the positive relationship between the retention of target firm scientists
and the likelihood of acquiring firms developing highly impactful knowledge.
METHODS
Sample and Data Sources
We used the Thomson VentureXpert database as the primary source of acquisition
transaction data. We collected information on all US transactions dated between 1 Jan-
uary, 1990 and 31 December, 2000 in the computer, semiconductor, biotech, and medi-
cal devices industries. Patenting is a key method of protecting intellectual property (IP)
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in these industries, providing us with a traceable history of knowledge creation and
transfer (Cohen et al., 2001). Our data allowed us to observe acquiring firm patenting
activities during the ten-year period following an acquisition. We excluded all transac-
tions that did not involve 100 per cent purchase of the target firm to eliminate joint ven-
tures or minority equity investments that may not involve full transfer of assets including
IP and human capital. We also confined our sample to acquiring firms that are publicly
traded to ensure access to information on firm size, R&D investment, and financial per-
formance. Our final sample captures 301 acquisition transactions.
Because we focus on conditions that may influence the impact of new knowledge cre-
ation following an acquisition, our level of analysis is the individual patent successfully
filed by an acquiring firm in the period after an acquisition. We thus compiled data on
all patents developed by acquiring firms in the ten-year period following an acquisition.
This resulted in a total sample size of 111,227 patents. Data on patent characteristics,
technology areas, and forward citations were drawn from the Harvard Business School
Patent Network Dataverse (Lai et al., 2013).
Measures
In order to capture the quality of knowledge created by acquiring firms, we use two out-
come variables. First, we operationalize the general range of knowledge impact through
the variable Knowledge Quality. For each successful patent application submitted by an
acquiring firm to the US Patent and Trademark Office (USPTO) in the ten-year period
following the date of an acquisition, we calculated this measure by counting external for-
ward citations of the focal patent (forward citations from which firm self-citations are
subtracted, offering an objective, third party view of patent quality). We then operation-
alize the highest range of knowledge impact through a binary variable, Development of
Breakthrough Knowledge. Following prior research (e.g., Ahuja et al., 2005; Kaplan and
Vakili, 2015; Phene et al., 2006; Srivastava and Gnyawali, 2011), this variable is coded
as 1 for acquiring firm patents that landed in the top 1 per cent of external forward pat-
ent citations, and 0 otherwise.
Hypothesis 1 examines how the retention of target firm scientists impacts new knowl-
edge creation outcomes. We rely on the patent record in collecting this information. If a
scientist employed in a target firm subsequently appears as an inventor on one or more
patents assigned to an entity other than the acquiring firm, we consider that individual
to have departed from the acquiring firm. The variable, Retention of Target Firm Scientists,
measures the proportion of the innovation team from a target firm that has not been
observed to migrate away from an acquiring firm as of the application date of a focal
patent in our data sample. Hypothesis 2 focuses on the moderating effect of Target Firm
Knowledge Complexity. This variable measures the level of the complexity associated with
patents acquired from a target firm. In constructing this measure, we draw from the
work of Sorenson and colleagues (2006). Finally, Hypothesis 3 addresses the moderating
effect of Target, Acquiring Firm Knowledge Similarity. Again, we rely on the measure devel-
oped by Rosenkopf and Almeida (2003). We also control for possible curvilinear effects
of technology similarity. Although some studies suggest the presence of such effects (e.g.
Basu et al., 2015; Cloodt et al., 2006), others only show a linear effect of knowledge
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similarity on innovation outcomes (e.g. Makri et al., 2010). We provide detailed meth-
odologies to calculate the two moderators in Appendix.
Control Variables
We controlled for factors that may influence the impact of newly created knowledge.
First, we captured the degree to which an acquiring firm patent draws directly from
prior sources of knowledge, either from knowledge obtained through the acquisition or
knowledge previously developed through its own R&D activities. Citation of Acquired
Knowledge is a binary variable coded as 1 if a focal patent includes a backward citation of
one or more patents developed by a target firm prior to the date of acquisition. Simi-
larly, Citation of Internally Developed Knowledge is a binary variable coded as 1 if a sample
patent cites one or more of the acquiring firm’s own internally developed inventions.
We also included a measure of the total post-acquisition knowledge productivity of an
acquiring firm, Cumulative Patents Following Acquisition that tracks the total number of suc-
cessful patent applications registered with the USPTO.
[3]
Further, we controlled for acquiring firm attributes in affecting its ability to develop
new knowledge. To capture the general level of human capital in a firm, we include
Acquiring Firm Number of Employees reported in the year of patent application. Acquiring
Firm R&D Expenditures captures the amount of R&D investment reported by the acquir-
ing firm in the application year of a focal patent, calculated on a per-employee basis by
dividing total R&D expenses by the total number of employees. Firms with greater
investment in R&D will plausibly have greater resources to devote to breakthrough
knowledge. We also capture the financial performance of a firm through the Acquiring
Firm Net Income, again calculated on a per-employee basis. Data for each of these three
variables were obtained from COMPUSTAT, a third-party database often used to track
financial performance of publicly traded firms. We also controlled for the value of
knowledge gained from a target firm. Target Firm Knowledge Quality captures the level of
quality or impact of patents held by a target firm at the time of an acquisition. This is
measured as the per-patent total count of external forward citations of target firm pat-
ents during the five years following an application. Acquiring firms that obtain higher
quality knowledge may be more likely to develop breakthrough knowledge. Finally, we
controlled for the general level of acquisition experience of an acquiring firm; Number of
Acquisitions captures the total number of within-sample acquisitions previously executed
by an acquiring firm at the time of a focal acquisition.
Model Specification and Estimation Techniques
We employed regression models suitable to each of our dependent variables. Knowl-
edge quality is a count outcome with an over-dispersed distribution, significantly skewed
toward values of zero. We thus used negative binomial regression, which is the proper
model specification for this type of outcome (Hilbe, 2011). The models testing break-
through knowledge involve an exceptionally rare outcome event, with only 1 per cent of
sampled patents falling in the breakthrough category. King and Zeng (2001) have devel-
oped a rare events logistic regression approach for such situations. We used their ‘relo-
git’ command in STATA to analyse breakthrough knowledge.
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RESULTS
Descriptive statistics and bivariate correlations for all study variables are provided in
Table I. Table II exhibits the results of our hypothesis tests.
Models 1 and 5 in Table II incorporate all control variables for our study. We note a
few consistent trends from these models. Direct citations of acquired knowledge and greater
financial resources (net income) of acquiring firms show consistent, positive effects on subse-
quent knowledge creation outcomes, leading to higher knowledge quality and greater likeli-
hood of breakthrough knowledge. Greater R&D expenditures reduce the quality of
innovations across all models, whereas acquisition experience has a positive effect. Acquir-
ing firms with more prior M&A experience appear to have an advantage in producing
impactful and breakthrough knowledge in the period following a focal acquisition.
Models 2 and 6 introduce the direct effect variables. The retention of target firm scien-
tists has a positive and significant effect across both outcomes – knowledge quality
(b5.198, p <.001) and knowledge breakthroughs (b5.833, p <.001). The positive, sig-
nificant coefficients for knowledge quality and the creation of breakthrough knowledge sug-
gest that retention of target firm scientists increases the likelihood that the acquiring firm
will develop exceptionally high quality knowledge, providing support for Hypothesis 1. In
the direct effect models, we also note that acquired knowledge complexity and knowledge
similarity have consistent, beneficial effects on increasing knowledge quality and odds of
breakthroughs. Although we did not present hypotheses for these direct effects, these results
are consistent with our theoretical framework and prior literature that complex acquired
knowledge likely provides greater value and that greater similarity between firms enhances
the positive benefits of the acquired knowledge (Lane and Lubatkin, 1998).
We tested the moderating effects of acquired knowledge complexity in models 3 and
7. The interaction between the retention of target firm scientists and knowledge com-
plexity is positive and significant in predicting knowledge quality (b5.395, p <.001)
and development of breakthrough knowledge (b51.322, p <.01). Thus, Hypothesis 2
is supported as these results suggest that retaining target firm scientists is particularly
important when they bring more complex knowledge to the acquiring firm.
Finally, models 4 and 8 test Hypothesis 3, predicting that greater similarity of knowledge
stocks between acquiring and target firms will undermine the benefits of retaining target firm
scientists. The results are consistent with our theory as the coefficients of the interaction
between knowledge similarity and retention of acquired scientists are negative and significant
in predicting knowledge quality (b5-.470, p <.001) and the development of breakthrough
knowledge (b520.898, p <.001). Thus, the retention of target firm scientists with knowledge
more similar to the acquiring firm has a less beneficial effect on subsequent knowledge creation
outcomes. We provide plots of the Hypotheses 2 and 3 interaction effects in Figures 1 and 2.
The interaction plot between knowledge complexity and target scientist retention in Fig-
ure 1 shows a clear difference in innovation impact for firms acquiring high versus low
complexity knowledge. The positive slope of the high complexity trend line implies that
greater benefits accrue to firms that retain target firm scientists who have tacit understand-
ing of the more complex knowledge. In contrast, the value of retaining target firm scientists
decreases when the acquired knowledge is less complex, as illustrated in the downward
sloping trend. In Figure 2, the interaction plot between knowledge similarity and target
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Table I. Descriptive statistics and bivariate correlations
Variable Mean S.D. 1 2345678910111213
1 Development of
Breakthrough
Technology
0.01 0.10
2 Knowledge Quality 2.47 7.37 0.736**
3 Retention of Target
Firm Scientists
0.71 0.32 0.017** 0.017**
4 Target Firm
Knowledge Complexity
0.61 0.21 0.035** 0.088** 20.134**
5 Target, Acquiring Firm
Knowledge Similarity
0.00 1.00 0.005 0.024** 20.125** 0.032**
6 Citation of Acquired
Knowledge
0.01 0.08 0.048** 0.072** 0.008** 20.008** 20.043**
7 Citation of Internally
Developed Knowledge
0.37 0.48 0.007* 0.046** 20.020** 0.000 0.121** 20.032**
8 Time Elapsed from
Acquisition (days)
1317.35 889.37 20.057** 20.161** 20.143** 0.161** 0.047** 20.004 20.125**
9 Cumulative Patents
Following Acquisition
3411.16 3930.91 20.033** 20.102** 0.179** 20.038** 0.132** 20.050** 20.022** 0.252**
10 Acquiring Firm
R&D Expenditures
49.85 51.74 20.016** 20.056** 20.025** 20.037** 20.128** 0.012** 20.107** 0.048** 20.071**
11 Acquiring Firm
Number of Employees
56.61 52.18 20.030** 20.040** 0.031** 0.172** 20.072** 20.055** 20.072** 20.033** 0.288** 20.247**
12 Acquiring Firm
Net Income
14.98 158.54 0.017** 0.032** 0.142** 0.077** 0.002** 0.000 0.019** 0.112** 0.100** 20.310** 0.117**
13 Target Firm
Knowledge Quality
19.68 23.47 0.001 20.021** 20.063** 20.139** 20.273** 0.040** 20.041** 20.031** 20.067** 0.133** 20.052** 0.050**
14 Number of Acquisitions 2.52 1.50 0.020** 0.034** 20.023** 0.075** 20.104** 20.001 20.109** 20.053** 0.132** 0.325** 0.272** 20.049** 0.182**
*Correlation is significant at the 0.05 level.
**Correlation is significant at the 0.01 level.
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Table II. Impact of new knowledge created by acquiring firms
Outcome variable Development of breakthrough technology
Model type Rare events logistic regression
Model 1 Model 2 Model 3 Model 4
Independent Variables
Retention of Acquired Firm
Scientists (Hypothesis 1)
0.833***
(0.111)
20.056
(0.322)
1.062***
(0.118)
Target Firm Knowledge
Complexity
1.467***
(0.112)
0.426
(0.362)
1.452***
(0.118)
Target, Acquiring Firm
Knowledge Similarity
0.192***
(0.060)
0.172**
(0.062)
0.956***
(0.162)
Target, Acquiring Firm
Knowledge Similarity
2
0.011
(0.037)
20.009
(0.040)
20.016
(0.045)
Retention of Scientists
X Target Firm
Knowledge Complexity
(Hypothesis 2)
1.322**
(0.437)
Retention of Scientists
X Knowledge
Similarity (Hypothesis 3)
20.898***
(0.175)
Control Variables
Citation of Acquired
Knowledge
1.590***
(0.160)
1.499***
(0.161)
1.520***
(0.161)
1.482***
(0.160)
Citation of Internally
Developed Knowledge
0.092
(0.061)
0.046
(0.062)
0.056
(0.063)
0.041
(0.062)
Cumulative Patents
Following Acquisition
26.94E-05***
(1.07E-05)
27.93E-05***
(1.19E-05)
27.85E-05***
(1.18E-05)
27.90E-05***
(1.15E-05)
Acquiring Firm
R&D Expenditures
20.020***
(0.002)
20.019***
(0.002)
20.019***
(0.002)
20.019***
(0.002)
Acquiring Firm
Number of Employees
20.014***
(0.001)
20.016***
(0.001)
20.015***
(0.001)
20.016***
(0.001)
Acquiring Firm Net Income 0.004***
(4.80E-04)
0.003***
(4.54E-04)
0.003***
(4.50E-04)
0.003***
(4.50E-04)
Target Firm
Knowledge Quality
20.006***
(0.001)
20.001
(0.001)
21.39E-04
(0.001)
20.002
(0.001)
Number of Acquisitions 0.490***
(0.026)
0.492***
(0.026)
0.484***
(0.026)
0.496***
(0.026)
Constant 24.080***
(0.073)
25.665***
(0.149)
24.962***
(0.271)
25.836***
(0.149)
Model Chi
2
860.54*** 1,113.17*** 1,126.70*** 1,141.58***
Sample Size - #of
Acquiring Firm Patents
111,227 111,227 111,227 111,227
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Table II. Continued
Outcome variable Knowledge quality
Model type Negative binomial regression
Model 5 Model 6 Model 7 Model 8
Independent Variables
Retention of Acquired Firm
Scientists (Hypothesis 1)
0.198***
(0.010)
20.092*
(0.052)
0.391***
(0.014)
Target Firm Knowledge
Complexity
0.923***
(0.034)
0.666***
(0.057)
0.962***
(0.034)
Target, Acquiring Firm
Knowledge Similarity
0.015
(0.014)
0.004
(0.014)
0.400***
(0.023)
Target, Acquiring Firm
Knowledge Similarity
2
29.76E-04
(0.006)
20.003
(0.006)
20.017**
(0.006)
Retention of Scientists
X Target Firm
Knowledge Complexity
(Hypothesis 2)
0.395***
(0.069)
Retention of Scientists
X Knowledge
Similarity (Hypothesis 3)
20.470***
(0.023)
Control Variables
Citation of Acquired
Knowledge
1.182***
(0.089)
1.163***
(0.088)
1.162***
(0.088)
1.155***
(0.008)
Citation of Internally
Developed Knowledge
0.250***
(0.015)
0.258***
(0.015)
0.258***
(0.015)
0.250***
(0.015)
Cumulative Patents
Following Acquisition
21.13E-04***
(2.05E-06)
21.16E-04***
(2.08E-06)
21.14E-04***
(2.09E-06)
21.20E-04***
(2.10E-06)
Acquiring Firm
R&D Expenditures
20.007***
(1.86E-04)
20.007***
(1.91E-04)
20.007***
(1.92E-04)
20.008***
(1.92E-04)
Acquiring Firm
Number of Employees
20.004***
(1.64E-04)
20.004***
(1.66E-04)
20.004***
(1.66E-04)
20.004***
(1.64E-04)
Acquiring Firm Net Income 4.83E-04***
(4.45E-05)
2.08E-04***
(4.89E-05)
1.80E-04***
(4.87E-05)
22.18E-04***
(5.21E-05)
Target Firm
Knowledge Quality
20.009***
(3.18E-04)
20.007***
(3.59E-04)
20.007***
(3.58E-04)
20.007***
(3.59E-04)
Number of Acquisitions 0.326***
(0.007)
0.308***
(0.007)
0.298***
(0.007)
0.322***
(0.007)
Constant 0.984***
(0.019)
0.284***
(0.029)
0.511***
(0.050)
0.123***
(0.030)
Model Chi
2
6,547.12*** 7554.39*** 7,587.43*** 7,916.66***
Sample Size - #of
Acquiring Firm Patents
111,227 111,227 111,227 111,227
1p<.1 *p<.05 **p<.01.
***p<.001, two-tailed significance for all variables.
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0
0.5
1
1.5
2
2.5
3
0.0 0.2 0.4 0.6 0.8 1.0
Knowledge Quality
Target Firm Scientist Retention
Interaction of Knowledge Complexity with Scientist Retention
Mean Minus 1 Std. Dev. Knowledge Complexity
Mean Knowledge Complexity
Mean Plus 1 Std. Dev. Knowledge Complexity
Mean-1SD +1SD
Figure 1. Interaction between knowledge complexity and scientist retention [Colour figure can be viewed
at wileyonlinelibrary.com]
0
1
2
3
4
5
6
0.00.20.40.60.81.0
Knowledge Quality
Target Firm Scientist Retention
Interaction of Knowledge Similarity with Scientist Retention
Mean Minus 1 Std. Dev. Knowledge Similarity
Mean Knowledge Similarity
Mean Plus 1 Std. Dev. Knowledge Similarity
Mean
-1SD +1SD
Figure 2. Interaction between knowledge similarity and scientist retention [Colour figure can be viewed
at wileyonlinelibrary.com]
98 H. D. Park et al.
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firm scientists retention shows an opposite effect. Greater (lesser) knowledge similarity and
higher scientist retention lead to lower (greater) knowledge quality.
Robustness Tests
We conducted an instrumental variable analysis of the direct effects of retention of target
firm scientists on knowledge quality and breakthrough knowledge to alleviate concerns of
endogeneity in our models. We first identified two plausible instruments for the retention
of acquired firm scientists – the unemployment rate in the US state in which the focal firm
is located and the amount of time elapsed since the closing date of the acquisition. These
instruments are likely to affect target firm scientists retention but not knowledge quality
and breakthroughs. A high state unemployment rate may discourage inventors from leav-
ing the security of their current job and hence boosts the level of scientist retention, but it is
very unlikely to influence knowledge quality or breakthroughs from a given acquisition.
Likewise, more inventors may exit a firm as more time elapses after an acquisition, but
more elapsed time in itself is not likely to translate into more quality or breakthrough
knowledge. We then ran generalized method of moments (GMM) instrumental variable
analyses (implemented using the ‘ivreg2’ function in STATA, with the ‘gmm2s’ option).
The results of these tests are shown in Table III, Models 9 and 10.
Our results are consistent with our baseline effects, and the reported instrumental
variable diagnostics demonstrate that our instruments are valid with respect to tests for
under-identification and orthogonality (Baum et al., 2007).
In addition, we ran a number of supplementary analyses to ensure robustness of our
results. First, we considered that the retention of target firm scientists may be an artefact
of the integration strategy pursued by the acquiring firm, influencing both the propor-
tion of target firm scientists who remain and the success of new knowledge subsequently
created by the acquiring firm. For example, some acquisitions may be more focused on
obtaining access to new markets or resources that are less associated with the knowledge
base and human capital of the target firm. To address this issue, we conducted a robust-
ness test using fixed effects at the acquired firm level. This eliminates any possible influ-
ence of omitted variables related to the acquisition, including but not limited to the
specific integration strategy employed by the acquiring firm for that particular target as
well as a host of other target firm-specific effects that may confound the influence of
inventor retention on knowledge quality and development of breakthroughs.
For this robustness test, we use fixed effects logistic regression to predict the binary
outcome of knowledge breakthroughs and quasi-maximum likelihood Poisson fixed
effects regression for knowledge quality. Although fixed effects count models are avail-
able in STATA for both Poisson and Negative Binomial distributions, the negative bino-
mial variant is not a true fixed-effects method in terms of controlling for all stable
covariates due to the incidental parameter problem, and it does not effectively contend
with over-dispersion (Allison and Waterman, 2002; Greene, 2007). For this reason, the
fixed effects Poisson model has been widely adopted for time-based count data (e.g.,
Carnahan and Somaya, 2013). Thus, we use Poisson Quasi-Maximum Likelihood
(PQML) fixed effects estimators. Research shows that the PQML fixed effects estimators
control for all stable covariates and effectively contend with both over- and under-
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dispersion in the dependent variable and the prevalence of zero values (Wooldridge,
1997, 2002). The results of the fixed effects robustness tests are shown in Table IV. It
shows that the retention of target firm scientists continues to have a positive, significant
effect on both outcomes.
We also implemented an alternative panel data structure by aggregating our observa-
tions at the firm-year level. Although this approach causes the analyses to be more
Table III. Instrumental variable analysis
Outcome variable
Development of
breakthrough technology Knowledge quality
Model 9 Model 10
Independent Variables
Retention of Acquired Firm Scientists (Hypothesis 1) 0.035* 9.111*
(0.020) (3.819)
Target Firm Knowledge Complexity 0.036*** 7.584***
(0.009) (1.637)
Target, Acquiring Firm Knowledge Similarity 0.002 0.618
(0.002) (0.395)
Target, Acquiring Firm Knowledge Similarity
2
20.001 20.4081
(0.002) (0.244)
Control Variables
Citation of Acquired Knowledge 0.050* 5.340**
(0.024) (1.862)
Citation of Internally Developed Knowledge 4.66E-04 0.696
(0.002) (0.508)
Cumulative Patents Following Acquisition 21.56E-06*** 24.60E-04***
(3.88E-07) (9.54E-05)
Acquiring Firm R&D Expenditures 29.80E-05 20.017
(6.67E-05) (0.014)
Acquiring Firm Number of Employees 21.03E-04* 20.010
(4.93E-05) (0.008)
Acquiring Firm Net Income 21.70E-05 20.005
(1.82E-05) (0.004)
Target Firm Knowledge Quality 1.38E-04 0.022
(8.83E-05) (0.022)
Number of Acquisitions 0.004 0.577
(0.002) (0.555)
Constant 20.029126.533*
(0.016) (2.984)
Model F-statistic 45.62*** 83.87***
Sample Size - #of Acquiring Firm Patents 111,227 111,227
Kleibergen-Paap rk LM statistic
(test of under-identification)
12.48** 9.72**
Hansen J statistic (test of over-identification) 2.70 0.03
Endogeneity test of endogenous regressor 0.07 4.84*
1p<.1 *p<.05 **p<.01.
***p<.001, two-tailed significance for all variables.
100 H. D. Park et al.
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coarse-grained and results in some loss of information, the direct effects of target firm
scientists retention remained robust across all tests, as did the interaction effects between
inventor retention and knowledge similarity. The interaction of inventor retention and
knowledge complexity, however, was not significant in this firm-year panel structure,
possibly due to the loss of time-based granularity in our measure of target firm scientists
retention (aggregated on an annual basis as opposed to the date of the focal patent in
the baseline analysis). Although we do not report the results of the firm-year structure
tests for space reasons, they are available from the authors upon request.
DISCUSSION AND CONCLUSION
We explored conditions under which acquiring firms were more likely to create highly
impactful new knowledge following an acquisition. We found that acquiring firms that
retained target firm scientists generally created more impactful new knowledge. How-
ever, acquired knowledge characteristics moderated this relationship such that acquiring
firms benefited more from retaining target firm scientists when the acquired knowledge
Table IV. Fixed effects analysis
Model type
Fixed effects
logistic regression
Quasi-maximum likelihood
Poisson fixed
effects regression
Outcome variable
Development of
breakthrough
technology
Knowledge
quality
Model 11 Model 12
Independent Variables
Retention of Acquired Firm Scientists (Hypothesis 1) 1.250*** 0.519*
(0.153) (0.269)
Control Variables
Citation of Acquired Knowledge 0.599*** 0.329*
(0.187) (0.151)
Citation of Internally Developed Knowledge 20.176** 0.023
(0.064) (0.052)
Cumulative Patents Following Acquisition 26.74E-04*** 25.22E-04***
(4.67E-05) (1.18E-04)
Acquiring Firm R&D Expenditures 20.030*** 20.015**
(0.003) (0.005)
Acquiring Firm Number of Employees 20.010*** 20.006
(0.003) (0.006)
Acquiring Firm Net Income 0.002* 5.84E-05
(7.49E-04) (4.57E-04)
Model Wald Chi
2
1,000.17*** 98,801.22***
Sample Size - #of Acquiring Firm Patents 111,227 111,227
1p<.1 *p<.05 **p<.01 ***p<.001, two-tailed significance for all variables.
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was more complex, but benefited less when the acquired knowledge was more similar to
their own.
This paper contributes to several streams of literature. First, we contribute to the liter-
ature related to mobility of knowledge workers through acquisition by highlighting the
importance of retaining target firm scientists to improve subsequent knowledge creation
outcomes. However, the benefit of retaining target firm scientists is contingent upon
organizational context and the characteristics of acquired knowledge, such as the com-
plexity and similarity to the existing knowledge stock of the acquiring firm. Although
prior research has examined the impact of acquired knowledge on post-acquisition
knowledge creation outcomes (e.g., Ahuja and Katila, 2001; Phene et al., 2006), there
has been a dearth of studies examining how retention of human capital and knowledge
characteristics jointly affect subsequent knowledge creation outcomes. More broadly,
this study complements the growing literature on how scientist mobility facilitates knowl-
edge transfer across firms (e.g., Campbell et al., 2012; Palomeras and Melero, 2010;
Singh and Agrawal, 2011; Somaya et al., 2008; Tzabbar, 2009).
Second, although KBV suggests that rare and valuable knowledge is the source of sus-
tained competitive advantage (e.g., Grant, 1996; Nonaka, 1994), few studies explore
antecedents of creating such knowledge. This paper provides insights on how acquisi-
tions, which enable firms to recombine distant knowledge sources, facilitate break-
through knowledge creation. More generally, this paper sheds light on the recently
emerging interests in knowledge governance (e.g., Foss, 2007; Kapoor and Adner, 2012;
Nickerson and Zenger, 2004). This line of research has examined the optimal gover-
nance structure that can improve knowledge creation, which in turn could lead to better
firm performance. However, this stream has paid limited attention on how changes in
firm boundaries through acquisition affect new knowledge creation. Our findings sug-
gest how the elimination of firm boundaries through acquisition provides an opportunity
to recombine two distinct knowledge stocks and yield superior subsequent knowledge
creation. In doing so, this paper complements the growing literature on how organiza-
tional design and boundary choices affect new knowledge creation (e.g., Cassiman and
Veugelers, 2006; Karim and Kaul, 2015; Puranam et al., 2006).
Practical implications of our study are straightforward. Our findings prescribe man-
agers to utilize both acquired knowledge and human capital resulting from an acquisi-
tion. Despite the challenges associated with utilizing externally-developed knowledge
(e.g., Katz and Allen, 1982), procedures and routines that ensure these practices could
help acquiring firms to get the most out of their acquisitions, particularly when they
acquire complex knowledge that may require integration of target firm scientists in addi-
tion to the acquired knowledge into their existing knowledge stock.
This study is not without its limitations, which provide opportunities for future
research. First, our study takes a sample of acquisitions in high-tech industries in the US
Although there are no reasons to believe that outcomes from acquisitions in other con-
texts would be different from ours, future studies may wish to corroborate findings in
other contexts. Second, we focused on knowledge transfer by observing patent data
through the retention of target firm scientists. It would be interesting to examine
whether retention of other type of human capital is equally important for other types of
knowledge. For example, Eckardt and colleagues (2014) found that the negative impact
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of production worker turnover on firm performance is greater in service firms than in
manufacturing firms. In our context, it would be interesting to study how the retention
of different ranks of scientist, e.g., scientists holding managerial positions versus labora-
tory positions, or different types of knowledge workers, e.g., scientists versus manufactur-
ing experts, would influence post-acquisition innovation performance.
Third, our study has considered acquisition as a vehicle for firms to access external
knowledge but did not explicitly compare it against alternative modes of absorbing
external knowledge such as strategic alliances (e.g., Arend et al., 2014) or corporate ven-
ture capital investments (e.g., Dushnitsky and Lenox, 2005). Future studies could com-
pare and contrast how knowledge complexity and similarity may affect knowledge
transfer outcomes following different access modes. Fourth, future studies could address
the issue of knowledge destruction, or more broadly knowledge loss as a result of acquisi-
tion or exchanges between firms (Martinez-Noya et al., 2013). In the context of technol-
ogy acquisitions, it is possible that human capital retention may introduce other
complexities that may actually deteriorate knowledge creation outcomes, potentially
leading to greater likelihood of developing knowledge flops. For example, Martinez-
Noya and colleagues (2013) examined the issue of appropriability hazards among sup-
pliers and clients, and how they might affect the transfer of knowledge between them.
Applying this perspective, it would be interesting to examine how appropriability haz-
ards at individual level due to exchange of expertise between retained and incumbent
scientists could influence subsequent knowledge creation for the acquiring firms.
Conclusion
As acquisitions enable acquiring firms to absorb human capital along with the knowl-
edge that they developed from the target firms, it is important to examine how such
mobility of workers from the target firms to acquiring firms affect post-acquisition
knowledge creation outcomes. We explained how the retention of scientists associated
with the acquired knowledge increased subsequent knowledge creation for the acquiring
firms. We then considered how acquired knowledge complexity further increased the
benefits of retaining target firm scientists, whereas knowledge similarity reduced those
benefits. We hope that our findings provide novel insights and practical implications on
how acquiring firms can improve their odds of creating impactful new knowledge.
ACKNOWLEDGMENTS
We thank seminar participants at Drexel University, the Strategic Management Society Conference in
Madrid, the University of Minnesota, and the University of Oregon, who have provided valuable feedback on
an earlier version of this paper. We also appreciate guidance by the Special Issue Editor Janet Bercovitz and
three anonymous reviewers.
NOTES
[1] Terms like ‘information’ and ‘know-how’ are also used to describe the two types of knowledge (Kogut
and Zander, 1992).
[2] Firms can either combine different knowledge stocks or find an alternative use of existing knowledge
(Carnabuci and Operti, 2013; Henderson and Clark, 1990). We focus on the former as it is more relevant
to our theorization of new knowledge creation through recombination following an acquisition.
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[3] Following prior studies (e.g., Park and Steensma, 2013; Tzabbar, 2009), we use the date of patent application
rather than grant because (1) the patent application date is more closely aligned to the timing of when the
knowledge is actually created, (2) the timing of the patent grant date is subject to the workload and activities of
the patent examiners’ office, outside of the control of the patenting firm and unrelated to its timeline of inno-
vation and technology development; and (3) USPTO and other patent databases provide data on patent
applications that were ultimately granted, and those records are indexed using application information.
APPENDIX: DEFINITIONS AND MEASURES OF MODERATING VARIABLES
Term Definition and measures
Knowledge
complexity
Knowledge complexity is defined as ‘the level of interdependence inherent in the sub-
components of a piece of knowledge’ (Sorenson et al., 2006)
To measure Target Firm Knowledge Complexity, we captured all of the technology sub-
classes listed in all patents held by the target firm at the time of acquisition. We then
calculated E
i
, the ease of recombination of these subclasses (propensity for the subclass
to be combined with other subclasses across the US patent record), given by the fol-
lowing (Sorenson et al., 2006):
Ei5Count of subclasses previously combined with subclass i
Count of previous patents in subclass i (1)
Following Sorenson et al. (2006), we used the period of 1980–1990 as the reference
window for this calculation. We then calculated the interdependence, k
j
, of each target
firm patent:
kj5Count of subclasses on patent j
Pi2jEi
(2)
This captures the total complexity of a given patent based on all of its referenced sub-
classes. Finally, we calculated the average complexity across all acquired patents to
aggregate this measure to the level of the target firm.
Knowledge
similarity
Knowledge similarity is defined as the extent to which two knowledge stocks share the
same origin for their development.
To measure Knowledge similarity, we first measure the Euclidean distance between the count vec-
tors of primary International Patent Classifications (IPCs) appearing in the patent portfolios of
the two firms (Rosenkopf and Almeida, 2003). This calculation results in continuous values
from zero to the square root of two, with higher values associated with greater technology diver-
gence. To convert this to a measure of knowledge similarity (and greatly simplify the task of
interpreting our hypothesis test results), we standardize the distance measure and reverse the
sign. This yields a similarity measure with a mean value of 0 and standard deviation of 1, with
higher values corresponding to greater similarity of knowledge stocks between the acquiring and
target firms.
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... The high turnover rate of knowledge workers, who constitute the highest proportion of the workforce, is part of today's volatile, uncertain, complex and ambiguous business environment (Del Giudice and Maggioni, 2014;Kianto et al., 2019;Lotti Oliva, 2014;Oliva F abio and Kotabe, 2019;Park et al., 2018;Shujahat et al., 2020;Wright et al., 2018). Consequently, the intra-sectoral and inter-sectoral mobility of knowledge workers and the flow of their personal knowledge becomes a unique and complex intra-organizational and inter-organizational phenomenon (Chatti, 2012;Del Giudice and Maggioni, 2014;Ismail et al., 2013;Liu et al., 2017;Nonaka, 1994;Piaget, 1937;Vygotsky, 1978). ...
... Firstly, when knowledge workers change jobs, their personal knowledge can flow across different organizations, as a special type of interorganizational process (Collet and Hedströ m, 2013;Mawdsley and Somaya, 2016;Park et al., 2018;Shujahat et al., 2020;Somaya et al., 2008;Wright et al., 2018). Recent empirical studies demonstrate that employee turnover exposes organizations to several knowledge management risks such as knowledge loss and knowledge leakage to other organizations (Park et al., 2018;Shujahat et al., 2020;Sumbal et al., 2018;Sumbal et al., 2020;Sumbal et al., 2017;Wright et al., 2018). ...
... Firstly, when knowledge workers change jobs, their personal knowledge can flow across different organizations, as a special type of interorganizational process (Collet and Hedströ m, 2013;Mawdsley and Somaya, 2016;Park et al., 2018;Shujahat et al., 2020;Somaya et al., 2008;Wright et al., 2018). Recent empirical studies demonstrate that employee turnover exposes organizations to several knowledge management risks such as knowledge loss and knowledge leakage to other organizations (Park et al., 2018;Shujahat et al., 2020;Sumbal et al., 2018;Sumbal et al., 2020;Sumbal et al., 2017;Wright et al., 2018). Secondly, to survive in today's complex and dynamically evolving environment, knowledge workers have to constantly engage in personal knowledge management practices (Chatti, 2012;Cheong and Tsui, 2011;Del Giudice and Maggioni, 2014;Drucker, 1968;Drucker, 1993Drucker, , 1999Ismail et al., 2013;Liu et al., 2017;Pauleen, 2009;Pauleen and Gorman, 2011;Tsui and Cheong, 2010;Zhang, 2009). ...
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... Human resource management is a critical element which matters for post-acquisition integration and performance (Larsson and Finkelstein, 1999). Scholars have emphasized the importance of retention of employees of acquired firms in facilitating post-acquisition knowledge transfer and integration, particularly for acquisitions in knowledge-intensive sectors (Park et al., 2018;Ranft and Lord, 2000). However, acquisitions are usually followed by large-scale employee departures (Krug et al., 2014;Walsh, 1988;Wu and Zang, 2009). ...
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Plain English Summary Over recent decades, the increasing importance of high-skilled knowledge workers has been reflected in the changing nature of acquisitions. In high-tech sectors, human capital has become a major asset that is valued or even targeted by many acquirers, especially when target firms are small technology ventures. However, extant literature has exclusively focused on the antecedents of post-acquisition turnover of executives in large public companies. How do acquisitions impact on the mobility of knowledge workers and managers in small technology firms? Drawing on the perspective of human capital theory, this study focuses on the role of technological and managerial skills of employees in post-acquisition employee turnover. Based on the matched employer–employee data of the Swedish high-tech sectors from 2007 to 2015, we find the following results. First, acquisitions increase the likelihood of employee departures. Second, the departures are mainly in the form of changing jobs. Third, the acquisition effects are weaker for employees with technological competences. Fourth, the acquisition effects are weaker for employees with managerial competences when acquirers have a strong employee retention motive. When acquirers do not have a strong retention motive, managers, compared to other employees, are more likely to exit the (national) labor market after acquisitions. Our findings show that retention of technological competences, compared to retention of managerial competences, is less dependent on the retention motive. This may suggest that technological capability is a more core source of competitiveness in a small technology firm. An implication of this study is that future research on post-acquisition employee mobility should go beyond management teams and give more attention to knowledge professionals with technological competences.
... Moreover, target firms' most valuable technological assets often reside in the human capital of talented scientists and engineers as well as in their social relations (Grant, 1996;Ranft and Lord, 2000). Previous studies document that mismanagement of acquisition implementation often disrupts the distinctive innovation capabilities of these individuals to the point that they may decide to leave the post-acquisition organization (Ernst and Vitt, 2000;Park et al., 2018). ...
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This study investigates the retention of target CEOs in the aftermath of acquisitions by comparing target founder and professional CEOs. Considering insights from resource‐based view, managerial rent perspective, studies on acquisition implementation, and the literature on founder‐CEOs, we argue that target founder‐CEOs are resourceful assets for acquirers for implementation purposes; ergo, they are more likely to be retained than target professional CEOs. Target founder‐CEOs, owing to their unique firm‐specific human capital, have greater acquisition implementation abilities than target professional CEOs. They also have greater monetary incentives to deploy their implementation abilities to the benefit of acquirers. Furthermore, we claim that these effects are stronger, thus contributing to a higher retention rate of target founder‐CEOs than their professional peers when acquisitions are technology‐driven, and target firms are young. Results from a sample of small high‐tech firms acquired by large incumbent firms confirm our predictions.
... With the knowledge resources of a firm embedded in its human capital (Badaracco & Badaracco, 1991;Youndt, Snell, Dean Jr, & Lepak, 1996), talented inventors recombine knowledge elements in new ways, spurring the development of new technologies and enabling the firm to successfully bring them to market (Subramaniam & Youndt, 2005). Retaining key human capital is thus critical to obtaining value (Park, Howard, & Gomulya, 2018;Ranft & Lord, 2000), and attracting such human capital may provide similar benefits. For both new and established firms, human capital in the form of greater technological experience enhances the success of business-level strategy (Shrader & Siegel, 2007). ...
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We consider how inventor- and firm-level knowledge characteristics co-determine an inventor’s propensity of joining another firm. Specifically, we examine the influence of knowledge impact, knowledge specialization, core status within the firm, and knowledge complexity on an inventor’s decision to depart the firm and join either an entrepreneurial venture or another established firm. We then examine how the incumbent firm’s knowledge complexity moderates the relationship between individual knowledge attributes and the inventor’s decision to join another firm. Our study demonstrates that individual knowledge attributes distinctively interact with the inventor’s current firm-knowledge complexity to determine the likelihood of mobility to an entrepreneurial or established firm. The knowledge environment of the incumbent firm may either prepare the inventor for mobility options or further embed the inventor’s work within the incumbent firm. We test our hypotheses using a panel data set of 33,826 inventors in the semiconductor industry.
... Relocated firms may also act as conduits for knowledge exchange between their previous and present location by spanning a "geographic hole" (Bell & Zaheer, 2007). Previous research has produced an extensive body of literature examining the role of alliances (Rosenkopf & Almeida, 2003), the role of knowledge worker mobility (Agrawal, Cockburn, & McHale, 2006;Park, Howard, & Gomulya, 2018) or the role of intermediary organizations (Wagner et al., 2014) for the acquisition of external knowledge. Surprisingly, firm relocation has been given hardly any attention as another possible mechanism for drawing from formerly distant knowledge sources by getting closer to them. ...
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