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Paper to be presented at
DRUID15, Rome, June 15-17, 2015
(Coorganized with LUISS)
Industry relatedness and post-acquisition innovative performance: the
moderating effects of acquirer?s capabilities and target size.
Elena Cefis
University of Bergamo
Department of Management, Economics and Quantitative Methods
[email protected]
Orietta Marsili
University of Bath
School of Management
[email protected]
Damiana Rigamonti
Aarhus University
School of Business and Social Sciences
[email protected]
Abstract
This paper examines how characteristics of acquiring and acquired firms influence the curvilinear (inverted U-shaped)
relationship between relatedness and post-acquisition innovative performance. Using a relatedness index based on
industry co-occurrence in a sample of 1,736 Dutch acquisitions, we find that acquirer?s internal R&D and acquisition
experience, and the small size of acquired firms, help to reach a balance between exploration of novelty and exploitation
of synergies in unrelated acquisitions, and to achieve higher post-acquisition performance. However, while the
acquirer?s R&D increases flexibility in the acquisition process in presence of deviations from the optimal level of
relatedness, acquisition experience may enhance rigidities.
Jelcodes:L25,D21
Industry relatedness and post-acquisition innovative performance: the
moderating effects of acquirer’s capabilities and target size.
Abstract. This paper examines how characteristics of acquiring and acquired firms influence the
curvilinear (inverted U-shaped) relationship between relatedness and post-acquisition innovative
performance. Using a relatedness index based on industry co-occurrence in a sample of 1,736
Dutch acquisitions, we find that acquirer’s internal R&D and acquisition experience, and the
small size of acquired firms, help to reach a balance between exploration of novelty and
exploitation of synergies in unrelated acquisitions, and to achieve higher post-acquisition
performance. However, while the acquirer’s R&D increases flexibility in the acquisition process
in presence of deviations from the optimal level of relatedness, acquisition experience may
enhance rigidities.
INTRODUCTION
Acquiring resources externally to the organization imposes a dilemma between novelty and
integration (Ahuja and Katila, 2001; Boschma, 2005; Cassiman et al., 2005; Cloodt, Hagedoorn,
and Van Kranenburg, 2006; Keil et al., 2008). Resources that are different from what firms
currently possess bring value through new combinations and the benefits of economies of scope.
On the other hand, resources that are similar to what firms already control can be integrated in
the current operations more effectively and at lower costs, and offer the benefits of economies of
scale. Thus, while some degree of overlap between external and internal resources is beneficial
for transfer and efficiency, too much overlap limits the advantages of an extended and renewed
resource basis. In this context, M&As are seen as instrumental for firms that aim at reconfiguring
the resources they control (Villalonga and McGahan, 2005) as they attempt to find a balance
between exploratory and exploitative search (Gupta, Smith, and Shalley, 2006; Phene, Tallman,
and Almeida, 2012). The argument that firms need to balance exploitation and exploration in
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their acquisition strategy, finds its empirical counterpart in the observed inverse-U relationship
that links post-acquisition performance to measures of relatedness between acquired and
acquiring firms (Ahuja and Katila, 2001; Cloodt et al., 2006; Grimpe and Hussinger, 2014). The
curvilinear relationship is regarded as indicative of the existence of a ‘point of balance’ between
the acquirer’s benefits of exploring novel and unique resources held by the acquired firm
(Barney, 1988; Graebner, Eisenhardt and Roundy, 2010; Granstrand and Sjôlander, 1990) and
the benefits of exploiting efficiencies and synergies, which are critical for the success of the postacquisition integration process (Capron and Mitchell, 2004; Ranft and Lord, 2002).
While the existence of a ‘point of balance’ in the association between relatedness and
post-acquisition performance is well documented in the literature, and the conditions under
which firms are more likely to use acquisitions to explore or to exploit have been shown to vary
with the characteristics of the acquirer, of the target, and the mode of acquisition (Phene et al.,
2012), little is known about how the characteristics of the point of balance vary with the nature
of the acquisition. Given the existence of a tipping point at which post-acquisition performance
stops increasing with relatedness and starts decreasing – where the balance of exploration and
exploitation optimizes the benefits of an acquisition – we explore which factors influence (a) the
position of the tipping point – i.e. the level of relatedness at which post-acquisition performance
is optimized; and (b) its sensitivity or tolerance – i.e. the extent to which performance is affected
by deviations from the optimal relatedness. To examine these factors, we propose a method to
estimate moderating effects in a curvilinear relationship, by means of variations in the
coordinates of the vertex of the parabola representing the relationship (the position of the tipping
or balance point), and in the focal length of the parabola (the curvature around the tipping point).
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As for the choice of moderating factors, we focus on the characteristics of M&A deals
that influence (i) the buyer’s capability to use acquisitions for externally searching resources to
support their innovation strategy, which is a function of the buyer’s internal R&D investments, as
established in organisational learning theory (Cohen and Levinthal, 1990; Nelson and Winter,
1982) and (ii) the buyer’s capability to manage the acquisition, by effectively detecting and
evaluating targets and implementing the acquisition integration process, which according to
transfer theory is a function of the buyer’s acquisition experience (Finkelstein and Haleblian,
2002) and of the target’s size as a measure of the deal complexity (Ellis et al., 2011). These three
factors, by influencing the ability to explore and exploit resources in M&A, contribute to
moderate the balance between novelty and integration, and its sensitivity to variations.
In the literature, the impact of relatedness on post-acquisition performance has been
addressed especially from a technological learning perspective, concentrating on technological
relatedness and the development of innovative capabilities successive to an acquisition, in the
context of high-tech sectors in which acquisitions are more likely to be technologically
motivated (Ahuja and Katila, 2001; Cloodt et al., 2006; Makri, Hitt and Lane, 2010). In this
study, we refer to the notion of industry relatedness as the degree to which the overall resources
bases of firms are related or connected across industries, consistent with the definition of
‘resource relatedness’ from an RBV perspective (Speckbacher, Neumann and Hoffmann, 2014).
Acquisitions in other industries than their own allow acquiring firms to realize economies of
scope in R&D through entry into new markets (Cassiman et al., 2005), to access co-specialized
complementary assets needed to commercialize their own innovations (Teece, 1986), or to
expand the application of their complementary assets to innovations produced elsewhere
(Puranam, Singh, and Zollo, 2006). These mechanisms potentially affect post-acquisition
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innovative performance across all industries, also when acquisitions are motivated by other
reasons than direct access to technological inputs. Furthermore, firms in an industry share
common learning trajectories, whose directions and boundaries are shaped by the nature of the
‘technological regime’ prevalent in the industry. The knowledge basis underlying such
trajectories and specific to a technological regime, is not limited to technological competencies,
but also encompasses knowledge of markets and customers, as well as an understanding of the
practices and conventions typical of doing businesses in a certain industry (Dosi, 1982; Nelson
and Winter, 1982).
We measure intra-industry relatedness by extending to M&A the method proposed by
Teece, Rumelt, Dosi and Winter (1994) for corporate diversification, and based on the principle
of ‘revealed relatedness’, as used by Neffke and Henning (2013) to measure industry skill
relatedness as revealed by employees’ mobility. Similarly, we calculate industry relatedness by
the frequency of resource transfer taking place from one industry to another in the market for
corporate control. One advantage of this relatedness index is to reflect the degree of connection,
instead of the degree of overlap, and therefore it is not conditional on a pre-assigned hierarchical
order of industry classification (Silverman, 1999). As well, the index has the advantage of being
directional as it takes into account the source and destination of resource transfer.
As indicator of innovative performance we rely on the revenues from innovative
products. With respect to other measures of technological performance, such as patents, which
have been used to study the impact of M&A (Ahuja and Katila, 2001; Valentini, 2012), the
turnover from innovative products is measured at the end of the commercialization process of an
innovation. In addition, because the impact of M&A on innovative performance may vary
depending on the originality of the innovation (Valentini, 2012), we assess the sensitivity of our
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analysis with respect to the degree of novelty of the innovation, which may affect the shape of
the trade-off between integration and novelty in resource acquisition. For radically new
innovations, the transfer of resources between unrelated industries can be more important than a
smooth integration process, while for innovations already known in the market, the ability to
integrate external resources may be more important than their novelty. To capture the novelty of
product innovations we follow the convention of distinguishing between revenues from ‘new to
the firm’ innovations and ‘new to the market’ innovations (Schneider and Veugelers, 2010).
THEORETICAL FRAMEWORK
It is well recognised in the literature that ‘distance’ or the complementary notion of ‘relatedness’
(Boschma, 2005) shapes the reciprocal ability to evaluate, transfer and integrate external
resources, and therefore it influences the success of the acquisition process in creating a
sustainable competitive advantage (Barney, 1988). Distant or unrelated resources are valuable
because they bring novelty to the acquiring firms, in various forms. First, through acquisitions
buyers gain access to novel product ideas, technologies, and practices that enable them to
increase their own innovative capabilities in the longer term (Arora and Gambardella, 1990;
Graebner et al., 2010; Kogut and Zander, 1992). Acquisitions are thus part of a firm’s innovation
search strategy (Ahuja and Katila, 2001; Laursen and Salter, 2006), in which ideas for new
products or services are traded through the market for corporate control (Gans and Stern, 2003).
Second, by acquiring firms in unfamiliar industry domains, acquirers become aware of new
market opportunities, and can realise economies of scope across divergent target markets, by
using experience and skills developed in one market space to others that are new to them
(Cassiman et al., 2005). Furthermore, firms with novel products may choose acquisitions from
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industries not often associated to their own core activity, as ways to gain access to
complementary assets that become necessary to commercialize their own innovations (Teece,
1986). Alternatively, acquirers search for potential targets that offer novel product ideas, which
they can match to their own complementary assets, expanding the application of their resources
and capabilities to innovations introduced elsewhere (Puranam et al., 2006).
However, to realize the value of ‘distant’ resources from outside the firm, these need to
be effectively transferred and integrated during the M&A process (Haspeslagh and Jemison,
1991). In fact, managers attribute great part of the value and rationale for M&A to the possibility
of achieving synergies by bringing together each other assets (Ranft and Lord, 2002). These
synergies are possible when there is a degree of similarity or commonality between the buyer
and the target, which facilitates the integration of the operation as the acquirer possesses already
the skills and knowledge to understand and absorb the acquired capabilities (Cohen and
Levinthal, 1990; Hagedoorn and Duysters, 2002). As distance increases, managers and engineers
on both sides may need to invest greater efforts and time in the acquisition integration process,
because of lack of shared understanding, common practices, and proved routines for knowledge
transfer. In fact, as attention and resources are diverted towards the integration process and away
from other strategic activities, such as R&D, the impact on post-acquisition innovation can
become negative (Hitt et al., 1996). Synergies can also take the form of increasing market power
when acquiring firms select targets in sectors that provide complementary products. With greater
market power acquirers can realize economies of scale at various stages of their innovation
process (R&D, marketing, sales, etc), creating stronger incentives to innovation, as well as
benefit of lead-time advantages from innovation more rapidly and more persistently (Cassiman et
al., 2005; Gerpott, 1995; Hagedoorn and Duysters, 2002). The combined outcome of the novelty
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effect, decreasing with relatedness, and the synergy effect, increasing with relatedness generate
the known inverted-U shaped relationship.
HYPOTHESIS 1: There is a curvilinear (inverted-U shaped) relationship between industry
relatedness in M&A and post-acquisition innovative performance
The curvilinear relationship can be interpreted as outcome of the coexistence of elements
of exploratory and exploitative search in the acquisition process (Graebner, 2004; Puranam et al.,
2006). When a balance between exploration of novelty and exploitation of synergies is achieved,
at the intersection between the synergy line and the novelty line in Figure 1, the acquisition is
positioned at the optimal level of relatedness with respect to innovative performance.
Figure 1: Novelty and synergies effects of industry relatedness on innovative performance.
The model in Figure 1 entails that when relatedness is low, the synergy-effect is dominant on the
novelty-effect, and therefore innovative performance increases with relatedness in the left-hand
side of the curve. At this end, it is crucial that the acquirer is able to integrate and create
synergies with highly unrelated resources, which can alter existing routines with considerable
impact on the organisation (Capron and Mitchell, 2004). In this case, a lack of exploitation of
synergies has more weight than the novelty contribution of resources per se, if novelty cannot be
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harvested without effective implementation (Van Den Bosch, Volberda, and De Boer, 1999).
However, beyond a certain threshold, the novelty-effect becomes dominant on the synergy
effect, and post-acquisition innovative performance decreases with relatedness at the right-hand
side of the curve. When relatedness is high, the benefit for innovative performance although easy
to achieve through a smooth integration, will be minimal. In fact, there has to be some degree of
novelty in an acquisition to enable learning and innovation by recombination of the acquirer’s
resource basis (Ahuja and Katila, 2001; Cloodt et al., 2006).
On the basis of the model in Figure 1, we formulate a moderating effect as a shift in the
lines representing the novelty and synergies components of the curvilinear relationship. A
moderating effect that enhances the ability to explore novelty from unrelated acquisitions at a
certain performance level shifts the ‘novelty line’ to the left-hand side. The parabola modeling
the curvilinear relationship will move accordingly, reaching the maximum level of innovative
performance at a lower degree of relatedness (fine dotted lines in Figure 2a). Conversely, a
moderating effect that enhances the ability to exploit synergies in unrelated acquisitions at a
certain performance level shifts the ‘synergy line’ towards the left-hand side, with a consequent
movement of the parabola towards lower levels of relatedness (fine dotted lines in Figure 2b).
Figure 2a: Moderating effect of increased
Figure 2b: Moderating effect of increased
exploration capability
exploitation capability
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Moderating effect of acquirer’s R&D expenditure
Potential buyers that have strong in-house innovative capabilities are more aware and better able
to appreciate the value of technological advances occurring elsewhere from their own industry
(Cohen and Levintal, 1990), and therefore they can more effectively search for solutions and
resources broadly, along new trajectories and uncommon directions compared to their industry’s
norms and common practices. As well, highly innovative firms can benefit more strongly from
acquisitions as vehicles to match their novel product ideas and technologies with unexplored
market opportunities (Cassiman et al., 2005), or to access complementary assets that are cospecialised to their distinctive innovative products (Teece, 1986), but not necessarily to the
dominant standard in the industry where they operate. Therefore it can be expected that potential
buyers that highly invest in R&D activities, are more likely to benefit from acquisitions as means
to realise the potential of their innovative efforts especially when relatedness to the target firm is
low. This is represented in Figure 1as a shift of the ‘novelty line’ towards the left-hand side of
lower levels of relatedness, in correspondence of an increase in the buyer’s R&D expenditures.
Furthermore, the in-house R&D efforts of acquiring firms influence their ‘absorptive
capacity’, i.e. their ability to assimilate external novel capabilities into current operations (Cohen
and Levinthal, 1990; Zahra and George, 2002). The close connection and interdependence
between the M&A implementation process and R&D is also revealed by the fact that firms
modify their technology sourcing strategy following an M&A. After a merger, firms increase
their in-house R&D investment while relying less on external R&D (Cefis, 2010), which is
indicative of an intensified commitment and effort within the organization to integrate and
assimilate the resources obtained by merging with or buying another firm.
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The buyer’s capability to absorb and integrate external resources embodied in the
acquisition target plays a more critical role when relatedness between the target and the acquiring
firm is low. In the scenario when novel resources are likely to require a far reaching
reconfiguration within the buyer’s existing organisation, post-acquisition innovative performance
is likely to be highly dependent on the effective integration of resources and realisation of
synergies with the target. Thus it is expected that buyers with higher investment in internal R&D,
mutually reinforcing absorptive capacity (Cassiman and Veugelers, 2006), are able to effectively
manage a lower degree of relatedness in the acquisition integration process, at a certain level of
innovative performance. In Figure 1, this is represented by a shift in the ‘synergy line’ towards
the left-hand side of the relatedness axis, for a higher level of buyer’s R&D expenditure. Thus,
with an increase in the buyer’s R&D expenditure, because of the combined effect of the shift in
the novelty line and in the synergy line, the curve of innovative performance as a function of
relatedness will move towards the top-left hand side, reaching the maximum level of innovative
performance, where balancing novelty and integration, at a lower degree of relatedness.
HYPOTHESIS 2: Acquirer’s prior R&D expenditure moderates the inverted-U relationship
between relatedness and post-acquisition innovative performance, by shifting the point of
balance towards a lower level of relatedness and a higher level of innovative performance.
Moderating effect of acquisition experience
Transfer theory has found application in the M&A literature to illustrate how ‘transfer effects’
impact the M&A integration process and the successive acquisition performance (Finkelstein and
Haleblian, 2002). The fundamental tenet is that past experience of an event helps to deal with the
same type of event in the future. When applied to M&As it is argued that past acquisitions
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provide knowledge about the processes and practices that are dominant in certain industries,
which can prove helpful to following deals (Lubatkin, 1983). Also, by repeatedly carrying out
M&As firms learn how to make acquisitions, and build routines, often formalised with the set up
of a dedicated M&A function, that help to realize future acquisitions (Barkema and Schijven,
2008). Transfer theory assumes a certain degree of similarity between events, as the learning
mechanisms are more effective when there is a certain fit between prior acquisition routines and
a new acquisition target, which can take the form of similarity in products (Hayward, 2002) or in
firm size (Ellis et al., 2011) of past and new acquisition targets.
In our model (Figure 1) we assume that the exploitation of synergies enabled by an
effective implementation process is critical when there is low relatedness between the target and
the acquirer, and resources of greater novelty need to be integrated. In this situation, an efficient
management of the post-M&A integration process can lead to improved innovative performance
(Cassiman and Colombo, 2006), as it allows to optimize the potential contribution of novel
resources to the acquirer’s innovation, through effective integration of routines. In contrast when
relatedness is high, integration routines and capabilities, and a well-planned integration process
are less critical, since resources can be transferred by simple replication or practices that are well
and implicitly understood by the parties. Accordingly, as relatedness with respect to potential
targets decreases, possessing greater acquisition experience allows buyers to maintain a certain
level of post-deal innovative performance. When represented in Figure 1, this assumption
implies that for an increase in the buyer’s acquisition experience, the ‘synergy line’ moves to the
left-hand side, towards lower levels of relatedness. Consequently the curve of innovative
performance as a function of relatedness shifts towards the left-top side, reaching a greater
maximum level of innovative performance in correspondence of a lower level of relatedness.
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HYPOTHESIS 3: Acquirer’s M&A experience moderates the inverted-U relationship between
industry relatedness and post-acquisition innovative performance, by shifting the point of
balance towards a lower level of relatedness and a higher level of innovative performance.
Moderating effect of target’s size
In the literature on post-merger performance the size of the target is often considered indicative
of complexity of the acquisition, and therefore an important determinant of the success of the
M&A process. Because of greater complexity, acquiring organisations with a large volume of
assets or number of employees (Kitching, 1967; Haspeslagh and Jemison, 1991) or a large
patents stock (Ahuja and Katila, 2001) will require integration procedures and routines that differ
from those for small targets (Ellis et al., 2011). In large acquisitions the integration process may
involve more steps and a more articulated plan, which slow down implementation (Capron and
Mitchell, 2004; Chakrabarti, Hauschildt and Sueverkruep, 1994; Haspeslagh and Jemison, 1991),
and demand a reallocation of managerial effort and resources away from other strategic
activities, such as internal renewal and innovation (Ahuja and Katila, 2001). Furthermore, as
large targets may have a more diversified product portfolio, the risks and uncertainties for the
acquiring firms associated with entering unfamiliar product markets will increase (Ellis et at.,
2011). Overall the literature indicate that in large acquisitions the value of the resources of the
acquired firm can be more difficult to capture during the integration process as compared to
small acquisitions. This negative effect is enhanced when relatedness between target and
acquirer is low. In the context of large acquisitions, with high levels of complexity and
uncertainty, possible mismatches in the integration process, have greater negative consequences
for post-deal performance when dissimilarities in products (or in other dimensions) between the
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target and the acquirers are high (Ellis et al., 2011). Thus scale and dissimilarity mutually
reinforce the complexity of the acquisition. Managers need to implement a process to integrate
on a large scale, technologies, knowledge, practices, and relationships they are not familiar with.
Ellis et al. (2001) argue that in large acquisitions, product dissimilarity represents an obstacle to
acquisition learning, because of the difficulty to apply to the current deal what an acquirer has
learned in other acquisition contexts, especially in previous small acquisitions. Thus, when
industry relatedness is low, and an effective integration of a distant target is crucial for the
success of the acquisition, a large-scale acquisition adds to the complexity of the process, with
potentially negative outcomes on the post-acquisition innovative performance.
When industry relatedness is high, and lack of novelty in the target resources hinder
innovativeness in the M&A deal, the acquisition of a large organization, with more established
routines and formalised processes and functions may exacerbate this negative effect when
compared to the acquisition of a smaller and flexible organization. Thus, overall, when acquiring
a large target, the buyer will be able to maintain a certain level of innovative performance, if the
target is related to the buyer and therefore easier to integrate in the existing organization. This is
equivalent to assume that in Figure 1, the ‘synergy line’ shifts to the right-hand side, towards
increasing levels of relatedness, for larger target size. Therefore the curve of innovative
performance in function of relatedness will move towards the bottom right-hand side of the
Cartesian plane, implying that the maximum level of post-deal innovative performance will be
lower and attainable at a higher degree of relatedness between target and buyer.
HYPOTHESIS 4: Target size moderates the inverted-U relationship between industry
relatedness and post-acquisition innovative performance, by shifting the point of balance
towards a higher level of relatedness and a lower level of innovative performance.
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METHODS
Data sources
We use data from the General Business Register (ABR) and the Community Innovation Surveys
(CIS) of firms operating in the Netherlands. Both datasets are collected and managed by the
Central Bureau of Statistics Netherlands (CBS). The ABR contains data, at the level of the
business legal unit, of the entire population of firms registered for fiscal purposes in the
Netherlands and includes economic and demographic information. The CIS is a EU data
collection effort, with the purpose of gathering harmonized data on the innovative activities and
performances of firms, carried out by the member states under Eurostat coordination. CIS data
and the derived indicators of innovative activities and performance have been used extensively in
studies on the economics and management of innovation (see among others Cassiman and
Veugelers, 2002; 2006; Klingebiel and Rammer 2014; Laursen and Salter, 2006; Leiponen and
Helfat, 2010; Love, Roper, and Vahter 2013; Mairesse and Mohnen, 2002). The Dutch part of
the CIS is conducted with two years frequency, each wave covering the three-year period prior to
the survey. Seven CIS waves, from 1994 to 2008, were available for this research.
Sample
From the ABR, we identify 36,375 acquisitions that took place in the Dutch market over the
period 1980-2005, but demographic data are available for 27,754 cases. The ABR data for these
deals were linked to the CIS considering tree time periods, as illustrated in Figure 3. First, we
considered the year in which the acquisition was carried out as observed in the ABR, and we
included the age, size and sector of activity, at that date, of both the acquirer and the target firms
( ABRt ( M &A) ). Second, we attached to the sample the data on the innovative activities of the
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acquiring firms, from the CIS wave preceding the acquisition year ( CISt (M &A)!1 ). Finally, we
added the data on the innovative performance of the acquiring firms, from the CIS wave
following the acquisition year ( CISt (M &A)+1 ). Some specific adjustments were made to account for
the biannual frequency of the CIS and the fact that the acquisition occurred in an odd or even
year. If the acquisition was in an even year, e.g. 1996, a typical data sequence would be
ABRt ( M &A) = ABR1996 , CISt (M &A)!1 = CISwave(1994!1996) , and CISt (M &A)+1 = CISwave(1998!2000) . If instead
the acquisition was in an odd year, e.g. 1997, the typical data structure would be
ABRt ( M &A) = ABR1997 , CISt (M &A)!1 = CISwave(1994!1996) , and CISt (M &A)+1 = CISwave(1998!2000) .
Figure 3: Sample structure.
This structure implies that post-acquisition innovative performance is observed after an
integration period ranging from one to four years, while the acquirer’s investment in innovation
is observed over a time period up to three years prior to the acquisition.
We were able to compose a sample with this longitudinal configuration over a time
period that covered ABR yearly data from 1996 to 2005, and all the seven CIS waves available:
CIS 2 (1994-1996), CIS2.5 (1996-1998), CIS3 (1998-2000), CIS3.5 (2000-2002), CIS4 (20022004), CIS 4.5 (2004-2006), and CIS 5 (2006-2008). The outcome was a final sample of 1,736
observations (unique deals), including manufacturing, service and construction firms.1
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
"
!The CIS is administered to a random sample of firms extracted by those registered in the ABR, year by year, and
stratified according to firm size, region and industry, with an average response rate around 60-70%. This implies
that not all the firms involved in an M&A as identified in the ABR are included in the CIS surveys.!
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The empirical model
In the CIS, data on innovative sales can only be observed for firms that engage in the innovation
process, and not for those that are not innovation-active. Estimating a standard regression model
on the subset of firms for which innovative sales can be measured raises a risk of sample
selection basis. Specifically, in our setting, sample selection basis may emerge if the probability
of inclusion of a deal in the estimation sample (i.e. the probability that the acquisition resulted in
an innovation) is affected by an unobservable variable, which also influences the level of postacquisition innovative sales. To mitigate this problem we apply a two-stage Heckman model
(Heckman, 1979), which has been used widely to control for sample selection bias in studies of
innovation performance (Mairesse and Mohnen, 2002; Cassiman and Veugelers, 2006).
Specifically, we apply the selection equation (Probit regression) of the first stage of the Heckman
model, to distinguish between deals that could have been motivated by different reasons to
acquire. This is to account for the possibility that acquisitions which are innovation driven,
compared to those carried out for other reasons, such as the increase of firms market share or
managerial hubris, are more likely to be included in the subset for estimation, as well as to
generate higher levels of innovative performance (Morck, Shleifer, and Vishny, 1990). This
allows calculating the inverse Mill’s ratio (!), which is added to the performance model of the
second-stage (the OLS regression) of the Heckman correction procedure, as a proxy of the effect
of the unmeasured firms’ characteristics on innovation sales, component that would have been
otherwise omitted leading to inconsistent estimators.
The ‘exclusion restrictions’ in the Heckman 2-stage estimator requires that at least one
independent variable in the selection equation, modelling the choice to engage in an innovation
driven acquisition, is unrelated (orthogonal) to the volume of sales realised from innovation after
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the acquisition. In the attempt to capture this component of the decision to acquire, we use
information from the CIS on the types of obstacles firms face in their innovation process (Cefis,
2010). In the CIS, respondents are asked to indicate whether the firm experienced obstacles that
prevented an innovation project to start, according to three categories: (i) financial constraints,
(ii) lack of qualified personnel and of information about the technology and (iii) lack of
information about the market. We assume that for those acquirers whose innovation projects
could not start because of lack of technological knowledge (including skills) and market
knowledge, acquisitions are more likely to be innovation driven as they provide vehicles to
acquire the necessary knowledge externally and to gain access to new markets. In contrast
acquirers who could not start innovation projects because of lack of financial resources,
acquisitions are likely to be motivated by other reasons than innovation, for example by
managers’ personal motivations and preference for rapid growth and diversification (Morck et
al., 1990). The lack of investments on R&D project may in fact reveal managerial risk aversion
and orientation towards short-term performance (Hoskisson, Hitt, and Hill, 1993). Earlier studies
based on CIS data have shown that obstacles to innovation are in fact “perceived” by companies
as constraints but they do not in fact discourage companies from innovating (Veugelers and
Cassiman, 1999). Instead, the perception of obstacles influences the way firms make decisions
about innovation strategy, in particular, the choice between producing a technology internally
and sourcing it externally (Veugelers and Cassiman, 1999). Accordingly, it is reasonable to
assume that while the existence of obstacles that are perceived to prevent innovation internally to
the firm, influences a company’s decision on whether or not to use acquisitions as external
sources of technological and market knowledge relevant for innovation; however, those obstacles
are not direct determinants of the firm’s level of innovative performance.
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Dependent variables
In the first stage of the Heckman model, the Probit model for the selection equation, we use a
dummy variable for weather or not the acquirer firm developed new or significantly improved
products or services after the acquisition, as identified in the CIS wave following the acquisition
year (Figure 3). In the second stage of the Heckman correction procedure, the regression model
for post-acquisition innovative performance, we consider the volume of sales (in logarithms)
originating from new and significantly improved products and services, which are reported in the
CIS wave following the acquisition year (Figure 3). For both indicators of innovative
performance, namely the development of an innovation and the revenues from the innovation
after the acquisition, we distinguish each measure on the basis of the degree of novelty,
specifically on whether the innovation consisted of products and services that were (a) improved
or new to the firm (but already available to competitors) and (b) new to the market. It is worth
noting that a firm can be an innovator, according to the dummy variable definition, but realize
innovative sales equal to zero if the product did not reach yet successful commercialisation.
Independent variables
Industry relatedness. Our measure of industry relatedness is adapted from the index proposed by
Teece et al. (1994) as an endogenous measure of coherence in corporate diversification. This
measure is based on the survivor principle of relatedness: because market competition selects out
inefficient organizations and the routines they embody, “activities which are more related will be
more frequently combined within the same corporation” (Teece et al., 1994, page 5). As
suggested in Bryce and Winter (2009), this endogenous notion of relatedness can be applied to a
wide range of issues in strategic management, using diverse definitions of co-occurrence, as for
example in Neffke and Henning’s (2013) measure of skill relatedness based on employees
18!
mobility across industries. Following this approach we consider an acquisition event to identify
the co-occurrence of the industry of the acquired firm, as source of resource transfer, and the
industry of the acquiring firm, as its destination. Adapting Teece et al. (1994), we calculate the
relatedness index by comparing the number of observed co-occurrences (as acquisitions), oij, of
the industry (i) of the acquirer and the industry (j) of the target, and the number that would have
been expected in randomly occurring pairs. Accordingly the relatedness index "ij is defined as:
!
where µij and ! ij are the mean and standard deviation of randomly distributed pairs, which are
best represented by an hypergeometric distribution (Teece et al., 1994). In the regression
analysis, the index has been calculated in natural logarithms after linearly transforming it to
assume only positive values, according to the expression:
Relatij = ln ["ij + | min ("ij) |].
In M&A research, industry relatedness is often defined on the basis of the number of SIC
codes that target and acquirer firm share. In contrast our index is based on the frequency that two
SIC codes actually combine together in acquisitions, irrespective of their distance in a predefined
hierarchical ordering of industry codes, property that makes the index more suitable to represent
a notion of relatedness from a RBV perspective (Silverman, 1999). Furthermore, in its definition,
the index is directed or unidirectional, at it accounts for the source and destination of the
connection between pairs. Accordingly the level of relatedness can vary between the pairs of
sectors (i, j) and (j, i), in which the first sector refers to the acquirer and the second to the target.
We calculated the relatedness index on the entire set of 36,375 domestic acquisitions for
the whole time period 1980-2005 for which ABR data were available. Industries are identified at
19!
3-digit level NACE code, generating 204 ! 204 possible combinations of pairs of target’s and
acquirer’s industries. !
Moderating variables
There are three moderating variables in our study: the acquirer’s R&D spending and acquisition
experience, and the target’s size. The acquirer’s R&D spending includes the costs of acquisition
of hardware/software and new machinery; the costs of market research aimed directly at the
market introduction of new products or services; and the costs for R&D personnel training. We
focus on in-house R&D because it is a company’s own investment in R&D that influences both
its ability to generate knowledge and to absorb externally produced knowledge (Cohen and
Levinthal, 1990). This variable is drawn from the CIS wave prior to the acquisition year. From
the ABR data we measure both the acquisition experience of the buyer using the total number of
acquisitions as a proxy, in line with Haleblian and Finkelstein (1999), and the target’s size
calculated as the number of employees in the year of acquisition (transformed in logarithm).
Control variables
In the analysis we control for a number of factors that can influence the choice of innovation
driven acquisitions, and the level of innovative performance after the acquisition. In the selection
model, we include acquirer’s characteristics, which are known to influence innovative
capabilities, namely firm size (measured by the number of employees), firm age (years since the
first inclusion of the firm in the national business register), and the nature of the technological
environment (Breschi, Malerba and Orsenigo, 2000; Pavitt, 1984), all calculated from the ABR
at the year of acquisition. In order to set apart different technological regimes among the
industries included in our sample (manufacturing, service and construction) we adopt the
20!
Eurostat aggregation, which groups industrial sectors at 3-digit NACE level into four broad
categories of technological intensity: medium-high technology, medium-low technology, for
manufacturing industries, knowledge intensive services (KIS) and less knowledge intensive
services (LKIS). Similar characteristics, i.e. firm size and age, and the technological regime were
also controlled for the target firms. Finally, two dummy variables identify the years in which
GDP in the Netherlands was at the highest (1997 and 1999) and lowest (2002 and 2003) levels.
Firms heterogeneity
An econometric concern in the regression analysis is unexplained heterogeneity. Differences in
post-acquisition innovative sales across acquiring firms may originate in unobservable firmspecific capabilities that produce systematic and persistent asymmetries in performance. This
leads to a problem of omitted variables in the error term of the regression model and
consequently to inconsistent estimators. A possible way to attenuate this problem and to control
for fixed effects is to introduce an autoregressive component by adding the lagged value of the
dependent variable (i.e. the innovative sales of the acquirer in years preceding the acquisition) to
the hand-right side of the performance equation (Bettis et al., 2014) as previous innovative
performance is an important predictor of future innovation (Geroski, Van Reenen, and Walters,
1997). This component reflects the existence of serial correlation in innovative performance due
to the fact that acquiring firms with superior idiosyncratic innovative capabilities will perform
persistently better than others, before and after the acquisition.
21!
Moderating effects of a curvilinear relationship
The existence of a non-linear relationship between an indicator of performance and an index of
relatedness has generally been formalised using a parabolic function, such as:
y = ax 2 + bx + c
The shape of the parabola is determined by its vertex which identifies the position of the
parabola on the xy-plane and by the focal length which defines the centre of the curvature, that is
how narrow or broad is the curvature. The vertex coordinates are given by:
" b
b 2 ! 4ac %
V = $!
,!
4a '&
# 2a
and the focal length is equal to:
f=
1
4a
where a is the absolute value of the coefficient a.
Moderating effects of a variable z to the non-linear relationship as expressed by the
parabola can be introduced in the form of a variation in the linear coefficient (b + ! z) . This is
equivalent to assume that the moderating effect of z shifts the vertex (and position) of the
parabola without changing the curvature of the parabola, as this is defined by the focal length
which only depends on the parameter a. Specifically, in the case that a < 0 as formulated in
Hypothesis 1, the parabola shifts towards the left hand side if the coefficient of the linear
interaction term is negative, ! < 0 , and toward the right hand side if positive, ! > 0 .
Accordingly, the outcome of a moderating effect as expressed by the linear interaction term
(with coefficient ! ) is to change the level of relatedness between target and acquirer at which
the maximum level of post-deal innovative performance is attained, without affecting the
curvilinear shape of the relationship. However, one can also assume that the moderating effect
extends to the shape of the relationship, accelerating or decelerating the pace at which the
22!
benefits or losses from increasing relatedness are realised. In this case the moderating effect can
2
be modelled through a variation in the quadratic coefficient (a + ! z ) , which defines the focal
point and therefore the curvature of the parabola. Accordingly, the outcome of a moderating
effect in the quadratic interaction term, for the curvilinear relationship as formulated in
Hypothesis 1is to broaden the parabola curvature if the coefficient is negative, ! < 0 , smoothing
out variations in innovative performance in relation to variations in relatedness, and to narrow
the parabola curvature if positive, ! > 0 , accentuating the extend of variation in innovative
performance in correspondence to variations in the relatedness index.
RESULTS
Table 1 provides the descriptive statistics and the matrix of correlation. The low correlation
between independent variables and acceptable variance inflation factor (VIF) statistics suggest
that multicollinearity of variables is not a problem in our analysis. Table 2 describes the sample.
[INSERT TABLES 1 AND 2 ABOUT HERE]
Table 3 reports the estimates of the selection equation for innovative acquisitions, in the first
stage of the Heckman model, calculated for both indicators of post-acquisition innovation status,
i.e. new-to-firm innovation (Model 1) and new-to-market innovation (Model 2). For both
indicators, we observe a higher probability to innovate post-acquisition among acquirers that
prior to the acquisition experienced strong obstacles to innovation due to lack of knowledge and
due to market uncertainty (the coefficients are statistically significant and positive), but low
financial constraints (the coefficient is statistically significant and negative). This confirms that
the nature of the obstacles to innovation for the acquirer is able to discriminate between two
types of acquisitions, and related motivations to acquire. Specifically it distinguishes acquisitions
23!
that are innovation related and driven by access to knowledge of technology and markets, from
acquisitions motivated from other reasons, such as managerial objectives, which shift financial
resources from long term renewal to rapid growth and diversification (Morck et al., 1990).
[INSERT TABLE 3 ABOUT HERE]
Innovation driven acquisitions are those included in the estimation of the second stage of the
Heckman correction procedure. The estimates coefficients of the OLS regression are reported for
two sets of models, in which post-acquisition innovative performance is measured by the
volumes of sales originating respectively from improved or new-to-the-firm products (Table 4a)
and from new-to-market products (Table 4b). Model 1 is the baseline model and includes only
direct linear effects. As for the control variables we find that post-acquisition innovative
performance is positively influenced by the acquirer’s size and, for new-to-firm innovations, by
the performance that was realised prior to the acquisition. Conversely, for new-to-market
innovations, the autoregressive term is not statistically significant, indicating that revenues from
more radical innovations are less persistent and more unpredictable over time.
[INSERT TABLES 4a AND 4b ABOUT HERE]
Model 2 adds the squared value of the relatedness index to test Hypothesis 1. The
coefficients of the relatedness index and its squared term are statistically significant across model
specifications, and of the expected signs for an inverted U-shaped curve, confirming the
hypothesis. While the signs of these coefficients are invariant across model specifications, their
absolute sizes are always higher in models (b) than in models (a). To assess the overall effect of
these differences we report in Table 5 the vertex and focal length of the estimated parabola for
each model. In the first line, the values of the parameters indicate that, for new-to-market
innovative sales (model 2b), the vertex of the parabola is positioned towards higher levels of
innovative performance at approximately the same level of relatedness (x=3.22, y=7.49), when
24!
compared to the vertex (x=3.17, y=5.22) of the parabola for new-to-firm innovative sales (model
2a). However, the focal length of the parabola for new-to-market innovation (0.29) is smaller
than for new-to-firm innovation (0.53). This suggests that acquisitions can produce greater
impact on post-deal performance in radical innovations, but variations from the optimal level of
relatedness produce a more significant drop in performance (the curvature is narrower). Thus,
relatedness can easily turn out to be too much or too little for achieving post-deal success in
radical innovations. Conversely, for new-to-firm innovations, the impact of acquisitions is
smaller for a similar degree of relatedness, but the outcome is less sensitive to variations from
the optimal level of relatedness (the curvature is broader). Thus, tolerance in relatedness for postacquisition performance is greater for less novel innovations, as compared to radical innovations.
[INSERT TABLE 5 ABOUT HERE]
To test for moderation effects, we add the interaction terms with the relatedness index,
both separately for each of the three moderating variables (Models 3, 4, and 5) and
simultaneously in the complete model. In particular, we consider two specifications of the
interaction term in the complete model: including only linear interactions, which represent a shift
of the parabola’s vertex (Model 6) and adding quadratic interactions, which also allow for
change in focal length (Model 7). In general, we observe that the estimated coefficient of the
interaction term of each moderator with industry relatedness is consistent in sign and statistical
significance when introduced separately (in model 3, 4 and 5) or concurrently (model 7). 2
Hypothesis 2 states that the acquirer’s own R&D spending moderates the curvilinear
relationship of Hypothesis 1, by shifting it towards higher levels of post-acquisition performance
reached at lower degrees of relatedness. In the most comprehensive model formulation (Model
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
#
!We also estimated the three models with only linear interactions when each moderator is entered separately. As
well, the estimates are consistent in sign and statistical significance with those obtained when entering the
moderators all together in the model with only linear interactions (Model 6). The results are available upon request. !!
25!
7), the estimates reveal that the moderating effect of R&D expenditure depends on both the
linear and quadratic terms (both coefficients are statistically significant, respectively with
negative and positive sign): a variation in R&D expenditure has the effect of changing the
position of the curve (the vertex) as well as the shape of the curve (the focal length). To visualize
this overall effect we plot the performance-relatedness curve for high and low levels of the
acquirer’s R&D expenditure (one standard deviation above and below the mean), respectively
for new-to-firm (Figure 3a) and new-to-market innovative sales (Figure 3b).3 We observe that
with a higher level of acquirer’s R&D expenditure the curve shifts to the left hand-side, with the
vertex moving towards higher values of innovative performance and lower relatedness, e.g. for
new-to-firm innovation the vertex coordinates change from (x=3.10, y=5.56) to (x=0.58,
y=6.66), as shown in Table 5 for Model 7. At the same time, because of the significance of the
quadratic interaction term, the curvature of the parabola also changes, by becoming wider: the
focal length increases from 0.86 to 1.17 for new-to-firm innovation and from 0.47 to 0.53 for
new-to-market innovation. This implies that with greater R&D investment, deviations from the
point of balance in the curvilinear relationship have smaller implications for innovative
performance. Thus, acquirers that invest more extensively in R&D internally are better able to
explore and exploit greater novelty of external resources (supporting Hypothesis 2), as well as to
benefit of greater flexibility, or tolerance, in the trade-off between novelty and integration.
In Hypothesis 3, we assume that a similar moderating effect exists for the buyer’s
experience in acquisitions. In the broader formulation of the model (Model 7), the coefficients of
the linear and quadratic interaction terms of acquisition experience with relatedness are both
statistically significant, respectively with positive and negative sign. The corresponding plots in
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
$
!The shift of the parabola towards the left hand-side with increasing R&D is also confirmed by the negative
coefficient of the linear interaction term in model 6, in which the curvature, by assumption, remains unchanged. !
26!
Figure 4 show that, with an increase in acquisition experience, the parabola shifts towards the
left-hand side, with the vertex moving towards a lower value of relatedness and a higher
maximum value of innovative performance: the coordinates change from (x=2.85, y=4.87) to
(x=2.57, y=6.37) for new-to-firm innovations, and from (x=3.38, y=6.61) to (x=2.69, y=7.87) for
new-to-market innovations (Table 5). This pattern is similar to what found for R&D expenditure,
and supports Hypothesis 3. 4 However, in contrast to R&D expenditure, the curvature becomes
narrower. The focal length decreases from 0.83 to 0.36 for new-to-firm sales and from 0.46 to
0.26 for new-to-market sales (Table 5). This finding implies that buyers with greater acquisition
experience are potentially able to attain a higher maximum level of innovative performance at a
lower degree of relatedness from the acquired firms (Hypothesis 3), but are also at risk of being
more adversely affected in achieving post-deal innovative performance if relatedness deviates
from the optimal level: tolerance to deviations from the point of balance is lower.
Finally, Hypothesis 4 predicts that the size of the acquired firm moderates the curvilinear
relationship, by shifting its point of maximum towards a lower level of innovative performance
and a higher degree of relatedness (in opposite direction to the effects of the acquirer’s R&D and
acquisition experience). The moderating effect of target size only depends on the linear
interaction term with relatedness, as the coefficient of the quadratic interaction term is not
statistically significant in any model specification (5 and 7). Therefore, with a variation in target
size, the point of balance (the vertex) of the curvilinear relationship changes in a significant way,
but not its tolerance (the focal length). Specifically, an increase in target size is associated with a
shift of the parabola towards the right-hand side, as indicated by the positive and statistically
significant coefficient of the linear interaction term in Model 6 ("=0.33 for new-to-firm and
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
%
!The shift of the parabola towards the left-hand side as acquisition experience increases is also confirmed by the
negative and statistically significant coefficient in the model with only linear interaction terms (Model 6), when
holding constant the curvature or focal length – and the tolerance of the curvilinear relationship.!
27!
"=0.24 for new-to-market sales).5 As shown in Table 5 for Model 6, with larger target size, the
vertex is positioned at a higher level of relatedness and unchanged or slightly increased level of
maximum post-deal innovative performance: the coordinates change from (x=3.09, y=5.02) to
(x=4.28, y=5.00) for new-to-firm and from (x=3.42, y=7.04) to (x=3.96, y=7.58) for new-tomarket sales. Overall for larger target size, the curvilinear relationship moves towards higher
levels of relatedness while the maximum level of innovative performance that could be achieved
post acquisition remains largely unchanged. This supports the first part of Hypothesis 4.
Acquiring a large target involves more complexity, which will demand greater relatedness
between target and acquirer, holding the expected innovative performance relatively invariant.
For sensitivity analysis, all the models were estimated using two indicators of innovative
performance. While some differences could be noted in the curvature of the relationship – with
more radical innovations being associated with lower tolerance to deviations from the point of
balance between exploration and exploitation – the directions of the moderating effects in the
performance equation are robust with respect to the two measurements used.
DISCUSSION AND CONCLUSIONS
The existence of a curvilinear relationship between relatedness in technology-acquisitions and
post-acquisition performance is well established in the M&A literature. Our findings confirm this
property, while offering a different qualification of relatedness, across all industries. Extending
the principle of ‘revealed relatedness’ (Teece et al., 1994; Neffke and Henning, 2013) to M&A,
we construct an index of relatedness in the resource bases that are common to firms operating in
a certain industry domain (Nelson and Winter, 1982), and which are transferred across industries
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
&
!The plots for the complete model including only the linear interaction terms (Model 6) are available upon request.!
28!
through the market for corporate control. Whereas measures based on overlapping SIC codes
reflect similarity, our measure accounts for the degree of (directional) connection or dependence
in resources, which can also result from complementarities across industries (Grimpe and
Hussinger 2014; Kim and Finkelstein, 2009; Makri et al., 2010). The index is consistent with the
definition of resource relatedness as used in the RBV context (Speckbacher et al., 2014).
While the existence of a curvilinear relationship between performance and relatedness in
acquisitions, is well established in the literature, our results especially suggest that both the
critical point where a balance is achieved, between the exploration of novelty and the
exploitation of synergies, and its tolerance to deviations, vary with the characteristics of the
acquirer and the acquired firm. To formalise this we represent the moderating effects to a
curvilinear relationship by means of variations in the shape parameters, i.e. the vertex
coordinates and the focal length, of the parabola modelling such relationship. Our results suggest
that the acquirer’s capabilities to (a) generate and absorb novelty through internal investments in
R&D, and (b) to manage the acquisition integration process by way of acquisition experience,
shift the point of balance, allowing to realise higher levels of post-acquisition innovative
performance at lower levels of relatedness. Thus, innovative and acquisition capabilities help the
potential buyer to search for and integrate acquisition targets in less explored directions, and to
realise better innovation outcomes after the acquisition. However, while enhanced innovative
capabilities of the buyer are associated with less performance sensitivity to deviations from an
optimal degree of relatedness to the target, acquisition experience may imply more sensitivity.
The characteristics of the acquired firm matter too. We observed that in acquisitions of
large target firms, the point of balance shifts towards a higher level of relatedness for relatively
invariant post-deal performance. A possible interpretation is that, because of a more complex and
29!
prolonged integration process, large targets can be effectively acquired only when the nature of
their activities is familiar to the acquirer, yet this type of acquisition may be less beneficial for
the innovations outcomes.
Observing a whole country market, we extend previous findings to a complete multiindustries context, coherently with existing research focused on the relationship between
technology relatedness and innovation performance in the high-tech segment (Ahuja and Katila,
2001; Cloodt et al., 2006). Furthermore our analysis adopts the sales from new and improved
products/services as indicators of innovative performance. With respect to patents, often used in
M&A research on innovative performance, which are measures of inventions, innovation sales
capture the realised outcome of the overall commercialisation process of new product ideas.
Innovative sales also represent a measure broadly applicable outside the high-tech sector and
allow overcoming some of the limitations associated to patent measures, due to the technologyor size specificity of the propensity to patent.
Finally, in line with the earlier suggestion that M&A research on relatedness effects
should take into account the novelty of the commercialised products (Makri et al., 2010) we
examined the post-acquisition innovative sales for products ‘new to the firm’ and ‘new to the
market’ separately. While we find that the curvilinear relationship of relatedness and
performance, as well the directions of moderation, are robust with respect to the two definitions,
differences also emerge. Specifically we observe that a higher degree of novelty of the
commercialised products (i.e. new-to-market versus new-to-firm) is associated with a higher
level of innovation sales post acquisition, but also with a narrower curvilinear relationship to
relatedness. This suggests what while the novelty of the commercialised products can potentially
generate better innovation outcomes after an acquisition, these are also associated with greater
30!
instability of the point of balance between novelty of the acquired resources and their effective
integration (i.e. deviations from the optimal combination are of greater consequence), which is
consistent with the strong uncertainty and unpredictability of radical innovations.
Our paper has also implications for managerial practice. Notably, it suggests that
managers that intend to perform acquisition with the aim of acquire external resources and
consequently generate innovation must take care of the scope of their search for potential targets
across industries, avoiding targets that are either too unrelated or too closely related. Moreover,
acquirers can predict or prevent some issues deeply valuing their internal capability such as R&D
ability, acquisition experience, as well as being aware of the complexity of large acquisitions. On
this basis they can pursue a higher innovation outcome by choosing an appropriate target or
improving their own routines to better integrate the new resources.
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34!
Table 1. Descriptive statistics and correlation matrix.
Variable
Mean
St.d.
1
2
1. New-to-firm innovative sales
7.42
3.06
1
2. New-to-market innovative sales
7.68
2.65
.79*
1
.13*
.04
3
!
3. Relatedness
3.74
1.17
4. Acquisition experience
1.55
1.23
.09*
.1
9. R&D expenditure
1.68
2.87
.26*
.27*
5. Acquirer age
2.31
1.28
.07*
.03
6. Acquirer size
5.77
1.16
.28*
7. Target age
1.94
1.18
8. Target size
2.35
10. Financial constraints
0.16
.29*
.00
.07
.16*
1.82
.09*
.13*
.24*
0.36
.05
.09*
.02
11. Lack of knowledge
0.29
0.45
-.02
.08
.04*
12. Market uncertainty
0.22
0.41
-.05
.02
-.02
0.05
0.22
.05
-.02
14. Acquirer low tech manuf.
0.13
0.34
.12*
.03
15. Acquirer high KI services
0.25
0.43
.10*
.11*
16. Acquirer low KI services
0.41
0.49
.00
17. Acquirer construction
0.13
0.34
18. Target high tech manuf.
0.03
0.18
.07*
-.06
19. Target low tech manuf.
0.08
0.27
20. Target high KI services
0.42
0.49
21. Target low KI services
0.39
0.48
22. Target construction
0.07
0.25
-.02
6
7
8
9
10
11
13
14
15
16
17
18
19
20
21
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
1
-.5*
1
.01
.05*!
1
.49*
.19*!
-.1*
-.01!
.06*
.05*!
.26*
.07*
-.02
!
1
.00!
1
!
.05*!
-.2*!
.12*!
.41*
1
.06*
.08*
.05*
-.06*
.04
.24*
-.1*
.1*
.02 .05* .44*
.26*
.05*
.03
.04
.02 .45* .59*
.19*
.04*
.09*
.03
.05
.18*
.04*
.00
1
1
.13*
.08*
.04*
.16*
.23*
.02
-.02
.18*
-.1*
.00
.15*
.37*
.00
.00
.13*
-.01
.06*
.06*
-.1*
.19*
.05*
.15*
.00
.13*
-.01
.12*
.24*
-.01
.00
-.03
.06*
.04*
.02
.00
.17* .09* -.06* -.08*
-.02
.00
.08* .11*
.16*
12
!
-.08
.09*
-.03
.07
.05
.1*
-.05
-.01
.34*
.04
-.03
.24*
.1*
-.01
.10*
.11*
-.01
.16*
5
!
1
.10*
.13*
.09*
.04*
13. Acquirer high tech manuf.
4
Note: N = 1736; * Statistically significant at the 10% level.
35!
.02
-.02
1
.00
1
.01 .13* .09* .09* .09* -.09*
-.16* -.12*
.02
.01
-.02 -.14* -.23*
.12* .05* -.08* -.08* -.14*
.00 -.09*
.04*
.1*
-.02
.03
1
1
-.2* -.33* -.49*
.02 .13* -.09* -.16* -.24* -.34*
.01
.04 .52*
-.01 -.11* -.11*
.06* .17* .06* .08* .07*
-.02 .62* -.17*
.26* -.28*
-.1*
.02
.02
1
.03
.00
-.1* -.06*
.01 .08*
1
-.03
1
-.2* -.09* -.05*
1
-.1* .64* -.48* .04* -.16* -.25*
1
-.2* -.44* .75* -.27* -.15* -.24* -.69*
-.02 -.08* -.14* -.21*
!
1
.56 -.05* -.08* -.23* -.22*
Table 2: Post-acquisition innovative status grouped by acquirer firm’s industry.
Post-acquisition
innovator
Acquirer industry
!
Total
% Innovator
!
!
!
High tech manufacturing
Low tech manufacturing
High Knowledge intensive service
Low knowledge intensive service
Construction
50
129
168
95
70
92
237
448
717
242
0.54
0.54
0.38
0.13
0.29
Total
512
1,736
0.29
Table 3: Estimates of selection equation for post-acquisition innovation
Variable!
Financial constraints
!
Lack of knowledge
!
Market uncertainty
!
Acquirer high tech manuf.
!
Acquirer low tech manuf.
!
Acquirer high KI services
!
Acquirer low KI services
!
Acquirer age
!
Acquirer size
!
Constant
!
LR !2 (9)
Pseudo R2
Log-likelihood
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
Model 1
New to the firm
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
-0.317***
(0.104)
0.377***
(0.092)
0.541***
(0.101)
1.278***
(0.148)
1.164***
(0.103)
0.776***
(0.086)
0.409***
(0.109)
-0.004
(0.027)
0.065**
(0.030)
-1.646***
(0.190)
306.33
!
!
0.145
-899.734
Model 2
New to the market
-0.213**
(0.107)
0.162*
(0.098)
0.559***
(0.105)
1.189***
(0.151)
0.962***
(0.108)
0.660***
(0.093)
0.339***
(0.118)
-0.044
(0.028)
0.105***
(0.032)
-1.967***
(0.205)
211.03
0.118
-783.309
Note: N = 1,736. Standard errors are in parentheses. * p < 0.10, ** p < 0.05, *** p < 0.01 (two-tailed test)
36!
Table 4a. Estimates of performance equation for post-acquisition new-to-firm innovative sales
Model 1a
!
Innovative sales
Relatedness
2
Relatedness
Acquisition experience
Model 2a
Model 3a
Model 4a
Model 5a
Model 6a
0.08** (0.03)
-0.27** (0.12)
0.07**
3.00**
(0.03)
(1.20)
0.09*** (0.03)
3.20*** (1.17)
0.05
1.41
(0.03)
(1.28)
0.08**
2.61**
(0.03)
(1.18)
0.07**
2.93**
!
-0.47*** (0.17)
-0.46*** (0.17)
-0.22
(0.18)
-0.55***
(0.17)
(0.03)
(1.24)
-0.50*** (0.17)
-0.30*
(0.17)
-0.89** (0.40)
-0.62
(0.39)
-3.39*** (1.28)
-1.06***
(0.39)
-0.17
(0.57)
-3.55*** (1.23)
0.33**
0.24*
(0.13)
1.14*** (0.27)
0.39***
(0.13)
0.38***
(0.13)
1.06***
(0.26)
R&D expenditure
0.15*** (0.05)
0.161*** (0.05)
0.66*** (0.14)
0.18*** (0.05)
0.17***
(0.05)
0.43***
(9.12)
0.67***
(0.13)
Acquirer age
-0.02
-0.03
-0.03
-0.06
-0.06
(0.11)
-0.07
(0.10)
-0.06
(0.10)
Acquirer size
0.94*** (0.15)
0.95*** (0.14)
0.74*** (0.15)
0.99*** (0.14)
0.86***
(0.14)
0.90***
(0.14)
0.73***
(0.14)
Target age
0.10
(0.12)
0.11
(0.12)
0.09
(0.11)
0.08
(0.11)
0.11
(0.12)
0.11
(0.11)
0.10
(0.11)
Target size
-0.00
(0.08)
-0.01
(0.08)
-0.06
(0.08)
-0.03
(0.08)
-1.37***
(0.27)
-1.21*** (0.24)
-0.97*** (0.27)
!
!
!
!
!
!
-0.07**
-0.27*** (0.06)
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
GDP high
0.01!
(0.57)!
-0.04!
(0.56)!
-0.55!
(0.57)!
-0.03!
GDP low
-0.18
(0.29)
-0.16!
(0.29)!
-0.07!
(0.28)!
-0.07!
Dummies for technological regimes
yes
Constant
-1.01
2
Relatedness ! R&D expenditure
(Relatedness ! R&D expenditure)
2
!
Relatedness ! Acquisition experience
(Relatedness ! Acquisition experience)
Relatedness ! Target size
(Relatedness ! Target size)
2
2
(0.16)
0.08***
1.48
!
Acquisition experience
-0.02
Model 7a
(0.03)
(1.16)
(0.11)
(0.14)
(0.11)
(0.11)
(0.11)
!
!
!
-0.29*** (0.06)!
!
!
!
0.005*** (0.001)!
!
!
!
!
!
!
0.67**
(0.33)!
!
-0.06*** (0.02)!
!
!
yes
yes
(1.51)
-5.59** (2.32)
-4.88**
Mills ($)
2.16*** (0.61)
2.13*** (0.60)
1.84*** (0.58)
Rho
0.667
0.668
0.613
Wald X^2
128.38***
144.51***
182.65***
(2.31)
-3.50
0.004*** (0.001)
-0.23**
(0.10)
-0.001
(0.002)
(0.55)!
0.148
(0.54)
0.34
(0.28)!
-0.05
(0.23)
0.01
yes
(2.50)
(0.11)
0.76**
0.33***
(0.06)
0.22**
(0.10)
0.001
(0.002)
(0.55)
-0.35
(0.55)
(0.28)
0.04
(0.27)
yes
yes
-2.33
(2.33)
-4.48*
(2.38)
-0.74
(2.55)
2.12*** (0.59)
1.85***
(0.58)
1.93***
(0.58)
1.78***
(0.57)
0.676
0.618
0.641
0.618
178.14***
193.87***
204.46***
249.50***
Note: N = 1,736. Standard errors are in parentheses. * p < 0.10, ** p < 0.05, *** p < 0.01 (two-tailed test)
"#!
(0.32)
-0.05*** (0.02)
0.39***
yes
(0.03)
!
!
Table 4b. Estimates of performance equation for post-acquisition new-to-market innovative sales
Model 1b
Innovative sales
-0.03
Relatedness
-0.42*** (0.13)
(0.04)
2
Relatedness
Acquisition experience
Acquisition experience
-0.01
(0.16)
2
Model 2b
-0.03
(0.03)
Model 3b
-0.02
(0.03)
Model 4b
-0.05
(0.03)
Model 5b
Model 6b
-0.02
(0.03)
-0.03
(0.03)
Model 7b
-0.02
(0.03)
(1.20)
5.63*** (1.16)
5.53*** (1.13)
3.65*** (1.25)
5.26***
(1.14)
5.49*** (1.09)
3.47***
-0.87*** (0.17)
-0.82*** (0.16)
-0.54*** (0.17)
-0.93*** (0.16)
-0.82*** (0.16)
-0.54*** (0.17)
-0.27
(0.40)
-0.11
(0.39)
-2.28*
-0.50
(0.39)
1.23**
(0.56)
-2.54**
(1.21)
0.08
(0.13)
0.04
(0.13)
0.85*** (0.26)
0.16
(0.13)
0.11
(0.13)
0.87***
(0.25)
-0.13
(0.11)
-0.09
(0.11)
-0.18*
-0.16
(0.11)
-0.19*
(0.10)
-0.16
(0.10)
(1.25)
Acquirer age
-0.11
Acquirer size
0.72*** (0.16)
0.75*** (0.15)
0.60*** (0.15)
0.80*** (0.15)
0.66***
(0.15)
0.70*** (0.14)
0.62***
(0.14)
Target age
0.06
(0.12)
0.02
(0.12)
0.03
(0.12)
-0.03
(0.11)
0.02
(0.12)
-0.01
0.004
(0.11)
Target size
0.15*
(0.08)
0.15*
(0.08)
0.10
(0.08)
0.11
(0.08)
-0.98*** (0.27)
-0.74*** (0.24)
-0.53**
(0.26)
R&D expenditure
0.16*** (0.05)
0.17***
0.42*** (0.12)
0.60***
(0.13)
-0.07** (0.03)
-0.22*** (0.05)
(0.11)
0.16*** (0.05)
Relatedness ! R&D expenditure
(Relatedness ! R&D expenditure)
0.53*** (0.13)
(0.10)
0.18*** (0.04)
-0.22*** (0.05)
2
0.004*** (0.001)
Relatedness ! Acquisition experience
(Relatedness ! Acquisition experience)
0.003*** (0.001)
0.57*
2
(0.33)
-0.44*** (0.11)
-0.06*** (0.02)
Relatedness ! Target size
(Relatedness ! Target size)
(0.05)
(0.11)
2
0.65**
(0.31)
-0.06*** (0.02)
0.32***
(0.10)
-0.000
(0.002)
0.24*** (0.06)
0.13
(0.10)
0.001
(0.002)
GDP high
-3.21*** (0.61)
-3.26*** (0.59)
-3.65*** (0.60)
-3.42*** (0.56)
-3.10*** (0.57)
-2.86*** (0.56)
-3.63*** (0.56)
GDP low
0.19
(0.28)
0.28
0.28
0.42
(0.26)
0.32
(0.26)
0.44*
0.40
(0.25)
Constant
3.00*
(1.68)
-5.84** (2.34)
-5.01** (2.33)
-3.68
(2.45)
-2.88
(2.39)
-5.46** (2.33)
-1.58
(2.50)
Mills ($)
0.85
(0.61)
0.68
0.53
0.74
(0.56)
0.45
(0.57)
0.43
0.58
(0.55)
Rho
0.356
0.302
0.243
0.341
0.208
0.208
0.285
Wald X^2
127.69***
166.27***
193.55***
226.52***
204.90***
237.46***
285.15***
(0.27)
(0.59)
(0.27)
(0.57)
Note: N = 1,736. Standard errors are in parentheses. * p < 0.10, ** p < 0.05, *** p < 0.01 (two-tailed test
"#!
(0.26)
(0.55)
Figure 3. Moderating effect of the acquirer pre-deal R&D expense (Model 7a and 7b).
Figure 4. Moderating effect of the acquirer’s experience (Model 7a and 7b).
Figure 5. Moderating effect of the target size (Model 7a and 7b).
39! !
Table 5. Parameters of the curvilinear relationship of post-acquisition innovation sales and relatedness
New to firm sales
Moderating variable
New to market
Vertex xcoordinate
Vertex ycoordinate
Focal
length
Vertex xcoordinate
Vertex ycoordinate
3.17
5.22
0.53
3.22
7.49
0.29
3.00
2.60
4.63
5.96
0.50
0.50
3.39
3.16
6.88
8.00
0.30
0.30
2.84
2.27
5.63
6.66
0.50
0.50
3.26
2.61
7.67
8.41
0.30
0.30
3.09
4.28
5.02
5.00
0.50
0.50
3.42
3.96
7.04
7.58
0.30
0.30
3.10
0.58
5.56
6.66
0.86
1.17
3.50
2.59
7.19
7.28
0.47
0.53
2.85
2.57
4.87
6.37
0.83
0.36
3.38
2.69
6.61
7.87
0.46
0.26
2.69
4.28
5.05
4.49
0.84
0.89
3.29
3.83
6.71
6.62
0.46
0.48
Model 2: Direct effects
Model 6: Linear moderating effects
R&D expenditure
Low
High
Acquisition experience
Low
High
Target size
Low
High
Model 7: Quadratic moderating effects
R&D expenditure
Low
High
Acquisition experience
Low
High
Target size
Low
High
40! !
Focal
length