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Technological distance and innovation in technology mergers: Bridging

through intra-organizational team design

Shinjinee Chattopadhyay (Univ. Illinois, Urbana-Champaign)

David H. Hsu (Wharton School, Univ. Pennsylvania)

ABSTRACT

Accessing distant technologies would seem to be an important logic in undertaking technology mergers, where innovation is the desired outcome. Doing so would broaden the domain of knowledge able to be recombined. We study the outcomes of technologically distant mergers (firms with unrelated technologies) and find that combining knowledge stocks that are too disparate leads to dampened innovative performance relative to that associated with combinations of intermediate points in the technology overlap space. We propose that such differences may arise from asymmetric learning roles assumed by merging firms. Post-merger, firms assume the role of a ‘student’ or a ‘teacher’ depending on their future goals to either broaden or deepen their knowledge base, the choice of which is contingent on knowledge distance; this shapes differential innovation outcomes. We furthermore argue and show empirical evidence that intra-organizational factors such as inventor team composition can help bridge knowledge distance: distant mergers that are characterized by more homogeneous target inventor teams experience superior innovation outcomes relative to those with more heterogeneity. Our findings provide insight into how intra-organizational design choices can contribute to firms’ achieving a balance between local and distant search.

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INTRODUCTION

Demand for technological acquisitions is often driven by firms’ desire to acquire new

technology and capabilities that it does not already possess (Uhlenbruck, Hitt, & Semadeni, 2006;

Vermeulen & Barkema, 2001; Rosenkopf & Almeida, 2003). There is also considerable interest on

the supply side of the technology acquisition market, as innovative ventures are increasingly reliant

on acquisitions to achieve entrepreneurial liquidity, as opposed to other channels such as public

offerings (e.g., Aggarwal & Hsu, 2014). Despite these aligned motivations on both sides of the

market, a wide-range of studies suggests disappointing post-merger innovation outcomes (e.g., Hitt,

Hoskisson & Ireland 1990; Hitt, et al., 1996; Rau & Vermaelen, 1998; Kapoor & Lim, 2007;

Cartwright & Schoenberg, 2006). At the high level, two explanations have been offered for the

performance shortfalls within the acquisitions literature: issues with combining stocks of

technological knowledge (e.g., Ahuja & Katila, 2001; Cloodt, Hagedoorn & Van Kranenburg, 2006)

and post-merger integration-based issues (e.g., Haspeslagh & Jemison, 1991; Puranam & Srikanth,

2007). However, these two sub-streams of literature have evolved largely independently of one

another. Our goal is therefore not only to theoretically contribute to each of these literatures, but also

to connect them.

The first literature has found that when merging firms share a moderate level of technological

relatedness they achieve the highest level of innovation following the merger; innovation decreases

as the relatedness increases or decreases from this optimal level (Ahuja & Katila, 2001; Makri, Hitt

& Lane, 2011). Past work emphasize the importance of both distant and local search in achieving this

outcome (Phene, Lindquist & Marsh, 2006; Vasudeva & Anand, 2011; Kaplan & Vakili, 2015). On

one hand, technologically distant elements broaden the set of possible recombinations (Fleming

2001), thereby promoting higher innovation novelty (Rosenkopf & Nerkar, 2001; Ahuja & Lampert;

2001; Cassiman et al. 2005; Laursen & Salter, 2006). On the other hand, drawing upon familiar

technological elements can reduce search costs as well as the variability of outcomes (Rosenkopf &

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Almeida, 2003; Carlisle, 2004; Taylor & Greve, 2006). In this paper, we propose an additional

mechanism that potentially plays a role in firms’ innovation performance, by contrasting the

innovation outcomes of mergers between technologically distant (or unrelated) firms to that of

proximal (related) firms. We first propose that differences in innovation performance may arise from

differential learning of merging firms. Following the merger, firms assume the role of a ‘student’ or

that of a ‘teacher’ (Lane & Lubatkin, 1998) depending on their future goals to either broaden or

deepen their knowledge base (Karim & Mitchell, 2000), the choice of which is contingent on

knowledge distance. Due to asymmetries in learning roles, target firms in distant mergers pay an

innovation penalty relative to target firms engaging in proximal mergers; for this reason, combining

knowledge stocks that are too disparate likely contributes to dampened innovative performance

relative to that associated with combinations of intermediate points in technology overlap space.

Instead of taking the view that combining disparate knowledge is hopeless, however, we next argue

that organizational design decisions related to inventor team composition can help bridge the

knowledge distance after a merger. Our paper adds to the discussion on the costs and benefits of

knowledge relatedness by conceptually linking it to firms’ motivations in seeking out targets and

hence, the implications for firms’ roles in learning. Past literature has been striving to provide

prescriptions on how organizations can achieve a balance between searching local and distant

technologies (Gupta et al., 2006; Laursen, 2012), and our paper provides insight on how intra-

organizational design choices can contribute to this balance.

The second literature that we connect to, that on post-merger integration as the basis for

innovative performance shortfalls, has either focused on individual productivity decline (Paruchuri,

Nerkar, & Hambrick, 2006) or emphasized organizational design elements (or lack thereof), such as

integration, (Puranam & Srikanth, 2007), structural form (Puranam, Singh & Zollo, 2006) or cultural

dissimilarities (Stahl & Voigt, 2008) as explanations for heterogeneous merger outcomes. The

literature has recognized that inventors are part of knowledge networks (Paruchari, 2010) and have

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socially embedded routines (Nerkar & Paruchari, 2005) that are disrupted in the aftermath of a

merger or acquisition. While recognizing the importance of organization-level design decisions and a

micro-level focus on innovation, we draw upon theories in the organizational creativity literature to

argue that such disruption to firm-level productivity can be bridged with intra-organizational design

choices such as inventor team composition. This represents an important yet understudied aspect of

post-merger integration choice. In addition, we tie the conversation from the first literature on

combining technological knowledge, with the theme of design in the post-merger integration

literature by arguing that a homogeneous team composition choice can help bridge shortfalls

associated with combining disparate knowledge domains, as would be the case in technologically

distant mergers.

To test our theories, we construct a firm-year panel dataset of life science mergers and

acquisitions taking place between 1980 and 2014. We identify 401 merger events, and build a panel

dataset by assembling invention, invention team, and organizational characteristics on a yearly basis

for a four-year time-window on either side of the merger event, yielding 2,671 firm-year

observations. Using a variety of empirical techniques, including specifications to mitigate the role of

unobserved and unmeasured differences, we find substantial empirical support for our arguments.

THEORETICAL BACKGROUND AND MOTIVATION

Technological distance and innovation outcomes

In the literature on technological acquisitions, a key finding is that the degree of

technological overlap between merging firms is related to their subsequent innovation output through

an inverted U shape (Ahuja & Katila, 2001; Makri, Hitt & Lane, 2011). This implies that innovation

output following mergers is highest for firms that have a moderate degree of technological overlap

but should be lower when the degree of overlap is less or more than this optimal level. Past literature

has attributed this to firms being able to trade-off between local and distant searches. In our first set

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of hypotheses we posit a different mechanism based on asymmetric roles played by the target and

acquirer in learning from each other. We examine these roles in the context of firms that are

technologically distant (no relatedness in knowledge bases) versus firms that are technologically

proximal (knowledge bases are related). Our binary classification of technological relatedness

departs from previous approaches that have looked at relatedness between firms on a continuum. We

choose this binary approach as it allows cleaner exposition on the distinct roles played by acquirers

and targets in learning from each other. We are also able to draw sharper contrasts in the motivation

of acquirers in choosing target firms. Moreover, while existing literature provides us with deeper

insight into how firms innovate when at and around the highest point of the inverted U, it does not

provide us with explicit prescriptions on firm behavior when at the extreme left end of the U shape.

We attempt to add to the conversation on the benefits and constraints of knowledge distance by

showing that the latter can also constitute a bridge too far. In our second set of hypotheses we argue

that this distance can be moderated by intra-organizational characteristics.

Past literature has emphasized that both local and distant knowledge are needed to explain

why firms need a moderate amount of overlap to reach optimal innovation following a merger.

Theories of technological recombination (March 1991; Fleming, 2001) emphasize that diversity and

breadth of the search space shape innovation outcomes due to the higher recombination of distant

elements (Ahuja & Katila, 2001; Rosenkopf & Nerkar, 2001; Makri, Hitt & Lane, 2011). Novel and

economically valuable or breakthrough innovations are likely to be the result of firms experimenting

in and exploring unfamiliar, pioneering or newly-developed technologies (Ahuja & Lampert; 2001).

While distant knowledge enhances the potential for novelty, local searches drawing on proximal

knowledge also contribute to the production of novel and economically valuable inventions (Katila

and Ahuja, 2002; Kaplan & Vakili, 2015; Vasudeva & Anand, 2013). Proximal knowledge allows

inventors to undergo smaller costs (Rosenkopf & Almeida, 2003) and be better positioned to select

appropriate knowledge components, thereby reducing the variability of outcomes (Taylor & Greve,

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2006) and duplication of efforts (Carlisle, 2004). Moreover, firms attain deeper understanding of

underlying assumptions and solutions that do not work and are able to apply this knowledge to future

problem solving (Weisberg, 1999; Fleming & Sorenson, 2004).

In this paper, we study distant versus proximal mergers to postulate that in addition to

recombination of local and distant external knowledge, as stated above, asymmetric learning by the

acquirer and target shapes innovation outcomes following a merger. A key finding in the alliance

literature is that firms do not symmetrically learn from one another (Hamel, 1991), but assume the

roles of ‘teacher’ and ‘student’ (Lane & Lubatkin, 1998). We draw upon this finding to theorize that

because of asymmetric learning roles, skills are apportioned asymmetrically and this shapes

inventions following the merger. The ability of firms to learn from one another is determined, among

other factors, by the characteristics of the knowledge shared and the ability to understand each

other’s research processes (Kogut & Zander, 1992). We suggest that mergers between distant firms

are motivated by different learning objectives relative to that between proximal firms. These

objectives endogenously shape the flow of knowledge between firms, thereby shaping the trajectory,

quality and quantity of innovation.

Acquisitions, like alliances, are a means of not just accessing each other’s knowledge but also

for firms to learn and internalize their partners’ skill set in order to build and develop in chosen areas.

Following acquisitions, acquirers have been found to retain those parts of targets’ technologies that

are distinct and new, as opposed to being familiar (Karim & Mitchell, 2000). This forms the basis of

the resource broadening argument: when firms seek to engage in path-breaking changes to broaden

their knowledge base they acquire unfamiliar technologies. Acquiring firms’ motivation in seeking

distant targets is to learn from the distant firm’s technology, in an effort to develop technologies that

are divergent to its existing set. The two merging firms will not learn symmetrically since the

primary motivation of acquirers in seeking such targets is to learn as a ‘student’ from the target firm,

which is the ‘teacher’. The flow of information therefore will primarily be from the target to the

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acquiring firm. This contrasts with the case of proximal mergers where acquirers seek access to

familiar technologies in order to continue to build on their existing knowledge bases and to pursue a

path of resource deepening and path dependent changes (Karim & Mitchell, 2000). When both the

target and acquirer share common knowledge based on technological overlap, there will be a two-

way flow of knowledge. Both firms will learn from each other and will simultaneously play the role

of a ‘teacher’ and a ‘student’.

There is evidence that external transfer of knowledge is negatively associated with firm

performance (Argote & Ingram, 2000). Firms that assume the role of a student will presumably

accrue disproportionate benefits from learning relative to a firm that plays the exclusive role of a

teacher. A teacher firm will pay a performance penalty from having scientific, financial and

managerial attention being diverted to the student in order to facilitate the assimilation of knowledge.

Take for example, when pharmaceutical firm Merck (student firm, as classified by Lane & Lubatkin,

1998) acquired biotech firm Sirna Therapeutics (teacher, using the same classification) in 2006 for

$1.1B, Sirna’s relatively thin clinical pipeline was not the main motivation of the merger; Merck’s

stated intentions were to collaborate on oncology by gaining new competence in Sirna’s core

intellectual property, its RNA interference technology which aims to silence the expression of

disease-related genes.1 Between 2003 and 2006 Sirna had filed for 175 patents, while between 2007

and 2010 it had filed for 89 patents, showing an almost 100 percent decline in productivity. Merck

ultimately sold Sirna to its competitor for $175M in cash and equity upfront payments with a modest

allowance for royalty payments upon successful product development. While there is a wealth of

similar anecdotal evidence on pharmaceutical-biotech mergers our argument is not limited to inter-

industry mergers, the core thesis can also be extended to within-industry mergers as well, with

mergers taking place between distant firms.

1 Nature Biotechnology. 2006 Dec; 24(12):1453. “A billion dollar punt”

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By this argument, we would expect that targets in distant mergers who assume the role of

teachers, to pay a performance penalty relative to target firms within proximal mergers, who assume

a dual role of teacher and student. Targets in distant mergers would be likely to experience a dip in

both quantity and quality of innovation relative to proximal mergers. We would then formulate our

first hypothesis as:

Hypothesis 1a. Inventions of targets following technologically distant mergers are lower in

quantity relative to those following proximal mergers.

Hypothesis 1b. Inventions of targets following technologically distant mergers are lower in

quality relative to those following proximal mergers.

Based on the resource deepening versus broadening argument we would expect firms in distant

mergers to purse a knowledge-broadening path, relative to those in proximal mergers. Controlling for

pre-existing technology base, we would then subsequently expect distant mergers to have a broader

knowledge base relative to proximal mergers. We would then formulate our second hypothesis as:

H2. Following the merger, technologically distant mergers are associated with a

broadening of firms’ technological knowledge base relative to proximal mergers.

The moderating role of invention-team composition on technologically distant mergers

The skills and knowledge embedded in the human capital of the acquired firm is of value to

the acquiring firm and is a motivator for acquisitions (Kozin & Young, 1994). Retaining key

managers and employees (Canella & Hambrick, 1992; Krishnan et al., 1997; Mayer & Kenney,

2004) and appointing target-firm managers to important roles (Ranft & Lord, 2002; Graebner, 2004)

are important factors facilitating integration, communication and eventual assimilation of knowledge

between firms. Puranam et al. (2003: pp 180), reporting a managerial interview from Cisco (a prolific

and highly successful acquirer), state: “Usually we purchase a specific piece of technology or a

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product. But that is only half the story, we also want the team which will generate innovation in the

future.”

A long-standing literature conceptualizes the process of invention and new scientific advance

as resulting from a unique combination of existing knowledge domains (e.g., Usher, 2013; Fleming,

2001; Uzzi, et al. 2013). The early literature in this domain did not necessarily consider different

ways of organizing the inventive activity – and mainly kept the individual inventor at the center of

the analysis. Later literature recognized the role of knowledge specialization at the individual level,

coupled with an invention team structure as an important way of organizing for productive outcomes

(Wuchty, Jones & Uzzi, 2007; Singh & Fleming, 2010), especially when examining invention team

composition for firm-level innovation (Aggarwal, Hsu & Wu, 2015). Following this literature, and

extending it to the domain of mergers, we propose that invention outcomes of mergers spanning

technological and organizational boundaries will be moderated by intra-organizational factors such as

the composition of invention teams. We allocate more attention to developing this claim, as it seems

to be novel to the literature.

The literature studying post-acquisition invention performance has largely focused on

inventor-level performance while remaining agnostic on team level productivity. Some of this work

has found that inventors’ productivity declines following an acquisition (Ernst & Vitt, 2000) but

eventually converge to that of the inventors of the acquiring firm over the longer term (Kapoor &

Lim, 2007). This is because one of the factors affecting scientists’ ability to innovate is the network

and context in which they operate. Workers who lose the most social status and centrality in the

aftermath of a merger or acquisition face the greatest disruption to their context and therefore suffer

the worst innovation outcomes (Paruchuri, Nerkar, & Hambrick, 2006). This literature has

emphasized the importance of the flow and organization of knowledge (Paruchari, 2010), as

inventors are part of knowledge networks and these networks are disrupted after an acquisition. The

disruption following an acquisition can be exacerbated or mitigated by the structure of teams; the

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organization of teams is therefore an important consideration of merging firms, as it has direct

bearing on how effectively newly-acquired knowledge can be used or assimilated. In this section, we

posit that the organizational design of teams is linked to the kind of knowledge the acquiring and

acquired firms are attempting to combine.

There is a large literature on the relationship between team diversity (defined along a variety

of dimensions) and outcomes at the team and organizational levels. Our purpose here is not to

provide an exhaustive review of this literature; rather it is to highlight the findings in existing work

most germane to our context. Team composition affects the quality and quantity of solutions

generated during problem solving, and shapes the depth and breadth of discussions. One set of

studies highlights the positive impact of team diversity. Team diversity in organizational experience

and work history has generally been found to be positively associated with a range of organizational

outcomes. Much of the literature has focused on top management team characteristics. Team

composition is an important factor shaping innovation as different team members may respond

differentially to the various steps involved in identifying problems and formulating / arriving at

solutions. When teams process complex and novel problems, teams composed of individuals with

diverse skills, expertise and cognitive processes are more successful than those with similar

characteristics (Bantel & Jackson, 1989), as these features allow teams to avoid groupthink (Janis,

1972). Amabile (1998) emphasized that homogeneous teams are not conducive to creative thinking

and building new expertise, often because of the lack of conflict. Job diversity, along with debate

among team members, has been found to positively influence financial performance (Simmons et al.,

1999). Diversity in demographic characteristics such as age, organizational tenure, educational

background, area of education, and functional expertise (Bantel & Jackson, 1989) have all been

found to be significant in predicting positive organizational innovation outcomes. Functional

diversity and heterogeneity in experiential background has also found to be an equally important

predictor of innovation. Teams who worked for different organizations prior to founding a new firm

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were more likely to engage in explorative behavior while those who worked for the same

organization were more likely to be exploitative in their search behavior (Beckman, 2006). Higher

educational diversity in teams is associated with unique approaches to problem solving, norms and

habits (Brown & Duguid, 1991, 2001), all of which can contribute to more exhaustive and deeper

problem-solving methods. More team diversity in inventor experience has been associated with a

higher flow of ideas between members (Rosenkopf & Almeida, 2003). Based on these studies, we

theorize that more diverse teams will be able to process distant information more effectively. We

would then expect to see a negative association between team similarity and innovation outcomes of

distant mergers, relative to proximal mergers. We therefore hypothesize:

Hypothesis 3a. Low intra-firm technological distance, via homogenous invention team

members’ technical experience, exacerbates the dampened innovation quantity of

technologically-distant mergers.

Hypothesis 3b. Low intra-firm technological distance, via homogenous invention team

members’ technical experience, exacerbates the dampened innovation quality of

technologically-distant mergers.

Other studies have shown a need for balance between overlapping and non-overlapping

knowledge held by team members, as highly diverse teams with little shared expertise will face high

coordination and communication costs, leading to dampened performance (Buckley & Carter, 2004;

Hambrick et al., 1996). Moreover, diversity in team experience has been found to be negatively

associated with task processes as it impedes collaborative productivity even though it encourages

higher creativity; teams composed of people from diverse functional backgrounds are sometimes

unable to develop a shared purpose and find it challenging to agree on courses of action (Ancona &

Caldwell, 1992).

Within the organizational creativity literature, diversity in specialization has been found to

facilitate certain aspects of team collaborations but impede others. Technological differences

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between team members make knowledge integration more challenging, especially when inventors

encounter novelty (Majchrzak et al., 2012). High degrees of specialization overlap between team

members is associated with barriers that impede integrative collaborative problem solving (Carlile,

2004; Dougherty, 1992; Leonard-Barton, 1995; Lovelace et al. 2001). Higher specialization is

associated with different world-views (Dougherty, 1992), interpretations (Boland & Tenkasi, 1995)

and practices (Bechky, 2003). These differences can create barriers to shared understanding among

team members, especially in the absence of common ties (Hansen, 1999) or shared processes that

allow team members to integrate and recombine ideas and technology (Eisenhardt & Santos, 2002).

The more diverse teams are, the higher are the differences in problem interpretation, which in turn

exacerbates the challenge of collaboratively generating solutions.

After the merger of technologically-distant firms, inventor teams encounter a larger (in

scope), unfamiliar, and more diverse search space. This can lead to wasted effort during the search

process (Koput, 1997). As a result of the merger, there may be too many new ideas to manage,

process, and choose among. A merger also brings about many organizational changes, which only

serves to heighten the knowledge integration challenge. In addition, because of the copious number

of ideas, very few of them may receive the requisite focus. Past work on managerial attention has

found that effective managers must “concentrate their energy, effort and mindfulness on a limited

number of issues” in order to achieve sustained strategic performance (Ocasio, 1997: 203). Highly

heterogeneous teams will experience a magnified attention problem when undergoing technological-

distant mergers – not only do team members have to navigate exchange of many ideas within the

team, they must encounter and process a completely unfamiliar technological base. Their attention

may simply be stretched too thin, leading to worse invention outcomes.

Theories of technological integration within teams (Tsoukas, 2009; Boland & Tenkasi 1995;

Carlile 2004) have argued that specialists in diverse teams have to externalize their deep

technological knowledge in a manner that facilitates the understanding of differences and

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dependencies between each specialists’ technology and allows the team to collectively traverse

boundaries to arrive at a solution. This entails questioning their own assumptions and search for

dependencies between their assumptions and core perspectives. After a merger, each inventor must

first span the boundaries of her own knowledge and that of the acquired firm to approach

collaborative recombination with other team members. In distant mergers with diverse teams, this

entails much higher effort in absorbing and processing new knowledge and then generating new

innovations. Majchrzak et al. (2011) found technological exchange through the minimization of

differences between team members was effective in technological integration within the team,

especially when the task environment is unfamiliar. These findings should predict that homogenous

teams would be better at processing diverse knowledge from distant mergers. For distant mergers, we

would therefore expect diverse teams to experience worse innovation outcomes than homogenous

teams. The above-mentioned theoretical predictions suggest that when teams are more uniform in

their technological background, there can be faster exchange of ideas through a reduction in conflict,

higher creative engagement and more flexibility in finding solutions, especially when dealing with

new problems. Knowledge distant mergers would therefore expect less coordination problems and

more efficient technological integration when inventor teams are homogeneous. This leads to our

final hypothesis:

Hypothesis 4a. Low intra-firm technological distance, via homogenous invention team

members’ technical experience, moderates the dampened innovation quantity of

technologically-distant mergers.

Hypothesis 4b. Low intra-firm technological distance, via homogenous invention team

members’ technical experience, moderates the dampened innovation quality of

technologically-distant mergers.

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DATA AND METHODS

A systematic, though imperfect set of measures for knowledge held by firms, is contained in

its patent portfolio.2 In the innovation literature there are three established measures of innovation

that we draw upon for this paper: the number of patents as a measure of the overall quantity of

innovation, the number of forward patent citations as a measure of the economic value of the patent

(Hall et al., 1999), and patent generality as a measure of the novelty of the invention in terms of

producing knowledge used in subsequent inventive efforts. We use the number of patents as a

measure of quantity and patent novelty and generality as measures of patent quality. The generality

measure (Trajtenberg, Henderson & Jaffe, 1997) of a patent is high when it is cited by other patents

belonging to many different technical fields, while it is low when it is cited by patents belonging to a

very concentrated set of fields; the measure therefore captures technology breadth-of-applicability in

follow-on invention.

We begin with the universe of all M&As in the life sciences industry in the US between the

years 1980 to 2014 that were listed in the database SDC Platinum, which is regarded as the “industry

standard” for information on M&As.3 We assemble a panel dataset of target firm-year level invention

profiles using data from the US Patent and Trademark Office. Since our focus is exploring the impact

of merger-driven technological distance, we eliminate mergers that were not primarily technology-

acquisition motivated. Consequently, we eliminate mergers in the categories of Health or Business

services and “Investor Group”, as acquisitions of firms in these categories are not likely to be aimed

at bolstering innovation but would be more likely to be aimed at leveraging non-technological

synergies. For example, a pharmaceutical firm would be likely to merge with a firm classified as

2 An example of a limitation of using patent data is that there may be different propensities to patent, both across- and within-industry. Proponents of using patent data to measure innovation note that there are very few alternative longitudinal data sources for which researchers can study the innovative output of private and public organizations. See, for example, Hagedoorn & Cloodt (2003) for a discussion of these type of tradeoffs in using patent data. 3 http://financial.thomsonreuters.com/en/products/data-analytics/market-data/sdc-platinum-financial-securities.html (accessed October 2016).

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“Investor Group” for the purpose of seeking funding rather than technological capabilities. Similarly,

firms categorized as “Health or Business Services” would be acquired with the intention of obtaining

service support rather than technology. We limit our sample to M&As to technology acquisitions

within the following sub-industries: Drugs, Chemicals and Allied Products, Electrical and Electronic

Equipment, Measuring and Medical Devices. This approach allows us to study acquisitions in similar

and related areas that were motivated by technological capabilities. Since we study innovation after

merger events, we focus on situations where more than a controlling share of the firm was acquired.

We therefore eliminate those mergers where the acquirer took less than 70 percent of the firm. The

results we present are not very sensitive to this exact threshold, however: our results hold for samples

ranging from acquisition stakes ranging from 50 to 100 percent. We further windsorized our data at a

valuation of $5 million USD on the left end and $3 billion USD on the right end in order to limit the

influence of outlying observations. This eliminates around five percent of the total observations.

Although our main results are robust to the inclusion of these observations, we do not present them in

this paper, as they represent events that are potentially motivated by reasons different from the

average, and are therefore not theoretically relevant to our study. We obtain firm-year level control

measures via Compustat for public firms and through the Thomson One database for private firms.

Variables

Dependent variables. The dependent variables are the number of forward citations of the target firm

(Number of forward citations), the number of patents (Number of patents), the generality of patents

(Generality of patents) and the number of patent classes (Number of classes) as a measure of

knowledge base.

Independent variables. Our first independent variable of interest is whether the merging firms share

technological overlap or not (Distant merger). This measure is based on the cosine similarity or

angular distance (Jaffe, 1986) between two firms in technology class experience. For each firm-year

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observation, we create a class experience vector based on the proportion of total patents the firm

patented in a given technology class in a given year. Thus, each entry of the class vector represents a

proportion of the firm’s patents that belonged to that class in that year. Cosine similarity is the

angular separation between the two merging firms in that year.4 This measure ranges between 0 and

1, where 0 indicates situations in which there is no similarity between the two firms and 1 indicates

that they are completely similar with regard to technology class patenting profile. When firms do not

share any overlap in technology (so they are technologically distant), we define Distant merger = 1

and when they do have any overlap (when the firms are technologically-proximal), then Distant

merger = 0.5

We conduct our analysis in a [-4,4] time window around the merger event year, as this

window is sufficiently long for merging firms to have overcome initial coordination issues and assess

possible innovation-related consequences of the event. Our results are also robust for [-3,3] and [-5,5]

windows. For years that include and follow the merger, we designate the focal event as Post = 1; for

those years that precede the merging event we designate the treatment as Post = 0.

We construct a similar overlap variable to measure the homogeneity of experience within a

target team. This variable, Team similarity, is a measure that ranges between 0 and 1 where 0

indicates that the team members have no similarity in past experience while 1 indicates that the team

has identical experience. We first measure the angular distance between the functional experience

between each pair of inventors on a team and then average this across all pairs of inventors. We then

aggregate this for all pairs at the organization to obtain the team homogeneity measure at a firm-year 4Our measure of technology overlap is a flow measure rather than a stock measure. This choice is guided by empirical considerations. Having a flow measure allows us to use a rich panel of invention data of firms before and after the merger, and also incorporate firm and year dummy variables to control for time-invariant heterogeneity. The theorization, empirical results, and interpretation of results do not differ based on whether we choose a flow or stock measure of distance based on angular separation. 5 Our theoretical motivation in the paper guides us to choose a dichotomous measure of technological distance. We also find empirically, that the continuous distribution of the cosine similarity measure has a large proportion (more than half) of the sample clustered around 0. This indicates that more than half of the mergers take place between firms with no technological overlap at all, and so should be treated as a separate category from those with overlap.

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level. For this measure, we create a class experience vector for each inventor based on the number of

patent classes the inventor has patented within for a given year. We calculate the angular distance

between each pair of inventors and then aggregate to the firm-year level to construct the measure of

team homogeneity.6

Control Variables. We use firm and year fixed effects to control for time-invariant firm-specific

factors such as structural differences. To account for time-varying, firm-specific heterogeneity we

control for firm performance measures such as the log of enterprise value (Log of Enterprise value).

Our results are robust to other measures such as size of assets and earnings before interest and tax

(EBIT). Where appropriate, we employ patenting controls that measure the characteristics of the

firm’s overall patenting experience: total number of patents held (Number of patents), total number

of patents in the previous year (Lagged number of patents), the number of classes the firms patent in

(Number of classes), number of classes in the previous year (Lagged number of classes) and

generality of patents in the previous year (Lagged generality of patents).

Empirical Specifications

Our analyses (related to innovation outcomes) are at the firm-year level of analysis, with

panel data of target characteristics. Our methodology is rooted in comparisons of firm-level

outcomes before and after the merger event for distant versus proximal firms, where proximal

mergers are the control group. As a result, our empirical approach is a difference-in-differences one,

where the pre- versus post-merger innovation outcomes are compared for the focal independent

variable(s) associated with the hypothesis tests.

6 Unlike the Distant merger variable, Team similarity is a continuous measure as we want to capture a measure of shared experience through this variable. This variable is continuous as we do not attempt to theoretically capture the difference between a similar and dissimilar team, but want to understand the gradient of the variation of innovation outcomes with team similarity.

18

To test Hypothesis 1a, which posits that targets of technologically distant mergers will be

associated with less quantity of patents relative to targets of proximal mergers, we estimate the

coefficients on quantity of patents (Number of patents). As our patent count variables are over-

dispersed in distribution we employ a negative binomial specification (given the count nature of the

dependent variable) on a panel of firm-year observations with conditional firm and year fixed effects

and bootstrapped standard errors in order to account for serial autocorrelation in the error terms. The

target, acquirer and year fixed effects account for the time-invariant heterogeneity within each firm

and over each year. For time-varying variables we use control variables that we identified in the prior

section. We estimate a regression with the variable Post distinguishing pre- from post-merger

outcomes, with pre-merger being our control group and Distant merger distinguishing distant versus

proximal mergers. The coefficient on Post * Distant merger therefore compares the outcomes of

distant versus proximal mergers before and after the merger, where proximal mergers are our control

group. To test Hypothesis 1b, which expects target firms of technologically distant mergers to be

associated with lower quality patents, we employ the same model as above, with generality of patents

(Generality of patents) and economic value of patents (Number of forward citations) being the

outcome. Here we use an ordinary least squares (OLS) specification, given the continuous

distribution of the outcome variable, with firm and year fixed effects and bootstrapped standard

errors to account for serial autocorrelation among the error terms.

To test Hypothesis 2, which is that technologically distant mergers are associated with

broadening relative to proximal mergers, we examine the knowledge base of the target following the

merger, while controlling for the size of pre-existing knowledge base. We use a negative binomial

estimation with the number of patent classes as the outcome variable, and the coefficient on the

interaction term Post * Distant merger giving the difference in knowledge base for distant vs.

proximal mergers.

19

To test Hypotheses 3a and 3b versus 4a and 4b, which predicts opposing team similarity

effects associated with technologically distant mergers, we use a three-way differences approach.

Here we compare before and after the merger (Post) of distant versus proximal firms (Distant

merger) in terms of team similarity (Team Similarity). We are thus interested in the coefficient on the

three-way interaction of the term: Post * Distant merger * Team Similarity. The estimated coefficient

gives the marginal effect of team homogeneity in distant firms relative to that on proximal firms in

post-merger years. We use the same outcome innovation outcome variables as before.

EMPIRICAL RESULTS

Summary Statistics

Our data consist of mergers of firms between the years 1980 to 2014. There are 401 unique mergers

in our sample and using a four-year window surrounding the merger up to 2,671 usable firm-year

observations (we lose some observations in the regressions due to control variable data constraints).

We calculate the angular separation of technologies as a flow variable, that is, on a firm-year basis.

When there is no overlap, the variable Distant merger is designated 1. On a firm-year basis, 66

percent of the firm-year observations have no overlap in patent classes as shown by the mean of

Distant merger in Table 1.7

The size of the deals ranges between $5 million to 2.9 billion USD with an average of $356

million USD. The mean enterprise value of a target firm is around $532 million USD with a standard

deviation of $699 million USD. A target firm has 13 patents each year with a standard deviation of

43 patents. It has 69 forward citations on its patents, with a standard deviation of 242. The mean

generality value of a target patent is 0.34. An acquirer in this sample holds 22 patents each year, with

a standard deviation of 71 with a mean generality of 0.16 The average within-team similarity 7 When we calculate the angular separation of merging firms as a stock variable - that is, on the entire stock of patents of the two firms, 50 percent of the sample is distant: that is, half of the merger events take place between firms with no overlap in any of their patent classes. These results are available on request from the authors.

20

measure of the target firm is 0.36 with a standard deviation of 0.29. There are four sectors within the

Life Sciences industry that are present in our sample, as mentioned in the past section. 85 percent of

our sample consists of mergers between firms in the same sector.

[Table 1 goes here]

Empirical Results

The specifications of Table 2 test Hypotheses 1a and 1b; each uses different outcome variables.

Model 1 of Table 2 shows that the number of patents granted is lower for distant firms (Distant

merger is negative) on average. The coefficient on Post is not significant but positive. The interaction

of Post and Distant merger is negative, indicating that post-merger, the increase in the number of

inventions for technologically distant firms is lower relative to those for proximal firms, supporting

Hypothesis 1a. In Model 2, the dependent variable is the number of forward citations or economic

value while in Model 3 it is the generality of patents or novelty of innovation; both taken together

provide evidence on the quality of innovation following the merger. The negative interaction effect

on Post * Distant merger provides evidence on Hypothesis 1b: that technologically distant mergers

are associated with lower quality relative to proximal mergers. In Model 4 we find support for

Hypothesis 2. The positive and significant coefficient on Post shows that that there is an expansion in

number of classes firms seek to patent in, following the merger. The positive coefficient on

Post*Distant merger indicates that the increase in the number of new classes is higher for distant

firms, when controlling for lagged number of classes. This indicates that that distant firms begin to

patent in newer areas relative to proximal mergers. We interpret this finding to be evidence that

distant mergers are associated with a knowledge broadening agenda.

[Table 2 goes here]

In Table 3 we test Hypothesis 3a and 3b vs. 4a and 4b. We find that team homogeneity

moderates the effect of knowledge distance for quantity and quality of innovation, thus showing

evidence in support of Hypothesis 4a and 4b. The three models use the same estimation method for

21

all three outcomes shown in Columns 1 (Number of patents), Column 2 (Number of forward

citations) and Column 3 (Patent generality). In all the specifications in this table, the negative

coefficients on Post * Distant merger indicate that, relative to proximal mergers, technologically

distant mergers are associated with lower number of patents, economic value and generality of

inventions; these results are consistent with our findings in Table 3. When we introduce within-team

homogeneity values measured by Team similarity, we find that the coefficient on the three-way

interaction term on Post * Distant merger * Team similarity is positive for all three models. This

indicates that team similarity moderates the negative effects of technologically distant mergers.

When teams are more similar, technologically distant mergers are associated with more positive

outcomes across the range of patent-based outcomes we examine.

[Table 3 goes here]

We do not find any results on acquirer team similarity measures. A possible explanation is

that acquirer team characteristics are endogenous to acquirer invention characteristics prior to the

merger event.

Robustness and Placebo tests

The most significant concern in interpreting our results is that firms may select to merge with distant

or proximal firms based on unobservable or unmeasured qualities, which can drive post-merger

invention outcomes. For instance, if distant firms decide to merge with the intention of obtaining

access to each other’s diverse product market rather than technological base, then these firms would

experience worse innovation outcomes post-merger as they may not invest resources into innovation.

We adopt two approaches to mitigating this concern.

First, we repeat all our tests based on a matched sample of firms using Coarsened Exact

Matching (CEM) techniques, which aims to make the sub-samples of study more uniform, with the

premise that observable differences in the control and focal samples may result from different

22

underlying data generation processes. We coarsen our pre-event data based on number of classes,

patents, assets, value of the merger deal and the number of inventors – the matched sample is shown

in Table 4.

[Table 4 goes here]

We then match firms based on this data and then run our regressions based on the matched

firms. This method yields a smaller sample of mergers, but this sample consists of firms that are

more similar on observable qualities compared to the original sample. We thus obtain a more

balanced pre-event sample. Table 5 shows the results of estimation on this matched sample – we find

the results to be consistent on the whole.

[Table 5 goes here]

Table 6 shows the moderation effects of target team similarity: the results remain consistent

with the original sample.

[Table 6 goes here]

While the CEM method yields a better-matched sample of observations, we are still limited

by the fact that we are matching on observables, and therefore cannot account for endogeneity

stemming from self-selecting based on unobservable qualities. We employ a second estimation

method to attempt to mitigate this issue. Assuming that firms who merge and those who announce

mergers but do not eventually merge are not systematically different in their correlates with

innovation, we conduct the same estimation on the sample of firms that announced but did not

complete the merger. We do not find our results to persist within this sample, that is, there is no

significant difference between distant merger outcomes versus proximal outcomes in this sample.

This suggests that the merger event is a driver of our results. While we cannot completely rule out a

third unobservable variable driving our results, the fact that we do not see our results persist in the

announced-but-not merged sample gives us some confidence that this is not the case. If there were a

third unobservable correlate that drives the innovation results then we would expect to see the results

23

persist in this sample as well. This gives us greater confidence to support our hypothesis that the

costs of sharing technology between two very distant firms impedes innovation after mergers.

We also conduct time-scale analysis on longer [-6,6] and shorter [-1,1] time frames. Our

results remain consistent in the longer time windows but do not manifest in a much shorter time

frame of less than two years. Our results are also robust to mergers of firms within the same and

different industry and we believe our results are not a consequence of structural differences

stemming from same-industry versus different-industry mergers. Thus our results are not, for

example, a result of structural differences between mergers between pharmaceuticals versus mergers

between a pharmaceutical firm with a biotechnology firm.

Despite these robustness checks, some limitations remain in the paper. First, we document

correlations rather than causal “treatment” effects in the empirics. We recognize that our econometric

correction efforts in the robustness checks imperfectly address the possibility of unobserved self-

selection. Second, our measure of team characteristics precludes those teams that are unsuccessful in

patenting thereby creating a potential selection issue. Lastly, our empirical analysis examines

subsectors within the life sciences industry and may not reflect outcomes associated with other

industries. Since there are many inherent industry-specific characteristics that can be influence our

assumptions and estimates, we must be cautious in extrapolating and generalizing our results to other

industries. This comprises an area of future work as well; it will be interesting to explore to what

extent the patterns we observe regarding technological distance acquisitions are upheld in different

industries.

DISCUSSION

We contrast technologically distant versus proximal mergers to propose that firms’

innovation trajectories are a consequence of their intention to broaden or deepen their knowledge

base together with the learning roles each entity takes in the transaction. Knowledge distance can

24

prove to be a bridge too far when not accompanied by familiar elements, but innovation outcomes

can be improved by organizational design, specifically shared experiences of team members. We

show a mechanism by which intra-firm technological proximity moderates inter-firm technological-

distance. As a result, the benefits of combining technologically distant firms might be aided via

inventor team composition choice.

This paper makes contributions to three literatures and brings them together. First, we link

the literature on innovation outcomes to that on team design and post-acquisition integration. Past

literature has focused on innovation outcomes of mergers of firms with varying degrees of

relatedness (Ahuja & Katila, 2001; Makri, Hitt & Lane, 2011), but in this work, we choose a binary

concept of relatedness to contrast the outcomes of technologically distant versus proximal mergers.

This illustrates how knowledge distance shapes firms’ learning roles, and connects with firms’

motivations to broaden or deepen their knowledge base. Our results refine the view from the prior

literature that an exploratory organizational stance is sufficient to generate valuable inventions. Both

distant and familiar technological elements play necessary roles in generating novel innovation.

Second, we contribute to the literature on post-acquisition integration innovation outcomes.

Organizational features, processes and actions all have effects on firms’ ability to effectively

leverage external knowledge obtained through acquisitions. Past work in this area has documented

the tradeoffs between leveraging external sources of knowledge through integration versus the

disruptive effects of such moves through the loss of autonomy (Haspeslagh & Jemison, 1991; Ranft

& Lord, 2002). While there is evidence that organizational characteristics such as experience of

acquirers (Puranam & Srikanth, 2007) and coordination between the acquirer and the target

(Puranam, Singh & Chaudhuri, 2009) can mitigate such disruptive effects of acquisition, there has

been less attention paid to the effect of intra-organizational features. For instance, we have learned

that the structural form of the combined organization – whether it is separated or integrated can

either enhance or dampen likelihood for innovation depending on the developmental stage of the

25

target (Puranam, Singh & Zollo, 2006), but we remain agnostic on the shape or composition of units

within such structures. Our paper adds to this literature by highlighting the role played by intra-

organizational characteristics such as team composition in mitigating post-acquisition challenges.

Third, we make a contribution to the literature studying effects of team composition on

organizational outcomes. While there is overwhelming evidence that diverse teams can facilitate

creative thinking and problem solving (as we delineate in the theory section), we illustrate contexts

under which homogeneity rather than diversity can positively shape innovation outcomes. While the

literature on top management teams has shown that teams composed of managers with

complementary functional backgrounds are more easily integrated into the new organization

(Krishnan et al., 1997; Simons et al., 1999), our findings raise the intriguing proposition that carrying

over results from the top management team literature to the production team context may be

inappropriate. More generally, we add to the growing literature on inventor teams (Ancona &

Caldwell, 1992; Williams & O’Reilly, 1998; Aggarwal, Hsu & Wu, 2015) by suggesting that

inventor team composition design may act as a bridge to accessing more distant external knowledge

in the context of technology mergers.

26

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TABLES

Table 1: Descriptive Statistics and Pairwise Correlations

Mean S.D. (1) (2) (3) (4) (5) (6) (7) (8) (9) (10)

(1) Distant merger 0.662 0.473 1 (2) Value of Transaction in

millions 356.673 579.304 -0.138 1 (3) Enterprise Value in

millions 532.137 699.695 -0.221 0.997 1 (4) Target Number of

forward citations 69.807 242.258 -0.019 0.072 0.159 1 (5) Target Number of Patents 13.554 43.742 -0.058 0.107 0.203 0.799 1

(6) Acquirer Number of forward citations 104.561 426.161 -0.31 0.031 0.046 0.022 0.026 1

(7) Acquirer Number of Patents 21.926 70.899 -0.373 0.087 0.159 0.03 0.066 0.854 1

(8) Target Patent generality 0.343 0.226 0.015 0.044 -0.088 0.06 -0.004 0.047 0.028 1

(9) Acquirer Patent generality 0.16 0.203 -0.529 0.027 0.063 -0.046 -0.048 0.243 0.25 0.097 1

(10) Target team similarity 0.362 0.286 -0.316 -0.042 -0.049 -0.082 -0.079 -0.041 -0.028 -0.09 -0.025 1

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Table 2: Average effects of technological distance after merger event (1) Model 1 (2) Model 2 (3) Model 3 (4) Model 4

VARIABLES Number of

patents Number of citations

Generality of patents

Number of classes

Distant merger -0.332*** -0.193*** -0.001 0.0134

(0.047) (0.051) (0.013) (0.0145)

Post 0.103 -0.032 -0.019 0.235***

(0.048) (0.057) (0.016) (0.018)

Post*Distant merger -0.186*** -0.337*** -0.052*** 0.117***

(0.061) (0.078) (0.020) (0.0224)

Log of Enterprise Value -0.102*** -0.004 0.035 -0.123

(0.024) (0.018) (0.037) (0.119)

Observations 2,615 2,590 2,671 2,671 Number of UniqueID 332 325 401 401 Event Year FE Yes Yes Yes Yes Firm FE Yes Yes Yes Yes

Standard errors in parentheses. * p<0.1; ** p<0.05; *** p<0.01 Notes: Dependent variables are for the target firm. Columns 1 and 2 were estimated using a conditional negative binomial with firm and year fixed effects, with bootstrapped standard errors to correct for serial autocorrelation. Column 3 was estimated using OLS with firm and year fixed effects, with bootstrapped standard errors. We analyze a [-4,4] window around the merger year. In Column 1, in addition to the shown control variables we include the lagged number of patents and classes. In Columns 2 and 3, in addition to the shown control variables, we control for the lagged number of forward citations, number of patents and number of classes. In Column 3, we include an additional control variable: lagged generality of patents. In Column 4 we control for the lagged number of classes.

34

Table 3: Average effects of technological distance when moderated by team similarity levels (1) Model 1 (2) Model 2 (3) Model 3

VARIABLES # patents # forward citations Patent generality Distant merger -0.310*** -0.166*** -0.002

(0.048) (0.052) (0.013)

Post 0.104 -0.068 -0.019

(0.075) (0.093) (0.025)

Post * Distant merger -0.309*** -0.606*** -0.104***

(0.097) (0.127) (0.031)

Team similarity 0.206** -0.0931 -0.0204

-0.0954 -0.128 -0.0492

Post * Team similarity 0.161 -0.245 -0.108**

(0.178) (0.238) (0.055)

Post * Distant merger * Team Similarity 0.538** 1.025*** 0.187***

(0.237) (0.316) (0.071)

Log of enterprise value -0.104*** -0.004 0.026

(0.025) (0.018) (0.027)

# classes 0.033*** 0.023*** 0.003**

(0.002) (0.003) (0.001)

Observations 2,459 2,439 2,505 Number of Unique mergers 321 315 378 Event Year FE Yes Yes Yes Firm FE Yes Yes Yes

Standard errors in parentheses * p<0.1 ** p<0.05 *** p<0.01 Notes: Dependent variables are for the target firm. Columns 1 and 2 were estimated using a conditional negative binomial with firm and year fixed effects, with bootstrapped standard errors to correct for serial autocorrelation. Column 3 was estimated using OLS with firm and year fixed effects, with bootstrapped standard errors. We analyze a [-4,4] window around the merger year. In Column 1, in addition to the shown control variables, we include the lagged number of patents. In Columns 2 and 3, in addition to the shown control variables, we control for the lagged number of forward citations and number of patents. In Column 3, we include an additional control variable: lagged generality of patents.

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Table 4: CEM Balancing

Pre-CEM Balancing Post CEM

Number of patents

2.099 (1.546)

-.7773 (1.286)

Number of forward citations -.8015

(9.777) -7.907 (6.373)

Number of classes .9084***

(.2988) -.1061 (.2094)

Notes: The table shows the difference in means (standard errors in parenthesis) of the difference in means between treatment (CB) vs. non-CB sample prior to the merger event for CEM and non-CEM samples. A balanced sample was created by matching on target firms’ number of classes and patents, acquirer firms’ number of classes and patents, and size of assets of the target firm.

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Table 5: Average Effects of technological distance after merger event on CEM sample (1) (2) (3)

VARIABLES # patents # forward citations Patent

generality of patents

Distant merger -0.262*** -0.118 0.007

(0.065) (0.075) (0.018)

Post 0.130* -0.141 -0.051**

(0.073) (0.093) (0.021)

Post * Distant merger -0.171* -0.526*** -0.086***

(0.100) (0.143) (0.030)

Log of Enterprise value -0.132*** 0.032 0.048

(0.051) (0.035) (0.043)

# classes 0.060*** 0.069*** 0.004*

(0.003) (0.006) (0.002)

Observations 953 947 1,037 Number of Unique Mergers 184 182 270 Event Year FE Yes Yes Yes Firm FE Yes Yes Yes

Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 Notes: Dependent variables are for the target firm. The sample above is that of CEM matched firms. A balanced sample was created by matching on target firms’ number of classes and patents, acquirer firms’ number of classes and patents, size of assets and price per share (if available) of the target firm. Columns 1 and 2 were estimated using a conditional negative binomial with firm and year fixed effects and bootstrapped standard errors to correct for serial autocorrelation. Column 3 was estimated using OLS with firm and year fixed effects and bootstrapped standard errors. We analyze a [-4,4] window around the merger year. In Column 1, in addition to the shown control variables, we include the lagged number of patents. In Columns 2 and 3, in addition to the shown control variables, we control for the lagged number of forward citations and number of patents. In Column 3, we include an additional control variable: lagged generality of patents.

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Table 6: Average Effects of technological distance when moderated by team similarity levels for CEM sample (1) (2) (3) VARIABLES # patents # forward citations Generality of patents Distant merger -0.253*** -0.038 0.012

(0.064) (0.078) (0.027)

Post 0.183 -0.142 -0.089

(0.113) (0.153) (0.060)

Post * Distant merger -0.436** -1.075*** -0.174 (0.178) (0.266) (0.106) Team similarity 0.419*** 0.070 -0.139***

(0.128) (0.150) (0.053)

Post * Team Similarity 0.111 -0.029 0.239*

(0.265) (0.362) (0.129)

Post * Distant merger * Team similarity 1.334*** 2.475*** 0.518***

(0.468) (0.706) (0.158)

Log of enterprise value -0.052 0.008 0.028

(0.065) (0.036) (0.043)

# classes 0.178*** 0.100*** 0.017*

(0.008) (0.013) (0.009)

Observations 811 807 894 Number of Unique Mergers 174 172 259 Event Year FE Yes Yes Yes Firm FE Yes Yes Yes

Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 Notes: Dependent variables are for the target firm. The sample above is that of CEM matched firms. A balanced sample was created by matching on target firms’ number of classes and patents, acquirer firms’ number of classes and patents, size of assets and price per share (if available) of the target firm. Columns 1 and 2 were estimated using a conditional negative binomial with firm and year fixed effects and bootstrapped standard errors to correct for serial autocorrelation. Column 3 was estimated using OLS with firm and year fixed effects and bootstrapped standard errors. We analyze a [-4,4] window around the merger year. In Column 1, in addition to the shown control variables, we include the lagged number of patents. In Columns 2 and 3, in addition to the shown control variables, we control for the lagged number of forward citations and number of patents. In Column 3, we include an additional control variable: lagged generality of patents.