Structural Recombination and Innovation:

The Roles of Technological Capability and Coherence

Samina Karim*Strategy & InnovationSchool of Management

Boston University595 Commonwealth Ave.

Boston, MA 02215Tel: 617-353-4289

Email: samina@bu.edu

Aseem KaulStrategic Management & Organization

Carlson School of ManagementUniversity of Minnesota321 Nineteenth Ave., S.Minneapolis, MN 55455

Tel: 612-625-8458Email: akaul@umn.edu

March, 2012

*corresponding author

Acknowledgments

We are grateful to seminar participants at the University of Minnesota and the University of Michigan for their comments and suggestions on earlier versions of this paper.

1

Structural Recombination and Innovation

ABSTRACT

This paper examines how structural recombination of business units impacts subsequent firm

innovation. We argue that structural recombination has a disruptive effect on the firm’s routines

and capabilities, causing innovation to decline following the merging together of business units.

The magnitude of this effect depends upon the strength and coherence of firm knowledge,

however, with the disruption being most harmful for firms with weak and diffuse technological

capabilities. In contrast, firms with strong and coherent technological capabilities may realize

gains from recombination of knowledge as a result of the change in intra-firm boundaries, so that

structural recombination may have a positive effect on innovation in such firms. Results from a

20-year panel of 71 firms operating in the U.S. medical sector confirm these arguments. The

study thus provides a contingent view of the effects of structural recombination on firm

innovation, while also highlighting the potential for structural change to be a means of unlocking

the potential for knowledge recombination within the firm.

Key words: recombination, innovation, reconfiguration, structure, knowledge, technological

capability, technological coherence

2

Structural Recombination and Innovation

INTRODUCTION

Structural recombination, or the change in organizational structure resulting from the

merging together of business units, is an important and widespread business phenomenon. Prior

work in the strategy literature has examined the consequences of such organizational

restructuring, often finding a negative effect on firm performance resulting from the

unanticipated disruptive effects of such changes (Bowman et al., 1999; McKinley and Scherer,

2000). These disruptive effects have prompted scholars to caution firms to use structural change

sparingly as a tool for performance improvement (Miller, 1982; Nadler and Tushman, 1997). The

focus of this work has been on the effect of these reconfigurations on firm financial performance,

however, leaving the question of how structural recombination impacts firm innovation largely

unexplored (see Karim, 2009 for a notable exception). Examining the effect of structural change

on innovation is important because a substantial body of recent work in the technology literature

has highlighted the recombinant nature of innovation (Kogut and Zander, 1992; Weitzman, 1998;

Fleming, 2001; Nerkar, 2003), suggesting the need to study how organizational restructuring

may impact the potential for knowledge recombination within the firm.

In this study, we attempt to bridge this gap in the literature by examining the effect of

structural recombination on firm innovation. Building off recent work (Karim, 2009), we argue

that structural recombination will have a disruptive effect on firm innovation, causing innovation

performance to decline following structural change. Moving beyond this average effect,

however, we propose a contingent theory of the effect of structural recombination on innovation,

3

with the extent of the decline depending upon the strength and coherence of the firm’s

technological knowledge. Where firm technological capabilities are weak and widely diffused,

we argue, the disruptive effects of structural recombination will be strongest, and innovation will

decline substantially. In contrast, firms with strong and coherent technologies will be able to

overcome the disruption caused by structural change more easily and may, in fact, benefit from

the change as the merger of business units unlocks the potential for knowledge recombination

within the firm.

These arguments are tested on a sample of 71 firms from the U.S. medical sector, using panel

data from 1978-1997 that combine a detailed record of the evolution of their business units with

information on firm patenting. Our results show that while structural recombination has, on

average, an insignificant effect on subsequent firm innovation, the impact of structural

recombination is strongly contingent on the firm’s existing knowledge resources. In particular,

structural recombination has a positive effect on innovation when firms have strong

technological capabilities or when their knowledge base is technologically coherent, but a

negative effect when firms are technologically diversified in incoherent ways.

These findings contribute to the literature on organizational change in general, and

organizational restructuring in particular, by shedding new light on the effect of structural change

on firm innovation. In particular, our work goes beyond the main disruptive effect of structural

change, to present a contingent perspective, with the outcome of structural recombination

depending upon the strength and coherence of firm knowledge. Moreover, by providing evidence

for a positive effect of structural recombination on innovation in firms with a strong and coherent

knowledge base, the study emphasizes the opportunity for firms to unlock the potential for

internal knowledge recombination through the redrawing of intra-firm boundaries. As such, our

4

study also contributes to the literature on technology and innovation, especially to work that

deals with internal boundary-spanning (Rosenkopf and Nerkar, 2001) as a means of knowledge

recombination within the firm (Hargadon and Sutton, 1997; Galunic and Rodan, 1998; Katila

and Ahuja, 2002). While the focus of this work has generally been on the transfer of knowledge

between divisions (Argote and Ingram, 2000; Miller, Fern and Cardinal, 2007; Tortoriello,

Reagans and McEvily, 2011), our findings suggest that an alternate way that firms can realize

intra-firm knowledge recombination is by redrawing the internal boundaries of the firm.

The paper is organized as follows. It begins with background on relevant literature and draws

on this literature to develop hypotheses. This is followed by methods and measures which outline

the data and sample, operationalization of variables, and the methodology of this study. Finally,

the paper concludes with a description of our results and a discussion of their implications.

BACKGROUND & HYPOTHESES

Structural Recombination

Structural recombination refers to changes in business units as their resources and market

activities are reorganized by merging units together, generally through the absorption of one unit

into another unit or the formation of a new business unit by combining existing units. Units may

have different origins, being either internally developed, acquired or the result of a former

recombination. Figure 1 provides several examples of structural recombination.

*** Figure 1 about here ***

While there is relatively little work directly examining the effect of structural change on firm

innovation, the phenomena of internal boundary changes broadly referred to as ‘reorganizations’

have been examined at various units of analysis in studies on organization design, acquisitions,

5

diversification and dynamic capabilities. Of these, the work that comes closest to the current

study is work in the dynamic capabilities literature, which studies the ‘patching’ and

‘reconfiguration’ of market activities by changing divisional boundaries, resulting in the

movement of the underlying resources within the firm (Brown and Eisenhardt 1997; Eisenhardt

and Brown 1999; Karim and Mitchell, 2004). Patching involves “…adding, deleting, splitting,

transferring or combining chunks of businesses” as firms frequently remap their structures to

react to changing market opportunities (Eisenhardt and Brown 1999: 73). Expanding upon this

concept, Karim and Mitchell (2004) defined reconfiguration as the act of patching to realign with

market conditions as well as purposeful experimentation and search for new opportunities. Firms

usually attempt to create value through structural recombination before divesting a unit (Karim

2006), though the effect of such reconfiguration on new product line entry (via internal

development, not acquisition) shows a U-shaped relationship (Karim, 2009).

It is worth noting that structural recombination as used here is different from, though related

to, several other terms that are prevalent in strategy literature, including the redeployment of

resources following acquisitions (Capron, Dussauge, and Mitchell 1998; Capron and Mitchell

1998), corporate restructuring through entry into new businesses and exit from existing ones

(Porter 1987; Bowman and Singh 1989; Hoskisson and Johnson 1992), and studies of ‘charter

changes’ that involve moving responsibility for a market activity from one division to another

(Galunic and Eisenhardt 1996, 2001; Berkinshaw and Lingblad 2005). Indeed, a key reason we

use the term ‘structural recombination’ is to distinguish our phenomenon of interest from these

other kinds of organizational change that also involve structure.

6

Structural Recombination as a Disruptive Process

Changes in organizational structure are an important means of organizational adaptation,

with firms ‘patching’ or ‘reconfiguring’ their market activities by changing divisional boundaries

to react to changing external and internal opportunities (Brown and Eisenhardt, 1997; Eisenhardt

and Brown, 1999, Karim and Mitchell, 2004). By merging together two or more business units

firms can both recombine their internal resources (Galunic and Rodan, 1998; Verona, 1999) and

realign them with market conditions (Eisenhardt and Brown, 1999; Karim and Mitchell, 2004).

These kind of structural changes are not without their challenges, however. As several

studies have suggested, structural changes are disruptive events, with unintended consequences

that may negate the potential benefits from the restructuring (Bowman et al., 1999; McKinley

and Scherer, 2000). Because structure is one of the core elements of an organization (Hannan

and Freeman, 1984; Hannan, Burton, and Baron 1996), with a complementary relationship to

other key elements such as strategy, culture, work, power and people (Miller and Friesen 1984;

Tushman and Romanelli 1985; Miller 1986; Miller 1996; Romanelli and Tushman 1994, Burton,

Lauridsen, and Obel 2002), structural changes are likely to be disruptive or costly to firm

performance (Miller, 1982). Changes in organizational structure will tend to disrupt the

communities of practice within the organization (Brown and Duguid, 1991), which, in turn will

compromise the collaboration between employees, negatively affecting their work, learning and

innovation (Brown and Duguid, 1991; Orr, 1996). Structural recombination may also result in

increased conflict within the organization. As units are recombined there may be conflict around

where to place the resources of the absorbed unit; this management of resources may be costly,

and may prove taxing to executives who need to make resource allocation decisions (Galunic and

Eisenhardt 1996, 2001; Helfat and Eisenhardt 2004). Structural change may also be associated

7

with greater complexity, uncertainty and ambiguity within the organization (Luscher and Lewis,

2008). As a result, firms may face ‘post-restructuring drift’ (Johnson, Hoskisson and Margulies,

1990) as managers attempt to work through the paradoxes and conflicting organizational goals

arising from the change (Cyert and March, 1963; Child, 1972; Luscher and Lewis, 2008) as well

as the disconnect between formal and informal structure (Miller, 1992; Gulati and Puranam,

2009). Studies of post-merger performance have long recognized the disruptive effects of

merging two organizations (Jemison and Sitkin, 1986; Buono and Bowditch, 1989; Greenwood,

Hinings and Brown, 1996), and many of the coordination and communication difficulties

discussed in this literature are likely to plague the merger of business units as well. Indeed, it is

precisely such disruptions that have prompted scholars to caution firms to use structural change

sparingly as a tool for performance improvement (Miller, 1982; Nadler and Tushman, 1997).

More specifically, for our purpose, changes in structure are likely to be especially disruptive

to the firm’s knowledge processes and capabilities. As several authors have pointed out,

organizational knowledge is often tacit (Nelson and Winter, 1982; Kogut and Zander, 1992),

situated within context (Glynn, Lant and Milliken, 1994; Fisher and White, 2000) and embedded

in routines (Cyert and March 1963; Nelson and Winter 1982; Levitt and March 1988). Since

these routines and processes are embedded within the organizational structure, the act of

changing boundaries and moving personnel may destroy some of these competencies. Structural

recombination may also be accompanied by the departure of employees who are unhappy with

the change (Dent and Goldberg 1999) and this may further deplete the tacit knowledge of the

firm (Nixon et al., 2004; Guthrie and Datta, 2008), with the departure of employees resulting in

the disruption of informal relationships (Dougherty and Bowman, 1995) and the loss of social

capital (Dess and Shaw, 2001). This in turn may have a disproportionate effect on firm learning

8

(Fisher and White, 2000) and create a negative environment that inhibits creativity (Amabile and

Conti, 1999).

These disruptions to the firm’s knowledge base may negatively affect the firm’s ongoing

innovation efforts, so that the effect of structural recombination on firm innovation may be

negative. There is some empirical evidence to suggest this. Karim (2009) examines structural

reconfiguration and entry into new markets and finds support for a U-shape learning effect,

though few firms achieve the positive upswing. She attributes this to the disruptive nature of

reconfigurations, and argues that like most other processes, successful structural recombination

is a learned process, one that the vast majority of firms do not gain enough experience with to

benefit from (Karim 2009). Overall, then, structural recombination may be harmful to firm

innovation, with prior work showing a negative effect of structural changes on innovation, on

account of the disruptive nature of reconfiguration. We therefore hypothesize that:

Hypothesis 1: Structural recombination will have a negative effect on firm innovation.

Structural recombination and knowledge recombination

While the discussion above predicts a negative effect of structural recombination on

innovation, there is also reason to believe that structural recombination has the potential to help

improve firm innovation performance. A substantial body of work in the innovation literature has

emphasized the role of knowledge recombination as a driver of innovation (Ahuja, 2000;

Fleming, 2001; Katila and Ahuja, 2002; Nerkar, 2003). In particular, organization scholars have

highlighted the role of knowledge recombination within the firm (Hargadon and Sutton, 1997;

Galunic and Rodan, 1998), through the spanning of intra-organizational boundaries (Rosenkopf

and Nerkar, 2001). While the focus of much of this work has been on the transfer or sharing of

9

knowledge across different parts of the organization within a given structure (Verona, 1999;

Argote and Ingram, 2000; Jansen, Van den Bosch, and Volberda, 2005; Miller et al., 2007;

Tortoriello et al., 2011), the potential for such internal knowledge recombination, coupled with

the insights from the organizational reconfiguration literature (Brown and Eisenhardt, 1997;

Karim and Mitchell, 2004) suggests that one way for firms to recombine internal knowledge may

be by redrawing their internal boundaries. Structural recombination may thus be adaptive as well

as disruptive (Amburgey, Kelly and Barnett, 1993) with the interruption of established routines

and communities prompting wider search (Zellmer-Bruhn, 2003) and creating opportunities for

intra-firm knowledge recombination (Rosenkopf and Nerkar, 2001; Katila, 2002).

The strength of this knowledge recombination effect is likely to differ from firm to firm.

Scholars studying firm innovation have long recognized the heterogeneity of a firm’s ability to

undertake knowledge recombination, referring to this ability as the firm’s combinative capability

(Kogut and Zander, 1992), second-order competence (Rosenkopf and Nerkar, 2001) or

architectural competence (Henderson and Cockburn, 2004). It follows that the effect of structural

recombination on subsequent firm innovation may also vary from firm to firm. In particular, we

argue that both the extent of disruption caused by the structural change, and the potential for

knowledge recombination following it, will depend upon the nature of the firm’s technological

knowledge base preceding the recombination. We therefore turn to consider the conditions under

which the effect of structural recombination on innovation may be less harmful or beneficial.

Technological Capability

An initial factor that is likely to moderate the effect of structural recombination on innovation

is the strength of the firm’s technology capability, or the quality of its knowledge. The inherent

10

potential of a firm’s technology is a critical part of its overall combinative capability (Kogut and

Zander, 1992), with the strength or quality of the existing knowledge having a positive effect on

the likelihood of valuable recombination (Nelson, 1982; Weitzman, 1998). Thus, firms with

strong technological capabilities are in a better position to leverage recombination across diverse

elements to generate valuable new innovations (Katila and Ahuja, 2002).

There are several reasons why firms with strong technological capabilities may be better

positioned to reap the benefits of structural recombination for innovation. First, as already

mentioned, strong technology capabilities will raise the potential value of recombinant

innovation, since recombinant knowledge that draws on strong existing capabilities is likely to be

more valuable (Nelson, 1982; Kogut and Zander, 1992; Weitzman, 1998).

Second, firms with strong technological capabilities may have a better understanding of the

technologies they are looking to combine, which in turn may raise the potential gains from

recombination. Scholars of innovation have emphasized the importance of cognitive search in

the innovation process (Gavetti and Levinthal, 2000), with firms that have a strong

understanding of the search space being able to achieve better outcomes in a more efficient

manner. At the same time, a major reason why attempts at organizational change are seen to fail

is the proclivity of firms to adapt for the sake of doing something different (Fiol and Lyles, 1985;

Cardinal and Opler, 1995), with the lack of a clear direction in experimenting resulting in

innovations that are less useful on average (Fleming, 2001). To the extent that firms with strong

technological capabilities have a better understanding of the technologies they are looking to

recombine, and a clearer insight into potentially valuable recombinations that could arise from

these technologies, they are likely to realize greater benefits from structural recombination.

11

Third, units within firms that have strong technology capabilities may also have superior

absorptive capacity (Cohen and Levinthal, 1990), and may be better able to absorb or engage

with technological knowledge from other business units to enable successful recombination.

Indeed, absorptive capacity is seen as being closely linked to firm combinative capabilities (Van

den Bosch et al., 1999).

Finally, the factors above will not only enhance the potential value of knowledge

recombination resulting from structural recombination, they may also reduce the disruption

associated with the structural change. A cognitively informed and guided search process is likely

to be less disruptive for the organization, and stronger intra-firm absorptive capacity may also

make the transition to the new organizational structure easier. In contrast, where technological

capabilities are weak, levels of stress within the organization may already be high before the

structural recombination, so that the disruption associated with it is likely to be worse.

Overall, then, we expect firms with strong technological capabilities to have greater potential

for valuable recombination, a superior ability to recombine their existing knowledge to generate

valuable innovations, and relatively limited disruption during the process of structural

recombination compared to firms with low technological capabilities. In addition, we expect

these firms to be smarter in how they structurally recombine business units, using their superior

technological understanding to identify the knowledge resources that they believe need to be

brought together. For all these reasons, we hypothesize:

Hypothesis 2: The negative relationship between structural recombination and

innovation will be dampened by the strength of the firm’s technological capabilities.

12

Technological Coherence

A second factor that may impact the effect of structural recombination on innovation is the

coherence of a firm’s technological portfolio. The potential for knowledge recombination within

the firm depends not only upon the strength of the firm’s technological knowledge, but also the

breadth or diversity of its knowledge. On the one hand, greater diversity of knowledge implies a

wider search space (Katila and Ahuja, 2002) with stronger absorptive capacity (Cohen and

Levinthal, 1990; Nicholls-Nixon and Woo, 2003) and greater potential for knowledge

recombination (Ahuja and Lampert, 2001). On the other hand, greater diversity of knowledge

may increase complexity (Wang and von Tunzellman, 2000), making experimentation unwieldy

as firm resources are stretched too thin (Hitt et al., 1996) and learning more difficult as firms

move into unfamiliar areas (Helfat, 1994). In line with these arguments, prior studies have found

a curvilinear relationship between technology diversity and innovation, with breadth of

knowledge having an initially positive effect on innovation, but innovation declining as

knowledge becomes too diverse (Ahuja and Lampert, 2001; Katila and Ahuja, 2002).

In this study, we argue that it is not so much the breadth of the firm’s technological

knowledge that matters as the extent to which the various technological areas of the firm are

related or unrelated to each other. In other words, it is not the firm’s technological diversity per

se, but the coherence of its technological portfolio (Teece et al., 1994; Nesta and Saviotti, 2005).

This idea of coherence was first articulated by Teece et al (1994), who argued and showed that

firms are expected to be ‘coherent diversifiers’, maximizing the value from potential synergies

by entering into lines of business that are related, in that they share common technological or

market characteristics. Studies have shown that firms tend to diversify their technologies in

coherent ways, “filling all the possible ‘technological gaps’ between [patent] classes” (Breschi et

13

al., 2003, p.86), and that such coherence has a positive effect on firm innovation performance

(Nesta and Saviotti, 2005). In addition, technological complementarity or coherence between two

firms has been shown to be critical to enabling recombination between the technology portfolios

of merging firms (Cassiman et al., 2005; Makri et al., 2010).

There are several reasons why technological coherence may positively moderate the effect of

structural recombination on subsequent innovation. First, high technological coherence will

mean that the various technological areas the firm operates in are closely related, so that the

potential for useful knowledge recombination will be greater given complementarities between

units (Teece et al., 1994; Cassiman et al., 2005; Makri et al., 2010). Second, the ability of

recombined units to work together to develop joint innovation will be greater because of the

lower coordination and communication costs between different units within the firm (Tushman,

1978; Teece et al., 1994; Breschi et al., 2003; Nesta and Saviotti, 2005). As several studies have

pointed out, intra-firm knowledge sharing is critically dependent on the existence of shared

language (Dougherty, 1992), frames (Fiol, 1994), coding schemes (Zander and Kogut, 1995) and

mental models (Galunic and Rodan, 1998) within the firm, and greater technological coherence

implies the existence of these common elements linking the knowledge of different units. Third,

greater technological coherence may also ameliorate the disruptive effects of structural change,

both because individuals in related or complementary knowledge spaces may already have

informal ties from being members of a shared community of practice (Brown and Duguid, 1991),

and because the formation of new ties may be easier given shared language and understanding.

Given these arguments, we expect that coherent technological diversity may benefit the firm

in its innovative efforts. Specifically, we propose that technological diversity in related areas will

14

diminish the negative impact of structural recombination on innovation, while unrelated

technological diversity will exacerbate this effect. Thus,

Hypothesis 3: The negative relationship between structural recombination and

innovation will be dampened by the coherence of the firm’s technological portfolio.

METHODS & MEASURES

Data and Sample

The empirical setting for this study is the medical sector, including its three main industries

of medical devices, pharmaceuticals, and healthcare service. Data was gathered from several

editions of the "Medical & Healthcare Marketplace Guide", namely those published in 1978,

1983, 1986, 1989, 1993 and 1997.1 The guides include information of firms operating in the

U.S., and include both domestic and international firms. Information is available on each firm

and its business units, along with the reconfiguration history of how units were recombined over

time. There is also information at the firm level about performance and degree of diversification

(outside of the medical sector).

This detailed information on firm organization and reconfiguration is augmented with data on

firm technology, specifically data on the patents filed by firms with the USPTO. Patent data has

been widely used in the technology literature to measure firm knowledge and innovation (Ahuja

and Lampert, 2001; Fleming, 2001; Rosenkopf and Nerkar, 2001; Nerkar, 2003; Miller, Fern and

Cardinal, 2007), and the richness of information available with this data allows us to construct

meaningful measures of each of our main constructs – technology quality and coherence, as well

as a measure of firm innovation overall. Note that the use of patent data also allows us to study

1 "The Medical & Healthcare Marketplace Guide" was published by International Bio-Medical Information Services, Inc. (Acton, MA, and Miami, FL; ed. Adeline B. Hale and Arthur B. Hale) in 1975, 1978, 1983, 1986, and 1989. Subsequent editions have been published by MLR Publishing Company (Philadelphia, PA) and by Dorland's Biomedical Publications (Philadelphia, PA).

15

innovation at a more granular level than prior work that has examined the effect of

reconfiguration on firms’ entry into new product markets (Karim, 2009). Patent data for the firms

in our sample is obtained by matching each firm to a few individual patents by searching for the

firm’s name in the USPTO database, and then searching the NBER patent database (Hall et al.,

2001) to identify all other patents belonging to the focal firm. Patent data are available for all

patents granted up to 2006, allowing us to study firm innovation for a significant period of time

after the structural recombination in our data.

We observe 250 firms that exist in 1978 starting with letters A, B or C, and track them over

the period 1978-1997. The industry breakdown consists of 181 medical device firms, 38

healthcare service firms, and 31 pharmaceutical firms. The minority of firms in the sample are

acquisition active (34%) with an average of 1.4 acquired units, and have multiple business units

(44%) with an average of 3.3 units. Most firms are not diversified (74% of firms) and the mean

of medical to non-medical sales is 0.7. Most firms are domestic (90%) and the average age is 51

years. Of these 250 firms, they exit the sample over time such that there are 158 firms remaining

in 1983, 132 firms in 1986, 117 firms in 1989, 98 firms in 1993, and 85 firms remaining in 1997.

By then, 97 firms had been acquired and 68 had been shut down. The full sample of 250 firms

includes a total of 866 unique business units over the course of the study period.

For this study, since our main construct of interest is structural recombination, we sample on

the firms that have multiple business units, are reconfiguration-active, survive for at least two

time periods (to include lagged terms), and for which patent data is available. This sampling

reduces our sample from 250 firms to 71 firms. Of these remaining 71 firms, within one time

period the average amount of reconfiguration is 1.26 events. The firms had an average (non-

logged actual value) of 14,000 employees and almost $611 million in medical sales.

16

Measures

Dependent variable

Our dependent variable for all hypotheses is firm innovation in a period. The variable

‘Innovation’ is measured as the change in the citation-weighted patent stock for a firm from time

‘t’ to ‘t+1’. Patents are counted in the year that they are applied for, though only patents that are

eventually granted are included in our measure. Following Hall, Jaffe and Trajtenberg (2005) we

use an annual discount rate of 15% to create a stock measure of patents. Thus, the value of patent

stock in any year is the sum of the number of patents applied for in that year and the stock of

patents in the previous year discounted by 15%. Moreover, because patents may vary in their

value (Griliches 1990; Trajtenberg 1990b; Ahuja and Lampert 2001), we weight each patent by

the number of citations it receives from other patents. This is done by first adjusting the citations

received by the patent by the average citations received by any patent in that same year and

within that same patent class2 (to account for differences in citation rates across patent classes

and cohorts), and then multiplying the patent by this (adjusted) number of citations. The use of

citation weights is a common feature in innovation literature, as is the adjustment of citation

counts to account for cohort and patent class (Hall et al., 2001; Hall et al., 2005). In addition,

weighting patents by citations means that we are closer to measuring the value of the knowledge

generated by the firm. Since we measure the change in patent stock, the measure captures how

innovative a firm has been during the period, or the extent of new knowledge it has created.

Independent variables

Our main independent variable is the amount of firm structural recombination during a

period. The variable ‘Recombination’ is a count, for a firm, of the number of events in which a

2 We use four-digit IPC classes as our measure of patent class

17

business unit was merged into another business unit (thus losing its original identity) for the

lagged time period ‘t-1’ to ‘t’.

The variable ‘Technology Capability’ is, for a firm in a given period, the average number of

citations it has received per patent, where the number of citations is adjusted for the average

citations in the focal patent’s year and patent class. This measure is similar to what others have

used in past papers as indication of innovative impact or quality (Fleming 2001; Ahuja and

Lampert 20013; Rosenkopf and Nerkar 2001; Nerkar 2003; Miller, Fern and Cardinal 2007;

Makri, Hitt and Lane 2010). In essence, it measures the average value of the firm’s patents

relative to other patents in the same year and patent class. Thus, a technology capability measure

of ‘1’ means that the firm’s patents are, on average, neither more nor less valuable than the

patents of other firms in the same technology areas. Values greater than 1 imply that the firm’s

patents are, on average, more valuable than those of other firms, while a value less than 1 implies

that they are less valuable.

‘Technological Coherence’ captures average relatedness of a firm’s patent portfolio in a

given period. To calculate this measure, we first construct a measure of relatedness between

patent classes by calculating the number of citations between patents in the two classes as a

proportion of the total citations by patents in either class. We then compare each patent in the

firm’s portfolio to every other patent in the portfolio and calculate the average relatedness of that

patent to all other patents in the firm’s portfolio based on the patent class that it belongs to. This

measure is then averaged across all patents to give us a measure of overall technological

coherence for the firm, as expressed in Equation 1 below. Mathematically:

Coherence = ∑pi.pj.rij / ∑pi . ∑pj (Equation 1)

3 Ahuja and Lampert (2001) specifically examined firms’ breakthrough inventions and thus observed the top 1% of an industry’s citation-weighted patents.

18

pi is the stock of firm patents in patent class i

rij is the relatedness of patent class i and j, measured as (no. of citations from i to j

+ no. of citations from j to i) / (no of citations from i + no. of citations from j)

This measure theoretically varies from 0 to 14, with higher values indicating greater

coherence, though in practice its range is more limited, since the relatedness between two patent

classes is never 1 (this would require that patents in each class exclusively cite patents in the

other class). Our measure is thus closely related to other measures of technological coherence in

the literature (Breschi et al., 2003; Nesta and Saviotti, 2005), except that they measure the

relatedness of two patent classes (or subcategories) by the proportion of patents that are assigned

to both classes, while we measure relatedness using the pattern of citations between classes.

Control variables

We include a number of controls. First, because our dependent variable is the change in

patent stock, we control for the level of a firm’s patent stock at time ‘t’. This calculation of this

measure is described in the dependent variable section above.

Second, while our theoretical focus is on the coherence of the firm’s technological portfolio

rather than its technological breadth per se, we include a control for the firm’s technological

diversity. Consistent with prior work (Gambardella and Torrisi, 1998; Garcia-Vega, 2006;

Wadhwa and Kotha, 2006) we measure technological diversity as one minus the Herfindahl

index of firm patenting across patent classes.

There are also several firm-level controls included in the models at time ‘t’. ‘Employees’ is

the natural log of the number of employees at the firm. ‘Medical Sales’ is the natural log of the 4 In the models presented below coherence is scaled by a factor of a 100 to make the coefficients easier to interpret

19

monetary sales of the firm (in thousands). ‘% Medical” is the ratio of the firm’s medical sales to

its total sales, and indicates extent of non-diversification (i.e. increasing amounts indicate

activity predominantly in the medical sector). ‘Firm age’ is the number of years since the firm

was incorporated. ‘Acquired Units’ is a count of the number of acquired business units at the

firm; this controls for the level of a firm’s acquisition activity. Finally, ‘Employment Growth’

controls for the percentage change in the firm’s number of employees between ‘t-1’ and ‘t’ to

account for the possibility that restructurings may be accompanied by downsizing.

Table 1 provides summary statistics and correlations for our sample.

*** Table 1 about here ***

Methodology

Given the panel nature of our data, as well as the continuous nature of our dependent

variable, we use fixed effects panel regressions (xtreg in STATA) with fixed effects for both firm

and year. Since we are interested in the effect of structural recombination on subsequent

innovation, and on the moderating role of pre-recombination technological capabilities, we use a

lagged structure, predicting change in patent stock between t+1 and t as a function of structural

recombination between t-1 and t, and technological capability and coherence at t-1. The basic

equation (see Equation 2 below) represented by model is:

Change in Patent Stock t→t+1

= α + β1. Recombination t-1→t + β2. Recombination t-1→t * Technology

Characteristicst-1 + β3. Technology Characteristicst-1 + β4. Patent Stockt

+ β. Controls t + γi + δt + ε (Equation 2)

20

where γi is a firm fixed effect, δt is a year fixed effect, and ε is an error term.

One issue with this analysis is that our main explanatory variable, structural recombination,

may be endogenous. In particular, firms may vary in their propensity to undertake structural

recombination depending on the nature of their technological knowledge, as well as the size of

their patent stock. To account for this possibility we run a first stage probit regression predicting

the likelihood of recombination as a function of firm technology variables and the lagged values

of the other controls in our main regression (i.e. their values prior to the structural

recombination). Following Heckman (1971) we then calculate the inverse mills ratio from the

predicted probability of recombination from this first stage regression, and include it as a control

in our main models. The results for this first stage regression are reported in Appendix A. In

addition to including all the independent variables from the second stage analysis, we also

include a measure for the total number of units in the first stage to act as an instrumental

variable, the logic being that, other things being equal, a firm with greater number of

independent units is at greater risk for structural recombination, but need not be more innovative

than another firm of the same size and technology characteristics.

A second issue with our analysis is the potential for autocorrelation, given that our dependent

variable is the change in patent stock and we include patent stock as an independent variable. To

ensure that our results are not biased by this problem, we run panel GLS models with first order

panel autocorrelation (xtgls in STATA). Results from this alternate specification (not shown, but

available upon request from the authors) are similar in direction and significance to those with

our main models.

21

RESULTS

A glance at the correlation matrix in Table 1 reveals that firms’ patent stock is highly

correlated with technological diversity and coherence as well as firm size and age. Large firms

(by number of employees) are positively correlated with older, more revenue generating, more

acquisition active firms. In addition, technological diversity and coherence are negatively

correlated. Because some of the correlations are high, we check for excess multicollinearity by

examining variance inflation factors and condition indices for the variables in our models. Our

tests do not indicate these to be problematic5.

Table 2 reports our analysis. Model 1 includes only controls variables, and model 2 adds our

main independent variable of structural recombination. The coefficient of structural

recombination in model 2 does not support Hypothesis 1, which predicts a negative relationship

between structural recombination and innovation. Although the coefficient is negative, as

expected, it is not significant.

*** Table 2 about here ***

Model 3 then examines the moderating effect of technology capability, by including the

interaction between structural recombination and technology capability. Model 3 indicates

support for Hypothesis 2; in the presence of strong technological capabilities, structural

recombination has a positive effect on innovation. This positive moderating effect is consistent

with our arguments for the benefits of strong technology capabilities in terms of greater potential

for recombination, more targeted recombination and search, and lower disruption following the

structural change. Interestingly, model 3 shows a negative and marginally significant (10% with

a two-sided test) coefficient for structural recombination, suggesting that once the countervailing

5 The mean VIF for our main models is 2.43, with none of the VIFs for the individual predictors, nor the overall condition index of the model exceeding 30, suggesting that multicollinearity problems are not severe Belsley (1991) and Belsley et. al. (1980).

22

effect of technology capability is accounted for, the main effect of structural recombination is to

disrupt subsequent innovation, as predicted.

Next, we turn our attention to technological coherence. Model 4 shows a positive and

significant coefficient for the interaction between technological coherence and structural

recombination, implying that firms with high coherence are likely to benefit from structural

recombination, consistent with Hypothesis 3. Again, the main effect of structural recombination

is negative and significant in model 4, suggesting that in the absence of technological coherence

structural recombination may actually harm firm innovation. Model 4 thus provides strong

support for the importance of relatedness of firm knowledge as a moderator of the effects of

structural recombination on innovation. In the absence of technological coherence, structural

recombination is, in fact, disruptive to firm innovation, in line with our main argument. In

contrast, where technological coherence is high, structural recombination may help unlock the

potential for knowledge recombination within the firm, leading to improved innovation

performance following the structural change.

Supplementary Analysis

These ideas are further explored in models 5 and 6, which test the effect of technological

coherence in firms with high and low technological diversity respectively. Because firms with

limited technological breadth will tend to have greater technological coherence, one concern

with the findings in model 4 is that they may simply capture a negative effect of diversity on

innovation following structural recombination. Moreover, our theoretical arguments suggest that

firms will see benefits from structural recombination if they are technologically diversified in

coherent ways, but that the disruptive effects of structural change will be especially strong if they

23

are diversified into a number of unrelated technological areas. Models 5 and 6 therefore study

how the effect of technological coherence varies with technological diversity6.

Model 5 shows the results for the subsample of firms with above median technological

diversity. The interaction between structural recombination and technological coherence is

positive and significant in this model, implying that, in line with our theory, firms that are

diversified in technologically coherent ways have the most to gain from structural

recombination. At the same time, the main effect of structural recombination in model 5 is

negative and significant confirming that structural recombination is disruptive to innovation in

firms with highly diffuse knowledge. In contrast, neither the main effect of structural

recombination, nor its interaction with technological coherence is significant in model 6, which

shows the results of our analysis in the subsample of firms with below median diversity. This

implies that coherence does not have a significant impact on the effect of recombination on

innovation in technologically focused firms. A comparison of the coefficients of the interaction

terms in models 5 and 6 (Paternoster et al., 1998), shows that the two coefficients are

significantly different (Z=2.81, p<0.01).

Overall, these results are consistent with our arguments. Although the main effect of

structural recombination in Model 2 is insignificant, we find support for both H2 and H3, with

the effect of structural recombination being positively moderated by technology capability and

technology coherence respectively. Moreover, once the moderating effect of these technology

variables is accounted for, we see a negative and significant main effect of structural

recombination, consistent with our arguments for the disruptive effects of structural change.

6 An alternate approach would be to include a three way interaction between structural recombination, technological coherence and technological diversity, but this would raise issues of multicollinearity and make the results hard to interpret. We therefore choose to adopt a split sample approach.

24

CONCLUSION

This paper aimed to study how structural recombination affects innovation, and how

technological characteristics of firms’ knowledge base moderate this relationship. Although we

find an insignificant effect of structural recombination on innovation overall, our analysis

indicates the relationship between structural recombination and innovation is dependent upon the

strength and coherence of the firm’s technological capabilities leading up to the structural

change. For firms that have strong technology capabilities, and are either focused or diversified

in technologically coherent ways, structural recombination can be an effective means to unlock

the potential complementarities between knowledge resources in different parts of the firm. In

contrast, where technological capabilities are weak, or where the firm is diversified into a

number of unrelated technological areas, structural recombination has a negative effect of

subsequent innovation. Interestingly, structural recombination has a stronger positive effect on

innovation for firms that are coherent diversifiers than for firms with a narrow technology focus,

highlighting the potential for recombinant innovation in coherent diversifiers.

These findings contribute to the existing literature in a number of ways. First, they shed new

light on the consequences of structural change by examining the effect of structural

recombination on innovation. While prior work suggests that structural recombination has a

negative effect on innovation (Karim, 2009)7, our study goes beyond this average effect to

examine the contingencies that moderate this effect, linking it to the strength and coherence of

the firm’s knowledge resources. As such, our study provides a more nuanced perspective on the

effects of structural recombination on innovation. In particular, our findings highlight the value

of coherent diversification (Teece et al 1994; Breschi, Lissoni and Malerba 2003; Nesta and

7 Karim (2009) measures innovation as entry into new product lines, a measure that is both less granular and conceptually distinct from the patent based measures used in our study

25

Saviotti 2005), and suggest that structural recombination is most beneficial where a combination

of strong capabilities and coherent diversification means that there are strong potential

complementarities between a firm’s knowledge resources.

Second, the findings of our study contribute to the literature on technology innovation,

highlighting the role of structural change as a means for firms to recombine their internal

knowledge resources. While this kind of internal boundary spanning (Rosenkopf and Nerkar,

2001) has been frequently discussed in the literature (Galunic and Rodan, 1998; Miller et al.,

2007), the focus of this work has been on the sharing of information between divisions within an

existing structure (Verona, 1999; Jansen et al., 2005; Tortoriello et al., 2011). This study shows

that another way in which firms can recombine its internal knowledge resources, and capture the

potential complementarities between them, is by changing its structural reconfiguration and

redrawing the boundaries between units.

The possibility of using structural change to enable knowledge recombination is important

not only because it suggests an alternate way of realizing intra-firm complementarities in

knowledge, but because knowledge recombination through structural change may offer certain

advantages over inter-unit knowledge transfer. As several recent studies have pointed out,

successful recombination of knowledge within the firm may require more than the simple

transfer of knowledge from one unit to the other, it may require the translation and

transformation of knowledge across semantic and pragmatic boundaries (Carlile and Rebentisch,

2003; Carlile, 2004). Knowledge transformation involves redefining and negotiating knowledge

to create a collective solution (Carlile and Rebentisch, 2003), and requires both constant

engagement between diverse technological domains, and the successful overcoming of political

and incentive issues (Carlile, 2004; Kellogg, Orlikowski and Yates, 2006). One way for firms to

26

enable this kind of higher-order knowledge transformation may be by changing the structure of

the organization, thus helping to overcome the pragmatic boundaries between knowledge

domains. Firms that undertake such structural recombination risk disrupting their existing

technological capabilities, of course, but the findings of this study suggest that in the presence of

strong technological capabilities and technological coherence, structural recombination may be

an effective way of enabling knowledge transformation.

The findings of our study also extend the technology literature in other ways. While prior

work has examined the direct effect of technological strength (Fleming 2001; Ahuja and Lampert

2001; Katila and Ahuja, 2002; Nerkar 2003), breadth (Gambardella and Torrisi 1998; Ahuja and

Lampert 2001; Nesta and Saviotti 2005; Garcia-Vega 2006) and coherence (Breschi et al., 2003;

Nesta and Saviotti, 2005) on firm innovativeness per se, we show that these factors also have an

indirect effect by moderating the effect of structural change on firm innovation. These findings

also complement work in the corporate strategy literature that emphasizes the complementarity

between partner knowledge as a driver of innovation performance following an acquisition

(Cassiman et al., 2005; Makri et al., 2010) and alliances (Sampson, 2007). Our study suggests

that diverse yet complementary knowledge may be important to knowledge recombination not

only between organizations, but also within them.

Finally, our study informs the literature on organizational reconfiguration and change. A

number of recent studies have examined the role of organizational ‘patching’ or ‘reconfiguration’

as a means to extend and deepen firm resources, and therefore as an integral part of a firm’s

dynamic capability (Brown and Eisenhardt, 1997; Eisenhardt and Brown, 1999; Karim and

Mitchell, 2004). Our study contributes to this work by examining the important but relatively

underexplored effect of structural change on the firm’s innovation performance (Karim, 2009).

27

In addition to contributing to the literature, our study also has important implications for

managers. Our results suggest that, in general, managers should be wary of pursuing structural

recombination for fear of its effects on subsequent innovation. While the effect of structural

recombination on innovation is not statistically significant in our main model, the negative sign

of this coefficient suggests that, on average, changes in organizational structure are more likely

to do harm than good to subsequent innovation. This may be especially true, moreover, for firms

that have weak technological capabilities or that are diversified into multiple and unrelated

technological areas. In other words, managers of firms with low quality and diffuse knowledge

looking to kick-start innovation would be ill-advised to do so by changing their organizational

structure. Instead, it is managers of firms with strong, focused and coherent technological

capabilities, who may benefit from using structural changes to unlock the recombinant potential

of their internal knowledge.

Our study could be improved in several ways. Controlling for the intent behind different

recombinations would certainly be useful, or even knowing the financial performance at the

business unit level, which would shed some light on intent. Further, we observe recombinations

in three to four year windows of time due to the availability of the medical database; more

granular annual data would capture even more recombinations that may have occurred within

firms. We were limited to 250 firms in the full sample due to the intricate mapping of firms’

business unit reconfiguration evolution; a larger sample or looking at other industries outside of

the medical sector could always serve as a good robustness check of our results.

In addition to these limitations, our study also suggests several avenues for future research.

While our study focuses on the effects of structural recombination, the contingent effects

highlighted here may have relevance to other forms of recombination, including combinations of

28

internal and external knowledge, and recombination of internal knowledge through inter-unit

collaboration. Future work could examine how these other means of recombinant innovation are

affected by the strength and coherence of firm knowledge. Future work could also examine how

these factors impact the acquisition and divestment decisions of the firm, as well as the decisions

on structural changes accompanying these corporate transactions.

In summary, our study examines the effects of structural recombination on subsequent firm

innovation, and the moderating role of the firm’s technology characteristics in this context. For

firms with weak and diffuse technological capabilities, structural recombination results in a

disruption of their existing routines and capabilities, causing subsequent innovation to decline.

By contrast, where a firm has strong technological capabilities or where it is technologically

diversified in coherent ways, structural recombination can help unlock the potential for intra-firm

knowledge recombination, resulting in innovation improvement. These findings extend our

understanding of organizational reconfiguration and change, highlighting the contingent nature

of the effect of such change on firm innovation. They also contribute to the literature on intra-

firm knowledge transfer, suggesting that structural recombination may be an alternate way to

unlock the potential for knowledge recombination within the firm.

29

REFERENCES

Ahuja, G. (2000). “Collaboration networks, structural holes, and innovation: A longitudinal study” Administrative Science Quarterly 45 (3) 425-455

Ahuja G. and R. Katila (2001). “Technological acquisitions and the innovation performance of acquiring firms: a longitudinal study”. Strategic Management Journal 22: 197-220

Ahuja, G. and C. M. Lampert (2001). "Entrepreneurship in the large corporation: A longitudinal study of how established firms create breakthrough inventions." Strategic Management Journal 22: 521-543.

Amabile, T.M. and R. Conti (1999). "Changes in the work environment for creativity during downsizing" Academy of Management Journal 42(6): 630-640

Amburgey, T.L, D. Kelly, and W.P. Barnett (1993). "Resetting the clock: The dynamics of organizational change and failure". Administrative Science Quarterly 38(1): 51-73

Argote, L. and P. Ingram (2000). "Knowledge transfer: A basis for competitive advantage in firms" Organizational Behavior and Human Decision Processes 82(1): 150-169

Argyres, N. (1996). "Capabilities, technological diversification and divisionalization." Strategic Management Journal 17: 395-410.

Argyres, N. and B.S. Silverman (2004) "R&D, organization structure, and the development of corporate technological knowledge" Strategic Management Journal 25: 929-958

Barney, J. (1991). “Firm resources and sustained competitive advantage.” Journal of Management 17(1): 99-120.

Baysinger B, Hoskisson RE. (1989). "Diversification strategy and R&D intensity in multi-product firms". Academy of Management Journal 32(2): 310–332

Belsley, D. A. (1991). Conditioning Diagnostics, Collinearity and Weak Data in Regression.New York, Wiley.

Belsley, D., Kuh, E. and R. Welsch (1980). Regression Diagnostics. New York, Wiley.

Birkinshaw, J. and M. Lingblad (2005). "Intrafirm competition and charter evolution in the multibusiness firm." Organization Science 16(6): 674-686.

Bowman, E. H. and H. Singh (1989). Corporate restructuring: Trends and consequences. Corporate Restructuring. R. Rock. New York, McGraw-Hill.

Bowman, E.H., H.Singh, M. Useem, and R. Bhadury (1999). "When does restructuring improve economic performance?" California Management Review 41(2): 33-54

Breschi, S., F. Lissoni and F. Malerba (2003). "Knowledge-relatedness in firm technological diversification." Research Policy 32: 69-87.

Brown, J.S., and P. Duguid (1991). "Organization learning and communities-of-practice: Toward a unified view of working, learning, and innovation" Organization Science 2(1): 40-57

30

Brown, S. L. and K. M. Eisenhardt (1997). “The art of continuous change: Linking complexity theory and time-paced evolution in relentlessly shifting organizations.” Administrative Science Quarterly 42: 1-34.

Buono, A.F. and J.L. Bowditch (1989) The Human Side of Mergers and Acquisitions San Francisco, CA: Jossey-Bass.

Burton, R.M., Lauridsen, J., and B. Obel (2002). "Return on assets loss from situational and contingency misfits." Management Science 48(11): 1461-1485.

Capron, L., P. Dussauge and W. Mitchell (1998). “Resource redeployment following horizontal acquisitions in Europe and North America, 1988-1992.” Strategic Management Journal 19: 631-661.

Capron, L., and W. Mitchell (1998). “Bilateral resource redeployment and capabilities improvement following horizontal acquisitions.” Industrial and Corporate Change 7(3): 453-483.

Cardinal, L. B. and T. C. Opler (1995). "Corporate diversification and innovative efficiency: An empirical study." Journal of Accounting and Economics 19: 365-381.

Carlile, P.R. (2004). "Transferring, translating and transforming: An integrative framework for managing knowledge across boundaries." Organization Science 15(5): 555-568

Carlile, P.R. and E.S. Rebentisch (2003). "Into the black box: The knowledge transformation cycle" Management Science 49(9): 1180-1195

Cassiman, B., M.G. Colombo, P. Garrone and R. Veugelers (2005) "The impact of M&A on the R&D process: An empirical analysis of the role of technological- and market- relatedness" Research Policy 34(2) 195-220

Child, J. (1972). “Organization structure, environment and performance: The role of strategic choice.” Sociology 6: 1-22.

Cohen, W. and D. A. Levinthal (1990). “Absorptive capacity: a new perspective on learning and innovation.” Administrative Science Quarterly 35: 128-152.

Cyert, R. M. and J. G. March (1963). A Behavioral Theory of the Firm. Englewood Cliffs, NJ: Prentice-Hall.

Dess, G.G. and J.D. Shaw (2001). "Voluntary turnover, social capital and organizational performance" Academy of Management Review 26(3): 446-456

Dougherty, D. (1992). "Interpretive barriers to successful product innovation in large firms" Organization Science 3(2) 179-202

Dougherty, D. and E.H. Bowman (1995). "The effects of organizational downsizing on product innovation" California Management Review 37(4): 28-44

Eisenhardt, K. M. and S. L. Brown (1999). "Patching: Restitching business portfolios in dynamic markets." Harvard Business Review 77(3): 71-82.

Fiol, C. and M. Lyles (1985). “Organizational learning.” Academy of Management Review10(4): 803-813.

31

Fiol, C.M. (1994). "Consensus, diversity and learning in organizations" Organization Science 5(3): 403-420

Fisher, S.R. and M.A. White (2000). "Downsizing in a learning organization: Are there hidden costs?" Academy of Management Review 25(1): 244-251

Fleming, L. (2001). "Recombinant uncertainty in technological search." Management Science47(1): 117-132.

Galunic, D. C. and K. M. Eisenhardt (1996). "The evolution of intracorporate domains: Divisional charter losses in high-technology, multidivisional corporations." Organization Science 7(3): 255-282.

Galunic, D. C. and K. M. Eisenhardt (2001). "Architectural innovation and modular corporate forms." Academy of Management Journal 44(6): 1229-1249.

Galunic, D. C. and S. Rodan (1998). "Resource recombinations in the firm: Knowledge structures and the potential for Schumpeterian innovation." Strategic Management Journal 19: 1193-1201.

Gambardella, A. and S. Torrisi (1998). "Does technological convergence imply convergence in markets? Evidence from the electronics industry." Research Policy 27: 445-463.

Garcia-Vega, M. (2006). "Does technological diversification promote innovation? An empirical analysis for European firms." Research Policy 35: 230-246.

Gavetti, G. and D. Levinthal (2000). "Looking forward and looking backward: Cognitive and experiential search." Administrative Science Quarterly 45 (1): 113-137

Glynn, M.A., T.K. Lant, and F.J. Milliken (1994). "Mapping learning processes in organizations" In C. Stubbart, J.P. Meindl, and J.F. Porac (Eds.) Advances in managerial cognition and organizational information processing Greenwich, CT. JAI Press 5: 43-83

Grant, R. M. (1996). "Towards a knowledge-based theory of the firm." Strategic Management Journal 17(Winter Special Issue): 109-122.

Greenwood, R., C.R. Hinings, and J. Brown (1994). "Merging professional service firms" Organization Science 5(2): 239-257

Griliches, Z. (1990). "Patent statistics as economic indicators: A survey." Journal of Economic Literature 28(4): 1661-1707.

Gulati, R. and P. Puranam (2009). "Renewal through reorganization: The value of inconsistencies between formal and informal organization" Organization Science 20(2): 422-440

Guthrie, J.P. and D.K. Datta (2008). "Dumb and dumber: The impact of downsizing on firm performance as moderated by industry conditions". Organization Science 19(1): 108-123

Hall, B. H., A. B. Jaffe and M. Trajtenberg (2001). "The NBER patent citation data file: Lessons, insights and methodological tools ." NBER Working Paper No.8498. National Bureau of Economic Research: Cambridge, MA.

Hall, B. H., A. B. Jaffe and M. Trajtenberg (2005). "Market value and patent citations" Rand Journal of Economics 36 (1): 16-38

32

Hannan, M. T., M. D. Burton, and J. N. Baron (1996). "Inertia and change in the early years: Employment relations in young, high-technology firms." Industrial and Corporate Change 5: 503-536.

Hannan, M. T. and J. Freeman (1984). "Structural inertia and organizational change." American Sociological Review 49: 149-164.

Hargadon, A. and R.I.Sutton (1997). "Technology brokering and innovation in a product development firm." Administrative Science Quarterly 42(4) 716-749

Helfat, C. (1994). CHECK "Evolutionary trajectories in petroleum firm R&D." Management Science 40: 1720-1747.

Helfat, C. E. and K. M. Eisenhardt (2004). "Inter-temporal economies of scope, organizational modularity, and the dynamics of diversification." Strategic Management Journal 25(13): 1217-1232.

Hitt MA, Hoskisson RE, Johnson RA, Moesel DD. (1996). "The market for corporate control and firm innovation" The Academy of Management Journal 39(5): 1084–1119

Hoskisson, R. E. and R. A. Johnson (1992). “Corporate restructuring and strategic change: The effect on diversification strategy and R&D intensity.” Strategic Management Journal 13: 625-634.

Huber, G. P. (1991). “Organizational learning: The contributing processes and the literatures.” Organization Science 2(1): 88-115.

Jansen, J.J.P., F.A.J. Van den Bosch, and H.W. Volberda (2005). "Managing potential and realized absorptive capacity: How do organizational antecedents matter?" Academy of Management Journal 48(6): 999-1015

Jemison, D.B. and S.B. Sitkin (1986) "Corporate acquisitions: A process perspective" Academy of Management Review 11(1): 145-163

Johnson, R.A., R.E. Hoskisson and N. Margulies (1990) "Corporate restructuring: Implications for organization change and development" In R.W. Woodman and W.A. Pasmore (Eds.) Research in organizational change and development Greenwich CT: JAI Press 4: 141-165

Karim, S. (2006). "Modularity in organizational structure: The reconfiguration of internally developed and acquired business units." Strategic Management Journal 27: 799-823.

Karim, S. (2009). "Business unit reorganization and innovation in new product markets."Management Science 55: 1237-1254.

Karim, S. and W. Mitchell (2004). "Innovating through acquisition and internal development."Long Range Planning 37: 525-547.

Katila, R. (2002). "New product search over time: Past ideas in their prime?" Academy of Management Journal 45(5): 995-1010

Katila, R. and G. Ahuja (2002). "Something old, something new: A longitudinal study of search behavior and new product introduction." Academy of Management Journal 45(6): 1183-1194.

33

Kellogg, K.C., W.J. Orlikowski, and J. Yates (2006). "Life in the trading zone: Structuring coordination across boundaries in postbureaucratic organizations" Organization Science17(1): 22-44

Kogut, B. and U. Zander (1992). “Knowledge of the firm, combinative capabilities, and the replication of Technology.” Organization Science 3(3): 383-397.

Levitt, B. and J. G. March (1988). “Organizational learning.” Annual Review of Sociology 14: 319-340.

Luscher, L.L. and M.W. Lewis (2008). "Organizational change and managerial sensemaking: Working through paradox." Academy of Management Journal 51(2): 221-240

Makri, M., M. A. Hitt and P. J. Lane (2010). "Complementary technologies, knowledge relatedness, and invention outcomes in high technology mergers and acquisitions." Strategic Management Journal 31: 602-628.

March, J. G. (1991). “Exploration and exploitation in organizational learning.” Organization Science 2(1): 71-87.

McKinley, W. and A.G. Scherer (2000). "Some unanticipated consequences of organizational restructuring" Academy of Management Review 25(4): 735-752

Miller, D. (1982). “Evolution and revolution: A quantum view of structural change in organizations.” Journal of Management Studies 19(2): 131-151.

Miller, D. (1986). “Configurations of strategy and structure: Towards a synthesis.” Strategic Management Journal 7: 233-249.

Miller, D. (1992). "Environmental fit versus internal fit". Organization Science 3(2) 159-178

Miller, D. (1996). “Configurations revisited.” Strategic Management Journal 17(7): 505-512.

Miller, D. and P. H. Friesen (1984). Organizations: A quantum view. Englewood Cliffs, NJ: Prentice-Hall.

Miller, D. J., M. J. Fern and L. B. Cardinal (2007). "The use of knowledge for technological innovation within diversified firms." Academy of Management Journal 50(2): 308-326.

Nadler, D. and M. L. Tushman (1997). Competing by Design: The power of organizational architecture. New York, Oxford University Press.

Nelson, R.R. (1982). "The role of knowledge in R&D efficiency." Quarterly Journal ofEconomics 97 (3): 453-470

Nelson, R. R. and S. G. Winter (1982). An Evolutionary Theory of Economic Change. Cambridge, MA, Belknap Press.

Nerkar, A. (2003). "Old is gold? The value of temporal exploration in the creation of new knowledge." Management Science 49(2): 211-229.

Nesta, L. and P. P. Saviotti (2005). "Coherence of the knowledge base and the firm's innovative performance: Evidence from the U.S. pharmaceutical industry." The Journal of Industrial Economics 53(1): 123-142.

34

Nicholls-Nixon, C.L. and C.Y. Woo (2003). "Technology sourcing and output of established firms in a regime of encompassing technological change" Strategic Management Journal24(7): 651-666

Nixon, R.D., M.A. Hitt, H. Lee, and E. Jeong (2004). "Market reactions to announcements of corporate downsizing actions and implementation strategies" Strategic Management Journal 25: 1121-1129

Orr, J. (1996). Talking About Machines: An Ethnography of a Modern Job. Ithaca, NY. IRL Press.

Palepu K. (1985). "Diversification strategy, profit performance and the entropy measure". Strategic Management Journal 6(3): 239-255

Palich LE, Cardinal LB, Miller CC. (2000). Curvi-linearity in the diversification-performance linkage: an examination of over three decades of research. Strategic Management Journal, 21: 155–174

Paternoster, R., R. Brame, P. Mazerolle, and A. Piquero (1998). "Using the correct statistical test for the equality of regression coefficients". Criminology 36(4): 859-866

Pavitt, K. L., M. J. Robson and J. F. Townsend (1989). "Technological accumulation, diversification and organisation in UK companies, 1945-1983." Management Science 35(1): 81-99.

Penrose, E. T. (1959). The Theory of the Growth of the Firm. New York, Wiley.

Porter, M. E. (1987). “From competitive advantage to corporate strategy.” Harvard Business Review 65(3): 43-59.

Romanelli, E. and M. L. Tushman (1994). "Organizational transformation as punctuated equilibrium: An empirical test." Academy of Management Journal 37(5): 1141-1166.

Rosenkopf, L. and A. Nerkar (2001). "Beyond local search: Boundary-spanning, exploration, and impact in the optical disk industry." Strategic Management Journal 22(4): 287-306.

Sampson, R.C. (2007). "R&D alliances and firm performance: The impact of technological diversity and alliance organization on innovation". Academy of Management Journal 50(2): 364-386

Schumpeter, J. A. (1934). The Theory of Economic Development. New Brunswick, NJ, Transaction Publishers.

Singh, J. (2005). "Is diversification really good for innovation?" Working Paper, INSEAD.

Teece, D. J. (1982). “Towards an economic theory of the multiproduct firm.” Journal of Economic Behavior and Organization 3: 39-63.

Teece, D. J., R. Rumelt, G. Dosi and S. Winter (1994). "Understanding corporate coherence." Journal of Economic Behavior and Organization 23: 1-30.

Tortoriello, M., R. Reagans and B. McEvily (2011). "Bridging the knowledge gap: The influence of strong ties, network cohesion and network range on the transfer of knowledge between organizational units". Organization Science orsc.1110.0688; published online before print August 24, 2011, doi:10.1287/orsc.1110.0688

35

Trajtenberg, M. (1990). "A penny for your quotes: Patent citations and the value of information." RAND Journal of Economics 21: 325-342.

Tushman, M.L. (1978). "Technical communication in R&D Laboratories: The impact of project work characteristics" Academy of Management Journal 21(4): 624-645

Tushman, M. and E. Romanelli (1985). “Organizational evolution: A metamorphosis model of convergence and reorientation.” Research in Organizational Behavior 7: 171-223.

Ven den Bosch, F.A.J., H.W. Volberda and M. de Boer (1999). "Coevolution of firm absorptive capacity and knowledge environment: Organizational forms and combinative capabilities." Organization Science 10 (5) 551-568

Verona, G. (1999). "A resource-based view of product development" Academy of Management Review 24(1): 132-142

Wadhwa, A. and S. Kotha (2006). "Knowledge creation through external venturing: Evidence from the telecommunications equipment manufacturing industry" Academy of Management Journal 49(4): 819-835

Wang, Q. and N. von Tunzelmann (2000) "Complexity and the functions of the firm: Breadth and depth" Research Policy 7(8): 805-818

Weitzman, M.L. (1998). "Recombinant growth". Quarterly Journal of Economics. 113(2): 331-360

Zander, U. and B. Kogut (1995). "Knowledge and the speed of transfer and imitation of organizational capabilities: An empirical test" Organization Science 6(1) 76-92

Zellmer-Bruhn, M.E. (2003). "Interruptive events and team knowledge acquisition" Management Science 49(4): 514-528

36

FIGURES

Figure 1Example of Structural Composition and Structural Reconfiguration at J&J, Inc.

Codman & Shurtleff, Inc.

(acquired pre-1975)

Kees Surgical Specialty

(acquired 1987)Applied

Fiberoptics(acquired 1982)

By 1989 recombine KSS into CS. By 1993 recombine

CS and J&J Ortho into new J&J Prof.

by 1975

Codman & Shurtleff, Inc.

by 1993by 1989by 1986

By 1986 recombine AF into CS.

Codman & Shurtleff, Inc.

J&J Orthopedics, Inc. (internally

developed 1986)

Entry into J&J Internally developed unit (I) Acquired unit (A) Mix of I units Mix of A units Mix of A and I units

J&J Professional, Inc. (created from

recombination 1993)

37

Table 1Summary Statistics & Correlation Matrix

Variable MeanStd. Dev. Min Max 1 2 3 4 5 6 7 8 9 10 11

1Patent Stock (citation-weighted) 186.44 451.48 0.05 2099.68 1.00

2 Recombination (t-1) 1.26 3.43 0.00 26.00 0.37 1.00

3Technology Capability 1.09 0.55 0.11 3.17 -0.11 -0.07 1.00

4Technology Coherence 21.37 16.46 2.03 69.87 -0.36 -0.19 -0.10 1.00

5Technological Diversity 0.61 0.30 0.00 0.97 0.39 0.21 0.04 -0.83 1.00

6 Employees 6.65 2.80 -2.30 12.11 0.53 0.44 -0.09 -0.44 0.49 1.00

7 Medical 10.48 2.60 6.21 16.02 0.48 0.44 -0.11 -0.38 0.44 0.78 1.00

8 % Medical 0.70 0.37 0.00 1.00 -0.45 -0.25 0.18 0.48 -0.48 -0.54 -0.23 1.00

9 Firm Age 51.36 34.55 9.00 159.00 0.51 0.38 -0.19 -0.27 0.27 0.49 0.52 -0.40 1.00

10 Acquired Units 1.42 3.03 0.00 17.00 0.36 0.56 -0.13 -0.23 0.28 0.55 0.54 -0.33 0.40 1.00

11 Employee growth 0.10 0.42 -1.00 3.64 -0.09 -0.04 0.05 -0.01 0.08 -0.02 -0.03 0.19 -0.19 -0.07 1.00

38

Table 2Main Results

Technological Diversity

High Low

1 2 3 4 5 6

Recombination (t-1)-4.90(3.99)

-4.38†(2.29)

-17.71***(4.03)

-22.33**(6.99)

1.53(3.53)

Recombination x Technology Capability

23.15*(9.68)

Recombination xTechnology Coherence

0.89***(0.24)

1.29**(0.43)

0.05(0.12)

Technology Capability-36.06(30.62)

-32.98(32.05)

-32.80(31.03)

-41.37(31.55)

-55.77(47.27)

-9.37*(4.56)

Technology Coherence-0.57(0.57)

-0.47(0.56)

-0.46(0.56)

-0.70(0.61)

-5.16*(2.24)

0.20*(0.08)

Patent Stock(citation-weighted)

-0.37*(0.14)

-0.36*(0.14)

-0.36**(0.13)

-0.36**(0.12)

-0.31*(0.14)

-1.36***(0.35)

Technological Diversity

33.75(41.10)

24.62(33.09)

20.62(32.58)

14.44(30.71)

Employees-21.74(13.35)

-21.59(13.69)

-24.53†(14.44)

-23.27(14.02)

-23.67(16.70)

-2.04(3.28)

Medical-15.30(24.15)

-11.10(20.47)

-8.10(18.42)

-6.88(18.88)

-8.59(67.87)

-0.89(0.88)

% Medical40.33

(86.16)46.29

(88.21)39.43

(94.74)75.56

(83.22)211.35

(174.22)34.97*

(14.81)

Firm Age-2.03(2.19)

-1.61(2.44)

-2.31(2.47)

-2.14(2.58)

-7.15(7.80)

0.06(0.14)

Acquired Units0.57

(6.90)-2.29(6.62)

-12.27(7.77)

-10.71(6.90)

-16.02(11.30)

-4.23†(2.43)

Employee growth47.17

(38.58)47.21

(40.91)64.46

(41.68)59.60

(42.93)66.54

(53.47)19.02*(7.80)

Inverse Mills-53.15(87.55)

-58.16(93.12)

-102.50(91.14)

-89.60(94.94)

-105.31(126.27)

-41.03*(17.77)

N(firms)

212(71)

212(71)

212(71)

212(71)

106(40)

106(41)

R-square (within) 0.302 0.324 0.367 0.376 0.449 0.624

Model Comparison [2]-[1] [3]-[2] [4]-[2]

F-test (model improvement)

1.50 5.72* 14.35***

Dependent variable is change in patent stock between t and t+1. All models include firm and period fixed effects, as well as a constant term. Figures in parentheses are robust standard errors. High and low technology diversity refers to values of technology diversity above and below the sample median respectively. All models have Prob>F less than 1%. Significance level (two-sided): † <10%, *<5%, **<1%, ***<0.1%

39

Appendix AFirst Stage Prediction Model

Predictor

Technology Capability0.32

(0.28)

Technological Coherence0.00

(0.03)

Patent Stock (citation-weighted)0.00†

(0.00)

Technological Diversity-0.11(1.27)

Employees0.12

(0.10)

Medical0.02

(0.11)

% Medical-0.76(0.57)

Firm Age0.01†

(0.00)

% Acquired Units0.35

(0.67)

Total Units0.15*

(0.07)

Recombination (lag)0.04

(0.08)

Employee growth-0.49(0.56)

N 212

Chi-square 37.01***

Random-effects probit model. Dependent variable is dummy variable that takes the value 1 if the firm undertook any structural recombination betweent and t+1, 0 otherwise. Independent variables are measured at t, except recombination lag, which is measured between t-1 and t. Model includes constant term. Figures in parentheses are robust standard errors. Significance level (two-sided): † <10%, *<5%, **<1%, ***<0.1%